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Wayland Moore Obituary (1935 – 2022) – Decatur, GA  Legacy.com
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It’s no exaggeration to say God of War: Ragnarok is one of the most hotly-anticipated games of 2022.
The series’ 2018 revival stunned critics and brought on board a new audience for whom the previous games had seemed too juvenile, weaving a tale of fathers, sons and grief into a gaming masterpiece. The bar for the sequel was raised absurdly high.
Reviews for Ragnarok have been pouring in over the past few days, ahead of the title’s debut on PlayStation 5 tomorrow, and it sounds like Kratos’ return is everything fans and critics alike have hoped for.
At the time of writing, the game has 133 critic reviews on Metacritic and an average rating of 94, making it the third highest-rated game of the year (behind Elden Ring and Portal Companion Collection, both on 96, and tied with Persona 5 Royal).
“Ragnarok defies the odds and manages to surpass its predecessor in every way,” writes Katcy Stephan in her 10/10 review for Variety.
“Ragnarok defies the odds and manages to surpass its predecessor in every way”
In his review for Push Square, which also gives 10/10, Robert Ramsey says the sequel maintains the impressive narrative presentation of the previous adventure – including the single-shot style of direction – and even improves on it in places thanks to “superb performances” by the game’s cast.
“Ragnarok is cinematic brilliance at times, but to say it’s like a movie would be doing the title’s top-notch gameplay a severe disservice,” he writes. “As we mentioned earlier, everything is pretty much as returning players will remember it; instead of starting over, the developer has basically branched things out in all directions. Both in and out of combat, Kratos is given a range of new toys to play with, adding meaningful depth across the board. But even if you’re unfamiliar with the first game, this sequel does an admirable job of introducing new bits and pieces at a steady, manageable rate.”
In his unscored review for Launcher, The Washington Post’s games site, Gene Park writes: “Ragnarok is one of the rare stories in any medium where the second experience is better than the first, rewarding the audience’s knowledge and attention to details,” he says. “As soon as the credits roll, you may be immediately tempted to start over, empowered with knowledge on how plot beats unfold and appreciating character and story arcs that pay off big time by the end.
“But you also don’t want to restart the game immediately, because Ragnarok is an even rarer type of game that offers more adventure after it ends. Some of its biggest emotional payoffs happen after the credits, featuring more playable adventures and secrets to find. It’s a generous game, offering stories that feel like a sequel to the game you just beat.”
As with the 2018 game, one of the most highly-praised aspects of God of War is the writing. In his 5/5 review for We Got This Covered, Shaan Joshi remarks on how the sequel goes beyond just exploring its central themes of prophecy, fate and breaking the cycle of violence.
“Ragnarok has its fingers in a lot of pies, tackling weighty topics including generational abuse and trauma, coping with grief and loss, and how compassion can be a strength, not a weakness,” he writes. “Addressing these is no easy task, which is why the writing team at Santa Monica Studio has to be applauded for handling them with nuance and tact.
“Hands down, this is easily one of the best stories ever featured in a video game, and it’s executed beautifully thanks to the ever-talented cast of voice and mocap actors. If that sounds like Ragnarok is a bit too serious for its own good, rest assured, there are plenty of laugh-out-loud moments that help balance things out.”
“With more efficiency, its story could be told in half the time, with half the characters”
The sheer amount of story, however, is the source of what little division there is among critics. Park, for example, says pacing is the “only area where the game seems to falter,” noting that 2018’s title moves a lot faster despite being about “a literal funeral march.”
“The story [also] plays coy about whether the actual Ragnarok event, the end of the Norse mythology, will occur, and this coyness drags out at the beginning, with quests that seem to meander. It becomes clear late in the game that the slow pace is meant to introduce new characters, so some patience may be required.”
In his unscored review for Eurogamer, which gives the game the site’s highest ‘Recommended’ accolade, Chris Tapsell notes that – as with so many sequels – everything in Ragnarok is dialled up compared to 2018’s God of War. This includes the size of the world, the length of the main quest, the number of characters and skills trees, and so on.
“With more efficiency, its story could be told in half the time, with half the characters, and in its tropey, misunderstanding-fuelled middle hours it sags under the weight of it all, falling into the mopier tendencies of the previous game but without the tight, double-act focus that kept it moving,” he writes.
“There are a huge amount of cutscenes, which flow beautifully in and out of the player-controlled action with more sophistication than ever… But there are still too many of them, forgotten lovers and brothers and probably someone’s cousin’s pet dog getting lengthy screen time each – likely, I suspect, because of an urge to ‘do justice’ to each minor character’s arc, to prevent them from feeling used by the story.”
Many critics discuss the length and sheer amount of content in the game, noting that Sony Santa Monica has openly said it originally planned a trilogy for Kratos’ Norse-themed adventures before deciding to resolve the story with Ragnarok.
Joshi is unconvinced a trilogy would have worked: “While Ragnarok is certainly no slouch (it took me just north of 40 hours to see the credits roll), I can’t say with confidence that two separate titles would have done enough to justify their existence.”
Meanwhile, Ramsey says Ragnarok “feels like it could have been split in half and sold as two separate pieces.”
He continues: “It absolutely dwarfs 2018’s God of War in every possible way, and we dare say that other developers, and indeed publishers, may have pushed for that aforementioned trilogy given the sheer scale of this project.
“Santa Monica Studio has delivered a game that makes its already excellent predecessor look like a prologue”
“But no, Santa Monica Studio has delivered a game that makes its already excellent predecessor look like a prologue. So much happens in Ragnarok that by the time the credits roll, it’s hard not to feel exhausted — in a good way. It’s a truly tireless title in terms of creative vision, and we can only imagine the amount of effort that went into crafting such a beast of a game.”
In terms of the gameplay, all reports suggest that this title is on par with, if not above and beyond, its critically acclaimed forebear. Tapsell notes that what few complaints were levelled at the 2018 revival have been “thoroughly mended here.”
“Enemy variety was a justified complaint, and clearly heard loud and clear at Santa Monica Studio (one previously ever-present enemy type is even acknowledged with some nicely self-deprecating humour). There’s no risk of repetition in Ragnarök. There are far more designs, but variety comes with more depth than that too. You’ll need to use a far wider range of abilities to get by – shield bashes, companion abilities, weapons – and adapt to attacks of different speed, height, and direction.”
The entire armour and weapon system, which increases Kratos’ power level based on the equipment he wields, has also been “revamped and streamlined for the better,” according to Joshi, while Tapsell is full of praise for the abundance and brilliance of the puzzles players will face outside of battle – although he notes Ragnorok demonstrates a “truly overbearing urge to offer you hints” before you’ve had a chance to solve some of these.
Park was particularly pleased with the side quests and other content found away from the main path, declaring that this “could equal the quests found in CD Projekt Red’s seminal 2015 hit, The Witcher 3: Wild Hunt.”
“So many other big-budget developers have tried to hit that peak, but I can confidently say Santa Monica Studio has surpassed that bar, having written side quests even more rewarding in [both] gameplay and story,” he says. “While the main story is a triumph, the side quests that lay off the beaten path are home to the game’s biggest battles, its most awe-inspiring sights and its biggest play areas.”
“Ragnarok is one of the rare stories in any medium where the second experience is better than the first”
Yet the primary narrative is still the source of the most praise among Ragnarok reviews. Variety’s Stephan notes that, despite being centred around a prophecy, Kratos’ journey “still manages to surprise at every turn.”
“Its storytelling is simply unmatched, with several cutscenes bringing me to tears (both of laughter and sadness),” she writes. “It sticks the landing, too, with an emotional gut-punch bringing its tale to a close.
“God of War Ragnarok is a 10 out of 10, checking every box with a combination of pulse-pounding action, humour and character development that will stay with players long after they’ve completed the campaign. It’s a masterpiece that proves even an old god can learn new tricks.”
Park concludes that it’s not just the relationship with the 2018 title, but the impact on all the previous outings, that is perhaps most impressive.
“The original trilogy was a nihilistic deconstruction of polytheism and the trap of fate,” he writes. “God of War: Ragnarok… subverts this bleak worldview into something more hopeful and joyful… The way Ragnarok transforms the series is remarkable.
“Popular entertainment these days is obsessed with lore to a fault. Disney’s Marvel and Star Wars franchises have entire councils of people devoted to keeping lore straight across these stories. Even 2022′s biggest game, Elden Ring, was essentially a story all about lore. Despite tapping into well-mined Norse mythology, Ragnarok is focused squarely on seeing and hearing its characters. Like Kratos, you will actually like spending time with them. The memories of these people will stay with you long after the credits roll. By the end, you will believe that even a god of war can earn himself some peace.”
James Batchelor
Editor-in-Chief
James Batchelor has been a journalist in the games industry since 2006, joining GamesIndustry in 2016, and also runs Non-Violent Game of the Day (@NVGOTD). He does play violent games, but always on Story/Easy mode.
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by Master Gardener Anne Bartlett
November 7, 2022
10:29 AM
Hamilton County Master Gardener Anne Bartlett gets into the holiday spirits this month by highlighting to of her favorite holiday herbs: sage and thyme. 
Sage: Indispensable for Good Health
It would not be Thanksgiving at my house without sage (Salvia officinalis). When I cook a turkey, I place leaves of this indispensable herb under the bird’s skin as well as in the bouquet garni I put in the cavity. 
Sage has long been thought to aid in the digestion of fatty foods. Indeed, salvia comes from a Latin verb meaning to be in good health. An old proverb that declares one who has sage in the garden will never grow old.
 Thyme: The Versatile Holiday Ingredient
At this time of year, I love to peruse cooking magazines in pursuit of fresh takes on holiday side dishes or a new twist for everyday standards. It is amazing how frequently thyme is a primary ingredient. 
This very versatile herb adds depth to savory dishes and cuts the mouth feel of fatty foods. Be forewarned that fresh thyme turns black in acidic dishes (think tomatoes), so always use the dried herb in those recipes. 
There are many varieties of thyme. The ones I see most frequently in our garden centers are German, English, and French thyme. German is the most cold-tolerant. English has the most intense flavor, while French is best for drying. The ideal time to harvest leaves for drying is while the plant is blooming. 
Growing Holiday Herbs
Both thyme and sage are Mediterranean natives. They need full sun and somewhat alkaline, well-drained soil. You may amend our heavy clay soil with expanded shale to improve drainage. However, I grow mine in containers and raised beds. 
By the way, the pretty, variegated sage cultivars have the same flavor as the basic green one. Both herbs are cold hardy here, so they will survive winter in the garden.
The Hamilton County Master Gardeners are here to provide you with the latest university research-based gardening information. Whether it’s plant or pest ID, sick plants or general gardening advice, we are here to help! 
To reach us:
Send an email to [email protected]. If you can’t email, call (423) 209-8560 and leave your name, phone number, best time to reach you and a description of your gardening question. If you need help diagnosing a problem, include a few details:
by Master Gardener Anne Bartlett
November 7, 2022
10:29 AM
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Michigan votes to protect abortion rights; Fetterman wins Pa. race for Senate  The Washington Post
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Goan Seamen’s Association of India requested the Union Minister Shripad Naik, besides the NRI commission for his intervention to release and bring back those Indian seafarers, who are held captive at Equatorial Guinea.All the crew including Indians are safe. Goan Seamen’s association President Frank Viegas, told reporters that the vessel has a total crew of 26 persons including Indians. The vessel was detained by the Equatorial Guinea naval ship in international waters on August 12, 2022 and brought to Luba port in Equatorial Guinea under naval escort and threat of lethal action against the vessel and crew if orders are not complied on 14th August 2022.

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NASHVILLE, Tenn. (WTVF) — A safety feature that first debuted in Latin America in 2019 is now expanding to six cities in the United States, and Nashville is one of those.
As of Monday, drivers and passengers of Uber rides may choose to record audio during trips.
If the feature is enabled in the Safety Toolkit in the app, either member of the ride can tap a blue shield icon on the map screen and hit “Start” on the “Record Audio” option. The recording will stop either when manually ended or shortly after the trip is completed.
“We are thrilled to expand the pilot of this important safety feature to additional cities,” said Andi Pimentel Dunn, Product Manager at Uber. “We’re always looking for ways to leverage our innovative technology to build features that help make Uber safer. We hope this audio recording feature will continue to help give riders and drivers peace of mind by encouraging comfortable and positive interactions while on trips.”
Uber tells users not to worry about access to the recordings. Once a recording is completed, Uber says the audio file will be encrypted and stored directly on the rider or driver’s device — whoever it was that chose to record.
To help protect privacy, not even Uber will be able to listen to the recording.
If a safety incident is reported to Uber, however, then a user will be able to attach the audio file to a report and share it with a safety support team. That is when the file would be decrypted and a trained safety agent will be allowed to review the audio.
“From the start, our goal was to design this feature in a way that keeps the recordings secure and protects the privacy of all parties involved.” said Uttara Sivaram, Head of Privacy and Security Public Policy. “That’s why we built this feature to immediately encrypt the audio file and store it directly on the user’s phone, ensuring that no one, not even Uber, is able to access the recording unless a user submits it to us to review. And if a driver chooses to enable this feature, it sends a message to the rider, alerting them that audio may be recorded during their trip.”

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Both are great for heavy tasks, but one has inking support, and the other is a lot more powerful.
When it comes to the best laptops to buy in 2022, you might have heard about the Lenovo ThinkPad X1 Extreme or the Surface Laptop Studio. Both of these devices are quite different when considering the internals and the design. However, they still pack powerful components inside for tasks like content creation, as well as gaming. They're also both in the same price range (when one of them is on sale, anyway) and are premium options from their respective manufacturers. The ThinkPad X1 Extreme is one of the best Lenovo laptops, and the Surface Laptop Studio is a great Surface PC.
If you're not a Microsoft or Lenovo purist, then you might be wondering which one of these you should buy. Well, let's take a look.
The Lenovo ThinkPad X1 Extreme Gen 5 is a powerful business laptop with Intel H-series processors and NVIDIA GeForce RTX graphics to handle demanding workloads and gaming.
The Surface Laptop 5 features a sleek design, runs Windows 11, and has a touchscreen. For the money, it's one of the best laptops around right now, and for the 15-inch model, it's one of the lightest.
Both the ThinkPad X1 Extreme Gen 5 and the Surface Laptop Studio are around the same price, but there's a caveat. That only applies whenever the ThinkPad X1 Extreme is on sale for $1,649.45. Usually, the starting price of the ThinkPad is $2,999. Without sales, the Surface Laptop Studio is cheaper, costing $1,600 for a model without a dedicated GPU.
That price goes up with the Surface Laptop Studio models with dedicated GPUs, starting at $2,099. However, there are more options for the ThinkPad X1 Extreme that can bump the price as high as $3,000 when on sale, and $6,000 when not on sale. If budget is important to you, the Surface Laptop Studio is cheaper if you can't catch the ThinkPad at the sales price.
To begin, we have the all-important design comparison, and it's the thing that most sets these devices apart. The Surface Laptop Studio is a smaller device than the ThinkPad X1 Extreme. It also has a convertible design, whereas the ThinkPad is a traditional clamshell laptop that uses high-end carbon fiber materials.
The Surface Laptop Studio's convertible design, however, is unlike your typical 2-in-1. Instead of using a kickstand or a hinge, this device has a pull-forward screen, so you can rest it near the trackpad and draw it closer to you for drawing or watching movies. It also retracts all the way dow with one more additional movement, so you can hold the device as a tablet. You even can flip the screen all the way around to showcase a presentation. This makes it quite the versatile device.
The 16-inch ThinkPad X1 Extreme, though, is a typical clamshell laptop that isn't close to being as fancy as the Surface Laptop Studio. The only fancy aspect is the carbon fiber lid, which is an optional add-on. This feels quite premium when compared to the aluminum on the Surface Laptop Studio.
Other than that, these products are quite heavy computers that aren't very portable. The ThinkPad is much bigger at 15.57 x 10 x 0.7 inches, over the Surface Laptop Studio's 12.72 x 8.98 x 0.746 inches. It's also heavier — 4.14 pounds against the Surface Laptop Studio's 4 pounds.
These two devices also vary a lot in the display department. As you can tell from the spec sheet, the Surface Laptop Studio has a single display option: a 14.4-inch, 3:2 aspect ratio screen tuned to 2400 x 1600 resolution and a 120Hz refresh rate. The ThinkPad X1 Extreme, meanwhile, has a bigger 16-inch screen with a lower 1920 x 1200 resolution as standard and a 16:10 aspect ratio.
However, the biggest difference regards the Surface Laptop Studio's support for the Surface Pen. Not every ThinkPad X1 Extreme display even has touch support (more on this in a bit), although it has more display options for screen resolution, brightness, and refresh rates.
If you're a creative type that depends on drawing a lot in apps like Photoshop, there's no doubt that the Surface Laptop Studio will be better for you. The display is smaller than the one on the ThinkPad, but it has a higher resolution. Plus, you can use the Surface Pen 2 to get a pen-to-paper feel with haptic feedback. The display also has a 120Hz refresh rate, which is good for smooth response rates when drawing or even editing videos.
Meanwhile, the ThinkPad X1 Extreme's display is more for those who will spend more time using a keyboard and mouse. The 16-inch screen is bigger than the 14-inch panel on the Surface Laptop Studio, so it's better for productivity. The 1920 x 1200 resolution option is standard but might feel limiting for some people. Thankfully, there are Quad HD+ and Ultra HD+ versions available. We suggest this Ultra HD+ version as it has touch support. There is also an option for a 165Hz panel, so if refresh rate is important, go for this one.
We understand that ports and connectivity are at the forefront of the laptop buying process. In 2022, no one wants to use a dongle. On that note, the ThinkPad X1 Extreme Gen 5 has better connectivity when compared to the Surface Laptop Studio. The ThinkPad X1 Extreme features two Thunderbolt 4 USB-C ports, two USB-A ports, HDMI 2.1, an SD card reader, a 3.5 mm combo headphone jack, and a Nano SIM slot. We love the inclusion of HDMI as well as USB-A, since it really helps you avoid dongles. ​​​​​​
It's also why we dislike the Surface Laptop Studio. That Microsoft device only has two Thunderbolt 4 ports, a Surface Connect port for charging only, and a 3.5 mm headphone jack. With the Laptop Studio, you'll need a dongle for almost everything that isn't yet USB-C.
In other areas of connectivity, the ThinkPad X1 Extreme has better Wi-Fi. It features support for Wi-Fi 6E and optional 5G, so you can use your laptop with Wi-Fi isn't available. The Surface Laptop Studio only has Wi-Fi 6 connectivity.
In considering the performance of these devices, we want to first note that the Surface Laptop Studio has older components. The 11th-generation Intel CPU inside is a year old, and the ThinkPad X1 Extreme has the latest and greatest 12th-generation CPU from Intel. There are more graphics options on the ThinkPad X1 Extreme, too, which gives it the win here.
Though both of these laptops have Intel's H-class processors, the 45-watt chips inside the ThinkPad are newer than the 35-watt chips inside the Surface Laptop Studio. That means the ThinkPad can benefit from Intel's hybrid architecture of performance and efficiency cores. In the case of the Core i5 and Core i7 12th-generation chips, there are 14 cores total: 6 performance, and 8 efficiency. The ThinkPad also has an option for Intel's Core i9 CPU, which packs a crazy amount of power thanks to the 5.0 GHz operating range. On all models of the ThinkPad, you're going to feel great system performance, especially with tasks that need raw CPU power.
Going back to the Surface Laptop Studio, Intel's 11th-generation Core i5 and Core i7 CPUs are still powerful for most video editing and gaming, or content creation. However, they are only quad-core and only run at 35 watts. This means CPU-intensive tasks will feel a bit slower on the Surface Laptop Studio compared to the ThinkPad X1 Extreme. However, the 35W CPUs inside are a lot less power-hungry than the 45W CPUs inside the ThinkPad X1 Extreme, so despite it having a smaller battery, you'll likely get better battery life with the Surface Laptop Studio.
On the GPUs, it's hard not to pick the ThinkPad just for this reason. There are three different options on the ThinkPad. One is the same Nvidia GeForce RTX 3050 Ti as the Surface Laptop Studio, but there is the Nvidia GeForce RTX 3060 with 6GB of GDDR6​​ memory or the Nvidia GeForce RTX 3080 Ti with 16GB of GDDR6 memory. A higher-end GPU will result in better performance for gaming, 3D rendering, or other creative tasks. These last two GPUs will outperform the one in the Surface Laptop Studio quite easily since they have more GDDR6 memory.
On the subject of memory, the ThinkPad X1 Extreme lets you upgrade the RAM, and there's a dual-channel DDR5 option. You can't upgrade RAM on the Surface Laptop Studio, but it uses slower DDR4x RAM. There's also secondary storage on the ThinkPad X1 Extreme, which isn't an option on the Surface Laptop Studio, though you can upgrade the SSD if you want.
Both of these are premium laptops with great specs for heavy tasks like gaming or content creation. That being said, if you can afford it, the ThinkPad X1 Extreme wins in this comparison. Buying this might mean you'd miss out on the convertible aspect of the Surface Laptop Studio, as well as inking support and a great display, but the performance will end up being much better. Intel's 12th-generation CPUs and the option for various Nvidia graphics give the ThinkPad a lot of power and make it one of the best ThinkPads on the market.
The Surface Laptop Studio is a versatile and powerful Surface PC with Intel H35 series processors and Nvidia RTX graphics.
The Lenovo ThinkPad X1 Extreme Gen 5 is a powerful business laptop with Intel H-series processors and NVIDIA GeForce RTX graphics to handle demanding workloads and gaming.
I have over six years of experience covering Microsoft, Surface, Windows, macOS and ChromeOS news and rumors for sites like Digital Trends and OnMSFT and WinBeta. I also write laptop reviews and how-to guides. I am a Microsoft fan and I have a drawer full of PCs and other devices. You can follow and interact with me on Twitter if you want to chat! I’m always there making new friends!

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Days before the midterm elections, former President Donald Trump energized Republicans to vote for incumbents Gov. Kim Reynolds and Sen. Chuck Grassley in Sioux City, Iowa. Trump also hinted he might run for president again. Iowa Public Radio’s Clay Masters details the Trump rally, as well as an event for Grassley’s challenger, Democratic candidate Mike Franken. Then, executive director of the Soy Transportation Coalition shares how historically low water levels in the Mississippi river are creating a barrage of traffic during the harvest season.
Later in the hour, an Iowa City resident drove two of his adult daughters to Chicago to vote at the Brazilian consulate. He shares why participating in the election was worth the hours long journey. Also, River to River celebrates a century of radio with another edition of What Dennis Found in the Basement.
Finally, groove into the weekend with tunes picked by Studio One host CeCe Mitchell, including a track from “Midnights” by Taylor Swift.
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The audience globally admires Punjabi Music which is not bound to any geographical boundaries, to which another name is going to be added – MTL Productions. The well-known international business enterprise ‘Maan Traders Limited’ by Gurvinder Maan is now making inroads into the entertainment industry by presenting its four new accomplished talents to the public.
Mehvish Dhillon, Deep Aman, Baba Raja, and Deep Mehraj, four up-and-coming singers mesmerised the crowd with their gorgeous and captivating voices while showcasing their forthcoming tracks through the label.
Living legend Babu Singh Maan wrote the lyrics for the songs that the production company will release, and Mr Wow, Birgi Veerz, Sonu Saggu, Lovey Akhtar, Manpreet Singh, and Judge Saab composed the music.
Additionally, well-known Masha Ali, Feroz Khan, Naveed Akhtar and Jasmeen Akhtar have also sung for the company and they joined the artists, transforming the evening into a heavenly stage with a groovy atmosphere. The exceptional live on-stage concert added even more allure to this evening of previously unseen Jugalbandi of all the artists.
Revealing the debut Sufi song of the MTL productions, Sufi singer Kanwar Grewal, along with artist & social activist – Sonia Maan, who is also featuring in the track, took over the stage with their arrival and released the teaser of his upcoming track and gave a final magnificent performance that captivated everyone’s hearts. 
The owner of MTL Productions, Gurvinder Maan says, “ Words are not enough to express my happiness and the warmth of this welcome that I have received from the audience. This new venture is like a dream come true for me, moreover, it’s my honour to have an opportunity to introduce such exceptional talent to the Punjabi audience. Most importantly working with great artists like Kanwar Grewal and Babu Singh Maan is beyond achievement.”
 
 
 
 
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Richmond, VA – Yesli Vega, an anti-abortion extremist, is continuing to try and deny the existence of the audio recording of her saying women who are raped cannot become pregnant.  


In a recent interview with Fox News, Vega flat-out lied about her controversial comments where she was asked if it would be harder for a woman who is a victim of rape to become pregnant. To which Vega responded with, “It wouldn’t surprise me, because it’s not something that’s happening organically. You’re forcing it….I can see why there is truth to that.”


WATCH FOX NEWS:



YESLI VEGA: “You know, people have asked me about it just because her allegations are so bizarre and not true and so I appreciate voters coming directly to me getting clarification, and once we talk about the issue and where I stand on it, they’re 100% on board and supporting me.”


In a separate interview with CBS19, Vega lied to a reporter when she said,  “Those were never my comments.”


Vega has been clear on her extreme anti-abortion stance. She’s indicated in a survey that she would cosponsor and support the Life at Conception Act — a bill that would institute a total ban on abortion with no exceptions and certain contraceptives. Vega also applauded the Supreme Court’s draft opinion to overturn Roe as an “amazing victory,” then doubted the likelihood that women who are raped could become pregnant. Vega has also said only God –should decide the life of the mother. 


“Yesli Vega can lie all she wants, but women across the Commonwealth and our country will never forget her disturbing comments about victims of rape,” said DPVA Spokesperson Gianni Snidle. “Yesli Vega’s terrifying determination to rip away women’s reproductive rights is a direct threat to all Virginia families — and Virginians will reject her at the ballot box tomorrow.” 


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Date: 8 November 2022
 
Authors: Aritra Ghosh, Senthilarasu Sundaram & Tapas K. Mallick
Source: Applied Energy, Volume 228, 2018 | https://doi.org/10.1016/j.apenergy.2018.07.040
(https://www.sciencedirect.com/science/article/pii/S0306261918310687)
 
Combined semi-transparent PV-vacuum glazing provides low overall heat transfer coefficient, reduces solar heat gain, generates clean electricity and admits comfortable daylight. In this work, thermal and electrical performances of a multicrystalline silicon based PV-vacuum glazing were characterised using indoor test cell. For this particular combined system, PV covered 32% of the glazing area. Two different combinations of PV-vacuum glazing systems were manufactured where for the first case vacuum glazing faced test cell external environment (VPS) and for the second case vacuum glazing faced test cell internal environment (SPV).
SPV type was found to have superior performance as PV cell achieved lower temperature than VPS type after 125 min exposure under 1000 W/m2 constant intensity from a simulator. For this type PV-vacuum glazing, overall heat transfer coefficient (U-value) was 0.8 W/m2 K and the solar factor was 0.42. U-value of this PV-vacuum glazing was 66% lower and the solar factor was 46% lower than PV double-glazing. Close power drop from PV due to elevated temperature was observed for both PV-double and PV-vacuum glazing.

Consumption of world energy increased by 40% from 1990 to 2007. Until 2035, another 8–10% increment is expected due to rapid growth of urbanization [1]. Transport, industry and buildings are the major energy consuming sectors. Building sector alone accounts 40% of global energy. To mitigate this high building energy demand, zero energy or low energy buildings are potential [2]. To achieve efficient buildings, energy efficient windows are essential which can control incoming excessive solar heat gain (reduce building cooling load), limit heat loss (reduce building’s heating load), and maintain comfortable daylight (possess discomfort glare). However, windows are the most essential building envelope by allowing visual connection between indoor space and external environment.
Building-integrated photovoltaic (BIPV) glazings are innovative and emerging glazing technology which has capability to replace conventional low performance building facade materials. In urban cities and places where rooftop and ground areas are limited, semi-transparent BIPV glazing or glazed façades are alternative advanced technology [3]. In a photovoltaic (PV) glazing, PV device is sandwiched between two glass panes [4]. This glass –PV –glass structure is advantageous over traditional PV device as they allow daylight into the indoor space while they are installed as a BIPV. Thus, a semi-transparent BIPV glazing controls entering solar heat gain and discomfort glare, introduces comfortable daylight, and generates electricity.
PV device for BIPV glazing includes first generation silicon, second generation amorphous silicon (a-Si) [5], cadmium telluride (CdTe) [6], CIGS, third generation DSSC [7] and perovskite [8]. Second and third generation PV devices have advantages over silicon as tuning the thickness modulation of transparency is possible. However, DSSC [9], and perovskite [10], [11] both have stability issue which hindrance them to be applied as practical glazing under outdoor environmental condition. Second generation a-Si is the most investigated PV device for PV glazing application as their see through structure allow natural daylight [12]. Second generation a-Si, CdTe and CIGS have currently reached the best laboratory efficiency of 11.9%, 21.7% and 21.4% respectively [13]. Crystalline silicon is still preferable over all those aforementioned PV as it offers high efficiency and high stability under outdoor environment.
Semi-transparent type PV glazing introduces daylight illuminance into the indoor space, which is not possible to obtain from an opaque silicon based PV glazing. Thus spaced type PV structure using crystalline silicon based glazing is attractive. These regular distributions of small area PV cells block the incident incoming solar radiation and the gap between cells allow daylight and near infrared radiation (NIR) [14]. Depends on the PV coverage different transparency level is achievable [15]. To obtain best PV cell coverage, local climatic conditions, buildings orientation and consumptions details are essential [16].
PV glazing has potential to reduce cooling load but increase seasonal heating loads [17] as it blocks incoming solar heat gain. For cold climate building, comfortable daylighting, high solar gains and low heat loss are required. Thus, for large glazed façades, vacuum glazing type is the best choice to provide allowable daylight and allow solar heat gain for reduction of the space heating demand during day time [2]. Vacuum glazing is composed of two low – emissivity (e) coated glass panes, arrays of small pillars between the two glasses to sustain the outside atmospheric pressure and leak free edge sealing [18], [19].
Vacuum glazing offers low heat transfer as vacuum between two glasses reduce conductive and convective heat transfer and presence of low emission coating reduces the radiative heat transfer [20]. First vacuum glazing was fabricated using high temperature melting point solder glass edge sealing which degrades low emission coating [21]. Low temperature indium alloy edge sealing was employed later on to enable the low-e coating [22]. Cerasolzer type CS186 was also investigated for replacement of costly indium edge sealant [23]. Metal-based opaque small pillars array was replaced by transparent pillars to make this glazing more aesthetic [24].
Vacuum glazing offers similar transmittance to a double glazing with 53% low heat loss compared to double glazing [18]. This higher transmittance often creates discomfort glare. Addition of solar heat gain-glare control PV material can control this excessive glare and possess suitable daylight. Addition of spaced type PV and vacuum glazing will form low heat loss clean power generating glazing as shown in Fig. 1. Spaced type semi-transparent PV-vacuum glazing is truly multifunctional by controlling solar heat gain, heat loss, glare and introduces allowable daylight. Interesting fact of this glazing is that no distortion of transmitted light through the non-PV covered part of the glazing. This indicates that daylight available outside will be very similar to inside after passing through this glazing as light passes through only three silica made glass pane and low emission coating.
a-Si PV based PV-vacuum glazing is the only reported combined PV-vacuum glazing work where total four glass panes were employed [25]. We report for the first time thermal performance of multicrystalline silicon based semi-transparent (32% PV cell coverage and 33% spectral solar transmittance through non-cover part) PV-vacuum glazing using indoor test cell characterisation. Total number of glass panes for this work was three. In this work,
Results of this work will be beneficial for retrofit or low energy building designer.
2.1. System fabrication
To realize semi-transparent PV-vacuum glazing, vacuum glazing dimension of 0.35 m × 0.2 m from NSG SPACIA, multicrystalline solar cell dimension of 0.155 m × 0.155 m, and Pilkington k glass single glazing dimension of 0.35 m × 0.2 m were combined. Three different systems of which two PV-vacuum glazing and one PV double glazing for comparison were fabricated for the experiment. For type 1 PV vacuum glazing, PV cell was placed on the top of single glazing, and after pouring sylgard encapsulation vacuum glazing was placed on the top of it.
Type 1 PV glazing is referred in this work as VPS PV-Vacuum glazing as shown in Fig. 2. For type 2 PV-vacuum glazing, PV cell was placed on the top of vacuum glazing and after sylgard encapsulation, single glazing was placed. Type 2 is referred as SPV PV-vacuum glazing for this work. Type 3 was prepared by glass PV glass structure to compare with the aforementioned two types of PV vacuum glazing. This type of glazing is indicated as SPS or PV-double glazing. For all three types of glazings, PV cell covered 32% whereas 68% area was non-covered by PV. Details of these three systems are mentioned in Table 1.
Table 1. Details of different types of systems.

A small-scale test cell dimension of 0.37 m × 0.22 m × 0.26 m was fabricated using 10 mm thick polystyrene to perform indoor characterisation. The ratio of test cell and glazing was 1:1. Outdoor characterisation using test cell offers several constraints and internal and external variables act simultaneously. Therefore it is difficult to differentiate a single responsible variable which influences the results [26]. Thus, in this work, indoor characterisation at laboratory environment was preferred. Glazing was attached on top of the test cell such that it faces the indoor simulator directly, which resembles similar to a vertical plane glazing facing south.
For VPS type PV-vacuum glazing, vacuum glazing faced laboratory room environment and single glazing faced test cell indoor environment. For SPV type PV-vacuum glazing single glazing faced laboratory room environment and vacuum glazing faced test cell indoor environment as shown in Fig. 3.
2.2. Indoor characterisation set up
Transmittance and reflectance at solar spectrum (280 nm -2500 nm) of prepared glazing systems were measured using an ultraviolet–visible–near infrared (UV/VIS/NIR) spectrophotometer (PerkinElmer® Lambda 1050) equipped with an integrating sphere (150 mm diameter) with 2 nm interval as shown in Fig. 4.
The indoor characterisation was performed using continuous indoor sun simulator exposure. This simulator is AAA type and its spectrum matches with solar spectrum between 250 nm and 3000 nm. Five thermocouples were employed to measure external and internal glass surface, test cell ambient and indoor laboratory ambient and PV cell temperature. Pico data logger recorded 5 min interval temperature data. Photograph of full experimental set up is shown in Fig. 5.
Thermal transmission and solar heat gain are the two major modes of heat transfer through a glazing technology [27]. Overall heat transfer coefficient indicates how well a glazing is insulated by evaluating the amount of heat that passes, in steady state, through the central part of the glazing. Evaluation of overall heat transfer coefficient depends on external, internal, thermal conductivity and thickness of glazing. Solar heat gain coefficient (solar factor) shows the admittance of solar heat gain through the glazing into an indoor space [28].
Overall heat transfer coefficient for this PV vacuum glazing was calculated using below equation

where
Qin is the incident radiation on the glazing [29]

Total heat transfer through the glazing is given by [30]

The total heat inside the test cell is given by [31]

The total heat losses through the test cell can be represented by equation

Overall heat transfer coefficient of this glazing is given by

Required parameters to find out U-value for this combined vacuum glazing is listed below Table 2.
Table 2. Parameters details for U-value calculation.

Solar factor (g) or solar heat gain coefficient indicates the transmitted solar energy through a glazing. Spaced type PV-vacuum glazing consists transparent and opaque portion. Thus g value for this system is given by Eq. (7) [32].

where solar transmission, reflection and absorption are given by Eqs. (8), (9), (10) respectively. In the Eq. (7), hout and hin are the external and internal heat transfer coefficient.

Net energy flows through a glazing in a building can be decided using Eq. (11) [33].

where α shown in Eq. (12) is the ratio of solar gains to thermal losses

4.1. Spectral performance
Spectral transmission, reflectance, absorption were measured for combined PV-vacuum and PV double glazing using UV–VIS-NIR spectrophotometer and calculated using Eqs. (8), (9), (10) respectively as shown in Fig. 6. Calculated solar transmission (Ts), reflection (ρs) and absorption (As) for non-covered part of PV-vacuum glazing were 33%, 40% and 27% respectively. These parameters qualitatively define the spectral behaviour of the entire PV-vacuum glazing. Higher transmission was around 550 nm. Obtained low transmission and higher reflection in near infrared (NIR) region were due to presence of low emission coating in the vacuum glazing. Average solar transmission was 77% whereas solar reflectance and absorption was 14% and 8% respectively for PV-double glazing.
4.2. Surface thermal performance
Fig. 7 shows the temperature variation of PV vacuum glazing under constant 1000 W/m2 solar radiation from an indoor simulator. This structure is SPV type PV-vacuum glazing where single glazing faced laboratory environment and vacuum glazing faced test cell internal environment. PV cell temperature increased from 32 °C to 86.4 °C after 125 min of exposure. First 40 min of exposure, cell temperature increased at 1.25 °C/min while laboratory room temperature increased at 0.025 °C/min.
During the same interval internal glass surface temperature increased at 0.625 °C/min. Next 80 min PV cell temperature increased from 80 °C to 86 °C. Temperature difference between PV cell and internal glass surface was due to the heat insulation properties of vacuum glazing. Vacuum glazing reduces conductive and convective heat flow whereas the presence of low-e coating restricts radiative heat flow. Thus, overall heat flow from the cell through the vacuum glazing is restricted.
Fig. 8 shows different temperature of VPS type PV vacuum glazing after 125 min exposure under 1000 W/m2 using an indoor simulator. In this case, vacuum glazing faced indoor laboratory environment and single glazing faced test cell internal environment. PV cell temperature increased from 26 °C to 97 °C with an increased rate of 1.36° C/min at the first 40 min. Test cell internal temperature increased at 0.45 °C/min during the same interval. PV cell temperature and internal glass temperature both increased nearly same rate. This is due to the presence of single glazing which allowed higher heat flow from PV cell through the glass.
Fig. 9 shows the temperature changes for PV double-glazing. PV cell temperature increased at 1 °C/min at first 40 min of exposure. This increment is very close to the SPV type PV vacuum glazing. Spectral behaviour of PV-double glazing showed (Fig. 6) higher solar transmission compared to PV-vacuum glazing (Fig. 6). Moreover, PV-vacuum glazing’s solar transmission reduced due to the presence of low-e coating.
However, in Fig. 9 the test cell internal temperature was low compared to both combinations of PV-vacuum glazing. The reason for this lower test cell internal temperature was due to the low thermal insulation of this double glazing. This double glazing was prepared by attaching two 4 mm thick single glazing with minimal (1 mm) air gap between them. Thus this combined glazing behaved similar to an 8 mm thick single glazing under indoor simulator exposure. Due to the thermal bridge between two glass panes, heat loses where higher compared to PV-vacuum glazing.
4.3. PV performance
PV cell performance for two different type combined PV-vacuum glazing was measured using I-V tracer as shown in Fig. 10. PV double glazing measurement was also conducted for comparison. Measurements were carried out for 125 min continuous exposure under 1000 W/m2 indoor simulator radiation. PV cell temperature dependent open circuit voltage (Voc) has been shown for three different types of glazing systems in Fig. 10. Voc has dependency on the characteristics of PV material.
Thus, variation of Voc depends on the saturation current and light generated current. PV cell temperature dependent Voc is given by Eq. (13) [34]. At high cell temperature, energy band gap of PV cell decreases (semiconductor bandgaps decrease with temperature [35]) and consequently Voc is decreased. Reduction of Voc for VPS type glazing was 25% from its initial conditions while for SPV and SPS type it was 20% and 16% respectively.

FF shows optimal current/voltage trade-off and the minimal extracting cost of photogenerated charges from the cell into the circuit [35]. At higher illumination due to increase of current flow resistive losses increase which can be seen from series resistance (Rs) dependent FF Eq. (14) [36]. In this work reduction of FF for SPV, VPS, SPS type were 13%, 23%, 19%. Decrease of FF was due to the increase in resistive losses for the higher continuous illumination.

Maximum power of PV cell can be expressed by Eq. (15). Thus, reduction of maximum power (Pm) is directly proportional to the reduction of open circuit voltage, and Fill factor (FF). Reduction of maximum power for SPV, VPS, and SPS type from initial conditions were 38%, 44%, 34% respectively. Maximum power drop of PV cell was not remarkably high for the presence of vacuum glazing compared to single glazing. Maximum power output reduced in higher order for VPS type glazing as PV cell temperature increased from 32.7 °C to 96 °C.

It is seen from Fig. 10 that the open circuit voltage (Voc) and fill factor (FF) and maximum power (Pm) decreased with higher PV cell temperature. These results are in agreement with the previously published literature [37], [38], [39], [40]. The relative change in FF, Voc and Pm for three different systems are listed in Table 3. Close power drop of PV for PV-vacuum and PV-double glazing enhances the possibility of using PV-vacuum for glazed façade application.
Table 3. Relative change of FF, Voc, Pm for VPS and SPS type PV vacuum glazing and PV double glazing.

4.4. Thermal transmission and solar factor (SHGC)
Thermal transmission of PV-vacuum glazing and PV double-glazing was calculated using Eq. (6). Calculation was performed after surface temperature reached close to steady state condition. Fig. 11 shows the overall heat transfer coefficient of PV-vacuum glazing. Calculated average U-value for this glazing was 0.8 W/m2 K. Previously, using outdoor test cell, thermal transmission of vacuum glazing and combined SPD-vacuum glazing were found to be 1.4 W/m2 K [18] and 1.0 W/m2 K [30] respectively.
Higher external wind speed influenced those results. In this work wind speed was negligible and temperature difference between test cell internal and laboratory environment govern the result. Fig. 12 shows the overall heat transfer coefficient of PV double-glazing. Average U–value for this system was 2.37 W/m2 K. Previously evaluated double glazing U-value for outdoor condition was 2.98 W/m2 K [29]. Thus, thermal transmission results of both PV-vacuum and PV double glazing are in agreement with the previously published work.
Higher temperature difference between test cell internal and external environment was found for PV-vacuum combined glazing due to lower U-value than PV double glazing. U-value reduction of 66% was possible using vacuum glazing integrated with PV glazing. Solar factor for these combined glazing was calculated using Eq. (7). For PV vacuum glazing the solar factor was 0.42 whereas for PV-double glazing solar factor was 0.79. Net energy through this PV vacuum glazing was evaluated using Eq. (12). Net energy higher than 1 indicates this glazing is suitable for cold climate and shows 46% higher energy gain compared to PV-double glazing. Table 4 listed the different thermal loss and solar gain of these two glazings.
Table 4. thermal loss, solar gain and ratio of solar gain thermal loss for PV-vacuum and PV-double glazing.

Experimentally thermal and electrical characteristics were performed for semi-transparent (32% area was covered by PV cell) combined PV- vacuum glazing and PV double glazing using indoor test cell. For PV-vacuum glazing, two different combinations SPV and VPS types were fabricated. Total three glass panes were employed to manufacture each type of PV-vacuum glazing system. Vacuum glazing was facing test cell indoor environment and single glazing was facing laboratory environment for SPV type whereas vacuum glazing was facing laboratory environment and single glazing was facing test cell environment for VPS type. Two glass panes PV double glazing was manufactured and employed to compare results with semi-transparent PV-vacuum glazing.
For all three types of glazing, PV cell temperatures were higher than glass surface and test cell indoor temperature. Maximum PV cell temperature was 97 °C for VPS type as presence of vacuum glazing on the external side works as a heat insulator. Rise of PV cell temperature was lower for SPV type due to presence of single glazing on the external side which allowed higher heat transfer from PV cells to environment. Semi-transparent SPV and VPS both offered equal U-value, solar factor (g-value) and transmission, however, PV cell performance was superior for SPV type.
PV cell performance for PV-vacuum glazing did not outperform in higher order compared to PV-double glazing. Moreover, 66% lower U-value and 42% lower solar factor make PV-vacuum glazing a suitable candidate for low energy building. For cold climatic condition, PV-vacuum glazing is potential due to low U-value. Net energy analysis also confirms that this PV-vacuum combination is useful for cold climate. Lower ambient temperature can alleviate or control the rise of PV cell temperature, which will enhance the power output. This is the first electrical performance investigation of PV cell using PV-vacuum combination.
This work has been conducted as part of the research project ‘Joint UK-India Clean Energy Centre (JUICE)’ which is funded by the RCUK’s Energy Programme (contract no: EP/P003605/1). The projects funders were not directly involved in the writing of this article. In support of open access research, all underlying article materials (data, models) can be accessed upon request via email to the corresponding author.
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Aerial shot of the Missouri S&T campus in fall. Photo by Terry Barner/Missouri S&T
Missouri S&T’s online master’s degree program in psychological science recently earned a top ranking from the website Forbes Advisor.
The finance-focused Forbes Advisor, which is dedicated to helping consumers make the best financial choices for their individual lives, recently picked Missouri S&T as one of its top 10 “Best Online Psychology Master’s Degrees.”
Forbes Advisor scored 45 colleges and universities that offer online programs in the United States using 17 data points in the categories of credibility (including accreditation and nonprofit status), affordability, student outcomes, student experience and cost of application fees. Specific characteristics that were considered within each category included accreditation, nonprofit status, the average price per year, financial aid offerings, Pell Grant recipient graduation rate, graduation rate and transfer-out rate.
“It comes as no surprise to me that our master’s degree in industrial-organizational psychology is ranked so highly,” says Dr. Clair Reynolds Kueny, interim chair and associate professor of psychological science at S&T. “S&T students receive a strong core foundation in psychology, as well as statistics and research methods. Our program develops individuals who understand the critical role humans play in organizational success as well as how to utilize human capital and people analytics to advance organizational effectiveness and strategy, and employee development and support.”
Missouri S&T’s online master’s degree in industrial-organizational psychology emphasizes the application of psychological science to enhance the performance and well-being of people in organizations. I-O psychologists develop assessments of people for selection and placement into jobs, effective training programs, strategies for organizational development, measurement of performance, and ways to promote quality of work-life.
About Missouri University of Science and Technology
Missouri University of Science and Technology (Missouri S&T) is a STEM-focused research university of over 7,000 students. Part of the four-campus University of Missouri System and located in Rolla, Missouri, Missouri S&T offers 101 degrees in 40 areas of study and is among the nation’s top 10 universities for return on investment, according to Business Insider. S&T also is home to the Kummer Institute, made possible by a $300 million gift from Fred and June Kummer. For more information about Missouri S&T, visit www.mst.edu.
On November 7, 2022. Posted in College of Arts, Sciences, and Business, College of Arts, Sciences, and Education, Department of Psychology, News, University News
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The landlord for Chicago’s tallest building, Willis Tower, promoted Britton Derkac to senior vice president and national head of leasing.
In his new role, which creates a dedicated leasing function within EQ, Derkac will handle all leasing activity within EQ’s portfolio, as well as manage strategic relationship with customers and brokers to retain customers and develop new business.
Derkac has nearly 20 years of commercial real estate experience including spending the last 14 with EQ Office. Previously, he was EQ’s senior vice president and portfolio director, working on value creation as well as EQ’s strategies for buying and selling properties.
“[Derkac’s] dedication to our partners makes him the ideal candidate and representative of the EQ Office brand,” said EQ Office CEO Alex Vouvalides in a statement. “We’re confident that under Brit’s leadership, EQ will continue to help companies attract, retain and inspire talent by creating effective work environments.”
“As the workplace continues to evolve, EQ Office has an opportunity to shape the customer experience — to become a strategic partner that works to deliver value and design space that not only fits —– but exceeds future needs,” said Derkac in the statement.
The portfolio of EQ Office, a unit of New York-based Blackstone Inc., includes the 110-story Willis Tower, which completed its $500 million renovation project this spring.
© 2022 American City Business Journals. All rights reserved. Use of and/or registration on any portion of this site constitutes acceptance of our User Agreement (updated January 1, 2021) and Privacy Policy and Cookie Statement (updated July 1, 2022). The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of American City Business Journals.

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Cameron Pohl is a tattoo artist thrust into the limelight through his time on Ink Master season 12. His career beginnings and decision to appear on the show is discussed in the article below.
Cam Pohl has always been interested in art. Since he was a child, it was his only pursuit of self-expression; and it appears to have turned into a lifelong career for this talented individual.
When passion becomes work, it can quickly turn into something less desirable for many people. But Cam Pohl is one of the lucky ones — immersing himself in the culture of tattooing for almost his entire working life has only deepened his love of the art.
In a 2019 interview, Cameron Pohl spoke about how his love of art blossomed to a career in tattooing. He reminisced on his time doodling and drawing in class rather than paying attention before heading to a graphic design course at community college, ultimately realizing the college scene wasn’t for him.
After what many may call a setback, Cameron Pohl entered a tattoo parlor in his hometown on a whim, asking, “Is there anything I can do?”
The studio’s employees were somewhat taken aback, but Pohl persevered, popping in three weeks in a row before someone finally said, “Do you want to mop the floors?” And he agreed. Little did he know that sentence would put him on a path to tattoo-related fame in a relatively short period.
Although he purchased an ink machine at 17, the now 30-year-old talented artist began his tattooing journey in 2013 at the tender age of 21, noting that it was a tremendously humbling experience.
However, he quickly realized that it was much more than a career path. It was a lifestyle. Every aspect of his life was rapidly encompassed in a huge culmination of art and culture, and he couldn’t have asked for anything more from his profession — especially since it led to him being cast on Ink Master season 12 in 2019.
Pohl’s Reservations About the Show
When asked during an interview if he was a fan of the program prior to appearing, he said he had heard about it but never taken it seriously. In his mind, it was all about the drama, not the artistry.
From seeing clips and hearing reviews of the show, he thought the producers focused on the aspects the public would best respond to. But after being cast, he stated his eyes were opened to what it takes to succeed.
Cameron Pohl’s Time on Season 12 of Ink Master
Many have often wondered how someone who wasn’t necessarily ready for the spotlight appeared on the show.
Interestingly, Cam Pohl states that was asked to appear in a previous season before the arrangement fell through, with the producers saying, “This isn’t your season, but we’ll keep you in mind.”
While he was frustrated at this turn of events due to dramatic schedule changes, he agreed to appear in season 12, giving Ink Master lovers a lot to talk about.
Fans of the show know Cameron Pohl as an outspoken, often-targeted individual. However, he displayed incredible artistic talent throughout his appearances, managing to prove himself despite the fewer years of experience he had compared with the competition.
In another interview, he talks of his time on the show and how he feels looking back.
Cameron Pohl mentioned the array of feelings he felt witnessing the opinions of those who he believed he was close with during filming. But despite that, he’s content with his actions and portrayal on the show, stating he stayed authentic to himself and his values.
As for the tattoo artistry, he mostly enjoyed the cross-stitch hamsa he created. He reveled in the challenge set by the judges as it was a style none of the artists expected.
Ultimately, Pohl enjoyed the experience and notoriety the show brought to his life and career.
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On November 8, 2022, the Moon passed fully into Earth's shadow and produced a total lunar eclipse. Totality took place between 5:17 a.m. and 6:42 a.m. EST (10:17 and 11:42 UTC), with the partial and penumbral phases of the eclipse continuing until 8:50 a.m. EST (13:50 UTC).
Explore the eclipse second-by-second with an interactive data visualization from NASA’s Scientific Visualization Studio and the Lunar Reconnaissance Orbiter:
November 8, 2022 Total Lunar Eclipse – Telescopic View
Last updated: Nov 8, 2022
NASA Official: Lori Glaze
Producer: Molly Wasser
Content Development: Andrea Jones, Caela Barry, Tracy Vogel
Graphics: Vi Nguyen

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Hydropower plants are known as major renewable energy sources, usually used to meet energy demand during peak periods. The performance of hydropower reservoir systems is mainly affected by their operating rules, thus, optimizing these rules results in higher and/or more reliable energy production. Due to the complex nonlinear, nonconvex, and multivariable characteristics of the hydropower system equations, deriving the operating rules of these systems remains a challenging issue in multi-reservoir systems optimization. This study develops a self-adaptive teaching learning-based algorithm with differential evolution (SATLDE) to derive reliable and precise operating rules for multi-reservoir hydropower systems. The main novelty of SATLDE is its enhanced teaching and learning mechanism with three significant improvements: (i) a ranking probability mechanism is introduced to select the learner or teacher stage adaptively; (ii) at the teacher stage, the teaching mechanism is redefined based on learners’ performance/level; and (iii) at the learner stage, an effective mutation operator with adaptive control parameters is proposed to boost exploration ability. The proposed SATLDE algorithm is applied to the ten-reservoir benchmark systems and a real-world hydropower system in Iran. The results illustrate that the SATLDE achieves superior precision and reliability to other methods. Moreover, results show that SATLDE can increase the total power generation by up to 23.70% compared to other advanced optimization methods. Therefore, this study develops an efficient tool to extract optimal operating rules for the mentioned systems.
Hydropower is one of the most common sources of renewable energy, which is highly flexible to generate and has low environmental contamination compared with the fossil^1,2,3,4. Most hydroelectricity is generated at large dams in the world, and several of the largest hydropower dams are located in the southern Iran. A dam reservoir maintains water to different target elevations such as flood control, hydropower production, and supplying irrigation demands^2,5,6. A multi-reservoir system is a set of reservoirs placed in the same river basin. Optimizing the systems in terms of operation is useful for the comprehensive development of the river basin^7,8. Extracting optimal operating rules for multi-reservoir hydropower systems is considered an arduous engineering problem because of the following reasons: (1) its optimization problem is commonly large-scale with a lot of constraints and unknown variables⁹; (2) due to different operational and physical constraints and opposite objectives¹⁰, it is not simple to achieve a suitable solution that meets all constraints¹¹ and (3) the hydropower systems are nonconvex, nonlinear and non-differentiable¹².
To optimize the problems related to the hydropower reservoir operation systems different types of optimization methods have been developed. In general, the optimization methods can be classified into three categories: (1) classical (traditional) techniques, (2) metaheuristic algorithms, and (3) hybrid algorithms. Traditional methods include linear programming (LP), nonlinear programming (NLP), and dynamic programming (DP). A metaheuristic optimization algorithm is a contemporary approach for guiding the search toward the optimal sections of the decision space in a given situation. A lot of various metaheuristic algorithms have been developed, comprising genetic algorithm (GA)¹³, differential evolution (DE)¹⁴, particle swarm optimization (PSO)¹⁵, gravitational search algorithm (GSA)¹⁶, cuckoo search (CS)¹⁷, artificial bee colony (ABC)¹⁸, gradient-based optimizer (GBO)¹⁹, Runge–Kutta optimization (RUN)²⁰, and weighted mean of vectors (INFO)²¹.
Metaheuristic techniques are substantially flexible beyond the classical optimization ones because they do not need the constraints and fitness functions to be differentiable, convex, and linear, and therefore the methods can be taken on for solving a mixed variety of optimization problems. In this regard, these techniques are not able to ensure the global optimum, but they globally search for it and therefore are expected to find a near-optimum solution. Also, these approaches can readily be coupled with simulation models without the requirement for making any simplifying assumption^{22,23,24,25,26}. Hybrid optimization algorithms use the strengths of various algorithms to achieve a more powerful optimization algorithm. For instance, Ahmadianfar et al. developed a hybrid optimization algorithm called A-DEPSO (i.e., Adaptive Differential Evolution with Particle Swarm Optimization) by combining the DE algorithm with PSO to optimize nonlinear and nonconvex hydropower systems²⁷.
By 2021, a wide range of optimization procedures have been proposed to solve hydropower systems. Zhang et al. developed a multi-elite guide PSO (MGPSO) approach to maximize the energy provided by a multi-reservoir system consisting of ten cascaded hydroelectric units²⁸. Asadzadeh et al. applied the Pareto Archived Dynamically Dimensioned Search heuristic multi-objective optimization algorithm to design the operation of the Great Lakes of the North America⁸. Taghian et al. used the GA algorithm to extract optimal hedging rule curves for a set of reservoirs built in a river in Iran²⁹. Ahmadianfar et al. improved the bat algorithm (BA) utilizing the DE algorithm and used to find better solutions for sophisticated multi-reservoir systems³⁰. Bozorg-Haddad et al. applied the biogeography-based optimization (BBO) to solve a single- and multi-reservoir system in Iran³¹. Their results confirmed the ability of the BBO algorithm to optimize the systems. Moravej and Hosseini-Moghari assessed the interior search algorithm (ISA) to solve problems in water resource management, and demonstrated that the ISA has an efficient performance to optimize complicated multi-reservoir problems³². Ehteram et al. optimized operation of the single- and multi-reservoir systems using shark algorithm (SA)³². They showed that the SA has the better efficiency to solve reservoir operation problems than the PSO and GA methods. Ahmadianfar et al. applied a powerful optimization technique namely hybrid of differential evolution (DE) and particle swarm optimization (PSO) with multi-strategy (MS-DEPSO) to solve multi-reservoir systems, which have purpose of hydropower generation and irrigation supply²³. The outcomes indicated that the proposed optimizer can effectively extract operating rules for multi-reservoir systems. Liu et al. evaluated the ability of lion swarm optimization (LSO) algorithm to optimize the dispatch of cascade hydropower stations located in China³³. They demonstrated that the LSO algorithm has a better efficiency than the GA, PSO, and improved cuckoo search (CS) algorithms in terms of reliability and accuracy. Feng et al. developed a modified version of sine cosine algorithm (SCA) called quasi-opposition SCA (QSCA) to precision optimization of multiple hydropower reservoir systems⁴. Their findings demonstrated that the suggested QSCA approach, which is based on the convergence rate and the quality of the solution, is a dependable and resilient method. It should be noted that all the studies mentioned above were single-objective. In applying optimization methods in the multi-objective water resources problems, many studies, such as Ahmadianfar et al., introduced a novel multi-objective PSO with DE algorithm (MOPSO-DE) to optimize a multi-reservoir and multi-objective system in Iran³⁴. They showed that the proposed method could significantly mitigate severe drought periods. Hatamkhani and Moridi applied the MOPSO algorithm and linked it to the water evaluation and planning system (WEAP) model to optimize long-term planning at a basin in Iran³⁵. The findings proved that the simulation–optimization model performed its duties correctly in the context of the best distribution and planning of water resources within the basin. Fang and Popole developed an efficient multi-objective PSO (MOPSO) improved by adopting self-organizing mapping (SOM) method³⁶. They applied the proposed algorithm on a hydropower system in China. The findings demonstrate that the model can obtain the best schedule by considering the environmental advantages and the benefits of power production. Despite the considerable success that has been obtained in practice, the utilization of the above optimizers is still confined by some drawbacks, such as parameter tuning, dimensionality course, and premature convergence^{4,21,23,37,38,39,40}.
Teaching–learning-based optimization (TLBO) introduced by Ref.⁴¹ emulates the teaching–learning procedure in a class. TLBO is an effective and simple optimizer with only one parameter tuning (i.e., the size of population). Several variants of TLBO have been recently developed, e.g. generalized oppositional TLBO (GOTLBO)⁴², self-adaptive TLBO (SATLBO)⁴³, and improved TLBO (ITLBO)⁴⁴. However, these variants of the TLBO algorithm suffer from low reliability, insufficient precision, especially for complex and high dimensional problems³⁸.
SATLDE is developed in this study to derive reliable and precise operating rules of hydropower reservoir operation systems. SATLDE uses a ranking mechanism to adaptively select a learner or teacher stage, instead of selecting both stages, to reduce the computing cost significantly, in each iteration. At the teacher stage, the teaching mechanism is enhanced to teach learners based on the level (performance) of each learner, aiming to assist all learners to achieve promising levels. Also, at the learner stage, an effective mutation operator is introduced based on the DE algorithm with self-adaptive control parameters to promote population diversity and boost wider global search ability, avoiding TLBO from being trapped into local optimal solutions. This paper evaluates the performance of SATLDE in the context of addressing benchmark and real-world multi-reservoir hydropower system problems.
The structure of paper can be arranged as follows. “Methodology” section expresses the TLBO and SATLDE algorithms. The performance criteria of optimization method are described in “Teaching learning-based optimization” section. The mathematical formulation and outcomes of ten-reservoir ordeals is illustrated in “Performance assessment of the optimizers” section. “Results and discussion” section formulates a real-world case study with four reservoirs, stating the experimental outcomes. In the last Section, results and complementary information are given.
Defining the operating rules of hydropower reservoir systems has the most significant impact on the performance of these systems; improving these rules could lead to an increased energy output that is either more dependable or more consistent. Specifying operating rules for these systems continues to be a challenging topic in multi-reservoir system optimization because of the complicated nonlinear and multivariable properties of the equations that make up the hydropower system. Therefore, providing an efficient optimization algorithm to solve these systems is an inevitable task. In this section, a benchmark multi-reservoir system with ten reservoir is modeled and the performance of proposed optimization method is assessed on this problem. Finally, optimal operating rules are extracted using the proposed optimization method for a real-world multi-reservoir system in Iran.
A well-known hypothetical multi-reservoir system namely the ten-reservoir system introduced by Ref.⁴⁵ is solved by all optimization algorithms introduced in the previous section. The problem is substantially more complex that the four-reservoir problem due to the large number of constraints and decision variables. Figure 1 displays the schematic of this system. As it can be seen from this figure, the system has 10 reservoirs with the purpose of hydropower generation at 12-time steps. Detail of this system were provided in Ref.⁴⁵. The fitness function of the problem is defined by Eq. (1),
where (F) denotes the fitness function, (l) denotes reservoir number, (L) is known as the total number of reservoirs, (T) denotes the total number of simulation periods, ({C}_{t}^{l}) is stated as the benefit of lth reservoir at period t, ({R}_{t}^{l}) is the lth reservoir release amount at period t.
Schematic of ten-reservoir problem.
The main constraints of this system are described by Eqs. (2)–(5),
where ({V}_{t+1}^{l}) and ({V}_{t}^{l}) are the volume of lth reservoir storage at period t + 1 and t respectively, ({In}_{t}^{l}) denotes the amount of inflow into lth storage at period t, ({R}_{min}^{l}) and ({R}_{max}^{l}) denote the minimum and maximum volume of release form lth reservoir storage at period t, ({V}_{min}^{l}) and ({V}_{max}^{l}) denote the lowest and highest volume of storage of lth reservoir storage at period span t. In this problem, the main decision variables are considered the amount of reservoir release (({R}_{t}^{l})) and the state variables are the volume of reservoir storage (({V}_{t}^{l})). Therefore, the constraints related to releases are directly handled by the proposed optimizer, while the constraints applied to the reservoir storage volumes are embedded as the penalty functions (PFs). In this regard, the fitness function defined in Eq. (1) can be redefined by embedding the following PFs,
where ({d}_{1}), ({d}_{2}), and ({d}_{3}) are the PF coefficients and are equal to 60^22,30. Accordingly, the fitness function can be expressed as,
In the current section, a real-world multi-reservoir system with the hydropower generation purpose is considered for examining the performance of SATLDE. This multi-reservoir system is situated in the Karoon–Dez (KD) basin, the southwest of Iran. In the KD basin, there are four reservoirs including Karoon3, Karoon1, Godar, and Dez. The map of study area is depicted in Fig. 2. This system was optimized during a 55-year operational time period (monthly time step). The diagram of this four-reservoir system is displayed in Fig. 3. In addition, the monthly inflow into the system is depicted in Fig. 4.
Location of Karoon–Dez basin.
Diagram of the real-world multi-reservoir system.
Monthly inflow into the multi-reservoir system.
The mathematical formulation of the multi-reservoir system operation is explained in this section. It should be noted that the proposed problem was simplified and turned into a single objective problem. Therefore, the fitness function and constraints are formulated based on this simplification. It is worth noting that the main goal of the proposed four-reservoir system is power production, so we formulated the problem based on maximizing power generated at each time step (monthly). The fitness (objective) function, limitations, and the proposed operating rule are all specified in great details.
The fitness function of the multi-reservoir hydropower system utilized here is to maximize the total power generated of the mentioned system, subject to different physical and technical constraints. In this research, the fitness function is to reach the lowest disparity between the maximum power produced by power plants and power generated by each of them over the operation periods, which is defined below,
where (Z) denotes the fitness function, ({Power}_{t}^{l}) is the energy yield of the lth reservoir over the tth time period, ({Power}_{max}^{l}) denotes the maximum power produced by the lth power plant over the tth time period. (g) is deemed 9.81, ({e}^{l}) is the efficiency of lth power plant at the tth period, ({havg}_{t}^{l}) denotes the water head of the lth power plant at the tth period, ({f}^{l}) denotes the plant factor of the lth power plant, ({h}_{t}^{l}) denotes the water level of lth reservoir during the tth time period, ({htail}^{l}) is the downstream water level of lth reservoir.
In this subsection, the main constraints of this problem are expressed as,
where ({Sp}_{t}^{l}) denotes the spill form the lth reservoir at the tth time period, ({Evp}_{t}^{l}) denotes the evaporation volumes from the lth reservoir at the tth time period, ({Area}_{t}^{l}) denotes the lth reservoir at the tth time period, ({hevp}_{t}^{l}) denotes the evaporation level of the lth reservoir at the tth time period.
For the multi-reservoir system, this section introduces a nonlinear operating rule. The rule is implemented by conditioning reservoir release on reservoir storage and inflow into each reservoir at each time step⁴⁶. Based on this rule, a suitable regression analysis is used to fit a nonlinear relationship to the data, which is formulated as,
where ({alpha }^{l}) and ({u}^{l}) are the main parameters of this rule for the lth reservoir, which are determined by the optimizer.
The main steps for extracting the optimal operating rules for the hydropower multi-reservoir system are described as,
Generate a set of random values (96 decision variables: 24 variables for each reservoir) using the optimization method within a feasible range (feasible ranges for (alpha) and (u) are (0, 1] and [− 400, 400], respectively) and calculating the reservoir release for each reservoir using Eq. (16).
Enter the releases as the input to the reservoir simulation model to calculate the volume of storage at period t + 1 utilizing Eq. (10).
Evaluate the storage volumes to be in the range of the allowable range.
Save the values of each created releases if they are in the range of search space.
Repeat steps (b)–(d) for calculating the volumes of releases and computing the fitness function for all time steps using Eq. (9). Next, the stopping criteria in the optimization process is evaluated, if it is met, then the simulation–optimization process is finished, else go to Step (b).
TLBO is known as an effective optimization algorithm⁴¹. This algorithm mimics the teaching process in a class and is deemed a population-based optimizer. The optimization process in TLBO includes two main stages: the teacher stage (TS) and learner stage (LS). In the first stage (i.e., TS), teacher tries share its data with the existing learners and in the LS, learners teach each other⁴¹.
There is a teacher and Np − 1 learners in the TLBO algorithm, where Np is the number of members in a population. In TS, the teacher shares its information with learners to promote the mean score of the class. In this algorithm, the teaching action can be formulated as,
where ({x}_{TS,k}) denotes the kth position calculated in TS, ({r}_{1}) denotes a random number in the interval of [0, 1]. ({x}_{T}) is the position of teacher (i.e., best-so-far position), (TF) is the factor of teaching and is equal to (round(1+rand)). (rand) denotes a random number in the interval of [0, 1]. ({x}_{M}) denotes the mean value of class, which is defined in Eq. (18).
After the position of all learners was updated in TS, they are assessed by the fitness function (f(x)). If (f({x}_{TS,k})) is better than (f({x}_{k})), then ({x}_{k}) is substituted with ({x}_{TS,k}); otherwise, ({x}_{k}) is maintained.
In LS, a learner boosts itself by choosing another random learner from the population, which is formulated in Eq. (19).
where ({x}_{l}) is the lth learner and (lne k). After creating the ({x}_{LS,k}), if (fleft({x}_{LS,k}right)) is superior than (fleft({x}_{k}right)), then accept ({x}_{LS,k}).
The TLBO method is a simple and effective optimization algorithm that solves the problem by mimicking the teaching–learning process in a classroom. In the TLBO, the optimization process is implemented by the teacher and learner stages, bring along a lot of computational time (2Np) compared with other optimization algorithms⁴³. In the TS, whereas the learner’s score is likely to be promoted, the enhancement of each learner’s score depends to some extent on their capability. In addition, in LS, a learner (({x}_{l})) is randomly selected to share information, which may lead to confined learning capability and ultimately a weak global searchability (exploration)^42,43,44. Accordingly, implementing a strategy that adaptively selects LT or LS based on the performance of learners can significantly decrease the computational time. Hence, TLBO is enhanced to the self-adaptive teaching learning-based with differential evolution (SATLDE) in this study, which is described in the subsequent sections.
Ranking probability strategy (RPS) proposed by Ref.⁴³ is used in SATLDE in order to better identify the degrees of various learners. According to RPS, the whole of the learners is sorted from the best to the worst based on their objective function value as in Eq. (20),
where (Ind) denotes the index of learners after sorting. (f) denotes the objective function values.
Next, the ranking of all learners is calculated as in Eq. (21).
Finally, the ranking probability of each learner ((Rankp)) is formulated as Eq. (22).
According to Eq. (22), the better learners have the larger (Rankp) while the worst learners have lower (Rankp).
The original TLBO uses the teacher (({x}_{T})) and the mean level of learners (({x}_{M})) for guiding all learners in the teacher stage (see Eq. (1)). Generally, learning levels of various learners is different from each other. Therefore, this study suggests employing an effective technique to assist diverse learners in achieving a better score. Accordingly, learners in a class can be divided into two groups. To specify these groups, ({x}_{M}) has a key role. With this consideration, learners are located with the better learning level in the first group with condition (fleft({x}_{k}right)<f({x}_{M})), while the second group ((fleft({x}_{k}right)ge f({x}_{M}))) belongs to the worse learners. In the proposed strategy, better ones are guided by ({x}_{T}), the teacher, and ({x}_{k}), the current learner. Additionally, in order to stay away from local solutions, two learners are picked at random and utilized to conduct the better learners. The proposed teacher stage procedure in SATLDE is presented by script (S1).
where (i1) and (i2) are two random integer number in the interval of [1, Np] and (i1ne i2ne k). It is noteworthy that (i2) is randomly selected from the union (Pcup Ar), where (P) is the current population and (Ar) is a set of inferior solutions at each iteration⁴⁷. ({alpha }_{k}) denotes the scale factor, which is calculated as follows,
where (randn) denotes a random number with a normal distribution. ({upsilon }_{alpha }) is equal to 0.5 in the first iteration and updated by utilizing Eq. (24),
where ({S}_{alpha }) is the set of all successful learner factors ({alpha }_{k}) at each iteration. (M) is the size of ({S}_{alpha }). (df) is the difference between the kth learner and successful learner and it can be defined according to the following script (S2).
As stated in “Real-world multi-reservoir system” section, TLBO uses a random learner to find a better region in the search space, leading to confined learning capability and weak global search. Hence, it is critical for the TLBO to improve the efficiency of the learning stage. In this research, an efficient mutant vector is utilized to create the new learner ({x}_{new,k}), which is expressed as,
According to Eq. (25), the knowledge of learners (({x}_{i1}), ({x}_{i2}), and ({x}_{k})) can be fully used and an exploration capability can be guaranteed. The proposed algorithm uses random vectors (({x}_{i1}-{x}_{i2})) to search globally in the solution space at each iteration. This mechanism is used to have a chance to explore most regions of the search space to find better solutions.
In order to improve the population variety, crossover is one of the most often used operators⁴⁸. This operator combines the new learner created by the TS or LS with the current learner at each iteration and generates a new learner, which is given as,
in which
where (jr) denotes an inter random number choosen from the interval of [1, D], (NFE) is the number of function evaluations, (MaxNFE) is maximum number of function evaluations, ({xp}_{k}) is a learner which is specified from between ({x}_{k}) and ({x}_{lb}) based on the condition (rand<(1-frac{NFE}{MaxNFE})). Accordign to Eq. (14), in the first iteration the learner ({xp}_{k}) is most probably equal to ({x}_{k}) and in the last iteration it is most probably equal to ({x}_{lb}). ({Cr}_{k}) is the kth crossover rate, which is determined based on Eq. (28),
where ({upsilon }_{Cr}) is equal to 0.5 in the first iteration and updated by using Eqs. (29) and (29-1),
in which
where ({S}_{Cr}) denotes the set of all successful learner crossover rate (({Cr}_{k})) at each iteration. Algorithm 1 gives the pseudo code of SATLDE algorithm. Also, the SATLDE flowchart is displayed in Fig. 5.
Flowchart of SATLDE.
The correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Willmott’s agreement Index (IA), the Legate and McCabe’s index ((text{E})), and centered root mean square error (cRMSE) are taken on to evaluate the efficiency of optimization methods, which are defined here.
where N is number of dataset (for four- and ten-reservoir problems is equal to 48 and 120, respectively), ({R}_{LP,i}) denotes the reservoir release obtained by LP model, ({R}_{OM,i}) denotes the reservoir release achieved by optimization methods, (overline{{R }_{LP}}) and (overline{{R }_{OM}}) denote the mean values of reservoir releases optimized by LP and optimization methods, respectively. Decisions based on these criteria are difficult, and for easier decision-making in order to select the best method, they can be turned into a single criterion. In this paper, a multi-index metric was applied to assess the performance of optimization methods, which is called the performance index (PI) and defined as,
where ({R}_{min}), ({E}_{min}), and ({IA}_{min}) are the minimum value of (R), (E), and (IA) calculated by all optimizers. Also, ({RMSE}_{max}), ({MAE}_{max}), and ({MAEP}_{max}) are the maximum value of (RMSE), (MAE), and (MAEP) computed by seven optimizers.
In this study an effective graphical method called Taylor diagram⁴⁹ is used to better show the efficiency of proposed method compared with the other methods. To implement this graph, it is used three performance indices, including standard deviation (SD), correlation coefficient (R), and centered root mean square error ((cRMSE)). This graph is plotted in a polar space in which its geometrical distance to the target point indicates the efficiency of each method⁴⁹. The (cRMSE) can be formulated as,
where ({SD}_{LP}) and ({SD}_{Alg}) refers the standard deviation of the optimal values of releases caluclated by the LP and optimization algorithms, respectively.
Optimal results of ten-reservoir problem calculated by all certain optimization techniques are pointed out in this section. The problem was solved by using LINGO 8.0 software and the global solution was 1194.44^30,50. The control parameter amounts of all optimizers for the problem are illustrated in Table 1. Also, the population size and (MaxNFE) for this problem are equal to100 and 600,000, respectively. Table 2 shows the outcomes achieved by the seven competitors, including the best, worst, mean, and SD for this problem in 30 different experiments. For ten-reservoir problem, only SATLDE can achieve the best values on four metrics (i.e., the best (1196.98), worst (1194.00), mean (1195.85), and SD (0.84)). A polar space is used to produce this graph, and the geometrical distance between the target point and the graph’s origin represents the effectiveness of each approach. From the results, it can powerfully certificate the importance of the proposed SATLDE in optimizing complicated multi-reservoir optimization problem. It should be noted that for this problem the value of global optimum is 1194.44, while the best value obtained by the SATLDE is 1196.98. Therefore, there are a difference between the optimum value achieved by the LP and SATLDE. This difference is due to the PF coefficients considered for this problem^30,50,51. In fact, the PF in Eq. (23) is not equal to zero, because the PF coefficient is small for this problem. Similar to the previous problem, the value of PF coefficient is not changed in this study, because this problem is a benchmark problem.
Table 3 presents the performance of seven optimization algorithms in this study, which are comprehensively evaluated by six performance metrics (i.e., R, RMSE, MAE, MAPE, E, and IA). The obtained results reported in Table 5 indicated that the SATLDE algorithm has the highest correlation and lowest error (R = 0.995, RMSE = 0.276, MAE = 0.061, MAPE = 4.809, E = 0.989, IA = 0.997) compared with the optimal value calculated by the other optimization algorithms.
According to Fig. 6, SATLDE has a superior performance compared to its competitors MS-DEPSO (0.52), MLBSA (0.64), HSLSO (0.77), jDE (0.81), SATLBO (0.85), and ITLBO (1.00). Consequently, it can be concluded based on this index that, the optimal results calculated by SATLDE have a significant compliance with the global optimal solution computed by the LP. The boxplot of all optimizers in Fig. 7 shows that SATLDE illustrates a superior performance compared with other optimizers with regard to the solution distribution.
Comparing PI metric for SATLDE and other optimizers for four-reservoir problem.
Boxplots of all optimizers for ten-reservoir problem.
The convergence graphs in Fig. 8 show that SATLDE has the fastest convergence speed compared with the other competitors for the ten-reservoir problem. Accordingly, it can be inferred SATLDE can achieve precise and reliable reservoir releases compared to the others. Figure 9 shows the monthly volume of reservoir releases and storage optimized by SATLDE and top two other optimization algorithms (i.e., SATLBO, MS-DEPSO, and MLBSA). Visually, the optimal release and storage achieved by SATLD have the significant correspondence with the global optimal values calculated by LP.
Convergence curves of all optimization techniques for ten-reservoir problem.
Reservoir volumes for ten-reservoir problem at two modes: (a) storage, (b) release.
The Taylor diagram of all optimization algorithms is depicted in Fig. 10, showing that, the proposed algorithm is closer to the global optimal solution (target point) compared to the other algorithms. This graph highlights once again the capacity of SATLDE to handle complex multi-reservoir systems.
Taylor diagram of all optimizers for ten-reservoir problem.
In this section, two non-parametric statistical tests namely the Friedman⁵² and Wilcoxon Signed Rank (WSR)⁵² tests are used to show the best method for optimizing multi-reservoir systems. Table 4 gives the results of Friedman test for two multi-reservoir problems. According to this test, SATLD has the best rank (1) compared with the six other optimizers.
Based on the WSR, R⁺ illustrates the sum of ranks over all different runs in which SATLDE outperformed the contestant optimizer, while R⁻ represents the sum of ranks over all runs where the contestant optimizer outperformed the SATLDE. In addition, in a statistical hypothesis test ((alpha) = 0.05), the p-value detects the importance of the outcomes. The results showed that the values of R⁺ and R⁻ obtained by WSR for the proposed method compared to other optimization methods are the same and equal 465 and 0, respectively. Accordingly, it can be said that SATLDE performs significantly better than the other optimizers.
Results of the multi-reservoir system as a real-world case study are provided in this section in detail. As stated in “Real-world multi-reservoir system” section, this system consists of four reservoirs and its main purpose is hydropower generation.
To indicate the robustness of the SATLDE, the statistical results of seven optimizers (i.e., HSLSO, MS-DEPSO, jDE, MLBSA, ITLBO, SATLBO) are reported in Table 5, comprising the best, mean, worst, and SD of the fitness function values. Seven optimization algorithms are independently run 30 times, while the (Np) and (MaxNFE) are set to 100 and 1000, respectively. The problem was optimized over a 55-year operational time step.
As demonstrated in Table 5, it can be obviously found that the best (207.63), mean (223.63), and worst (255.08) of the fitness function values obtained by SATLDE are obviously better than the control optimizers. In addition, SATLDE makes about 49%, 32%, 83%, 29%, 32%, and 65% improvements in the SD values compared with HSLSO, ITLBO, jDE, MS-DEPSO, SATLBO, and MLBSA, respectively. Besides, from Table 6 also finds that the SATLDE is more reliable and accurate than other optimizers according to the performance.
Box plot graph is used to illustrate the variation rate of the fitness function calculated by the proposed SATLDE and other optimization methods during 30 different runs. Figure 11 depicts the Box plot of 7 optimizers for the multi-reservoir system. The figure indicates the seven optimization algorithms’ maximum, minimum, and average fitness functions. In addition, the Box plot shows the density range of fitness function over 30 runs. It can be seen that compared with six other optimization algorithms, the location of SATLDE is clearly lower, and the range of distribution is smaller, indicating the priority of the optimization technique. Therefore, SATLDE can be utilized as an efficient tool for optimizing multi-reservoir problems.
Boxplots of all optimizers for real world multi-reservoir problem.
Figure 12 illustrates the convergence graphs of seven methods for the multi-reservoir problem. The figure displays the average values of the fitness function for all optimizers, obtained in 30 different runs. It has been discovered that the SATLDE has a faster convergence speed than the other approaches after 12,500 NFE. In fact, the proposed method can quickly explore feasible solutions by increasing the NFE. The high accuracy and speed up of the SATLDE convergence are due to the adaptive parameters and different strategies added to the proposed algorithm.
Convergence graphs of all optimizers for the multi-reservoir system.
Table 6 gives the total power generated by all optimization methods for each reservoir of the multi-reservoir system. It can be found from the table that the power generated by SATLDE for Karoun3 (6.35E+05 MW), Karoun1 (8.46E+05 MW), Godar (8.51E+05 MW), and Dez (2.78E+05 MW) are more than those produced by the other methods. In addition, the total power generated by SATLDE (2.61E+06 MW) is more than the MLBSA (2.46E+06), HSLSO (2.55E+06), ITLBO (2.11E+06 MW), jDE (2.39E+06 MW), MS-DEPSO (2.53E+06 MW), and SATLBO (2.47 + 06 MW), respectively. Based on the results, the best optimization method to produce power is the SATLDE, followed by the HSLSO, MS-DEPSO, SATLBO, MLBSA, jDE, and ITLBO, respectively.
Figure 13 displays the monthly distribution of power generated by all optimizers in the form of a heat map plot. It can be observed that the proposed SATLDE can produce more monthly power than those generated by the six other optimizers. It is worth noting that in the SATLDE, most of the power generated occurs in months 1 to 9 (January to September).
Monthly average of hydropower energy generated by all optimizers.
Figure 14 depicts the distribution of monthly power generated by SATLDE in the form of a Box plot. Based on the figure, the maximum and minimum energy produced belong to the 6th and 10th, respectively. Moreover, the monthly maximum, minimum, and average volumes of reservoir storage achieved by the proposed method are shown in Fig. 15. According to the findings of this study, SATLDE outperforms the MS-DEPSO, HSLSO, MLBSA, jDE, and ITLBO in determining the optimal power generated from the multi-reservoir system. This is the case, although all six optimizers can be used effectively to reach optimal operating rules for the multi-reservoir system successfully.
Monthly generated power gained by SATLDE.
Monthly maximum, minimum, and average of reservoir storage gained by SATLDE.
A self-adaptive teaching learning-based with differential evolution (SATLDE) is developed for optimizing hydropower multi-reservoir systems. SATLDE uses a ranking probability mechanism to select the teacher or learner stage; then a boosted teacher stage utilizing various teaching ways is applied to move the learners to enhance themselves based on their levels. Finally, an efficient mutation operator is employed to the learner stage for improving global search capability. In addition, an adaptive method is used for parameter settings. SATLDE applies a learner factor according to the adaptive approach to store the optimal control parameter values at each iteration.
The efficiency of SATLDE has been evaluated by optimizing a well-known benchmark multi-reservoir system, i.e., the ten-reservoir problem. For the ten-reservoir problem, SATLDE shows superior results, with the best fitness function value equal to 1196.98, while MLBSA, HSLSO, ITLBO, jDE, MS-DEPSO, and SATLBO can converge to 1168.20, 1142.39, 1091.07, 1133.47, 1187.02, and 1138.69, respectively. SATLDE demonstrates significant efficiency in reliability, precision, and robustness. Therefore, the proposed algorithm can be employed in other multi-reservoir systems in order to quickly extract the optimal operating rules for multi-reservoir systems, thus improving energy efficiency.
To exhibit the ability of SATLDE in optimizing real-world problems, a multi-reservoir system consists of four reservoirs located in Iran was used as a case study. In this research, a nonlinear operating rule is developed to maximize the energy produced from the multi-reservoir system. In fact, SATLDE and six other optimizers are applied to derive the optimal operating rules. It statistical analysis of results shows that, SATLDE can present the superior ability in obtaining more promising solutions as compared with the six optimization methods. In addition, SATLDE could produce remarkably more power (2610 GW) than the MLBSA (2460 GW), HSLSO (2550 GW), ITLBO (2110 GW), jDE (2390 GW), MS-DEPSO (2530 GW), and SATLBO (2470 GW). Consequently, results show that SATLDE is able to present more reliable and accurate operating rules for the multi-reservoir system. Accordingly, it can be considered an effectual alternative for optimizing operating rules of other complex hydropower multi-reservoir systems.
For future study, the proposed SATLDE can be ensembled with other optimization methods such as the GBO algorithm. Moreover, we will investigate the ability of proposed method to derive optimal parameters of photovoltaic models. In addition, although the data in the present study shows both wet and dry periods, we recommend that future work focus on including climate change scenarios to ensure that the operating rules can respond to a broader range of climate conditions. Finally, SATLDE can be used for solving machine learning problems.
All data used in the study are available from the corresponding author by request.
The source codes of the paper and online web service for any question and Supplementary Material including all equations of the algorithms will be publicly available at http://imanahmadianfar.com.
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This study was funded by Vice-Chancellor for Research and Technology, Behbahan Khatam Alanbia University of Technology, (project code. 1401- 17-G).
Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran
Iman Ahmadianfar & Arvin Samadi-Koucheksaraee
Department of Civil Engineering, University of Manitoba, Winnipeg, MB, Canada
Masoud Asadzadeh
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I.A.: Conceptualization, Methodology, Software, Writing—Original draft preparation, Visualization, Investi- gation, A.S.-K.: Writing—Original draft preparation, Visualization, Investigation, M.A.: Supervision, Reviewing and Editing, Visualization, Investigation.
Correspondence to Iman Ahmadianfar.
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Ahmadianfar, I., Samadi-Koucheksaraee, A. & Asadzadeh, M. Extract nonlinear operating rules of multi-reservoir systems using an efficient optimization method. Sci Rep 12, 18880 (2022). https://doi.org/10.1038/s41598-022-21635-0
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Received: 25 March 2022
Accepted: 29 September 2022
Published: 07 November 2022
DOI: https://doi.org/10.1038/s41598-022-21635-0
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