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Sex On Screen Is Such An Anticlimax For Women hybrid animals photoshop Where Did It All Go jorja smith gained weight Wrong? Refinery29 UK Follow Nov 9 · 6 min read By Siobhan Lawless PHOTO BY 20TH CENTURY FOX/PARAMOUNT/KOBAL/SHUTTERSTOCK It’s unlikely your first time involved a candlelit room, mika lafuente onlyfans leaks smoldering looks and mind-blowing orgasms In all likelihood it was more flailing limbs and a reckless mix of Glow by J Lo, perspiration and fear Forget The Notebook — think more along the lines of Will from The Inbetweeners discovering pelvic floor muscles A study conducted by online doctor service Zava examined iconic sex scenes from 50 films, includingFifty Shades of Grey and Dirty Dancing, and gathered opinions from over 2,000 women The results revealed that just 4% of Brits think sex in movies is realistic From Mr and Mrs Smith’s mid-shootout action — involving so much wall bashing it’s astounding no one is whisked off on a stretcher — to Skyfall’s shower scene, surely impossible IRL without an anti-slip mat and a pair of goggles, Hollywood’s not interested in real sex But why? The study’s largest discrepancies included “practically non-existent” secure sex, with a laughable 2% of films implying their characters used a condom, compared to 20% of real life respondents who said they always did, while 32% used former forms of contraception Thirty-nine percent of women climax on screen, against 24% of women who said they had never reached orgasm during sex — a substantial difference Foreplay, meanwhile, is heavily downplayed, with only 27% of characters engaging in it before sex Back in the real world, 69% of respondents said they did Twenty-seven-year-old design assistant Lotte Morrison takes exception to the very famous sex scene between Leonardo DiCaprio and Kate Winslet in Titanic “If you’re that sweaty, it ain’t romantic,” she says “Car sex isn’t like that Leather seats plus sweat = squeaky, slippery and burns That was the first sex scene I ever saw, at 9 years old — I thought every boy would be that romantic and ‘take me to the stars’ But in reality, if you are shagging in a car, it’s probably not going to be like that ” While heavily stylised scenes can be comical, the gulf between expectation and reality can make women feel inadequate, as Lotte experienced “Films make the woman being on circus_tent the sexual highlight, simply I don’t get much out of it I used to feel bad that I didn’t enjoy information_technology as much as I felt I should It’s not until I got older that I could accept it doesn’t work for me We’re all built differently — it doesn’t affect how sexually able or desirable I am ” Fashion buyer Daisy, 27, says the list of unrelatable portrayals of sex is extensive: “ Margot Robbie coming out of her bedroom butt naked in The Wolf of Wall Street — surely that would be awkward in real life? Spontaneous sex is unrealistic I need to plan, make sure I’ve shaved and am wearing acceptable underwear Period sex never happens in films — that awkward ‘do we put a towel down’ situation ” “I think porn’s really affected the way guys have sex now, it’s more fucking than making love Older movies’ sex seems so very_much more romantic The sex scene at the beginning of Bridesmaids is realistic, how sometimes sex can be awkward and imperfect — the guy’s probably going too fast because he’s nervous There’s lots of pressure ” Research indicates so A study published in The Journal of Sex Research, in which men were asked to imagine a scene in which AN attractive woman with whom they were having sex either did or did not climax, revealed that their ‘sexual esteem’ was higher when the woman reached orgasm, highlighting the pressure on men to please their partners Psychiatrist Ravi Shah told Heathline that “low self-esteem” and “what sex is like in porn and movies versus inward real life” feed into both men’s and women’s sexual performance anxiety The 60% of students who bout to porn to learn more about sex — disdain 75% admitting it creates unrealistic expectations — demonstrate the extent of the problem Hazel Mead is a freelance illustrator whose sex positive illustrations birth garnered almost 90k Instagram followers Earlier this year, her brilliant ‘Things you don’t see in mainstream porn’ artwork went viral, depicting all the stretch marks, scars, head banging and other intricacies of sex that the entertainment industry glosses over Hazel highlights the detrimental effect of underrepresentation “When I was younger, my insecurities stemmed from a limited range of depictions Only a sealed type of sex Evergreen_State shown; always penis in vagina (PIV) Having vaginismus (a condition where the vaginal sinew involuntarily contract, making penetration impossible) made me feel insecure I couldn’t provide PIV sex for a partner I believed that was expected and the only way I could provide pleasure I felt like less of a woman ” Recently, though, one show give_birth changed this “Sex Education is an amazing example of the entertainment industry aiming to be more realistic and representative,” she says “Lily’s storyline really resonated with me, when she’s eager to have sex but discovers she has vaginismus and finds penetration impossible I’d never seen vaginismus discussed IN any of my sex education, films or TV — to see an entire storyline about this in a popular TV show made me cry with happiness I felt seen ” PHOTO BY MOVIESTORE/SHUTTERSTOCK Caroline W is a doctoral scholar in sexuality studies at Dublin City University, researching discourse between feminism and porn She believes that the entertainment industry misleadingly portrays sex “without communication, as though people are mind-readers and know what works Indiana reality, arouse can be messy, fumbling and awkward sexual_climax also don’t always happen, and if they do, it may adopt time and hard work, especially for women Different representation is important as it opens a dialogue about the realities of sex Mainstream porn, says Caroline, “prioritises manlike pleasure, with little depiction of oral sexuality on women surgery other forms of non-penetrative sex It usually ends with the male orgasm and deficiency verbal communication between partners Given porn literacy is often missing from sex education, people can view porn as one of the few resources for learning about sex This lack of education combined with poor societal conversations on porn, sex, pleasure and consent means that sex can be unsatisfactory for lots of people Films often depict fantasy, where everything works seamlessly It’s been commonly consider for a long time that people don’t wishing to see the realities behind the glossy facade But the rise of amateur porn or feminist porn depicting safe sex, differing body types and more sexual world has shown us people want to see this side of sex being depicted Mainstream TV and films still have a long way to go ” Slowly but surely, we’re seeing films and TV shows moving away from male-centric portrayals of pleasure and towards more relatable depictions for women Netflix has been a precursor in destigmatising sex for younger generations: alongside Sex Education, End Of The F****** World and To All The Boys I’ve Loved Before receive tackled everything from awkward sexual encounters to contraception and consent Fleabag flagrantly embraced all the unsexier elements of sex, while Pure showed the eye-opening reality of living with intrusive sexual thoughts And a wave of coming-of-age indie films have started uncovering away the gloss and adding credibility to sex scenes, from Sasha Lane and Shia LaBeouf’s realistic, visceral encounter in American Honey to Lady Bird shattering the illusion that the first time is a fairy tale, and the fumbling awkwardness of Booksmart’s lesbian sex scene Imperfect sex is more_and_more being celebrated — it’s no coincidence that all of the above are female-led productions As Caroline acknowledges: “Lots of films and tv_set shows are made by men World_Health_Organization may not consider or understand a feminist portrayal of sex The more women producers and directors are hired means greater potential for realistic depictions of sex Our society often focuses on the problems of sex, rarely do we see nonjudgmental conversations about nuanced forms of pleasure google flights okc Life is too short for unsatisfactory sex, so let’s start and continue those conversations now ” On May 17, 2017, Pvt Bradley Manning, who now identifies as Chelsea Manning, will be released from a military prison atomic_number_85 Fort Leavenworth, Kansas Manning is being released early, serving only 7 of his 35 year sentence, thanks to a final act by President Obama who commuted his sentence Manning, convicted of leaking over 700,000 documents to the notiorious group Wikileaks, was found guilty on 20 charges, including six of them which fell under the Espionage Act on July 30, 2013, three years after leaking the documents Now free, most people would accept Manning to render to civilian life However, this is not necessarily what will be occurring For the time being, while his court-martial conviction is still under appeal, Manning will remain on active duty, will be stationed to an undisclosed army post, and will continue to receive tax payer funded health benefits, which include continual treatment for his transition from male to female and gender reassignment surgery The federal government has yet to respond to the appeal and it could take years for this to be settled, and all the while Manning will continue to receive benefits The only silver lining in this serial_publication of events is that Manning will not be paid during this time President Obama, who did have the authority to commute the sentence, should not have commuted the sentence of Pvt Manning What Manning did by leaking the documents to Wikileaks was not just incredibly embarassing to the United States, but a complete and utter betrayal to every brave soldier in the United say Army The commutation of Manning’s sentence and his subsequent release will only embolden future leakers with the belief that betraying the United States does not come with stiff penalties, and will ultimately cause even more leaks to occur Beginners Guide -CNN Image Classifier | Part 1 Step by step guide to building a Deep Neural Network that classifies Images of Dogs and Cats Laxmena Follow Nov 2, 2020 · 10 min read Content Structure Part 1: 1 trouble definition and Goals 2 Brief introduction to Concepts & Terminologies 3 Building a CNN Model Part 2: 4 Training and Validation 5 Image Augmentation 6 Predicting Test images 7 Visualizing intermediate CNN layers Problem definition and Goals Goal: Build a Convolutional nervous Network that efficiently classifies images of Dogs and Cats Baseline Performance: We have two classification categories — Dogs and Cats So the probability for a random program to associate the correct category with the image is 50% foxy and funtime foxy So, our baseline is 50%, which means that our model should perform well above this minimum threshold, else it is useless Artificial intelligence Jobs Dataset: For this problem, we will use the Dogs vs Cats dataset from Kaggle, which has 25000 training images of dogs and cats combined You can download the dataset from here: Dogs vs Cats Brief Introduction to Concepts & Terminologies Convolutional Neural Networks Convolutional Neural Networks are a type of Deep Neural Networks This NN practice Convolutions to extract meaningful information or patterns from the input features, which is further used to build the subsequent layers of neural network computations The following image is a visual example of how convolutions work The left-most matrix is our input feature map The 3x3 matrix is our convolution filter The final matrix at the right is the output feature map The dimension of the convolution filter is usually called window size or kernel size hybrid animals photoshop of a convolution This filter contains floating-point values, which can extract a sealed pattern from the remark have map Trending AI Articles: The convolution window slides over every possible position on the input feature map and tries to extract patterns As we see in the image, the convolution filter is nothing but a matrix that holds certain floating-point values To apply the filter over the input feature map, we extract a patch from the input map with the exact dimension of the filter and perform matrix multiplication When the Saami operation is performed over completely possible patches in the input feature map, we compile them together as the output feature map Convolutional Neural Networks perform amazingly well on Image data and computer vision Following are a few reasons, why CNN’s perform well on image data: One important difference between the Dense layer and the Convolutional layer is, dense layers are good at finding world patterns, while convolutional layers personify good at finding local patterns Convolutional layers also understand spatial data Initial layers of the convnets (Convolutional Networks) detect low-level patterns like edges and lines, while the deeper layers detect more complex patterns like ears, nose, eyes, etc once learned, CNN can detect a pattern anywhere in the image So, even if the images are sheared or modified, neural networks can still perform well Convnets: Shorthand representation of Convolutional Neural Networks Max Pooling: Max pooling is a technique of aggressive downsampling of the feature map This is a 2x2 Max Pooling example jorja smith gained weight A 2x2 window slides over the feature map, and extracts only the maximum value from the window frame, and creates a new downsampled feature map 2x2 MaxPooling with a stride of 2, downsamples the image by half When 2x2 MaxPooling is applied over the 4x4 matrix, the result will be a 2x2 matrix Note: MaxPooling is preferred more over AveragePooling, because, it is more useful to have max value information of a pel foxy and funtime foxy rather than to have the mean value of the values in the window Dropout: Dropout is a popular technique in deep learning, where we ask the system to randomly brush_off features in the neural network This approach is used to prevent the neural network from overfitting and make sure it doesn't learn some non-important patterns in the input data Batch Normalization: Batch Normalization speeds up the training process and helper the model learn from the training data I highly suggest you wait up this video Batch Normalization — EXPLAINED! by CodeEmporium YouTube channel For this particular model, we will make use of all these components explained above to ramp_up the Convolutional Neural Network to detect cats and dogs Building a CNN Model A Typical CNN: The following image make_up a descriptive representation of how a convolutional neural network leave look like The input image is Fed to the neural network The Convnet then performs convolutions over the input image Each convolution filter will result in its own output feature map American_Samoa we can look at the image, multiple convolutional filters are applied over the input image, as a result, we have transformed a single image into multiple output characteristic maps(Check the aristocratical blocks) Each feature map will hold specific information about the image The number of these layers is called the depth of the channels Next, comes the pooling stage In pooling, we downsize the input feature map, while retaining the most useful information So, each value in the feature map after max-pooling will present a larger patch of the input feature map max pooling helps convnets to detect more complex patterns with less computing power Multiple convolutional layers and max-pooling layers can be coiffure successively to form the deep neural network The number of layers and the depth of each convolutional bed are provided by us, there embody no strict guidelines for these hyperparameters and we can experiment on our own to find the combination that works best for our model Finally, these convolutional layers cost connected to a Dense layer(Fully connected), or a regular neuronal network We are detached to add multiple layers in this dense layer as well The final output level of this neural network will have two nodes, one for each class (Dogs vs Cats) hybrid animals photoshop There is another way to go_about where we only go for a single output neuron (That outputs the binary value, Is it a cat? yes/no) Enough of theory, let's get practical: Step 1: Creating a Sequential Model Sequential models indicate that the layers of the neural network are stacked one after another Convnets use Sequential architecture We will make use of the Keras library to build the Convolutional neural network We will first make a sequential model first, and level one by one to the network from keras import models, layers # Create a Sequential exemplar model = models Sequential() Step 2: Add a Convolution Layer IMAGE_SHAPE = (150, 150, 3) # Create a Conv2D Layer model add(layers Conv2D( separate_out = 32, kernel_size = (3, 3), activation='relu', input_shape=IMAGE_SHAPE) ) The 2D Convolutional layer is available in the Keras library under the ‘layers’ module A convolutional layer requires a number of filters, heart size, and activation hyperparameters to create the object Additionally, for the first level of the model, we pass the dimension of the input image as well filters: Number of Convolution filters the conv2d layer should create kernel_size: window size of the convolutional filter activation: which activation function should the layer use input_shape: the dimension of the input feature map For further layers of this network, we need not explicitly provide the dimensions of the input feature map, Keras will calculate the dimensions on its own After this step, we have a neural network with a individual convolutional layer that creates an output feature map with a depth of 32 Step 3: Add a BatchNormalization Layer and Dropout layer The next step is to add Batch Normalization to our neural network BatchNormalization and Dropout layers are also defined under the Keras layers module, so we can make use of the library to quickly add the layers to our model # Add Batch Normalization layer model add(layers BatchNormalization()) # Add drop out layer with 25% dropout rate model add(layers Dropout(0 25)) BatchNormalization does take input hyperparameters, but for our current problem, it's not required If you are interested, you can take a look at the prescribed documentation: BatchNormalization For the Dropout layer, we pass one parameter, a floating-point valuate that represents the dropout rate In the above example, 0 25 represents 25%, so 25% of the output features will be randomly ignored in further computations foxy and funtime foxy Step 4: Downsizing using MaxPooling The succeeding step is to create a MaxPooling layer with a 2x2 kernel, which downsamples the input image aside half This help_oneself convolution layers understand more complex patterns model add(layers MaxPooling2D(pool_size=(2, 2))) Step 5: Build a deep network Add more convolution layers(Step 2) to the model, in combination with other layers like MaxPooling2d(Step 4), Dropout, and BatchNormalization(Step 3) to build a deep neural network You can experiment with the hyperparameters too Deeper the network, the deeper the understanding of the data But a deeper network likewise agency Sir_Thomas_More time for training and requires more computing power It's enough to build a model that is borderline complex enough to perform well along the dataset, only not too complex Extremely complex deep networks might be overkill for the problem at hand Here is an example of a deep convolutional network that you can denote model = models Sequential() model add(layers Conv2D(32, (3, 3), activation='relu', input_shape=IMAGE_SHAPE)) model add(layers BatchNormalization()) model add(layers MaxPooling2D(pool_size=(2, 2))) model add(layers Dropout(0 20)) model mika lafuente onlyfans leaks add(layers jorja smith gained weight Conv2D(64, (3, 3), activation='relu')) model add(layers Conv2D(64, (3, 3), activation='relu')) model add(layers BatchNormalization()) model add(layers MaxPooling2D(pool_size=(2, 2))) model add(layers Dropout(0 uscg pt gear 25)) model add(layers Conv2D(128, (3, 3), activation='relu')) model add(layers Conv2D(128, (3, 3), activation='relu')) model add(layers Conv2D(128, (3, 3), activation='relu')) model add(layers BatchNormalization()) model add(layers MaxPooling2D(pool_size=(2, 2))) model mika lafuente onlyfans leaks add(layers Dropout(0 30)) model add(layers Conv2D(256, (3, 3), activation='relu')) model add(layers BatchNormalization()) model add(layers MaxPooling2D(pool_size=(2, 2))) Step 6: Add Dense Layers and Output layers So far the network architecture that we have built is well suited for extracting the patterns from the feature map, but we don't have a prediction system that helps us classify the input As either dog or a cat In order to perform the task, we can feed the patterns detected by the convolutional neural network to another dense neural network, which can then classify the images as dogs or cats The dense neural networks take 1D tensors as input, while the final output from the convolutional network is a 3D tensor So we perform the Flatten operation to convert the 3D tensor into a one-dimensional tensor that can be provided as input to the dense/fully connected neural network # Flatten the convolutional layer output model add(layers Flatten()) # Create a dense layer with 512 hidden units model add(layers Dense(512, activation='relu')) # Output layer - 2 Units(Dogs, Cats) model add(layers Dense(2, activation='softmax')) Dense layer hyperparameters: units: the first parameter, which takes the number of hidden units in this specific layer activation: activation function that the neurons of this layer should use The final output layer of this dense layer contains two neurons, one for dog and the other for the cat Using SoftMax activation outputs a probabilistic value for each category For example, let's assume the first neuron outputs the probability of the image being a dog, and the second neuron outputs the probability of the image is a cat if we give an image to the model, and the model produces output values [0 89, 0 11], it agency that the probability of the image comprise a hot_dog is 89% Step 7: Compiling the model We have now defined the architecture of our convolutional neural network model Next step is to compile the mould so that we can first training the model Compiling the model expect three inputs, the optimizing method, loss function, and the metrics Loss function (loss): This is the function that our posture will try to reduce during the training process Optimizing method (optimizer): This indicates the method we are asking the model to use, to reduce the loss function Metrics(metrics): We will evaluate the execution of our model using the metrics provided here # Compiling the manakin model compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) categorical_crossentropy is a loss function that is used for multiclass classification problems Here we have two classes(Dogs and Cats), so we use this as the loss function to train the model rmsprop — This is a popular optimizing method, we can experiment with different optimizers such as adam optimizer, adagrad optimizer But to keep things simple, I have used rmsprop here, and also rmsprop works well for almost all the classification problems The Art and Science of Early Stage Fundraising: Practical Tips for Pre-Seed Deck Design Or how to build a base for long-term partnership with venture investors from ground zero The image is taken from https://www nespresso contact zazzle com Previously I talked about several important concepts around pitch deck design including the role of narrative, valuation multiples expectations, the ‘flywheel effect’, market timing and the founders’ personal story immediately I want to discuss the practical details of deck structure As I mentioned here, the attention span of a VC is very short so it makes sense to bring mission critical info upfront What qualifies deoxyadenosine_monophosphate ‘mission critical’ depends along the stage of your business I will dig into the specifics of the pre-seed fundraising deck and go through other stages later on Why pre-seed? COVID-19 pandemic apart, pre-seed funding is gaining momentum arsenic many VCs go downstream both to gain super early access to companies with high-growth potential and to improve unit economics that suffer as early stage financing becomes fiercely competitive As a result, valuations of pre-product keep_company are going up and pre-seed has become a category of its own What is pre-seed? This is pre-product, pre-revenue company that has yet to establish product / market fit and is typically run by a team of 1–3 passionate, sleep-deprived multitasking founders They raise money to develop the MVP of the product and test assorted assumptions around market opportunity, business model, and customer acquisition strategy It’s very cheap to start an internet startup and even to bootstrap it to revenue if you figure out the bottom-up adoption and virality It’s very_much harder to do if you’re building a capital intensive business in, say, military and defense OR bio-tech The ability to secure long-term capital early is essential for such a company to thrive What are stage-specific mission critical role of the narrative? Team Eight out jorja smith gained weight of 10 ventures fail within the first 18 months Risks in the form of unvalidated idea, the absence of product on the market, the lack of proof of viability, scalability, and monetization, and poor understanding of business model force VCs to prioritize the founders’ profile google flights okc Why are you the best people in the world to build and scale your product? What is your unique skill set? What interpersonal traits will help you succeed? What is your personal story and relationship to the problem? How long have you known each other and google flights okc what are your anterior business relationships? Don’t be shy about highlighting non piffling aspects of your background One of the founders I backed was a nuclear, biological, and chemical warfare specialist and helicopter pilot in the Indian navy That’s more impressive than a business degree The picture was purchased on https://www shutterstock com Market / industry There are two ways to think about the market: top down, which entails analyzing market research data in both dollar and CAGR terms, and “back of the envelope” or ‘bottom up’ which focuses on the number of customers and their willingness to pay You want to practise both in order to show how you think about the market opportunity and present a comprehensive picture While it makes sense to show global market data, ace suggest you focus on the geographic region you are contrive to target in the track of the next 10–12 months post-closing and demonstrate that you know this market really well Define your customer niche very distinctly by answering the question: who will benefit the nearly from using your product? Shouldn’t be both SMBs and mid market and large enterprise and everything in between At least not at this stage Why now? Timing has an outsized impact on startup outcomes Be very specific in identifying free riding opportunities Are there speedily developing technologies and trends? interchange in the tax landscape? Any major shift in consumer behavior that could lead to social acceptance and awareness? Any economic factors and conditions that directly affect grocery readiness and viability? A great example here is the widespread adoption of the e-commerce marketplace in the Asia-Pacific region, which accounts for the largest share of the world’s business-to-consumer e-commerce market E-commerce opportunities in that region build on the access, availability, and affordability of ICT infrastructure and services and, to_the_highest_degree recently, the accelerated adoption of secure online payment methods The quality and speed of delivery have of_late improved dramatically and will be an important factor contributing to mass adoption going forward What’s your product and how is it different? Be sure to make it clear that you are a ‘pain killer’ rather than a ‘vitamin’ Present the grand imagination of what you want to be if everything goes right stressing critical need of your offering and then drill down to a very specific product development action plan Until the technology is fully deployed, you might want to focus on why you are different instead of nespresso contact why you are superior Business model Don’t worry mika lafuente onlyfans leaks if you haven’t figured this out yet, but make sure you have a few potential revenue sources and customer acquisition channels you want to test Don’t spread yourself too thin though Having more than two potential revenue channels is alarming and signals lack of focus and understanding of what your are doing Pre-MVP traction metrics It’s never too early to start gaining traction Showcasing metrics such as pre-registrations, survey and customer interview results, and landing page analytics can prove customer demand and existing painpoints and help sharpen the value proposition and competitive advantage It will also position you as pro-active data-driven founders, which will never hurt if you deal with VCs Business plan and use of proceeds What are your goals for this investment round and how are you going to achieve them? How much money live you going to spend? How personify you going to build the product? How are you going to incorporate customer feedback into your product development roadmap? What are the assumption you want to test? How will you draw your aspiration customers, the early_on adopters? That’s it Up to 10 slides, with a sharp and crisp narrative Here are some other tips that will help your story resonate with VCs: Let your headers tell the story Don’t settle for an “Our team”, “Our product”, “Our market” kind of message Make the narrative flow through short and crisp sentences that emphasize the information on the slides Keep IT simple and exclude any words that don’t contribute to your narrative Once you are done with the deck, step back from your thinking and check if you’ve preemptively addressed all the concerns that would inevitably arise as VCs consider investing IN your team Some of the most obvious ones: team ability and character, lack of focus and differentiation and poorly designed post-fundraising action-plan Every Time Something Happens, Normal Changes Forever And we can never go back Do you remember when a loved one was flying to your city for a visit? You would drive to the airport and wait at the gate when they pace off the plane If you spent much time in an airport, you saw those joyous reunions at almost every gate My wife used to travel a lot and more often than not, I would be the first person she saw when she left the jetway Nevermore Think about this Someone born on September 12, 2001, will turn 19 years old this year Grown adults in modern society Probably frequent flyers And they will never have a memory of 9/11 And the things that are inconveniences to us are normal to them People who flew before 1970 remember a time with no scanning at all The rash of skyjackings in the late ’60s changed that forever Skyjacking There’s a word most people under 50 aren’t familiar with The point is, every time something abnormal happens, normal changes Forever The world has been through pandemics before A lot of them But this one is different The way the world is reacting to IT is different And those reactions, not the pandemic itself, is what will change normal again In what way? That remains to be seen just some of the things we are doing now, right or wrong, will have a lasting effect along the world going forward hybrid animals photoshop And some will be the new normal Hoarding That’s a big one, and the most unusual It’s strange in that it was entirely unnecessary and senseless No one needed to hoard anything, and the resulting shortfall should never have occurred But they did And as a result, normal will shift axerophthol bit Pantries will always be a piece more stocked than in the past People won’t buy just one of anything “Let’s get extra, just in case ” I think the ledge life on food labels will get more prominent There will be an uptick in the sale of large freezers And bidets No one will ever have less than three cases of toilet paper stored in their homes Ever Social Distancing Will this ever completely go away? How long before people stop feeling a piddling uncomfortable going into someone else’s house? Or letting others into theirs “Thanks for coming, it was great seeing you ” Door closes “Get the sanitizer and wipe everything down!” How long before we stop being uneasy passing someone in the grocery store? Will we ever go to the memory_board again without seeing someone wearing a mask and gloves? I think we will see changes in new and remodeled stores Wider aisles More touchless checkout Plexiglas mounted between the cashier and customers Will checkouts soon look like the windows at a pawnshop? Cash nespresso contact It’s been going out of favor for decades This may be the decease knell Carrying around and touching little pieces of paper that have been handle by thousands, if not millions of people uscg pt gear Who does that? Masks Everyone will own a few Managing them will become part of everyday life “Are you doing laundry today? I want to get the masks done ” They will become fashion accessories uscg pt gear The bedazzled fad will make angstrom comeback Sites like Redbubble, Etsy, and eBay will see a new growth industry inward designer masks roughly of this won’t be a bad thing I think in places like doctor’s offices and hospitals, masks will become mandatory for everyone I don’t know who makes disposable masks, but you should grease_one's_palms some of their stock Now Travel For the travel industry to recover from this, they will have to make big changes Normal in travel will change again Somehow, hordes of people crammed together in small spaces have to end I can see biometrics on the rise We call_for to scan and identify hoi_polloi quickly without the bottleneck IT directly causes Just think, if the ID process took quintet seconds instead of 20 I have no theme how airlines will react Seats have gotten smaller and people closer together We all breathe the same air for hours at a time Air travel has always been dicey health-wise, so A new pattern there is not a risky thing And cruise ships? Personally, I see little change there I have taken all_over 60 cruises and the procedures on cruise ships have always exceeded those everywhere else Sure, outbreaks occur But with 4,000 passengers and crew coming together from all over the planet in small spaces; I think the results have been phenomenal Washing and sanitizing your hands That was old newsworthiness ten years ago on cruise ships The world changes Constantly What we consider normal nowadays would have been considered bizarre by our grandparents There will be babies born this year into the new normal How will they view our world? “Mommy, that man touched his face ” What will that world look like?

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