Internet of Things (IoT) based healthcare systems offer significant potential for improving the delivery of healthcare services in humanitarian engineering, providing essential healthcare services to millions of underserved people in remote areas worldwide. However, these areas have poor network infrastructure, making communications difficult for traditional IoT. This paper presents a real-time chest X-ray classification system for hospitals in remote areas using FastDDS real-time middleware, offering reliable real-time communication. We fine-tuned a ResNet50 neural network to an accuracy of 88.61%, a precision of 88.76%, and a recall of 88.49\%. Our system results mark an average throughput of 3.2 KB/s and an average latency of 65 ms. The proposed system demonstrates how middleware-based systems can assist doctors in remote locations.

Flying cars and Martian holidays are perhaps among the things we dream of when looking ahead to the year 2100. But it may surprise you to know that dogs could undergo a huge transformation too, as they adapt amid the crippling impacts of climate change. Experts at Love Your Dog asked artificial intelligence (AI) to envision what future pooches could like based on predictions of canine evolution. The results may just surprise you, as dogs are expected to be far more fox-like one day with huge ears and even hairless legs. 'Physically, we can expect dogs that resemble the famous Chinese Crested Dog, with a small size, little or almost no hair (considered hypoallergenic), and a calm and friendly temperament,' said Jessica D'avilia and Brenda Vitorino, of the Federal University of Rio Grande do Sul.

Brain-computer interface company Neuralink announced on 25 May that it has received approval from the US Food and Drug Administration (FDA) for a clinical study in humans. Neuralink made the announcement on Twitter: "We are excited to share that we have received the FDA's approval to launch our first-in-human clinical study." The tweet said that the approval "represents an important first step that will one day allow our technology to help many people". The firm also said that the recruitment is not yet open for the trial, and it has yet to give any further details about what the trial will entail. Neuralink was formed in 2016 by Elon Musk and a group of scientists and engineers with the ultimate aim of making devices that interface with the human brain – both reading information from neurons as well as feeding information directly back into the brain.

Kate Daniels, from Willow Farm in Worcestershire, told BBC Radio 4's Today programme that while the scientists might have been impressed, "I don't think this will come as a surprise to anyone that works with pigs".

Like humans, pigs have idiosyncratic faces, and new players in the Chinese pork market are taking notice, experimenting with increasingly sophisticated versions of facial recognition software for pigs. China is the world's largest exporter of pork, and is set to increase production next year by 9%. As the nation's pork farms grow in scale, more farmers are turning to AI systems like facial recognition technology – known as FRT – to continuously monitor, identify, and even feed their herds. This automated style of farming has the potential to be safer, cheaper and generally more effective: In 2018, pig farmers in China's Guangxi province trialling FRT found that it slashed costs, cut down on breeding time, and improved welfare outcomes for the pigs themselves. But it also has the potential to leave behind independent, small-scale farmers, who cannot afford to introduce this kind of technology to their operations.

Like humans, pigs have idiosyncratic faces, and new players in the Chinese pork market are taking notice, experimenting with increasingly sophisticated versions of facial recognition software for pigs. China is the world's largest exporter of pork, and is set to increase production next year by 9%. As the nation's pork farms grow in scale, more farmers are turning to AI systems like facial recognition technology – known as FRT – to continuously monitor, identify, and even feed their herds. This automated style of farming has the potential to be safer, cheaper and generally more effective: In 2018, pig farmers in China's Guangxi province trialling FRT found that it slashed costs, cut down on breeding time, and improved welfare outcomes for the pigs themselves. But it also has the potential to leave behind independent, small-scale farmers, who cannot afford to introduce this kind of technology to their operations.

Elon Musk has showed off his company Neuralink's brain-computer interface for the first time. In an announcement on 28 August, Neuralink unveiled prototypes of its device and showed off pigs with the devices implanted in their brains. The device resembles a coin with extremely thin wires coming from one side of it. It is designed to be implanted in the skull, with the wires embedded a few millimetres into the surface of the brain. Those wires can then detect when neurons are firing, or emit their own electrical signals to make the neurons fire.

As Plastic Dinosaur has wandered around the large warehouse space it would call home if it had that concept, it's been learning as it observes the mobile bipeds it shares the space with. They are more nimble, erect and noisy than it is, for now. As it dreams, it has started to recognize a pattern. When these bipeds reach out with their upper limbs and touch things, the environment changes in ways it's curious about. It learns to pay more attention to what human hands touch.

In classic CNNs, each neuron in the first layer represents a pixel. Then, it feeds this information forward to next layers. The next convolutional layers group a bunch of neurons together, so that a single neuron there can represent a whole frame (bunch) of neurons. Thus, it can learn to represent a group of pixels that look something like a snout, especially if we have many examples of those in our dataset, and the neural net will learn to increase the weight (importance) of that snout neuron feature when identifying if that image is of a dog. However, this method solely cares about the existence of the object in the picture around a specific location; but it is insensitive to the spatial relations and direction of the object.

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, yet markers are intrusive (especially for smaller animals), and the number and location of the markers must be determined a priori. Here, we present a highly efficient method for markerless tracking based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in a broad collection of experimental settings: mice odor trail-tracking, egg-laying behavior in drosophila, and mouse hand articulation in a skilled forelimb task. For example, during the skilled reaching behavior, individual joints can be automatically tracked (and a confidence score is reported). Remarkably, even when a small number of frames are labeled ($\approx 200$), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.