Wildlife is flourishing in the exclusion zone around the disabled Fukushima Daichii nuclear reactor in Japan, images from remotely-operated cameras have revealed. Researchers spotted more than 20 species in areas around the reactor, including wild boar, macaques and fox-like raccoon dogs. The findings help reveal how wildlife populations respond in the wake of catastrophic nuclear disaster like those that occurred at Fukushima and Chernobyl. Humans were evacuated from certain zones around the the Fukushima reactor following radiation leaks caused by the Tōhoku earthquake and tsunami of 2011. Wildlife ecologist James Beasley of the University of Georgia, in the US, and colleagues used a network of 106 remote cameras to capture images of the wildlife in the area around the Fukushima Daiichi power plant over a four-month period.

Computer vision, or abbreviated to CV, is an increasingly important technology in the field of artificial intelligence. Those involved in its development believe that it has endless possibilities and a wealth of applications in a range of fields. These include developing non-invasive health care treatments to self-driving vehicles and virtual shopping experiences. Through the course of this article, we will seek to explain exactly what computer vision is and the applications of computer vision in all major industries. We will also look at its current limitations as well as how it is already being applied. Computer vision has the potential to transform a number of operations and sectors. As it grows in importance, its potential and applications will be key to helping it enhance your organization. Computer vision is a branch of artificial intelligence that enables computers to see and identify images, processing them as humans would. Using images from cameras and videos, deep learning models enable machines to accurately identify and classify the objects. Computer vision can be confused with image processing. However, computer vision is a more high-level process. It deals with the analysis of an image. In the CV process, the input is an image while the output is the interpretation of an image.

In this research, an extensive literature review was performed on the recent developments of the ambulance routing problem (ARP) and ambulance location problem (ALP). Both are respective modifications of the vehicle routing problem (VRP) and maximum covering problem (MCP), with modifications to objective functions and constraints. Although alike, a key distinction is emergency service systems (EMS) are considered critical and the optimization of these has become all the more important as a result. Similar to their parent problems, these are NP-hard and must resort to approximations if the space size is too large. Much of the current work has simply been on modifying existing systems through simulation to achieve a more acceptable result. There has been attempts towards using meta-heuristics, though practical experimentation is lacking when compared to VRP or MCP. The contributions of this work are a comprehensive survey of current methodologies, summarized models, and suggested future improvements.

The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.

A model and expansion plan have been developed to optimally determine microgrid designs as they evolve to dynamically react to changing conditions and to exploit energy storage capabilities. In the wake of the highly electrified future ahead of us, the role of energy storage is crucial wherever distributed generation is abundant, such as microgrid settings. Given the variety of storage options that are recently becoming more economical, determining which type of storage technology to invest in, along with the appropriate timing and capacity becomes a critical research question. In problems where the investment timing is of high priority, like this one, developing analytical and systematic frameworks for rigorously considering these issues is indispensable. From a business perspective, these strategic frameworks will aim to optimize the process of investment planning, by leveraging novel approaches and by capturing all the problem details that traditional approaches are unable to. Reinforcement learning algorithms have recently proven to be successful in problems where sequential decision-making is inherent. In the operations planning area, these algorithms are already used but mostly in short-term problems with well-defined constraints and low levels of uncertainty modeling. On the contrary, in this work, we expand and tailor these techniques to long-term investment planning by utilizing model-free approaches, like the Q-learning algorithm, combined with simulation-based models. We find that specific types of energy storage units, including the vanadium-redox battery, can be expected to be at the core of the future microgrid applications, and therefore, require further attention. Another key finding is that the optimal storage capacity threshold for a system depends heavily on the price movements of the available storage units in the market.

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth when thousands of IoT devices stream data to the cloud concurrently. In this paper, we propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. Specifically, we first construct three anomaly detection DNN models of increasing complexity, and associate them with the three layers of HEC from bottom to top, i.e., IoT devices, edge servers, and cloud. Then, we design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. The selection is formulated as a contextual bandit problem and is characterized by a single-step Markov decision process, with an objective of achieving high detection accuracy and low detection delay simultaneously. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud. In addition, our evaluation also shows that it outperforms other baseline schemes.

It had already been an eventful day in Iran: The country had just launched missiles at United States forces based in Iraq and an airliner carrying at least 176 people crashed shortly after takeoff from Tehran on Wednesday, killing everyone on board. Then just before dawn, a 4.5-magnitude earthquake struck southern Iran at a depth of about six miles, the United States Geological Survey reported, in the same region as the troubled Bushehr nuclear power plant. It struck just as Iranian leaders were trumpeting their strike on two Iraqi bases housing United States forces, in retaliation for last week's American drone strike that killed Maj. No casualties were immediately reported, but rescue teams were working at the site, Jahangir Dehqani, managing director of the Bushehr crisis management agency, told the state-run IRNA news agency. The quake was reported about 30 miles from the Russian-built Bushehr nuclear plant.

AI, which refers to algorithms that learn from and find patterns in huge quantities of data, is fundamentally about giving the right actionable information to someone at the moment of making a decision. Only AI can do that at scale. AI can be used all across the enterprise, in sales, marketing, customer retention, customer support, fraud reduction--anything that requires a prediction based on data and actionable recommendations in order to make an informed business decision. But a recent International Data Corporation survey of global organizations that are already using AI solutions found only 25% have developed an enterprise-wide AI strategy. Most organizations reported failures among their AI projects, with a quarter of them reporting up to a 50% failure rate.

The paper announces the new long-term challenge for improving the performance of automatic speech recognition systems. The goal of the challenge is to investigate methods of correcting the recognition results on the basis of previously made errors by the speech processing system. The dataset prepared for the task is described and evaluation criteria are presented.

Climate change impacts and adaptations are the subjects to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.