Newly, the rates of energy and material consumption to augment industrial pro-duction are substantially high, thus the environmentally sustainable industrial de-velopment has emerged as the main issue of either developed or developing coun-tries. A novel approach to supply chain management is proposed to maintain economic growth along with environmentally friendly concerns for the design of the supply chain network. In this paper, a new green supply chain design approach has been suggested to maintain the financial virtue accompanying the environ-mental factors that required to be mitigated the negative effect of rapid industrial development on the environment. This approach has been suggested a multi-objective mathematical model minimizing the total costs and CO2 emissions for establishing an environmentally sustainable closed-loop supply chain. Two opti-mization methods are used namely Epsilon Constraint Method, and Genetic Al-gorithm Optimization Method. The results of the two mentioned methods have been compared and illustrated their effectiveness. The outcome of the analysis is approved to verify the accuracy of the proposed model to deal with financial and environmental issues concurrently.

Commercial and industrial applications of artificial intelligence and machine learning are unlocking economic opportunities, transforming the way we do business, and even helping to solve complex social and environmental problems. In fact, generative applications of this technology have become tools for environmental sustainability. With machine learning's capability to analyze and make predictions using massive pools of data, these applications are now able to accurately model climate change and fluctuations, so that energy infrastructures and energy consumption can be re-engineered accordingly. Ironically, training large-scale models via deep neural networks requires vast computational power. It also produces a great deal of thermal energy from each of the associated graphics processing units (GPUs) or tensor processing units (TPUs) used.

The thrust of nuclear physics is studying the universe down to its smallest subatomic parts. Now, two physicists at the Department of Energy's Thomas Jefferson National Accelerator Facility have secured more than $2 million in federal funding dedicated to research projects that harness the power of data analytics to make that work faster and more efficient. David Lawrence and Chris Tennant are among 14 scientists at seven DOE national laboratories whose proposals were awarded a total of $37 million to be allocated over three years. "Artificial Intelligence and machine learning have the potential to transform a host of scientific disciplines and to revolutionize experimentation and operations at user facilities in the coming years," Chris Fall, director of DOE's Office of Science, said in announcing the funding. "These awards will help ensure America remains on the cutting edge of these critical technologies for science."

SAN RAMON, California – Tesla is working on new battery technology that CEO Elon Musk says will enable the company within the next three years to make sleeker, more affordable cars that can travel dramatically longer distances on a single charge. But the battery breakthroughs that Musk unveiled Tuesday at a highly anticipated event didn't impress investors. They were hoping Tesla's technology would mark an even bigger leap forward and propel the company's soaring stock to even greater heights. Tesla's shares shed more than 6 percent in extended trading after Musk's presentation. That deepened a downturn that began during Tuesday's regular trading session as investors began to brace for a potential letdown.

For a hitherto relative unknown, scoring a $113 million Series C at this time is bound to get some attention. The amount of attention is bound to grow upon learning that the company is backed by, and works with, the likes of Bp, its AI technology is based on IP from NASA and Caltech, and it looks like the closest thing to the vision for AI in the real world today. Beyond Limits, an industrial and enterprise-grade AI technology company active in energy, utilities, and healthcare, today announced a milestone Series C funding round with $113 million closed and another approximately $20 million committed. This round is led by Group 42, a prominent AI and cloud computing company, and Bp ventures, an existing two-time investor and customer of the company. ZDNet caught up with Beyond Limits CEO and Founder AJ Abdallat to discuss business, technology, and applications.

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models' predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field.

Billions of birds die annually from collisions with windows, communication towers, wind turbines, and other human-made objects. One reason is that birds see a reflection of the sky in the object and think they're flying into an unobstructed path. This is even a problem for solar panel facilities, which see up to 138,000 bird deaths per year in the US from collisions with equipment. Though damage to the solar panels is minimal, officials worry about the impact these structures have on local wildlife. To combat the problem, the Department of Energy (DOE) has awarded Argonne National Laboratory $1.3 million to develop a system that can automatically monitor bird activity.

We propose an architecture for training generative models of counterfactual conditionals of the form, 'can we modify event A to cause B instead of C?', motivated by applications in robot control. Using an 'adversarial training' paradigm, an image-based deep neural network model is trained to produce small and realistic modifications to an original image in order to cause user-defined effects. These modifications can be used in the design process of image-based robust control - to determine the ability of the controller to return to a working regime by modifications in the input space, rather than by adaptation. In contrast to conventional control design approaches, where robustness is quantified in terms of the ability to reject noise, we explore the space of counterfactuals that might cause a certain requirement to be violated, thus proposing an alternative model that might be more expressive in certain robotics applications. So, we propose the generation of counterfactuals as an approach to explanation of black-box models and the envisioning of potential movement paths in autonomous robotic control. Firstly, we demonstrate this approach in a set of classification tasks, using the well known MNIST and CelebFaces Attributes datasets. Then, addressing multi-dimensional regression, we demonstrate our approach in a reaching task with a physical robot, and in a navigation task with a robot in a digital twin simulation.

Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.

Reinforcement learning is a promising model-free and adaptive controller for demand side management, as part of the future smart grid, at the district level. This paper presents the results of the algorithm that was submitted for the CityLearn Challenge, which was hosted in early 2020 with the aim of designing and tuning a reinforcement learning agent to flatten and smooth the aggregated curve of electrical demand of a district of diverse buildings. The proposed solution secured second place in the challenge using a centralised 'Soft Actor Critic' deep reinforcement learning agent that was able to handle continuous action spaces. The controller was able to achieve an averaged score of 0.967 on the challenge dataset comprising of different buildings and climates. This highlights the potential application of deep reinforcement learning as a plug-and-play style controller, that is capable of handling different climates and a heterogenous building stock, for district demand side management of buildings.