This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis. The problem is significant because in many practical applications it is required to adapt the model when new information is available and/or the system undergoes changes, without the need to store an increasing amount of data as time proceeds. Indeed, in this context, many problems arise, such as the well known Catastrophic Forgetting and Capacity Saturation ones. We propose an adaptation algorithm inspired by Moving Horizon Estimators, deriving conditions for its convergence. The described method is applied to a simulated chemical plant, already adopted as a challenging benchmark in the existing literature. The main results achieved are discussed.

Today\'s world of space\'s primary concern is the uncontrolled growth of space debris and its probability of collision with spacecraft, particularly in the low earth orbit (LEO) regions. This paper is aimed to design an optimized micro-propulsion system, Cold Gas Thruster, to de-orbit the PSLV debris from 668km to 250 km height after capturing process. The propulsion system mainly consists of a storage tank, pipes, control valves, and a convergent-divergent nozzle. The paper gives an idea of the design of each component based on a continuous iterative process until the design thrust requirements are met. All the components are designed in the CATIA V5, and the structural analysis is done in the ANSYS tool for each component where our cylinder tank can withstand the high hoop stress generated on its wall of it. And flow analysis is done by using the K-$\epsilon$ turbulence model for the CD nozzle, which provides the required thrust to de-orbit PSLV from a higher orbit to a lower orbit, after which the air drag will be enough to bring back to earth\'s atmosphere and burn it. Hohmann\'s orbit transfer method has been used to de-orbit the PSLV space debris, and it has been simulated by STK tools. And the result shows that our optimized designed thruster generates enough thrust to de-orbit the PSLV debris to a very low orbit.

A novel neural network (NN) approach is proposed for constrained optimization. The proposed method uses a specially designed NN architecture and training/optimization procedure called Neural Optimization Machine (NOM). The objective functions for the NOM are approximated with NN models. The optimization process is conducted by the neural network's built-in backpropagation algorithm. The NOM solves optimization problems by extending the architecture of the NN objective function model. This is achieved by appropriately designing the NOM's structure, activation function, and loss function. The NN objective function can have arbitrary architectures and activation functions. The application of the NOM is not limited to specific optimization problems, e.g., linear and quadratic programming. It is shown that the increase of dimension of design variables does not increase the computational cost significantly. Then, the NOM is extended for multiobjective optimization. Finally, the NOM is tested using numerical optimization problems and applied for the optimal design of processing parameters in additive manufacturing.

The last decade the MAVs have steadily gained interest in the fields of real-life applications, such as infrastructure inspection [1], underground mine tunnel inspection [2], and bridge inspection [3]. The key objective in all these usecases is the online collection of critical information like images, 3D models, and other sensorial data to create safer conditions for the personnel while reducing the overall inspection time. Fully autonomous performance of the MAVs is one of the key challenges in deploying them in real-world applications, while it should overcome uncertainties in localization, limited on-board computation power, delays in control layers, dynamic/static obstacles, etc. Meanwhile, advances in technologies, such as 5G [4] telecommunications technology, enable the use of cloud and edge computing for MAV applications. In this case, the heavy computational processing for multiple processes, such as mapping, localization, and path planning can be carried on the edge computing side, while retaining a fast bi-directional link with the MAV. However, one of the main challenges in such networked applications is the limited bandwidth, the time delays, and the overall package losses that degrade the overall control performance and could lead the system to instability.

Random forest (RF) regression model is used to predict the lattice constant, magnetic moment and formation energies of full Heusler alloys, half Heusler alloys, inverse Heusler alloys and quaternary Heusler alloys based on existing as well as indigenously prepared databases. Prior analysis was carried out to check the distribution of the data points of the response variables and found that in most of the cases, the data is not normally distributed. The outcome of the RF model performance is sufficiently accurate to predict the response variables on the test data and also shows its robustness against overfitting, outliers, multicollinearity and distribution of data points. The parity plots between the machine learning predicted values against the computed values using density functional theory (DFT) shows linear behavior with adjusted R2 values lying in the range of 0.80 to 0.94 for all the predicted properties for different types of Heusler alloys. Feature importance analysis shows that the valence electron numbers plays an important feature role in the prediction for most of the predicted outcomes. Case studies with one full Heusler alloy and one quaternary Heusler alloy were also mentioned comparing the machine learning predicted results with our earlier theoretical calculated values and experimentally measured results, suggesting high accuracy of the model predicted results.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

As the global population has expanded over time, modernizing agriculture with the aid of innovations like AI has been humanity's prevailing approach to staving off famine. A variety of mechanical and chemical innovations delivered during the 1950s and 1960s represented the third agricultural revolution. The adoption of pesticides, fertilizers and high-yield crop breeds, among other measures, transformed agriculture and ensured a secure food supply for many millions of people over several decades. Concurrently, modern agriculture has emerged as a culprit of global warming, responsible for one-third of greenhouse gas emissions, namely carbon dioxide and methane. Meanwhile, inflation on the price of food is reaching an all-time high, while malnutrition is rising dramatically.

Engineering methods are centered around traditional notions of decomposition and recomposition that rely on partitioning the inputs and outputs of components to allow for component-level properties to hold after their composition. In artificial intelligence (AI), however, systems are often expected to influence their environments, and, by way of their environments, to influence themselves. Thus, it is unclear if an AI system's inputs will be independent of its outputs, and, therefore, if AI systems can be treated as traditional components. This paper posits that engineering general intelligence requires new general systems precepts, termed the core and periphery, and explores their theoretical uses. The new precepts are elaborated using abstract systems theory and the Law of Requisite Variety. By using the presented material, engineers can better understand the general character of regulating the outcomes of AI to achieve stakeholder needs and how the general systems nature of embodiment challenges traditional engineering practice.

Heterogeneous catalysis is key for chemical transformations. Understanding how catalyst active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurate determination of catalytic mechanisms and predictably developing catalysts. We combine in-situ observation and Machine Learning accelerated first-principle atomistic simulations to uncover the mechanism of restructuring for Pt catalysts under a pressure of carbon monoxide CO. We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, that then detach from the step edge to create sub-nano-islands on the terraces, where undercoordinated sites are stabilized by the CO adsorbates. These studies open an avenue to achieve an atom-scale understanding of structural dynamics of more complex metal nanoparticles under reaction.

Detect Technologies announces a global agreement with Vedanta for deployment of T-Pulse, their internationally deployed AI-based workplace safety software. Vedanta Resources Limited is a globally diversified natural resources company and is among the top producers of major commodities, including zinc-lead-silver, iron ore, steel, copper, aluminium, oil and gas. The group engages more than 65,000 employees and contractors, primarily in India, Africa, Ireland and Australia. Managing EHS for such a diverse and spread-out organisation is a massive challenge. Driven by its commitment to GOAL ZERO, Vedanta started exploring AI–based solutions, which can infuse efficiency in this process.