This paper discusses 2d malware image  detection and classifying with AI based Model using  Machine Learning. It aims to use previous data and learn  and train automatically to identify and classify malware  families. Results are based on several parameters.

The integration of artificial intelligence and science has resulted in substantial progress in computational chemistry methods for the design and discovery of novel catalysts. Nonetheless, the challenges of electrocatalytic reactions and developing a “large-scale language model” in catalysis persist, and the recent success of ChatGPT’s(Chat Generative Pre-trained Transformer) few-shot methods surpassing BERT (Bidirectional Encoder Representation from Transformers) underscores the importance of addressing limited data, expensive computations, time constraints and structure-activity relationship in research. Hence, the development of few-shot techniques for catalysis is critical and essential, regardless of present and future requirements. This paper introduces the Few-Shot Open Catalyst Challenge 2023, a competition aimed at advancing the application of machine learning technology for predicting catalytic reactions on catalytic surfaces, with a specific focus on dual-atom catalysts in hydrogen peroxide electrocatalysis. To address the challenge of limited data in catalysis, we propose a machine learning approach based on MLP-Like and a framework called Catalysis Distillation Graph Neural Network (CDGNN).Our results demonstrate that CDGNN effectively learns embeddings from catalytic structures, enabling the capture of structure-adsorption relationships. This accomplishment has resulted in the utmost advanced and efficient determination of the reaction pathway for hydrogen peroxide, surpassing the current graph neural network approach by 16.1%. Consequently, CDGNN presents a promising approach for few-shot learning in catalysis.

Traditionally the methodology to interconnect the MAC layer and the PHY layer is by a interface called Media Independent Interface which is a complicated with a high pin count and costly more suitable for high speed ethernet data transfer about 100Mbps. For this project, speed upto 10Mbps is suitable which is a complicated task to do with MII interface. These complexities and limitations of the conventional Media Independent Interface(MII) for interfacing MAC and PHY lead to the development and implementation of Open Alliance SPI Protocol for 10BASET1-MACPHY. The objective of the project is to develop the open alliance protocol stack architecture for MAC-PHY chip to enable seamless control and ethernet data transactions. Furthermore to test and debug the functionality on the necessary hardware and verify the same. Based on the objectives defined in the previous section, the complete project can be divided into multiple phases, with each phase aiming to be a successive step which contributes in part to achieve the final goal. These steps which are to be performed in order are as follows: Firstly developing a high level architecture or code flow to be followed. The initial step of development is the OASPI library. Following development of the physical abstraction layer which is a wrapper to the SPI driver API’s. Lastly development of the application code to test and debug the functionality of the entire code flow. After the software development is complete, verification of the functionality with various test cases is done. By incorporating the OASPI protocol stack various limitations of the MII interface were resolved like :Enhancing Bandwidth, Data Transmission Reliability, Interoperability and Compatibility, Energy Efficiency, Network Scalability, Provisioning for Quality of Service (QoS),Network security and privacy, Cost-effectiveness, Time-Sensitive Networking and complexity reduction.

At the French-German Research Institute of Saint-Louis (ISL) railguns are investigated with respect to military scenarios. Currently, the different railguns installed at ISL use rails with a flat contact surface. For lateral guidance of the armarture, the barrel is equipped with guiding bars made of reinforced plastic mounted on the sides of the rails. This solution is prone to damage of the guiding bars. In this investigation rails with a convex shaped contact surface are installed and used in a 2 m long railgun. It is experimentally shown, that using these modified rails, the guiding bars can be eliminated from the barrel setup, thus simplifying the barrel. Further on, in a comparison to launches with flat rails, the convex rail implementation is more efficient, resulting in a higher armature velocity at the same primary energy. Additional improvement was obtained by an armature modification.

A document by Hao Cheng . Click on the document to view its contents.

This research paper explores the transformative potential of immersive media, specifically virtual reality (VR), in enhancing empathy among users. Empathy is vital in fostering understanding, compassion, and social cohesion. Immersive media, with its ability to transport individuals into simulated environments and perspectives, offers unique opportunities to bridge the empathy gap. This paper examines how VR can boost empathy, including perspective-taking, emotional connection, experiential learning, breaking down barriers, and empathy-building simulations. It discusses existing studies and empirical evidence supporting the effectiveness of VR in promoting empathy. The paper acknowledges the ethical considerations and challenges associated with using VR for empathy enhancement, such as avoiding stereotype reinforcement and ensuring inclusive design. By highlighting the potential of immersive media in cultivating empathy, this research paper contributes to the growing field of empathy research and provides insights for the development and application of VR experiences aimed at fostering empathy and understanding in diverse contexts.

In recent years, safe reinforcement learning (RL) with the actor-critic structure has gained significant interest for continuous control tasks. However, achieving near-optimal control policies with safety and convergence guarantees remains challenging. Moreover, few works have focused on designing RL algorithms that handle time-varying safety constraints. This paper proposes a safe RL algorithm for optimal control of nonlinear systems with time-varying state and control constraints. The algorithm’s novelty lies in two key aspects. Firstly, the approach introduces a unique barrier force-based control policy structure to ensure control safety during learning. Secondly, a multi-step policy evaluation mechanism is employed, enabling the prediction of policy safety risks under time-varying constraints and guiding safe updates. Theoretical results on learning convergence, stability, and robustness are proven. The proposed algorithm outperforms several state-of-the-art RL algorithms in the simulated Safety Gym environment. It is also applied to the real-world problem of integrated path following and collision avoidance for two intelligent vehicles – a differential-drive vehicle and an Ackermann-drive one. The experimental results demonstrate the impressive  sim-to-real transfer capability of our approach, while showcasing satisfactory online control performance.

This study presents a detailed review of a contemporary, holistic safety concept for a smart cluster, comprising a multipurpose display and a head unit. The focus is on elucidating the fundamental regulatory requirements for smart clusters within the regulatory frameworks of the United States and the European Union, and drawing connections to the safety requirements and concept of the smart cluster. The paper explores a range of safety mechanisms and architectures designed to actualize these proposed safety requirements. For each mechanism, we provide an in-depth analysis of its benefits and drawbacks, as well as a thorough explanation of its operational logic. This comprehensive evaluation offers valuable insights into developing safer and more efficient smart clusters in line with international regulatory standards.

This article presents an improved antenna array pattern synthesis method, which can be effectively applied to synthesize of the radiation pattern of arbitrary linear and planar arrays with equal space between the array elements. In mathematics theory, method of the least squares is a routine way to discover the best fit curve to the specified data. Least square method (LSM) with Richardson technique will be a more direct technique to synthesize the radiation pattern of any linear and planar, equally spaced antenna arrays. The goal of them is to find the amplitude of each element of the under-studying array. Simulation results demonstrate that pattern synthesis of linear antenna arrays using the proposed method, provides considerable enhancements compared to the other ones.

The vertical distribution of soil properties is crucial in accurately representing various environmental processes such as freeze-thaw cycles and diurnal variations. In this paper, considering the complexity of the multi-parameter features of layered soil, we evaluate the potential to retrieve the vertical distribution of the moisture and temperature of soil using multi-channel passive microwave observations. To enhance the inversion rate and accuracy, a novel Physics-Driven Artificial Neural Network (P-ANN) inversion algorithm combining multi-angle (30 to 50 degree), multi-frequency (L-, C-, and X-band), and multi-polarization (horizontal and vertical polarization) passive observations is proposed. In this approach, the multi-channel physical brightness temperature simulations corresponding to the predicted soil state parameters are integrated into the loss function of a standard fully connected neural network, enabling efficient convergence with limited sampling data in the training process. Testing results exhibit that the inversion performance of P-ANN is superior than that of conventional neural network approaches which only adopts errors in soil states in the loss function to train the network. Test also shows the proposed P-ANN approach outperforms traditional optimization algorithms in dealing with layered soil retrieval. In order to further improve the retrieval accuracy, an advanced local optimization scheme is also proposed, where the output from P-ANN is further treated as the initial value to a local optimization algorithm, achieving even closer results to the true values without excessive computational resources. In addition, to estimate the reliability of the model predictions,  this paper also establishes a statistical relationship between the soil inversion error and the error of corresponding brightness temperatures from the testing process. When the trained neural network is in operation, the error of brightness temperature is calculated through the physical model, and the reliability of retrieval soil results is then acquired by putting the calculated brightness temperature errors into the pre-established statistical relationship. The proposed concepts and approaches have demonstrated the feasibility of using P-ANN model with multidimensional observations to invert the layered soil structure. The proposed approach holds great potentials for various remote sensing applications as well as a wide range of inverse problem challenges.

Inertia assessment is an important step for frequency stability analysis in power systems. Typically, the total inertia of all devices in the system is summed up to obtain the system inertia. However, such method may be optimistic when there are a considerable number of converter-interfaced generations (CIGs). Particularly, if the CIGs saturate under large disturbances, their inertia to be provided will be significantly reduced. To this end, this letter proposes a method to assess the system inertia considering the power limits. Then, the distribution of system inertia under uncertain power limits is also analyzed. Simulations are conducted to validate the analysis.

Individuals using passive prostheses typically rely heavily on their biological limb to complete sitting and standing tasks, leading to slower completion times and increased rates of osteoarthritis and lower back pain. Powered prostheses can address these challenges, but have control methods that divide sit-stand transitions into discrete phases, limiting user synchronization across the motion and requiring long manual tuning times. This paper extends our preliminary work using a thigh-based phase variable to parameterize optimized data-driven impedance parameter trajectories for sitting, standing, and walking, with only two classification modes. We decouple the stand-to-sit and sit-to-stand equilibrium angles through a knee velocity-dependent scaling term, reducing the model fitting error by approximately half compared to our previous results. We then experimentally validate the controller with three individuals with above-knee amputation performing sitting and standing transitions to/from three different chair heights. We show that our controller implemented on a powered knee-ankle prosthesis produced biomimetic joint mechanics, resulting in significantly reduced sit/stand loading asymmetry and time to complete a 5x sit-to-stand task compared to participants’ passive prostheses. Integration with a previously developed walking controller also allowed sit/walk transitions between different chair heights. The controller’s biomimetic assistance may reduce the overreliance on the biological limb caused by inadequate passive prostheses, helping improve mobility for people with above-knee amputations.

A document by Shruti Sapra . Click on the document to view its contents.

The sets of expressions for calculation of sixteen components of magnetic stiffness over torque between two current-carrying circular filaments arbitrarily positioning in the space are derived by using mutual inductance and Grover's method. The expressions are presented in the analytical form via integral equations, whose kernels include the elliptic functions of first and second kinds. The derived sets of formulas were mutually validated and results of calculation of components of magnetic stiffness agree well to each other.

The primary objective of this study is to introduce a novel concept, namely spinal geometric compensation, to the realm of spine biomechanics. Mechanical low back pain has long been associated with lumbar segmental instability, wherein the degree of segmental mobility profoundly impacts the curvature and range of motion (ROM) of the thoracolumbar spine. Consequently, this research endeavors to shed light on the intricate geometric compensatory mechanisms employed by the spine to uphold congruency. A validated and representative finite element model of the thoracolumbar spine was developed, to comprehensively evaluate the influence of passive tissues such as the lumbar intervertebral disc, iliolumbar ligament, the rib cage, and the lower lumbar facet cartilage-capsular ligament on lumbar segmental instability and spinal geometric compensation during flexion and extension rotations.

The ergonomic configuration of processes is becoming increasingly important, especially considering the changing demographics and increasing shortage of skilled workers. Exoskeletons are widely discussed as a means of protecting employees from overstraining at the level of personal protective measures. The field of industrial exoskeletons research is still relatively new and has many unanswered questions. For example, there have not yet been sufficient studies on the influence of exoskeletons on the movements of employees. This publication discusses the effects of exoskeletons in manual processes. For this purpose, exemplary physical activities are carried out in a pilot study by a subject collective, whereby the tasks are executed with and without an exoskeleton. During the execution, a motion capturing system is used to record the movement data. Different back-supporting exoskeletons are taken into account in the study. The evaluation is based on the joint angles of the participants while performing tasks with and without exoskeletons. It is shown that the use of exoskeletons has a significant effect on the movement patterns, with a distinction made between rigid and soft support structures.

The magnetic properties in Pt/Co and Pt/Co/Mx/Pt [1] multilayers (with Mx= different caping materials), such as perpendicular magnetic anisotropy (PMA), are of particular interest for spintronic devices. In particular, it is important to obtain a strong PMA [2] on the flexible substrate in the field of wearable devices and health monitoring. However, the impact of different stacks deposition and modulation conditions on the magnetic properties of the film lacks systematic research. Here, we investigate the magnetic properties in Pt/Co/Pt, Pt/Co/RuO2/Pt structures deposited by sputtering with different hard and flexible substrates, different layer thicknesses, different deposition temperatures of the buffer layer and different capping layers. We found that the coercive field and magnetic dynamic behavior of the films vary with these factors. More-over, depositing the buffer layer under high temperature can effectively adjust the crystalline state of the films, thus affecting the performance of the films. Also, in order to break the symmetry of Pt/Co/Pt we add different thin film materials between Co and the Pt capping. In addition, we study the effect of light He+ ions irradiation on the defects and the magnetics parameters on Pt/Co/Pt. It is expected that ultra-fast magnetic moment switching can be achieved by optimizing the conditions, in order to reduce the power consumption of the device. The experimental results also show that the optimized film conditions can also obtain strong PMA on the flexible substrate. These findings help to understand magnetic properties in these structures and show the promising prospect for wearable devices and other spintronic devices.

This correspondence paper analyzes the performance of a multiuser underlay uplink non-orthogonal multiple access network. A subset of users are selected for concurrent uplink transmission in any coherence interval while ensuring that the interference temperature limit (ITL) of the primary receiver is satisfied by the overall interference from all these users. Analytical expressions are derived for non-outage probability and throughput of the secondary network. Channel knowledge is utilized for intelligent apportioning of the ITL between all transmitting nodes in a novel channel-aware ITL apportioning(CA-ITLA) scheme. The performance of the scheme is compared to benchmark schemes like static ITL apportioning (S-ITLA) and orthogonal multiple access (OMA). It is shown that exploiting channel state information (CSI) allows the CA-ITLA scheme to achieve large throughput gains. It is shown that indirectly exploiting primary channel knowledge through a CSI dependent ITL results in even larger throughputs. Useful upper bounds onperformance are also derived with static and CSI-dependent ITL. Monte Carlo simulation results validate the presented analysis