Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development

With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.

Employee Satisfaction： A Case Study at Bank Muamalat Malaysia Berhad

Employee satisfactions emerge as the central subject in this study because employee satisfaction is one of the most important factors in determining the success of an organization. Leading from such probe, this study formulates a model to test factors that drive towards employee satisfaction. The study considers the most positive and unique factors that most organization has, thus suggests that core factors for employee satisfaction notably are leadership style, working environment, employee rewards and welfare, and organization culture. The analysis involved two phases of qualitative and quantitative techniques. First, the study is based on the management of Bank Muamalat Malaysia Berhad （BMMB） to explore how far the relevant factors opined the literature review synchronized with the issue being examined. In the second phase, the study developed a questionnaire, based on the findings to reach out 1,216 employees of BMMB in 1999. This study found few differences in the factors that affected employee satisfaction. Various independent variables hypothesized were significant. In view of more than one independent variable, the multiple regression analysis is adopted in this study. It is assumed that these independent variables： leadership style, working environment, employee rewards and welfare, and organization culture, explained the employee satisfaction variation. The results show that there is significant relationship between employee satisfaction and these independent variables. The statistical package for the social sciences was used to analyze the data. It is anyhow the result shows that the independent variable： organizational culture-the most important variable that determines employee satisfaction.

Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

Modeling the dynamical behavior of the interaction of T-cells and human immunodeficiency virus with saturated incidence

In the last forty years,the rise of HIV has undoubtedly become a major concern in the field of public health,imposing significant economic burdens on affected regions.Consequently,it becomes imperative to undertake comprehensive investigations into the mechanisms governing the dissemination of HIV within the human body.In this work,we have devised a mathematical model that elucidates the intricate interplay between CD4^(+)T-cells and viruses of HIV,employing the principles of fractional calculus.The production rate of CD4^(+)T-cells,like other immune cells depends on certain factors such as age,health status,and the presence of infections or diseases.Therefore,we incorporate a variable source term in the dynamics of HIV infection with a saturated incidence rate to enhance the precision of our findings.We introduce the fundamental concepts of fractional operators as a means of scrutinizing the proposed HIV model.To facilitate a deeper understanding of our system,we present an iterative scheme that elucidates the trajectories of the solution pathways of the system.We show the time series analysis of our model through numerical findings to conceptualize and understand the key factors of the system.In addition to this,we present the phase portrait and the oscillatory behavior of the system with the variation of different input parameters.This information can be utilized to predict the long-term behavior of the system,including whether it will converge to a steady state or exhibit periodic or chaotic oscillations.

A Two-layer Framework for Mitigating the Con-gestion of Urban Power Grids Based on Flexible Topology with Dynamic Thermal Rating

The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.