Hydrogen generation rate is one of the most important parameters which must be considered for the development of engineering solutions in the field of hydrogen energy applications. In this paper, the kinetics of hydrogen generation from oxidation of hydrogenated porous silicon nanopowders in water are analyzed in detail. The splitting of the Si-H bonds of the nanopowders and water molecules during the oxidation reaction results in powerful hydrogen generation. The described technology is shown to be perfectly tunable and allows us to manage the kinetics by: (i) varying size distribution and porosity of silicon nanoparticles; (ii) chemical composition of oxidizing solutions; (iii) ambient temperature. In particular, hydrogen release below 0 °C is one of the significant advantages of such a technological way of performing hydrogen generation.

This study aims to identify the most suitable IoT communication protocol for a digital twin architecture, focusing on meeting the demands of real-time data transmission with stability and security. The research methodology involves a comparative performance analysis of MQTT over TCP, MQTT over WebSocket, and HTTP protocols, evaluating their latency, connectivity stability, and IoT application feasibility using an ESP32 microcontroller and DHT22 sensor setup. The experimental results demonstrate that MQTT over TCP achieves the lowest average latency at 290.5 ms, while MQTT over WebSocket exhibits more stable latency profiles with a 193.2 ms standard deviation; HTTP, despite its broader compatibility, showed higher average latency of 342.6 ms with a 307.9 ms standard deviation. These findings provide valuable insights for digital twin implementations, enabling developers to make informed protocol selections based on specific requirements, whether prioritizing real-time performance through MQTT protocols or emphasizing system compatibility and simplicity through HTTP.

The intricate and dynamic nature of agricultural supply chains imposes stringent demands on optimization methodologies, necessitating multi-objective considerations, privacy safeguards, and decision transparency to address pivotal challenges in ensuring food security and sustainable development. This study introduces a Dynamic Heterogeneous Multi-Objective Graph Neural Network (DHMO-GNN) model, meticulously tailored for optimizing agricultural supply chains. It integrates five core modules: data preprocessing and heterogeneous graph construction, dynamic graph neural networks, multi-objective optimization, interpretability enhancement, and federated learning collaboration. The model adeptly captures temporal dynamics through sequential attention mechanisms and incremental updates, harmonizes cost, delivery time, and carbon emissions via multi-task learning and Pareto optimization, augments decision transparency with GNNExplainer and SHAP, and surmounts data silos by leveraging federated learning alongside differential privacy. Empirical evaluations on the Chengdu Hongguang Town Farmers’ Market dataset demonstrate that the centralized DHMO-GNN variant achieves a hypervolume indicator (HV) of 0.849, surpassing baseline models; the federated variant exhibits only a 2.6% decline under privacy constraints, underscoring its robustness. Ablation studies further corroborate the synergistic contributions of each module. This research furnishes an efficacious and trustworthy framework for the intelligent management of agricultural supply chains, holding profound implications for advancing digital transformation and green development.

The article is given to the issue of guideline of hotness supply and cooling in the room. A robotized framework for checking the unique attributes of such sensors is depicted, which is a product and equipment complex for setting up a test seat and dissecting the boundaries of sensors for dynamic temperature control and cooling. The framework fills the roles of controlling the Google Coral USB Accelerator, designing the ADC (Analog-digital converter) and deciding the sufficiency recurrence and stage recurrence qualities of temperature sensors, switches, leak sensors and cooling taking into account the latest values from temperature and humidity sensors, fixing attributes and monitoring in SCADA (Supervisory Control And Data Acquisition) Genesis64 program. The plan of the test seat, the summed-up calculation of the framework activity and the screen type of the program activity are introduced. The product of the robotized framework for temperature control and cooling in the room is created based on SCADA Genesis 64 programs and technologies with OPC UA (Unified Architecture) and ModBUS TCP data receive protocol.

Enhancing Sustainable Biogas Generation Through a Real-Time Digital Twin of a Modular Bioreactor

Coronary artery disease (CAD) is a leading cause of global mortality, demanding accurate and early risk assessment. While machine learning models offer strong predictive power, their clinical adoption is often hindered by a lack of transparency and reliability. This study aimed to develop and rigorously evaluate a calibrated, interpretable machine learning framework for CAD prediction using 56 routinely collected clinical and demographic variables from the Z-Alizadeh Sani dataset (n = 303). A systematic protocol involving comprehensive preprocessing, class rebalancing using SMOTE, and grid-search hyperparameter tuning was applied to five distinct classifiers. The XGBoost model demonstrated the highest predictive performance, achieving an accuracy of 0.9011, an F1 score of 0.8163, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.92. Post hoc interpretability analysis using SHAP (Shapley Additive Explanations) identified HTN, valvular heart disease (VHD), and diabetes mellitus (DM) as the most significant predictors of CAD. Furthermore, calibration analysis confirmed that the mode’s probability estimates are reliable for clinical risk stratification. This work presents a robust framework that combines high predictive accuracy with clinical interpretability, offering a promising tool for early CAD screening and decision support.