Search Results (35,758)

In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs, California weather data. This extension paper addresses three critical advances. First, we provide a rigorous theoretical proof demonstrating that the DCRNN’s Markovian reduction of strongly path-dependent dynamics yields a bounded approximation error, with the error decaying exponentially in the mixing time of the underlying graph diffusion process. Second, we extend the framework to diverse weather regimes—from stable Mediterranean climates (San Diego) to highly variable marine west coast (Seattle), monsoon (Mumbai), and typhoon-prone regions (Hong Kong)—quantifying how weather-induced path dependence affects the required hidden state dimension and forecasting accuracy. We present comprehensive Leave-One-Out Cross-Validation (LOOCV) results across six cities, demonstrating consistent generalization with R² drops of less than 0.1% under out-of-sample testing. A controlled baseline comparison under matched training protocols shows that the graph-free GRU achieves comparable or higher R² on the one-step prediction task, which we attribute to the near-cumulative structure of the target and the small evaluation graph. We frame the DCRNN’s contribution around its theoretical guarantee and its potential advantage on larger graphs and longer horizons. We also characterize conditions under which the model expects to fail, specifically when weather stochasticity violates the geometric mixing assumption or when the effective temporal correlation length exceeds the GRU’s memory capacity. Third, we outline physics-informed enhancements that are proposed as future development: CFD-integrated loss functions, differentiable Model Predictive Control (MPC) heads, and a modular design enabling alternative turbine configurations. We also propose a standardized rooftop solar thermal deployment architecture with 200 m × 100 m, 100 m × 100 m, and 100 m × 50 m modules designed for data center footprints with pre-allocated HVAC space. We conclude with a stage-gated validation roadmap progressing from unit tests to hardware-in-the-loop simulation to full-scale FEED-site deployment. The completed contributions of this paper are the theorem, its empirical assumption verification, the multi-climate LOOCV study, the matched-protocol baseline comparison, and the sensor-failure robustness analysis. The remaining components are described as proposed extensions. Full article

Pyrazines are the key odorants in food contributing to baked and nutty aromas. In order to improve practicality, a novel electrochemical olfactory biosensor for pyrazine analysis was developed using an olfactory receptor (OR5K1) as the recognition element and AuNPs-PB/ZIF-8@SWCNT/Ti₃C₂ MXene as the sensing matrix. The sensing application and molecular recognition mechanism of OR5K1 toward pyrazines were explored using molecular docking and in silico site-directed mutagenesis. We explored the sensing application and molecular recognition mechanism of OR5K1 toward pyrazines using molecular docking and in silico site-directed mutagenesis. The sensor achieved a linear detection range of 10⁻¹⁴ to 10⁻⁹ M with a low detection limit of 10⁻¹⁴ M. The biosensor exhibited significantly higher current responses toward pyrazine compounds compared to interfering substances, including ethanol, hexanal, acetone, and phenol, demonstrating excellent selectivity, and retained 79% of its initial signal after 12 days of storage, indicating good stability. The change in the reduction peak current (ΔI) in the presence of pyrazine was used as an analytical signal. When applied to four malt samples (pilsner, munich, crystal, and caramel malts), the biosensor showed ΔI responses ranging from 4.6 ± 0.2 µA to 108 ± 6 µA, which corresponded well with the total pyrazine contents determined by GC-TOF/MS (0.092–0.391 mg/kg), with a correlation coefficient of 0.96. Molecular docking revealed binding energies ranging from −3.9 to −6.0 kcal/mol, suggesting spontaneous interactions between OR5K1 and pyrazines, with Leu14, Met81, Asn84, Phe17, Phe85, and Lys90 identified as potential key residues and hydrogen bonds, hydrophobic interactions, and π−π stacking as primary driving forces. This work provides a sensitive and selective biosensor for pyrazine detection, and the elucidated recognition mechanism offers a molecular basis for understanding roasted aroma perception, supporting applications in food quality control and flavor analysis. Collectively, this study offers new insights for designing olfactory receptor-based electrochemical biosensors and facilitates future exploration of food aroma–receptor interaction mechanisms. Full article

Although the world is rich in deepwater oil and gas resources, deepwater drilling faces the challenge of a narrow safety density window. Dual-gradient drilling is an effective approach to address this narrow safety density window, and subsea pump-lift dual-gradient drilling is currently one of the most widely used methods. To improve handling capacity and effectiveness under well kick conditions, we established a simplified hydrodynamic model for subsea pump-lift dual-gradient drilling that accounts for the subsea pump’s head-flow performance curve. Based on this hydrodynamic model, we proposed automatic control methods based on throttling regulation and variable-speed regulation and conducted simulation analyses. The results indicate that both control methods can safely, efficiently, and automatically handle well kicks; throttle control has shorter response times and higher precision, while variable-speed control regulates bottom hole pressure by adjusting the subsea pump’s rotational speed and offers better energy-saving performance. These findings provide new insights into handling well kicks in subsea pump-lift dual-gradient drilling. Full article

Renewable-powered electric vehicle charging can support sustainable transport electrification by coupling low-carbon electricity generation with charging demand. Autonomous microgrids (MGs) combining wind, solar, and energy storage offer a pathway to this integration, including at locations with limited grid access. However, it can be challenging to monitor and manage the energy of standalone microgrids because they are time-varying and nonlinear. Solar and wind energy were the main sources of power for the microgrid. A microgrid’s primary load is thought to be an electric vehicle charging station (EVCS). The EVCS can charge quickly and uses a lot of power. Additionally, an energy storage system (ESS) is incorporated into the microgrid. This research evaluates a fractional-order-super-twisting sliding mode controller (FOSTSMC) for DC-bus regulation and ESS-assisted power balancing to support reliable renewable-powered fast EV charging. The FOSTSMC scheme includes three key parameters that are optimally tuned using the starfish optimization algorithm (SFOA). Regarding changes in wind velocity and solar irradiance, the response of the FOSTSMC was contrasted to that of a conventional proportional-integral (PI), super-twisting sliding mode controller (STSMC), and the fractional-order-PI (FOPI) regulators. MATLAB/Simulink (R2023a version 9.14) was used to simulate and model the MG. The findings show that the introduced FOSTSMC enhanced the MG’s transient response when compared to the other controllers. The proposed optimal FOSTSMC provides an improvement in the peak overshoot of 41.8% and 47.6% in the settling times over the best values of the other controllers. Moreover, simulation-based evaluation using NASA POWER weather profiles for solar irradiance and wind speed are applied to the proposed system to validate energy management effectiveness. Despite changes in wind velocity, solar intensity, and other parameters, the EVCS charging process and the DC-bus voltage tracked the set point with the least amount of disruption. To prove the effectiveness of SFOA, it is compared to particle swarm optimization (PSO). Full article

Energy-autonomous Internet of Things (IoT) nodes are becoming important components of distributed fieldbus and wireless networks used in building automation, industrial monitoring and wider smart systems. Their operation is constrained not only by the amount of harvested and stored energy, but also by sensing activity, communication cost, computational workload and required service quality. This review analyzes these dependencies from a cross-layer perspective linking energy harvesting and power management, field-level IoT nodes, wireless communication technologies, and edge–fog–cloud computing. The main contribution is a conceptual decision framework derived from the literature synthesis, linking service adaptation, communication-path feasibility and coordination scope to the placement of sensing, processing and inference functions. The analysis shows that energy autonomy cannot be achieved by optimizing individual nodes only. Wireless connectivity, network topology and communication overhead directly affect the feasibility of higher-level processing, while edge and fog resources can reduce field-node load and improve local service continuity. The proposed framework therefore combines energy feasibility, communication conditions, service requirements and coordination scope. The resulting guidelines are particularly relevant to building automation and smart IoT systems, supporting interoperable, adaptive and energy-efficient distributed wireless architectures. Full article

The rapid expansion of autonomous microsystems, implantable sensors, wireless sensor nodes, Internet-of-Things devices, distributed electronics, and heterogeneous system-on-chip platforms has intensified the demand for compact electrochemical energy-storage systems that can be integrated directly with microfabricated devices. Among the various negative electrode materials, silicon is particularly attractive for miniaturized lithium-ion batteries because of its high theoretical lithium-storage capacity, abundance, compatibility with mature semiconductor processing, and direct availability as both an active material and a structural platform. However, the practical implementation of silicon anodes in on-chip and miniaturized batteries remains difficult because lithiation-induced volume expansion, fracture, unstable solid-electrolyte interphase formation, loss of electrical contact, and process-integration constraints become more severe as the battery footprint is reduced to the microscale. In contrast to conventional slurry-cast silicon electrodes, silicon anodes for microbatteries can exploit microfabrication strategies such as thin-film deposition, photolithography, deep reactive ion etching, metal-assisted chemical etching, nanoimprint lithography, laser patterning, template-assisted growth, atomic layer deposition, and wafer-level encapsulation. These methods enable deterministic control over electrode geometry, areal loading, porosity, current-collector contact, diffusion length, mechanical compliance, interfacial chemistry, and compatibility with complementary metal-oxide-semiconductor and microelectromechanical-system platforms. This review summarizes the recent progress in microfabrication strategies for silicon anodes in on-chip and miniaturized batteries, emphasizing the relationship between the process route, electrode architecture, mechanical stability, electrochemical performance, and manufacturability. Full article

Large language models (LLMs) are increasingly used in programming tasks, including code generation, debugging, validation, optimization, test generation, and educational support. Their use as graders of student programming assignments is, however, still not fully understood, especially in object-oriented programming (OOP). In this context, correctness depends not only on the final output of a program but also on class structure, encapsulation, abstraction, and design decisions. This paper introduces a dataset of 271 C++ student solutions to eight OOP examination problems, together with the problem statements and the reference grades produced by the university auto-grader. We evaluate four locally served open-weight models—phi4-mini, gpt-oss:20b, qwen3.6:27b, and qwq:32b—under one endpoint, one prompt, and deterministic decoding, and we report four properties that a deployable grader must have. The first is output-format compliance, which ranges from 88.9% to 98.9% across models: qwq:32b failed to terminate on 30 of 271 submissions even at a doubled generation budget, spending 7.7 MJ, 29.9% of its total energy, to produce nothing. The second is agreement with the reference grades on the 30-point course scale, where qwen3.6:27b performs best (MAE 3.23 points, $r=0.85$) and phi4-mini worst (MAE 10.90, $r=0.17$); all four models grade systematically more strictly than the auto-grader. The third is reproducibility, which fails at two levels. Repeating an identical run, three models returned identical grades at temperature 0 with a fixed seed while gpt-oss:20b varied by up to 21 points on the same submission; restricting the serving daemon to one concurrent request makes it reproducible, locating that failure in request batching rather than in sampling. Grading the same submissions as part of a different set, however, no model is reproducible: agreement with the original run ranges from 13 to 33 of 40 submissions, and a control run with the original client excludes our instrumentation as the cause. The fourth is degeneracy: phi4-mini awarded an identical grade of zero to every submission in three of the eight examinations, 109 submissions in total, so its aggregate agreement statistics describe a model that is not grading. On a subset graded blind by two instructors, each instructor agreed with the auto-grader more closely than the two agreed with each other, so the residual error of the best-performing model is of the same order as ordinary disagreement between human graders. We also report a negative result: a declaration-level reference could be reconstructed for only five of the eight examinations, because the auto-grader merges missing declarations and wrong return values into single checks. Together these results indicate that agreement with a reference is an insufficient basis for choosing a local grading model, and that compliance, determinism, degeneracy, and the cost of failed generations must be measured alongside it. Full article

A multi-faceted research framework integrating experimental tests, finite element (FE) simulations, and parametric analysis was adopted to investigate the compression–shear hysteretic performance of CFRP-confined concrete-filled steel tubes (CFRP-CFST). Nine circular specimens were designed, with the axial compression ratio and transverse CFRP confinement coefficient as key variables, to conduct material performance tests, laying a foundation for subsequent research. Displacement-controlled cyclic loading was applied in the tests to obtain hysteretic curves, observe the failure process, and calculate stiffness degradation and strength degradation. FE models were established using the constitutive relationships of corresponding materials and material parameters derived from tests (e.g., concrete plastic damage coefficients), and their reliability was verified by comparing simulation results with experimental data. Further stress analysis throughout the loading process was performed to reveal the stress distribution and evolution laws of concrete, steel tubes, and CFRP during loading. Finally, parametric analysis was carried out to explore the effects of material strength, transverse CFRP layers, and axial compression ratio on the hysteretic performance of the members. The results indicate that the specimens exhibit a stable four-stage mechanical response and typical failure modes, including steel tube shear fracture, CFRP tensile fracture, and concrete shear fracture. The established FE models can reliably predict the hysteretic characteristics and failure mechanisms of the specimens. While material strength, CFRP layers, and axial compression ratio significantly enhance the peak bearing capacity, they have little impact on the initial elastic stiffness or the overall trend of the skeleton curve. In addition, the steel tube and CFRP maintain effective synergy, improving the ductility and energy dissipation capacity of the members. Full article

Simulation-based reinforcement-learning locomotion depends on the physical fidelity of the underlying model. This work presents a subsystem-level modelling, validation, and control framework for a 12-DoF biped robot, combining Denavit–Hartenberg kinematics, Euler–Lagrange dynamics, a Discrete Euler–Lagrange reference integrator, Hunt–Crossley contact, and Soft Actor-Critic training in PyBullet. Validation is scoped. For the fixed-hip leg, numerical damped-least-squares inverse kinematics achieved a round-trip error of 0.017 ± 0.022 mm, while a gravity path-integral test produced a residual of 0.006 J. On a one-DoF reference problem, DEL bounded energy error under a coarse-step stress test, whereas at the 1 ms training step, RK4 was more accurate; no RL-scale DEL advantage was established. Contact realism remained inconclusive because the available prescribed-penetration analysis was not a dynamically consistent whole-body impact test. The same nominal parameters were used in PyBullet for locomotion training, without establishing numerical equivalence between the two simulators. Across three asymmetric-reward runs, forward walking dominated final evaluations, but sustained velocity ranged from 0.62 to 1.24 m/s under unequal training budgets. An exploratory hybrid architecture reached 2.38 m/s in one run without controlled ablation. These results demonstrate subsystem-level diagnostics while identifying full-body validation, contact calibration, equal-budget replication, and architectural ablation as necessary future work. Full article

This study estimates the conditional short- and long-run association between domestic-currency oil prices and real industrial value added in a panel of ten selected oil-importing economies over 1993–2024. The analysis employs a cross-sectionally augmented autoregressive distributed lag mean-group (CS-ARDL) framework that accommodates cross-sectional dependence, heterogeneous dynamics, non-stationarity, and cointegration. The domestic-currency oil-price variable is constructed by converting the OPEC basket price into each country’s local currency using the official exchange rate, thereby capturing the effective nominal oil-cost exposure faced by domestic industrial producers. Gross fixed capital formation, foreign direct investment, labor-force participation, trade openness, and inflation are included as control variables. The short-run results show that a rise in domestic-currency oil prices is associated with a statistically significant decline in real industrial value added. The error-correction coefficient is negative and significant, indicating that approximately 35.8% of short-run disequilibrium is corrected within one period. In the long run, domestic-currency oil prices have a negative and statistically significant association with industrial value added in both the CS-ARDL and CS-DL specifications. The estimated long-run elasticities are −0.610 and −0.310, respectively, indicating that sustained increases in the effective domestic price of imported oil constrain industrial performance. Gross fixed capital formation, labor-force participation, and trade openness are positively associated with industrial value added, whereas foreign direct investment and inflation are statistically insignificant. For the sampled oil-importing economies, the findings highlight the importance of reducing industrial exposure to imported-oil costs through energy efficiency, energy diversification, and policies that limit exchange-rate vulnerability. Given possible endogeneity, the estimates are interpreted as conditional associations rather than definitive causal effects. Full article

Energy management in ship integrated power systems (IPSs) requires real-time dispatch of diesel generators, battery storage, and shore power under strict operational constraints. Existing deep reinforcement learning (DRL) approaches are economically competitive but cannot guarantee that device constraints are satisfied during training or deployment. This paper proposes MA-SRL, a safe reinforcement learning framework for ship IPSs that couples an execution-layer Safe Projection Layer (SPL) with a training-stage Lyapunov-based policy update. The SPL projects each raw action onto the feasible set of the current mode before execution, whenever that set is non-empty, and quantifies the departure as a constraint-cost signal that drives a Mode-Dependent Constrained Markov Decision Process (MD-CMDP), making feasibility observable to the learner. The Lyapunov update is designed to control the expected discounted constraint cost through a budget condition during training. Under the nominal scenario, this signal drives the raw policy close to the feasible set, lowering constraint violation by 2.2–6.0× and cost by 7.7–16.9% over DRL baselines, with the lowest constraint violation retained under storm conditions. The same architecture and settings are replicated on a second vessel, plant, route, and operating profile, attaining the lowest cost and constraint violation of all compared methods, and sustaining the lowest distance shortfall and unmet load under a single-generator-loss contingency. Full article

Reliable solar insecticidal lamp Internet of Things (SIL-IoT) equipment underpins green pest control and precise pest monitoring. Healthy ranges of light, photovoltaic and thermal variables shift with daylight, weather, energy availability and installation conditions, while labelled faults remain scarce and imbalanced. We introduce a diagnosability-aware operating-state health baseline (DA-OHB) for calibration-based cross-node diagnosis of data-observable faults in SIL-IoT equipment. Candidate faults were screened by data observability, mechanistic expressibility and availability as curated telemetry event labels. Photovoltaic–light, electrical-box/air-temperature, power and rolling-state features were combined with operating-state gates, direction-sensitive evidence scores and target-node healthy false-alarm calibration. We evaluated DA-OHB on July–August 2025 field records from four devices deployed in Chuzhou, China, for three maintenance-relevant faults: light-intensity sensor open circuit, light-intensity/solar-panel-current mismatch, and electrical-box/air-temperature mismatch. Under leave-one-device-out aggregation with an early healthy calibration subset from each target node, mean F1-scores were 0.996, 0.823 and 0.770; mean area under the precision–recall curve values were 1.000, 0.890 and 0.963. Across five seeds, DA-OHB F1-scores were 0.996 ± 0.000, 0.823 ± 0.000 and 0.763 ± 0.004. F1 reflects the target-node mechanism with sufficient fault evidence, whereas F2 and F3 demonstrate cross-node evidence from multiple devices. Field diagnosis of SIL-IoT equipment thus benefits from linking alarms to valid operating states, fault directions and node-specific healthy calibration. DA-OHB provides an interpretable basis for SIL-IoT maintenance under curated telemetry labels; effects on pest-count estimates and agricultural decisions require separate evaluation. Full article

Optical-based internal arc fault control devices (IACDs) play a crucial role in the speed of detection of arc faults in low-voltage switchgear assemblies. They can utilize different types of sensors (point and line) and contain different types of output (semiconductor and relay). The configuration of an IACD will influence the total detection time of such a system. This article presents a screening methodology of comparing the detection speed of commercially available optical-based IACDs. It describes the method used to measure the speed of operation of IACDs using variable illuminance conditions generated by a commercial photographic flash unit. The results are presented and compared using box plots and individuals–moving range (I-MR) charts. Box plots offer at glance comparison of influence of different test conditions on the speed of operation, and I-MR charts are used to visually assess the measurement points. The measurements also indicate a dependence of the reaction times of the tested IACD on the energy of the flash. This information is useful in designing the proper placement of optical sensors and allows the selection of a system configuration that will ensure the fastest reaction time under given test conditions applicable to low-voltage switchgear assemblies. Full article

Anode foil is a key component of aluminum electrolytic capacitors, and its performance largely depends on the anodic oxidation, or formation, process. During formation, electrical current converts the aluminum surface into a thin insulating oxide film that determines the capacitor’s voltage resistance and reliability. Electrolyte conductivity controls how easily ions move through the solution: excessively low conductivity increases resistive energy losses, whereas excessively high conductivity can cause electrical discharges that damage the oxide film. This study investigated the effects of low (800 μS·cm⁻¹), medium (1500 μS·cm⁻¹), and high (2700 μS·cm⁻¹) conductivities on energy consumption and oxide-film quality during formation at 520 V. The corresponding energy consumptions were 761.635, 707.381, and 754.238 kJ, respectively. Medium conductivity achieved the lowest energy consumption, representing reductions of 7.1% and 6.2% compared with low and high conductivities, respectively. However, the oxide film formed at medium conductivity showed lower crystallinity, revealing a trade-off between energy efficiency and film quality. Based on these results, a four-stage process was developed by applying high conductivity at ≤300 V, medium conductivity at 300–400 V, and low conductivity at 400–520 V. Compared with the conventional single-stage process, the optimized process reduced energy consumption by 8.4% and increased the voltage rise rate by approximately 6%. This voltage-dependent conductivity strategy provides a practical approach to producing high-quality anode foil with lower energy consumption. Full article