Search Results (28,288)

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems. Full article

Electricity demand forecasting plays a crucial role in energy planning and power system operation. However, it is affected by numerous factors and complex relationships, making accurate prediction challenging. Therefore, from the perspective of sample diversity in the base dataset, we propose an improved stacking-based ensemble regression algorithm to enhance the accuracy of electricity demand forecasting. Firstly, a continuous sampling strategy is constructed between the sample integration selection probability and the base dataset using D²-Sampling and KNN; secondly, multiple base regression models are integrated through stacking to improve the predictive performance. In the electricity demand forecasting experiments conducted on three different datasets and across multiple base models, the proposed improved stacking ensemble learning regression algorithm (DK-Stacking) achieved the best performance. This symmetric experimental evaluation ensured consistent and balanced assessment of the model performance across datasets and models, highlighting the robustness and generalization of the proposed algorithm. Compared to the ANN, SVR, and RF models, its prediction accuracy increased by more than 1 percentage point. Even when compared to the optimized XGBoost model, it showed an improvement of 0.44 percentage points. Overall, the proposed DK-Stacking demonstrates symmetry-inspired robustness in electricity demand forecasting through the balanced treatment of datasets and model integration. Full article

Background: The belief that “sugar feeds cancer” is widespread and has strongly influenced public perceptions, patient behavior, and dietary recommendations, despite uncertainty regarding its scientific validity. This belief largely stems from misinterpretation of the Warburg effect, which describes altered glucose metabolism in cancer cells rather than dietary sugar dependence. The objective of this review was to critically evaluate whether dietary sugar intake directly contributes to cancer development or progression by examining the totality of epidemiological, experimental, and mechanistic evidence. Methods: We conducted a narrative review of human epidemiological studies, experimental animal and cell-based models, and mechanistic investigations published between 1980 and July 2025. Evidence was synthesized across cancer types, sugar sources, and biological pathways, with careful consideration of study design, exposure relevance, and key confounders, including obesity, insulin resistance, and overall dietary patterns. Results: Across cancer types, epidemiological evidence showed predominantly null or inconsistent associations between sugar intake and cancer risk or outcomes, with positive findings largely confined to metabolically susceptible subgroups and often attenuated after adjustment for adiposity and energy intake. Experimental studies suggested potential tumor-promoting effects under non-physiological conditions, while mechanistic data indicated that sugar influences cancer risk indirectly through insulin signaling, inflammation, and metabolic dysfunction rather than direct tumor fueling. Conclusions: Current evidence does not support the hypothesis that dietary sugar directly “feeds” cancer in humans. Overemphasis on sugar avoidance risks nutritional and psychological harm, particularly among cancer patients. Evidence-based guidance should prioritize overall dietary quality, metabolic health, and patient well-being rather than isolated sugar restriction. Full article

The present study investigates the potential of poplar (Populus spp.) biomass from phytoremediation plantations as a feedstock for downdraft fixed bed gasification. The biomass was characterized in terms of moisture, ash content, elemental composition (C, H, N, O), and calorific values (HHV and LHV), confirming its suitability for thermochemical conversion. Gasification tests yielded a volumetric syngas production of 1.79 Nm³ kg⁻¹ biomass with an average composition of H₂ 14.58 vol%, CO 16.68 vol%, and CH₄ 4.74 vol%, demonstrating energy content appropriate for both thermal and chemical applications. Alkali and alkaline earth metals (AAEM), particularly Ca (273 mg kg⁻¹) and Mg (731 mg kg⁻¹), naturally present enhanced tar reforming and promoted reactive gas formation, whereas heavy metals such as Cd (0.27 mg kg⁻¹), Pb (0.02 mg kg⁻¹), and Bi (0.01 mg kg⁻¹) were detected only in trace amounts, posing minimal environmental risk. The results indicate that poplar pruning residues from phytoremediation sites can be a renewable and sustainable energy resource, transforming a waste stream into a process input. In this perspective, the integration of soil remediation with syngas production constitutes a tangible model of circular economy, based on the efficient use of resources through the synergy between environmental remediation and the valorization and sustainable management of marginal biomass—i.e., pruning residues—generating environmental, energetic, and economic benefits along the entire value chain. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

Driver distraction, particularly mobile phone use while driving, remains one of the leading causes of road traffic incidents worldwide. In response to this issue and leveraging recent technological advances and increased access to intelligent systems, this research presents the development of an application running on an intelligent embedded architecture for the automatic detection of mobile phone use by drivers, integrating computer vision, inertial sensing, and edge computing. The system, based on the YOLOv8n model deployed on a Jetson Xavier NX 16Gb—Nvidia, combines real-time inference with an inertial activation mechanism and cloud storage via Firebase Firestore, enabling event capture and traceability through a lightweight web-based HMI interface. The proposed solution achieved an overall accuracy of 81%, an inference rate of 12.8 FPS, and an average power consumption of 8.4 W, demonstrating a balanced trade-off between performance, energy efficiency, and thermal stability. Experimental tests under diverse driving scenarios validated the effectiveness of the system, with its best performance recorded during daytime driving—83.3% correct detections—attributed to stable illumination and enhanced edge discriminability. These results confirm the feasibility of embedded artificial intelligence systems as effective tools for preventing driver distraction and advancing intelligent road safety. Full article

Speed prediction is fundamental to optimizing energy management strategies. Common evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) focus primarily on the numerical deviation between predicted and actual speeds. However, when applied to hybrid vehicle energy management strategy optimization, speed prediction models based on these metrics show a random deviation between energy consumption results and the theoretical optimal, indicating that these metrics are not effective in this application domain. To explore a more effective method for evaluating the practical application of speed prediction curves, this study uses multiple metrics to assess numerous speed prediction curves and analyses the correlation between each metric and the deviation from the optimal energy consumption during energy management strategy optimization. The results show that considering acceleration is more aligned with the needs of energy management strategy optimization than merely evaluating the proximity of speed values. Specifically, the standard deviation of the acceleration time ratio deviation performs better than traditional metrics like RMSE and MAE in distinguishing the effectiveness of speed prediction curves. The smaller the standard deviation of the acceleration time ratio deviation between the predicted and actual speed curves, the closer the energy consumption results of energy management based on the predicted speed curve are to the theoretical optimal. Full article

Partial demagnetization of multiple in-wheel motors changes torque distribution characteristics and can reduce vehicle stability, which poses a challenge for in-wheel motor drive electric vehicles (IWMDEVs) to maintain a balance between safety and efficiency. To address this issue, a hierarchical multi-objective adaptive fault-tolerant control (FTC) strategy based on wheel terminal torque compensation is developed. In the upper layer, a nonlinear model predictive controller (NMPC) generates the desired total driving force and corrective yaw moment according to vehicle dynamics and driving conditions. The lower layer employs a quadratic programming (QP) scheme to allocate the wheel torques under actuator and tire constraints. Two adaptive coefficients—the stability–efficiency weighting factor and the current compensation factor—are updated through a randomized ensembled double Q-learning (REDQ) algorithm, enabling the controller to adaptively balance yaw stability preservation and energy optimization under different fault scenarios. The proposed method is implemented and verified in a CarSim–Simulink–Python co-simulation environment. The simulation results show that the controller effectively improves yaw and lateral stability while reducing energy consumption, validating the feasibility and effectiveness of the proposed strategy. This approach offers a promising solution to achieve reliable and energy-efficient control of IWMDEVs. Full article

The vibration compaction behavior of fully graded fresh concrete differs fundamentally from that of conventional two-graded concrete. Based on measured vibration responses of an internal vibrator and sinking-ball tests, an energy transfer model for fully graded concrete was established by incorporating the effects of aggregate-specific surface area, paste–aggregate ratio, dynamic damping, and natural frequency, and the spatiotemporal attenuation of vibration energy in fresh concrete was systematically analyzed. Experimental results indicate that fully graded concrete exhibits a higher energy absorption capacity during the early stage of vibration, with a maximum energy absorption rate of 423 W and a peak energy transfer efficiency of 76.3%, both of which are significantly higher than those of two-graded concrete at the same slump. However, as a dense aggregate skeleton rapidly forms, the energy absorption efficiency of fully graded concrete decreases more rapidly during the middle and later stages of vibration, showing a characteristic pattern of “high initial absorption followed by rapid attenuation.” Through segregation assessment and porosity analysis, a safe vibration energy range for fully graded concrete was quantitatively determined, with lower and upper energy thresholds of 159.7 J·kg⁻¹ and 538.5 J·kg⁻¹, respectively. In addition, the experiments identified recommended vibration durations of 30–65 s and effective vibration influence radii of 22–85 mm for fully graded concrete under different slump conditions. These findings provide a quantitative basis for the control of vibration parameters and energy-oriented construction of fully graded concrete. Full article

The study presents a stability analysis of an axially compressed column resting on a Winkler foundation with a stepwise variation in stiffness. The solution is based on an energy approach using the Rayleigh quotient, and the original buckling mode function is proposed to capture the localization of deformations in the region of foundation discontinuity. The theoretical model was verified numerically for rectangular-section columns by comparing the results with simulations performed in COSMOS/M and ABAQUS systems. The differences in critical load values did not exceed 1.7%. The investigation showed that increasing the stiffness contrast leads to stronger buckling localization within the weaker foundation segment. The developed model can be used for preliminary assessment of the load-carrying capacity of structural elements interacting with a non-homogeneous distributed foundation. Full article

With growing global energy demand, deep and ultra-deep wells have become a focal point in oil and gas development. Wellbore temperature variations significantly impact drilling and completion operations in such wells. To analyze the temperature distribution in ultra-deep wellbores, a numerical model based on the Gauss–Seidel iterative algorithm was developed. This model explicitly accounts for the convective heat transfer coefficient and the distinct thermophysical properties of drilling fluids in both the drill string and the annulus. By employing adaptive meshing, it significantly enhances computational efficiency while ensuring accuracy. This study investigated the influence of key parameters—including drilling fluid density, specific heat capacity, drill pipe thermal conductivity, and formation properties—on bottom-hole temperature. The results show that the average deviation between the actual wellbore temperature and the model-predicted temperature is 0.5%. The heat transfer dynamics model for ultra-deep wells is validated by the close agreement between theoretical predictions and field data. This study offers a valuable theoretical basis for wellbore temperature management and the control of drilling fluid cooling systems, supporting safer and more efficient development of ultra-deep resources. Full article

Accurate forecasting of meteorological parameters is essential for the reliable operation and performance optimization of photovoltaic (PV) power plants. Among these parameters, ambient temperature and global horizontal irradiance (GHI) have the most direct impact on PV output. This study investigates the integration of machine-learning-based (ML) weather forecasts into PV energy modeling and quantifies how forecast accuracy propagates into PV generation estimation errors. Three commonly used ML algorithms—Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF)—were developed and compared. Antalya (Turkey), representing a Mediterranean climate zone, was selected as the case study location. High-resolution meteorological data from 2018–2023 were used to train and evaluate the forecasting models for prediction horizons from 1 to 10 days. Model performance was assessed using root mean square error (RMSE) and the coefficient of determination (R²). The results indicate that RF provides the highest accuracy for temperature prediction, while ANN demonstrates superior performance for GHI forecasting. The generated forecasts were incorporated into a PV power output simulation using the PVLib library. The analysis reveals that inaccuracies in GHI forecasts have the largest impact on PV energy estimation, whereas temperature forecast errors contribute significantly less. Overall, the study demonstrates the practical benefits of integrating ML-based meteorological forecasting with PV performance modeling and provides guidance on selecting suitable forecasting techniques for renewable energy system planning and optimization. Full article

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment. Full article

Microgrids are evolving from simple hybrid systems into complex, multi-energy platforms with high-dimensional optimization challenges due to technological diversification, sector coupling, and increased data granularity. This review systematically examines the intersection of microgrid optimization and metaheuristic algorithms, focusing on the period from 2015 to 2025. We first trace the technological evolution of microgrids and identify the drivers of increased optimization complexity. We then provide a structured overview of metaheuristic algorithms—including evolutionary, swarm intelligence, physics-based, and human-inspired approaches—and discuss their suitability for high-dimensional search spaces. Through a comparative analysis of case studies, we demonstrate that metaheuristics such as genetic algorithms, particle swarm optimization, and the gray wolf optimizer can reduce the computation time to under 10% of that required by an exhaustive search while effectively handling multimodal, constrained objectives. The review further highlights the growing role of hybrid algorithms and the need to incorporate uncertainty into optimization models. We conclude that future microgrid design will increasingly rely on adaptive and hybrid metaheuristics, supported by standardized benchmark problems, to navigate the growing dimensionality and ensure resilient, cost-effective, and sustainable systems. This work provides a roadmap for researchers and practitioners in selecting and developing optimization frameworks for the next generation of microgrids. Full article

Background: Early diagnosis of Alzheimer’s disease (AD) is essential for slowing disease progression and mitigating cognitive decline. However, conventional diagnostic methods are often invasive, time-consuming, and costly, limiting their utility in large-scale screening. There is an urgent need for scalable, non-invasive, and accessible screening tools. Methods: We propose a novel screening framework combining a pre-trained multimodal large language model with structured MMSE speech tasks. An artificial intelligence-assisted multilingual Mini-Mental State Examination system (AAM-MMSE) was utilized to collect voice data from 1098 participants in Sichuan and Chongqing. CosyVoice2 was used to extract speaker embeddings, speech labels, and acoustic features, which were converted into statistical representations. Fourteen machine learning models were developed for subject classification into three diagnostic categories: Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). SHAP analysis was employed to assess the importance of the extracted speech features. Results: Among the evaluated models, LightGBM and Gradient Boosting classifiers exhibited the highest performance, achieving an average AUC of 0.9501 across classification tasks. SHAP-based analysis revealed that spectral complexity, energy dynamics, and temporal features were the most influential in distinguishing cognitive states, aligning with known speech impairments in early-stage AD. Conclusions: This framework offers a non-invasive, interpretable, and scalable solution for cognitive screening. It is suitable for both clinical and telemedicine applications, demonstrating the potential of speech-based AI models in early AD detection. Full article