Search Results (17,855)

This study proposes a stress field data-driven prediction method that combines a finite element thermo-mechanical coupling model with a multi-machine learning framework. This method takes the inversion of stress based on the shrinkage behavior of deposition layers as the core logic, extracts the node displacement shrinkage during the cooling to solidification process of the melt pool in the thermal coupling simulation as the key feature input, and constructs extreme gradient boosting (XGBoost), Gaussian process regression (GPR), and deep convolutional neural network (DCNN) models, respectively, to achieve accurate prediction of nodal effect stress and triaxial stress in the laser directed energy deposition (L-DED) node process. The experimental results show that the XGBoost algorithm performs the best in various stress prediction indicators, and its generated stress distribution cloud map is highly consistent with the thermal coupling simulation results, suggesting a strong correlation between deposition layer shrinkage behavior and the stress field under the investigated conditions. In addition, compared to traditional finite element simulations, this method significantly improves computational efficiency while ensuring prediction accuracy, providing a new approach for rapid assessment of residual stresses. Full article

To address the poor mechanical adaptability of conventional equipment to 40 cm narrow-row sesame cultivation and the high weeding resistance and energy consumption of traditional weeding tools, this study developed an integrated bionic weeding and plant protection vehicle. The vehicle features a modular structure capable of three-row weeding and four-row plant protection, coupled with an extended-range hybrid powertrain. Its parallel linkage design enables terrain adaptation, ensuring consistent weeding depth of 3–6 cm and stable spraying height. Combined with an adjustable spraying width and a “detection–feedback–adjustment” mechanism to prevent plant collisions, the vehicle is fully compatible with the agronomic requirements of narrow-row cultivation. Inspired by mole cricket forelegs, the vehicle’s bionic weeding wheel blade model incorporates quantified biological features: quadratically fitted claw toe contours (R² > 0.97), a toe base height-to-width ratio of 1:2, and a toe groove radius-to-toe height ratio of 1:1. This design achieves a reliable biological-to-engineering translation. EDEM-based Discrete Element Method (DEM) simulations confirm that the bionic wheel outperforms conventional designs: the average torque is 17.4% lower (7.75 vs. 9.38 N·m), the soil disturbance rate is 8.2 percentage points higher (95.2% vs. 87.0%), and soil particle motion is more ordered (average velocity: 0.52 vs. 0.58 m/s), effectively reducing energy waste and improving weeding efficiency. Full article

Photovoltaic (PV) power generation is inherently intermittent due to unpredictable irradiance variations, posing significant challenges for grid integration. While conventional power smoothing strategies mitigate short-term fluctuations, they do not explicitly enforce the tracking of a scheduled power trajectory. This paper proposes a dispatchable PV framework that integrates a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for precise day-ahead power forecasting with a real-time supercapacitor (SC) compensation strategy. The CNN-LSTM network captures complex spatiotemporal meteorological dependencies to generate a robust day-ahead reference trajectory. Concurrently, a supercapacitor energy storage system (SC-ESS) integrated at the DC-link level via a bidirectional buck–boost converter actively balances the instantaneous mismatch between this forecast trajectory and the actual PV generation. Unlike filter-based hybrid methods, the SC-ESS is employed as a direct forecast error actuator in a closed-loop control scheme. This strategy strictly enforces real-time forecast tracking while preserving maximum power point tracking (MPPT) and DC-link voltage stability. Simulations and laboratory experiments under rapidly varying irradiance confirm that the proposed method significantly reduces power deviations from the forecast reference and improves short-term power predictability without imposing excessive stress on the SC. This forecast-aware strategy effectively enhances the dispatchability of PV systems, providing a practical solution for grid-supportive operation. Full article

Parameter estimation of photovoltaic (PV) models is crucial for the theoretical analysis and performance evaluation of PV cells and modules, with the objective of enhancing their efficiency and reliability, thereby supporting the long-term sustainability of solar energy systems. Nevertheless, the nonlinear and multimodal characteristics of PV models make the task of accurate parameter estimation challenging. This paper proposes an improved differential evolution algorithm, named opposition-based parent selection differential evolution (OBPSDE), to enhance the reliability and robustness of PV parameter estimation. The method integrates a parent-selection mechanism with an opposition-based learning strategy to exploit both solution quality and population diversity during the search process. The proposed method is evaluated using measured data from several PV cells and modules (RTC France, PVM752GaAs, PWP201, and STP6-120/36) for parameter estimation of the double-diode model (DDM). Its performance is compared with standard DE, DE variants, and four metaheuristic algorithms using statistical metrics including root mean square error (RMSE), individual absolute error (IAE), and mean absolute error (MAE). The results indicate that OBPSDE achieves stable performance, competitive computational cost, and improved convergence behavior, with RMSE values of 6.93726 × 10^–4 for RTC France, 5.89070 × 10^–5 for PVM752GaAs, 1.93772 × 10^–3 for PWP201, and 1.39519 × 10^–2 for STP6-120/36. Additionally, the improved parameter estimation accuracy may support more reliable performance prediction and analysis of PV systems, contributing to effective PV system modeling and diagnostic applications. Full article

Formaldehyde (HCHO) is a typical volatile organic compound (VOC) that poses significant risks to human health. Long-term exposure, even at low concentrations, has been associated with various malignant diseases, including nasopharyngeal, colon, and brain cancers. Common technologies for HCHO abatement include ventilation, adsorption, photocatalysis, and catalytic oxidation. Among these methods, catalytic oxidation is regarded as the most promising due to its high removal efficiency, low cost, minimal energy consumption, and no toxic by-products. In recent years, supported catalysts with excellent room-temperature activity and high dispersibility have attracted considerable attention. These catalysts can usually be divided into two categories: noble metal catalysts and non-noble metal catalysts. Zirconia (ZrO₂) has become an ideal support owing to its advantages of high specific surface area, abundant and tunable acid–base sites, and strong metal–support interaction (SMSI). Various modification strategies have been developed to improve the catalytic performance of ZrO₂-based systems, such as the construction of phase interfaces and the stabilization of single-atom species. This review summarizes the recent research progress of ZrO₂-based systems for the catalytic oxidation of formaldehyde. It provides a detailed discussion of the physicochemical properties of ZrO₂ supports and the reaction mechanisms involved, and highlights achievements in crystal phase regulation, elemental doping, metal–support interaction, and composite modification. Finally, future challenges and development directions for these catalysts are also outlined. Full article

Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. Full article

To address the frequency stability challenges arising from the high penetration of renewable energy, this study proposes a coordinated frequency regulation strategy for wind-power–hydrogen coupled systems, considering the Equivalent State of Charge (ESOC). While wind-power–hydrogen integration offers significant regulation potential, frequent ESOC excursions toward operational limits may lead to power interruptions and increased durability-related stress on hydrogen units. To resolve this, a refined mathematical model comprising wind turbines, electrolyzers, and fuel cells is first established to characterize system dynamics. The proposed method adopts an ESOC-based partitioning control logic: within normal ESOC ranges, the hydrogen storage system provides rapid frequency support via virtual inertia control; when ESOC reaches operational thresholds, the hydrogen unit seamlessly transitions out of service to prolong its lifespan, while the wind turbine dynamically compensates for the power deficit through adaptive droop control. Compared with other methods, the strategy proposed in this paper, implemented via DIgSILENT/PowerFactory simulations, improves the frequency nadir by 0.02 Hz during load increases and reduces the frequency peak by 0.04 Hz during load shedding. Under stochastic disturbances, the absolute steady-state frequency error is maintained below 0.02 Hz, while frequency deviations are strictly confined within ±0.5 Hz. These improvements significantly enhance both grid resilience and the operational safety of hydrogen units. Full article

Accurate thermal characterization of building insulation materials is essential for reliable energy performance assessment, regulatory compliance, and the development of high-performance envelopes. On one hand, the growing adoption of innovative insulating products, such as nanoporous materials, aerogel-based composites, bio-based panels, and thin insulating coatings, helps to enhance buildings’ energy efficiency by means of sustainable raw materials. On the other hand, conventional measurement techniques encounter significant challenges, due to their heterogeneity, reduced thickness, and unconventional geometries. In this study, an intra-laboratory comparison of three widely used methods for thermal conductivity determination is presented: the Transient Plane Source (TPS, Hot Disk) method, the Guarded Hot Plate (GHP) method, and the Heat Flow Meter (HFM) method. A total of twelve insulating materials, spanning super-insulating cores, insulating renders, bio-based panels, and nanocomposite coatings, were experimentally characterized under controlled laboratory conditions. A view on the analyzed insulating materials’ cradle-to-grave environmental impact is also given, to enhance the users’ awareness for the highly informed choice. The results highlight systematic differences between transient and steady-state approaches, with TPS measurements generally exhibiting larger deviations for materials characterized by surface roughness, limited thickness, or strong internal heterogeneity. In contrast, GHP and HFM methods show closer agreement when specimen geometry and stabilization requirements are satisfied. The influence of contact resistance, probing depth, specimen preparation, and uncertainty propagation is critically analyzed for each technique. The study provides practical insights into the applicability limits of commonly used thermal characterization methods and emphasizes the importance of selecting measurement techniques in relation to material morphology and testing constraints. These findings support more reliable thermal property assessment of emerging insulation materials and contribute to improved consistency between laboratory measurements and energy performance evaluations for buildings. Full article

As critical infrastructure for seasonal natural gas peak-shaving, the operation of underground gas storage (UGS) must consider multiple factors including risk, economics, efficiency, and technology. Traditional UGS operation schemes are heavily dependent on subjective experience and lack intelligent methods to fully leverage historical data. This shortcoming leads to higher risks and increased compressor energy consumption. Taking S UGS as an example, the sensitivity factors of injection-production capacity are analyzed based on geological development and multi-cycle injection-production operation data. With injection-production rates as a decision variable and while considering safety and economic factors, objective functions and constraints are defined from the formation, wellbore, and surface. The proposed injection and production cycles are both 15 days, and the total injection and production volumes are 1200 × 10⁴ m³ and 800 × 10⁴ m³. An optimization model was constructed using the INSGA-Ⅱ and TOPSIS to determine the optimal gas injection-production volume allocation scheme. Compared with the initial scheme, the optimal injection-production volume allocation scheme reduces compressor energy consumption by 49.19% and 49.80% and formation pressure standard deviation by 78.88% and 77.21%, respectively. This effectively lowers injection-production energy consumption while improving safety, thereby ensuring the long-term safe and efficient operation of UGS. Full article

The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production management, reliant on empirical fixed-cycle schedules, fails to adapt to dynamic reservoir behavior and wellbore conditions, leading to suboptimal energy efficiency and recovery. This study presents a physics-constrained, data-driven framework for adaptive intermittent production optimization, specifically designed to address the coupled geological-engineering complexities of such reservoirs. The methodology integrates three core innovations: (1) a hybrid flowing bottomhole pressure (FBHP) decline model coupling a “Three-Segment” wellbore pressure calculation with inflow performance relationship (IPR) curves, enabling dynamic characterization of pressure depletion; (2) a shut-in pressure buildup prediction framework combining a physically interpretable dual-exponential recovery mechanism—representing near-wellbore elastic expansion and far-field formation recharge—with a Random Forest Regression algorithm to capture the influence of geological and operational heterogeneity; and (3) a “Dual-Curve Intersection Method” that autonomously determines optimal pumping and shut-in durations by intersecting predicted pressure decline and recovery curves under geological constraints. Field implementation on 15 low-production wells in the Jiyuan Oilfield—a representative low-permeability asset—demonstrated robust performance: average pump efficiency improved from 14.3% to 14.49%, and average single-well electricity savings reached 15.61%. This work establishes a closed-loop intelligent control framework that bridges reservoir geology, wellbore hydraulics, and machine learning, offering a scalable solution for enhancing energy efficiency and production sustainability in low-permeability and unconventional resources. Full article

The common root characteristic of CO₂ and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO₂ and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O₂/CO₂ cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO₂ and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO₂ emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement. Full article

Background/Objectives: Digital parent training (DPT) programs offer scalable solutions for childhood disruptive behaviors but face significant engagement challenges. Although content tailoring may enhance outcomes, its clinical impact remains under-examined. This study aimed to (a) describe the correspondence between program recommendations, parental choices and engagement, and (b) examine how initial decisions are associated with subsequent engagement and therapeutic outcomes. Methods: A secondary analysis of three randomized trials included 151 parents of children (ages 3–7) with disruptive behaviors. Participants were classified as ‘Recommendation-Adherent’ (n = 63) or ‘Beyond-Recommendation’ (n = 88) based on whether initial content selections matched or exceeded program recommendations. Clinical outcomes (child behavior, parenting styles) and objective usage metrics were assessed at baseline and post intervention. Results: Many parents chose to expand the intervention scope beyond clinical recommendations (e.g., 91.5% selected the non-recommended Emotion Regulation module). However, this proactive initial intention did not increase objective engagement; groups did not differ significantly in total usage time, login days, or module completion rates. Although both groups showed comparable improvements in child behavior, intending to adhere to the recommended pathway was associated with significantly greater reductions in permissive parenting (laxness; p = 0.029) after adjusting for baseline differences. Conclusions: The findings highlight a discrepancy between parents’ intent to expand intervention scope and their actual engagement capacity. While the decision to adhere to a tailored pathway was associated with specific improvements in permissive parenting, the observational nature of the study precludes causal claims. Nevertheless, the results suggest that guided tailoring may serve as a protective function against choice overload. Aligning program demands with the practical realities of parental effort could help families focus finite energy on essential clinical targets. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

With the rapid development of new energy vehicles, fast and reliable prediction of power battery collision damage has become increasingly important. Traditional finite-element analysis is computationally expensive and difficult to deploy for rapid prediction under varying conditions. Although learning-based methods are faster, they usually rely on single-fidelity data: high-fidelity data is accurate but scarce and costly, while low-fidelity data is abundant but less reliable. Existing multi-fidelity methods alleviate this issue, yet often suffer from imbalanced sample allocation and weak cross-fidelity modeling. Moreover, current adaptive sampling strategies cannot dynamically determine the appropriate fidelity for different regions of the design space. To address these challenges, we propose HNGP-LCA, a multi-fidelity active learning framework for battery pack collision damage prediction. Our method consists of two components: (1) an Ensemble Nested Gaussian Process module that integrates single-layer and double-layer nested Gaussian process regression to better capture high–low fidelity correlations; and (2) a Location Information Cost-aware Active Learning strategy that leverages positional information to reconstruct expected improvement under different fidelities, enabling dynamic fidelity selection during sampling. Experiments on multiple synthetic benchmarks and a real battery pack engineering case demonstrate that HNGP-LCA achieves a better trade-off among accuracy, efficiency, and cost than strong baselines such as NARCO and MFBO. In the engineering case, it improves prediction accuracy by 0.6% over NARCO and 1.29% over MFBO, while reducing dependence on expensive high-fidelity data. These results show that HNGP-LCA provides an effective and practical solution for battery collision damage prediction. Full article

Background/Objectives: Persistent gastrointestinal (GI) symptoms following oncologic treatment represent a major unmet need in survivorship care, often managed symptomatically without addressing underlying biological mechanisms. This study aimed to evaluate the clinical efficacy and biological correlates of an artificial intelligence (AI)-guided, personalized microbiome modulation strategy in oncology patients with chronic secondary GI dysfunction. Methods: We conducted a prospective, single-arm, open-label validation study including 29 adult female oncology patients with persistent GI symptoms lasting ≥3 months. Participants underwent baseline multidimensional assessment integrating shotgun metagenomic sequencing, inflammatory and nutritional biomarkers, and clinical symptom profiling. An AI-guided platform generated individualized dietary, supplement, and lifestyle recommendations. Outcomes were assessed at baseline and after a 3-month intervention, focusing on intra-individual changes in stool frequency (primary endpoint), self-reported energy, microbiome composition, and metabolic biomarkers. Paired statistical analyses, correlation testing, and multivariable regression were performed. Results: After three months, stool frequency significantly decreased (4.69 ± 2.41 to 2.07 ± 1.19 episodes/day; p < 0.0001), accompanied by a marked increase in energy levels (4.00 ± 1.04 to 7.24 ± 1.12; p < 0.0001). Microbiome analysis revealed consistent enrichment of butyrate-producing and barrier-supportive taxa, including Faecalibacterium prausnitzii, Eubacterium rectale, Roseburia intestinalis, Akkermansia muciniphila, and Bifidobacterium longum. Butyrate-related biomarkers and vitamin-associated parameters (B-complex, vitamin D) showed significant improvement, while lactate levels normalized. Changes in Bifidobacterium longum were independently associated with stool frequency reduction (β = −0.783, p = 0.0082). Conclusions: AI-guided personalized microbiome modulation was associated with significant clinical improvement and biologically coherent microbial and metabolic shifts in oncology patients with persistent GI symptoms. These findings support a precision supportive-care approach targeting microbiome restoration, warranting further validation in randomized controlled trials. Full article