Search Results (12,476)

The transition to renewable energy is a central objective of the European Union’s energy and climate policies, yet adoption rates differ significantly across Member States. This study analyses the economic, structural, and energy determinants of renewable energy adoption in the EU-27 over the period 2008–2023, using panel data models with country and year fixed effects and clustered standard errors. The results indicate that the relationship between renewable energy and its main determinants is limited and heterogeneous across countries. Most explanatory variables do not exhibit consistent and statistically significant effects across model specifications. In particular, research and development expenditure does not show a robust impact, while GDP per capita is associated with negative coefficients in several specifications, suggesting the presence of structural constraints and path dependency. Energy-related variables also display weak and unstable relationships. The findings suggest that renewable energy adoption is shaped by context-specific and heterogeneous dynamics rather than by uniform drivers. The study contributes by highlighting the limited explanatory power of standard macroeconomic indicators and supports the need for differentiated policy approaches across Member States. Full article

Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (sim2real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles. Full article

Japan’s import market for wood pellets has expanded rapidly since the introduction of the feed-in tariff (FIT) scheme in 2012, with imports exceeding six million tonnes in 2024, positioning Japan as the world’s second-largest wood pellet importer. Despite this expansion, empirical evidence on its demand structure remains limited. This study employs a Dynamic Linear Approximate Almost Ideal Demand System (Dynamic LA-AIDS) model incorporating demand inertia stemming from long-term fuel supply contracts to analyze Japan’s wood pellet import demand from 2012Q1 to 2025Q3. The results reveal a distinct two-tiered structure: North American pellets behave as a strategic necessity, exhibiting price-inelastic demand and a tendency toward a stable long-run procurement pattern following price and expenditure shocks, suggesting procurement practices that prioritize supply security under long-term contracts. In contrast, Vietnamese pellets behave as a price-sensitive commodity, displaying price-elastic demand and relatively sustained responsiveness following such shocks. These results indicate a dual procurement strategy under the FIT scheme that balances stability and cost flexibility. Importantly, the Japanese demand structure differs from the more uniformly price-inelastic patterns observed in the EU and South Korean markets, providing new insights into how institutional frameworks shape biomass allocation and market responsiveness in renewable energy systems. Full article

Valve stiction remains a persistent nonlinear phenomenon in industrial control loops, often inducing limit-cycle oscillations that degrade control performance, compromise stability, and reduce process efficiency. Reliable detection of stiction is therefore essential for condition-based maintenance and improved operational performance. This study proposes a Hybrid Wavelet–Convolutional Neural Network (HW-CNN) framework for the detection of valve stiction in closed-loop systems. The approach employs the continuous wavelet transform (CWT) to generate time–frequency scalograms that preserve localized energy distributions associated with stick–slip behavior, including transient release events and sustained oscillatory patterns. These representations are subsequently processed using a fine-tuned deep residual neural network to enable automated feature extraction and classification. Unlike conventional signal-based or generic time–frequency learning approaches, the proposed framework is designed to retain control system-specific dynamics within the feature representation, thereby improving the separability of stiction-induced signatures under varying operating conditions. The methodology is evaluated using both simulated control loop data and real industrial datasets obtained from the International Stiction Database (ISDB), ensuring evaluation under controlled and practical conditions. To enhance reliability, performance metrics are reported as averages over repeated experimental runs. The results demonstrate that the proposed HW-CNN framework achieves an accuracy of 96.1% and an F1-score of 96.0% on simulated datasets, and 90.4% accuracy with an F1-score of 90.0% on industrial data. Additional analysis indicates that the model maintains consistent detection capability despite increased variability in real-world conditions. Furthermore, interpretability is supported through Grad-CAM analysis, which shows that the network focuses on physically meaningful regions within the scalograms corresponding to known stiction dynamics. The findings confirm that the integration of wavelet-based feature encoding with deep residual learning provides a robust and interpretable framework for valve stiction detection. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

With the fast penetration of renewable energy resources (RERs) in modern power grids, system inertia is gradually decreasing, whereby threatening frequency stability. Grid-forming (GFM) permanent magnet synchronous generator (PMSG) wind turbines have emerged as a promising solution for supporting and maintaining power system stability. Nevertheless, many studies neglect the inherent intermittency and limited power capability of RERs. As a result, the dynamic interactions between machine-side and grid-side converters are often neglected, and the DC link is commonly modeled as either an ideal voltage source or a controlled current source, which may lead to inaccurate representations of system dynamics. As a solution, this paper investigates the influence of RER intermittency and power constraints on DC-link dynamics and their impact on the frequency support performance of GFM PMSGs. First, the overall system is configured using back-to-back voltage source converters, and the system’s dynamic equations are presented. Afterwards, the impact of wind speed variations is thoroughly discussed, alongside a critical examination of the requirements specified in IEEE Standard 2800-2022. Furthermore, a supervisory curtailment strategy is proposed to ensure overall system stability under severe load disturbances when the PMSG is unable to supply the required power. Finally, detailed case studies are conducted to: (1) assess the influence of variable wind speed and DC-link voltage control on the dynamic response of PMSGs, and (2) compare the performance of the accurate DC-link dynamic model with conventional idealized and simplified models. Full article

Flying Ad Hoc Networks (FANETs) composed of multiple unmanned aerial vehicles (UAVs) are a promising solution for emergency wireless communications when terrestrial infrastructure is unavailable. However, ensuring reliable Quality of Service (QoS) in these highly dynamic networks remains challenging due to topology changes, varying propagation conditions, and congestion. This work proposes a lightweight machine learning-based QoS optimization framework for multi-UAV emergency communications that combines realistic mobility modeling, empirical channel measurements, and adaptive traffic prioritization. UAV mobility patterns are generated with ArduSim, while LoS/NLoS propagation models are derived from real UAV flight experiments and integrated into ns-3. Multiple supervised machine learning algorithms—including Decision Trees, Random Forest, Support Vector Machines, k-NN, Gradient Boosting, and CatBoost—are trained using four input features derived from the network state: ${\mathrm{CBR}}_{\mathrm{src}}$, ${\mathrm{QP}}_{\mathrm{src}}$, ${\mathrm{CBR}}_{\mathrm{dst}}$, and ${\mathrm{QP}}_{\mathrm{dst}}$. Simulation results show that the proposed AI SMOTE EMERGENCY scheme, based on CatBoost, improves the Packet Delivery Ratio (PDR) by approximately 43% over the No-QoS baseline, achieving 89–93% delivery across all four application ports. Compared with EDCA, the proposed scheme maintains reliable delivery for all services, increases emergency throughput by 34–36%, and reduces end-to-end delay by about 70%. In addition, the higher delivery reliability translates into clear communication energy benefits, reducing energy waste across all evaluated topologies when compared with the No-QoS baseline. The inference time remains below 0.002 s, supporting real-time QoS adaptation in resource-constrained UAV networks. Full article

To improve the operational efficiency of the park source-load-storage system and reduce operation costs and the wind-solar curtailment rate, this paper establishes a Park Integrated Energy System (PIES) model with multiple energy storage and vehicle-to-grid (V2G) components and proposes an adaptive comprehensive fitness multi-objective particle swarm optimization algorithm. First, each component of the PIES is modeled. Second, electric vehicle (EV) scheduling boundaries, determined by wind and PV output, as well as a dynamic charging-discharging incentive mechanism, are designed to enhance renewable energy accommodation. Finally, an adaptive comprehensive fitness index is defined, and convergence and particle-update strategies are improved to achieve better scheduling performance. Simulation results verify that the proposed PIES model achieves optimal performance in terms of carbon-emission cost, total operation cost, and wind-solar curtailment rate. Meanwhile, the improved algorithm also outperforms traditional multi-objective methods in PIES scheduling. Full article

Hybrid robotic manufacturing systems integrating additive and subtractive processes enable fabrication of complex, high-value components but are typically executed sequentially, resulting in long cycle times. Concurrent execution of Directed Energy Deposition (DED) and milling promises productivity gains but introduces coupled thermal, mechanical and spatial interactions that challenge conventional process planning. This work addresses the methodological problem of planning milling operations in the presence of an ongoing DED process. The concurrent planning task is formulated as a mixed-integer, nonlinear, multi-objective optimisation problem capturing sequencing and orientation decisions, cutting parameters and enabling temporal coupling to the deposition trajectory. A hierarchical, surrogate-assisted optimisation framework is proposed, combining unified decision-variable encoding, deterministic decoding and staged feasibility enforcement to ensure robotic executability. Disturbance mechanisms such as thermal interaction, particulate interference and pose-dependent dynamic compatibility are incorporated as modular objective abstractions, enabling systematic trade-offs between machining productivity and preservation of deposition process integrity. The proposed framework is demonstrated on a representative case study, enabling analysis of the interaction between spatial sequencing, temporal feasibility and disturbance-aware optimisation. The case study provides a controlled instantiation and illustrates its application to concurrent additive–subtractive planning under explicitly modelled temporal and disturbance constraints. Full article

Hypertension is a severe global public health issue. Food-derived angiotensin-converting enzyme (ACE)-inhibitory peptides have shown great potential as safe and effective alternatives to synthetic antihypertensive drugs. Camel milk is rich in bioactive peptides. This study aimed to screen for ACE-inhibitory peptides from hydrolyzed camel casein, explore their inhibitory mechanisms and endothelial protective effects in vitro, and reveal their potential antihypertensive pathways using network pharmacology. This study screened three peptides with angiotensin-converting enzyme (ACE) inhibitory activity from enzymatically hydrolyzed camel casein components: MVPFLQPK, VPFLQPKVM, and QKWKFL, with IC₅₀ values of 277.1, 396.9, and 486.9 μmol/L, respectively. Enzyme inhibition kinetics analysis indicated that MVPFLQPK exhibited a non-competitive inhibition pattern, VPFLQPKVM exhibited a mixed inhibition pattern, and QKWKFL exhibited a competitive inhibition pattern. Molecular docking revealed that all three peptides formed hydrogen bond interactions with ACE, and QKWKFL and VPFLQPKVM directly bound to the enzyme’s active site to inhibit substrate catalysis. Molecular dynamics simulation further confirmed the high stability of the three peptide–ACE complexes, with binding free energies from −34.24 to −51.19 kcal/mol. The primary contributing forces include hydrogen bonds, van der Waals interactions, electrostatic forces, and nonpolar solvation effects. Network pharmacology analysis suggested that these peptides may exert synergistic antihypertensive effects by regulating multiple blood pressure-related pathways, including the renin–angiotensin system, renin secretion, and calcium signaling pathways, by acting on key targets such as ACE, REN, SRC, and MMP9. Cell experiments demonstrated that all three peptides exhibited no cytotoxicity in the Ang II-induced HUVEC injury model, significantly promoted NO release, inhibited ET-1 secretion, and possessed endothelial protective potential. This study investigated the in vitro ACE-inhibitory mechanism of peptides derived from camel milk and their potential role in blood pressure regulation, providing experimental evidence for subsequent in vivo activity validation and the development of functional camel milk protein products. Full article

The study investigates the application of machine learning techniques for classifying rockbursts among non-destructive tremors recorded in the Rydułtowy part of the ROW hard coal mine in the Upper Silesian Coal Basin, Poland. The mining environment is dominated by ultra-thick, high-strength sandstone strata, which significantly increase the likelihood of high-energy tremors. The interaction of geological/geomechanical, mining, technical/technological, and seismic factors is highly nonlinear, rendering deterministic analytical approaches insufficient for reliable rockburst identification. A dataset comprising 99 records, including 16 dynamic phenomena, was divided into training and testing subsets, with 75% of the data used to evaluate the discriminative power of the input variables and to train the machine learning models. Three parameters consistently exhibit the highest predictive relevance: peak particle velocity, seismic energy, and the rock mass bursting tendency index. Ten machine learning classifiers were evaluated using stratified 10-fold cross-validation. Ensemble-based models—particularly XGBoost, AdaBoost and Random Forest—demonstrated the most stable and accurate performance. The results indicate that machine learning models provide an effective computational framework for supporting rockburst hazard assessment in geologically complex mining conditions associated with ultra-thick sandstone strata. Full article

Background/Objectives: SHP2 (PTPN11) is a key regulator of RAS/MAPK signaling and a well-validated target in cancer and developmental disorders. Designing ligands for its catalytic site is challenging due to the pocket’s intrinsic flexibility and the presence of conserved structural water molecules critical for ligand recognition, which limits traditional discovery approaches. This study aimed to systematically identify and prioritize novel SHP2-binding candidates using a computational strategy that accounts for these challenges. Methods: An integrative computational workflow was applied, combining water-aware docking, large-scale virtual screening of 714,409 compounds, MM/GBSA binding free-energy analysis, AI-driven chemical space modeling using ChemBERTa, and microsecond-scale molecular dynamics (MD) simulations. The high-resolution catalytic PTP domain of SHP2 structure was analyzed to identify conserved water molecules (W711, W716, W726, W776) essential for reproducing the crystallographic binding mode of the reference ligand 3LU. Candidates were prioritized based on docking scores, physicochemical criteria, structural inspection, MM/GBSA energetic profiles, and occupancy of distinct chemical space regions. Results: Seven compounds were selected. SwissADME analysis confirmed favorable drug-likeness and GI absorption, with no BBB permeation. ChemBERTa embeddings revealed substantial structural novelty relative to known SHP2 inhibitors. 1 μs molecular dynamics simulations suggested stable binding of compound 4 (2-(3-methyl-2,6-dioxopurin-7-yl)acetate) and persistent interactions with the conserved water network. MM/GBSA evaluation subsequently highlighted its energetically coherent profile. Conclusions: The workflow prioritizes compound 4 as a promising and structurally innovative SHP2-binding candidate. This integrative strategy provides a generalizable approach for targeting proteins with flexible pockets, critical water networks, and limited scaffold diversity, offering a roadmap for challenging computational ligand-prioritization projects. Full article

Against the background of accelerated green energy development and the deep integration of intelligent sensing technologies, wind power forecasting is evolving toward a multimodal sensor collaborative perception paradigm within nonlinear multi-source integrated energy systems. To address the limitations of conventional methods, including the lack of dynamic importance modeling and constrained stability under complex wind conditions, a forecasting framework based on multimodal sensor importance perception is proposed. This study emphasizes the framework’s role in decoding the complex nonlinear dependencies between atmospheric drivers and turbine responses. Through a multimodal feature encoding architecture, unified temporal representations of meteorological environments and turbine operational states are established. A sensor-importance-aware attention mechanism and a cross-modal relational modeling strategy are introduced to adaptively allocate contributions under varying contexts. Furthermore, prediction compensation and uncertainty characterization modules are integrated to enhance robustness. Systematic experiments on real-world multi-source data validate the method. Overall performance comparisons demonstrate that MAE, RMSE, and MAPE reach 30.48, 42.37, and 9.16 percent, respectively, with the coefficient of determination $R^2$ achieving 0.957, significantly outperforming the Transformer baseline. In multi-horizon tasks, the model exhibits superior error accumulation suppression, with twelve-step forecasting errors remaining at 41.27 and 56.48. These findings reveal that the framework captures the context-dependent nonlinear mapping of energy systems, providing effective technical support for green energy dispatch and intelligent sensing applications. Full article

Accurate forecasting of water demand enables optimized peak-load management, alleviating pressure during high-demand periods and improving the operational efficiency of urban secondary water supply systems—a critical component in the energy-efficient and sustainable operation of buildings. However, existing water demand prediction methods in some regions suffer from low accuracy and excessively long prediction cycles, posing challenges for real-time water scheduling in building-scale systems. To address these challenges, this study develops a hybrid predictive framework that integrates a BP neural network with the Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms for enhanced parameter optimization. Using hourly water consumption data from a representative residential district, the proposed model is compared against standalone machine learning models—Extreme Learning Machines (ELM), Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Model performance is rigorously evaluated using the coefficient of determination, mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). The PSO-GWO-BP hybrid model achieves a predictive accuracy of 97.06%, yielding the lowest MAE, MSE, RMSE, and MAPE, as well as the highest R among all models considered, thereby significantly outperforming the benchmark standalone models. Furthermore, the high-precision short-term prediction outputs enable dynamic regulation of secondary water tank refill thresholds, facilitating refined water allocation and enhanced operational management of building water supply systems. These findings demonstrate the considerable application potential of the proposed hybrid model in enhancing both water resource efficiency and energy utilization performance in the daily operation of green buildings, providing reliable technical support for intelligent and low-carbon building water supply management. Full article

Selective laser melting (SLM) offers a promising route for fabricating high-performance Cu–Sn alloys; however, the extremely transient thermal behavior of the molten pool and its influence on microstructural evolution and mechanical properties remain insufficiently understood. In this study, a finite element model based on ABAQUS was developed to simulate the transient temperature field and molten pool dynamics of Cu–10Sn alloy during the SLM process. By systematically varying the volumetric energy density (VED), the interplay among molten pool geometry, thermal characteristics, microstructure, and mechanical performance was investigated through a combination of numerical simulation and experimental investigation. The results reveal that increasing VED significantly enlarges the molten pool dimensions, elevates the peak temperature, and intensifies the maximum heating and cooling rates, thereby altering solidification conditions. At a VED of 208.33 J/mm³, the molten pool reached its maximum dimensions, with a length of 230 μm, a width of 161 μm, and a depth of 85 μm, resulting in the highest relative density within the investigated range (98.33%). Under this processing condition, the Cu–10 wt.% Sn (Cu–10Sn) alloy exhibited microhardness values of 190 HV near the solidified areas of melt pool interior and 208.4 HV near the solidified areas of melt pool boundary, accompanied by an ultimate tensile strength of 494 MPa. These findings elucidate the critical role of molten pool thermal behavior in governing microstructural refinement and mechanical properties of SLM-fabricated Cu–10Sn alloys and provide a mechanistic basis for understanding the effect of process parameters. Full article