Search Results (14,226)

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

The management of modern water supply systems requires a detailed analysis of consumption patterns in order to optimize pump operation schedules, reduce energy costs, and support the development of intelligent management systems. Traditional clustering algorithms are applied for these tasks; however, their limitation lies in the need to predefine the number of clusters. The aim of this study was to develop and validate a non-parametric method for clustering daily water consumption profiles based on a modified DBSCAN algorithm. The proposed approach includes the automatic optimization of neighborhood radius and the minimum number of points required to form a cluster. The input data consisted of half-hourly water supply and electricity consumption values for the water supply system of Gomel (Republic of Belarus), supplemented with the time-of-day factor. As a result of the multidimensional clustering, two stable regimes were identified: a high-demand regime (6:30–22:30), covering about 46% of the data and accounting for more than half of the total water supply and electricity consumption, and a low-demand regime (0:30–6:00), representing about 21% of the data and forming around 15% of the resources. The remaining regimes reflect transitional states in morning and evening periods. The obtained results make it possible to define the temporal boundaries of the regimes and to use them for data labeling in the development of predictive water consumption models. Full article

Wood is a renewable, carbon-sequestering, and structurally versatile material that has supported human civilization for millennia and continues to play a central role in advancing sustainable development. Although its low density, high specific strength, and esthetic appeal make it highly attractive, its intrinsic flammability presents significant challenges for safety-critical uses. This review offers a comprehensive analysis that uniquely integrates three key domains, covering advanced processing technologies, flame-retardant functionalization strategies, and multifunctional applications. Clear connections are drawn between processing approaches such as delignification, densification, and nanocellulose extraction and their substantial influence on improving flame-retardant performance. The review systematically explores how these engineered wood substrates enable more effective fire-resistant systems, including eco-friendly impregnation methods, surface engineering techniques, and bio-based hybrid systems. It further illustrates how combining processing and functionalization strategies allows for multifunctional applications in architecture, transportation, electronics, and energy devices where safety, durability, and sustainability are essential. Future research directions are identified with a focus on creating scalable, cost-effective, and environmentally compatible wood-based materials, positioning engineered wood as a next-generation high-performance material that successfully balances structural functionality, fire safety, and multifunctionality. Full article

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings. Full article

The integration of renewable energy sources has become a strategic necessity for sustainable energy management and supply security. This study evaluates the performance of eight metaheuristic optimization algorithms in scheduling a renewable-based smart grid system that integrates solar, wind, and battery storage for a factory in İzmir, Türkiye. The algorithms considered include classical approaches—Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), the Whale Optimization Algorithm (WOA), Krill Herd Optimization (KOA), and the Ivy Algorithm (IVY)—alongside hybrid methods, namely KOA–WOA, WOA–PSO, and Gradient-Assisted PSO (GD-PSO). The optimization objectives were minimizing operational energy cost, maximizing renewable utilization, and reducing dependence on grid power, evaluated over a 7-day dataset in MATLAB. The results showed that hybrid algorithms, particularly GD-PSO and WOA–PSO, consistently achieved the lowest average costs with strong stability, while classical methods such as ACO and IVY exhibited higher costs and variability. Statistical analyses confirmed the robustness of these findings, highlighting the effectiveness of hybridization in improving smart grid energy optimization. Full article

The optimal control of cooling water systems is of great significance for energy saving inchiller plants. Previously optimal control methods optimize the flow rate, temperature or temperature difference setpoints but cannot control pumps and cooling tower fans directly. This study proposes a direct optimal control method for pumps and fans based on derivativecontrol strategyby decoupling water flow rate optimization and airflow rate optimization, which can make the total power of chillers, pumps and fans approach a minimum. Simulations for different conditions were performed for the validation and performance analysis of the optimal control strategy. The optimization algorithms and implementation methods of direct optimal control were developed and validated by experiment. The simulation results indicate that total power approaches a minimum when the derivative of total power with respect to water/air flow rate approaches zero. The power-saving rate of the studied chiller plant is 13.2% at a plant part-load ratio of 20% compared to the constant-speed pump/fan mode. The experimental results show that the direct control method, taking power frequency as a controlled variable, can make variable frequency drives regulate their output frequencies to be equal to the optimized power frequencies of pumps and fansin a timely manner. Full article

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Vibration loads during deep drilling are one of the main causes of reduced service life of drilling tools and emergency failure of downhole motors. This work investigates the adaptive operation of an original elastic element based on an open cylindrical shell used as part of a drilling shock absorber. The vibration protection device contains an adjustable radial clearance between the load-bearing shell and the rigid housing, which provides the effect of structural nonlinearity. This allows effective combination of two operating modes of the drilling shock absorber: normal mode, when the clearance does not close and the elastic element operates with increased compliance; and emergency mode, when the clearance closes and gradual load redistribution and increase in device stiffness occur. A nonconservative problem concerning the contact interaction of an elastic filler with a coaxially installed shaft and an open shell is formulated, and as the load increases, contact between the shell and the housing, installed with a radial clearance, is taken into account. Numerical finite element modeling is performed considering dry friction in contact pairs. The distributions of radial displacements, contact stresses, and equivalent stresses are examined, and deformation diagrams are presented for two loading modes. The influence of different cycle asymmetry coefficients on the formation of hysteresis loops and energy dissipation is analyzed. It is shown that with increasing load, clearance closure begins from local sectors and gradually covers almost the entire outer surface of the shell. This results in deconcentration of contact pressure between the shell and housing and reduction of peak concentrations of equivalent stresses in the open shell. The results confirm the effectiveness of the adaptive approach to designing shell shock absorbers capable of reliably withstanding emergency overloads, which is important for deep drilling where the exact range of external impacts is difficult to predict. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

This study employed an integrated computational approach to discover novel nucleoprotein (NP) inhibitors for influenza A virus (IAV). Beginning with virtual screening of over 10 million compounds using Schrödinger’s Glide module (HTVS, SP, XP docking), the workflow identified promising candidates with favorable binding energies. Subsequent molecular mechanics/generalized born surface area (MM-GBSA) calculations and 100 ns molecular dynamics (MD) simulations prioritized 16 compounds for experimental validation. Surface plasmon resonance (SPR) assays revealed that compounds 8, 13, and 14 demonstrated superior target engagement, showing equilibrium dissociation constants (K_D) of 7.85 × 10⁻⁵ M, 3.82 × 10⁻⁵ M, and 6.97 × 10⁻⁵ M, respectively. Molecular dynamics, alanine scanning mutagenesis, and quantum mechanics/molecular mechanics (QM/MM) analysis were conducted to analyze the binding modes, providing a reference for the design of subsequent compounds. These findings validate the efficacy of structure-based virtual screening in identifying high-affinity NP inhibitors and provide insights for the development of broad-spectrum anti-influenza therapeutics. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

With the rapid evolution of artificial intelligence technologies in power systems, data-driven time-series forecasting has become instrumental in enhancing the stability and reliability of power systems, allowing operators to anticipate demand fluctuations and optimize energy distribution. Despite the notable progress made by current methods, they are still hindered by two major limitations: most existing models are relatively small in architecture, failing to fully leverage the potential of large-scale models, and they are based on fixed nonlinear mapping functions that cannot adequately capture complex patterns, leading to information loss. To this end, an LLM-Empowered Kolmogorov–Arnold frequency learning (LKFL) is proposed for time series forecasting in power systems, which consists of LLM-based prompt representation learning, KAN-based frequency representation learning, and entropy-oriented cross-modal fusion. Specifically, LKFL first transforms multivariable time-series data into text prompts and leverages a pre-trained LLM to extract semantic-rich prompt representations. It then applies Fast Fourier Transform to convert the time-series data into the frequency domain and employs Kolmogorov–Arnold networks (KAN) to capture multi-scale periodic structures and complex frequency characteristics. Finally, LKFL integrates the prompt and frequency representations through an entropy-oriented cross-modal fusion strategy, which minimizes the semantic gap between different modalities and ensures full integration of complementary information. This comprehensive approach enables LKFL to achieve superior forecasting performance in power systems. Extensive evaluations on five benchmarks verify that LKFL sets a new standard for time-series forecasting in power systems compared with baseline methods. Full article

This study focuses on optimizing the machinability of Al-Fe-Cu (8000 series) alloys by developing new compositions with varying Fe and Cu contents and evaluating their mechanical, microstructural, and energy performance. For this purpose, 6061 Al alloy was melted in an induction furnace and cast into molds, and samples containing 2.5% and 5% Fe were produced. Microstructural features were analyzed using Python-based image processing, while Specific Energy Consumption (SEC) theory was applied to assess machining efficiency. An alloy with 2.5% Fe and 2.64% Cu showed superior mechanical properties and the lowest energy consumption. Increasing cutting speed and depth of cut notably decreased SEC. Machine learning (ML) analysis confirmed strong predictive capability, with R² values above 0.80 for all models. Decision Tree (DT) achieved the highest accuracy for SEC prediction (R² = 0.98634, MAE = 0.02209, MSE = 0.00104), whereas XGBoost (XGB) performed best for SCEC (R² = 0.96533, MAE = 0.25578, MSE = 0.10178). Response Surface Methodology (RSM) optimization further validated the significant influence of machining parameters on SEC and specific cutting energy consumption (SCEC). Overall, the integration of machine learning (ML), response surface methodology (RSM), and energy equations provides a comprehensive approach to improve the machinability and energy efficiency of 8000 series alloys, offering practical insights for industrial applications. Full article

MXenes, a rapidly emerging family of two-dimensional transition metal carbides and nitrides, have attracted considerable attention in recent years for their potential in next-generation energy storage technologies. In solid-state batteries (SSBs), they combine metallic-level conductivity (>10³ S cm⁻¹), adjustable surface terminations, and mechanical resilience, which makes them suitable for diverse functions within the cell architecture. Current studies have shown that MXene-based anodes can deliver reversible lithium storage with Coulombic efficiencies approaching ~98% over 500 cycles, while their use as conductive additives in cathodes significantly improves electron transport and rate capability. As interfacial layers or structural scaffolds, MXenes effectively buffer volume fluctuations and suppress lithium dendrite growth, contributing to extended cycle life. In solid polymer and composite electrolytes, MXene fillers have been reported to increase Li⁺ conductivity to the 10⁻³–10⁻² S cm⁻¹ range and enhance Li⁺ transference numbers (up to ~0.76), thereby improving both ionic transport and mechanical stability. Beyond established Ti-based systems, double transition metal MXenes (e.g., Mo₂TiC₂, Mo₂Ti₂C₃) and hybrid heterostructures offer expanded opportunities for tailoring interfacial chemistry and optimizing energy density. Despite these advances, large-scale deployment remains constrained by high synthesis costs (often exceeding USD 200–400 kg⁻¹ for Ti₃C₂T_x at lab scale), restacking effects, and stability concerns, highlighting the need for greener etching processes, robust quality control, and integration with existing gigafactory production lines. Addressing these challenges will be crucial for enabling MXene-based SSBs to transition from laboratory prototypes to commercially viable, safe, and high-performance energy storage systems. Beyond summarizing performance, this review elucidates the mechanistic roles of MXenes in SSBs—linking lithiophilicity, field homogenization, and interphase formation to dendrite suppression at Li|SSE interfaces, and termination-assisted salt dissociation, segmental-motion facilitation, and MWS polarization to enhanced electrolyte conductivity—thereby providing a clear design rationale for practical implementation. Full article