Search Results (6,907)

Multiple uncertainties in traffic demand fluctuations and infrastructure vulnerability during seismic events pose significant challenges for the resilience assessment of highway transportation networks (HTNs). While Monte Carlo simulation remains the dominant approach for uncertainty propagation, its high computational cost limits its scalability, particularly in metropolitan-scale networks. This study proposes an EQResNet framework for accelerated post-earthquake resilience assessment of HTNs. The model integrates network topology, interregional traffic demand, and roadway characteristics into a streamlined deep neural network architecture. A comprehensive surrogate modeling strategy is developed to replace conventional traffic simulation modules, including highway status realization, shortest path computation, and traffic flow assignment. Combined with seismic fragility models and recovery functions for regional bridges, the framework captures the dynamic evolution of HTN functionality following seismic events. A multi-dimensional resilience evaluation system is also established to quantify network performance from emergency response and recovery perspectives. A case study on the Sioux Falls network under probabilistic earthquake scenarios demonstrates the effectiveness of the proposed method, achieving 95% prediction accuracy while reducing computational time by 90% compared to traditional numerical simulations. The results highlight the framework’s potential as a scalable, efficient, and reliable tool for large-scale post-disaster transportation system analysis. Full article

The growing demand for power and the rising awareness of the need to reduce carbon footprints have led to wider acceptance of photovoltaic (PV)-integrated microgrids. PV-based microgrids have numerous significant advantages over other distributed energy resources; however, creating a dependable protection scheme for the DC microgrid is difficult due to the closely resembling current and voltage profiles of PV array faults and line faults in the DC network. The conventional methods fail to clearly discriminate between them. In this regard, a fault-resilient scheme exploiting the inherent characteristics of Fourier–Bessel Series Expansion and Empirical Wavelet Transform (FBSE-EWT) has been utilized in the present work. In order to enhance the efficacy of the bagging tree-based ensemble classifier, Artificial Gorilla Troop Optimization (AGTO) has been used to tune the hyperparameters. The hybrid protection approach is proposed for accurate fault detection, discrimination between scenarios (source-side fault and line-side fault), and classification of various fault types (pole–pole and pole–ground). The discriminatory attributes derived from voltage and current signals recorded at the DC bus using the hybrid FBSE-EWT have been utilized as an input feature set for the AGTO tuned bagging tree-based ensemble classifier to perform the intended tasks of fault detection and discrimination between source faults (PV array faults) and line faults (DC network). The proposed approach has been found to outperform the decision tree and SVM techniques, demonstrating reliability in terms of discriminating between the PV array faults and the DC line faults and resilience against fluctuations in PV irradiance levels. Full article

The increasing depletion of high-grade nickel sulfide deposits and the growing demand for nickel have intensified global interest in oxidized nickel ores (ONOs), particularly those located in Kazakhstan. This study presents a comprehensive review of the mineralogical and chemical characteristics of ONOs from the Shevchenkovskoye cobalt–nickel ore deposit and other Kazakhstan deposits, highlighting the challenges they pose for conventional beneficiation and metallurgical processing. Current industrial practices are analyzed, including pyrometallurgical, hydrometallurgical, and pyro-hydrometallurgical methods, with an emphasis on their efficiency, environmental impact, and economic feasibility. Special attention is given to the potential of hydro-catalytic leaching as a flexible, energy-efficient alternative for treating low-grade ONOs under atmospheric conditions. The results underscore the necessity of developing cost-effective and sustainable technologies tailored to the unique composition of Kazakhstani ONOs, particularly those rich in iron and magnesium. This work provides a strategic framework for future research and the industrial application of advanced leaching techniques to unlock the full potential of Kazakhstan’s nickel resources. Full article

Given the urgent need to decarbonize the global energy system, green hydrogen has emerged as a key alternative in the transition to renewables. However, its production via electrolysis demands high water quality and raises environmental concerns, particularly regarding reject water discharge. This study employs an experimental and analytical approach to define optimal water characteristics for electrolysis, focusing on conductivity as a key parameter. A pilot water treatment plant with reverse osmosis and electrodeionization (EDI) was designed to simulate industrial-scale pretreatment. Twenty water samples from diverse natural sources (surface and groundwater) were tested, selected for geographical and geological variability. A predictive algorithm was developed and validated to estimate useful versus reject water based on input quality. Three conductivity-based categories were defined: optimal (0–410 µS/cm), moderate (411–900 µS/cm), and restricted (>900 µS/cm). Results show that water quality significantly affects process efficiency, energy use, waste generation, and operating costs. This work offers a technical and regulatory framework for assessing potential sites for green hydrogen plants, recommending avoidance of high-conductivity sources. It also underscores the current regulatory gap regarding reject water treatment, stressing the need for clear environmental guidelines to ensure project sustainability. Full article

Compared to commuting, grocery shopping trips, despite their profound implications for mixed land use and transportation planning, have received limited attention in travel behavior research. Drawing upon a travel diary survey conducted in a fast-growing metropolitan region of the United States, i.e., Salt Lake County, UT, this research investigated a variety of influential factors affecting mode choices associated with grocery shopping. We analyze how built environment (BE) characteristics, measured at seven spatial scales or different ways of aggregating spatial data—including straight-line buffers, network buffers, and census units—affect travel mode decisions. Key predictors of choosing walking, biking, or transit over driving include age, household size, vehicle ownership, income, land use mix, street density, and distance to the central business district (CBD). Notably, the influence of BE factors on mode choice is sensitive to different spatial aggregation methods and locations of origins and destinations. The straight-line buffer was a good indicator for the influence of store sales amount on mode choices; the network buffer was more suitable for the household built environment factors, whereas the measurement at the census block and block group levels was more effective for store-area characteristics. These findings underscore the importance of considering both the spatial analysis method and the location (home vs. store) when modeling non-work travel. A multi-scalar approach can enhance the accuracy of travel demand models and inform more effective land use and transportation planning strategies. Full article

The growing demand for sustainable materials has led to increased interest in biodegradable polymer fibers and nonwoven mats due to their eco-friendly characteristics and potential to reduce plastic pollution. This review highlights how mechanical properties influence the performance and suitability of biodegradable polymer fibers across diverse applications. This covers synthetic polymers such as polylactic acid (PLA), polyhydroxyalkanoates (PHAs), polycaprolactone (PCL), polyglycolic acid (PGA), and polyvinyl alcohol (PVA), as well as natural polymers including chitosan, collagen, cellulose, alginate, silk fibroin, and starch-based polymers. A range of fiber production methods is discussed, including electrospinning, centrifugal spinning, spunbonding, melt blowing, melt spinning, and wet spinning, with attention to how each technique influences tensile strength, elongation, and modulus. The review also addresses advances in composite fibers, nanoparticle incorporation, crosslinking methods, and post-processing strategies that improve mechanical behavior. In addition, mechanical testing techniques such as tensile test machine, atomic force microscopy, and dynamic mechanical analysis are examined to show how fabrication parameters influence fiber performance. This review examines the mechanical performance of biodegradable polymer fibers and fibrous mats, emphasizing their potential as sustainable alternatives to conventional materials in applications such as tissue engineering, drug delivery, medical implants, wound dressings, packaging, and filtration. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

To meet the demands for real-time and accurate fault warning of wind turbine gear transmission systems, this study proposes an innovative intelligent warning method based on the integration of digital twin and multi-source data fusion. A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. Experimental validation shows that this model outperforms traditional methods in prediction accuracy. Specifically, it achieves average improvements of 0.3945, 0.546 and 0.061 in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²) metrics, respectively. Building on the above findings, a monitoring and early warning platform for the wind turbine transmission system was developed, integrating digital twin visualization with intelligent prediction functions. This platform enables a fully intelligent process from data acquisition and status evaluation to fault warning, providing an innovative solution for the predictive maintenance of wind turbines. Full article

Wellington, New Zealand, is highly vulnerable to disaster-induced food security crises due to its geography and geological characteristics, which can disrupt transportation and isolate the city following disasters. Urban agriculture (UA) has been proposed as a potential alternative food source for post-disaster scenarios. This study examined the potential of urban agriculture for enhancing post-disaster food security by calculating vegetable self-sufficiency rates. Specifically, it evaluated the capacity of current Wellington’s community gardens to meet post-disaster vegetable demand in terms of both weight and nutrient content. Data collection employed mixed methods with questionnaires, on-site observations and mapping, and collecting high-resolution aerial imagery. Garden yields were estimated using self-reported data supported by literature benchmarks, while cultivated areas were quantified through on-site mapping and aerial imagery analysis. Six post-disaster food demand scenarios were used based on different target populations to develop an understanding of the range of potential produce yields. Weight-based results show that community gardens currently supply only 0.42% of the vegetable demand for residents living within a five-minute walk. This rate increased to 2.07% when specifically targeting only vulnerable populations, and up to 10.41% when focusing on gardeners’ own households. However, at the city-wide level, the current capacity of community gardens to provide enough produce to feed people remained limited. Nutrient-based self-sufficiency was lower than weight-based results; however, nutrient intake is particularly critical for vulnerable populations after disasters, underscoring the greater challenge of ensuring adequate nutrition through current urban food production. Beyond self-sufficiency, this study also addressed the role of UA in promoting food diversity and acceptability, as well as its social and psychological benefits based on the questionnaires and on-site observations. The findings indicate that community gardens contribute meaningfully to post-disaster food security for gardeners and nearby residents, particularly for vulnerable groups with elevated nutritional needs. Despite the current limited capacity of community gardens to provide enough produce to feed residents, findings suggest that Wellington could enhance post-disaster food self-reliance by diversifying UA types and optimizing land-use to increase food production during and after a disaster. Realizing this potential will require strategic interventions, including supportive policies, a conducive social environment, and diversification—such as the including private yards—all aimed at improving food access, availability, and nutritional quality during crises. The primary limitation of this study is the lack of comprehensive data on urban agriculture in Wellington and the wider New Zealand context. Addressing this data gap should be a key focus for future research to enable more robust assessments and evidence-based planning. Full article

Hericium coralloides is a highly valued gourmet and medicinal species with growing market demand across East Asia, though industrial production remains limited by cultivation challenges. This study investigated the molecular characteristics, biological traits, domestication potential, and cultivation protocols of Hericium coralloides strains collected from the Changbaishan Nature Reserve (Jiling, China). Optimal conditions for mycelial growth included mannose as the preferred carbon source, peptone as the nitrogen source, 30 °C incubation temperature, pH 5.5, and magnesium sulfate as the essential inorganic salt. The fruiting bodies had a protein content of 2.43% g/100 g (fresh sample meter). Total amino acids comprised 53.3% of the total amino acid profile, while essential amino acids accounted for 114.11% relative to non-essential amino acids, indicating high nutritional value. Under optimized domestication conditions—70% hardwood chips, 20% cottonseed hulls, 8% bran, 1% malic acid, and 1% gypsum—bags reached full colonization in 28 days, with a 15-day maturation phase and initial fruiting occurring after 12–14 days. The interval between flushes was 10–12 days. The average yield reached 318.65 ± 31.74 g per bag, with a biological conversion rate of 63.73%. These findings demonstrate that Hericium coralloides possesses significant potential for edible and commercial applications. This study provides a robust theoretical foundation and resource reference for its artificial cultivation, supporting its broader industrial and economic utilization. Full article

High-performance waterproof and breathable nanofiber membranes (WBNMs) are in great demand for various advanced applications. However, the fabrication of such membranes often relies on fluorinated materials or involves complex preparation processes, limiting their practical use. In this study, we present an innovative approach by utilizing silicon-containing polyurethane (SiPU) as a single-component, fluorine-free raw material to prepare high-performance WBNMs via a simple one-step electrospinning process. The electrospinning technique enables the formation of SiPU nanofibrous membranes with a small maximum pore size (d_max) and high porosity, while the intrinsic hydrophobicity of SiPU imparts excellent water-repellent characteristics to the membranes. As a result, the single-component SiPU WBNM exhibits superior waterproofness and breathability, with a hydrostatic pressure of 52 kPa and a water vapor transmission rate (WVTR) of 5798 g m⁻² d⁻¹. Moreover, the optimized SiPU-14 WBNM demonstrates outstanding mechanical properties, including a tensile strength of 6.15 MPa and an elongation at break of 98.80%. These findings indicate that the single-component SiPU-14 WBNMs not only achieve excellent waterproof and breathable performance but also possess robust mechanical strength, thereby enhancing the comfort and expanding the potential applications of protective textiles, such as outdoor apparel and car seats. Full article

Under normal conditions, the novel zinc blende beryllium oxide (zb BeO) exhibits in a metastable crystalline phase, which is less stable than its wurtzite counterpart. Ultrathin zb BeO epifilms have recently gained significant interest to create a wide range of advanced high-resolution, high-frequency, flexible, transparent, nano-electronic and nanophotonic modules. BeO-based ultraviolet photodetectors and biosensors are playing important roles in providing safety and efficiency to nuclear reactors for their optimum operations. In thermal management, BeO epifilms have also been used for many high-tech devices including medical equipment. Phonon characteristics of zb BeO at ambient and high-pressure P ≠ 0 GPa are required in the development of electronics that demand enhanced heat dissipation for improving heat sink performance to lower the operating temperature. Here, we have reported methodical simulations to comprehend P-dependent structural, phonon and thermodynamical properties by using a realistic rigid-ion model (RIM). Unlike zb ZnO, the study of the Grüneisen parameter γ(T) and thermal expansion coefficient α(T) in zb BeO has revealed atypical behavior. Possible reasons for such peculiar trends are attributed to the combined effect of the short bond length and strong localization of electron charge close to the small core size Be atom in BeO. Results of RIM calculations are compared/contrasted against the limited experimental and first-principle data. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

The efficiency of electric motors is highly dependent on their operating temperature, with lower temperatures contributing to enhanced performance, reliability, and extended service life. This study presents a comprehensive review of state-of-the-art cooling technologies and evaluates their impact on the thermal behavior of a commercial motor–generator system in high-demand applications. A baseline model of a permanent magnet synchronous motor (PMSM) was developed using MotorCAD 2023^® software, which was supported by reverse engineering techniques to accurately replicate the motor’s physical and thermal characteristics. Subsequently, multiple cooling strategies were simulated under consistent operating conditions to assess their effectiveness. These strategies include conventional axial water jackets as well as advanced oil-based methods such as shaft cooling and direct oil spray to the windings. The integration of these systems in hybrid configurations was also explored to maximize thermal efficiency. Simulation results reveal that hybrid cooling significantly reduces the temperature of critical components such as stator windings and permanent magnets. This reduction in thermal stress improves current efficiency, power output, and torque capacity, enabling reliable motor operation across a broader range of speeds and under sustained high-load conditions. The findings highlight the effectiveness of hybrid cooling systems in optimizing both thermal management and operational performance of electric machines. Full article