Search Results (4,608)

COP21 represents a starting point for several nations to develop and implement energy transition strategies to face and mitigate climate change, making the electrical power sector crucial in achieving the established goals and commitments. This research presents an analysis to identify the key factors in power system planning by integrating an economic dispatch model (ED) based on linear programming to determine vulnerable aspects of power generation and transmission in strategic planning scenarios that could jeopardize the country’s energy transition. The analysis is illustrated through a case study of the Mexican Electrical Power System (SEN) during the year 2025. The case study shows that the reserve margin fluctuated due to the variable renewable energy installed despite having a vast installed capacity to supply the country’s total demand. In addition, the results showed that most of the transmission lines had a congestion frequency higher than 90% of their capacity during most of the year. Two regions were identified as the best options for reducing greenhouse gas emissions by installing new power plants. Finally, most technologies reflected an under-generation, suggesting high dependence on some fuels to supply the Mexican demand. The model’s programming is freely available in GitHub. Full article

The targets for reducing greenhouse gas emissions, combined with increased electrification and the increased use of intermittent renewable energy sources, create significant challenges in matching supply and demand within distribution grid constraints. Demand response (DR) can shift electricity demand to align with constraints, reducing peak loads and increasing the utilisation of renewable generation. In countries like Aotearoa (New Zealand), peak loads are driven primarily by the residential sector, which is a prime candidate for DR. However, traditional deterministic and stochastic models do not account for the important variability in behavioural-driven residential demand and thus cannot be used to design or optimise DR. This paper presents a behavioural agent-based model (ABM) of residential electricity demand, which is validated using real electricity demand data from residential distribution transformers owned by Powerco, an electricity distributor in Aotearoa (New Zealand). The model accurately predicts demand in three neighbourhoods and matches the changes caused by seasonal variation, as well as the effects of COVID-19 lockdowns. The Pearson correlation coefficients between the median modelled and real demand are above 0.8 in 83% of cases, and the total median energy use variation is typically within 1–4%. Thus, this model provides a robust platform for network planning, scenario analysis, and DR program design or optimisation. Full article

Children with neurodevelopmental disabilities often struggle with motor control and stability, impacting their ability to perform functional tasks such as lifting and carrying objects. This study explores positional performance during bimanual box-lifting tasks in children aged 9–18 years with neurodevelopmental disabilities. A total of 83 participants, including 62 with unilateral spastic cerebral palsy and 21 with non-unilateral spastic cerebral palsy, performed tasks using the Activity of Daily Living Testing and Training Device. Tasks were conducted at maximal (80–100% force) and submaximal (40–80% force) levels of force control, with positional performance measured in six directions using Inertial Measurement Unit sensors. Statistical analyses included the Wilcoxon signed-rank test for levels of force control comparisons, Kruskal–Wallis tests for group differences, and Spearman correlations to assess relationships between maximal and submaximal performance. The results revealed that four of six positional parameters were worse in the maximal zone than in the submaximal zone ($p<0.05$), highlighting the challenges of higher force demands. Additionally, positive correlations between maximal and submaximal performance suggest consistency across levels of force control. Maximal levels of force control increased variability, with submaximal performance proven to be a reliable predictor of maximal capabilities. This finding offers a safer and more efficient method for assessing motor performance. Overall, these results underscore the importance of targeted rehabilitation strategies focused on improving stability and precision in children with neurodevelopmental disabilities so they can perform daily tasks more independently. Full article

Capacity expansion models for electricity grids typically use deterministic optimization, addressing uncertainty through ex-post analysis by varying input parameters. This paper presents a stochastic capacity expansion model that integrates uncertainty directly into optimization, enabling the selection of a single strategy robust across a defined range of uncertainties. Two cost-based risk objectives are explored: “risk-neutral” minimizes expected total system cost, and “risk-averse” minimizes the most expensive 5% of the cost distribution. The model is applied to the U.S. Midwest grid, accounting for uncertainties in electricity demand, natural gas prices, and wind generation patterns. While uncertain gas prices lead to wind additions, wind variability leads to reduced adoption when explicitly accounted for. The risk-averse objective produces a more diverse generation portfolio, including six GW more solar, three GW more biomass, along with lower current fleet retirements. Stochastic objectives reduce mean system costs by 4.5% (risk-neutral) and 4.3% (risk-averse) compared to the deterministic case. Carbon emissions decrease by 1.5% under the risk-neutral objective, but increase by 3.0% under the risk-averse objective due to portfolio differences. Full article

The global demand for clean and sustainable energy has driven the rapid growth of wind power. However, wind farm managers face the challenge of forecasting wind power for efficient power generation and management. Accurate wind speed forecasting (WSF) is vital for predicting wind power; yet, the variability and intermittency of the wind make forecasting wind speeds difficult. Consequently, WSF remains a challenging area of wind research, driving continuous improvement in the field. This study aimed to enhance the optimization of multifeature-driven short multistep WSF. The primary contributions of this research include the integration of ReliefF feature selection (RFFS), a novel approach to variational mode decomposition for multifeature decomposition (NAMD), and a recursive non-linear autoregressive with exogenous inputs (NARXR) neural network. In particular, RFFS aids in identifying meteorological features that significantly influence wind speed variations, thus ensuring the selection of the most impactful features; NAMD improves the accuracy of neural network training on historical data; and NARXR enhances the overall robustness and stability of the wind speed forecasting results. The experimental results demonstrate that the predictive accuracy of the proposed NAMD–NARXR hybrid model surpasses that of the models used for comparison, as evidenced by the forecasting error and statistical metrics. Integrating the strengths of RFFS, NAMD, and NARXR enhanced the forecasting performance of the proposed NAMD–NARXR model, highlighting its potential suitability for applications requiring multifeature-driven short-term multistep WSF. Full article

Electric Road Systems (ERS) are emerging technologies that enable electricity transfer to electric vehicles in motion. However, their implementation presents challenges due to high energy demands and infrastructure requirements. This technology offers a significant opportunity for decarbonizing road freight transport, one of the most carbon-intensive sectors, contributing to the European Union’s climate goals. This study hypothesizes that implementing an inductive ERS for freight transport along the AP-7 corridor in Spain will generate environmental benefits—primarily through greenhouse gas (GHG) emission reductions—that outweigh the associated socioeconomic costs, making it a viable decarbonization strategy. To test this hypothesis, an impact assessment framework based on Cost–Benefit Analysis (CBA) is conducted, incorporating climate change and other environmental benefits. The framework is applied to a section of the Mediterranean Highway Corridor AP-7 in Spain. The results indicate that the most significant benefits are derived from positive environmental impacts and lower vehicle operation costs. Through a sensitivity analysis, our research identifies key variables affecting the system’s socioeconomic profitability, including payload capacity, volatility of energy prices and shadow prices of GHG emissions. The study provides insights for policymakers to optimize ERS deployment strategies, ensuring maximum social benefits while addressing economic and environmental challenges. Full article

Infantry soldiers must cover long distances carrying heavy and bulky combat equipment. Since the beginning of their training, Spanish Marines have undergone this characteristic and demanding test. However, little is known about its effects on neuromuscular function and recovery in the days following the test. Twenty-six Spanish Marines completed the test, three of whom suffered injuries and had to withdraw from the study, resulting in a final sample of twenty-three Marines. These participants underwent evaluations before (pre), immediately after (post), and 24 and 48 h post-exercise, following a 30 km endurance march carrying their 34 kg combat equipment. A repeated-measures ANOVA, paired-samples t-test, and effect size (ES) analysis were conducted; the results are presented as mean ± SD. The significance level was set at p ≤ 0.05. The variables and p-values of changes over time are presented. Isometric mid-thigh pull (IMTP) (p = 0.004), countermovement jump (CMJ) (p ≤ 0.001), rating of fatigue scale (ROF) (p ≤ 0.001), maximum pull-ups in two minutes (PUmax) (p ≤ 0.001), body mass (BM) (p ≤ 0.001), hand grip strength (HGS): dominant (p = 0.180) and non-dominant (p = 0.616), and incident reports (IRPE) showed a significant increase over time and between the first 10 km and last 5 km in fatigue, muscle pain, joint pain, shortness of breath, excessive sweating (p ≤ 0.001), and muscle tremors (p = 0.028), except for palpitations (p = 0.189). In conclusion, the results indicate that the test had a significant impact on neuromuscular function, with no recovery observed in overall strength and lower limb power after 48 h, even though their perceived fatigue decreased substantially. The resilient spirit of operational military units and their philosophy of always being ready for combat could increase the injury rate. Full article

The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches for optimizing their composition and properties. This study aimed at the application of machine learning for the prediction and optimization of the functional properties of composites based on a thermoplastic matrix with various fillers (two types of fibrous, four types of dispersed, and two types of nano-dispersed fillers). The experimental methods involved material production through powder metallurgy, further microstructural analysis, and mechanical and tribological testing. The microstructural analysis revealed distinct structural modifications and interfacial interactions influencing their functional properties. The key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate mechanical strength. Carbon fibers at 20 wt. % significantly improved wear resistance (by 17–25 times) while reducing tensile strength and elongation. Basalt fibers at 10 wt. % provided an effective balance between reinforcement and wear resistance (by 11–16 times). Kaolin at 2 wt. % greatly enhanced wear resistance (by 45–57 times) with moderate strength reduction. Coke at 20 wt. % maximized wear resistance (by 9−15 times) while maintaining acceptable mechanical properties. Graphite at 10 wt. % ensured a balance between strength and wear, as higher concentrations drastically decreased mechanical properties. Sodium chloride at 5 wt. % offered moderate wear resistance improvement (by 3–4 times) with minimal impact on strength. Titanium dioxide at 3 wt. % enhanced wear resistance (by 11–12.5 times) while slightly reducing tensile strength. Ultra-dispersed PTFE at 1 wt. % optimized both strength and wear properties. The work analyzed in detail the effect of PTFE content and filler content on composite properties based on machine learning-driven prediction. Regression models demonstrated high R-squared values (0.74 for density, 0.67 for tensile strength, 0.80 for relative elongation, and 0.79 for wear intensity), explaining up to 80% of the variability in composite properties. Despite its efficiency, the limitations include potential multicollinearity, a lack of consideration of external factors, and the need for further validation under real-world conditions. Thus, the machine learning approach reduces the need for extensive experimental testing, minimizing material waste and production costs, contributing to SDG 9. This study highlights the potential use of machine learning in polymer composite design, offering a data-driven framework for the rational choice of fillers, thereby contributing to sustainable industrial practices. Full article

Hydrology relates to many complex challenges due to climate variability, limited resources, and especially, increased demands on sustainable management of water and soil. Conventional approaches often cannot respond to the integrated complexity and continuous change inherent in the water system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing the most important facets of hydrological research, including soil and land surface modeling, streamflow, groundwater forecasting, water quality assessment, and remote sensing applications in water resources. In soil and land modeling, AI techniques could further enhance accuracy in soil texture analysis, moisture estimation, and erosion prediction for better land management. Advanced AI models could also be used as a tool to forecast streamflow and groundwater levels, therefore providing valuable lead times for flood preparedness and water resource planning in transboundary basins. In water quality, AI-driven methods improve contamination risk assessment, enable the detection of anomalies, and track pollutants to assist in water treatment processes and regulatory practices. AI techniques combined with remote sensing open new perspectives on monitoring water resources at a spatial scale, from flood forecasting to groundwater storage variations. This paper’s synthesis emphasizes AI’s immense potential in hydrology; it also covers the latest advances and future prospects of the field to ensure sustainable water and soil management. Full article

Together with the rapidly growing world population and increasing usage of electrical equipment, the demand for electrical energy has continuously increased the demand for electrical energy. For this reason, especially considering the increasing inflation rates around the world, using an electricity energy meter, which works with the least operating error, has great economic importance. In this study, an artificial neural network (ANN)-based prediction methodology is presented to estimate an active electricity meter’s combined maximum error rate by using variable factors such as current, voltage, temperature, and power factor that affect the maximum permissible error. The estimation results obtained with the developed ANN model are evaluated statistically, and then the suitability and accuracy of the presented approach are tested. At the end of this research, it is understood that the obtained results can be used by high accuracy rate to estimate the combined maximum working error of an active electricity energy meter with the help of a suitable ANN model based on the internal variable factors. Full article

The variability of renewable energy sources, storage limitations, and fluctuations in residential demand affect the reliability of sustainable energy systems, resulting in energy deficits and the risk of service interruptions. Given this situation, the objective of this study is to diagnose and optimize the reliability of a residential microgrid based on photovoltaic and wind power generation and battery energy storage systems (BESSs). To this end, genetic algorithms (GAs) and ant colony optimization (ACO) are used to evaluate the performance of the system using metrics such as loss of load probability (LOLP), loss of supply probability (LPSP), and availability. The test system consists of a 3.25 kW photovoltaic (PV) system, a 1 kW wind turbine, and a 3 kWh battery. The evaluation is performed using Python-based simulations with real consumption, solar irradiation, and wind speed data to assess reliability under different optimization strategies. The initial diagnosis shows limitations in the reliability of the system with an availability of 77% and high values of LOLP (22.7%) and LPSP (26.6%). Optimization using metaheuristic algorithms significantly improves these indicators, reducing LOLP to 11% and LPSP to 16.4%, and increasing availability to 89%. Furthermore, optimization achieves a better balance between generation and consumption, especially in periods of low demand, and the ACO manages to distribute wind and photovoltaic generation more efficiently. In conclusion, the use of metaheuristics is an effective strategy for improving the reliability and efficiency of autonomous microgrids, optimizing the energy balance and operating costs. Full article

The cultivation of Mitragyna speciosa (kratom) has gained significant interest due to its diverse alkaloid profile, increasing its commercial and medicinal demand. Using controlled hydroponic techniques, this study investigates the effects of varying light intensity and water potential on kratom growth, mitragynine (MG) accumulation, and total alkaloid content (TAC). While the interaction between light and water potential was generally not significant, water potential emerged as the dominant factor affecting plant growth and alkaloid accumulation. The highest MG accumulation (0.63% w/w) was recorded under moderate water potential (−0.4 MPa). In contrast, the highest TAC (8.37 mg alkaloid equivalent per gram dry weight) was observed under the combined effect of low light and mild water potential (−0.4 MPa). Leaf age also played a key role, with younger leaves (second and third pairs) accumulating significantly higher MG levels (0.74% w/w) than older leaves (0.40% w/w). Additionally, leaf thickness was positively associated with MG levels, suggesting a potential link between plant morphology and alkaloid biosynthesis. However, low water potential (−0.7 MPa) significantly reduced both growth and MG content, highlighting the importance of optimizing environmental conditions for sustained bioactive compound production. These findings demonstrate the physiological adaptability of kratom to variable environmental stresses and their influence on alkaloid accumulation. This knowledge can be applied to precision cultivation strategies to enhance the sustainability of kratom farming while optimizing the production of bioactive compounds for pharmaceutical and agricultural applications. Full article

Electric vehicles (EVs) must be used as the primary mode of transportation as part of the gradual transition to more environmentally friendly clean energy technology and cleaner power sources. Vehicle-to-grid (V2G) technology has the potential to improve electricity demand, control load variability, and improve the sustainability of smart grids. The operation and principles of V2G and its varieties, the present classifications and types of EVs sold on the market, applicable policies for V2G and business strategy, implementation challenges, and current problem-solving techniques have not been thoroughly examined. This paper exposes the research gap in the V2G area and more accurately portrays the present difficulties and future potential in V2G deployment globally. The investigation starts by discussing the advantages of the V2G system and the necessary regulations and commercial representations implemented in the last decade, followed by a description of the V2G technology, charging communication standards, issues related to V2G and EV batteries, and potential solutions. A few major issues were brought to light by this investigation, including the lack of a transparent business model for V2G, the absence of stakeholder involvement and government subsidies, the excessive strain that V2G places on EV batteries, the lack of adequate bidirectional charging and standards, the introduction of harmonic voltage and current into the grid, and the potential for unethical and unscheduled V2G practices. The results of recent studies and publications from international organizations were altered to offer potential answers to these research constraints and, in some cases, to highlight the need for further investigation. V2G holds enormous potential, but the plan first needs a lot of financing, teamwork, and technological development. Full article

Considering the coexistence of semantic and bit transmissions in future networks, transmission policy design is crucial for heterogeneous semantic and bit communications to improve transmission efficiency. In this paper, we investigate downlink semantic and bit data transmissions from the access point (AP) to several semantic users and a bit user, and we propose a TDMA–NOMA-based transmission scheme to efficiently utilize wireless communication resources. The transmission time and power resource allocation problem is formulated with the aim of maximizing the throughput of the bit user while guaranteeing the semantic demands of the semantic users are met. Due to the time-varying channel conditions and mixed continuous–discrete variables, we propose a parameterized deep Q network (P-DQN)-based algorithm to solve the problem, where an actor network is employed to output continuous parameters, and a deep Q network is used to find the optimal discrete actions. the simulation results show that the proposed P-DQN-based algorithm converges faster than other learning methods. The simulations also demonstrate that the proposed TDMA–NOMA-based transmission scheme can improve the average bit throughput by up to 20% while meeting the semantic demands compared to other multiple access schemes. Full article

Dynamic time history analysis has long been regarded as an acceptable and reliable method for the seismic design of structures. The methodology for conducting such analyses, particularly for modern structures with advanced seismic resisting systems, is generally not covered by codal guidelines and is often categorized as “alternative” analysis. Resilient rocking wall systems with low-damage hold-downs fall within the “alternative” design category for most international standards, and designs must include dynamic time history analysis. However, the analysis results are influenced by factors such as ground motion selection, scaling methodologies, modeling considerations employed, and the assumptions embedded within the numerical model. This study takes a practical approach and assesses their impact on the structural response and seismic demand determination of a selected mass timber archetype featuring a rocking wall system with friction connections. The investigation into modeling considerations explores various damping models, time history analysis methods, and the associated variables within these models. It is demonstrated that varied seismic demands can result from different selections and modeling assumptions. However, with careful and rational engineering judgment and selection during the analysis process, reasonably close and acceptable seismic demands can be achieved. Furthermore, the authors provide recommendations and insights to enhance the analysis and design demand determination process. Full article