Search Results (3,607)

Non-linear systems of equations play a fundamental role in various engineering and data science models, where accurate solutions are essential for both theoretical research and practical applications. However, solving such systems is highly challenging due to their inherent non-linearity and computational complexity. This study proposes a novel hybrid iterative method with fourth-order convergence. The foundation of the proposed scheme combines the Walrus Optimization Algorithm and a fourth-order iterative technique. The objective of this hybrid approach is to enhance global search capability, reduce the likelihood of convergence to local optima, accelerate convergence, and improve solution accuracy in solving non-linear problems. The effectiveness of the proposed method is checked on standard benchmark problems and two real-world case studies, hydrocarbon combustion and electronic circuit design, and one non-linear boundary value problem. In addition, a comparative analysis is conducted with several well-established optimization algorithms, based on the optimal solution, average fitness value, and convergence rate. Furthermore, the proposed scheme effectively addresses key limitations of traditional iterative techniques, such as sensitivity to initial point selection, divergence issues, and premature convergence. These findings demonstrate that the proposed hybrid method is a robust and efficient approach for solving non-linear problems. Full article

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems. Full article

Gender-based violence (GBV) in urban contexts is a complex, multifactorial phenomenon shaped by socioeconomic, territorial, and contextual factors. This study aims to develop a predictive model for GBV-related crimes in Valencia (Spain), using open geospatial data and advanced machine learning techniques to support the identification of high-risk areas and guide targeted interventions. A 25 m grid was generated to homogenize crime data and independent variables, including socioeconomic indicators, urban services, real estate information, and traffic intensity. Multiple models were tested—Multiple Linear Regression (MLR), Decision Tree (DT), and Random Forest (RF). Linear models were found to be insufficient for explaining GBV patterns (R² ≈ 0.45), while RF and DT achieved high predictive accuracy (R² ≈ 0.97 and 0.95, respectively. The variables with the greatest influence were traffic intensity, average monthly income, unemployment rate, and proximity to nightlife venues. To enhance the interpretability of the most accurate models, we applied SHAP (SHapley Additive exPlanations) to quantify the contribution of each predictor and elucidate the direction and magnitude of their effects on model predictions. These findings demonstrate the utility of geospatial ML techniques in understanding the spatial dynamics of GBV and in supporting urban safety policies. While the current model focuses on static spatial predictors and does not explicitly model temporal dynamics or spatial autocorrelation, future research will integrate these aspects, along with participatory data, and test the model’s applicability in other cities to enhance its robustness and generalizability. Full article

In precision agriculture, accurate, non-destructive estimation of fruit volume is crucial for quality grading, yield prediction, and post-harvest management. While vision-based methods provided some usefulness, fruits with complex geometry—such as mangosteen (Garcinia mangostana L.)—are difficult due to their large calyx, which may lead to difficulties in solving using traditional form-modeling methods. Traditional geometric solutions such as ellipsoid approximations, diameter–height estimation, and shape-from-silhouette reconstruction often fail because the irregular calyx generates asymmetric protrusions that violate their basic form assumptions. We offer a novel study framework employing both multi-view instance segmentation and hybrid geometrical feature modeling to quantitatively model mangosteen volume with traditional 2D imaging. A You Only Look Once (YOLO)-based segmentation model was employed to explicitly separate the fruit body from the calyx. Calyx inclusion resulted in dense geometric noise and reduced model performance ($R^2<0.40$). We trained eight regression models on a curated and augmented 900 image dataset ($N=720$, test $N=180$). The models used single-view and multi-view geometric regressors ($V\propto A^{1.5}$), polynomial hybrid configurations, ellipsoid-based approximations, as well as hybrid feature formulations. Multi-view models consistently outperformed single-view models, and the average predictive accuracy improved from $R^2=0.6493$ to $R^2=0.7290$. The best model is indeed a hybrid linear regression model with side- and bottom-area features—($A_s^{1.5}$, $A_b^{1.5}$)—combined with ellipsoid-derived volume estimation—($V_{\mathrm{ellipsoid}}$)—which resulted in $R^2=0.7290$, a Mean Absolute Percentage Error (MAPE) of 16.04%, and a Root Mean Square Error (RMSE) of 31.9 ${\mathrm{cm}}^3$ on the test set. These results confirm the proposed model as a low-cost, interpretable, and flexible model for real-time fruit volume estimation, ready for incorporation into automated sorting and grading systems integrated in post-harvest processing pipelines. Full article

This paper proposes an analysis of different logics (heuristic and linear) of managing renewables scenarios including two different operating conditions and their relative degradation: fixed and variable point. The synergy between two storage technologies, such as Li-ion batteries and the hydrogen power-to-power solution (electrolyzer, H₂ tank, and fuel cells), is evaluated to ensure the balance of the power grid. This paper presents a numerical model of the smart grid developed in MATLAB/Simulink. A detailed performance evaluation of each component was performed to meet an electrical load (30 kW-peak) of a smart renewable energy community. From the optimization process, a fuel cell of 6 kW, an electrolyzer of 18 kW, a tank of 40 m³ at 200 bars, as well as a battery of 75 kWh were selected. The fuel cell operates during autumn and winter due to the lack of photovoltaic power generation, while its contribution is reduced during the summer period. In the heuristic logic, the minimum and maximum hydrogen levels are 18% and 60% of the tank volume (40 m³), respectively, while in the linear logic, they are 33% and 65%. The average value of the state of charge (SOC) of the battery is similar in both logics (0.51 vs. 0.53). Regarding hydrogen produced from the electrolyzer, the linear logic allows it to produce a quantity 7% higher than the heuristic one; therefore, the linear logic allows it to properly manage the electrochemical systems. The dynamic operation results in more significant degradation of hydrogen systems, making them less suitable; thus, to preserve the devices (up to 25% of lifetime more), a fixed-point operation is recommended. The cost comparison does not show relevant differences between the two scenarios, while a steep increase in the costs is shown when the fuel cell is operated in dynamic mode. Finally, the total emissions associated with renewable microgrids are 30 times lower than the traditional grid scenario, demonstrating the potential of renewable energy communities. Full article

This study explores the potential of Precision Agriculture (PA) to optimize top-dressing nitrogen (N) fertilization in rainfed barley under drought conditions in Central Catalonia (Spain). Efficient N management is critical in Mediterranean dryland winter cereal systems, where water scarcity and environmental regulations limit fertilization strategies. Two plots (2.93 ha and 1.80 ha) were zoned using soil apparent electrical conductivity (ECa) and elevation data obtained with the VERIS 3100 ECa soil surveyor. An on-farm experimental design tested four N dose rates (0 kg N/ha, 32 kg N/ha, 64 kg N/ha, and 96 kg N/ha) across two management zones per plot. Yield data were collected using a combine harvester equipped with a yield monitor and were mapped using geostatistical methods. A linear model (ANOVA) was used to analyze barley yield (kg/ha at 13% moisture), with nitrogen rate and soil zone (management class) as explanatory factors. Results showed low average yields (~1200 kg/ha–1300 kg/ha) due to severe water stress during the 2022–2023 season. Non-fertilized plots (N0) and those receiving moderate (N64) or high fertilization (N96) achieved the best performance, with the latter likely enhancing crop N uptake during the post-stress recovery period. In contrast, low fertilization (N32) proved less effective. Marginal return analysis supported variable-rate N application only in one plot, whereas under drought conditions, a no-fertilization strategy proved more suitable in the other. Ultimately, additional trials conducted under more favourable climatic scenarios are necessary to assess and validate the effectiveness of Precision Agriculture-based fertilization strategies in rainfed barley. Full article

Integrating environmental, social, and governance (ESG) reporting with the Sustainable Development Goals (SDGs) is important for achieving corporate sustainability. The rapid evolution of regulations like the Corporate Sustainability Reporting Directive (CSRD), and the fragmented research landscape create uncertainty for strategic planning. This paper addresses the critical gap related to the lack of predictive data into future research trends at the ESG-SDG nexus. The research begins with a bibliometric analysis using two software programs R-Biblioshiny 5.2.0 and VOSviewer 1.6.20, to process data extracted from the Web of Science (Clarivate). Selected key terms regarding sustainability reporting concepts and reporting standards, as well as the engagements of auditors were used to filter the database information. Starting from the bibliometric analysis of 361 publications completed during January 2015–September 2025, the study performs further a quantitative measurement bibliometrics using RStudio 4.5.2 and provides a novel ensemble forecasting model (AutoRegressive Integrated Moving Average, Error, Trend, Seasonal Components, and Linear regression with SDG factors) that cartograph the alignment of the current research field and forecast its evolution. The results reveal that terms regarding reporting “CSRD” and sustainability assurance, “ISSA 5000” are the most dominant research fronts, strongly aligned with SDG 12, 13 and 17. The forecasting model predicts sustained growth in this area. The study contributes by providing a forward-thinking strategic map for researchers, policymakers and businesses, transforming sustainability integration from a compliance task into systematic, data-driven approach for priority setting strategy. Full article

Robust, national-scale quantification of soil organic carbon (SOC) dynamics in China’s paddy fields has been hindered by widely divergent estimates and a lack of comprehensive driver attribution. To address this, we developed a new empirical model from a comprehensive database of 746 long-term field observations (125 sites) to identify predominant drivers and quantify national-scale SOC stock dynamics from 1980 to 2018. The model explained 43% of the variance in topsoil SOC change. Organic matter input was the dominant driver (21.83% variance), with livestock manure demonstrating the highest C sequestration efficiency, followed by green manure and straw. Soil pH, latitude (as a climate proxy), and initial SOC content were also critical controllers. We estimate that China’s paddy topsoils (0–20 cm) acted as a significant C sink from 1980 to 2018, accumulating 242.51 ± 85.80 Tg C (an average rate of 6.65 Tg C yr⁻¹), bringing the 2018 national stock to 1220.48 ± 85.80 Tg C. Spatially, sequestration was highest in central (e.g., Hunan) and northeastern (e.g., Heilongjiang) China, while Chongqing experienced a net SOC loss. Crucially, our study provides a new long-term benchmark that reconciles previous, higher estimates from shorter timeframes, empirically demonstrating that sequestration rates are non-linear and diminish over time. These findings confirm that the C sequestration potential of paddy soils, while substantial, is finite and requires spatially targeted management of organic inputs and soil pH to maintain. Full article

This study examined the effect of competition stress on recovery time in female collegiate soccer players. Thirty NCAA Division I athletes were monitored over 35 consecutive days using Polar Team Pro wearable devices, which captured exercise duration, distance covered, energy expenditure, sprint count, speed, heart rate, training load, and recovery duration. Data were collected across 20 practices and 7 competitions, totaling 845 observations. Linear regression was used to assess whether formal competition independently influenced recovery duration, controlling for time of day and workload variables. Athletes averaged 20.1 ± 1.1 years of age. Across all sessions, the mean exercise duration was 59.5 ± 38.7 min, with an average distance of 2.6 ± 2.1 km, and energy expenditure of 387.2 ± 283.5 kcals. Recovery duration was significantly longer after competition (51.3 ± 59.6 h) compared to practice (13.0 ± 15.8 h, p < 0.001). The regression model indicated that formal competition predicted an additional 51 h of recovery time (β = 50.540; p < 0.001), independent of physical workload. Recovery following formal competition is significantly prolonged, holding multiple components of workload constant. These findings offer novel insights into female athlete recovery and highlight the importance of sex-specific approaches in sports science. Full article

Nonlinear water waves (NWWs) can be generated by the vertical bottom disturbance, which represents the conceptual processes of the rise of seabed rupture under seismic loads. To explore the correlation between the disturbance parameters and the wave features, a Reynolds-averaged Navier–Stokes (RANS) model is applied, with the flow turbulence and fluid–structure interaction (FSI) being resolved by the k–ɛ model and the immersed boundary method (IBM), respectively. The free surface is tracked using the volume of fluid (VOF) method. After validating against the theoretical solutions and experimental results, the effects of disturbance duration and bulk on the wave features at the source region (the generation stage) and offshore direction (the propagation stage) are systematically discussed. The fixed maximal vertical displacement is considered, with four moving durations and five disturbance widths being simulated, resulting in four disturbance velocities and five disturbance bulks. The results indicate that the proposed RANS model can accurately create various wave patterns (including the linear, solitary, and tsunami-like waves) generated by bottom disturbances. Special attentions are paid to the tsunami-like wave. The wave evolution exhibits strong dependence on disturbance duration and width, with shorter durations triggering earlier soliton fission and longer widths accelerating phase celerity. These findings highlight the critical role of disturbance parameters in governing soliton formation and energy propagation patterns, which are vital in disaster forecasting. Full article

This study employs molecular dynamics simulations to investigate the frictional behavior of fullerene nano-additives on Fe-C alloy surfaces under varying loads and temperatures, focusing on boundary lubrication conditions. The results show that the x-direction friction force exhibits minimal sensitivity to normal pressure due to the high rigidity of fullerene molecules, which limits variations in real contact area and atomic interactions. In contrast, temperature has a significant effect: as it rises, enhanced atomic vibrations and thermal activation lower energy barriers for sliding. The coefficient of friction (COF) consistently decreases with both increasing load and temperature, driven by the mechanism of thermally activated motion. Although partial rotational motion from sliding to rolling friction was not explicitly observed in the simulations, the study remains within the sliding-dominated regime, highlighting the importance of temperature over load in controlling friction. A linear relationship between $\ln COF$ and $1/k_{B}T$ yields an average activation energy of ~0.03 eV, supporting a thermally activated friction mechanism. By introducing a composite parameter that combines load and temperature effects, the study provides a predictive framework for modeling friction behavior under thermo-mechanical coupling. These findings enhance the understanding of the friction-reducing capabilities of fullerene additives and offer a foundation for designing advanced nano-lubricants in boundary lubrication systems. Full article

The Maximum Entropy (MaxEnt) model, integrated with ArcGIS (a geographic information system), was employed to project potential species distribution under current conditions and future climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5) for the 2050s, 2070s, and 2090s. Model optimization involved testing 1160 parameter combinations. The optimized model (FC = LQ, RM = 0.1) exhibited significantly improved predictive performance, with an average AUC of 0.967. Under current conditions, the estimated core suitable habitat spans 35.62 × 10⁴ km², primarily located in southern China. Future projections indicated a non-linear trajectory: an initial contraction of total suitable area by mid-century, followed by a substantial expansion by the 2090s, particularly under high-emission scenarios. Simultaneously, the distribution centroid shifted northwestward. The primary factors influencing distribution were the annual mean temperature (Bio1, 41.1%) and the precipitation of the coldest quarter (Bio19, 20.0%). These findings establish a critical scientific basis for developing climate-adaptive conservation strategies, including the identification of priority climate refugia in Fujian province, China, and planning for assisted migration to northwestern regions. Full article

Precise 3D shape correspondence is a fundamental prerequisite for critical applications ranging from medical anatomical modeling to visual recognition. However, non-isometric 3D shape matching remains a challenging task due to the limited sensitivity of traditional Laplace–Beltrami (LB) bases to local geometric deformations such as stretching and bending. To address these limitations, this paper proposes a Sinkhorn-Regularized Elastic Functional Map framework (SRE-FMaps) that integrates entropy-regularized optimal transport with an elastic thin-shell energy basis. First, a sparse Sinkhorn transport plan is adopted to initialize a bijective correspondence with linear computational complexity. Then, a non-orthogonal elastic basis, derived from the Hessian of thin-shell deformation energy, is introduced to enhance high-frequency feature perception. Finally, correspondence stability is quantified through a cosine-based elastic distance metric, enabling retrieval and classification. Experiments on the SHREC2015, McGill, and Face datasets demonstrate that SRE-FMaps reduces the correspondence error by a maximum of 32% and achieves an average of 92.3% classification accuracy (with a peak of 94.74% on the Face dataset). Moreover, the framework exhibits superior robustness, yielding a recall of up to 91.67% and an F1-score of 0.94, effectively handling bending, stretching, and folding deformations compared with conventional LB-based functional map pipelines. The proposed framework provides a scalable solution for non-isometric shape correspondence in medical modeling, 3D reconstruction, and visual recognition. Full article

Human West Nile virus (WNV) infections can have severe neurological health effects, especially among those over 50 years of age. As changes in weather patterns lead to more frequent and intense droughts, there is a public health need for improved understanding of drought associated WNV risks. While multiple studies have reported an association between drought conditions and human WNV cases, this information has not yet been synthesized systematically across studies. Our review aims to evaluate the existing evidence of an association between drought and human WNV cases while considering the impacts of different study regions, methodological approaches, drought metrics, and WNV case definitions. We conducted a systematic literature search of peer-reviewed epidemiological studies that examined a potential association between drought and human WNV cases. Our inclusion criteria targeted studies that employed measures of drought beyond precipitation and reported effect estimates along with measures of error. The literature search and screening process resulted in the inclusion of nine papers with study periods spanning from 1999 to 2018. The included peer-reviewed publications employed a wide variety of study designs and methods, such as linear mixed-effects models, generalized linear models using simultaneous autoregression, generalized additive models, Bayesian model averaging, and a case-crossover design using conditional logistic regression models. We summarize the key findings and provide study quality evaluations for each of the nine included studies. Studies that analyzed drought indices averaged over a seasonal period of three to four months reported positive associations between drought and WNV. However, studies that analyzed drought indicator variables averaged over weekly periods of time had less consistent results. We discuss potential mechanisms underlying the observed associations between drought and human WNV cases. Full article

Accurate wind information is essential for safe and efficient port operations, yet many small and medium-sized coastal ports lack dense meteorological instrumentation. This paper presents a data-driven framework for wind speed prediction at such ports by leveraging long-term historical measurements from nearby reference stations. Focusing on a real-world case study at the Chalkida port in Greece, the framework integrates both deterministic and Machine Learning (ML) models trained on historical wind patterns of archived wind data from four surrounding locations. We examine both short- and long-horizon prediction periods, using recently acquired wind measurements at the target port for model validation. Deterministic baselines include simple and weighted averaging schemes, while supervised ML methods, such as Multiple Linear Regression, Decision Trees, Support Vector Regression, Random Forests, and Gradient Boosting, are trained to capture complex spatiotemporal patterns. Experimental results highlight that ensemble-based ML models, particularly Gradient Boosting, achieve superior accuracy in short-term forecasting, while the optimal predictor varies with the forecast horizon. The proposed approach enables the deployment of virtual wind stations in data-scarce ports and can be periodically updated to dynamically select the most suitable model, thereby supporting climate adaptation strategies, localized wind monitoring, and operational planning without requiring dense local instrumentation. Full article