Search Results (6,076)

Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources and a fixed pilot structure, which degrade the performance and effectiveness of traditional estimation techniques, particularly in dynamic environments. Recent advances in deep learning offer significant potential for addressing these issues by improving estimation accuracy and modelling complex channel dynamics. Though deep learning-based methods introduce trade-offs in computational complexity and accuracy, these are crucial constraints in latency-sensitive V2V scenarios. This article presents a comprehensive review of deep learning-based channel estimation techniques, analysing methods for the IEEE 802.11p standard and critically examining their limitations in both classical and deep learning-based approaches. Additionally, the article highlights improvements introduced by IEEE 802.11bd, which features an enhanced pilot structure and advanced modulation schemes, providing a more robust framework for adaptive, efficient channel estimation. By identifying future research pathways that balance delay, complexity, and accuracy, an intelligent and effective transportation system can be established. Full article

This study investigates optimal and non-optimal MACD parameter ranges in the gold market using a simulation-based framework and examines their implications for trading strategy design and risk-adjusted performance. By systematically identifying optimal and non-optimal MACD parameter ranges together with appropriate stop-loss and take-profit levels, this study addresses an issue that has not been explored in the existing literature on gold markets. The empirical results reveal a clear contrast between the optimal and non-optimal groups. Importantly, the superior performance of the optimal strategies emerges at the group level, rather than being driven by isolated exceptional models. Annual analysis further shows that models in the optimal groups respond effectively to overall market direction, taking long (short) positions under upward- (downward-) biased market conditions. Additional analyses examine fixed-ratio stop-loss and take-profit rules, identifying parameter–strategy combinations that balance risk control and profit realization. A cross-market comparison between the gold and stock markets highlights significant heterogeneity in optimal parameter ranges and investment horizons, underscoring the market-specific nature of MACD-based trading rules and the limits of cross-asset parameter transferability. Overall, these findings provide deeper insights into market-specific trading dynamics, going beyond the provision of an empirical benchmark and a methodological reference for MACD-based trading strategy design in the gold market. Full article

A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) and the scarcity of real-time, sentence-level translation systems. This paper presents a real-time system for sentence-level recognition of continuous SSL and direct mapping to natural spoken Arabic. The proposed system operates end-to-end on live video streams or pre-recorded content, extracting spatio-temporal landmark features using the MediaPipe Holistic framework. For classification, the input feature vector consists of 225 features derived from hand and body pose landmarks. These features are processed by a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset to perform direct sentence-level classification over a vocabulary of 50 continuous Arabic sign language sentences, supported by an idle-based segmentation mechanism that enables natural, uninterrupted signing. Experimental evaluation demonstrates robust generalization: under a Leave-One-Signer-Out (LOSO) cross-validation protocol, the model attains a mean sentence-level accuracy of 94.2%, outperforming the fixed signer-independent split baseline of 92.07%, while maintaining real-time performance suitable for interactive use. To enhance linguistic fluency, an optional post-recognition refinement stage is incorporated using a large language model (LLM), followed by text-to-speech synthesis to produce audible Arabic output; this refinement operates strictly as post-processing and is not included in the reported recognition accuracy metrics. The results demonstrate that direct sentence-level modeling, combined with landmark-based feature extraction and real-time segmentation, provides an effective and practical solution for continuous SSL sentence recognition in real-time. Full article

The continuous increase in power density of electronic devices imposes stringent requirements on the design of lightweight, high-efficiency heat sinks. To overcome the limitations of conventional single-gradient or monomaterial heat sinks—namely, their suboptimal heat-transfer efficiency and poor structural adaptability—this study proposes a dual-gradient, triply periodic minimal surface (TPMS)-based multimaterial heat sink architecture fabricated from CuCrZr and AlSi₇Mg. Thermal performance was quantified experimentally using infrared thermography, while the underlying flow-field mechanisms were investigated numerically via computational fluid dynamics (CFD) simulations employing the standard k–ε turbulence model. With the TPMS material volume ratio fixed at 3:3 (CuCrZr:AlSi₇Mg), the Z-axis gradient configuration P-Z4-5 delivered the best overall thermal performance, achieving a heat-transfer coefficient (HTC) of 1557.63 W·m⁻²·K⁻¹ and a thermal resistance as low as 1.83 K·W⁻¹ at an inlet velocity of 5 m·s⁻¹. In contrast, the Y-axis gradient configuration P-Y3-6 yielded the most uniform temperature distribution, exhibiting a maximum surface temperature difference of only 21.5 °C under the same inlet condition. Velocity and turbulence distribution analyses reveal that the dual-gradient design enhances both the narrow-tube effect and flow-induced disturbances; furthermore, increasing the inlet velocity from 5 m·s⁻¹ to 21.65 m·s⁻¹ significantly intensifies vorticity-driven fluid mixing. Among all configurations evaluated, P-Z4-5 exhibited the highest j/f factor (i.e., the ratio of Colburn j-factor to Fanning friction factor), followed by P-Z3.5-5.5 and P-Z3-6. These findings establish a promising new pathway for the development of high-performance, lightweight heat sinks tailored for next-generation high-power electronics. Full article

Objectives: This study systematically evaluated and quantitatively synthesized preclinical evidence on the effects of magnesium (Mg) incorporation into or coating of titanium dental implants on osseointegration and peri-implant bone formation. Methods: Electronic searches of PubMed, Scopus, and Web of Science were performed up to May 2025 to identify animal studies evaluating Mg-modified titanium implants. Eligible studies compared Mg-incorporated or Mg-coated implants with non-modified titanium controls and reported quantitative histomorphometric outcomes. Primary outcomes included the values of bone-to-implant contact (BIC) and bone area (BA) around implants. Study quality was assessed using the ARRIVE 2.0 guidelines. Meta-analyses were performed using weighted mean differences with 95% confidence intervals under fixed- or random-effects models based on heterogeneity. Results: Eleven preclinical animal studies conducted in rabbit and rat models were included. Mg was incorporated using various surface-modification techniques, including ion implantation, Mg-substituted hydroxyapatite coatings, mesoporous titania layers, and nanotubular structures. Overall, the studies’ quality was high, with most studies rated as excellent and with a low-to-moderate risk of bias. Furthermore, the meta-analysis revealed a significant increase in BIC for Mg-modified implants compared with uncoated implants (Z = 4.38, p < 0.001), implying improved osseointegration. Meanwhile, pooled BA values showed no significant differences between the groups (Z = 0.93, p = 0.35). Conclusions: Mg coating onto or incorporation into titanium implant surfaces can improve BIC in preclinical models, indicating improved osseointegration in the early stages. Full article

Retrieval-Augmented Generation (RAG) has been observed to encounter challenges in heterogeneous query scenarios characterised by varying evidence requirements and reasoning depths. In order to address this limitation, the present paper puts forward a proposal for an Adaptive Multi-Source RAG framework (AMSRAG) that integrates query complexity awareness with confidence-aware fusion. The framework performs query complexity classification with a pretrained language model, calibrates the classification confidence to guide the dynamic scheduling of retrieval paths and the adjustment of fusion weights, and enables a controllable balance between answer quality and retrieval efficiency through hierarchical path selection and cross-source weighting. The experiments conducted on multiple open-domain question-answering datasets demonstrate that the query complexity classifier achieves an accuracy of 85.9% and a Macro-F1 score of 85.4%. These outcomes indicate the potential for the classifier to generate a reliable decision signal, which can subsequently be utilised to guide the process of adaptive retrieval and fusion. The proposed framework demonstrates a marked improvement in terms of both answer accuracy and retrieval relevance when compared to the fixed-pipeline RAG. In scenarios involving high-confidence queries, the system has been shown to effectively avoid redundant retrieval, thereby reducing the average number of retrievals. In instances of low-confidence complex queries, the system has been shown to enhance evidence coverage and completeness of answers through multi-source retrieval and confidence-weighted fusion. This study proposes a novel methodology for enhancing the adaptability and resource efficiency of RAG systems in response to heterogeneous query conditions. Full article

Background: Amid rising trade, urbanization, and carbon emissions in MENA countries, sustainable logistics faces major constraints. This study develops an enhanced Green Logistics Performance Index (GLPI) using min-max normalization and Principal Component Analysis (PCA) to integrate the World Bank’s Logistics Performance Index (LPI) and Yale’s Environmental Performance Index (EPI). The study uses fixed-effects panel regression on data from 20 MENA countries (2018–2024), identifies key drivers, and applies ARIMA and LSTM models for 2030 projections. The prior ratio-based GLPI suffered from scale sensitivity and volatility; this refined version provides improved stability and predictive utility for Green Supply Chain Management (GSCM). Methods: Panel data from 20 MENA countries (2018–2024) were analyzed. The enhanced GLPI normalizes and weights LPI and EPI scores via PCA. Fixed-effects regression identifies drivers, while ARIMA and LSTM enable scenario-based forecasting (baseline, optimistic, and pessimistic). Results: Renewable energy share positively influences GLPI, while trade openness has a negative effect. Projections indicate the regional GLPI will reach about 0.65 by 2030, with Saudi Arabia potentially achieving 25% higher under optimistic conditions. Conclusions: The refined GLPI advances GSCM theory by operationalizing triple bottom line trade-offs through a robust, predictive metric. It bridges descriptive limitations in prior literature, enabling forward-looking insights into sustainable logistics in emerging economies, with potential applicability beyond MENA. Full article

High-performance difference patterns (DPs) are critical for compact and integrated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to constraints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quadratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geometric configuration of the monopulse array. Finally, three numerical examples employing a radome-enclosed linear array, a uniform planar array, and a radome-enclosed planar array are presented to demonstrate the effectiveness of the proposed method in achieving the monopulse array DP synthesis with high directivity and symmetric main lobes. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

This paper investigates a fractional-order mathematical model for the co-infection dynamics of pneumonia and typhoid fever using the Liouville–Caputo derivative. We establish the existence, uniqueness, non-negativity, and boundedness of solutions using Banach’s fixed point theorem and fractional comparison principles. The Hyers–Ulam and generalized Ulam–Hyers–Rassias stability of the system are rigorously proved; this stability analysis is epidemiologically significant because it guarantees that small perturbations in initial conditions or model parameters—inevitable in real-world data collection—do not lead to unbounded deviations in disease trajectory predictions. To approximate solutions numerically, we develop a Laplace-Based Optimized Decomposition Method (LODM) and validate its convergence against a modified predictor–corrector scheme. The LODM provides a semi-analytical series solution, while the predictor–corrector method serves as a numerical benchmark; this dual approach ensures reliability of simulations. Numerical simulations illustrate the influence of the fractional order $\xi$ on system dynamics. Quantitative comparison between $\xi =1$ (integer order) and $\xi <1$ (fractional order) demonstrates that fractional modeling reduces peak infection by 12–18% and delays epidemic peaks by 15–30 days, confirming that memory effects capture long-term epidemiological dependencies that integer-order models fail to reproduce. A biological interpretation links the fractional order to immune memory, pathogen persistence, and intervention latency. This study provides both theoretical and numerical evidence supporting the use of fractional calculus in epidemiological modeling. Full article

This study develops a novel AI-based trading framework designed to consistently generate profits across cyclical bullish and bearish futures markets. Unlike conventional strategies that rely on static rules or a single predictive model, the proposed framework introduces a dual-agent deep reinforcement learning (DRL) architecture, where one agent specializes in bullish conditions and the other in bearish conditions, while a trading decision selector dynamically predicts market regimes and allocates execution accordingly. This design enables the system to adapt to regime shifts and mitigate risks arising from market volatility and extreme events. Using Mini Taiwan Stock Exchange Index Futures (MTX) as a case study, a four-year historical backtest is conducted covering multiple disruptive periods, including the tax adjustment and the Russia–Ukraine conflict. The empirical results show that, under a monthly capital reset and loss-compensation rule with a fixed investment of TWD 500,000 per month, the proposed framework achieves an average cumulative return of 2240%, an annualized return of 109%, and a Sharpe ratio of 0.31, with the cumulative ROI exceeding twice the MTX index growth over the same period. Although the Sharpe ratio remains moderate, this outcome reflects the framework’s emphasis on directional trading and absolute return maximization, where profitable trades outweigh intermittent losses despite higher short-term volatility. These findings suggest that adaptive, regime-aware DRL architectures are particularly effective for futures trading in markets characterized by frequent trend reversals, offering both methodological innovation and practical applicability under realistic market conditions, with strong returns achieved at a moderate risk-adjusted level. Full article

This study examines the impact of corporate sustainability practices on firm performance, sustainable development, and value by focusing on ESG performance, sustainability committees, and sustainability reporting. While prior literature documents a general association between ESG performance and firm value, limited attention has been paid to the role of sustainability governance structures and their contribution to sustainable development outcomes, particularly SDGs disclosure, in a multi-country setting. Sustainable development is proxied by an SDGs disclosure index constructed using firm-level disclosures aligned with the 17 Sustainable Development Goals based on LSEG (Refinitiv) ESG item-level data. The analysis controls for firm size, leverage, profitability, industry-, and country-level institutional factors to ensure robust results. Using panel data comprising 36,438 firm-year observations from 6073 companies across OECD member countries from 2017 to 2022, this study employs a fixed-effects model based on diagnostic tests, including the Hausman and Breusch–Pagan tests. The findings revealed that higher ESG performance scores positively influence both sustainable development outcomes and market value. Moreover, the presence of sustainability committees and broader sustainability reporting further strengthens these relationships. These results highlight the importance of institutional sustainability governance in translating ESG commitments into measurable firm values and SDG-related outcomes. This study provides novel empirical evidence on how sustainability-focused governance mechanisms enhance corporate contributions to sustainable development, offering important implications for managers and policymakers as well as directions for future research. Full article

SSRMS refers to a Space Station Remote Manipulator System. The robotic arm of the Wentian module can complete tasks such as supporting astronauts’ extravehicular activities, installing and maintaining payloads, and inspecting the space station. The seven-joint SSRMS manipulator is critical for space missions. This study aims to build its kinematic model via screw theory. It simplifies SSRMS to right-angle rods, defines joint screw axes, twist coordinates, and initial pose matrix. Using the PoE (Product of Exponentials) formula, the 7-DOF forward kinematics equation is derived. In addition, it derives fixed joint angle for inverse kinematics, including analytical solutions and numerical solutions. It elaborates analytical solutions for fixing joints 1/7 and 2/6 and numerical solutions for fixing joints 3/4/5, solves all joint angles via kinematic decoupling, and addresses special cases. Experiments with China’s space station small arm parameters show the probability of meeting the accuracy threshold ${10}^{-4}$ is 99.79%, verifying model effectiveness, while noting singularity-related weak solving areas. This provides a reliable basis for subsequent inverse kinematics optimization. Full article

Background: Concerns that human papillomavirus (HPV) vaccination may adversely affect ovarian reserve contribute to vaccine hesitancy, yet longitudinal data with paired anti-Müllerian hormone (AMH) measurements are limited. We evaluated whether HPV vaccination was associated with short-term changes in AMH compared with an unvaccinated control group. Methods: In this retrospective cohort, women aged 18–45 years who completed a three-dose 9-valent HPV vaccination (Gardasil 9®, Merck Sharp & Dohme LLC, West Point/Pennsylvania/USA) schedule and had AMH measured before dose 1 and after dose 3 were compared with unvaccinated controls who had two AMH measurements during routine gynecologic evaluation. AMH change was summarized as absolute change (ΔAMH), percent change, and log change. To compare rates of AMH change while accounting for heterogeneous follow-up and confounding, AMH was analyzed on the natural log scale using a linear mixed-effects model with a random intercept for participant and fixed effects for time (years), group, and a time×group interaction, adjusted for age, current smoking, gravidity, and parity. Annual percent change was derived from model coefficients. Prespecified sensitivity analyses repeated the primary model under follow-up restrictions and after restricting baseline AMH to 1.0–5.0 ng/mL. Results: The cohort included 158 vaccinated and 106 control women. Baseline AMH was similar between groups (median 1.88 vs. 1.94 ng/mL), while the follow-up interval was shorter in vaccinated women (6.7 vs. 8.9 months). Unadjusted AMH decline was smaller in vaccinated women (median ΔAMH −0.13 vs. −0.27 ng/mL; p = 0.015; median percent change −10.9% vs. −20.6%; p = 0.006). In the adjusted mixed-effects model, controls showed an estimated AMH decline of −27.6% per year (95% CI −35.5% to −18.7%; p < 0.001). The time × group interaction was positive (β = 0.170, 95% CI 0.027 to 0.312; p = 0.020), corresponding to a slope ratio of 1.185 (95% CI 1.02–1.366) and an implied annual change of −14.2% per year (95% CI −21.0% to −6.7%) in vaccinated women. Results were broadly consistent in follow-up-restricted sensitivity analyses; however, in the baseline AMH 1.0–5.0 ng/mL restricted cohort (vaccinated n = 82, control n = 67), the interaction was attenuated and not statistically significant (β = 0.082, p = 0.237). Conclusions: In this retrospective cohort with paired AMH measurements, HPV vaccination was not associated with evidence of clinically meaningful short-term impairment in ovarian reserve as assessed by AMH. Observed differences in AMH alterations were modest and should be interpreted cautiously, given residual confounding, measurement variability, and reduced precision in restricted-cohort analyses. Full article

Achieving rural social sustainability requires both income growth and a reduction in rural income inequality. Strengthening farmland property rights is widely expected to contribute to these goals, yet the evidence remains limited. Building on a “property rights–factor allocation–income” framework, this study uses rural micro panel data from CHARLS (2011–2018) and combines two-way fixed effects with a chain multiple-mediation model to examine how farmland property rights strength (FPRS) relates to these outcomes. The results show the following: (i) FPRS has a dual total effect, raising household per capita income (0.683) while reducing the Gini coefficient ($-0.032$); (ii) effect decomposition indicates that the impacts are dominated by the direct effect, accounting for 96.47% and 98.37% of the total effects on per capita income and the Gini coefficient, respectively; (iii) the indirect transmission is structurally asymmetric, with income growth relying on seven “independent–chain” mediation paths involving land, labor, and capital, whereas inequality convergence operates only through farmland transfer-out and (iv) stronger property rights further reshape income composition by activating both agricultural and non-agricultural income through differentiated direct effects and mediated paths. This study identifies underlying mechanisms and offers policy implications for strengthening the direct effect of farmland property rights reform and improving factor allocation channels to achieve rural social sustainability outcomes. Full article