Search Results (594,100)

Early-stage post-conflict regions must rebuild essential infrastructure while avoiding development pathways that create long-term environmental and institutional costs. This study examines how green reconstruction, policy sequencing, and sustainable regional competitiveness may co-evolve in Karabakh and Eastern Zangezur during 2025–2040. A scenario-based System Dynamics (SD) model developed in Vensim compares Baseline Reconstruction, Eco-Tourism Promotion, Green Investment, an Integrated Policy Mix, and Accelerated Reconstruction. The normalized simulations indicate that stand-alone interventions generate partial gains and trade-offs, whereas the full Integrated Policy Mix produces the most balanced outcome under its stated, more favourable policy settings. Eco-tourism promotion increases attractiveness and demand but may intensify carrying-capacity stress when services and environmental management lag; accelerated reconstruction can create institutional overload when implementation demands exceed absorptive capacity. A matched policy-intensity comparison does not show uniform mixed-package dominance over both stand-alone alternatives, so the full Integrated advantage cannot be interpreted as a pure coordination effect. An exploratory global uncertainty analysis preserves the original ranking under the specified perturbation design. The results are assumption-dependent policy experiments rather than empirically estimated forecasts and support conditional, phased, and capacity-sensitive reconstruction strategies. Full article

Secure programming and secure design are often managed as separate activities, leaving a gap between architectural intent and the implementation that reaches production. This paper presents a structured conceptual synthesis of twenty core publications, supplemented by standards and recent work on AI-assisted development. It makes three contributions. First, it frames secure programming as the implementation-facing realization of secure design within a closed feedback loop. Second, it uses an explicit 0–5 rubric to produce an illustrative comparison of Waterfall, Iterative, Spiral, and Agile with DevSecOps across six security-integration dimensions. These profiles are structured author judgments, not empirical measurements of security effectiveness. Third, it proposes an operational Secure SDLC–DevSecOps framework that links six stages through named artifacts, accountable roles, traceability rules, release gates, exceptions, and outcome measures. A comparison with NIST SSDF, OWASP SAMM, Microsoft SDL, ISO/IEC 27034, and OWASP implementation guidance shows that the individual practices are established; the framework’s intended contribution is the project-level control loop that connects design decisions to pipeline evidence and production feedback. The illustrative profiles place Spiral and Agile with DevSecOps close together under the baseline weights, while sensitivity scenarios show that their ordering depends on whether design-time risk analysis or delivery-time verification is emphasized. The synthesis therefore supports contextual tailoring rather than a universal ranking. Full article

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $150,000$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article

This study aims to verify and improve the Electrical Equivalent Circuit Model (EECM) for a regenerated VW e-Golf cell and to develop a verification and optimization framework that enhances simulation accuracy. The model is based on an identified set of EESB elements derived from enhanced measurements of the regenerated cell and is compared with the original cell. A global EESB model is implemented in the PLECS environment, comprising a charge/discharge block and a Voc versus SOC evaluation. Parameters are obtained from measurements of the regenerated VW e-Golf cell and augmented with SOC-dependent polynomial relationships for individual model components. The methodology was applied to identify EESB elements for the regenerated VW e-Golf cell and to produce an EESB model aligned with the identification results. Verification compares simulated and experimental curves in critical SOC regions (0–10%, around 30%, and during relaxation) and cross-validates regenerated versus original cells. Results show that SOC-based polynomial estimates extend the valid range of EESB elements to 0–10% SOC and improve agreement with measured trajectories. Optimization reduces the computational load and improves accuracy, particularly in critical SOC regions, supporting a robust verification framework for regenerated battery cells and guiding further research and implementation in BMS and simulation environments. Full article

Background: Diabetic retinopathy (DR) is a major cause of visual impairment. Its clinical determinants are well-established, whereas socioeconomic associations vary across settings. Objective: To estimate the observed proportion of DR and examine associations between clinical, demographic, and socioeconomic factors and DR among adults attending a hospital-based screening program in Oslo, Norway. Methods: This cross-sectional study analyzed 118 adults with diabetes. DR was graded from wide-field retinal images. A complete-case multivariable logistic regression model included age, diabetes duration, HbA1c, BMI, diabetes type, education level, and economic activity. Results: DR was present in 67 participants (56.8%). The adjusted model included 93 participants (53 with DR and 40 without DR). Longer diabetes duration was associated with higher odds of DR (adjusted odds ratio [aOR] 1.27 per year, 95% CI 1.14–1.41; p < 0.001), whereas age showed an inverse association (aOR 0.89 per year, 95% CI 0.82–0.97; p = 0.006). HbA1c, BMI, diabetes type, education, and economic activity were not statistically significant after adjustment. Conclusions: No statistically significant associations between the measured socioeconomic factors and DR were identified in this study sample. Clinically relevant associations and causal inference cannot be assumed because of possible selection bias and cross-sectional study design. Full article

Generative artificial intelligence (GenAI) can support engineering problem solving, but whether AI-assisted practice transfers to independent performance after the tool is removed remains unclear. This multicentre controlled trial evaluated a traceable five-stage GenAI-assisted learning configuration in agricultural engineering education. Twenty-eight second- and third-year classes from six universities in Uzbekistan were assigned within nine teacher blocks by deterministic constrained minimization to GenAI (14 classes) or structured active-control (14 classes) groups. Both groups completed the same 16-week module, tasks, software, contact time, feedback, and verification requirements. They differed in the source and adaptivity of a provisional alternative used after an independent attempt: bounded adaptive GenAI dialogue versus a version-controlled curated alternative. The full assigned cohort included 656 students; likelihood-based available-outcome analyses included 641 immediate and 589 delayed outcomes. Kenward–Roger analyses estimated an adjusted immediate difference of 2.78 points (95% confidence interval (CI) [2.02, 3.55]; p < 0.001; model-based d = 0.72) and a delayed difference of 2.34 points (95% CI [1.54, 3.14]; p < 0.001; d = 0.51). The results show a positive adjusted association for the evaluated traceable instructional configuration, but deterministic post-baseline allocation limits causal interpretation. Full article

Peak summer surface ozone (O₃) threatens human health, crop productivity, and ecosystem stability in eastern China, but similar O₃ trends may reflect contrasting anthropogenic drivers. Using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Multi-Resolution Emission Inventory for China (MEIC), we examined July maximum daily 8 h average (MDA8) O₃ over the Bohai Plain and the Yangtze River Delta (YRD) from 2014 to 2020. Emission substitutions under fixed 2014 meteorology isolated the O₃ responses to changes in total emissions and individual pollutants. Although O₃ levels were high in both regions by 2019 and declined sharply in 2020, the two regions showed opposite responses to emission changes. Over the Bohai Plain, substituted inventories reduced MDA8 O₃ in all later scenarios, with a 4.57 ppb decrease for the 2020 inventory. Over the YRD, emission changes increased O₃ by up to 4.70 ppb. VOC substitution produced the largest decrease over the Bohai Plain at 5.46 ppb, whereas substituting 2020 NO_x emissions increased YRD O₃ by 4.22 ppb. The joint NO_x and VOC substitution increased YRD O₃ by 4.97 ppb, revealing a nonadditive response. Formation sensitivity also diverged, with the western Bohai Plain shifting toward VOC-sensitive conditions and NO_x sensitivity strengthening in the northern YRD. These results show that similar peak summer O₃ evolution can conceal fundamentally different emission responses. These findings support region-specific multipollutant control strategies for the two regions. Full article

Efficient scheduling of special materials is essential for improving the operational efficiency of material transportation systems. However, multiple processing stages, heterogeneous transportation resources, and complex transfer paths pose significant challenges to efficient scheduling. To address these challenges, this paper proposes an improved reinforcement learning (RL)-based scheduling algorithm. First, a scheduling optimization model is established with the objectives of minimizing makespan and balancing resource utilization. Second, an improved action-value update strategy is developed to reduce Q-value estimation bias, thereby improving policy convergence and scheduling performance. Experimental results show that the proposed Improved DQN outperforms greedy, genetic, and standard DQN algorithms across different task and resource scales. In particular, it achieves an average relative error rate of 27.48% with 500 tasks and a maximum error rate of only 6.51% under different resource configurations, demonstrating its effectiveness and scalability for complex special material scheduling. Full article

Against the backdrop of global forest degradation and timber supply constraints, improving green production efficiency in forestry while securing timber supply is an important challenge for sustainable forest management. Treating China’s National Reserve Forest Policy (NRFP) as a quasi-natural experiment, this study uses panel data for 114 prefecture-level cities from 2011 to 2022, including 67 treated and 47 control cities. We employ a staggered difference-in-differences estimator based on group-time average treatment effects, together with transmission-channel analysis, difference-in-difference-in-differences (DDD), and a spatial difference-in-differences–spatial Durbin model (Spatial DID-SDM). The baseline estimates yield an overall ATT of 0.0149, indicating an average increase of 0.0149 index points in forestry GTFP for treated cities relative to the estimated counterfactual without the NRFP. This effect is approximately 1.49% of the sample mean of forestry GTFP (0.998) and remains stable across multiple robustness and sensitivity checks. NRFP implementation is separately associated with increases in capital deepening (CD) and physical labor productivity (PLP), while the lagged values of CD and PLP are each positively associated with forestry GTFP; these results are consistent with CD and PLP as potential transmission channels. The estimated NRFP effect is significantly weaker in resource-based cities and significantly stronger in economy-efficiency-oriented regions. Spatial effect decomposition shows that both the direct and indirect effects of the NRFP are positive and statistically significant. Under the alternative spatial weight matrix, both effects retain their direction and statistical significance, while the magnitude of the indirect effect remains broadly stable, suggesting that the positive spatial spillover effect of the NRFP on forestry GTFP is reasonably robust to the spatial weight matrix specification. These findings provide city-level empirical evidence for evaluating NRFP performance and informing differentiated policy design. Full article

Community Mobile Health Vehicles (CMHVs) represent an innovative healthcare delivery model that integrates medical services into residents’ daily community life. By extending healthcare coverage to underserved populations, CMHVs can improve access to health management services while enhancing the efficiency of healthcare resource utilization. However, existing studies have primarily focused on health outcomes or the adoption of digital health technologies, with limited attention paid to users’ willingness to utilize CMHVs and their relationship with sustainability dimensions, including social equity, economic efficiency, and environmental responsibility. Drawing on survey data collected from community residents in China, this study employs Exploratory Factor Analysis (EFA) to identify the key determinants influencing users’ willingness to use CMHVs. Furthermore, the Entropy Weight Method (EWM) is applied to prioritize both the identified factors and their corresponding design strategies according to their relative importance. The results reveal five principal determinants: Cognitive Ease, System Adaptability, Institutional Trustworthiness, Technical Reliability, and Environmental Compliance. These factors and their associated design strategies exhibit varying levels of importance in promoting user engagement, optimizing resource allocation, and facilitating community integration. Based on the findings, this study proposes a set of sustainability-oriented design prioritization strategies for CMHV service systems. The proposed framework provides both theoretical and empirical insights for urban healthcare service planning and supports the coordinated achievement of economic, social, and environmental sustainability goals. The study further offers evidence-based guidance for the implementation, continuous improvement, and long-term sustainability of CMHVs within urban healthcare and mobile health service systems. Full article

Driving style, reflected in recurrent patterns of acceleration, braking, speed selection, following distance, gear use, and lane-changing behavior, plays an important role in road safety and is also associated with fuel consumption, emissions, passenger comfort, and vehicle wear. This paper presents a structured review of recent research on driving style analysis, with particular emphasis on its relationship with road-safety outcomes and risk indicators. Following a PRISMA-oriented methodology, studies published between 2015 and 2025 were identified, screened, and synthesized to examine how driving styles are defined, detected, classified, and evaluated. The review shows a clear shift toward data-driven approaches, including feature-based machine learning and representation-learning methods using support vector machines, ensemble models, convolutional neural networks, recurrent neural networks, and hybrid deep learning architectures. Common data sources include smartphone inertial and GNSS signals, CAN/OBD vehicle data, telematics platforms, naturalistic driving datasets, and camera-based perception systems. Safety impact is most often assessed through crashes, near-miss events, traffic conflicts, time-to-collision measures, harsh maneuvers, and composite risk scores. Across the reviewed literature, aggressive and unstable driving patterns are generally associated with reduced safety margins and increased risk, although comparability remains limited by inconsistent label definitions, heterogeneous datasets, indirect safety proxies, and varied validation protocols. The paper also proposes a reproducible database architecture linking drivers, trips, driving events, and safety events to support transparent analysis, benchmark development, and future implementation in fleet monitoring, driver feedback, and connected vehicle applications. Full article

Background: Psychological flourishing is a key indicator of optimal mental health and professional well-being, particularly among psychologists who are routinely exposed to emotionally demanding clinical environments. Although self-compassion has consistently been associated with positive psychological outcomes, few studies have integrated traditional statistical methods with machine learning approaches to predict psychological flourishing among psychologists. Objective: This study aimed to examine the relationship between self-compassion and psychological flourishing among psychologists in Saudi Arabia, identify the unique contribution of self-compassion dimensions, evaluate the predictive performance of supervised machine learning models, and compare their performance with traditional multiple linear regression. Methods: A cross-sectional correlational design was employed, involving 224 psychologists practicing in Saudi Arabia. Participants completed the Self-Compassion Scale and the Flourishing Scale. Descriptive statistics, Pearson’s correlation, and multiple linear regression analyses were performed using IBM SPSS Statistics version 29.0. In addition, Random Forest Regression and Support Vector Regression (SVR) models were implemented in Python using scikit-learn version 1.8.0 to predict psychological flourishing based on self-compassion dimensions together with demographic and professional characteristics. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Results: Overall, self-compassion was positively associated with psychological flourishing (r = 0.627, p < 0.001). Multiple linear regression showed that self-kindness had a significant positive independent association with psychological flourishing (β = 0.292, p = 0.001), whereas over-identification had a significant negative independent association (β = −0.206, p = 0.009). The regression model explained 40.5% of the variance in psychological flourishing (R² = 0.405, p < 0.001). In the held-out test-set comparison using the same predictor set, predictive performance was similar across multiple linear regression (R² = 0.273; RMSE = 3.583; MAE = 2.849), Random Forest Regression (R² = 0.282; RMSE = 3.562; MAE = 2.771), and Support Vector Regression (R² = 0.272; RMSE = 3.587; MAE = 2.768), with no substantial predictive advantage of the machine-learning models over the linear benchmark. Conclusions: Self-compassion, particularly self-kindness and over-identification, was significantly correlated with psychological flourishing among psychologists. Machine-learning models demonstrated predictive performance comparable to traditional regression, with no substantial predictive advantage over the linear benchmark, indicating that increased model complexity did not improve out-of-sample prediction in the present sample. Full article