Search Results (32,112)

In autonomous driving, detecting small and occluded objects remains a substantial challenge due to the complexity of real-world environments. To address this, we propose RSO-YOLO, an enhanced model based on YOLOv12. First, the bidirectional feature pyramid network (BiFPN) and space-to-depth convolution (SPD-Conv) replace the original neck network. This design efficiently integrates multi-scale features while preserving fine-grained information during downsampling, thereby improving both computational efficiency and detection performance. Additionally, a detection head for the shallower feature layer P2 is incorporated, further boosting the model’s capability to detect small objects. Second, we propose the feature enhancement and compensation module (FECM), which strengthens features in visible regions and compensates for missing semantic information in occluded areas. This module improves detection accuracy and robustness under occlusion. Finally, we propose a lightweight global cross-dimensional coordinate detection head (GCCHead), built upon the global cross-dimensional coordinate module (GCCM). By grouping and synergistically enhancing features, this module addresses the challenge of balancing computational efficiency with detection performance. Experimental results demonstrate that on the SODA10M, BDD100K, and FLIR ADAS datasets, RSO-YOLO achieves mAP@0.5 improvements of 8.0%, 10.7%, and 7.2%, respectively, compared to YOLOv12. Meanwhile, the number of parameters is reduced by 15.4%, and model complexity decreases by 20%. In summary, RSO-YOLO attains higher detection accuracy while reducing parameters and computational complexity, highlighting its strong potential for practical autonomous driving applications. Full article

The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively. Full article

Agricultural systems are complex social–ecological systems shaped by interactions among diverse stakeholders including governments, enterprises, farmers, consumers, and financial institutions. To examine policy-driven sustainability transitions, this study focuses on three principal actors—government regulatory agencies, agricultural enterprises, and farmers—whose strategic interactions critically determine transition outcomes. The aim is to drive agricultural systems toward sustainability in China. This study develops a three-party evolutionary game model involving the government, enterprises, and farmers to explore how policy-driven incentives influence sustainable development practices. The model incorporates both static and dynamic reward–punishment mechanisms, calibrated with empirical data, to examine behavioral dynamics across stakeholders. The results indicate that fluctuations in enterprise and government engagement contribute to instability in agricultural sustainability transitions. While static reward mechanisms mitigate peak fluctuations, they are insufficient to fully stabilize enterprise commitment, with actors oscillating between sustainable and conventional agricultural practices. Linear dynamic reward mechanisms offer partial stabilization but lack the capacity to maintain long-run Nash equilibrium. In contrast, nonlinear dynamic mechanisms effectively align stakeholder incentives, fostering a stable and enduring shift toward sustainable agricultural systems. This study underscores the importance of tailored dynamic strategies to build resilient agricultural systems with integrated sustainability objectives. Full article

Background/Objectives: We wished to determine whether patients of providers subscribing to Health Information Exchange (HIE) services experience a reduction in emergency room visits and inpatient admissions compared to those who do not subscribe and whether the type of HIE service (online access to records via an HIE portal and/or integration with their electronic health record (EHR) and push notifications) a provider uses further impacts utilization. Methods: The research team utilized the Colorado All-Payer Claims Database (CO APCD) from 2017 to 2019, intentionally excluding the impact of COVID-19 on HIE services. A pre–post analysis was conducted 15 months before and after the provider subscribed to HIE solutions to quantify the effects when compared to control clinics. Results: Subscription to HIE solutions was associated with lower inpatient (IP) admissions than those in a control group for all insurance payer types (p = 0.001). The reduction in Medicaid patients was more pronounced when their providers subscribed to either service alone or in combination and was associated with significantly fewer ED and IP admissions than those for control clinics (p = 0.003, p < 0.05 for HIE portal alone and p < 0.0001, p = 0.002 for both HIE portal and integrated EHR solutions). HIEs enable providers to coordinate care and access patients’ previous test results and clinical histories, potentially reducing the need for high-cost emergency department and inpatient services. This, in turn, appears to substantially impact low-resource individuals, who have historically been the highest users of these services. Conclusions: HIE provider subscription is associated with a reduction in patients’ use of inpatient care after enrolling in HIE products across all insurance types. Medicaid patients experienced the most significant decrease in both emergency room (ED) visits and inpatient admissions when compared to control clinics. Full article

From the perspective of “dynamic supply–demand coordination,” this study evaluates the development level of China’s economic “dual circulation” across 30 provinces during 2001–2020. Employing Kernel density estimation, natural breakpoint method, and exploratory spatial–temporal data analysis (ESTDA), we provide a comprehensive examination of the spatiotemporal evolution and developmental dynamics of China’s “dual circulation” economy. Furthermore, a nested matrix linking the quantile response types of driving factors with spatiotemporal transition types is constructed to uncover the mechanisms underlying these transitions, in order to form a unified understanding of the significance of China’s implementation of the economic “dual circulation” strategy against the background of high-quality development and lay a solid theoretical foundation for the empirical measurement of China’s economic “double circulation”. The results reveal the following: (1) Despite the “dual circulation” development level of Chinese provinces steadily improving over time, a marked east-to-west gradient of regional imbalance remains; (2) The spatial correlation of the “dual circulation” development level across provinces is significant, with changing trends influenced by neighboring provinces, showing both “concentration” and “differentiation” characteristics; (3) The spatial agglomeration trend of China’s “dual circulation” economy continues to strengthen, with distinct characteristics of “high rigidity + low mobility.” The low mobility of provinces locked in low-level spatial patterns will become a key limiting factor for their overall transition; (4) The quantile response types of the driving factors for the “dual circulation” development level in each province exhibit nest ability with their spatiotemporal transition types. The driving and constraining patterns of various driving factors coexist and interact. Full article

Rapid industrialization and urbanization have driven China’s economic growth but also worsened air pollution, posing serious challenges to sustainable development and public health. Balancing economic growth and environmental protection has become essential for achieving the UN Sustainable Development Goal of “Climate Action.” The National Ecological Civilization Construction Demonstration Zone policy, a major institutional innovation for green transition, aims to integrate ecological protection, industrial upgrading, and spatial governance to achieve both economic and environmental goals. Using county-level panel data from 2010 to 2022, this study applies a difference-in-differences (DID) approach, treating the phased establishment of NECCDZs as an exogenous policy shock. It further explores the mediating effects of green innovation capability and land use efficiency. The results show that the NECCDZ policy significantly reduces PM2.5 concentrations in pilot regions, and the findings remain robust under multiple tests. Improvements in green innovation and land use efficiency are identified as key transmission mechanisms, while policy effects vary across city hierarchies, industrial base types, and regions. Overall, the NECCDZ policy demonstrates the effectiveness of institutionalized ecological governance and offers policy insights for developing countries seeking coordinated progress in economic growth and environmental sustainability. Full article

The pattern of the front contact metallization critically influences solar cell efficiency. This study introduces a novel explicit geometry optimization approach for designing the front contact metallization patterns. In the proposed approach, the front contact patterns are represented by wide Bezier curves with variable widths, where each curve’s geometry is defined by both control points and control circles. The control point coordinates and the control circle radii are taken as design variables. To ensure physical feasibility during the design process, one of the end control points of each curve is fixed at the current extraction point. Unlike geometry optimization techniques employing fixed-width Bezier curves, our approach provides enhanced design flexibility through continuous width modulation along the front contact paths. Simulation experimental validation across the simple solar cell geometries demonstrates the proposed method’s superior performance relative to both the solid isotropic material with penalization (SIMP) approach and geometry optimization method using a fixed-width Bezier. Furthermore, the optimized front contact metallization structures outperform the conventional H-pattern designs. Specifically, for a solar cell with a size of 3.5 cm, compared to a solar cell with conventional H-pattern front contact electrodes, the conversion efficiency, open-circuit voltage, short-circuit current, and fill factor of the solar cell with curve-shaped front contact metallization are relatively increased by 0.415%, 0.0011 V, and 5.091 A·m⁻², and 0.904%, respectively, while the material coverage ratio is reduced by 1.974%. The methodology’s versatility is further evidenced by its successful adaptation to free-form solar cell configurations. Full article

In the present study, we aimed to derive analytical solutions of the homotopy analysis method (HAM) for the time-fractional Navier–Stokes equations in cylindrical coordinates in the form of a rapidly convergent series. In this work, we explore the time-fractional Navier–Stokes equations by replacing the standard time derivative with the Katugampola fractional derivative, expressed in the Caputo form. The homotopy analysis method is then employed to obtain an analytical solution for this time-fractional problem. The convergence of the proposed method to the solution is demonstrated. To validate the method’s accuracy and effectiveness, two examples of time-fractional Navier–Stokes equations modeling fluid flow in a pipe are presented. A comparison with existing results from previous studies is also provided. This method can be used as an alternative to obtain analytic and approximate solutions of different types of fractional differential equations applied in engineering mathematics. Full article

This paper proposes an adaptive control framework for constrained nonlinear systems subject to sensor and actuator deception attacks, in the presence of actuator saturation. To enforce state constraints under sensor attacks, a modified coordinate transformation is integrated with a barrier Lyapunov function (BLF) design, ensuring controller feasibility and constraint satisfaction even when state measurements are compromised. Moreover, the compounded effects of actuator saturation and actuator-side attacks are explicitly analyzed within a unified BLF framework, and an adaptive decoupling-compensation strategy is introduced to mitigate their influence. Simulation results demonstrate the effectiveness and robustness of the proposed approach. Full article

This study examines how supply chain structure diversification drives high-quality enterprise development in the digital economy. Using panel data from Chinese listed non-financial firms (2009–2023), we find that diversification of both suppliers and customers significantly improves firms’ total factor productivity (TFP), and the results remain robust after controlling for endogeneity. Mechanism analyses show that diversification enhances innovation capability, sustainability performance, and risk resilience, while digital supply chains strengthen these effects by improving information flow and coordination. Heterogeneity tests reveal that the impact is greater for firms with higher operational efficiency, cultural synergy, and information transparency. Overall, the findings highlight that diversified and digitally integrated supply chains are essential for innovation-driven, resilient, and sustainable enterprise growth. Full article

Digital technology adoption can be beneficial for sustainable development of firms. This study seeks to illuminate how it works, based on the contingent affordance actualization theory that emphasizes both action potential and achievement context of technology. Specifically, it considers supply chain innovation as the underexplored mechanism and environmental munificence as the context factor. With the matched multisource data of 157 human resources service firms in China, the empirical findings show that supply chain innovation mediates the relationship between digital technology adoption and sustainable performance. Additionally, environmental munificence, the extent of the resources available in an environment, weakens this indirect relationship. By demonstrating these relations, this study provides firms with insights that allow them to utilize both the functional and coordinated action potentials of digital technology to conduct supply chain innovation, which in turn enhances sustainable performance. During this process, firms are further advised to be watchful to cope with organizational inertia when environmental munificence is high. Full article

Background: Midpalatal suture expansion is effective in both growing and adult patients, and Miniscrew-Assisted Rapid Palatal Expansion (MARPE) provides greater skeletal effects and fewer dentoalveolar side effects than traditional expanders. However, asymmetric expansion remains a challenge, often influenced by pre-existing craniofacial asymmetries, appliance design, and suture morphology. In this case report, we describe asymmetric expansion with 3D-guided piezocorticotomy-assisted MARPE and its management with directly printed aligners (DPAs). Methods: A patient with facial asymmetry, a narrow maxillary arch, and multiple dentoalveolar deformities underwent pre-treatment evaluation, including root inclination analysis and CBCT imaging. A MARPE appliance with 3D-guided piezocorticotomy assistance was applied; post-expansion orthodontic treatment was digitally planned and performed with directly printed aligners. Results: During MARPE activation, asymmetric midpalatal suture disarticulation was observed, with greater displacement on the left side due to jackscrew orientation and root proximity. Post-expansion orthodontic correction with DPAs allowed precise root positioning, spatial redistribution, and improved occlusal symmetry. Over 20 months, significant improvements were achieved in midline orientation, axial root inclination, and transverse arch coordination. Conclusions: The reported case underscores the importance of pre-treatment evaluation for asymmetries and careful appliance design in MARPE protocols; in addition, it demonstrates that directly printed aligners, supported by digital planning, can provide accurate and efficient dentoalveolar correction following asymmetric expansion. Full article

The central nervous system is highly vulnerable to oxygen deprivation during the neonatal period, leading to long-term neurological damage. Growth hormone (GH) has shown neuroprotective and neuroregenerative effects in response to hypoxic injury. This study investigated GH effects on cell survival, inflammatory, and glial activation markers in the developing cerebellum, as well as its impact on motor coordination and anxiety-like behaviors in adulthood following neonatal hypoxia. Global hypoxia was induced in postnatal day 2 Wistar rats (8% O₂, 2 h), followed by subcutaneous GH treatment (0.1 mg/kg/d) for five days. Neonatal hypoxia triggered a sustained inflammatory response in the developing cerebellum, with increased expression of TLR-4, IL-1β, TNF-α, IL-6, COX-2, iNOS, and pNF-κB, persistent gliosis, myelin disruption, and Purkinje cell loss, leading to impaired adult behavior. GH exhibited a biphasic effect—initially proinflammatory, then anti-inflammatory—ultimately downregulating proinflammatory markers and activating prosurvival pathways (pStat5, pErk1/2, pAkt, Bcl-2, TNF-R2, IGF-1). GH also reduced microglial (Iba-1) and astrocytic (GFAP) hypertrophy, restored MBP and β-III tubulin levels, enhanced Purkinje cell survival, and improved motor coordination and anxiety-like behavior in adulthood. These findings demonstrate that GH modulates the cerebellar inflammatory response and supports its therapeutic potential to counteract neuroinflammation and dysfunction following neonatal hypoxic injury. Full article

Extracted peatlands experience strong hydrological fluctuations due to drainage, vegetation succession, and climatic variability, yet long-term, high-frequency groundwater data remain scarce in Northern Europe. Our dataset presents two years (June 2023–May 2025) of 30-min groundwater table depth (WTD) measurements from six wells installed across contrasting Greenhouse Gass Emission Site Types (GEST 5, 6, 15, 20) in the Kaigu peatlands, central Latvia. Each well was equipped with an automatic pressure transducer (TD-Diver, van Essen Instruments) recording absolute pressure (m H₂O). The dataset also includes metadata on coordinates, installation elevation, well construction, and manual control measurements. All values are unprocessed, i.e., they represent original logger outputs without atmospheric or elevation correction, enabling users to apply their own calibration or referencing methods. This is the first openly available high-frequency extracted peatland groundwater pressure dataset from the Baltic region and provides a foundation for hydrological modelling and rewetting designs. Full article

Oral mucosal wound healing is a rapid, precisely regulated process distinct from cutaneous repair due to the specialized anatomical, microbial, and physiological features of the oral cavity. This review outlines the sequential healing phases—hemostasis, inflammation, proliferation, and remodeling—and examines the coordinated roles of keratinocytes, fibroblasts, endothelial cells, and immune cell subsets in tissue restoration. Central molecular pathways, including PI3K/Akt, JAK/STAT, Ras/MAPK, TGF-β/SMAD, and Wnt/β-catenin, along with growth factors such as TGF-β, FGF, EGF, and VEGF, are discussed in relation to their regulatory influence on cell behavior and extracellular matrix dynamics. Unique intraoral factors—namely saliva-derived histatins and a distinct resident microbiota—promote accelerated re-epithelialization and attenuated fibrosis. Systemic conditions such as diabetes, aging, and tobacco exposure are identified as key modulators that compromise repair efficiency. Emerging therapeutic strategies, including stem-cell-based interventions, microbiota modulation, bioengineered scaffolds, and photobiomodulation, offer translational potential to enhance clinical outcomes in oral tissue regeneration. Full article