Search Results (1,271)

The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm named YOLOv11-TrunkLight. The core innovations of the algorithm include (1) a novel StarNet_Trunk backbone network, which replaces traditional residual connections with element-wise multiplication and incorporates depthwise separable convolutions, significantly reducing computational complexity while maintaining a large receptive field; (2) the C2DA deformable attention module, which effectively handles the geometric deformation of tree trunks through dynamic relative position bias encoding; and (3) the EffiDet detection head, which improves detection speed and reduces the number of parameters through dual-path feature decoupling and a dynamic anchor mechanism. Experimental results demonstrate that compared to the baseline YOLOv11 model, our method improves detection speed by 13.5%, reduces the number of parameters by 34.6%, and decreases computational load (FLOPs) by 39.7%, while the average precision (mAP) is only marginally reduced by 0.1%. These advancements make the algorithm particularly suitable for deployment on resource-constrained edge devices of inspection robots, providing reliable technical support for intelligent forestry management. Full article

This paper provides a theoretical investigation of the OR-product (∨-product) in soft set theory, an operation of central importance for handling uncertainty in decision-making. A comprehensive algebraic analysis is carried out with respect to various types of subsets and equalities, with particular emphasis on M-subset and M-equality, which represent the strictest forms of subsethood and equality. This framework reveals intrinsic algebraic symmetries, particularly in commutativity, associativity, and idempotency, which enrich the structural understanding of soft set theory. In addition, certain missing results on OR-products in the literature are completed, and our findings are systematically compared with existing ones, ensuring a more rigorous theoretical framework. A central contribution of this study is the demonstration that the collection of all soft sets over a universe, equipped with a restricted/extended intersection and the OR-product, forms a commutative hemiring with identity under soft L-equality. This structural result situates the OR-product within one of the most fundamental algebraic frameworks, connecting soft set theory with broader areas of algebra. To illustrate its practical relevance, the int-uni decision-making method on the OR-product is applied to a pilot recruitment case, showing how theoretical insights can support fair and transparent multi-criteria decision-making under uncertainty. From an applied perspective, these findings embody a form of symmetry in decision-making, ensuring fairness and balanced evaluation among multiple decision-makers. By bridging abstract algebraic development with concrete decision-making applications, the results affirm the dual significance of the OR-product—strengthening the theoretical framework of soft set theory while also providing a viable methodology for applied decision-making contexts. Full article

Developing sustainable and high-performance sorbents for efficient CO₂ capture is essential for mitigating climate change and reducing industrial emissions. In this study, phosphorus-doped porous carbons (LPSP-T) were synthesized via a one-step activation–doping strategy using lotus petiole biomass as a precursor and sodium phytate as a dual-function activating and phosphorus-doping agent. The simultaneous activation and phosphorus incorporation at various temperatures (650–850 °C) under a nitrogen atmosphere produced carbons with tailored textural properties and surface functionalities. Among them, LPSP-700 exhibited the highest specific surface area (525 m²/g) and a hierarchical porous structure, with abundant narrow micropores (<1 nm) and phosphorus-containing surface groups that synergistically enhanced CO₂ capture performance. The introduction of P functionalities not only improved the surface polarity and binding affinity toward CO₂ but also promoted the formation of a well-connected pore network. As a result, LPSP-700 delivered a CO₂ uptake of 2.51 mmol/g at 25 °C and 1 bar (3.34 mmol/g at 0 °C), along with a high CO₂/N₂ selectivity, fast CO₂ adsorption kinetics and moderate isosteric heat of adsorption (Q_st). Furthermore, the dynamic CO₂ adsorption capacity (0.81 mmol/g) was validated by breakthrough experiments, and cyclic adsorption–desorption tests revealed excellent stability with negligible loss in performance over five cycles. Correlation analysis revealed pores < 2.02 nm as the dominant contributors to CO₂ uptake. Overall, this work highlights sodium phytate as an effective dual-role agent for simultaneous activation and phosphorus doping and validates LPSP-700 as a sustainable and high-performance sorbent for CO₂ capture under post-combustion conditions. Full article

Considering the world’s growing interest in health-promoting phytochemicals, the current research investigated the development of a dual-captured Ginkgo Biloba and Green Tea Extract into Collagen-Nanostructured Lipid Nanocarriers (Col-NLC-GBil-GTE) for an enhanced therapeutic efficacy against hepatic, colon or breast cancer. NLC considerably reduced cell viability; the most advanced cytotoxicity profile was determined on human colon adenocarcinoma cells (LoVo) and liver cancer cells (HepG2), e.g., tumor cell viability was 21.81% in the presence of Col-NLC-GBil-GTE, similar to that determined for Cisplatin. Col-NLC exhibited apoptosis in HepG2 and LoVo cells and no significant apoptosis induction in normal HUVECs. A 20% increase in apoptosis for HepG2 cells was registered for 100 μg/mL NLC-GBil-GTE compared to Cisplatin (Cis-Pt), e.g., a 63.4% total apoptosis for NLC-GBil-GTE versus a 52.6 apoptosis induced by 100 μg/mL of a chemotherapeutic drug. According to the cell cycle outcomes, an accumulation of hepatocyte HepG2 tumor cells in the G0/G1 phase was detected upon treatment with 100 mg/mL of NLC- and Col-NLC-GBil-GTE, simultaneously with a drastic decrease in the S phase, which may indicate a cell number reduction that enters in the division cycle. The simultaneous delivery of GBil and GTE by synchronizing their bioactivities offers several advantages; Col-NLC-GBil-GTE can be viewed as a noteworthy strategy for consideration in connection with antitumor therapeutic protocols. Full article

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

The rapid rise of e-commerce is intensifying pressure on last-mile delivery networks, making the strategic placement of parcel lockers an urgent urban challenge. In this work, we adapt multilayer two-mode Social Network Analysis to the parcel-locker siting problem, modeling city-scale systems as bipartite networks linking spatially resolved demand zones to locker locations using only open-source demographic and geographic data. We introduce two new Social Network Analysis metrics, Dual centrality and Coverage centrality, designed to identify both structurally critical and highly accessible lockers within the network. Applying our framework to Milan, Rome, and Naples, we find that conventional coverage-based strategies successfully maximize immediate service reach, but tend to prioritize redundant hubs. In contrast, Dual centrality reveals a distinct set of lockers whose presence is essential for maintaining overall connectivity and resilience, often acting as hidden bridges between user communities. Comparative analysis with state-of-the-art multi-criteria optimization baselines confirms that our network-centric metrics deliver complementary, and in some cases better, guidance for robust locker placement. Our results show that a network-analytic lens yields actionable guidance for resilient last-mile locker siting. The method is reproducible from open data (potential-access weights) and plug-in compatible with observed assignments. Importantly, the path-based results (Coverage centrality) are adjacency-driven and thus largely insensitive to volumetric weights. Full article

Chimeric compounds represent a promising strategy in cancer therapy by simultaneously targeting multiple pathways responsible for tumour growth and survival. Their structure comprises two or more pharmacophores connected through suitable chemical linker. These dual or multi-functional drugs can interact with several biological targets for a more pronounced pharmacological effect. In order to identify new multi-targeting agents with anticancer efficacy, we designed and synthesised a series of novel multi-functional molecules by covalently linking antitumor compounds dichloroacetate (DCA) and Nutlin-3a. The design was aimed at addressing two critical events in cancer: (1) the Warburg effect and (2) the dysregulations of protein p53 pathway, both of which are directly linked to the predominant survival and aggressive proliferation of malignant cells. DCA reactivate oxidative phosphorylation by inhibiting mitochondria pyruvate dehydrogenase kinase (PDK), thereby unlocking the Warburg metabolism of cancer cells and its antiapoptosis state. Concurrently, Nutlin-3a restores the protective function of the “genome guardian” p53 protein, by blocking its antagonist oncoprotein E3 ligase MDM2. Chimeric compounds were obtained using a chemoenzymatic multi-step procedure that included a key lipase-catalysed asymmetric reaction. Biological evaluation of the synthesised Nutlin-DCA chimeras in a panel of three cancer cell lines demonstrated promising results in vitro. Specifically, compounds rac-19a, rac-19b, rac-20a, rac-20b and enantioenriched 20a caused a statistically significant reduction in cell viability at micromolar concentrations. These findings suggest that targeting both the Warburg effect and the p53 pathway with a single molecule is a viable approach for future cancer therapeutic development. Full article

Keratoconus (KC) is a progressive, multifactorial corneal ectatic disorder characterized by localized stromal thinning and irregular astigmatism, with incidence and prevalence varying markedly among populations. These differences are influenced by environmental exposures, behavioral factors, and genetic predisposition. A positive family history is a well-established high-risk factor, and KC has also been documented in association with syndromic disorders such as Down syndrome, connective tissue disorders, and certain metabolic diseases. Over the past decades, numerous candidate genes have been investigated, encompassing those involved in extracellular matrix (ECM) assembly, collagen synthesis and cross-linking, oxidative stress defense, wound healing, and transcriptional regulation. Modern genomic approaches, including genome-wide association studies (GWAS), linkage analyses, and next-generation sequencing, have identified multiple loci and variants with potential pathogenic roles. Nonetheless, several genes have also been systematically tested and found to show no association in specific populations, highlighting the genetic variability of KC and the potential influence of population-specific factors. This dual landscape of positive and negative genetic findings underscores the complexity of KC pathogenesis and the necessity for ethnically diverse cohorts. In this review, we synthesize current evidence on genes implicated in KC, integrating confirmed pathogenic variants, associations, and negative findings across diverse populations, to provide a comprehensive overview of the genetic architecture of KC and to outline priorities for future research aimed at improving diagnosis, risk stratification, and therapeutic development. Full article

With the rapid development of intelligent connected vehicle technology, false data injection (FDI) attacks have become a major challenge in the Internet of Vehicles (IoV). While deep learning methods can effectively identify such attacks, the dynamic, distributed architecture of the IoV and limited computing resources hinder both privacy protection and lightweight computation. To address this, we propose FedIFD, a federated learning (FL)-based detection method for false data injection attacks. The lightweight threat detection model utilizes basic safety messages (BSM) for local incremental training, and the Q-FedCG algorithm compresses gradients for global aggregation. Original features are reshaped using a time window. To ensure temporal and spatial consistency, a sliding average strategy aligns samples before spatial feature extraction. A dual-branch architecture enables parallel extraction of spatiotemporal features: a three-layer stacked Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies, and a lightweight Transformer models spatial relationships. A dynamic feature fusion weight matrix calculates attention scores for adaptive feature weighting. Finally, a differentiated pooling strategy is applied to emphasize critical features. Experiments on the VeReMi dataset show that the accuracy reaches 97.8%. Full article

Distributed photovoltaic grid-connected converters adopting droop control can provide dual support for voltage and frequency in the distribution system. However, under fault conditions, droop-controlled inverters will face the problem of transient synchronization instability, and their transient characteristics are significantly affected by fault conditions, control parameter configurations, and other factors. Nevertheless, at present, the transient operation boundaries of droop inverters, considering key sensitive parameters, are unclear, and the transient stability control mechanism is lacking, which poses a threat to the safe and stable operation of distributed photovoltaic systems. To this end, this paper fully considers the influences of control parameters and fault severity and conducts a multidimensional quantitative characterization of the transient stability boundaries of droop-controlled inverters. Furthermore, a stability enhancement control structure for droop-controlled inverters, based on power angle deviation feedforward, is proposed, and an adaptive configuration method for feedforward coefficients is put forward to ensure the safe and stable operation of droop inverters at different fault sag depths. Finally, the accuracy of the theoretical analysis and the proposed control structure is verified through simulations and experiments. Full article

Urban heat islands are intensifying under the dual pressures of global climate change and rapid urbanization, posing serious challenges to ecological sustainability and human well-being. Among the factors influencing urban thermal environments, vegetation and green spaces play a critical role in mitigating heat accumulation through canopy cover, evapotranspiration, and ecological connectivity. In this study, a comparative analysis of Seoul and Busan—two representative metropolitan areas in South Korea—was conducted using land surface temperature (LST) data derived from Landsat 8 and a set of multi-source spatial indicators. The nonlinear effects and interactions among built environment, socio-economic, and ecological variables were quantified using the Extreme Gradient Boosting (XGBoost) model in conjunction with Shapley Additive Explanations (SHAP). Results demonstrate that vegetation, as indicated by the Normalized Difference Vegetation Index (NDVI), consistently exerts significant cooling effects, with a pronounced threshold effect observed when NDVI values exceed 0.6. Furthermore, synergistic interactions between NDVI and surface water availability, measured by the Normalized Difference Water Index (NDWI), substantially enhance ecological cooling capacity. In contrast, areas with high building and population densities, particularly those at lower elevations, are associated with increased LST. These findings underscore the essential role of green infrastructure in regulating urban thermal environments and provide empirical support for ecological conservation, urban greening strategies, and climate-resilient urban planning. Strengthening vegetation cover, enhancing ecological corridors, and integrating greening policies across spatial scales are vital for mitigating urban heat and improving climate resilience in rapidly urbanizing regions. Full article

The swift advancement of the digital economy presents new pathways toward achieving common prosperity in China. Based on microdata derived from the China Family Panel Studies (2010–2022), this study employs the “Broadband China” pilot policy as a quasi-natural experiment to explore how digital economy development influences multidimensional relative poverty. We develop a multidimensional relative poverty index encompassing economic, health, education, and living condition aspects utilizing the Alkire–Foster dual cutoff method and employ a staggered Difference-in-Differences design for empirical analysis. Results show that the policy leads to an average decrease of 1.8 percentage points in the probability of multidimensional relative poverty across households. The effect is more pronounced in central and western regions, rural households, and those with a high proportion of non-labor force, particularly in the dimensions of economic, health, and living conditions dimensions. Mechanism analysis via interaction term regression indicates that increased population mobility and improved informal employment are key channels. These findings suggest that enhancing digital infrastructure and tailoring mobility and employment policies to fit regional and urban–rural contexts can effectively alleviate multidimensional relative poverty. This study contributes empirical evidence connecting the advancement of the digital economy to poverty alleviation and aligns with the United Nations Sustainable Development Goal 1 (No Poverty). Full article

Against the global biodiversity crisis, arid and semi-arid regions are sensitive indicators of terrestrial ecosystems. However, research on their habitat quality (HQ) evolution mechanism faces dual challenges: insufficient multi-scale dynamic simulation and fragmented driving mechanism analysis. To address these gaps, this study takes northern China’s arid and semi-arid regions as the object, innovatively constructing a “pat-tern-process-mechanism” multi-dimensional integration framework. Breaking through single-model/discrete-method limitations in existing studies, it realizes full-process integrated research on regional HQ spatiotemporal dynamics. Based on 1990–2020 Land Use and Land Cover Change (LUCC) data, the framework integrates the InVEST and PLUS models, solving poor continuity between historical assessment and future projection in traditional research. It also pioneers combining the XGBoost-SHAP model and Geographically and Temporally Weighted Regression (GTWR): XGBoost-SHAP quantifies nonlinear interactive effects of natural, socioeconomic, and landscape drivers, while GTWR explores spatiotemporal heterogeneous mechanisms of landscape pattern evolution on HQ, effectively addressing the dual challenges. Results show the following: (1) In 1990–2020, cultivated and construction land expanded, with grassland declining most notably; (2) Overall HQ decreased by 0.82%, with high-value areas stable in the west and northeast, low-value areas concentrated in the central region, and 2030 HQ optimal under the Ecological Protection (EP) scenario; (3) Natural factors contribute most to HQ change, followed by socioeconomic factors, with landscape indices being least impactful; (4) Under future scenarios, landscape Patch Density (PD) has the most prominent negative effect—its increase intensifies fragmentation and reduces connectivity. This study’s method integration breakthrough provides a quantitative basis for landscape pattern optimization and ecosystem management in arid and semi-arid regions, with important scientific value for promoting integration of landscape ecology theory and sustainable development practice. Full article

Reducing hydrodynamic resistance remains a central concern in modern ship design. The Simulation-Based Design technique offers high-fidelity optimization through computational fluid dynamics, but this comes at the cost of computational efficiency. In contrast, surrogate models trained offline can accelerate the process but often compromise on accuracy. To address this issue, this study proposes a hybrid optimization framework connecting a computational fluid dynamics solver and a convolutional neural network surrogate model within a dual-mode mechanism. By comparing selected computational fluid dynamics evaluations with surrogate predictions during each iteration, the system is able to balance the precision and efficiency adaptively. The framework integrates a particle swarm optimizer, a free-form deformation modeler, and a dual-mode solver. Case studies on three benchmark hulls including KCS, KVLCC1, and JBC have shown 3.40%, 3.95%, and 2.74% resistance reduction, respectively, with computation efficiency gains exceeding 44% compared to the traditional Simulation-Based Design process using full computational fluid dynamics. This study provides a practical attempt to enhance the efficiency of hull form optimization while maintaining accuracy. Full article

Oxidative stress, resulting from an imbalance between reactive oxygen species (ROS) production and antioxidant defenses, is a major factor in chronic diseases such as cardiovascular disorders, neurodegeneration, diabetes, and cancer. Despite extensive research, current reviews often discuss antioxidants or inflammatory pathways separately, which limits their translational impact. The primary objective of this review is to present an integrated analysis of oxidative stress and inflammation, connecting molecular mechanisms with clinical evidence. We focus on the dual roles of natural and synthetic antioxidants in managing redox balance, regulating inflammatory signaling, and targeting new molecular pathways. Unlike previous work, this review emphasizes recent clinical findings, ongoing therapeutic challenges, and innovative strategies, including combination approaches and synthetic derivatives designed to improve effectiveness. By combining biochemical, preclinical, and clinical perspectives, we highlight both established knowledge and critical gaps. Ultimately, this review highlights the clinical significance of redox biology, clarifies the potential of antioxidant-based treatments, and outlines future research directions essential for translating these insights into effective therapies for chronic disease management. Full article