Search Results (140)

This paper introduces a unified and systematic design methodology for robotic systems that is generalizable across a wide range of applications. It integrates rigorous mathematical formalisms such as kinematics, dynamics, control theory, and optimization with advanced simulation tools, ensuring that each design decision is grounded in provable theory. The approach defines clear phases, including mathematical modeling, virtual prototyping, parameter optimization, and theoretical validation. Each phase builds on the previous one to reduce unforeseen integration issues. Spanning from conceptualization to deployment, it offers a blueprint for developing mathematically valid and robust robotic solutions while streamlining the transition from design intent to functional prototype. By standardizing the design workflow, this framework reduces development time and cost, improves reproducibility across projects, and enhances collaboration among multidisciplinary teams. Such a generalized approach is essential in today’s fast-evolving robotics landscape where rapid innovation and cross-domain applicability demand flexible yet reliable methodologies. Moreover, it provides a common language and set of benchmarks that both novice and experienced engineers can use to evaluate performance, facilitate knowledge transfer, and future-proof systems against emerging application requirements. Full article

The rapid evolution of pathogens and the limited genetic diversity of hosts are two major factors contributing to the plant pathogenic phenomenon known as the loss of disease resistance in maize (Zea mays L.). It has emerged as a significant biological stressor threatening the global food supplies and security. Based on previous cross-species homologous gene screening assays conducted in the laboratory, this study identified the maize disease-resistance candidate gene ZmNLR-7 to investigate the maize immune regulation mechanism against Bipolaris maydis. Subcellular localization assays confirmed that the ZmNLR-7 protein is localized in the plasma membrane and nucleus, and phylogenetic analysis revealed that it contains a conserved NB-ARC domain. Analysis of tissue expression patterns revealed that ZmNLR-7 was expressed in all maize tissues, with the highest expression level (5.11 times) exhibited in the leaves, and that its transcription level peaked at 11.92 times 48 h post Bipolaris maydis infection. Upon inoculating the ZmNLR-7 EMS mutants with Bipolaris maydis, the disease index was increased to 33.89 and 43.33, respectively, and the lesion expansion rate was higher than that in the wild type, indicating enhanced susceptibility to southern corn leaf blight. Physiological index measurements revealed a disturbance of ROS metabolism in ZmNLR-7 EMS mutants, with SOD activity decreased by approximately 30% and 55%, and POD activity decreased by 18% and 22%. Moreover, H₂O₂ content decreased, while lipid peroxide MDA accumulation increased. Transcriptomic analysis revealed a significant inhibition of the expression of the key genes NPR1 and ACS6 in the SA/ET signaling pathway and a decrease in the expression of disease-related genes ERF1 and PR1. This study established a new paradigm for the study of NLR protein-mediated plant immune mechanisms and provided target genes for molecular breeding of disease resistance in maize. Overall, these findings provide the first evidence that ZmNLR-7 confers resistance to southern corn leaf blight in maize by synergistically regulating ROS homeostasis, SA/ET signal transduction, and downstream defense gene expression networks. Full article

The rapid development of deepfake technologies has led to the widespread proliferation of facial image forgeries, raising significant concerns over identity theft and the spread of misinformation. Although recent dual-domain detection approaches that integrate spatial and frequency features have achieved noticeable progress, they still suffer from limited sensitivity to local forgery regions and inadequate interaction between spatial and frequency information in practical applications. To address these challenges, we propose a novel forgery-aware guided spatial–frequency feature fusion network. A lightweight U-Net is employed to generate pixel-level saliency maps by leveraging structural symmetry and semantic consistency, without relying on ground-truth masks. These maps dynamically guide the fusion of spatial features (from an improved Swin Transformer) and frequency features (via Haar wavelet transforms). Cross-domain attention, channel recalibration, and spatial gating are introduced to enhance feature complementarity and regional discrimination. Extensive experiments conducted on two benchmark face forgery datasets, FaceForensics++ and Celeb-DFv2, show that the proposed method consistently outperforms existing state-of-the-art techniques in terms of detection accuracy and generalization capability. The future work includes improving robustness under compression, incorporating temporal cues, extending to multimodal scenarios, and evaluating model efficiency for real-world deployment. Full article

Food security constitutes a fundamental pillar of future sustainable development. A systematic evaluation of the impact of urban expansion on food security is critical to advancing the United Nations Sustainable Development Goals (SDGs), particularly “Zero Hunger” (SDG 2). Drawing on bibliographic data from the Web of Science Core Collection, this study employs the bibliometrix package in R to conduct a comprehensive bibliometric analysis of the literature on the “urban expansion–food security” nexus spanning from 1982 to 2024. The analysis focuses on knowledge production, collaborative structures, and thematic research trends. The results indicate the following: (1) The publication trajectory in this field exhibits a generally increasing trend with three distinct phases: an incubation period (1982–2000), a development phase (2001–2014), and a phase of rapid growth (2015–2024). Land Use Policy stands out as the most influential journal in the domain, with an average citation rate of 43.5 per article. (2) China and the United States are the leading contributors in terms of publication output, with 3491 and 1359 articles, respectively. However, their international collaboration rates remain relatively modest (0.19 and 0.35) and considerably lower than those observed for the United Kingdom (0.84) and Germany (0.76), suggesting significant potential for enhanced global research cooperation. (3) The major research hotspots cluster around four core areas: urban expansion and land use dynamics, agricultural systems and food security, environmental and climate change, and socio-economic and policy drivers. These focal areas reflect a high degree of interdisciplinary integration, particularly involving land system science, agroecology, and socio-economic studies. Collectively, the field has established a relatively robust academic network and coherent knowledge framework. Nonetheless, it still confronts several limitations, including geographical imbalances, fragmented research scales, and methodological heterogeneity. Future efforts should emphasize cross-regional, interdisciplinary, and multi-scalar integration to strengthen the systematic understanding of urban expansion–food security interactions, thereby informing global strategies for sustainable development. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

Recent advancements in deep learning have driven the rapid proliferation of deepfake generation techniques, raising substantial concerns over digital security and trustworthiness. Most current detection methods primarily focus on spatial or frequency domain features but show limited effectiveness when dealing with compressed videos and cross-dataset scenarios. Observing that mainstream generation methods use frame-by-frame synthesis without adequate temporal consistency constraints, we introduce the Spatiotemporal Attention 3D Network (STA-3D), a novel framework that combines a lightweight spatiotemporal attention module with a 3D convolutional architecture to improve detection robustness. The proposed attention module adopts a symmetric multi-branch architecture, where each branch follows a nearly identical processing pipeline to separately model temporal-channel, temporal-spatial, and intra-spatial correlations. Our framework additionally implements Spatial Pyramid Pooling (SPP) layers along the temporal axis, enabling adaptive modeling regardless of input video length. Furthermore, we mitigate the inherent asymmetry in the quantity of authentic and forged samples by replacing standard cross entropy with focal loss for training. This integration facilitates the simultaneous exploitation of inter-frame temporal discontinuities and intra-frame spatial artifacts, achieving competitive performance across various benchmark datasets under different compression conditions: for the intra-dataset setting on FF++, it improves the average accuracy by 1.09 percentage points compared to existing SOTA, with a more significant gain of 1.63 percentage points under the most challenging C40 compression level (particularly for NeuralTextures, achieving an improvement of 4.05 percentage points); while for the intra-dataset setting, AUC is enhanced by 0.24 percentage points on the DFDC-P dataset. Full article

Two-dimensional human pose estimation (2D HPE) has become a fundamental task in computer vision, driven by growing demands in intelligent surveillance, sports analytics, and healthcare. The rapid advancement of deep learning has led to the development of numerous methods. However, the resulting diversity in research directions and model architectures has made systematic assessment and comparison difficult. This review presents a comprehensive overview of recent advances in 2D HPE, focusing on method classification, technical evolution, and performance evaluation. We classify mainstream approaches by task type (single-person vs. multi-person), output strategy (regression vs. heatmap), and architectural design (top-down vs. bottom-up) and analyze their respective strengths, limitations, and application scenarios. Additionally, we summarize commonly used evaluation metrics and benchmark datasets, such as MPII, COCO, LSP, OCHuman, and CrowdPose. A major contribution of this review is the detailed comparison of the top six models on each benchmark, highlighting their network architectures, input resolutions, evaluation results, and key innovations. In light of current challenges, we also outline future research directions, including model compression, occlusion handling, and cross-domain generalization. This review serves as a valuable reference for researchers seeking both foundational insights and practical guidance in 2D human pose estimation. Full article

Micromachines, small-scale engineered devices prepared to carry out exact tasks at the micro level, have garnered great interest across different fields such as drug delivery, chemical synthesis, and biomedical applications. In emerging applications, micromachines have indicated great potential in advancing click chemistry, a highly selective and efficient chemical technique widely applied in materials science, bioconjugation, and pharmaceutical development. Click chemistry, distinguished by its rapid reaction rates, high efficiency, and bioorthogonality, serves as a robust method for molecular assembly and functionalization. Incorporating micromachines into click chemistry processes paves the way for precise, automated, and scalable chemical synthesis. These tiny devices can effectively transport reactants, boost reaction efficiency through localized mixing, and enable highly exact site-specific modifications. Moreover, micromachines driven by external forces such as magnetic fields, ultrasound, or chemical fuels provide exceptional control over reaction conditions, significantly enhancing the selectivity and efficiency of click reactions. In this review, we explore the interaction between micromachines and click chemistry, showcasing recent advancements, potential uses, and future prospects in this cross-disciplinary domain. By leveraging micromachine-supported click chemistry, scientists can surpass conventional reaction constraints, opening doors to groundbreaking innovations in materials science, drug discovery, and beyond. Full article

The subtropical grasslands of South America are known as Pampa, span parts of Brazil, Uruguay, and Argentina, and are undergoing rapid and alarming transformations due to agricultural expansion, habitat fragmentation, and climate change. Despite this, these areas harbor a remarkable diversity of Cactaceae, including a high proportion of endemic and threatened species. This study offers the first comprehensive inventory of native and endemic cactus taxa in the Pampean province of the Chacoan domain, integrating data from georeferenced herbarium records, biodiversity databases, and fieldwork. A total of 111 native taxa were identified, of which 62% are endemic to the region. Spatial analyses reveal that many species occur outside protected areas, with hotspots of richness and endemism located near international borders and in poorly studied regions. These findings underscore the urgent need to reassess conservation priorities in Pampa, where biodiversity is being lost at an accelerating pace. By identifying critical areas for conservation and highlighting gaps in species assessments, the present study contributes essential data to support public policy, conservation planning, and the establishment of cross-border strategies for the protection of this unique and vulnerable flora. Full article

Digital platform ecosystems confront critical management challenges as they overcome path dependence amid rapid technological change. This study explores cross-category innovation as a key strategic action, using a longitudinal case study of ByteDance to analyze how digital technology drives ecosystem evolution, and constructs a “technology-driven–strategic action–ecosystem evolution” framework to examine the interplay between technological capabilities and strategic actions. Findings identify two stages: in the category emergence stage, platforms establish a core business ecosystem via identity, legitimacy, and differentiation strategies, leveraging technologies like algorithmic recommendation to shape user cognition and market legitimacy. In the category spanning stage, platforms leverage platform envelopment, open innovation, and status strategies to expand cross-category ecosystems, enabling technological spillover and integrated innovation across new domains. The findings reveal a co-evolution mechanism of cross-category innovation strategy and ecosystems, where the cross-category innovation strategy serves as both a driving force for ecosystem evolution and acquires new strategic opportunities. This study offers insights for building sustainable ecosystems that transcend industry boundaries and enhance resilience. Full article

Landslides are characterized by their suddenness and destructive power, making rapid and accurate identification crucial for emergency rescue and disaster assessment in affected areas. To address the challenges of limited landslide samples and data complexity, a landslide identification sample library was constructed using high-resolution remote sensing imagery combined with field validation. An innovative Dual-Coded Segmentation Network (DS Net), which realizes dynamic alignment and deep fusion of local details and global context, image features and domain knowledge through the multi-attention mechanism of Prior Knowledge Integration (PKI) module and Cross-Feature Aggregation (CFA) module, significantly improves the landslide detection accuracy and reliability. To objectively evaluate the performance of the DS Net model, four efficient semantic segmentation models—SegFormer, SegNeXt, FeedFormer, and U-MixFormer—were selected for comparison. The results demonstrate that DS Net achieves superior performance (overall accuracy = 0.926, precision = 0.884, recall = 0.879, and F1-score = 0.882), with metrics that are 3.5–7.1% higher than the other models. These findings confirm that DS Net effectively improves the accuracy and efficiency of landslide identification, providing a critical scientific basis for landslide prevention and mitigation. Full article

To address the challenges of detection and acquisition caused by trajectory prediction errors during the midcourse–terminal guidance handover phase in cross-domain unmanned aerial vehicles (CDUAV), this study proposes a collaborative multi-interceptor detection coverage optimization method based on predictive error pipeline modeling. Firstly, we employ nonlinear least squares to fit parameters for the motion model of CDUAV. By integrating error propagation theory, we derive a recursive expression for error pipelines under t-distribution and establish a parametric model for the target’s high-probability region (HPR). Next, we analyze target acquisition scenarios during guidance handover and reformulate the collaborative detection problem as a field-of-view (FOV) coverage optimization task on a two-dimensional detection plane. This framework incorporates the target HPR and the seeker detection FOV models, with an objective function defined for coverage optimization. Finally, inspired by wireless sensor network (WSN) coverage strategies, we implement the starfish optimization algorithm (SFOA) to enhance computational efficiency. Simulation results demonstrate that compared to Monte Carlo statistical methods, our parametric modeling approach reduces prediction error computation time from 15.82 s to 0.09 s while generating error pipeline envelopes with 99% confidence intervals, showing superior generalization capability. The proposed collaborative detection framework effectively resolves geometric coverage optimization challenges arising from mismatches between target HPR and FOV morphology, exhibiting rapid convergence and high computational efficiency. Full article

Sustainability research encompasses diverse theories and frameworks focused on promoting sustainable economic ($E$), social ($S$), and environmental ($Env$) systems. However, integrated approaches to sustainability challenges have been impeded due to the absence of a unified analytical framework in the field. This study investigated how foundational and emerging theories, including resilience thinking, systems theory, and planetary boundaries, could be synthesized to develop an Integrated Sustainability Model (ISM) that captures nonlinear feedback, adaptive capacities $A_i\left(t\right)$, and threshold effects across these domains. The ISM model employs a system dynamics approach, where the rates of change for $E$, $S$, and $Env$ are governed by coupled differential equations, each influenced by cross-domain feedback (${\alpha }_i$ and ${\beta }_i$), adaptive capacity functions, and depletion rates (${\gamma }_i$). The model explicitly incorporates boundary constraints and adaptive capacity, operationalizing the dynamic interplay and co-evolution of sustainability dimensions. Grounded in an integrative perspective, this research introduces the Synergistic Resilience Theory (SRT), which proposes optimal sustainability arises from managing economic, social, and environmental systems as interconnected, adaptive components of a resilient system. Theoretical analysis and conceptual simulations demonstrated that high adaptive capacity and positive cross-domain reinforcement foster resilient, synergistic growth, while reduced capacity or breaches of critical thresholds ($En{v}_{\mathrm{m}\mathrm{i}\mathrm{n}}$ and $S_{\mathrm{m}\mathrm{i}\mathrm{n}}$) can lead to rapid decline and slow recovery. These insights illuminate the urgent need for integrated, preventive policy interventions that proactively build adaptive capacity and maintain system resilience. This research, by advancing a mathematically robust and conceptually integrative framework, provides a potent new lens for developing empirically validated, holistic sustainability strategies within sustainability research. Full article

Amid the dual imperatives of global trade expansion and low-carbon transition, intelligent maritime shipping has emerged as a central driver for the innovation of international logistics systems, now entering a critical window period for the deep integration of Internet technologies and automated port infrastructure. While existing research predominantly focuses on isolated applications of intelligent technologies, systematic evaluations of the synergistic effects of technological integration on maritime ecosystems, policy compatibility, and contributions to global carbon emission governance remain under-explored. Leveraging bibliometric analysis, this study systematically examines 488 publications from the Web of Science (WoS) Core Collection (2000–2024), yielding three pivotal findings: firstly, China dominates the research landscape, with a 38.5% contribution share, where Artificial Intelligence (AI), the Internet of Things (IoT), and port automation constitute the technological pillars. However, critical gaps persist in cross-system protocol standardization and climate-adaptive modeling, accounting for only 2.7% and 4.2% of the literature, respectively. Secondly, international collaboration networks exhibit pronounced “Islamization”, characterized by an inter-team collaboration rate of 17.3%, while the misalignment between rapid technological iteration and existing maritime regulations exacerbates industry risks. Thirdly, a dual-track pathway integrating Cyber–Physical System (CPS)-based digital twin ports and open-source vertical domain-specific large language models is proposed. Empirical evidence demonstrates its efficacy in reducing cargo-handling energy consumption by 15% and decision-making latency by 40%. This research proposes a novel tripartite framework, encompassing technological, institutional, and data sovereignty dimensions, to resolve critical challenges in integrating multi-source maritime data and managing cross-border governance. The model provides academically validated and industry-compatible strategies for advancing sustainable maritime intelligence. Subsequent investigations should expand data sources to include regional repositories and integrate interdisciplinary approaches, ensuring the adaptability of both technical systems and international policy coordination mechanisms across diverse maritime ecosystems. Full article

Introduction: Sub-Saharan Africa in general, and the Democratic Republic of the Congo (DRC) in particular, is undergoing an epidemiological transition characterized by a more rapid increase in the number of non-communicable diseases (NCDs). However, the level of readiness of the DRC’s healthcare facilities (HFs) to manage these diseases is unknown. Thus, our study aimed to assess these HFs’ level of readiness to manage cardiovascular disease (CVD) and diabetes. Methodology: This cross-sectional study involved 1412 HFs in the DRC, selected by stratified random sampling. They are representative of the country’s 26 provinces. The World Health Organization (WHO) Service Availability and Readiness Survey (SARA) was used. The “readiness” outcome was a composite measure of the capacity of HFs to manage CVD and diabetes. The readiness indicator comprised four domains, and a score of ≥70% indicated “readiness” to manage CVD and diabetes. Informed consent was obtained from the stakeholders, and the ethics committee held a positive opinion. Statistical analyses were performed using STATA 17 software. Results: The average readiness scores of the DRC’s HFs to manage CVD and diabetes are less than 50%, being 38.3% (37.3–39.3) and 39.8% (38.7–40.9), respectively. These scores were less than 40% for CVD and diabetes in rural HFs. They were less than 30% for CVD and diabetes in primary-level HF. No province possesses over 50% of health facilities equipped to address cardiovascular illnesses, and only four provinces (Haut Uele, Kinshasa, Nord Kivu, and Sud Kivu) possess over 50% of health facilities equipped to address diabetes. The provinces with health facilities exhibiting the least preparedness in managing cardiovascular illnesses and diabetes are Nord Ubangi and Sankuru. Only 0.07% (0.01–0.5) of HFs obtained a score ≥ 70% for CVD management, and 5.9% (4.8–7.3) obtained this score for diabetes management. Conclusions: Significant deficiencies must be rectified to enhance service delivery in the management of cardiovascular disease (CVD) and diabetes. Most primary-level and rural facilities demonstrated inadequate preparedness for CVD and diabetes screening and management, exhibiting low readiness scores and limited-service availability in the assessed domains. While secondary-level services are relatively accessible, critical gaps persist that must be addressed to improve readiness for CVD and diabetes care. Healthcare facilities should possess the capacity to deliver recommended services across various tiers, ensuring both service readiness and availability. Full article