Search Results (19,512)

Lycium barbarum L. is an important economic crop in Qinghai province, China. However, root rot seriously reduces the economic results of L. barbarum. Here, we collected the diseased L. barbarum roots from Nuomuhong Farm of Haixi Mongolian and Tibetan Autonomous Prefecture, Qinghai Province, China, to clarify the diversity, pathogenicity, and biological characteristics of its root rot pathogens. A total of 125 isolates were collected, and based on morphological characteristics and rDNA ITS, TEF-1α, and RPB2 genes sequence analysis, they were identified as Fusarium equiseti, F. avenaceum, F. solani, F. citri, F. acuminatum, F. culmorum, F. sambucinum, F. incarnatum, F. oxysporum, F. tricinctum, Microdochium bolleyi, and Clonostachys rosea. These fungi were used to inoculate the roots of 1-year-old L. barbarum seedlings using scratching and root-irrigation inoculation methods, and all isolates caused root rot. This is the first report that M. bolleyi, F. avenaceum, and F. citri caused root rot in L. barbarum. And the best media, the lethal temperatures, and the optimum carbon sources and nitrogen sources of the 12 pathogen species were determined in this study. Moreover, our findings provide a theoretical foundation for root rot management in the future. Full article

This paper examines how narrative authority shapes craft evolution by analyzing two Indian craft development initiatives: the Golden Eye exhibition (1985) and the Jawaja project (1970s–1990s). Drawing on narrative theory and design research, I argue that the capacity to author one’s own story from lived experience is fundamentally generative—creating conditions for autonomous evolution. While designers routinely claim narrative authority as a basis for innovation, craftspeople are positioned as subjects within frameworks others establish. Through an analysis of these cases and my embedded relationship to them, I propose five dimensions of narrative authority—source authority, generative capacity, framework control, ambiguity privilege, and validation—that reveal how structural positioning rather than capability determines whose stories count as legitimate bases for evolution. The paper demonstrates that even well-intentioned interventions cannot create genuine self-reliance without addressing the epistemic conditions that make craftspeople’s narratives recognizable as authoritative knowledge. Full article

Adam Smith’s The Theory of Moral Sentiments (1759) and The Wealth of Nations (1776) offer a distinctive perspective on moral development that avoids succumbing to the limitations of capitalism and utilitarianism by supporting both moral agency and the importance of enabling structures and systems in commerce. Corruption of moral sentiments cannot be averted by enforcing only mechanical structures and systems of compliance with governance rules, regulations, and disciplinary processes to control employees. Compliance then follows a means-to-an-end logic for maximising profit, which becomes a barrier for autonomous moral development or is even incapable of moral decision-making, as suggested by Hannah Arendt. Smith’s originality lies in grounding this analysis with an affirmative view of human nature and liberty, which enables him to move beyond purely legalistic or moralistic approaches to understand and counter moral failure. Smith offers a distinctive perspective on moral development in commerce, integrating human cognition, moral philosophy, and enabling structural and systemic design that avoids the displacement of responsibility noted by Albert Bandura. For Smith, the corruption of moral sentiments is distorted by the natural need for praise from others at all costs, as opposed to praiseworthy conduct. His remedy is a two-fold process of moral education in which the impartial spectator extends the natural desire for praise to prioritise honour and integrity in behaviour that is praiseworthy. However, moral education also requires a structural social space that is not prescriptive or legalistic to enhance the freedom to develop morally by exercising the choice to strive towards ethical behaviour. In this manner, self-interest enables moral development through natural means that prioritise honourable conduct and perpetuates sympathetic sentiment in which the well-being of others is considered. Full article

This study examines how Association of Southeast Asian Nations (ASEAN) is framed in Chinese, Japanese, and South Korean English-language mainstream media during four high-salience geopolitical events (2023–2025). Methodologically, it employs a qualitative comparative framing and discourse analysis of 28 systematically selected news articles from leading outlets in each media system, coded using Entman’s four framing functions (problem definition, causal attribution, moral evaluation, and treatment recommendation) and supplemented by representational logics and explicitly stated platform-governance indicators. Drawing on framing theory, representation, platform governance, and critical geopolitics, the analysis finds that ASEAN is portrayed not as an autonomous actor but as a flexible signifier within nationally inflected narratives. Chinese media emphasize regional cooperation and developmental connectivity, Japanese outlets foreground liberal-normative order and security alignment, and South Korean coverage prioritizes technocratic and pragmatic partnership. The study argues that ASEAN’s mediated visibility is shaped by recurring editorial framing patterns and, where explicitly invoked, by infrastructural and platform-related cues, revealing ongoing narrative contestation over regional power and legitimacy in East Asia. Full article

Growing consumer demand for sustainable food products has amplified the use of “green” marketing claims, yet transnational agri-food supply chains face a critical “perception–reality gap” due to data fragmentation and the absence of independent verification, fostering significant greenwashing risks. This study explores how the synergistic integration of Blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) can bridge this gap. Utilizing a PRISMA-inspired qualitative systemic analysis and scenario modeling, we propose the “Converging Technologies for Sustainable Agri-Food” (CTSAF) model, formalized through a mathematical Green Claim Veracity Index (Vi) and AI-driven anomaly detection algorithms. The analysis evaluates three maturity-level scenarios against expert-calibrated Key Performance Indicators (KPIs). Results demonstrate that while traditional and blockchain-only systems remain vulnerable to the “Oracle Problem”, the integrated CTSAF model (Scenario III) achieves “Very High” performance in data accuracy and audit efficiency. By transforming passive record-keeping into an autonomous governance layer, this framework provides a strategic roadmap for substantiating environmental claims in alignment with the EU Green Claims Directive and the Digital Product Passport framework. Full article

Robotic peach harvesting represents a pivotal strategy for reducing labor costs and improving production efficiency. The fundamental prerequisite for a harvesting robot to successfully complete picking tasks is the accurate recognition of fruit growth posture subsequent to target identification. This study proposes a novel methodology for peach growth posture recognition by integrating an enhanced YOLOv8 algorithm with the RTMpose keypoint detection framework. Specifically, the conventional Neck network in YOLOv8 was replaced by an Atrous Feature Pyramid Network (AFPN) to bolster multi-scale feature representation. Additionally, the Soft Non-Maximum Suppression (Soft-NMS) algorithm was implemented to suppress redundant detections. The RTMpose model was further employed to locate critical morphological landmarks, including the stem and apex, to facilitate precise growth posture recognition. Experimental results indicated that the refined YOLOv8 model attained precision, recall, and mean average precision (mAP) of 98.62%, 96.3%, and 98.01%, respectively, surpassing the baseline model by 8.5%, 6.2%, and 3.0%. The overall accuracy for growth posture recognition achieved 89.60%. This integrated approach enables robust peach detection and reliable posture recognition, thereby providing actionable guidance for the end-effector of an autonomous harvesting robot. Full article

Short-term traffic prediction is vital for intelligent transportation systems, enabling adaptive congestion control, real-time signal management, and dynamic route planning for autonomous vehicles (AVs). This study introduces a custom Transformer-based deep learning framework for joint forecasting of traffic flow and vehicle speed, leveraging handcrafted positional encoding and stacked multi-head attention layers to model multivariate traffic patterns. Evaluated against baselines including Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Tree, and Random Forest on the Next-Generation Simulation (NGSIM) dataset, the model achieves 94.2% accuracy (Root Mean Squared Error (RMSE) 0.16) for flow and 92.1% accuracy for speed, outperforming traditional and deep learning approaches. A hybrid evaluation metric, integrating RMSE and threshold-based accuracy tailored to AV operational needs, enhances its practical relevance. With its parallel processing capability, this framework offers a scalable, real-time solution, advancing AV ecosystems and smart mobility infrastructure. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

Obesity is a multidimensional condition characterized by autonomic imbalance, metabolic inflexibility, impaired physical resilience, and ectopic adiposity, pathophysiological alterations that arise long before overt cardiometabolic disease becomes clinically detectable. Despite this, current cardiometabolic risk scores continue to rely predominantly on biochemical and anthropometric variables, such as BMI, waist circumference, glucose, and lipid levels. While these markers are practical, inexpensive, and validated across large population cohorts, growing evidence shows that they offer limited incremental predictive value and fail to capture early functional and structural abnormalities. The recent literature highlights the prognostic importance of autonomic dysfunction, reduced metabolic flexibility, diminished cardiorespiratory fitness, impaired muscular strength, and ectopic fat depots including visceral and epicardial adiposity, independently of the traditional anthropometric indices. The domains remain absent from traditional algorithms such as the Metabolic Syndrome criteria, the Framingham Risk Score, and SCORE2. As a result, cardiometabolic risk is frequently underestimated in key subgroups, including young adults with obesity, individuals with high visceral adiposity but normal BMI, those with subclinical myocardial dysfunction, and metabolically unhealthy normal-weight phenotypes. This narrative review synthesizes current evidence on obesity-related cardiometabolic impairment, highlights major gaps in established risk scores, and supports the conceptual development of the C.O.R.E. (Cardio-Obesity Risk Evaluation) Indicator Model—a hypothesis-generating, non-validated multidomain framework integrating autonomic, metabolic, functional, and structural markers to enable earlier risk phenotyping in future studies. Full article

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures. First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

To address the current reliance on manual labor in traditional logistics sorting operations, which leads to low sorting efficiency and high operational costs, this study presents the design of an unmanned logistics vehicle based on the Robot Operating System (ROS). To overcome bounding-box loss issues commonly encountered by mainstream video-stream image segmentation algorithms under complex conditions, the novel SMITE video image segmentation algorithm is employed to accurately extract key regions of mail items while eliminating interference. Extracted logistics information is mapped to corresponding grid points within a map constructed using Simultaneous Localization and Mapping (SLAM). The system performs global path planning with the A* heuristic graph search algorithm to determine the optimal route, autonomously navigates to the target location, and completes the sorting task via a robotic arm, while local path planning is managed using the Dijkstra algorithm. Experimental results demonstrate that the SMITE video image segmentation algorithm maintains stable and accurate segmentation under complex conditions, including object appearance variations, illumination changes, and viewpoint shifts. The PaddleOCR text recognition algorithm achieves an average recognition accuracy exceeding 98.5%, significantly outperforming traditional methods. Through the analysis of existing technologies and the design of a novel parcel-grasping control system, the feasibility of the proposed system is validated in real-world environments. Full article

Digital fringe projection profilometry (DFPP) is a widely used technique for full-field, non-contact 3D surface measurement, offering precision from the sub-micrometer-to-millimeter scale depending on system geometry and fringe design. This review provides a consolidated synthesis of advances reported between 2022 and 2025, covering projection and imaging architectures, phase formation and unwrapping strategies, calibration approaches, high-speed implementations, and learning-based reconstruction methods. A central contribution of this review is the integration of these developments within a metrological perspective, explicitly relating phase–height transformation, fringe parameters, system geometry, and calibration to dominant uncertainty sources and error propagation. Recent progress highlights trade-offs between sensitivity, robustness, computational complexity, and applicability to non-ideal surfaces, while learning-based and hybrid optical–computational approaches demonstrate substantial improvements in reconstruction reliability under challenging conditions. Remaining challenges include measurements on reflective or transparent surfaces, dynamic scenes, environmental instability, and real-time operation. The review outlines emerging research directions such as physics-informed learning, digital twins, programmable optics, and autonomous calibration, providing guidance for the development of next-generation DFPP systems for precision metrology. Full article

Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed to grasp drones that use low-altitude visual servo technology for landing on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated with multi-source information sensors for the treatment of air-to-water-related airplane crashes. Dynamic model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on a shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The surface grasp experiments have verified the efficacy of this grasp paradigm when compared to the traditional autonomous landing method. Full article