Search Results (711)

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°²), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

Crude oil and grain, as two pivotal global commodities, exhibit significant price co-movement that profoundly affects national economic stability and food security. From the perspective of systems theory, the energy and grain markets do not exist in isolation but rather form a highly coupled complex system, characterized by nonlinear feedback, cross-market risk contagion, and cascading effects. This study systematically investigates the transmission mechanisms from international crude oil prices to the domestic prices of Chinese four major grains, employing the DY spillover index, Vector Error Correction Model (VECM), and a mediation effect framework. The empirical findings reveal three key insights. First, rising international crude oil prices significantly strengthen the pass-through of global grain prices to domestic markets, while simultaneously weakening the effectiveness of domestic price stabilization policies. Second, higher crude oil prices amplify international-to-domestic price spillovers by increasing maritime freight costs, a key channel in global grain trade logistics. Third, elevated oil prices stimulate demand for renewable biofuels, including biodiesel and ethanol, thereby boosting international demand for corn and soybeans and intensifying the transmission of price fluctuations in these commodities to the domestic market. These findings reveal the key pathways through which shocks in the energy market affect food security and highlight the necessity of studying the “energy–food” coupling mechanism within a systems framework, enabling a more comprehensive understanding of cross-market risk transmission. Full article

China faces the persistent challenge of uneven spatiotemporal water resource distribution, constraining economic and social development while exacerbating regional disparities. Achieving co-evolution between water source systems and water use systems is thus a critical proposition in water resources management. Based on synergetics theory, this study takes Henan Province, a typical water-scarce social–ecological system, as the research object, and constructs a quantitative analysis framework for supply–demand bidirectional synergy. It systematically reveals the evolution patterns of water resource systems under the mutual feedback mechanism between water sources and water use. Findings indicate that between 2012 and 2022, the synergy degree of Henan’s water resource system increased by nearly 40%, exhibiting significant spatiotemporal differentiation: spatially “lower north, higher south”, and dynamically shifting from demand-constrained to supply-optimized. Specifically, the water source system’s order degree showed a “higher northwest, lower southeast” spatial pattern. Since the operation of the South-to-North Water Diversion Middle Route Project, the provincial average order degree increased significantly (annual growth rate of 0.01 units), though with distinct regional disparities. The water use system’s order degree also exhibited “lower north, higher south” pattern but achieved greater growth (annual growth rate of 0.03 units), with narrowing north–south gaps driven by improved management efficiency and technological capacity. This study innovatively integrates water source systems and water use systems into a unified analytical framework, systematically elucidating the intrinsic evolution mechanisms of water resource systems from the perspective of supply–demand mutual feedback. It provides theoretical and methodological support for advancing systematic water resource governance. Full article

Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings. Full article

Rapid advancements in artificial intelligence and the Internet of Things (IoT) have fueled the growth of furniture, transforming traditional home environments into intelligent living spaces. As consumer adoption accelerates, understanding user concerns and sentiment trends becomes crucial for brands to refine product offerings and enhance market competitiveness. This study systematically investigates consumer concerns and sentiment trends toward furniture products by analyzing user-generated reviews across two major e-commerce platforms: Jingdong and Taobao. Leveraging advanced text-mining methods including TF-IDF keyword extraction, hierarchical clustering, Graph of Words–Latent Dirichlet Allocation (GoW-LDA) topic modeling, and BERT-based sentiment analysis, this research identifies critical user preferences, product satisfaction factors, and platform-specific behavioral patterns. Results reveal distinct cross-platform differences; Jingdong users prioritize service quality, brand trust, and logistical efficiency, whereas Taobao users emphasize product aesthetics, material selection, and cost-effectiveness. The sentiment analysis demonstrates that Jingdong users exhibit more consistent and positive feedback, while sentiment on Taobao displays higher variability due to product-quality discrepancies and price sensitivity. Full article

Background: Inappropriate antibiotic prescribing by dental practitioners is a significant problem in low- and middle-income settings, such as India, where there are no guidelines for dental prescribing. This study aims to report, in a step-by-step process, the co-development of a computer-based stewardship educational intervention with Indian stakeholders to reduce inappropriate antibiotic prescribing by primary care dental practitioners in India. Methods: The development process of our intervention was guided by the Medical Research Council framework for developing and evaluating complex interventions. In alignment with the framework’s core elements, a co-production research approach was employed. Engagement with local stakeholders, including primary care dental practitioners, academic dentists, and those from the Indian Dental Association, facilitated the development of a contextually appropriate intervention that was informed by a prior needs assessment (a systematic review and a policy document analysis conducted in India) and evidence from global literature. The intervention was refined through iterative feedback from stakeholders and pre-testing. Results: An educational antibiotic stewardship intervention was co-developed in collaboration with stakeholders from Chennai, a major city in southern India. The final intervention comprised three components: 1. A one-page chairside guide summarising common areas of dental antibiotic use for easy reference in clinical settings; 2. A training module based on the chairside guide; and 3. A patient information sheet to facilitate dentists’ communication with patients. The intervention components were designed to be clear, practical, and contextually relevant, with the potential to enhance clinical decision-making and promote evidence-based antibiotic prescribing practices. Conclusions: This research paper describes, in a structured manner, how an educational antibiotic stewardship intervention for dental practitioners in India was co-developed by researchers and local stakeholders. Further feasibility testing is required to address uncertainties identified at the conclusion of the development process, including those related to dentists’ perceptions of the intervention, the utility of the intervention tools, and prescription recording. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Background/Objectives: Incomplete spinal cord injury (iSCI) represents a significant challenge in neurorehabilitation, with conventional limitations including recovery plateaus and declining patient motivation. Virtual reality (VR) and augmented reality (AR) have emerged as promising technologies to supplement traditional therapy through gamification and multisensory feedback. This systematic review and meta-analysis evaluates the effectiveness of VR and AR interventions for improving balance and locomotor function in patients with incomplete spinal cord injury. Methods: A systematic review was conducted following PRISMA guidelines, with searches in PubMed, Scopus, Web of Science, Science Direct, and Google Scholar. Randomized controlled trials and high-quality controlled studies evaluating VR/AR interventions in patients with iSCI (American Spinal Injury Association Impairment Scale [AIS] classifications B, C, or D) for a minimum of 3 weeks were included. A random-effects meta-analysis (Standardized Mean Difference, SMD; 95% Confidence Interval, CI) was conducted for the balance outcome. Results: Eight studies were included (n = 142 participants). The meta-analysis for balance (k = 5 studies) revealed a statistically significant improvement with a large effect size (SMD = 1.21, 95% CI: 0.04–2.38, p = 0.046). For locomotor function, a quantitative meta-analysis was not feasible due to a limited number of methodologically homogeneous studies; a qualitative synthesis of this evidence remained inconclusive. Substantial heterogeneity was observed in the balance analysis (I² = 81.5%). No serious adverse events related to VR/AR interventions were reported. Conclusions: VR/AR interventions show potential as an effective adjunctive therapy for improving balance in patients with iSCI, though the benefit should be interpreted with caution due to considerable variability between studies. The current evidence for locomotor function improvements is insufficient to draw conclusions, highlighting a critical need for more focused research. Substantial heterogeneity indicates that effectiveness may vary according to specific intervention characteristics, populations, and methodologies. Larger multicenter studies with standardized protocols are required to establish evidence-based clinical guidelines. Full article

Medical doctors are at risk of poor mental health, linked to their working conditions. However, little distinction is made between private and public practice where working conditions differ. This review examines the relationship between perceived working conditions, psychological health, and patient care among doctors in private practice, considering how differences between private and public practice impact these outcomes and the implications of working across sectors. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search encompassed Academic Search Premier, Business Source Premier, PsycInfo, PsycArticles, and Medline. Included studies were coded in line with the Job Demands–Resources model. The initial search identified 309 papers, with 14 being selected for final full-text review. Higher job demands were associated with higher levels of burnout, while social resources, job crafting, and healthy coping mechanisms were linked with lower levels of burnout. Working in private practice was associated with higher demands. Doctors in private practice had more negative perceptions of performance, less satisfactory leadership, and a lack of feedback. However, private practice also offered better work–life balance, more control, and greater reward. Notably, no study was found that examined the implications of dual roles where doctors worked across both sectors, which is common. A clear definition of private practice as well as a more granular understanding of work-related risks posed to private practice and dual roles should be the focus of future research. Full article

Small and medium-sized enterprises face significant challenges in effectively implementing design thinking due to limited resources, leadership skepticism, and a paucity of suitable frameworks. This study addresses these challenges by developing and validating a web-based Design Thinking and Innovation Strategy Toolbox tailored to SME needs. The Toolbox is designed to align with the ISO 56001:2024 Innovation Management Systems standard and was developed through systematic literature reviews and expert interviews, shaping practical modules based on previously identified barriers and success factors. A multi-round Delphi study with 14 experienced consultants refined the Toolbox, focusing on usability, ISO compliance, and practical relevance. The results indicate strong consensus among experts regarding its clarity, adaptability, and alignment with SME constraints, while also highlighting areas for improvement such as visual design and continuous feedback mechanisms. Preliminary validation suggests that the Toolbox can support SMEs in improving sustainable innovation, strategic alignment, and capability development. By addressing contextual constraints, this research contributes to the field of design-led innovation in SMEs by offering a practical, ISO-compliant tool that connects theory and practice in resource-limited environments. Full article

High-precision spiral trajectory tracking for aquaculture net-cage inspection is hindered by uncertain hydrodynamics, strong coupling, and time-varying disturbances acting on an underactuated autonomous underwater vehicle. This paper adapts and validates a model–data-driven learning-aided adaptive robust control strategy for the specific challenge of high-precision spiral trajectory tracking for aquaculture net-cage inspection. At the kinematic level, a serial iterative learning feedforward compensator is combined with a line-of-sight guidance law to form a feedforward-compensated guidance scheme that exploits task repeatability and reduces systematic tracking bias. At the dynamic level, an integrated adaptive robust controller employs projection-based, rate-limited recursive least-squares identification of hydrodynamic parameters, along with a composite feedback law that combines linear error feedback, a nonlinear robust term, and fast dynamic compensation to suppress lumped uncertainties arising from estimation error and external disturbances. A Lyapunov-based analysis establishes uniform ultimate boundedness of all closed-loop error signals. Simulations that emulate net-cage inspection show faster convergence, higher tracking accuracy, and stronger robustness than classical adaptive robust control and other baselines while maintaining bounded control effort. The results indicate a practical and effective route to improving the precision and reliability of autonomous net-cage inspection. Full article

Permafrost degradation, driven by the thawing of ground ice, results in the progressive thinning and eventual loss of the permafrost layer. This process alters hydrological and ecological systems by increasing surface and subsurface water flow, changing vegetation density, and destabilising the ground. The thermal and hydraulic conductivity of permafrost are strongly temperature-dependent, both increasing as the soil warms, thereby accelerating thaw. In addition, thawing permafrost releases large quantities of greenhouse gases, establishing a feedback loop in which global warming both drives and is intensified by permafrost loss. This paper reviews the mechanisms and consequences of permafrost degradation, including reductions in strength and enhanced deformability, which induce landslides and threaten the structural integrity of foundations and critical infrastructure. Permafrost has been investigated and modelled extensively, and various approaches have been devised to address the consequences of thawing permafrost on communities and the built environment. Some techniques focus on keeping the ground frozen via insulation, while others propose local replacement of permafrost with more stable materials. However, given the scale and pace of current changes, systematic remediation appears unfeasible. This calls for increased efforts towards adaptation, informed by interdisciplinary research. Full article

(1) Objective: The biosynthesis of medicinal secondary metabolites in Dendrobium nobile Lindl. is regulated by complex environmental, hormonal, and microbial interactions. However, the mechanisms by which subtle variations in plant elevation shape metabolite accumulation through plant–microbe–hormone networks remain largely unexplored. (2) Methods: We conducted a multi-omics investigation of D. nobile cultivated under simulated wild conditions at four elevation gradients (347–730 m) in Chishui, China. High-throughput transcriptome sequencing and ITS-based fungal community profiling were combined with hormone quantification and functional prediction (FUNGuild), enabling integrated analysis of hormone pathway activation, microbial structure–function dynamics, and dendrobine levels. (3) Reults: This study systematically investigated D. nobile cultivated under simulated wild conditions across four elevation gradients (347–730 m) in the Danxia region of Chishui, China. We identified a dual regulatory mechanism underlying the elevation-dependent accumulation of dendrobine alkaloids, involving both plant hormone signaling and endophytic fungal communities. Transcriptomic analyses revealed coordinated upregulation of key hormone pathway genes, including DELLA, PYR/PYL, SnRK2, COI1-JAZ-MYC2, and NPR1-TGA, particularly in CY01Y samples at 670 m elevation from ChiYan base in Chishui city, which corresponded to the highest dendrobine content. Concurrently, functional prediction of the ITS-based fungal sequencing data revealed that CY01Y harbored a stable, functionally enriched fungal community dominated by saprotrophs, fungal parasites, and plant pathogens. (4) Conclusions: Through integrative hormone profiling, gene expression, and microbial function analysis, we propose that elevation-induced environmental cues reshape hormone pathways both directly and indirectly via microbial feedback. Specific microbial taxa were identified as potential modulators of hormone signaling and secondary metabolism. The coordinated interaction between plant hormones and endophytic fungi supports a hormone–microbiome–metabolite network that dynamically regulates dendrobine biosynthesis in response to micro-elevation variation. Full article

Deep hole structures are widely used in the fields of aerospace, engineering machinery, marine, etc. During the deep hole machining processes, especially for boring procedures, the vibration phenomenon caused by the large aspect ratio of boring tools seriously restricts the machining accuracy and production efficiency. Therefore, extensive research has been devoted to the design and development of damped boring tools with different structures to suppress machining vibration. According to varied vibration reduction technologies, the damped boring tools can be divided into active and passive categories. This paper systematically reviews the advancements of vibration reduction principles, structure design, and practical applications of typical active and passive damped boring tools. Active damped boring tools rely on the synergistic action of sensors, actuators, and control systems, which can monitor vibration signals in real-time during the machining process and achieve dynamic vibration suppression through feedback adjustment. Their advantages include strong adaptability and wide adjustment capability for different machining conditions, including precision machining scenarios. Comparatively, vibration-absorbing units, such as mass dampers and viscoelastic materials, are integrated into the boring bars for passive damped tools, while an energy dissipation mechanism is utilized with the aid of boring tool structures to suppress vibration. Their advantages include simple structure, low manufacturing cost, and independence from an external energy supply. Furthermore, the potential development directions of vibration damped boring bars are discussed. With the development of intelligent manufacturing technologies, the multifunctional integration of damped boring tools has become a research hotspot. Future research will focus more on the development of an intelligent boring tool system to further improve the processing efficiency of deep hole structures with difficult-to-machine materials. Full article