Search Results (295)

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article

Green inclusive growth is a crucial strategic choice for achieving high-quality development in China. This study constructs an indicator system encompassing economic, social, and ecological dimensions to quantitatively measure the level of green inclusive growth across 31 provinces (cities, autonomous regions) in China from 2001 to 2021. The regional disparities, spatiotemporal evolution trends, and convergence characteristics are analyzed using the Dagum Gini coefficient, kernel density function, and σ-convergence and conditional β-convergence. The findings indicate the following: (1) China’s green inclusive growth generally exhibits a “high in the east, low in the west” spatial distribution pattern, with western regions demonstrating a catching-up trend. (2) The regional disparities in China’s green inclusive growth levels are showing a trend of gradual narrowing, though imbalances within eastern and western regions remain relatively pronounced. (3) The kernel density curve of China’s green inclusive growth maintains a “unimodal” shape, with no significant polarization or multi-polar differentiation. (4) Both the national level and the four major regional clusters exhibit σ-convergence and conditional β-convergence in green inclusive growth, demonstrating the effectiveness of policies aimed at reducing regional disparities. (5) Social capital, human capital, technological innovation, material capital investment, foreign direct investment, urbanization level, and government fiscal expenditure all have a positive promoting effect on China’s green and inclusive growth. These results provide decision-making references for promoting coordinated regional development and guiding the inclusive and green transformation of China’s economic growth. Full article

Forest soil conservation is pivotal for controlling soil erosion and ensuring ecological security. Taking the Qilian Mountains Area in China as the research region, this study used ArcMap 10.8 software to process data for six prefecture-level cities in the area from 2008 to 2023. The Revised Universal Soil Loss Equation (RUSLE) model was applied to quantify the forest soil conservation amount and evaluate its ecosystem service value (ESV). Their spatiotemporal variations and dynamic evolution patterns were analyzed, alongside the influence of soil organic matter (OM) and nitrogen (N), phosphorus (P), and potassium (K) contents. The results showed that the average contents of OM, N, P and K in the forest soils of the Qilian Mountains Area were 24.22 g·kg⁻¹, 1.54 g·kg⁻¹, 0.70 g·kg⁻¹, and 19.96 g·kg⁻¹, respectively, with significant regional heterogeneity. Haibei Tibetan Autonomous Prefecture (HBTAP) had the highest while Jinchang City (JC) had the lowest. From 2008 to 2023, the average annual forest soil conservation amount and its ESV of the region were 1.749 × 10⁹ tons and 2.0444 × 10¹⁰ yuan, respectively, both showing a fluctuating trend of initial increase followed by a decrease. Spatially, HBTAP ranked first in average annual forest soil conservation amount per unit area and ESV. Jiuquan City (JQ) had the lowest forest soil conservation amount per unit area, and JC the lowest ESV. Forest soil conservation and its ESV in the region were affected by the contents of soil nutrients (OM and N, P, K elements), vegetation types and quality, topography, climate, and human activities (including ecological governance), which collectively intensified the spatiotemporal heterogeneity. These findings provide a theoretical basis for precise regional ecological protection and differentiated restoration strategies in arid regions. Full article

The growing integration of generative artificial intelligence in logistics demands efficient simulation modeling. This study evaluates generative large language models, Perplexity and ChatGPT, for discrete-event simulation in ExtendSim. It focuses on modeling a real, complex manufacturing system, yielding 9721 tons of output. The following three scenarios were assessed: autonomous model creation, output estimation from process descriptions and parameters, and copilot-guided manual building. LLMs cannot autonomously construct ExtendSim models due to the lack of APIs. Output estimation only matched benchmarks after iterative prompt refinement, achieving errors of 0.1% for Perplexity and 1.2% to 22.8% for ChatGPT. Estimation without substantial human intervention proved infeasible. Only the copilot approach appeared viable despite initial errors. It enabled a validated model with 9718 tons output after resolving 25 errors for Perplexity and 22 for ChatGPT through iterative refinement. Approximately 28% (Perplexity) or 32% (ChatGPT) of the errors were hallucinations. The copilot approach reduced development time from several days to 8–10 h. Human expertise remained essential for verifying model outputs and addressing hallucinations and logical flaws. Consequently, this approach may be less feasible for inexperienced users. The copilot paradigm offers practical acceleration for experienced users; however, its limitations underscore the need for API integration and retrieval-augmented generation enhancements. Full article

Symmetry and structural balance play a fundamental role in the collective behavior of networked agent systems (NASs). In particular, cluster formation tracking, representing the emergence and maintenance of symmetric group structures, has attracted significant attention due to its wide applications in robotics and autonomous systems. However, most existing approaches assume autonomous leaders, which may not be applicable in scenarios where human intervention is required. With this in mind, this paper addresses the cluster formation tracking problem for NASs with collision avoidance, where the leader receives control inputs from a human-in-the-loop (HiTL), making the leader a non-autonomous system. A distributed control protocol is developed so that followers can track the trajectories of their designated leaders using only relative information from neighboring agents. Sufficient conditions are established to guarantee collision-free cluster formation tracking, and Lyapunov-based analysis is employed to prove the asymptotic convergence of the subgroup tracking errors. In the proposed framework, human intervention is incorporated through external commands applied to the leaders, which makes the leader dynamics non-autonomous while preserving the distributed nature of the follower controllers. Simulation studies on a 13-agent network with three subgroups show that all followers achieve the desired time-varying cluster formations under HiTL-driven leader motions, with convergence times ranging from 4.21 s to 5.12 s. Moreover, the final tracking errors of all followers are reduced below $9.07\times {10}^{-5}$, while the minimum pairwise distances within each subgroup remain strictly above the prescribed safety threshold. These quantitative results verify both the effectiveness of the proposed protocol and the practical feasibility of integrating HiTL commands into collision-free cluster formation tracking. Full article

The ability to autonomously navigate and explore complex 3D environments in a purposeful manner, while integrating visual perception with natural language interaction in a human-like way, represents a longstanding research objective in Artificial Intelligence (AI) and embodied cognition. Vision-Language Navigation (VLN) has evolved from geometry-driven to semantics-driven and, more recently, knowledge-driven approaches. With the introduction of Large Language Models (LLMs) and Vision-Language Models (VLMs), recent methods have achieved substantial improvements in instruction interpretation, cross-modal alignment, and reasoning-based planning. However, existing surveys primarily focus on traditional VLN settings and offer limited coverage of LLM-based VLN, particularly in relation to Sim2Real transfer and edge-oriented deployment. This paper presents a structured review of LLM-enabled VLN, covering four core components: instruction understanding, environment perception, high-level planning, and low-level control. Edge deployment and implementation requirements, datasets, and evaluation protocols are summarized, along with an analysis of task evolution from path-following to goal-oriented and demand-driven navigation. Key challenges, including reasoning complexity, spatial cognition, real-time efficiency, robustness, and Sim2Real adaptation, are examined. Future research directions, such as knowledge-enhanced navigation, multimodal integration, and world-model-based frameworks, are discussed. Overall, LLM-driven VLN is progressing toward deeper cognitive integration, supporting the development of more explainable, generalizable, and deployable embodied navigation systems. Full article

Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care. Full article

Context: Sport-related concussion (SRC) management has evolved substantially over the past 25 years. Early paradigms emphasized prolonged physical and cognitive rest; however, growing evidence has demonstrated that recovery following SRC is multidimensional and influenced by interacting neurological, vestibular, autonomic, cervical, cognitive, and psychological systems. Consequently, contemporary clinical practice has shifted toward active, multifaceted rehabilitation approaches. Objective: We aimed to synthesize and contextualize the evidence supporting a multifaceted approach to sport-related concussion management from 2000 through 2025, with emphasis on implications for athletic training practice. Data Sources: A structured literature search was conducted using PubMed, SPORTDiscus, CINAHL, and Web of Science to identify peer-reviewed publications related to SRC evaluation, management, and rehabilitation. Study Selection: Studies published between 1 January 2000, and 31 December 2025 involving human participants with sport-related concussion or sport-like mechanisms of mild traumatic brain injury were included. Evidence from randomized controlled trials, cohort studies, systematic and narrative reviews, and major consensus or position statements was considered. Data Extraction: Relevant studies were reviewed and synthesized across key domains of SRC management, including aerobic exercise, vestibular and oculomotor rehabilitation, cervical spine management, multimodal and profile-based rehabilitation, return-to-learn strategies, psychological and behavioral health considerations, and implementation patterns within athletic training settings. Results: A total of 182 publications contributed evidence to one or more components of multifaceted SRC management. Across domains, evidence supports early, symptom-limited aerobic exercise; targeted vestibular and cervical rehabilitation; structured return-to-learn planning; and the integration of psychological support. Multimodal rehabilitation and profile-based clinical categorization approaches were associated with shorter recovery timelines and improved functional outcomes compared with rest-only strategies. Despite strong evidence, implementation variability persists across athletic training settings. Conclusions: Evidence accumulated over the past 25 years supports a shift toward active, individualized, and multidisciplinary approaches to SRC management. Athletic trainers are uniquely positioned to coordinate multifaceted care addressing the diverse contributors to concussion recovery. Full article

Background: Adapted Physical Activity (APA) prescription is a complex decision-making process that integrates clinical guidelines and individual patient characteristics and remains strongly dependent on clinician experience. Generative artificial intelligence (AI) has recently emerged as a potential decision-support tool in exercise prescription; however, its interaction with professional expertise is still unclear. This study compared the perceived quality of APA protocols developed by expert professionals, novice professionals supported by AI, and AI operating autonomously across multiple orthopedic conditions. Methods: In this observational cross-sectional study, five real orthopedic prescriptions (scoliosis, low back pain, osteoporosis, high risk of falls, and osteoarthritis) were used to generate three APA protocols per condition: expert professional (EP), novice professional with AI support (NAI), and AI alone. All protocols were created using an identical standardized prompt and anonymized. A multidisciplinary panel of 135 professionals blindly evaluated the protocols using a structured questionnaire assessing effectiveness, safety, appropriateness, clarity, and progression. Overall quality scores were compared using Friedman tests with post hoc Wilcoxon signed-rank tests. Results: Across all conditions, EP protocols achieved the highest quality scores, followed by NAI, while AI-alone protocols consistently received the lowest ratings (all p < 0.05). NAI protocols showed intermediate performance, partially reducing the expertise gap. Post hoc analyses showed that EP protocols received significantly higher rating than AI protocols in all conditions (p < 0.01). NAI protocols received significantly higher rating than AI protocols in most conditions (p < 0.01) except osteoporosis (p = 0.362). Differences between EP and AI were most pronounced for safety (p < 0.01), appropriateness (tailoring p < 0.01), and progression (p < 0.05), whereas EP–NAI differences were smaller and condition-dependent. AI-alone protocols showed greater variability across pathologies. Conclusions: Professional expertise remains the main determinant of APA protocol quality. AI support can improve protocol structure and perceived quality when used by novice professionals but does not replace expert clinical reasoning. AI-generated protocols without human oversight are not yet suitable for autonomous APA prescription, supporting a complementary, expertise-dependent role of AI in exercise programming. Full article

Real-world traffic is highly dynamic, with pedestrians exhibiting unpredictable movements. Pedestrians’ poses are essential cues for predicting their actions, enabling vehicles to respond proactively and reduce accident risks. In autonomous driving, the distance between vehicles and pedestrians is critical, making 3D human pose estimation crucial. In this context, pedestrian pose estimation has been actively studied, and recently, light detection and ranging (LiDAR) sensors have attracted attention due to their accurate 3D depth information and privacy benefits. However, existing LiDAR-based 3D pose estimation methods mainly process 3D data directly, requiring high computational cost and memory. In this paper, we propose a lightweight LiDAR-based 3D human pose estimation method specifically designed for deployment in autonomous driving systems. Unlike conventional 3D direct processing methods, our approach strategically reduces computational complexity by projecting point clouds into 2D depth images and leveraging a lightweight MoveNet, followed by efficient 3D lifting. Furthermore, we introduce a self-occlusion correction algorithm to improve robustness under side-view and bending poses, where depth-based projections often suffer from distortion. Experimental results on benchmark datasets demonstrate that the proposed method achieves competitive pose estimation accuracy while substantially improving efficiency, highlighting its practicality and scalability for real-time autonomous vehicle applications. Full article

Autonomous trucks can be used in different loading combinations, including different axle configurations, tire types, and lateral wander mode scenarios. In this research, four different truck types have been selected with varying gross weights and axle configurations. The four different truck types include a 5-axle long-haul semi-truck, a 6-axle electric autonomous truck, a 6-axle autonomous truck platoon leader, and a 5-axle autonomous truck platoon follower. Furthermore, three different tire footprint scenarios, consisting of a conventional dual wheel assembly, a wide base tire, and a new generation wide base tire, have been used. In order to utilize the possibility of lateral wander programmed into the autonomous trucks, three different lateral wander models, including zero lateral wander, a human-driven probabilistic lateral wander, and an optimum uniform wander mode, have been used. Finite element analysis has been employed to incorporate the effects of various scenarios on a conventional pavement section. Results showed improved pavement life with the use of uniform wander mode, where trucks T1 and T2 improved the pavement life by 47% and 56%, respectively, when compared to truck T3. Furthermore, the use of uniform wander mode decreases rutting and fatigue damage by 36% and 28%, respectively, on average for all scenarios. The use of new generation wide-base tires is recommended, since it reduces damaging strains by 38% when compared to the dual tire configuration. Full article

This pilot study investigated whether ambient biogenic volatile organic compounds (bVOCs)—scent profiles emitted by botanic glasshouse vegetation—could contribute to quantifiable human health and wellbeing outcomes. Over 11 months in 2024 (January–December), human participant trials were conducted at the Oxford Botanic Garden to compare the physiological and psychological effects associated with spending 30 min exposures in five different vegetation-rich glasshouses, each characterised by a distinct and complex bVOCs profile, with those of a plant-free room. Pre- and post-intervention assessments were conducted on 43 participants, using the State-Trait Anxiety Inventory (STAI), heart-beat rate (beats per minute), and heart rate variability (HRV): the latter two are widely used as an index of regulation of the autonomic nervous system. Significant reductions in STAI anxiety scores and decreases in heart-beat rate were observed, while HRV indices remained stable, relative to the plant-free room, following glasshouse exposure. Distinct scent profiles in the glasshouses included compounds that have previously shown associations with therapeutic effects in clinical settings, indicating the potential of these scented vegetation-rich glasshouse environments to promote the beneficial health effects observed in this study. Overall, these findings highlight the potential public health value of aromatic plant species and the importance of incorporating them into urban green space planning and policy. Full article

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article

In recent years, autonomous liquid pouring systems have gained more relevance, with applications from daily service tasks to complex industrial operations. While seemingly simple for humans, this task poses major challenges for automated systems, as it requires precise control and adaptation to varying container geometries, liquid properties, and environmental conditions. This review examines the state-of-the-art on liquid pouring through five research questions: (1) What are the characteristics of the liquids used in the experiments? (2) What are the characteristics of the containers used in the experiments and how do they affect the performance of the pouring tasks? (3) What techniques are used to control liquid pouring (i.e., to control the robotic arm or device)? (4) What metrics are used to assess the methods for pouring liquid? (5) What devices are used to measure poured volume? This scoping review follows the Arksey and O’Malley framework, and uses the PRISMA-ScR protocol to filter the articles. A total of 285 studies published between 2018 and 2025 were screened from IEEE Xplore, SpringerLink, ScienceDirect, Web of Science, and EBSCOhost, of which 23 met the inclusion criteria. Results showed that the most widely used methods for autonomous liquid pouring were classical control methods—PID, PD (30.4% of the studies). Conversely, the least widely used methods for autonomous liquid pouring were learning, imitation learning, and probabilistic models (15% of the studies). Full article