Search Results (750)

Speedcubing, the competitive practice of fast solving the Rubik’s Cube, has gained global popularity both as a sporting and an educational activity. Aside from its recreational value, speedcubing may contribute to broader social and developmental outcomes. This study aims to examine the potential of speedcubing as a tool for sustainable social development, concentrating on its educational, psychological, and social implications and its relationship to selected United Nations Sustainable Development Goals (SDGs). An anonymous online survey consisting of 26 items (22 used for the main analysis and 4 demographic items) was conducted among 112 participants associated with the speedcubing community, including active competitors, coaches, and parents. The questionnaire addressed accessibility, cognitive and social competencies, and perceived educational and social benefits, as well as user preferences regarding digital tools supporting learning. The results indicate that participation in speedcubing supports the development of analytical thinking, problem-solving skills, perseverance, and self-control. Respondents also emphasized its educational value, accessibility, and role in fostering fair play and social integration. These findings suggest that speedcubing may contribute to several Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 and SDG 12 (Sustainable Cities and Communities; Responsible Consumption and Production). Full article

Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (COD_Mn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The COD_Mn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article

Background: Globally, rates of children with physical and mental health problems are increasing. Health issues in early childhood often persist into adulthood, highlighting the need to ensure children are supported to flourish from the start of life. Objectives: This protocol describes methods used to test the acceptability and feasibility of a novel digital Flourishing Intervention (designed to empower parents and promote child wellbeing), comprising a Flourishing Check (a newly developed online questionnaire) and a Pathway Tool (an online directory of high-quality, evidence-based programmes and resources). Methods: Using a randomised feasibility trial, participants (N = 600 parents of children aged 0–5 years) will complete the Flourishing Check. The intervention group (n = 400) will access the Flourishing Check and Pathway Tool, whereas a waitlist control group (n = 200) will access the Flourishing Check only. Results: The primary aim is to assess the acceptability and feasibility of the intervention through a mixed-methods design incorporating quantitative data from pre- and post-intervention questionnaires and qualitative data from focus groups. This will be assessed using a traffic light system, which will inform if and how to proceed to a future effectiveness trial. Secondary aims are to assess changes in parent and child outcomes. Primary outcomes will be assessed using descriptive statistics and thematic analysis. Secondary outcomes will be analysed using mixed-effects regression models. Conclusions: We anticipate that the Flourishing Intervention will be feasible and acceptable to parents. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12626000187347). Full article

This study addresses the challenge of early-stage recognition of control chart patterns in statistical process control, which is critical for timely detection of process abnormalities in real-time manufacturing environments. Unlike most existing approaches that focus on fully developed patterns, this work targets partially developed patterns within a fixed observation window to enable proactive intervention. A multi-layer perceptron (MLP) classifier was developed using statistical features, and a structured design of experiments (DOE) approach was employed to optimize both the feature set and network parameters. Simulated X-bar chart data representing six pattern types were used, and candidate features were systematically evaluated using fractional factorial design. The results identified an effective feature subset consisting of autocorrelation, mean, mean square value, standard deviation, slope, and cumulative sum. The optimized MLP achieved an offline accuracy of approximately 86%, while online implementation yielded an overall accuracy of 70.6% with acceptable error rates and average run length performance (ARL₀ = 207.3, ARL_I = 10.9). The findings demonstrate that, despite greater difficulty in online recognition, the proposed approach provides a practical and interpretable solution for early detection in quality control systems. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

In water-scarce regions such as Xinjiang, China, agricultural development is constrained not only by limited water resources but also by a strong reliance on water-saving irrigation technologies. Drip irrigation is a key measure for improving irrigation efficiency and promoting the sustainable development of water-saving agriculture. However, defects arising during the manufacture of labyrinth Drip emitters—the core components of drip irrigation systems—can undermine system reliability, leading to channel blockage and non-uniform irrigation. To tackle this issue, a defect detection approach is developed by integrating Digital Twin technology with an enhanced YOLOv8 model for online inspection of labyrinth Drip emitters on drip irrigation tape production lines. In parallel, a self-built dataset covering six defect categories is established. Supported by the DT framework, the standard YOLOv8 network is refined to strengthen its capability in identifying complex micro-defects. Specifically, DySnakeConv is introduced to better represent the curved and slender characteristics of labyrinth channels; DySample is incorporated to improve the reconstruction and representation of fine-grained details; an Efficient Multi-Scale Attention module is adopted to capture richer contextual information while suppressing background noise; and Inner-SIoU is applied to optimize the bounding-box regression process. Experimental results show that the model achieves 89.6% precision, 90.9% recall, and 93.9% mAP50. Compared with the baseline YOLOv8, precision, recall, and mAP50 are improved by 7.3, 3.9, and 3.3 percentage points, respectively. Under the same training conditions, the proposed model outperforms YOLOv10 and YOLOv11 in accuracy-related metrics. Specifically, compared with YOLOv11, precision, recall, and mAP50 are improved by 4.8, 5.0, and 2.6 percentage points, respectively; compared with YOLOv10, they are improved by 10.0, 7.7, and 7.3 percentage points, respectively. Meanwhile, the model maintains a lightweight size of 3.7 M parameters and a real-time inference speed of 150.2 FPS, demonstrating a favorable accuracy–efficiency trade-off. By extending manufacturing-level quality control to agricultural applications, the approach helps ensure uniform irrigation and improve water-use efficiency, providing practical technical support for precision agriculture in arid regions. Full article

With the rapid growth of e-commerce, recommender systems have been widely adopted across diverse online services by presenting products aligned with user preferences. Moreover, review-based recommender systems have been studied to alleviate the sparsity of interaction data. However, many studies directly use full review texts, which may contain redundant semantics or noise that is irrelevant to recommendations, thereby degrading data quality and recommendation performance. To address this limitation, this study proposes summarized reviews fusion for adaptive recommendation (SuReFAR), which predicts ratings by summarizing reviews into key information using a multi-summarization strategy. Specifically, SuReFAR utilizes TextRank and bidirectional and auto-regressive transformers (BART) to generate extractive and abstractive summaries of user and item review sets, respectively. Subsequently, we apply an attention mechanism to emphasize salient information within each summary representation and fuse multiple summary representations by adaptively controlling their contributions through a gated multimodal unit (GMU) to predict ratings. We conducted experiments on Amazon and Yelp review datasets, demonstrating that the proposed model consistently outperforms baseline models and captures user preferences more effectively via personalized summary representations. Full article

In nanoscale wafer defect inspection, raster scan imaging imposes sub-micrometer requirements on motion stage tracking accuracy, while trajectory changes and load variations pose significant challenges to traditional control methods. This paper proposes a Real-time Iterative Compensation based Adaptive Robust Control (RICARC) strategy. Within this framework, the ARC module incorporates RLS-based online parameter estimation, a PID-type feedback control term, and a robust control term to suppress lumped disturbances. On this basis, the RIC module establishes a discrete prediction model based on the ARC closed-loop system and iteratively generates optimal feedforward compensation signals at each sampling instant to further suppress residual tracking errors. Experimental results across five operating scenarios, including periodic, dual-frequency, and S-curve trajectories, as well as payload variation, and strong external disturbances, demonstrate that RICARC consistently achieves sub-micrometer RMS accuracy ranging from 0.120 to 0.240 μm, reducing RMS errors by over 75% compared with conventional ARC, effectively enhancing imaging quality in nanoscale wafer defect detection systems. Full article

Background/Objectives: Increasing evidence implicates the microbiome–gut–brain axis in Autism Spectrum Disorder (ASD) pathophysiology, prompting interest in probiotics as a therapeutic strategy, although findings remain inconsistent. This systematic review aimed to evaluate the clinical efficacy of probiotic supplementation on core ASD symptoms, examine the outcome measures used, and provide insights into optimal probiotic interventions. Methods: This review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Scopus, Web of Science, Ovid, ProQuest, and Wiley Online Library were searched for studies published between January 2015 and July 2025. Randomized, non-randomized, and open-label clinical studies evaluating oral probiotic supplementation in children and adolescents with ASD were included. Outcomes assessed core symptom domains using validated instruments. Study selection, data extraction, and risk-of-bias assessment (RoB 2 and ROBINS-I) were performed independently by multiple reviewers. Due to methodological heterogeneity, the findings were synthesized narratively. Results: Fourteen studies involving 924 children and adolescents with ASD across seven countries or regions were included, of which ten were randomized controlled trials. Eight studies reported significant improvement in core ASD symptoms, predominantly within the social and communication domain. The most frequently used assessment tools were the Social Responsiveness Scale (SRS), Autism Treatment Evaluation Checklist (ATEC), and Autism Diagnostic Observation Schedule (ADOS). Lactobacillus reuteri supplementation for at least three months was consistently associated with improvement in social behavior. Conclusions: L. reuteri supplementation possibly improves social and communication function in children with ASD. However, there is limited high-quality evidence on this. Evidence for other core domains remains limited and inconsistent, highlighting the need for well-designed, multicenter trials using standardized outcome measures and strain-specific hypotheses. Full article

Texture characteristics are critical quality evaluation indicators for soft foods. Traditional texture profile analysis (TPA) relies on probe–sample contact and may cause irreversible structural damage, limiting its application in nondestructive or online detection. In this study, a non-contact and nondestructive Controlled Airflow–Laser Texturemeter (CAFLT) system was developed to achieve rapid multi-parameter texture characterization. The system integrates programmable airflow loading with laser displacement sensing to implement a TPA-like double-cycle loading protocol, simultaneously acquiring time–applied airflow pressure (T–AP) and time–displacement (T–D) responses. Gelatin–maltose composite gels with graded Bloom strengths (CL50–CL250) were used as model samples. Texture-related descriptors were extracted using a dual-curve feature framework and compared with traditional TPA measurements. The CAFLT system produced a double-peak response pattern resembling that of traditional TPA and showed clear monotonic trends with increasing gel strength. Hardness_CAFLT exhibited a strong correlation with the reference TPA hardness value (r = 0.97). In addition, Gumminess_CAFLT showed a positive association with traditional gumminess (r = 0.87), but should be interpreted within the CAFLT-specific loading framework. Multivariate principal coordinates analysis further demonstrated clear multivariate discrimination among samples. Additionally, the time-domain descriptor t_Peak1 showed a strong power-law relationship with Bloom strength ($R^2=0.96$), indicating enhanced sensitivity to mechanical differences under small-deformation conditions. Overall, the CAFLT system provides a feasible approach for non-contact, nondestructive, and quantitative texture evaluation of soft foods, and shows strong potential for real-time quality monitoring and intelligent food inspection. Full article

Aiming at the problems of difficult hard-constraint enforcement and weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard-constraint safety assurance: a Constraint-Discounted Soft Actor–Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; and a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensures the smoothness and feasibility of the trajectory, and has a good adaptive capacity to complex environments with unknown obstacle configurations, thus providing an efficient solution for the autonomous and safe operation of manipulators. Full article

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

This paper investigates a five-level active neutral-point-clamped (5L-ANPC) converter operating in rectifier mode with unbalanced DC-side loads, where neutral-point (NP) deviation may deteriorate grid-current quality. Conventional space-vector pulsewidth modulation (SVPWM) is typically derived under the split-capacitor-voltage symmetry assumption; when NP deviation occurs, fixed sector boundaries and ideal volt–second balance calculations can lead to sector misclassification and synthesis errors. To address this issue, an NP-aware SVPWM scheme is proposed by reconstructing sector criteria using real-time capacitor voltages and correcting the vector dwelling-time computation to improve modulation accuracy under imbalance. Based on the power-transfer mechanism, an average-power boundary condition is further derived to quantify the admissible upper/lower load power ratio that allows NP regulation without additional hardware, and its validity is examined under resistive-load cases. Moreover, for battery-type loads exhibiting voltage-source characteristics, the control objective is extended from voltage symmetry to controllable power/charge allocation by establishing a mapping between the small-vector duty ratio and the branch average-power ratio, with constrained online solution and smoothing to mitigate coefficient jitter. Experimental validation is conducted on an OPAL-RT OP5707-based hardware-in-the-loop platform, where both single-phase and three-phase 5L-ANPC systems are implemented according to different verification objectives. The derived boundary condition for resistive loads is examined in the single-phase system, while the proposed modulation and battery-load power-allocation strategy are verified in the three-phase system. The three-phase arrangement is adopted for the battery-load case in order to avoid the second-order power ripple inherent to single-phase operation. Full article

Combine harvesters are core modern grain production equipment with high reliability, critical for food security. Yet their metal parts suffer severe grain-induced wear during operation, directly reducing efficiency, increasing grain loss, and raising maintenance costs and environmental burdens. This paper clarifies the grain-induced wear source characteristics and the dominant mechanisms and hazards for combine harvester metal surfaces, as well as summarizes the research progress of four key protection strategies: wear-resistant materials, surface engineering, structural and parameter optimization, and maintenance and remanufacturing. Based on the latest research data, the working principles, performance advantages and application scenarios of various protective technologies were analyzed. Current research faces several challenges: insufficient systematic wear data for multiple crops, unclear multi-factor coupled wear mechanisms, limited low-cost and long-lasting protective technologies, and the absence of online wear monitoring techniques. Finally, the directions for future research focus, such as the systematic research on the wear characteristics of multiple crops, the deepening of the wear mechanism of multi-factor coupling, the development of green, low-cost and long-term protection technologies, and the development of online wear monitoring and active control systems, are explored, providing theoretical support and technical reference for the transformation of wear control in combine harvesters, from passive maintenance to active protection throughout the entire life cycle. Such future work supports the high-quality development of agricultural mechanization and ensures food security. Full article

Ridesplitting is a promising strategy to enhance vehicle efficiency in urban mobility services during mega-events. However, designing dispatching algorithms that effectively balance high service rates with acceptable passenger delays under high-demand, reservation-based scenarios remains a significant challenge. To address this issue, this study proposes a novel two-stage dispatch framework: Offline Grouping and Online Matching (OGOM). In the offline stage, the request grouping problem is formulated as a weighted hypergraph maximum matching (WHMM) problem. A sequence inference (SI) method is introduced to accelerate the construction of candidate ridesplitting trips, and the WHMM problem is solved optimally using the Gurobi solver. In the online stage, the dispatch process is completed within an event-based simulation environment built with MATSim. The framework is validated through a comprehensive case study of the Hangzhou Asian Games. The results demonstrate that the proposed OGOM framework achieves a mean service rate of 92.12%, representing an 8.74% improvement over a rolling horizon batching benchmark. Concurrently, the average passenger delay is maintained between 2 and 4 min across all simulation runs. Furthermore, the framework reduces the average request completion distance by over 30% compared to a non-ridesplitting baseline. The proposed SI method also shows a 49.35% reduction in computation time for hypergraph construction compared to conventional methods. These findings confirm that the OGOM framework provides an effective and scalable operational strategy for mega-event ridesplitting services, simultaneously improving service quality through optimized supply–demand matching and controlled passenger delays. Full article