Search Results (2,740)

The tractor road, as the core scene for autonomous driving of grain transport vehicles, is unstructured, complex, and obstacle-rich, leading to poor real-time performance and accuracy of joint road and obstacle detection with existing YOLOv5s. Furthermore, the reliability of passable area evaluation is low solely based on environmental factors. Therefore, YOLOv5s-C2S is proposed, fusing multi-scale features, attention mechanism, and dynamic features for joint detection. Firstly, YOLOv5s-CC is proposed for road detection by fusing context and spatial details and introducing Criss-Cross attention. Secondly, YOLOv5s-SGA is proposed for obstacle detection by grouped and spatial convolution, parameter-free attention, and adaptive feature fusion. By reusing YOLOv5s-CC weights, YOLOv5s-C2S shares low-level features and decouples high-level specificity. Based on the tractor road and obstacle information, combined with vehicle factors, a weighted scoring–based comprehensive method for passable area evaluation is proposed. Finally, the method was verified through experiments with an intelligent tracked grain transport vehicle using self-constructed datasets, including VOC_Road (11,927 images) and VOC_Obstacle (21,779 images). Compared with existing YOLOv5s, Deeplabv3+, FCN, Unet and SegNet, the mAP₅₀ of road detection by YOLOv5s-CC increased by over 1.2%. Compared with existing YOLOv5s, R-CNN, YOLOv7, SSD and YOLOv8n, the mAP₅₀ of obstacle detection by YOLOv5s-SGA increased by over 2%. Compared with YOLOv5s-SD, the mAP₅₀ of joint detection by YOLOv5s-C2S increased by 9.3%, and the frame rate increased by 7.0 FPS. The proposed passable area evaluation method exhibits strong robustness and reliability in complex environments, meeting the accuracy and real-time requirements in autonomous driving of grain transport vehicles. Full article

Chryseobacterium indologenes MA9 is a causative agent of root rot disease in Panax notoginseng (P. notoginseng), with its high incidence being a major manifestation of continuous cropping barriers, severely hindering the sustainable development of the P. notoginseng industry. In this study, a novel lytic bacteriophage, MA9V-3, was isolated from wastewater, targeting C. indologenes MA9. The phage produced clear plaques, ranging from 1 to 3 mm in diameter, with a surrounding halo. Phage MA9V-3 achieved an adsorption rate of up to 80% after 30 min of contact with C. indologenes MA9, a latent period of approximately 40 min, and an average burst-size if 160 PFU/cell. Transmission electron microscopy revealed that phage MA9V-3 possesses an icosahedral head and a contractile tail, exhibiting a typical myovirus-like morphology. According to the latest ICTV taxonomy, MA9V-3 belongs to the class Caudoviricetes, and the phage’s biocontrol efficacy and inhibitory capacity were evaluated at different multiplicity of infection (MOI s). The results showed that the highest titer recorded at 1.6 × 10¹⁰ PFU/mL. Whole-genome sequencing revealed that MA9V-3 is a double-stranded circular DNA virus, with a genome length of 103,203 bp, GC content of 34.29%, and 150 open reading frames (ORFs), one of which is related to tRNA. Only 13 of these ORFs encode known functional sequences, likely due to the limited available gene data for such phages in the database, with additional details on hypothetical proteins yet to be uncovered. Comparative database analysis confirmed that the phage genome contains no antibiotic resistance or toxin-related genes. Phage therapy experiments were performed using MA9V-3 and two other phages screened in our laboratory. The experimental results showed that phage MA9V-3 may be a potential candidate for effectively controlling the infection of Panax notoginseng by C. indologenes MA9, and offering valuable insights into the potential application of phage therapy for managing bacterial plant diseases. Full article

The retrofit of existing residential buildings plays a critical role in reducing energy consumption and carbon emissions in the building sector. However, previous retrofit evaluations often fail to account for the age-related thermal and lighting requirements of residents in aging residential buildings, thereby overlooking the substantial behavioral heterogeneity that shapes retrofit effectiveness. This study evaluates the comprehensive performance of different building envelope retrofit strategies, considering occupants’ thermal and visual comfort, from the perspectives of energy efficiency, economic feasibility, and environmental sustainability. First, age-specific differences in occupancy patterns, thermal preferences, and lighting requirements between elderly and non-elderly comparison group occupants were systematically extracted from the literature. Then, a typical high-rise residential building was modeled in EnergyPlus to serve as the reference building, within which the differentiated occupant behavior models were implemented, and the pre-retrofit condition was defined as the baseline scenario. Next, six commonly applied exterior wall insulation materials and different glass configurations and window frames were parameterized and evaluated under varying insulation thicknesses and remaining building service life scenarios. Finally, the energy-saving performance, economic benefits, and carbon reduction potential of envelope retrofit measures were quantitatively assessed across three primary functional zones (bedroom, living room, and study), using area-normalized indicators. The results indicate that, in the retrofit of existing residential buildings, bedrooms and study rooms exhibit greater retrofit benefits than living rooms, primarily due to longer occupancy durations and higher heating demand. In terms of retrofit strategies, exterior wall insulation consistently outperforms window retrofitting in energy-saving potential, with energy-saving rates of approximately 3.2–4.3% depending on functional zone, material type, and insulation thickness. Among the evaluated materials, vitrified microbead insulation performs best overall in terms of energy, economic, and carbon benefits at 40–60 mm thickness. These findings support occupant-informed, low-carbon retrofit decision-making for existing residential buildings. Full article

Declining birth rates are reshaping higher education across East Asia, accelerating the large-scale underutilization and, in some contexts, partial abandonment of university campus assets. Although adaptive reuse has been widely discussed, campus transformation is often framed primarily as a programmatic or policy problem, with limited attention to the inherited spatial logic embedded in campus morphology. This study revisits Le Corbusier’s concept of unlimited growth as a generative framework for campus transformation. Rather than treating it as a museum-specific historical typology, the research reinterprets unlimited growth as a scalable spatial logic defined by modular continuity, circulation hierarchy, and open-ended sequencing. To enhance reproducibility and operational clarity, the study formalizes a typo-morphological decoding protocol—modules, circulation, and growth sequence—and applies it through plan-, section-, and diagram-based analysis. Through comparative examination of three museum precedents—Sanskar Kendra Museum, the National Museum of Western Art (Tokyo), and the Chandigarh Museum and Art Gallery—the study extracts a set of transferable spatial mechanisms: modular increment, circulation-centered ordering, directional displacement, and fifth-façade ecological continuity. These mechanisms are then translated into an operational right-sizing model and tested through a design-operational demonstrator on a single anonymized Taiwanese campus experiencing demographic contraction. The findings indicate that unlimited growth functions not merely as a formal principle but as a spatial governance logic that supports phased consolidation, adaptive recomposition, and system-level coherence under long-term uncertainty. Importantly, this framework contributes to sustainability by reducing land consumption through spatial consolidation, minimizing unnecessary new construction, enabling adaptive reuse of existing campus assets, and improving long-term resource-use efficiency through phased right-sizing and ecological continuity. This study further advances a reproducible, mechanism-based methodological framework for institutional spatial transformation, providing a transferable approach for large-scale campus restructuring under conditions of long-term demographic and environmental uncertainty. Full article

Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems. Full article

Motor vehicle encroachment into non-motorized lanes is a common but hard-to-verify violation in urban intersections, especially when monitored from unmanned aerial vehicles (UAVs) or high-mounted overhead views. Existing rule-based solutions built on horizontal bounding boxes and center-point/line-crossing criteria are sensitive to perspective distortion, occlusion, and frame-to-frame jitter, resulting in unstable decisions and low evidential value. This paper presents a cascaded UAV-view system that closes the loop from perception to evidence output through detection–segmentation–recognition–decision. First, we adopt a two-stage detection cascade: a lightweight vehicle detector localizes vehicles using axis-aligned bounding boxes, and a dedicated YOLOv5n-based oriented bounding box (OBB) license plate detector, constructed via architecture grafting and weight transfer, is then applied within each vehicle region of interest (ROI) to localize rotated license plates under large pose variation and small-target conditions. Second, a U-Net lane region segmentation module provides pixel-level spatial constraints to define an enforceable lane occupancy region. Third, a perspective rectification step is integrated with the PP-OCRv4 optical character recognition (OCR) framework to improve license plate recognition reliability for tilted plates. Finally, an area ratio criterion and an N-frame temporal counter are used to suppress transient misdetections and stabilize alarms. On a representative 100-sample controlled encroachment benchmark, the proposed system improves detection accuracy from 67.0% to 92.0% and reduces the false positive rate from 32.35% to 5.88% compared with a baseline horizontal bounding box (HBB)-based rule. The system outputs both violation alarms and license plate evidence, supporting practical deployment for multi-view traffic governance. Full article

The European Union aims to cut greenhouse gas emissions by 55% by 2030 and reach climate neutrality by 2050, targets that depend on expanding renewable generation in the European energy system. While photovoltaic (PV) capacity has grown quickly in several member states, others remain far behind. This paper frames that divergence as a systems planning problem: installed MW expands through diffusion-like dynamics, but the conversion of investment into energizable capacity is filtered by grid-integration constraints and institutional throughput. The study develops a macro-level framework for systems-level assessment and decision support to guide PV capacity planning and budget allocation using official 2012–2022 data for 22 EU countries. We combine (i) unsupervised clustering of standardized national deployment trajectories, (ii) bounded logistic fits interpreted as an operational diffusion-with-saturation representation that yield comparable growth parameters and maturity years (80–90% of the estimated ceiling), and (iii) a proportional reallocation scenario for countries below 5 GW in 2022. Three clusters emerge—steady growth, early plateau, and atypical paths—and an analytically tractable maturity indicator integrates capacity, rate, and timing in a single measure. In a 10 GW reallocation scenario, average progress toward the 5 GW benchmark rises from 9.8% to 23.1%, closing about 14.8% of the aggregate shortfall. The allocation experiment reveals a clear asymmetry: systems with an existing installed base convert additional MW into benchmark progress more efficiently than very low-baseline systems, where binding constraints are more likely to sit in permitting, interconnection queues, and hosting capacity rather than in finance alone. Turning these allocations into usable capacity depends on timely interconnection and power-electronics integration and on grid-enablement constraints such as interconnection readiness, inverter compliance, and local hosting capacity in high-penetration areas. The contribution is a transparent, updateable decision-support pipeline that links observed trajectory regimes to a maturity “clock” and an auditable allocation baseline, making the trade-off between closing capacity gaps and respecting feasibility filters explicit in an EU system with heterogeneous national subsystems. The proposed approach links macro-level maturity clusters to operational feasibility signals in the grid integration layer, showing that modeling-based allocation can improve system progress but cannot substitute grid-enablement measures, highlighting the importance of regional coordination in the EU energy system under heterogeneous national trajectories. Full article

Long-period ground motions (LPGMs), rich in low-frequency content, can resonate with long-period structures like high-rise buildings, leading to severe damage. As seismic design shifts from safety toward resilience, limited attention to LPGMs makes it difficult to ensure the seismic resilience of long-period structures. This study used Perform-3D software to model three high-rise reinforced concrete (RC) frame–shear wall structures with varying periods and one with infill walls for resilience assessment. The resilience indicators and seismic resilience grades under LPGMs and ordinary ground motions (OGMs) were compared using the Standard for Seismic Resilience Assessment of Buildings (GB/T38591-2020) and the Guideline for Evaluation of Seismic Resilience Assessment of Urban Engineering Systems (RISN-TG041-2022), which are national standards in China. The results show that the structural response under LPGMs is significantly different from that under OGMs. In particular, the influence of LPGMs on displacement-sensitive non-structural components is much greater than OGMs. Resilience indicators were higher under LPGMs. The presence of infill walls notably reduced resilience indicators, with a stronger effect under OGMs. Based on GB/T38591-2020, the seismic resilience of each structure generally decreases by 1–2 grades under LPGMs, while evaluations based on RISN-TG041-2022 show similar ratings under both LPGMs and OGMs. Full article

High-frame-rate (HFR) ultrasound imaging enables the acquisition of up to several thousand frames per second, substantially improving the temporal resolution of echocardiography. This technical advancement allows visualization of rapid mechanical and hemodynamic events that are not captured by conventional systems. In this review, we summarize the methods used to achieve HFR acquisition and examine their application across three principal domains: deformation imaging, mechanical wave imaging, and blood flow imaging. In deformation imaging, clinical studies have demonstrated higher feasibility for myocardial motion tracking and more reliable temporal deformation parameters. Mechanical wave imaging has emerged as a complementary domain, using HFR acquisition to capture transient mechanical events and estimate regional myocardial stiffness under both physiological and pathological conditions. In flow imaging, improved temporal resolution enables detailed visualization of rapid intracardiac flow and the evaluation of complex hemodynamic patterns. This technology expands the scope of functional and quantitative cardiac assessment and is emerging as a valuable modality for noninvasive diagnosis and monitoring in cardiovascular disorders. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

Background: Real-time and accurate analysis of breast ultrasounds is crucial for diagnosis but remains challenging due to issues like low image contrast and operator dependency. This study aims to address these challenges by developing an integrated framework for real-time lesion detection and tracking. Methods: The proposed system combines Contrast-Limited Adaptive Histogram Equalization (CLAHE) for image preprocessing, a transfer learning-enhanced YOLOv11 model following a continual learning paradigm for cross-center generalization in for lesion detection, and a novel Detection-Based Tracking (DBT) approach that integrates Kernelized Correlation Filters (KCF) with periodic detection verification. The framework was evaluated on a dataset comprising 11,383 static images and 40 ultrasound video sequences, with a subset verified through biopsy and the remainder annotated by two radiologists based on radiological reports. Results: The proposed framework demonstrated high performance across all components. The transfer learning strategy (TL12) significantly improved detection outcomes, achieving a mean Average Precision (mAP) of 0.955, a sensitivity of 0.938, and an F1 score of 0.956. The DBT method (KCF + YOLO) achieved high tracking accuracy, with a success rate of 0.984, an Intersection over Union (IoU) of 0.85, and real-time operation at 54 frames per second (FPS) with a latency of 7.74 ms. The use of CLAHE preprocessing was shown to be a critical factor in improving both detection and tracking stability across diverse imaging conditions. Conclusions: This research presents a robust, fully integrated framework that bridges the gap between speed and accuracy in breast ultrasound analysis. The system’s high performance and real-time efficiency underscore its strong potential for clinical adoption to enhance diagnostic workflows, reduce operator variability, and improve breast cancer assessment. Full article

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

Integrating Lean Construction (LC), the Circular Economy (CE), and Construction 5.0 (C5.0) remains challenging in emerging delivery contexts. This difficulty increases when procurement routines determine which practices become enforceable across tendering, contracting, and site execution. This study prioritized barriers to LCCE5.0 implementation in Morocco and translated expert judgments into actionable recommendations. A structured literature review informed the barrier inventory and conceptual framing. The study proposed a three-layer, life-cycle LCCE5.0 framework that links governance, operational routines, and digital enablers. It operationalized 40 critical barrier factors across six dimensions and five life-cycle macro-phases. A two-round Delphi study was conducted with 22 Moroccan experts using a 7-point Likert scale. Barriers were ranked using Round 2 (T2) medians with ties resolved using the interquartile range. Top-box agreement (ratings of 6–7) and consensus tiers were reported. The ranking showed strong stability across rounds, with 92.5% of barrier factors remaining stable. Kendall’s W at T2 equaled 0.817 (p < 0.001), indicating high panel consensus. Results indicated that constraints clustered in upstream governance. Three procurement-centered regulatory and contractual barriers topped the ranking (Mdn_T2 = 7). These barriers reflected missing CE procurement guidelines, limited weighting of environmental criteria, and the absence of circularity and digital requirements in tenders. Six additional barriers reinforced this procurement bottleneck. They included limited owner commitment, weak enforcement authority, limited top-management commitment, and regulatory instability. They also included low interorganizational trust, limited risk-sharing contracts, and tool-centered deployment of LCCE5.0 practices. These findings support procurement-focused recommendations to institutionalize auditable circular requirements and data-enabled verification in tendering and contracting routines. The proposed LCCE5.0 mechanism and the resulting recommendations require empirical validation beyond this Delphi-based prioritization. Full article

To address challenges related to complex background interference and insufficient multi-scale target feature extraction in lettuce growth stage detection. The lightweight YOLOv10 detection model and the specific characteristics of lettuce field data were used. The CNCM channel non-local mixture mechanism and ASF adaptive spatial frequency attention mechanism were incorporated to optimize lightweight modules, including DownSample, Zoom_cat, and ScalSeq, within the original model. Consequently, an improved CCASF-YOLOv10 model was constructed, integrating multi-scale feature fusion and enhanced target feature extraction. Experimental results demonstrate that, in an NVIDIA A40 GPU testing environment, the model achieves an accuracy rate of 91.9%, a recall rate of 91.6%, mAP@0.5 of 95.3%, and mAP@0.5:0.95 of 72.9%. The parameter size is 11.9 M, and the single-frame inference speed is 24.76 ms, indicating a favorable balance between detection precision, model efficiency, and real-time inference. Furthermore, an intelligent machine vision detection system for lettuce growth-stage monitoring and precise field control was developed using the CCASF-YOLOv10 model. This system facilitates the industrial advancement of lettuce cultivation. Full article

Background and Objectives: Stroke is a leading cause of long-term disability in older adults, who often face persistent motor, cognitive, and functional challenges. Conventional stroke rehabilitation programs often involve highly repetitive motor tasks, which may reduce patient motivation and contribute to suboptimal adherence over time. Virtual reality (VR) offers an engaging alternative; however, much of the existing research has focused on specialized rehabilitation-oriented VR systems rather than off-the-shelf commercial platforms. This study evaluated older stroke survivors’ acceptance, tolerability, and lived experiences of a short VR-based rehabilitation session using a commercial game on a commercial wearable VR system. Methods: A single-session convergent mixed-methods design was employed. Thirteen community-dwelling older stroke survivors (mean age 79.2 ± 7.1 years; 9 males, 4 female) completed a 15 min VR session using a commercial wearable VR system. The Technology Acceptance Model (TAM) questionnaire and Simulator Sickness Questionnaire (SSQ) assessed acceptance and tolerability, while semi-structured interviews explored lived experiences. Qualitative data were thematically analyzed. Results: Participants reported high acceptance across all TAM domains (overall M = 4.35 ± 0.79, scale 1–5). Enjoyment/intention to use was rated highest (M = 4.77 ± 0.42), while perceived usefulness was lowest (M = 4.15 ± 0.77). VR was well tolerated: the SSQ total score was 17.38 ± 1.73, with most symptoms rated at the mild level only. Exploratory Spearman correlations revealed a significant positive association between age and SSQ total score (r_h = +0.568, p = 0.043). Thematic analysis identified five themes: (1) usability and accessibility; (2) therapeutic value; (3) engagement and motivation; (4) social and clinical support; and (5) physical and cognitive demands. Conclusions: A commercial wearable VR system was found to be acceptable, safe, and engaging for older stroke survivors. With supervision and therapeutic framing, it may serve as a motivating adjunct to conventional rehabilitation. Full article