Search Results (13,126)

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably. Full article

Cleaning historical silk textiles is a particularly sensitive operation that requires precise control to prevent mechanical or chemical damage. In this study, we investigate using flexible PVA–borax-based gels to remove soot from silk, i.e., polyvinyl alcohol–borax (PVA-B) gels and polyvinyl alcohol–borax–agarose double network gels (PVA-B/AG DN) loaded with different cleaning agents—namely, 30% ethanol and 1% Ecosurf EH-6—in addition to plain gels loaded with water. These gel formulations were tested on simplified model systems (SMS) and were applied using two methods: placing and tamping. The cleaning results were compared with a traditional contact-cleaning approach; micro-vacuuming followed by sponging. Visual inspection, 3D opto-digital microscopy, colorimetry, and machine-learning-assisted (ML) soot counting were exploited for the assessment of cleaning efficacy. Rheological characterization provided information about the flexibility and handling properties of the different gel formulations. Among the tested systems, the DN gel containing only water, applied by tamping, was easy to handle and demonstrated the highest soot-removal effectiveness without leaving residues, as confirmed by micro-Fourier Transform Infrared (micro-FTIR) analysis. Scanning electron microscope (SEM) micrographs proved the structural integrity of the treated silk fibers. Overall, this work allows us to conclude that PVA–borax-based gels offer an effective, adaptable, and low-risk cleaning strategy for historical silk fabrics. Full article

Accurate, subject-specific estimation of cervical muscle forces is a critical prerequisite for advancing spinal biomechanics and clinical diagnostics. However, this task remains challenging due to substantial inter-individual anatomical variability and the invasiveness of direct measurement techniques. In this study, we propose a novel data-driven biomechanical framework that addresses these limitations by integrating massive-scale personalized musculoskeletal simulations with an efficient Feedforward Neural Network (FNN) model. We generated an unprecedented dataset comprising one million personalized OpenSim cervical models, systematically varying key anthropometric parameters (neck length, shoulder width, head mass) to robustly capture human morphological diversity. A random subset was selected for inverse dynamics simulations to establish a comprehensive, physics-based training dataset. Subsequently, an FNN was trained to learn a robust, nonlinear mapping from non-invasive kinematic and anthropometric inputs to the forces of 72 cervical muscles. The model’s accuracy was validated on a test set, achieving a coefficient of determination (R²) exceeding 0.95 for all 72 muscle forces. This approach effectively transforms a computationally intensive biomechanical problem into a rapid tool. Additionally, the framework incorporates a functional assessment module that evaluates motion deficits by comparing observed head trajectories against a simulated idealized motion envelope. Validation using data from a healthy subject and a patient with restricted mobility demonstrated the framework’s ability to accurately track muscle force trends and precisely identify regions of functional limitations. This methodology offers a scalable and clinically translatable solution for personalized cervical muscle evaluation, supporting targeted rehabilitation and injury risk assessment based on readily obtainable sensor data. Full article

Sorghum (Sorghum bicolor L.)–soybean (Glycine max L.) intercropping produces a significant yield advantage over monocropping. However, a comprehensive synthesis is lacking to quantify yield benefits. This article provides a systematic review, a primary meta-analysis, and an exploratory meta-analysis to quantify the land productivity advantage of sorghum–soybean intercropping, explore the impact of planting configuration, and critically assess the methodological robustness of the existing literature. A random-effect meta-analysis of Land Equivalent Ratio (LER), with a primary analysis on studies with reported and calculated variance only (n = 23 treatments from six studies) and an exploratory analysis on the full dataset, which includes studies with imputed variances (n = 103 treatments from 21 studies). Group-specific analyses examined row configurations. The exploratory meta-analysis showed a pooled LER of 1.31 (95% CI: 1.25–1.36), suggesting an approximately 31% average land productivity gain (LER > 1). Configuration beyond a 1:1 row ratio showed potential for higher yield gains (LER = 1.43 for 2:2). Critically, over 75% of studies required variance data imputation. The analysis, limited to studies with reported or calculated variance data, showed a higher LER of 1.55 (95% CI: 1.41–1.69), but with extreme heterogeneity (I² = 96.2%). This highlights substantial outcome variability and inconsistent statistical reporting in the literature, limiting robust synthesis. Future research must prioritize long-term, well-replicated experiments with reported standardized variance and configuration evaluations to enable precise, locally relevant intercropping recommendations. Full article

Background/Objectives: Isolated cleft lips and/or palates often require orthognathic treatment. Traditional planning based on 2D images and plaster models limits precision; therefore, virtual surgical planning (VSP) and Computer-Aided Design and Computer-Aided Manufacturing (CAD/CAM) technologies are increasingly being used. The aim of this scoping review was to analyze the techniques, outcomes, and gaps in research on VSP in orthognathics for patients with isolated (non-syndromic) clefts. Methods: Searches were conducted in July 2025 in seven databases (including PubMed, Scopus, and Cochrane) without language restrictions, in accordance with the PRISMA guidelines for scoping reviews. Of the 2836 records, 36 publications were eligible after deduplication and full-text screening, and their Level of Evidence (LoE) was assessed using the Oxford CEBM scale. A risk of bias assessment was also conducted according to JBI tools. Results: The identified studies primarily comprised LoE III and IV; there were no systematic reviews or randomized controlled trials (LoE I). Descriptions of bimaxillary procedures and LeFort I osteotomies dominated. The most commonly used software was ProPlan CMF, Dolphin 3D, and Rhinoceros, although other tools have emerged in recent years. The available studies suggest that VSP increases translational and rotational accuracy and facilitates individualized treatment, and bimaxillary procedures bring better functional and aesthetic outcomes in patients with severe maxillary hypoplasia. Conclusions: Despite the growing interest in VSP in orthognathics, the scientific evidence is limited and mostly of lower quality. Well-designed prospective studies are needed to assess the long-term stability, quality of life, and cost-effectiveness of modern technologies. Full article

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research. Full article

Background: The shift toward precision orthodontics necessitates population-specific cephalometric databases. Reliance on Eurocentric norms for ethnically diverse populations—particularly underrepresented Middle Eastern groups—represents a significant evidence gap. This study establishes initial normative cephalometric data for Yemeni and Northern Turkish Cypriot (NTC) adults. Methods: This retrospective comparative study analyzed 400 lateral cephalograms from skeletal Class I adults (200 Yemeni and 200 NTC; age 18–40; gender-balanced). Twenty standardized parameters were assessed using VistaDent OC™ software (version 4.2.61, GAC Orthodontic Software solutions, Birmingham, AL, USA). Analyses included *t*-tests, MANOVA, effect size computations (Cohen’s *d*), and variance partitioning. The False Discovery Rate method controlled multiple comparisons. Results: Yemeni adults exhibited a more vertical facial growth pattern (indicated by a lower Jarabak ratio: 60.18 ± 4.50% vs. 65.79 ± 5.20%; *d* = 1.15) and pronounced soft-tissue convexity (N-A-Pog: 5.76 ± 1.20 mm vs. 3.82 ± 1.10 mm; *d* =1.69). NTC adults showed a mild skeletal Class II tendency (ANB: 4.51 ± 1.70° vs. 3.35 ± 1.50°; *d* = 0.72). Ethnicity accounted for 21.3% of craniofacial variance (partial η² = 0.213). Conclusions: This study provides foundational cephalometric reference data for two underrepresented populations. The significant morphological distinctions quantified here underscore the necessity of developing population-specific norms. These data should be considered as one component within comprehensive, individualized diagnostic frameworks in orthodontics, rather than standalone diagnostic criteria. Full article

Accurate interpretation of missense variants in cancer-associated genes remains a critical challenge in precision oncology, as most sequence-based predictors lack mechanistic explanations. Receptor tyrosine kinases like FGFR2 exemplify this problem: their function depends on conformational dynamics, yet most variants remain classified as variants of uncertain significance (VUS). In this paper we present DyVarMap, an interpretable structural-learning framework that integrates AlphaFold2-based ensemble generation with physics-driven refinement, manifold learning, and supervised classification using five biophysically motivated geometric features. Applied to FGFR2, the framework generates diverse conformational ensembles, identifies metastable states through nonlinear dimensionality reduction, and classifies pathogenicity while providing mechanistic attributions via SHAP analysis. External validation on ten kinase-domain variants yields an AUROC of 0.77 with superior calibration (Brier score = 0.108) compared to PolyPhen-2 (0.125) and AlphaMissense (0.132). Feature importance analysis consistently identifies K659–E565 salt-bridge distance and DFG motif dihedral angles as top predictors, directly linking predictions to known activation mechanisms. Case studies of borderline variants (A628T, E608K, L618F) demonstrate the framework’s ability to provide structurally coherent mechanistic explanations. DyVarMap bridges the gap between static structure prediction and dynamics-aware functional assessment, generating testable hypotheses for experimental validation and demonstrating the value of incorporating conformational dynamics into variant effect prediction for precision oncology. Full article

During tendon healing, collagen III expression precedes that of collagen I. The collagen I-to-III ratio at a certain time point post-laceration serves as an indicator of the healing status. Consequently, it is crucial to understand how different therapeutic approaches to support tendon healing affect the collagen I-to-III ratio in the extracellular matrix of a healing tendon, particularly across distinct anatomical zones. We compared the impact of a platelet-derived growth factor-BB (PDGF-BB) treatment via controlled release from coaxially electrospun DegraPol^® (Ab medica, Cerro Maggiore, Italy) hollow-fiber mesh with a treatment by the vehicle alone (no PDGF-BB) in the rabbit Achilles tendon full transection model and provide data on the collagen I-to-III ratio 3 weeks post-operation. For this purpose, we compared a dual-color Herovici staining to two single IHC labeling, for collagen I and collagen III, respectively. Herovici staining (HV) was expected to offer a more precise approach (pink-to-blue histogram) than the two separately labeled IHC stainings, both with chromogenic DAB labeling (red-to-green histogram), despite an anticipated positive correlation of the data assessed by these methods. Different zones were compared, i.e., native tendon tissue, reactive zone at interface to implant, hot zone within the core of the healing tendon and the zone within the scaffold, meaning the collagen deposited within the fibers of the implanted DegraPol^® tube, respectively. The analysis revealed that the ratios obtained via HV correlated weakly with the ratios obtained by IHC. Based on HV, PDGF-BB therapy led to higher collagen I-to-III ratios in all zones, except for the zone within the scaffold pores, while IHC did not reveal significant differences. Notably, collagen I-to-III ratios were not higher in immediate proximity, but rather distal from the PDGF-BB releasing implant, specifically in the core of the healing tendon tissue. Hence, a PDGF-BB therapy is suggestive of greater collagen maturation in specific zones of the healing tendon. Full article

Building cracks are significant indicators of structural integrity. Conventional fracture detection methodologies, however, are characterized by extended durations, significant labor requirements, and limitations in both precision and operational effectiveness. Findings are also subject to subjective and technical constraints inherent in manual assessments. To overcome these challenges, this paper introduces an enhanced YOLOv8-based methodology for developing a building crack detection system, thereby achieving high precision, operational efficiency, and cost-effectiveness. Initially, classified and segmented datasets of building fractures were obtained from field photography, online image aggregation, and open-source databases, thereby providing the basis for training the experimental model. Subsequently, the Swin Transformer window multi-head self-attention mechanism was implemented to augment small-object recognition capabilities and reduce computational demands, thereby enabling the development of an enhanced image segmentation module. Utilizing the U-Net’s segmentation capabilities, a rotated split method was implemented to quantify fracture width and derive geometric parameters from the segmented crack regions. In order to evaluate the effectiveness of the model, two experiments were conducted: one to demonstrate the performance of the classification category and the other to show the capabilities of the segmentation category. The result is that the proposed model has high accuracy and efficiency in the frac detection task. This approach effectively enhances fracture detection in structural safety evaluations of these buildings, providing technical support for relevant management decisions. Full article

In precision oncology research, achieving joint modeling of tumor grading and treatment response, together with interpretable mechanism analysis, based on multimodal medical imaging and clinical data remains a challenging and critical problem. From a sensing perspective, these imaging and clinical data can be regarded as heterogeneous sensor-derived signals acquired by medical imaging sensors and clinical monitoring systems, providing continuous and structured observations of tumor characteristics and patient states. Existing approaches typically rely on invasive pathological grading, while grading prediction and treatment response modeling are often conducted independently. Moreover, multimodal fusion procedures generally lack explicit structural constraints, which limits their practical utility in clinical decision-making. To address these issues, a grade-guided multimodal collaborative modeling framework was proposed. Built upon mature deep learning models, including 3D ResNet-18, MLP, and CNN–Transformer, tumor grading was incorporated as a weakly supervised prior into the processes of multimodal feature fusion and treatment response modeling, thereby enabling an integrated solution for non-invasive grading prediction, treatment response subtype discovery, and intrinsic mechanism interpretation. Through a grade-guided feature fusion mechanism, discriminative information that is highly correlated with tumor malignancy and treatment sensitivity is emphasized in the multimodal joint representation, while irrelevant features are suppressed to prevent interference with model learning. Within a unified framework, grading prediction and grade-conditioned treatment response modeling are jointly realized. Experimental results on real-world clinical datasets demonstrate that the proposed method achieved an accuracy of $84.6\%$ and a kappa coefficient of $0.81$ in the tumor-grading prediction task, indicating a high level of consistency with pathological grading. In the treatment response prediction task, the proposed model attained an AUC of $0.85$, a precision of $0.81$, and a recall of $0.79$, significantly outperforming single-modality models, conventional early-fusion models, and multimodal CNN–Transformer models without grading constraints. In addition, treatment-sensitive and treatment-resistant subtypes identified under grading conditions exhibited stable and significant stratification differences in clustering consistency and survival analysis, validating the potential value of the proposed approach for clinical risk assessment and individualized treatment decision-making. Full article

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Detecting rip currents from satellite imagery offers valuable information for the characterization and assessment of this coastal hazard. While recent advances in deep learning have enabled automatic detection from close-view beach images, the broader geospatial context available in far-view satellite imagery has not yet been fully exploited. The main challenge lies in identifying rips as small objects within large and visually complex scenes that include both beach and non-beach areas. To address this, we proposed a detection pipeline which partitions high-resolution satellite images into small regions on which rip currents are detected using a deep learning object detection model that merges the results. The merged results are processed by applying a deep learning classification model to filter out non-beach scenes, followed by applying the detection model on augmented images to remove spurious detection. The proposed pipeline achieved an overall accuracy of 98.4%, a recall of 0.890, a precision of 0.633, and an F2 score of 0.823 on the testing dataset, demonstrating its effectiveness in locating rip currents within complex coastal scenes and its potential applicability to other regions. In addition, a new rip image dataset containing far-view satellite imagery was constructed. With the new dataset, we demonstrated a potential application of the proposed method in characterizing rip occurrences and found that rip currents tended to occur at open beaches under moderate-energy, onshore-directed waves conditions. Overall, the proposed pipeline, unlike existing near-real-time rip current monitoring systems, provides a high-accuracy offline analysis tool for rip current assessment using satellite imagery. Along with the new dataset introduced in this work, it can represent a valuable step towards expanding available resources for improving automated detection methods and rip current research. Full article

Existing research on the linear rolling guide has predominantly focused on performance under ideal conditions or isolated error types, while systematic studies concerning multi-error coupling mechanisms and their impact on internal contact parameters remain limited. To address this, a comprehensive static model based on Hertz contact theory is proposed that simultaneously incorporates ball diameter, raceway radius, and raceway curvature center distance errors. This model is validated using finite element analysis (FEA) in ABAQUS, and the numerical results verify the feasibility and effectiveness of the proposed analytical model. Analysis of single, combined, and random errors indicates that geometric errors significantly influence vertical stiffness, load distribution, and critical load-carrying capacity. For example, as the ball diameter error varies from −2.5 to 2.5 μm, the vertical stiffness increases by a factor of 3.8, while a representative negative error combination reduces the critical load by nearly 40%. Additionally, random error analysis reveals that larger manufacturing tolerance ranges lead to increased fluctuation in ball contact forces, raising performance uncertainty. These findings establish the proposed model as a theoretical foundation for the precision design and load-bearing assessment of linear rolling guides under static conditions. Full article

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the precision of carbon sink assessments and provides critical structural parameters for ecosystem modeling. Most studies rely on a single SAR sensor or a limited range of SAR features, which restricts their ability to represent vegetation structural complexity and reduces biomass estimation accuracy. Here, we propose a phased fusion strategy that integrates backscatter intensity, interferometric coherence, texture measures, and polarimetric decomposition parameters derived from dual-frequency ALOS-2, GF-3, and Sentinel-1A SAR data. These complementary multi-dimensional SAR features are incorporated into a Random Forest model optimized using an Adaptive Genetic Algorithm (RF-AGA) to estimate forest total and component estimation. The results show that the progressive incorporation of coherence and texture features markedly improved model performance, increasing the accuracy of total AGB to R² = 0.88 and canopy biomass to R² = 0.78 under leave-one-out cross-validation. Feature contribution analysis indicates strong complementarity among SAR parameters. Polarimetric decomposition yielded the largest overall contribution, while L-band volume scattering was the primary driver of trunk and canopy estimation. Coherence-enhanced trunk prediction increased R² by 13 percent, and texture improved canopy representation by capturing structural heterogeneity and reducing saturation effects. This study confirms that integrating coherence and texture information within the RF-AGA framework enhances AGB estimation, and that the differential contributions of multi-dimensional SAR parameters across total and component biomass estimation originate from their distinct structural characteristics. The proposed framework provides a robust foundation for regional carbon monitoring and highlights the value of integrating complementary SAR features with ensemble learning to achieve high-precision forest carbon assessment. Full article