Search Results (97,988)

Diabetes complications may increase frailty rates among the elderly, leading to falls, immobility, dependency, hospitalizations, and death. The study aimed to assess any association between frailty status and glycaemic control among older adults with type 2 diabetes mellitus at Kenyatta National Hospital, Kenya. We conducted a cross-sectional study of 430 older individuals aged 60+ years with type 2 diabetes at a specialized diabetes clinic using a modified FRAIL scale. Mean age was 69.1 years; 65.7% were female and 76.2% completed primary school. Frailty prevalence was 3.8%, pre-frailty constituted 24.3%, and robust/non-frail comprised 71.9%. It was associated with age, social status, health knowledge, duration of DM, blood pressure, body mass index, high-density lipoprotein-C, and renal failure. Mean fasting plasma glucose (FPG) was 8.7 mmol/L, with 60% having FPG > 7 mmol/L; mean glycated haemoglobin (HbA1C) was 8.0%, with 41% having HbA1C > 8%. Glycaemic control was correlated with number of medications, blood pressure, and lipidaemia, but not age, sex, or social status. No correlation was found between frailty and glycaemic control: frailty versus FPG (r = 0.038, p = 0.459; χ² = 0.699, p = 0.705) and HbA1C (r = −0.009, p = 0.877; χ² = 0.046, p = 0.977). Low frailty prevalence was noted, with no association to glycaemic control. Our findings provide evidence for conducting frailty assessments in chronic disease care. Full article

Short-form video content has gained importance as a popular form of digital media due to the rising popularity of social media platforms and the decreasing attention spans of consumers. However, a major obstacle to popularity detection in short-form content is the heterogeneous nature of the data, encompassing textual, visual, and metadata components. To tackle this challenge, we propose FUSEPOP, a robust multi-modal architecture. The proposed framework utilizes ResNet-50 for visual feature extraction and XLM-RoBERTa for encoding multilingual textual information. FUSEPOP employs a mutual information-based modality weighting mechanism with logarithmic smoothing and a 0.7 weight ceiling to balance contributions from each input stream. Furthermore, FUSEPOP implements a robust stacked generalization strategy trained via stratified 5-fold cross-validation. This approach utilizes a logistic regression meta-learner to dynamically synthesize predictions from random forest, XGBoost, and a neural network-based classifier. Experimental results show that this architecture significantly outperforms benchmark models, achieving an accuracy of 0.980 and an average F1-score of 0.964 on the feature configuration selected for this study, and remains competitive on a literature-aligned alternative configuration. These findings confirm that the proposed model successfully detects popularity on short-form social media content. Full article

With the rapid development of public digital cultural resources, the lack of cross-lingual information retrieval (CLIR) services catering to multilingual users in practical applications has created significant language barriers. This hinders the promotion of public digital culture and results in the underutilization of relevant resources. To address this need, this paper constructs M-APE, a shared semantic model that operates without reliance on parallel corpora. Through a three-step process comprising the generation, fine-tuning, and optimization of a shared semantic space, M-APE establishes a common semantic framework for diverse languages. The model utilizes a Chinese semantic space, transferred and trained on authentic public cultural corpora, as its input. Evaluation based on bilingual dictionary induction quality demonstrates that M-APE significantly enhances semantic sharing performance between Chinese and Indo-European languages, represented here by English and French, achieving an average cross-family transformation accuracy of 56.6%. Furthermore, focusing on the CLIR needs of multilingual users within China’s public cultural engineering projects, this study develops a Chinese-English-French cross-lingual information retrieval framework by integrating M-APE into public cultural domain tasks. Experimental results indicate that the proposed method achieves superior cross-lingual retrieval performance in terms of average metrics. Full article

Background: Emotional distress (ED) is common among patients with gynecological malignancies and is associated with reduced quality of life and suboptimal health outcomes. Total-score approaches may overlook the complex interrelationships among individual emotional symptoms. Objective: This study provides a theory-informed contextual application and empirical boundary test of symptom network analysis, organized by the Stress Process Model (SPM), to examine not only how ED symptoms cluster and connect with psychosocial correlates and quality-of-life domains, but also whether psychosocial stratification is reflected in altered symptom topology or primarily in differences in distress burden. Methods: A cross-sectional study was conducted among 415 patients with gynecological malignancies recruited from a tertiary hospital in China. ED was assessed using the Brief Profile of Mood States-Short Form (BPOMS-SF30). An exploratory three-track screening strategy was used to derive a focused 16-node set of frequent negative mood symptoms. Gaussian graphical models with EBICglasso regularization were estimated for the symptom network and for extended networks including demographic/clinical variables, SPM-related psychosocial variables, and quality-of-life indicators. Results: The ED network showed dense positive connectivity, with strong within-domain clustering and several cross-domain associations. Exhaustion, restlessness, and irritability were relatively more relationally prominent in the primary network, although centrality stability was low to moderate across models. Fatigue-related symptoms were closely connected with anxiety, depressive symptoms, and impaired quality of life. Among psychosocial variables, self-perceived burden showed the strongest conditional association with fatigue. Adjusting for demographic and clinical variables did not materially alter the core symptom network, and no significant subgroup differences in global strength or overall structure were observed across psychosocial strata. Conclusions: In this sample, psychosocial risk stratification appeared to relate more to the overall severity and burden of distress than to major reorganization of symptom topology. The study therefore contributes primarily as a theory-informed contextual application of network methods and as an empirical boundary test showing that several psychosocial strata did not exhibit major topological differences. Because the retained nodes were selected for prevalence, association strength, and selection stability, the observed prominence of fatigue- and activation-related symptoms should be interpreted as conditional on this focused symptom subset. Overall, the findings are correlational, exploratory, and hypothesis-generating. Full article

Background/Objectives: Sphingosine-1-phosphate (S1P) is implicated in glycemic control. However, its circulating levels and clinical significance in type 2 diabetes mellitus (T2DM) remain controversial. We assessed plasma S1P levels in T2DM patients, its associations with metabolic parameters and complications, and explored its biomarker potential and non-linear (U-/J-shaped) relationships. Methods: This cross-sectional study enrolled 140 patients with T2DM and 63 matching healthy controls. Plasma S1P was measured by competitive ELISA. Statistical analyses included comparisons, correlation, ROC analysis, multivariable logistic regression, and quadratic/spline regression for U-shaped relationships. Results: Plasma S1P was significantly elevated in T2DM patients [1256.7 (149.4–1510.0) ng/mL] compared to controls [1075.1 (202.0–1510.0) ng/mL; p < 0.001]. S1P correlated positively with age, disease duration, HbA1c, insulin resistance, TyG index, triglycerides, systolic blood pressure, and negatively with HDL-C. Patients with complications had higher S1P than those without (p = 0.001), with progressive increases from retinopathy to nephropathy to mixed complications. Insulin-treated patients exhibited the highest S1P levels (p < 0.001). ROC analysis showed moderate diagnostic accuracy (AUC = 0.724). S1P is an independent associated factor with complications (OR = 1.18 per 100 ng/mL, p = 0.003). Non-linear analysis revealed a U-shaped relationship with HDL-C (optimal S1P: 1100–1350 ng/mL) and a J-shaped relationship with complication risk (threshold ~1250 ng/mL). Conclusions: Plasma S1P is elevated in T2DM and correlates with disease severity, glycemic control, insulin resistance, and complications. S1P demonstrates moderate biomarker potential and exhibits non-linear U-/J-shaped relationships with metabolic parameters, suggesting an optimal therapeutic window of 1100–1280 ng/mL. These findings support S1P as a marker of cumulative disease burden and a potential therapeutic target. Full article

Idiopathic pulmonary fibrosis (IPF) and autoimmune usual interstitial pneumonia (aUIP) share overlapping clinico-radiological features, complicating differential diagnosis. Electronic nose (eNose) technology characterizes exhaled breath profiles (“breathprints”) and may offer a non-invasive diagnostic approach in fibrotic interstitial lung diseases. To evaluate whether eNose breathprint analysis can discriminate between IPF and aUIP. In this cross-sectional study of 60 patients (34 IPF, 26 aUIP), breathprints were analyzed using principal component analysis (PCA, retaining eigenvalues > 1). Group differences were assessed via independent t-tests. Linear discriminant analysis (LDA) with leave-one-out cross-validation evaluated the discriminatory performance of PC combinations. PCA identified four principal components, with PC1 explaining 96% of the total variance. PC1 scores were significantly higher in aUIP compared to IPF (mean difference −0.53; 95% CI −1.04 to −0.02; p = 0.04); PC2-PC4 showed no significant differences (p > 0.3). LDA utilizing PC1 and PC3 achieved a cross-validated classification accuracy of 73.3% (95% CI 60.7–84.4, p < 0.05). eNose-derived breathprints showed preliminary discriminatory potential between IPF and autoimmune UIP, supporting further validation of this non-invasive adjunctive approach. Breathomics represents a promising non-invasive adjunctive tool for phenotyping fibrotic interstitial lung diseases, though larger validation studies integrating clinical and biological data are warranted. Full article

This manuscript studies the construction of nonlinear feedback shift registers that generate binary de Bruijn sequences by means of the cross-join pairs method. We explicitly state the original contribution of our study as follows: We combine the classical theorem of Mykkeltveit and Szmidt with constructive algorithms, implementation details for small orders, and an interpretation in terms of Fryers’ polynomials. We also clarify the relation to earlier methods, discuss computational complexity and practical limitations, formalize the definition of the weighted adjacency matrix, and expand the conclusion with applications and directions for future work. Numerical results for small orders demonstrate the feasibility of the approach. Full article

Accurate and trustworthy prediction of lithium-ion battery aging remains challenging due to multi-mechanistic degradation, cell-to-cell variability, and distribution shift between laboratory calibration and deployment. Fractional-order models have been proposed to capture long-memory effects in electrochemical systems; however, it remains unclear when such memory is empirically identifiable and beneficial within the common prognostics abstraction of state-of-health (SOH) versus cycle index. This work develops a fully reproducible computational pipeline for mechanistic battery aging based on a Caputo fractional differential equation (FDE) and evaluates its cross-cell generalization on open NASA cycling data. Parameters are identified using bounded robust nonlinear least squares and validated under a strict transfer protocol: calibration on cells B0005/B0006 and evaluation on held-out cells B0007/B0018 without refitting. The fractional model is benchmarked against a classical ODE surrogate, an ECM-inspired resistance-proxy baseline, and one-step-ahead machine-learning predictors. Uncertainty quantification is performed via parameter bootstrap and subsequently calibrated using conformal correction to target nominal coverage under transfer. Results show that the fractional order tends to collapse toward the integer-order limit (α → 1) in this dataset, indicating limited evidence of additional long-memory at the SOH-versus-cycle level under the considered protocol, while robust identification remains essential for stability. Calibrated prediction intervals achieve near-nominal coverage on held-out cells, highlighting the importance of UQ calibration under cell-to-cell shift. The proposed scripts and environment specifications enable direct replication and facilitate future extensions to stress-aware fractional models and hybrid physics–ML approaches. Full article

In robotic grinding of complex curved surfaces, the low stiffness of serial robots causes tool tip deflection and degrades surface quality. The axial symmetry of grinding discs introduces a free rotational parameter at each waypoint, converting a standard 6-DOF robot into a functionally redundant system. However, this redundancy has not been systematically exploited for stiffness optimization along the trajectory. This paper proposes a hierarchical dynamic programming framework to optimize the redundancy angle sequence over the entire grinding trajectory. A kinematic transformation parameterizes the flange target by the redundancy angle, enabling enumeration of feasible candidate configurations over a discretized grid. A composite stiffness index that accounts for the normal, feed, and cross-feed grinding force components is formulated at the contact point. Hierarchical constraint filtering removes configurations that violate posture, singularity, velocity, acceleration, and stiffness constraints. The Viterbi algorithm then recovers the minimum-cost path that balances stiffness performance and joint motion smoothness. Finally, a post-processing step based on a cubic smoothing spline generates $C^2$-continuous joint trajectories. Simulations on a UR5 robot grinding a curved surface evaluate the proposed framework against fixed-angle, greedy, and flange-stiffness baselines. The proposed method improves the mean composite stiffness by 31.7% and 17.9% over the fixed-angle and flange-stiffness baselines, respectively, and reduces the maximum joint jump by two orders of magnitude compared with the greedy strategy. Experimental validation on a UR5 robot confirms that the smoothed trajectory is accurately tracked while the stiffness threshold is preserved. A multi-trajectory analysis further shows that the stiffness threshold is maintained across all grinding trajectories. These results demonstrate the effectiveness of the proposed framework for redundancy optimization in robotic grinding with tool spin symmetry. Full article

Background: Untargeted proteomics enables quantitative host cell protein (HCP) determination in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. Methods: A prospective total-error (TE) validation of label-free ddaPASEF proteomics was performed. A stable isotope-labeled whole-proteome standard was spiked into NISTmAb at seven levels (20–80 ng) and analyzed in four independent assays (198 injections), supporting one-way random-effects ANOVA with Welch–Satterthwaite adjustment. Peptide-level identification error was evaluated by dual entrapment. Results: Empirical false-discovery proportions were below 1% at q = 0.01. Weighted least-squares regression (R² = 0.993) confirmed stable proportional compression with 81–85% recovery. Repeatability dominated the variance structure (median CV 2.7%); intermediate precision SD ranged from 0.69% to 3.81%. Both 95% β-expectation and 95/95 content tolerance intervals were contained within ±30% at all levels, defining a validated range of 20–80 ng. Abundance-stratified TE profiling revealed concentration-dependent calibration heterogeneity, with stratum-specific intervals within ±35% defining an abundance-aware LLOQ of 3.6 ppm (P95 = 3.87 ppm). Robustness under independent search software (FragPipe v24.0, CCC = 0.998) and cross-platform acquisition (Astral, CCC = 0.980) remained within ±30% limits. Conclusions: This constitutes the first prospective ICH Q2(R2)-aligned validation of untargeted proteomics for HCP quantification, with a transferable statistical framework for high-dimensional analytical methods. Full article

Despite being fundamental to modern infrastructure, the cement and concrete industry is a major contributor to global carbon emissions, necessitating urgent decarbonisation strategies to mitigate climate change and achieve net-zero targets by 2050. This review explores technological pathways and innovations essential for lowering carbon emissions, including low-carbon materials, energy-efficient processes, carbon capture, utilization and storage (CCUS), and advanced production technologies. It also highlights the importance of supportive policy frameworks, financial incentives, and international collaboration in accelerating the transition to a low-carbon industry. While challenges such as high initial costs, resistance to change, and knowledge gaps persist, these can be addressed through innovation, education, and robust financial mechanisms. Furthermore, circular economy principles, sustainable procurement practices, and continued research and development are emphasized as critical enablers of the industry’s transformation. The paper concludes with recommendations for future actions, highlighting the role of cross-sector cooperation, research funding, and knowledge sharing in achieving a sustainable and decarbonised cement and concrete sector that can “go green” for eco-constructions. Full article

3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexities. While existing methods rely on fixed geometric or appearance-based associations, they struggle to handle ambiguous cases and detection failures. We present an adaptive multi-level 3D MOT framework that achieves robust tracking through three key innovations: (1) multi-granularity temporal modeling that captures both fine-grained short-term motion and coarse long-term trends via dual-scale spatio-temporal attention, enabling accurate motion prediction across different object dynamics; (2) Transformer-based Appearance Association that employs cross-attention to model global inter-object relationships, resolving ambiguous associations in crowded scenarios where geometric cues alone fail; and (3) scene-adaptive learned thresholds that automatically adjust association strictness based on object density, motion complexity, and occlusion levels, avoiding the one-size-fits-all limitations of fixed thresholds. Our hierarchical four-level tracking strategy progressively handles cases from easy geometric matching (Level 1) to complex interval-frame recovery (Level 4), with SOT-based virtual detection generation bridging detector failures. Extensive experiments on the nuScenes benchmark demonstrate state-of-the-art performance. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Acrylamide is a neurotoxic compound formed during thermal food processing that can cross the placental barrier and potentially affect fetal brain development. This study aimed to evaluate the effects of maternal acrylamide exposure on parvalbumin-immunoreactive (PV-IR) neurons in the pyramidal layer of the subiculum (Sub) and hippocampus of weaning rats. Pregnant Wistar rats received 3 mg/kg b.w. of acrylamide orally for 5 or 10 days during the prenatal period. After weaning, offspring brains were analyzed using immunohistochemistry, morphometry, and quantitative assessment of PV-IR neuron density and staining intensity in the pyramidal layers of the Sub, Cornu Ammonis 1 (CA1), and Cornu Ammonis 3 (CA3). The results demonstrated a significant increase in PV-IR neuron density in the Sub and CA1 after prolonged maternal exposure, accompanied by a predominance of weakly stained cells and decreased mean immunostaining intensity. Morphometric analyses revealed region-specific changes: enlarged cell area and perimeter with reduced nuclear-to-cytoplasmic ratio in the Sub, whereas CA1 and CA3 showed smaller cell dimensions and altered shapes. In conclusion, maternal acrylamide exposure is associated with region-dependent alterations in the morphology and immunoreactivity of PV-IR neurons within the offspring hippocampus. Full article

Traffic accident detection is a critical component of intelligent transportation systems (ITS), enabling timely incident response and traffic management. While most existing approaches rely on temporal information from video sequences, such methods are not always applicable in resource-constrained surveillance environments. This study investigates the feasibility of detecting traffic accidents from single static images by formulating the task as a binary classification problem. Representative architectures, including Vision Transformer (ViT), Swin Transformer, and ResNet-50, are systematically evaluated on the Car Crash Dataset (CCD) under multiple training configurations. To assess generalization capability, cross-domain evaluation is conducted using an external crash video dataset (ECVD) constructed to approximate real-world deployment conditions. Experimental results show that all models achieve strong performance under in-domain evaluation. However, cross-domain testing reveals substantial performance degradation, particularly in recall, indicating limited generalization capability under domain shift. Qualitative analysis further shows that missed detections are associated with weak visual cues, occlusion, and complex traffic environments, while false positives are caused by visually ambiguous patterns resembling accident scenarios. Unlike prior studies that primarily report performance improvements, this work provides empirical evidence that model behavior in static-image-based accident detection is governed by dataset composition rather than architectural design. Therefore, static-image-based accident detection should be interpreted as a coarse-level screening tool rather than a fully reliable decision-making system. This study highlights the importance of data-centric design and cross-domain evaluation for improving real-world applicability. Full article