Search Results (899)

Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most engineering and industrial measurement applications, the accuracy and density of LiDAR point clouds are highly dependent on laser scanners, leading to significant variability that critically affects registration quality. Key factors influencing point cloud accuracy include scanning distance, incidence angle, and the surface characteristics of the target. Notably, in short-range scanning scenarios, incidence angle emerges as the dominant error source. Building on this insight, this study systematically investigates the relationship between scanning incidence angles and point cloud quality. We propose an incident-angle-dependent weighting function for point cloud observations, and further develop an improved weighted Iterative Closest Point (ICP) registration algorithm. Experimental results demonstrate that the proposed method achieves approximately 30% higher registration accuracy compared to traditional ICP algorithms and a 10% improvement over Faro SCENE’s proprietary solution. Full article

Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on microbiota composition, metabolic activity, and immune markers in fecal samples from patients with anxiety on SSRIs, using the SHIME^® (Simulator of the Human Intestinal Microbial Ecosystem) model. Methods: The fecal microbiotas of four patients using sertraline or escitalopram were inoculated in SHIME^® reactors simulating the ascending colon. After stabilization, a 14-day probiotic intervention was performed. Microbial composition was assessed by 16S rRNA sequencing. Short-chain fatty acids (SCFAs), ammonia, and GABA were measured, along with the prebiotic index (PI). Intestinal barrier integrity was evaluated via transepithelial electrical resistance (TEER), and cytokine levels (IL-6, IL-8, IL-10, TNF-α) were analyzed using a Caco-2/THP-1 co-culture system. The statistical design employed in this study for the analysis of prebiotic index, metabolites, intestinal barrier integrity and cytokines levels was a repeated measures ANOVA, complemented by post hoc Tukey’s tests to assess differences across treatment groups. For the 16S rRNA sequencing data, alpha diversity was assessed using multiple metrics, including the Shannon, Simpson, and Fisher indices to evaluate species diversity, and the Chao1 and ACE indices to estimate species richness. Beta diversity, which measures microbiota similarity across groups, was analyzed using weighted and unweighted UniFrac distances. To assess significant differences in beta diversity between groups, a permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis test. Results: Probiotic supplementation increased Bifidobacterium and Lactobacillus, and decreased Klebsiella and Bacteroides. Beta diversity was significantly altered, while alpha diversity remained unchanged. SCFA levels increased after 7 days. Ammonia levels dropped, and PI values rose. TEER values indicated enhanced barrier integrity. IL-8 and TNF-α decreased, while IL-6 increased. GABA levels remained unchanged. Conclusions: The probiotic combination of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 modulated gut microbiota composition, metabolic activity, and inflammatory responses in samples from individuals with anxiety on SSRIs, supporting its potential as an adjunctive strategy to mitigate antidepressant-associated dysbiosis. However, limitations—including the small pooled-donor sample, the absence of a healthy control group, and a lack of significant GABA modulation—should be considered when interpreting the findings. Although the SHIME^® model is considered a gold standard for microbiota studies, further clinical trials are necessary to confirm these promising results. Full article

In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on similarity metrics to establish cross-domain class matching, making it difficult to simultaneously account for intra-class sample size variations and inherent inter-class differences. To address this problem, existing studies have introduced a class weighting mechanism within the prototype network framework, determining class weights by calculating inter-sample similarity through distance metrics. However, this method suffers from a dual limitation: susceptibility to noise interference and insufficient capacity to capture global class variations, which may lead to distorted weight allocation and consequently result in alignment bias. To solve these issues, we propose a novel class-discrepancy dynamic weighting-based cross-domain FSL (CDDW-CFSL) framework. It integrates three key components: (1) the class-weighted domain adaptation (CWDA) method dynamically measures cross-domain distribution shifts using global class mean discrepancies. It employs discrepancy-sensitive weighting to strengthen the alignment of critical categories, enabling accurate domain adaptation while maintaining feature topology; (2) the class mean refinement (CMR) method incorporates class covariance distance to compute distribution discrepancies between support set samples and class prototypes, enabling the precise capture of cross-domain feature internal structures; (3) a novel multi-dimensional feature extractor that captures both local spatial details and continuous spectral characteristics simultaneously, facilitating deep cross-dimensional feature fusion. The results in three publicly available HSIC datasets show the effectiveness of the CDDW-CFSL. Full article

Background/Objectives: Prehabilitation is a multimodal intervention introduced in preparation for various surgical procedures. The most effective treatment for obesity is bariatric surgery. Physiotherapy during prehabilitation for bariatric surgery may be an effective method of functional capacity improvement. We aimed to evaluate the impact of an individual outpatient 12-week, exercise-based physiotherapy program featuring prehabilitation on functional status, exercise tolerance, everyday mobility, and fatigue among patients qualified for bariatric surgery. Methods: The completion of an individual outpatient 12-week, exercise-based physiotherapy program during prehabilitation was an inclusion criterion for the study group. Participants included in the study and control groups were assessed twice, after enrollment into the prehabilitation program (the first assessment) and after prehabilitation but before surgery (the second assessment). Both assessments involved functional tests (a six-minute walking test [6MWT], a timed up and go test [TUG], a chest mobility test, anthropometric measures, a mobility index [Barthel], and a modified Borg scale). The collected anthropometric data and values from the 6MWT were used to create original linear models. This study followed STROBE recommendations. Results: The study group and control group did not differ statistically in terms of their anthropometric data. Statistically significant results were obtained between the first and second assessments in both groups in terms of body weight and waist circumference. However, only the study group showed improved results in the TUG test (p = 0.0001) and distance in the 6MWT (p = 0.0005). The study group presented with the normalization of blood pressure (BP) after exertion in the second assessment (systolic BP p = 0.0204; diastolic BP p = 0.0377), and the 6MWT results were close to the norms. According to the original linear model used to predict performance in the 6MWT, the primary modifiable determinant of exercise tolerance was the participant’s weight, while gender served as a non-modifiable determinant. Conclusions: Exercise-based physiotherapy in prehabilitation was associated with improved functional capacity in patients preparing for bariatric surgery, contributing to the improvement in 6MWT results in relation to the norms as well as exercise tolerance. Body weight may be an independent factor determining distance in the 6MWT for patients undergoing prehabilitation for bariatric surgery. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

Bregman divergences form a class of distance-like comparison functions which plays fundamental roles in optimization, statistics, and information theory. One important property of Bregman divergences is that they generate agreement between two useful formulations of information content (in the sense of variability or non-uniformity) in weighted collections of vectors. The first of these is the Jensen gap information, which measures the difference between the mean value of a strictly convex function evaluated on a weighted set of vectors and the value of that function evaluated at the centroid of that collection. The second of these is the divergence information, which measures the mean divergence of the vectors in the collection from their centroid. In this brief note, we prove that the agreement between Jensen gap and divergence informations in fact characterizes the class of Bregman divergences; they are the only divergences that generate this agreement for arbitrary weighted sets of data vectors. Full article

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

Artificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively. Full article

This study proposes a novel method to assess urban park accessibility by incorporating perceived utility based on both park area and distance. Departing from conventional models that treat accessibility as a function of geometric proximity alone, we define park utility as a distance-discounted benefit of park area, thereby allowing for a more behaviorally grounded measure. A customized discounting function is introduced, where larger park sizes proportionally reduce perceived travel cost, and walking speed adjustments are applied based on demographic user profiles (children, adults, and older adults). The methodology was implemented using a Python-based v.3.12.9 geospatial workflow with network-based distance calculations between 18,614 census block groups and all urban parks in Seoul. Population-weighted utility scores were computed by integrating park size, distance, and age-specific mobility adjustments. The results reveal significant intra-urban disparities, with a citywide deficit of 4,066,046 m in population-weighted distance, particularly in areas with large populations but insufficient proximity to high-utility parks. Simulation analyses of 30 candidate sites demonstrate that strategic park placement can yield substantial utility improvements (maximum gain: 493,436 m), while indiscriminate expansion may not. These findings offer spatial decision support for optimizing limited public resources in urban green infrastructure planning and underscore the need to consider both park scale and user-specific walking behavior in evaluating accessibility. Full article

Improving community walkability can encourage older adults to walk, which is beneficial for enhancing their physical activity level (PAL) and keeping healthy. The first purpose of this study was to formulate an optimized community Walk Score measurement system from the perspective of Chinese seniors. It will be optimized from the aspects such as community service facility selection, weight determination, and distance decay function calculation. The second purpose was to verify its validity by exploring the correlation between Walk Score and subjective/objective community environment variables based on Spearman correlation analysis and the ANOVA method. The third purpose was to examine the relationship between Walk Score and Chinese seniors’ PAL and health outcomes by means of ordered/binary logistic regression. The results show the following: (1) Walk Scores are significantly correlated with partial objective environmental variables. (2) Walk Score was related to older adults’ physical activity level. (3) There was no significant relationship between Walk Score and two health outcomes. Walk Score can provide a supporting basis for urban renewal, older-community renovation, age-friendly community planning and design, and public health practitioners or policymakers. Full article

Background and Aims: Vitamin D is currently well regarded for its pleiotropic effects on the immune system, stimulating an anti-inflammatory response and enhancing immune tolerance. Vitamin D deficiency is an established risk factor for multiple sclerosis (MS). Additionally, lower vitamin D serum levels are associated with worse disease outcomes. However, current randomized clinical trials provide conflicting evidence about the beneficial role of vitamin D on disease progression. Most studies have evaluated the effect of vitamin D supplementation on clinical and radiological activity, yet very few have examined the impact on brain atrophy. Methods: A 4-year observational, non-interventional study design was applied to evaluate the association between vitamin D supplementation and disease progression. Altogether, 132 relapsing–remitting multiple sclerosis patients were enrolled in the study (97 subjects in the group with vitamin D supplementation and 35 subjects in the group without supplementation). The analyzed groups were similar in terms of age, body mass index, sun exposure, comorbidities, nicotinism, duration of the disease, and current treatment. The number of relapses, Expanded Disability Status Scale assessments, and the number of new/enlarged T2-weighted lesions and gadolinium-enhancing lesions in magnetic resonance imagining analyses, as well as 25-hydroxyvitamin D serum levels, were assessed every 12 months of a 4-year follow-up, whereas brain atrophy was assessed at the baseline and after 36 months using two-dimensional measurements. Results: After 36 months, a significant increase in atrophy was observed in both groups; however, patients without vitamin D supplementation had a significantly higher increase in intercaudate distance, third ventricle width, and bicaudate ratio after 36 months of observation (p < 0.05). Vitamin D supplementation among the studied group did not affect other disease activity outcomes. Conclusions: Our study revealed an observed association between vitamin D supplementation and reduced brain atrophy in patients with MS. Randomized controlled trials are required to establish the impact of vitamin D supplementation on brain atrophy progression. Full article

Background: Considering that the prevalence of obesity has risen rapidly in recent decades, the aim of this study was to investigate the effects of a carbohydrate mouth rinse (CMR) on the outcomes of aerobic training among adult men with obesity, focusing particularly on the effects of repeated use on body composition and exercise performance. Methods: The intervention targeted 20 men with obesity in their 20s and 30s randomly assigned to either a CMR group (n = 10) or a placebo mouth rinse (PMR) group (n = 10). Both groups completed treadmill-based aerobic training three times per week for eight weeks. Prior to each session, participants used a mouth rinse at 60, 40, and 20 s before the start of each exercise, holding either a 6% maltodextrin solution (CMR) or purified water (PMR) in their mouths for 5 to 10 s before expectorating. Pre- and post-intervention assessments included body composition (body weight and body fat percentage), resting metabolic rate (RMR), maximal oxygen uptake (VO₂max), and exercise performance (rate of perceived exertion [RPE], exercise distance, speed, and time). Data were analyzed using 2 × 2 repeated measures analysis of variance. Results: Following the intervention, the CMR group showed significantly greater improvements than the PMR group did in body fat percentage, RMR, VO₂max, exercise distance, speed, and time (p < 0.01). However, the interaction effect for RPE was not statistically significant between the groups (p = 0.175). Overall, the repeated use of the CMR during aerobic training contributed to enhanced exercise performance and favorable physiological changes without additional caloric intake. Conclusions: A CMR may be a practical and non-caloric ergogenic aid to support exercise performance and metabolic function in individuals with obesity. Its repeated use during aerobic training appears to be effective and safe, especially when fasting while exercising, when improving endurance without compromising fat loss is essential. Full article

Leaf shape is a crucial visual cue for plant recognition. However, distinguishing among plants with high inter-class shape similarity remains a significant challenge, especially among cultivars within the same species where shape differences can be extremely subtle. To address this issue, we propose a novel shape representation and an advanced heterogeneous fusion framework for accurate leaf image retrieval. Specifically, based on the local polar coordinate system, multiscale analysis, and statistical histograms, we first propose local polar coordinate feature representation (LPCFR), which captures spatial distribution from two orthogonal directions while encoding local curvature characteristics. Next, we present heterogeneous feature fusion with exponential weighting and Ranking (HFER), which enhances the compatibility and robustness of fused features by applying exponential weighted normalization and ranking-based encoding within neighborhood distance measures. Extensive experiments on both species-level and cultivar-level leaf datasets demonstrate that the proposed representation effectively captures shape features, and the fusion framework successfully integrates heterogeneous features, outperforming state-of-the-art (SOTA) methods. Full article

Intermodal transport (IT), which implies the combination of several different types of transport to achieve a more efficient and economical movement of goods, is of increasing importance in modern supply chains. In the conditions of globalization, growth of trade flows and increasingly pronounced requirements for sustainability, effective planning and management of intermodal routes have become crucial, which is why their evaluation and ranking are essential for making strategic and operational decisions. Accordingly, this paper aims to identify the most favorable alternative for developing intermodal transport. Deciding on the choice of the most important intermodal route requires consideration of a large number of criteria, often of a mutually conflicting nature, which places this problem in the domain of multi-criteria decision-making (MCDM). Accordingly, this paper develops a hybrid decision-making model in a fuzzy environment, which combines fuzzy DELPHI (FDELPHI), fuzzy factor relationship (FFARE), and fuzzy axial-distance-based aggregated measurement (FADAM) methods. The model enables the identification and evaluation of relevant criteria, as well as the ranking of defined variants under the requirements and attitudes of various stakeholders. The practical application and effectiveness of the developed model were demonstrated and confirmed by a case study for Bosnia and Herzegovina (B&H). The sensitivity analysis showed that even with changes in the weights of the criteria or the elimination of the most important criteria, the solution remains consistent and reliable. This indicates the robustness of the model and suggests that changes in the parameters do not lead to significant changes in the final results. This confirms the validity of the proposed model and increases confidence in its applicability in practice. Full article

Background: Traditional neonatal assessments rely on anthropometric measures such as weight, body size, and head circumference. However, recent studies suggest that objective movement quantification may serve as a complementary clinical indicator of development in preterm infants. Methods: This study evaluates non-invasive computer vision-based quantification of neonatal movement using contactless pose tracking based on computer vision. We analyzed approximately 800,000 postural data points from ten preterm infants to identify reliable algorithms, optimal recording duration, and whether whole-body or regional tracking is sufficient. Results: Our findings show that 30 s video segments are adequate for consistent motion quantification. Optical flow methods produced inconsistent results, while distance-based algorithms—particularly Chebyshev and Minkowski—offered greater stability, with coefficients of variation of 5.46% and 6.40% in whole-body analysis. Additionally, Minkowski and Mahalanobis metrics applied to the lower body yielded results similar to full-body tracking, with minimal differences of 0.89% and 1%. Conclusions: The results demonstrate that neonatal movement can be quantified objectively and without physical contact using computer vision techniques and reliable computational methods. This approach may serve as a complementary clinical indicator of neonatal progression, alongside conventional measures such as weight and size, with applications in continuous monitoring and early clinical decision-making for preterm infants. Full article