Search Results (5,763)

The proliferation of intelligent sensor networks in urban surveillance and remote sensing has triggered the explosive growth of unstructured visual sensor data. Accurately retrieving targets from these massive streams based on complex cross-modal user intents remains a critical bottleneck for efficient intelligent perception. Composed Image Retrieval (CIR) addresses this by enabling retrieval via a multi-modal query that combines a reference image with semantic control signals. However, existing methods often struggle with abstract instructions in real-world scenarios. Consequently, models often suffer from feature distribution shifts due to focus ambiguity, as well as semantic erosion caused by highly entangled visual and textual features. To address these challenges, we propose a geometry-based Selective Orthogonal Projection Network (SOP). First, the Selective Focus Recovery module quantifies instruction uncertainty via information entropy and calibrates shifted query features to the true target distribution using structural consistency regularization. Second, to ensure data fidelity, we introduce Orthogonal Subspace Projectionand Geometric Composition Fidelity. These mechanisms employ Gram–Schmidt orthogonalization to decouple features into a constant visual base and an orthogonal modification increment, restricting semantic modifications to the null space. Extensive experiments on FashionIQ, Shoes, and CIRR datasets demonstrate that SOP significantly outperforms SOTA methods, offering a novel solution for efficient large-scale sensor data retrieval and analysis. Full article

Regional production-consumption imbalances and deficient multimodal connectivity in grain circulation systems have rendered traditional segmented transport inefficient, loss-intensive, and costly, constraining overall supply chain performance. In China, the persistent north-to-south and west-to-east grain transfer patterns, driven by regional production–consumption imbalances, have imposed significant challenges on the grain circulation system, making multimodal transport optimization a critical priority for national food security. Multimodal transport, a critical logistics optimization strategy, integrates diverse transport modes and hub nodes to enable end-to-end coordination, thereby enhancing circulation efficiency and food security. This study systematically reviews the transport configurations and modal characteristics of grain multimodal transport, and employs bibliometric analysis with the VOSviewer tool to map publication trends and keyword co-occurrence networks. Subsequently, recent advances in transshipment hub location selection and route optimization in multimodal transport systems are examined. Finally, existing technical bottlenecks are summarized, and future research directions are outlined from the perspectives of intelligent logistics, green and low-carbon development, coordinated operations, and supply chain resilience. Full article

Artificial intelligence (AI) has been increasingly applied to leukemia research, spanning diagnostic, prognostic, therapeutic, and translational domains. However, the rapid growth and methodological diversity of this literature present challenges for existing reviews, which are often constrained by limited scope, narrow clinical focus, or reliance on either manual or purely bibliometric approaches. As a result, cross-domain relationships, evolving methodological trends, and the interaction between data modalities and clinical objectives remain insufficiently understood. This paper presents a systematic, AI-assisted literature analysis of AI applications in leukemia, combining scalable machine-driven discovery with author-led qualitative interpretation. Using a PRISMA-guided screening process, a corpus of 2338 peer-reviewed publications retrieved from Scopus (1990–2024) is analyzed through semantic text representation and unsupervised clustering. An iterative human–machine process is employed to identify and refine 23 analytical parameters grouped into five macro-parameters, enabling structured organization of the research landscape across diagnostic, prognostic, therapeutic, genetic, and methodological dimensions. Building on this structured representation, in-depth qualitative analysis is conducted by the authors across parameters and macro-parameters, synthesizing methodological developments, data usage patterns, application domains, and commonly used datasets. The resulting analysis provides a coherent, interpretable mapping of AI-driven leukemia research, supporting cross-domain comparison and identification of research concentrations, fragmentation, and emerging directions. By integrating large-scale automation with domain-informed qualitative analysis in a reusable analytical pipeline, this work contributes a rigorous and transferable framework for structured literature analysis in leukemia and related biomedical domains. Full article

Public transport is a critical instrument for mitigating traffic congestion, reducing environmental pollution, and promoting social inclusion in urban areas. This study presents the results of a quantitative survey conducted among 406 residents of Łódź, Poland, aimed at identifying the determinants of public transport use and the factors influencing modal choices. The findings indicate that 89% of respondents had used public transport within the past three years, with over half reporting the use of both buses and trams. However, public transport is predominantly chosen out of necessity rather than preference, driven by limited access to private vehicles, absence of a driver’s license, or the high costs of car ownership. Environmental considerations and service quality factors play a comparatively minor role. User satisfaction with public transport services in Łódź is moderate, and current users express limited intention to increase their usage or actively recommend the system, suggesting constrained potential for demand growth. In contrast, non-users declare a willingness to shift to public transport if travel costs are reduced and service quality is improved. Measures aimed at restricting private car use demonstrate limited motivational impact, whereas enhancing the reliability, accessibility, and affordability of public transport emerges as the most effective strategy. Methodologically, the study contributes by combining bibliometric mapping with quantitative survey analysis, providing a replicable framework for assessing urban mobility determinants in other cities with similar socio-economic and transport contexts. Full article

Background: Perianal fistulizing Crohn’s disease (CD) is associated with significant morbidity and remains difficult to treat. Although advanced medical therapies are widely used, much of the available evidence derives from heterogeneous fistula populations or luminal CD trials, with limited perianal-specific synthesis and inconsistent outcome definitions. We conducted a systematic review focusing exclusively on perianal-specific clinical, radiologic, and composite outcomes in adults with perianal fistula (PAF) CD. Methods: We performed a systematic review in accordance with PRISMA 2020. Electronic databases were searched from inception through November 2025. We included randomized controlled trials and cohort studies enrolling adults with CD reporting outcomes specific to PAF. Interventions included biologics and small-molecule therapies, compared with placebo or other therapies. Due to substantial heterogeneity in outcome definitions and study designs, a meta-analysis was not performed. Risk of bias was assessed using Risk of Bias 2 (RoB 2) for randomized trials and the Newcastle–Ottawa Scale for observational studies. Results: Seven studies including >1200 participants with PAF-CD met inclusion criteria. Follow-up ranged from 24 weeks to 5 years. Across studies, outcome definitions and assessment modalities varied. Upadacitinib demonstrated significantly higher clinical fistula closure compared with placebo across multiple dose regimens at 52 weeks. In observational comparisons, ustekinumab and vedolizumab were associated with higher clinical closure rates than anti-TNF therapies. However, infliximab demonstrated higher closure rates than adalimumab as a first-line treatment. The definition for radiologic remission was less consistent across studies and often did not parallel clinical outcomes. Composite clinical–radiologic remission and response were reported in a limited number of studies, with filgotinib showing higher composite outcomes in comparison to placebo in a phase 2 trial. Surgical interventions, relapse outcomes, biomarkers [C-reactive protein (CRP)/fecal calprotectin (FCP)], and patient-reported outcomes were variably reported and not consistently significant across comparisons. Conclusions: Evidence for advanced therapies in PAF CD remains limited by heterogeneity in endpoint definitions, subjectivity in clinical observation, inconsistent radiologic reporting, and reliance on subgroup or observational comparisons. While anti-TNF therapy remains the most established option in guidelines, emerging data suggest significant benefits with ustekinumab, vedolizumab, and JAK inhibitors in selected patients. There is a need for PAF-specific, adequately powered randomized trials using standardized composite clinical and radiologic endpoints. Full article

This paper examines the use of high-rigidity Tau-robots in friction stir welding, where process loads are very high. The rigidity of Tau-robots increases at the expense of the workspace. Therefore, the right configuration of the Tau-robot is sought to reconcile rigidity and workspace requirements. This is studied by use of kinematics, followed by static and modal analysis. In particular, by extending an existing kinematic model employing free vectors, the robot workspace was derived in non-dimensional parametric form and was then maximized through evolutionary optimization. However, finite element static and modal analysis that were carried out subsequently may prove, as in a case demonstrated here, that the optimized configuration may not withstand high loads, typically axial forces of 15 kN and torques of 80 Nm, and it may also be susceptible to forced vibrations in the typical spindle rotation range up to 3000 rpm. As a rectification measure, it was shown how a modified configuration by placing robot kinematic chain bases further apart and shortening robot links achieves higher rigidity, axial displacement being reduced by one or two orders of magnitude to below 1 mm and increases critical modal frequency 3 to 5 times depending on the workspace position, of course sacrificing part of the workspace, i.e., reducing it 3-fold to enclose welding lines in a rectangle of dimensions 700 × 800 mm. In the quest for the appropriate robot configuration desired dimensions of parts to be welded and available standard components are briefly considered, too. Full article

Drone-based RGBT person detection facilitates critical applications such as search and rescue, owing to its high maneuverability and inherent capability to mitigate visual occlusion. However, despite the complementary nature of RGBT systems, existing detectors often overlook the specific impact of occlusion during the fusion process, leading to feature contamination and subsequent detection failures. In this work, we address this limitation by formally defining two categories of occlusion: “soft occlusion,” where targets remain partially visible in at least one modality, and “hard occlusion,” which involves complete obstruction. To tackle these challenges, we propose Unveiling Occluded Targets (UOT), a novel multi-modal fusion framework that implements a Quality–Occlusion Arbitration (QOA) mechanism. By leveraging both quality-related and occlusion-related cues, UOT dynamically arbitrates the fusion process to maximize information recovery from the clearer modality. Extensive experiments on the RGBTDronePerson and VTUAV-det datasets demonstrate significant improvements, achieving an $mA{P}_{50}^{all}$ of 53.42% and an $mA{P}_{50}^{tiny}$ of 54.70% in densely occluded scenes. Qualitative analysis further confirms UOT’s robustness in reliably identifying targets obstructed by sparse foliage. Full article

Background: Infrared thermography has emerged as a non-invasive imaging modality capable of capturing physiological alterations associated with skin cancer; however, its diagnostic utility remains insufficiently characterized. This study aimed to compare the diagnostic performance of active (cooling-induced) and passive infrared thermography in distinguishing benign from malignant skin lesions. Methods: In this clinical study conducted at Barretos Cancer Hospital, 64 individuals contributed 100 skin lesions, of which 68 (56 malignant, 12 benign) met criteria for diagnostic accuracy analysis. Long-wave infrared images were acquired under steady-state conditions (passive thermography) and after standardized cooling (active thermography). Diagnostic performance was assessed using thermal contrast patterns and temperature differentials (ΔT), with histopathology as the reference standard. Results: Active thermography markedly outperformed passive imaging. Active thermography achieved a sensitivity of 91.1% and specificity of 75.0%, whereas passive thermography demonstrated low sensitivity (17.9%) despite perfect specificity (100%). Malignant lesions displayed substantially higher ΔT values, particularly during active thermal recovery. ROC analysis confirmed the superiority of the active technique (AUC = 0.871) over passive imaging (AUC = 0.650), with an optimal ΔT cutoff of 0.70 °C yielding high discriminative accuracy. Conclusions: Active infrared thermography enhances thermal contrast and provides significantly improved diagnostic sensitivity compared with passive imaging. As a low-cost, non-invasive technique, active thermography shows promise as a complementary tool for skin cancer triage, particularly in settings with limited access to dermatologic evaluation. Full article

The integration of shape-memory alloy (SMA) wires into composite laminates offers a promising approach for active vibration damping. Towards this goal, this study investigates the damping behavior of hybrid random mat E-glass/epoxy composite plates with embedded SMA wires under electrically active and inactive conditions. The composites are tested using a Laser Doppler Vibrometer (LDV) and an impact hammer to assess the effect of SMA wire activation on the natural frequencies and vibration behavior of composites. For a fixed number of active SMA wires, differences in vibration behavior are evaluated between outer- and inner-wire activation configurations in both two-ply and four-ply composite plates. The results show that SMA wire activation significantly affects damping behavior, while the mode shapes remain unchanged. The magnitude and frequency of the first natural frequency as well as the quality factor (Q-factor) decrease in composites with activated SMA wires compared to the inactive configuration, indicating enhanced energy dissipation. Under the fully active condition, a reduction in vibrational amplitude of approximately 42–60% and a frequency shift of approximately 10–17% are observed. Compared to outer-wire activation, inner-wire activation results in greater reductions in vibration magnitude, reaching approximately 7–13%. Full article

Customer Lifetime Value (CLV) estimation over the observed transactional horizon is a fundamental challenge in hospitality analytics, supporting revenue management, personalization, and long-term customer relationship strategies. However, existing models predominantly rely on structured behavioral data while overlooking the emotional intelligence embedded in guest narratives. This study proposes an interpretable multimodal deep learning (DL) framework that bridges behavioral and emotional intelligence for CLV estimation by integrating structured booking records with unstructured hotel review text. Model interpretability is ensured through SHAP analysis for structured attributes, LIME for local textual explanations, and attention visualization for modality interaction analysis. Experimental evaluation on large-scale hospitality datasets demonstrates that the proposed multimodal framework outperforms traditional machine learning models, unimodal deep learning baselines, and classical ensemble learners, yielding consistent improvements across multiple error metrics and a notable increase in goodness of fit. The results confirm that emotional intelligence extracted from guest reviews significantly enhances CLV estimation and provides actionable insights for hospitality decision-making, supporting the deployment of transparent and explainable artificial intelligence (XAI) systems for strategic customer value management. Full article

Background: Local bone quality of the proximal humerus is a key determinant of fracture risk and implant stability in osteoporotic bone. Beyond established HU-based calibration, CT-osteoabsorptiometry (CT-OAM)-derived indices and microarchitecture-oriented workflows warrant systematic cross-modality evaluation. Methods: Twelve proximal humeral heads from six body donors (age 65–86 years; bilateral specimens) were analyzed using paired clinical CT and high-resolution micro-CT. Bone quality was quantified by (i) a HU-calibrated cancellous vBMD method (Krappinger et al.), (ii) a CT-OAM-inspired workflow reporting an ROI-averaged mean-intensity index in arbitrary units (a.u.), and (iii) a calibrated Bone Microarchitecture Analysis (BMA) workflow in Analyze 15.0. Paired tests, linear regression, and repeated-measures ANOVA after z-standardization were applied. Results: HU calibration yielded a mean trabecular vBMD of 114.37 ± 35.15 mg/cm³ on clinical CT. The BMA workflow produced higher CT-based values (207.37 ± 23.78 mg/cm³, p < 0.001) and markedly higher micro-CT values (469.34 ± 30.99 a.u.), indicating a systematic level shift between calibration frameworks. The CT-OAM index averaged 166.94 ± 40.12 a.u. on clinical CT and 455.89 ± 132.63 a.u. on micro-CT. Cross-modality agreement was very strong for CT-OAM (R² = 0.888) and moderate for BMA (R² = 0.502). After z-standardization, no significant differences were detected between the three CT-based approaches. Conclusions: A CT-OAM-inspired ROI-mean index and a BMA-based workflow provide complementary, transferable readouts of proximal humeral bone quality across clinical CT and micro-CT, with stronger cross-modality rank consistency for CT-OAM. Absolute density values differ systematically between calibration frameworks and should not be interpreted as directly interchangeable. These approaches support opportunistic, site-specific bone quality assessment from routine CT, but require prospective validation against fixation-related outcomes and robust scanner-independent standardization. Full article

Extended Reality (XR) technologies are increasingly used to create immersive and interactive systems across domains such as education, training, health, and entertainment. As these systems become more complex and multisensory, evaluating user experience (UX) in XR environments requires approaches that go beyond traditional usability assessments and consider perceptual, cognitive, emotional, and interaction-related factors. However, existing UX evaluation efforts in XR often rely on isolated instruments or domain-specific studies, lacking a systematic and reusable evaluation methodology. This paper proposes a human-centered methodology for evaluating user experience in extended reality applications, integrating UX dimensions and XR-specific characteristics into a structured and coherent evaluation process. The methodology is grounded in a multi-phase research process that includes a comprehensive literature review, expert consultation, correlation analysis between UX dimensions and XR features, and formal specification of evaluation phases and activities. Based on this process, the proposed methodology supports evaluators in selecting appropriate UX evaluation methods and instruments according to the characteristics and experiential goals of XR applications. The methodology defines a set of UX dimensions tailored to immersive environments, capturing perceptual, cognitive, emotional, and interaction aspects that are critical for the design and evaluation of adaptive and human-centered XR systems. An expert-based validation was conducted to assess the clarity, usefulness, and applicability of the methodology, leading to refinements in its structure and descriptions. The methodology promotes a human-centered approach by considering user perception, emotional impact, and contextual experience across XR modalities. It additionally contributes to the field by offering a reusable process for UX evaluation in XR, supporting more consistent, transparent, and human-centered assessment practices. It also provides a foundation for future empirical studies and the development of evaluation approaches inspired by natural and adaptive human–environment interactions. Full article

Composite bone replicas are widely used in biomechanical testing as alternatives to cadaveric specimens, with numerical models often complementing or replacing experiments. The reliability of these models depends strongly on accurate material parameters. This study investigates a fourth-generation Sawbones composite L5 vertebra, updating cortical material properties under isotropic and transversely isotropic modelling assumptions. Finite element models were calibrated using free-free experimental modal analysis, revealing differences between manufacturer-provided material properties and the measured specimen behaviour. For both models, matching the specimen mass required reducing the cortical density from 1.64 g/cm³ to 1.423 g/cm³. In the isotropic model, the Young’s modulus was reduced from 16,000 MPa to 6500 MPa. In the transversely isotropic model, longitudinal and transverse Young’s moduli were reduced from 16,000 MPa and 11,000 MPa to 6400 MPa and 5500 MPa, respectively, while the shear moduli decreased from 4370 MPa and 6350 MPa to 3500 MPa and 2540 MPa. In both models, the Poisson’s ratio was increased from 0.26 to 0.30. These updates reduced the average eigenfrequency error to 6.12% (isotropic) and 5.83% (transversely isotropic), with the first five modes errors reduced to 3.10% and 2.80%, respectively, substantially improving numerical representation of L5 vertebral mechanics. The updated vertebral FE model and accompanying workflow enhance the reliability of future FE analyses, improve interpretation of Sawbones vertebra biomechanical results, and support vibration-based biomechanical applications such as implant fixation assessment. Full article

Background/Objectives: Molecular subtyping of pediatric B-cell acute lymphoblastic leukemia (B-ALL) has improved patient outcomes through stratification and selection of targeted therapies. Despite extensive genomic and transcriptomic profiling of this cancer, few studies to date have characterized the proteomic landscape, although proteins are the direct targets of many therapeutic agents. Methods: In this study, we demonstrate the utility of multi-omic integration of global transcriptomic, proteomic, and phosphoproteomic profiles of samples from patients diagnosed with either of two B-ALL subtypes—Ph-like (BCR::ABL1-like) and ETV6::RUNX1. Through individual and multi-omic analysis, we recapitulate known transcriptomic findings and identify novel subtype-specific proteomic and phosphoproteomic biomarkers. Conclusions: Our findings suggest a previously undescribed role for calcium-dependent signaling processes in Ph-like B-ALL, which has the potential to serve as a novel avenue for targeted treatments. By integrating multiple -omics modalities, we identify not only features of interest but also begin to unravel the regulatory interactions driving subtype-specific mechanisms of leukemogenesis. This integrated analytic approach paves the way for enhanced precision medicine for precise subtyping and treatment selection for pediatric leukemia patients. Full article

Red blood cell (RBC) membrane flickering arises from the interplay between thermal fluctuations, cytoskeletal elasticity, and metabolically driven non-equilibrium processes, making it a sensitive reporter of membrane mechanical state. Here, we introduce a computational microscopy framework that integrates bright-field morphometry with high-speed flickering spectroscopy to phenotype single-cell RBC mechanics under flavonoid exposure. As a proof of concept, human erythrocytes from a single donor were incubated with structurally distinct flavonoids (quercetin, apigenin, and rutin) prepared at sub-hemolytic concentrations, ensuring preservation of membrane integrity. Static shape descriptors and dynamic fluctuation spectra were extracted from segmented cell contours and analyzed through Fourier-mode decomposition to obtain compound-specific mechanical signatures. While gross morphology remained largely discocytic across conditions, flavonoid treatment induced reproducible alterations in flickering spectra and effective mechanical parameters, revealing distinct dynamical phenotypes that depend on flavonoid structure. In particular, aglycone flavonoids exhibited modulation patterns that differed from the glycosylated compound, consistent with differential membrane interactions. The combined analysis of geometry and dynamics provided enhanced discriminative power compared to either modality alone. These results establish computational microscopy as a sensitive, label-free approach to map compound-specific perturbations of RBC membrane mechanics and flickering, with potential applications in membrane biophysics, drug–membrane interaction screening, and single-cell mechanical phenotyping. Full article