Search Results (15,228)

With the escalating frequency of urban and forest fires driven by climate change, the development of intelligent and robust fire detection systems has become imperative for ensuring public safety and ecological protection. This paper presents a comprehensive multi-module fire detection framework based on visual computing, encompassing image enhancement and lightweight object detection. To address data scarcity and to enhance generalization, a projected generative adversarial network (Projected GAN) is employed to synthesize diverse and realistic fire scenarios under varying environmental conditions. For the detection module, an improved YOLOv8n architecture is proposed by integrating BiFormer Attention, Agent Attention, and CCC (Compact Channel Compression) modules, which collectively enhance detection accuracy and robustness under low visibility and dynamic disturbance conditions. Extensive experiments on both synthetic and real-world fire datasets demonstrated notable improvements in image restoration quality (achieving a PSNR up to 34.67 dB and an SSIM up to 0.968) and detection performance (mAP reaching 0.858), significantly outperforming the baseline. The proposed system offers a reliable and deployable solution for real-time fire monitoring and early warning in complex visual environments. Full article

Background: Diastasis recti abdominis (DRA) is a common postpartum condition typically managed with rehabilitation exercises. Given its high prevalence and postpartum barriers in attending in-person sessions, telerehabilitation may offer a feasible alternative. This small pilot study evaluates the preliminary effectiveness and user satisfaction of a 12-week telerehabilitation exercise program for women with postpartum DRA. Methods: Parous women with DRA participated in a 12-week trunk stabilization program, including synchronous and asynchronous sessions, from April 2024 to May 2025. The primary outcome was satisfaction (Telehealth Usability Questionnaire—TUQ_Greek and two additional custom-made questions). Secondary outcomes included inter-recti distance (IRD), trunk muscle endurance tests, body image (BISS_Greek), and adherence to exercise. Results: Thirteen participants aged 37.54 ± 5.49 completed the pilot intervention. Satisfaction was high (TUQ_Greek = 6.28 ± 0.60), with 84.62% (11/13 subjects) rating telerehabilitation as very satisfactory. Statistically significant reductions in IRD were observed at 2 cm (large effect, d = 1.00; 95% CI [0.12 to 0.47]) and 5 cm (large effect, d = 0.81; 95% CI [0.08 to 0.58]) above the umbilicus (p < 0.05). Post-intervention, most trunk muscle endurance tests improved significantly (p < 0.05) at 4 and 12 weeks (large effect, η² = 0.44 to–0.56). Body image (BISS_Greek) also improved post-intervention (p < 0.05, medium to large effect, d = −0.73; 95% CI [(–1.75 to –0.16]). Mean adherence reached 71.37%. Conclusions: This small pilot supports the feasibility and acceptability of a telerehabilitation program as well as its effectiveness in improving key clinical outcomes. However, since this was a small pilot, generalizability might be limited by the small sample size and should be confirmed in larger studies. Full article

We present a comparative analysis of three hybrid methodologies for transforming 3D kidney tumor segmentations of volumetric NIfTI data into highly accurate network representations. Exploiting the KiTS23 dataset, we evaluate edge-preserving reconstruction pipelines integrating anisotropic diffusion, multiscale Gaussian filtering and KNN-based network optimisation. Model 1 uses Gaussian smoothing with Marching Cubes, while Model 2 uses spline interpolation and Perona-Malik filtering for improved resolution. Model 3 extends this structure with normal sensitive vertex smoothing to preserve critical anatomical interfaces. Quantitative metrics (Dice score, HD₉₅) demonstrated the advantage of Model 3, which achieved a 22% reduction in the Hausdorff distance error rate compared to conventional methods while maintaining segmentation accuracy (Dice > 0.92). The proposed unsupervised pipeline bridges the gap between clinical interpretability and computational accuracy, providing a robust infrastructure for further applications in surgical planning and deep learning-based classification. Full article

Muscle dysmorphia (MD) is a body image disorder characterized by an obsessive preoccupation with muscularity and compulsive behaviors such as excessive exercise, rigid dieting, and frequent body checking. MD has been linked to obsessive–compulsive traits and the use of anabolic–androgenic steroids (AASs), yet these associations have not been comprehensively synthesized. This systematic review and meta-analysis examined the relationships between MD, obsessive–compulsive symptomatology, and AASs or performance-enhancing drug use. Following PRISMA 2020 guidelines and PROSPERO preregistration (CRD42025640206), we searched four major databases for peer-reviewed studies published between 2015 and 2025. Ten studies (five quantitative, five qualitative) met the inclusion criteria. Meta-analytic findings revealed a moderate positive correlation between MD symptom severity and obsessive–compulsive traits (r ≈ 0.24), and significantly higher MD symptoms among AAS users compared to non-users (Cohen’s d ≈ 0.45). Odds of MD were markedly higher in steroid-using populations. Thematic synthesis of qualitative studies highlighted compulsive training routines, identity conflicts, motivations for AAS use, and limited engagement with healthcare services. These findings suggest that MD exists at the intersection of obsessive–compulsive psychopathology and substance-related behavior, warranting integrated interventions targeting both dimensions. The study contributes to understanding MD as a complex, multi-faceted disorder with significant clinical and public health relevance. Full article

In recent decades, technological developments in archaeological geophysics have led to growing data volumes, so that an important bottleneck is now at the stage of data interpretation. The manual delineation and classification of anomalies are time-consuming, and different methods for (semi-)automatic image segmentation have been proposed, based on explicitly formulated rulesets or deep convolutional neural networks (DCNNs). So far, these have not been used widely in archaeological geophysics because of the complexity of the segmentation task (due to the low contrast between archaeological structures and background and the low predictability of the targets). Techniques based on shallow machine learning (e.g., random forests, RFs) have been explored very little in archaeological geophysics, although they are less case-specific than most rule-based methods, do not require large training sets as is the case for DCNNs, and can easily handle 3D data. In this paper, we show their potential for geophysical data analysis. For the classification on the pixel level, we use ilastik, an open-source segmentation tool developed in medical imaging. Algorithms for object classification, manual reclassification, post-processing, vectorisation, and georeferencing were brought together in a Jupyter Notebook, available on GitHub (version 7.3.2). To assess the accuracy of the RF classification applied to geophysical datasets, we compare it with manual interpretation. A quantitative evaluation using the mean intersection over union metric results in scores of ~60%, which only slightly increases after the manual correction of the RF classification results. Remarkably, a similar score results from the comparison between independent manual interpretations. This observation illustrates that quantitative metrics are not a panacea for evaluating machine-generated geophysical data interpretation in archaeology, which is characterised by a significant degree of uncertainty. It also raises the question of how the semantic segmentation of geophysical data (whether carried out manually or with the aid of machine learning) can best be evaluated. Full article

In response to the growing importance of vocational education for youth employability, this study examines students’ perceptions of dual vocational education and training (dVET) in Bulgaria, focusing on the following determinants of student loyalty (SL) and word-of-mouth communication (WOM) in the secondary education context: brand associations, brand relevance, brand image, image of dVET, service quality, and student satisfaction, based on previously validated scales adapted to the Bulgarian context. A structured questionnaire was administered to a target population of 608 students across nine vocational secondary schools in the Plovdiv region. A total of 507 usable surveys were collected from students in 11th and 12th grades who were actively participating in work-based learning. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS 4 software. The findings indicate that brand image is the strongest direct predictor of the image of dVET. Furthermore, student satisfaction stands out as the most influential antecedent of WOM. The indirect pathways from service quality to both SL and WOM, mediated by student satisfaction, underscore the pivotal role of satisfaction as a transmission mechanism. The study contributes to the limited empirical research on branding in dVET and offers insights for policymakers, school administrators, and employers seeking to improve the attractiveness of these pathways. Full article

Streak Tube Imaging Lidar (STIL) offers significant advantages in long-range sensing and ultrafast diagnostics by encoding spatial-temporal information as streaks, and hence decodes 3D images using tailored algorithm. However, under low-photon conditions that caused either long-range or reduced exposure time, the reconstructed image suffer from low contrast, strong noise and blurring, hindering the application in various scenarios. To address this challenge, we propose a Multi-Sparsity Constraints and Adaptive Regularization (MSC-AR) algorithm based on the Maximum a Posteriori (MAP) framework, which jointly denoises and deblurs degraded streak images and efficiently solved using the Alternating Direction Method of Multipliers (ADMM). MSC-AR considers gradient sparsity, intensity sparsity, and an adaptively weighted Total Variation (TV) regularization along the temporal dimension of the streak image which collaboratively optimizing image quality and structural detail, thus better 3D restoration results in low-photon conditions. Experimental results demonstrate that MSC-AR significantly outperforms existing approaches under low-photon conditions. At an exposure time of 300 ms, it achieves millimeter-level RMSE and over 88% SSIM in depth image reconstruction, while maintaining robustness and generalization across different reconstruction strategies and target types. Full article

Introduction: Maxillary setback in orthognathic surgery has been extensively discussed regarding its effects on bone healing and facial soft tissue profile; however, its impact on upper airway volume remains unclear. Objective: We evaluate the influence of maxillary setback combined with counterclockwise (CCW) rotation of the occlusal plane on upper airway dimensions. Methods: A retrospective observational case series was conducted with eight patients diagnosed with Class II malocclusion who underwent orthognathic surgery involving maxillary setback and CCW mandibular rotation. All procedures were performed by the same surgeon. Preoperative (T1) and 6-month postoperative (T2) facial CT scans were analyzed using Dolphin Imaging software11.7 to measure airway volume (VOL), surface area (SA), and linear distances D1, D2 and D3. Statistical analysis was performed using the Wilcoxon test with a 5% significance level. Results: Significant skeletal changes were observed, including 10.2 mm of mandibular advancement, 5.2 mm of hyoid advancement, and 4.1° of CCW rotation. Although increases in airway volume and surface area were noted, they did not reach statistical significance (p = 0.327 and p = 0.050, respectively), but suggesting a favorable trend toward airway adaptation. Conclusions: Maxillary setback combined with CCW rotation appears to safely correct Class II skeletal deformities without compromising upper airway space. These preliminary findings highlight the technique’s potential for both functional and aesthetic outcomes, warranting further long-term studies. Full article

With strategic mineral exploration extending to deep and complex geological settings, traditional methods increasingly struggle to dissect metallogenic systems and locate ore bodies precisely. This synthesis of current progress in muon imaging (a technology leveraging cosmic ray muons’ high penetration) aims to address these exploration challenges. Muon imaging operates by exploiting the energy attenuation of cosmic ray muons when penetrating earth media. It records muon transmission trajectories via high-precision detector arrays and constructs detailed subsurface density distribution images through advanced 3D inversion algorithms, enabling non-invasive detection of deep ore bodies. This review is organized into four thematic sections: (1) technical principles of muon imaging; (2) practical applications and advantages in ore exploration; (3) current challenges in deployment; (4) optimization strategies and future prospects. In practical applications, muon imaging has demonstrated unique advantages: it penetrates thick overburden and high-resistance rock masses to delineate blind ore bodies, with simultaneous gains in exploration efficiency and cost reduction. Optimized data acquisition and processing further allow it to capture dynamic changes in rock mass structure over hours to days, supporting proactive mine safety management. However, challenges remain, including complex muon event analysis, long data acquisition cycles, and limited distinguishability for low-density-contrast formations. It discusses solutions via multi-source geophysical data integration, optimized acquisition strategies, detector performance improvements, and intelligent data processing algorithms to enhance practicality and reliability. Future advancements in muon imaging are expected to drive breakthroughs in ultra-deep ore-forming system exploration, positioning it as a key force in innovating strategic mineral resource exploration technologies. Full article

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies. Full article

Background/Objectives: Tooth autotransplantation is a natural tooth replacement method that preserves the periodontal ligament, supporting root development and alveolar bone remodeling. Unlike dental implants, autotransplanted teeth maintain sensory function and adapt better to the mouth. Although once overlooked, new surgical, imaging, and regenerative advances have revived interest in this technique. This narrative review explores the renewed interest in tooth autotransplantation by assessing its benefits, success rates, technological advancements, and role in modern dentistry while evaluating its advantages, limitations, and potential impact on dental care. Methods: A narrative approach was used to provide a comprehensive and descriptive overview of current knowledge on tooth autotransplantation. A literature search was conducted in PubMed, Scopus, and Google Scholar using keywords such as “tooth autotransplantation”, “biological tooth replacement”, “periodontal ligament”, and “dental implants alternative”. English-language articles published between 2000 and 2025 were included, covering clinical trials, reviews, and relevant case reports. Selection focused on studies discussing biological mechanisms, clinical techniques, technological advances, and treatment outcomes. Results: Success rates range from 80% to 95%, with better predictability in younger patients with immature donor teeth. Long-term viability depends on preserving the PDL and performing atraumatic extractions. However, challenges such as root resorption, ankylosis, and appropriate case selection remain significant considerations. Technological advancements, including CBCT, 3D-printed surgical guides, and biomimetic storage media, have improved surgical precision and clinical outcomes. Conclusions: Tooth autotransplantation is an effective and cost-effective alternative to dental implants, particularly for growing patients or when implants are not suitable. While success depends on surgical skill and proper case selection, improvements in imaging and regenerative techniques have made outcomes more predictable. Future advances in bioengineering, AI-based planning, and regenerative therapies are likely to expand their use in modern dentistry. Full article

Synthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness. Existing adversarial SAR target detection approaches mainly focus on the image domain and neglect the critical role of signal propagation, making it difficult to fully capture the connection between the physical space and the image domain. To address this limitation, we propose an Echo Signal-Guided Adversarial Example Generation method for SAR target detection (SAR-ESAE). The core idea is to embed adversarial perturbations into SAR echo signals and propagate them through the imaging and inverse scattering processes, thereby establishing a unified attack framework across the signal, image, and physical spaces. In this way, perturbations not only appear as pixel-level distortions in SAR images but also alter the scattering characteristics of 3D target models in the physical space. Simulation experiments in the Scenario-SAR dataset demonstrate that the SAR-ESAE method reduces the mean Average Precision of the YOLOv3 model by 23.5% and 8.6% compared to Dpatch and RaLP attacks, respectively. Additionally, it exhibits excellent attack effectiveness in both echo signal and target model attack experiments and exhibits evident adversarial transferability across detection models with different architectures, such as Faster-RCNN and FCOS. Full article

Treatment-resistant schizophrenia (TRS) affects up to one in three individuals with schizophrenia and is associated with a significant clinical, social, and economic burden. Different from treatment-responsive forms, TRS appears to involve other biological mechanisms extending beyond dopaminergic dysfunctions. This review outlines current knowledge on the molecular and cellular basis of TRS, focusing on alterations in glutamate signaling, imbalances between excitatory and inhibitory activity, disruptions in D-amino acid metabolism, and evidence of neuroinflammation, oxidative stress, and mitochondrial or endoplasmic reticulum dysfunction. Data from genomics, proteomics, metabolomics, preclinical models, and postmortem studies suggest that TRS may have a peculiar neurobiological substrate. Further, multimodal brain imaging studies reveal differences in brain structure, white matter integrity, and network connectivity when compared to treatment-responsive individuals. Altogether, these findings support a shift from the traditional dopamine hypothesis toward a more comprehensive model that includes multiple immune, metabolic, and synaptic factors. Understanding the possible interplay of these complex mechanisms may lead to the identification of potential biomarkers that may help to predict antipsychotic response, as well as the development of more targeted treatments. Early recognition and a deeper biological insight into TRS are essential for improving care and guiding personalized therapeutic strategies. Full article

Background: Immunotherapy has revolutionized oncology care, but clinical response to immune checkpoint inhibitors (ICIs) remains unpredictable, and treatment carries substantial risks and costs. The EpiSwitch^® CiRT blood test is a novel 3D genomic assay that stratifies patients by probability of ICI benefit using a binary, blood-based classification: high (HPRR) or low (LPRR) probability of response. Methods: This interim analysis of the ongoing PROWES prospective real-world evidence study evaluates the clinical utility of CiRT in 205 patients with advanced solid tumors. The primary endpoint was treatment decision impact, assessed by pre-/post-test physician surveys. Secondary endpoints included treatment avoidance, time to ICI initiation, concordance with clinical response, early discontinuation rates, and exploratory health economic modeling. Longitudinal use, resistance monitoring, and equity analysis by social determinants of health (SDoH) were also explored. Results: CiRT results influenced clinical decision-making in a majority of cases. LPRR status was associated with higher rates of treatment avoidance and early discontinuation due to immune-related adverse events (IrAEs). In contrast, HPRR patients experienced greater clinical benefit and longer ICI exposure. CiRT classification was not associated with short-term imaging-based response outcomes, supporting its role as an independent predictor. Given that ICI therapy and supportive care can cost more than $850,000 per patient, CiRT offers potential value in avoiding ineffective treatment and associated toxicities. Conclusions: CiRT demonstrates meaningful clinical utility as a non-invasive, predictive tool for guiding immunotherapy decisions across tumor types. It enables more precise treatment selection, improves patient outcomes, and supports value-based cancer care. Full article

Background: Splenic arterial embolization (SAE) is a well-established technique in the non-operative management of splenic trauma and aneurysms. While its short-term safety and efficacy have been widely documented, medium- and long-term impacts on splenic volume and function remain under-investigated. This study aimed to evaluate volumetric changes and hematological parameters following SAE, with emphasis on its role in preserving splenic integrity and potential integration with AI-enhanced imaging technologies. Methods: We retrospectively analyzed 17 patients treated with SAE between January 2014 and December 2023. Volumetric measurements were performed using computed tomography (CT) with 3D reconstructions before and after SAE. Patients were divided into two groups based on indication: polytrauma (n = 8) and splenic artery aneurysm (n = 9). Hematological parameters including white blood cells (WBCs), red blood cells (RBCs), and hemoglobin (Hb) were evaluated in correlation with clinical outcomes. Statistical significance was assessed using Student’s t-test, and power analysis was conducted. Results: Among the trauma group, mean splenic volume decreased from 190.5 ± 51.2 cm³ to 147.8 ± 77.8 cm³ (p = 0.2158), while in the aneurysm group, volume decreased from 195.4 ± 78.9 cm³ to 143.7 ± 81.4 cm³ (p = 0.184). Though not statistically significant, these changes suggest post-procedural splenic remodeling. The technical success of SAE was 100%, with no cases of late follow-up infarction, abscess, immunological impairment, or secondary splenectomy required. Hematologic parameters remained within normal limits in follow-up assessments. Conclusions: SAE represents a safe and effective intervention for spleen preservation in both traumatic and aneurysmal conditions. Although a reduction in splenic volume has been observed, white blood cell counts, a reliable indicator of splenic function, have remained stable over time. This finding supports the preservation of splenic function following SAE. Full article