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Childhood trauma, substance use and mental health: Exploring differences across two Tunisian jails
Introduction The increasing rate of incarceration represents a significant public health issue worldwide. However, focusing on mental health issues and exploring gender differences among this vulnerable population is lacking in the literature. We aimed to assess differences in childhood trauma, substance use, and mental health among two Tunisian jails. Materials and methods Two cross-sectional studies were conducted in two Tunisian prisons with different gender compositions, one involving male prisoners in April 2023 and the other involving female prisoners in July 2024. We used Arabic-validated versions of the Adverse Childhood Experiences-International Questionnaire, the Hospital Anxiety and Depression Scale, and the Rosenberg Self-Esteem Scale. Substance use and suicidal thoughts were assessed through a yes or no question. The questionnaire was anonymous and participation was voluntary. Results A total of 568 prisoners answered the questionnaire. The majority were males (71%) with a median age of 31 years [2640]. We found that participants from female prison reported higher rates of childhood sexual abuse (25.6% vs 7.8%, p < 0.001). Physical abuse was more common among the male prison population (74.6% vs 58.8%, p < 0.001). Community and collective childhood violence were significantly more prevalent among participants from the male prison (94.8% vs 74.5%, p < 0.001 and 57.3% vs 43.6%, p = 0.003, respectively). Substance use was more prevalent among participants in the male prison across all substance types assessed. The female prison population reported more severe anxiety symptoms (69.1% vs 23.6%, p < 0.001) and depressive symptoms (96.4% vs 80.1%, p < 0.001). About 46% of participants in the female prison reported suicidal thoughts during incarceration, compared with 35.1% in the male prison (p = 0.014). The male prison population was more likely to have lower self-esteem (87.4% vs 69.1%, p < 0.001). Conclusion Implementing rehabilitation programs for prisoners is essential to reduce incarceration rates and mitigate these alarming negative outcomes.
AR/VR based digital exhibitions as a tool for successful presentation of new products and ideas at trade shows and expos
Directly probing the carrier transfer length in 2D-material transistors
A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence
Author Correction: Vitellogenin receptor mediates heat adaptability of oocyte development in mud crabs and zebrafish
Validity of multiple human pose estimation tools for measuring knee impact angles in video-captured falls of older adults
Falls are a major cause of injury in older adults. Although bending the knees during a fall has been shown to reduce stress on the hip, knee motion during falls is not well understood because laboratory fall studies are limited by safety concerns and marker occlusion in motion capture systems. AI-based pose estimation may help overcome these challenges, but its accuracy in measuring joint angles during falls has not yet been validated. We evaluated three pose estimation models (OpenPose, VideoPose3D, WHAM) for analyzing knee kinematics in video-captured falls. A total of 121 videos of 13 older adults (64.0 ± 5.9 years) falling sideways, utilizing diverse fall strategies (knee block, stick-like, tuck-and-roll), in a lab setting were analyzed. Each model generated time series of knee angles from the videos, from which knee flexion angles at ground impact were calculated and compared to ground truth data from a motion capture system. Agreement with the ground truth was assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), and bias, analyzed across viewing planes (sagittal vs. frontal) and leg sides (impact vs. opposite). WHAM demonstrated the highest accuracy (MAPE:13.61 ± 10.55%) with minimal bias (<10%), consistently performing well across all views and leg sides. OpenPose performed similar to WHAM in the sagittal view (MAPE:14.38 ± 9.63%) but poorly in the frontal view (MAPE:71.33 ± 17.24%) due to substantial underestimation (bias:-71.33 ± 17.24%). VideoPose3D showed poor accuracy across all conditions (MAPE:39.09 ± 20.54%). WHAM also characterized differences in knee flexion kinematics between fall strategies (e.g., least vs. most knee flexion) but did not fully reproduce side-specific kinematic differences between legs, particularly for tuck-and-roll falls. This is the first study to validate pose estimation algorithms for estimating knee impact angles from video-captured falls in older adults. Future work should fine-tune WHAM using fall-specific data to further improve its performance in tracking body movements during falls.
Uncertainty aware stochastic sampling for efficient object detection
Abstract The effectiveness of deep learning models is strongly influenced by the quality of training data. Traditional training approaches assume that all samples contribute equally to the learning process, leading to uniform data sampling. However, this assumption overlooks the substantial variation in informational content across samples. This paper introduces a novel and computationally lightweight data prioritization methodology for object detection that dynamically adjusts the sampling probability of training data according to its relevance during learning.The proposed Relative Detection Error (RDE) is a new temporal instability metric defined in a joint classification–regression setting, where classification corresponds to object category prediction and regression corresponds to bounding-box parameter estimation. By explicitly quantifying prediction variability over time, RDE identifies samples with higher learning value and guides an exploration-regularized stochastic sampling policy. As a result, the method improves both classification and localization accuracy while imposing minimal computational overhead and integrating seamlessly into standard training pipelines. The approach is validated using YOLO architectures on diverse datasets, demonstrating consistent improvements and strong generalization across models and domains. Experimental results show that prioritizing high-value samples yields higher F1 scores and mean Average Precision, alongside more efficient and stable convergence. The project source code is available at: https://kp-labs-bme.github.io/Object-Detection-Prioritization/
An integrated, scaled approach to resolve TSC2 variants of uncertain significance
Identification of shipping signals with few-shot learning: A distribution-aware approach
Effective identification of shipping signals in underwater environments is essential for maritime operations and ecosystem monitoring. Traditional models require extensive data for each ship type, posing a significant challenge owing to the difficulty of collecting diverse signals, particularly for vessels with security constraints. Few-shot learning offers a promising solution by enabling ships identification from minimal data through accurate template matching. This study proposes a novel few-shot learning approach that leverages stochastic information within and between ship types to improve identification accuracy using limited labeled data. The proposed model is designed based on a Siamese prototype network that integrates intra- and inter-category dissimilarities, employing cosine distance to estimate similarity while accounting for variance within the data. It achieves robust performance even when trained on limited samples with an average accuracy of 87.81% in five-way identification. In addition, its ability to generalize to unseen ship classes highlights its potential for real-time marine applications, further confirming the effectiveness of few-shot learning in constrained data scenarios. This approach provides valuable insights into designing adaptive, efficient systems for underwater signal detection and has potential applications across a wide range of acoustic processing tasks.
Multitarget therapeutic potential of sulforaphane in ethidium bromide-induced neurotoxicity in multiple sclerosis-like pathology: comparison with omaveloxolone and dimethyl fumarate on neuroprotection and systemic recovery
Unravelling the key factors governing O2 evolution upon charging a reversible LiOH-based nonaqueous Li | |O2 battery
Leveraging microcredentials for sustainability literacy in higher education: A case study of reflective thinking and learning impact in science
Microcredentials are becoming increasingly popular in higher education. Despite their growing popularity, there is limited exploration of microcredentials’ potential for lifelong learning and their role in sustainability education within curricula. Furthermore, the use of and evaluation of reflective thinking linked to science and sustainability within these online offerings is unexplored. Undertaken in 2024, a large undergraduate science subject, Complex Case Studies in Science (n = 435), embedded a short microcredential titled ‘Sustain ability : Think, Care, Do’ to foster sustainability learning outcomes. The microcredential introduces learners to key concepts of sustainability, understanding diverse worldviews, unpacking their values and how to think systemically, as well as the relevance of these literacies to their science discipline. Our paper explores the learning of a participating cohort of undergraduate students (n = 33) in developing sustainability literacies through this online intervention to culminate in a final reflective assessment of the subject. Using reflexive thematic analysis, we analysed the students’ reflective assessment task. Our findings demonstrate how an online microcredential enhanced science students’ understanding of sustainability, re-evaluation of their daily practices and professional identities, and the development of literacies that grapple with complexity and interdisciplinary solutions across sustainability domains. We also quantified their engagement through behavioural analytics, which varied greatly as expected with an independent self-paced online course. We share these insights as a blueprint for higher education practitioners to integrate online sustainability microcredentials into their curricula at scale.
A GIS-based fuzzy-AHP framework for delineating hydrogeologically and socially sensitive recharge zones in Southern Odisha, India
Continuous electricity from charged total dissolved solids in wastewater using a wood-based ion-selective power generator
Abstract Exploring the potential for secondary utilization of wastewater is a prudent strategy to achieve “take-make-use-reuse” circular economy. Taking advantages of wood’s hierarchical structure and large surface area, in this project, we fabricate surface-encapsulated anion-selective and cation-selective wood membranes (comprising up to 98% eco-friendly materials) through a two-step process: dip-coating with either positively charged 2(dimethylamino)ethyl methacrylate or negatively charged acrylic acid, followed by energy-efficient sunlight-induced polymerization. The output voltage and current of a single modified wood cell (20 × 20 × 3 mm 3 ) in modulated wastewater from flue gas desulfurization are 55 mV and 0.6 µA, respectively, tenfold higher than that of untreated wood cells. When five cells are connected in series, the output voltage reaches 0.27 V, sufficient to power simple electronic devices. This underscores its potential for scaling up and its viability for future applications in industrial power plants.
Lightweight real-time detectors of apple-leaf diseases operating on embedded devices
Agricultural leaf disease detection is crucial for early intervention and yield protection in precision agriculture. Among representative economic crops, such as apples, leaf lesions are typically small and appear in complex backgrounds, making accurate detection performed on resource-constrained embedded devices challenging. To address this, we propose a lightweight small-object detection models, namely the dynamic Differential Compensation Lightweight-YOLO (DCL-YOLO) model and its pruned version (DCL-YOLO-P), based on YOLO11n. A novel Dual-Aspect Feature Complementary Mapping (DAFCM) module type is embedded in their backbone to recover lost semantic and spatial information, while the original YOLO11n’s neck is replaced by an Efficient Enhanced Cross-Scale Feature Fusion (EE-CSFF) module, which incorporates Gated Differential Convolutional Fusion (GDCF) modules to strengthen cross-scale information flow and small-object representation. Experimental results obtained on the ALDSOD dataset show that, compared with the YOLO11n baseline, DCL-YOLO improves recall from 81.9% to 84.6%, mAP 50 from 86.8% to 88.4%, and mAP 50:95 from 47.0% to 47.8%, while also reducing the parameter count from 2.58 M to 1.91 M and Giga Floating-Point Operations (GFLOPs) from 6.3 to 5.5. After applying Layer-Adaptive Magnitude-based Pruning (LAMP), the parameter count and GFLOPs are further reduced to 0.75 M and 2.7, respectively, with mAP 50 and mAP 50:95 still exceeding the baseline by 1.2 and 0.5 percentage points, respectively. When deployed on an embedded device, the pruned model achieved 15.2 FPS and 139 msec per image, confirming its applicability in real-time scenarios. Furthermore, cross-domain validation, performed on the Global Wheat Head Detection (GWHD) dataset, indicates the stable generalization capabilities of the proposed models across environmental domain shifts. The DCL-YOLO’s source code is publicly available at: https://github.com/q123-code/dcl-yolo .