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Cold object detection and classification allow reliable identification and categorization of low-temperature objects using non-contact thermal analysis, thereby complementing standard heat-focused techniques. It supports accurate monitoring of cooling behaviour, improves fault detection, quality control and remains powerful under poor lighting or visually challenging situations. However, cold object classification remains challenging when using conventional visible-light images due to the absence of discriminative temperature information. As of now, dedicated and systematic research on cold object detection and classification using thermal images are still very limited, highlighting a clear research gap. To address this limitation, this work proposes a thermal image-based cold object detection and classification framework using machine learning algorithms, designed to achieve effectiveness, reduced cost, and lower processing time. In addition, a dedicated dataset is developed by capturing time-dependent temperature variations of multiple cold object categories, enabling well ordered analysis and classification based on their thermal behaviour. Several machine learning models, including Decision Tree, Random Forest, XGBoost, and other classification algorithms, were developed and assessed using the proposed dataset. Among these models, the Random Forest classifier gives the highest classification accuracy of 99.35%, demonstrating its effectiveness in capturing temporal thermal variations for accurate cold object detection and identification. This work establishes a new research direction in thermal image analysis by shifting the focus from heat anomaly classification toward reliable cold object classification.

This study introduces a new 10-item, self-administered children's voice questionnaire (CVQ-10), designed to quantify children's voice handicap and assess its validity and reliability. Observational, prospective, cross-sectional study. The 10&#xa0;items that comprise the new CVQ-10 were extracted from the full CVQ. The selected items were chosen based on their Cohen's d scores. The new questionnaire was administered to 208 children (80 dysphonic and 128 nondysphonic), aged 6-17 years. In addition, the parents of these children completed the Pediatric Voice Handicap Index (pVHI) and a brief anamnesis questionnaire. Furthermore, after 2&#xa0;weeks, a subset of 30 randomly selected children (15 dysphonic and 15 nondysphonic) completed the CVQ-10 again, to examine test-retest reliability. At that time, another subset of 30 children was randomly selected to complete the full CVQ questionnaire to evaluate validity. The CVQ-10 demonstrated high reliability (Cronbach's &#x3b1; =&#xa0;0.94). Test-retest reliability yielded a Pearson correlation coefficient of r =&#xa0;0.91 (P&#xa0;< 0.001). A highly significant group difference was found between the dysphonic and nondysphonic groups of children (t[206] = 17.00, P&#xa0;< 0.001), with a calculated cutoff of&#xa0;nine&#xa0;points.&#xa0;A significant positive correlation was found between children's subjective self-evaluation on the CVQ-10 and their parents' responses on the pVHI questionnaire (r = 0.766, P&#xa0;< 0.001). The CVQ-10 is shown to be a valid and reliable instrument. Data confirms its single factor contract. This demonstrates that children are capable of providing a reliable and consistent description of their subjective voice handicap. The CVQ-10 differentiates dysphonic from nondysphonic children and can serve as a brief and easy to-use instrument for evaluating dysphonic children in clinical and research settings.

Engineered bacteria offer unique opportunities for biosensing because they combine genetically programmable biological activity with tunable surface interfaces. However, constructing living sensing systems that enable programmable recognition, reliable signal output, and robust performance in complex samples remains challenging. Here, we report an interfacially engineered bacterial sensing platform for dual-mode microRNA detection. Engineered Escherichia coli expressing enhanced green fluorescent protein (eGFP) were used as living signal carriers, and a polyphenol coating was deposited to create a stable, functionalizable interface. This coating preserved bacterial fluorescence while enabling immobilization of nucleic acid hairpin probes. Upon target recognition, catalytic hairpin assembly (CHA) was triggered, driving the programmable bridging and aggregation between bacteria and magnetic beads. This process converts molecular recognition into a magnetically separable assembly event, enabling target enrichment and background reduction. Meanwhile, enriched bacteria provide fluorescence output via intracellular eGFP, while Fe3+ released from the coating under acidic conditions generates a Prussian blue colorimetric signal. Together, these processes establish a fluorescence-colorimetric dual-mode sensing platform with limits of detection of 5.33 pM for the fluorescence mode and 14.1 pM for the colorimetric mode. In serum samples from glioma patients, the platform effectively distinguished patients from healthy controls, with dual-mode analysis showing improved discrimination performance compared to single-mode detection. This work demonstrates that interfacial engineering transforms engineered bacteria into multifunctional living sensing units and provides a practical strategy for developing reliable biosensing systems for liquid biopsy applications.

Maritime Search and Rescue (SAR) operations are often challenged by vast search zones, poor visibility, and extreme lighting conditions, especially during nighttime missions. This study investigates the use of computer vision and object detection algorithms to automate life jacket detection and improve SAR effectiveness. To address the absence of domain-specific datasets, a custom image dataset featuring multiple life jacket types was developed. A two-fold methodology was adopted: evaluating the performance of YOLO object detection models (versions 5 through 12) on the dataset, and incorporating advanced image preprocessing techniques to enhance detection under challenging lighting conditions. The results demonstrate that preprocessing significantly improves detection performance in both overexposed and underexposed scenarios. Among all evaluated models, YOLOv10 achieved the strongest combination of precision and real-time inference speed (43.9 FPS on Tesla T4 GPU), making it a promising candidate for time-sensitive rescue applications. While individual cells of Tables&#xa0;5, 6, 7, 8 and 9 show other detectors achieving higher precision under specific lighting &#xd7; preprocessing combinations, YOLOv10 offers the best aggregate trade-off across the evaluated criteria. This work contributes a scalable benchmark solution for improving SAR outcomes by enabling faster and more reliable identification of individuals in distress at sea.

Accurate classification of skull image orientations is a foundational prerequisite for advanced downstream applications, including automated sex estimation, ethnicity classification, and 3D cranial reconstruction. However, geometric variations and anatomical feature overlaps in cranial photographs present persistent challenges for computer vision models. This study introduces a confidence-based prediction rejection framework to establish a high-fidelity image preprocessing pipeline across five standard viewpoints: anterior (Front), posterior (Back), lateral (Side), anterolateral (Diagonal-Front), and posterolateral (Diagonal-Back). A dataset of 4,673 images derived from 43 dry skulls of Thai individuals was used to develop four convolutional neural network (CNN) models: ResNet50, VGG-16, MobileNetV2, and a Custom CNN. All models were evaluated using 20-fold cross-validation and validated on an independent test set of 760 images from 8 distinct skulls. VGG-16 achieved the highest cross-validation accuracy of 94.59% &#xb1;1.50% and lowest loss of 0.15&#x2009;&#xb1;&#x2009;0.04, whereas ResNet50 demonstrated superior generalization on the independent dataset. ResNet50 and the Custom CNN also exhibited stable and congruent convergence trajectories during optimization. A threshold-based filtering mechanism was deployed to mitigate false-positive errors, with &#x3c4;&#x2009;>&#x2009;0.90 identified as the optimal confidence cutoff. At this threshold, ResNet50 achieved an accuracy of 98.49% at a rejection rate of 29.87%. This framework serves as a reliable computational foundation for ensuring accurate skull view classification in forensic anthropology, craniofacial surgery, ethnicity classification, and sex estimation.

Measuring health, a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity, in Down syndrome (DS) was recently achieved through creation of a new health measure. However, additional information on health-related medical topics and how health measurement varies in demographic groups from that cohort has not been presented. We surveyed caregivers of individuals age 0-21 with Down syndrome (DS) from a national sample about the health of their son or daughter with DS as part of a broader study to create a valid, reliable health measure in DS. In this manuscript, we present secondary analysis of survey items on health-related topics including co-occurring medical conditions, recurrent infections, and healthcare maintenance. We then conducted an analysis to compare caregiver-reported demographic traits to total health scores through regression modeling to identify predictors of health status. Survey responses from 542 caregivers of individuals with DS were received; rates of co-occurring medical conditions generally aligned with past results, rates of recurrent infections showed lower rates of persistent fungal infections in our cohort (p&#x2009;<&#x2009;0.0001) and completion of healthcare guidelines did not correlate with health scores (p&#x2009;=&#x2009;0.13). In regression modeling, parent sex of respondent (male) and better parent health correlated with better total health scores of individuals with DS. Health-related topics show prevalence rates aligning with past literature, lower rates of some infections, and imperfect guideline adherence. Fathers and parents who feel that they are in better health reported better health of their sons and daughters with DS. Trial Registration: ClinicalTrials.gov, NCT04631237.

Diffusion magnetic resonance imaging (dMRI) provides powerful insights into brain microstructure, but conventional microstructural modeling methods require long acquisition times for covering sufficient diffusion directions and are computationally intensive. While deep learning has shown promise in reducing the direction requirement and accelerating the modeling, traditional architectures such as CNNs often struggle to capture the highly nonlinear relationships between multi-shell diffusion signals and microstructural properties. We present MicroKAN, a novel framework built upon Kolmogorov-Arnold Networks with adaptive spline-based activations, specifically designed to represent complex biophysical models with enhanced flexibility and efficiency. MicroKAN supports both supervised and self-supervised paradigms: the supervised variant learns mappings from data to reference metrics, while the self-supervised variant estimates model parameters directly by reconstructing signals through the forward diffusion process, eliminating the need for ground-truth labels. Evaluated on diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) across multiple datasets, MicroKAN substantially accelerates acquisition and improves the fidelity of microstructural parameter estimation. Beyond supervised training, its self-supervised formulation shows strong robustness to distribution shifts, enabling reliable performance even without annotations. Furthermore, transfer learning with minimal labeled data preserves high accuracy, underscoring the framework's adaptability to diverse scenarios. These advances establish MicroKAN as a versatile and efficient tool for dMRI analysis, offering new opportunities to accelerate neuroscience research and expand the clinical utility of microstructural imaging. Our source code is available at https://github.com/JustlfC03/MicroKAN.

In vivo confocal microscopy is a valuable tool for evaluating corneal subbasal nerve plexus (SBNP). However, lack of standardised, repeatable, and reliable methods is a challenge, particularly in clinical, multi-centre, and longitudinal settings. To address this, we developed a standardised imaging method using the inferior whorl (IW) region of the SBNP as a reference for imaging a predefined 1.5 mm2 central region of interest (ROI). No systematic differences were observed either between the two operators or between test-retest assessments performed by the same operator. A full 400&#xa0;&#x3bc;m diameter circle around the IW was imaged in 85% of the mosaics. The inter-operator repeatability for the x- and the y-coordinates when defining the centre of the IW was 0.996 (95% CI [0.991, 0.999]) and 0.997 (95% CI [0.994, 0.999]), respectively. For 61% of the mosaics, the ROI was accurately positioned and fully imaged. By anchoring the ROI to the IW, the approach enables more repeatable and anatomically consistent imaging, supporting improved comparability across examinations. While the method shows promise, further refinement and technical development are required to enhance its reliability and robustness.

Although liver biopsy in patients with acute cellular rejection (ACR) is the gold standard for diagnosis, its invasive nature highlights the need for reliable noninvasive biomarkers. With evidence suggesting that peripheral blood eosinophil levels may be associated with rejection, we evaluated the relationship between eosinophil levels and ACR to determine whether eosinophil dynamics reflect rejection severity. We retrospectively analyzed 151 liver transplant recipients who underwent 398 liver biopsies between 2012 and 2022. To analyze eosinophil changes, we collected data on eosinophil counts, eosinophil percentages, liver enzymes, bilirubin, international normalized ratio, and rejection activity index (RAI) scores from biopsy day (day 0) and day 5 after treatment; patients with and without rejection (established histopathologically with RAI) were compared. We analyzed correlations between RAI severity and eosinophil levels. Liver enzyme levels were significantly higher in patients with versus without ACR (P < .05). In the rejection group, eosinophil count and percentage decreased markedly after treatment (P < .001), whereas no significant change was observed in the non-rejection group. Higher RAI scores were associated with increased eosinophil count (P = .029) and percentage (P = .021) on day 0 and a more pronounced decline on day 5 (both P < .001). Peripheral blood eosinophil levels were associated with ACR and exhibited characteristic changes that reflected rejection severity and therapeutic response. Eosinophil monitoring may be a useful noninvasive adjunct in the early detection and follow-up of ACR. Larger prospective multicenter studies are needed to validate these findings.

Weakly supervised video anomaly detection (WSVAD) is fundamentally constrained by the absence of frame-level annotations, which leads to noisy instance selection in Multiple Instance Learning (MIL) and weak correspondence between temporal video segments and semantic descriptions. Vision-language models address this by enabling cross-modal alignment between visual features and textual labels, but when these representations are learned in Euclidean space, they struggle to capture subtle semantic variations and often produce ambiguous instance ranking under weak supervision. To address this limitation, we propose PoinCLIP-VAD, a vision-language framework that performs cross-modal fusion in hyperbolic space. The model embeds visual and textual features into a shared Poincar&#xe9; ball geometry, where non-linear distance scaling provides a more expressive representation of latent semantic relationships induced by cross-modal interactions, without relying on predefined hierarchical structures. This geometry-consistent formulation enables more reliable similarity estimation and better preserves distinctions between normal and anomalous patterns. The framework adopts a dual-block architecture consisting of a classification block for coarse anomaly scoring and a video-text alignment block for fine-grained correspondence using negative Poincar&#xe9; distance. Extensive experiments on benchmark datasets demonstrate that PoinCLIP-VAD achieves an AUC of 90.62% on UCF-Crime and an AP of 86.93% on XD-Violence, confirming improved anomaly discrimination and more consistent cross-modal alignment under weak supervision.

Navigating the complexities of modern power systems with a high share of renewable energy sources (RES) requires effective control strategies. This research presents a new Coordinated Voltage Control (CVC) framework for RES-integrated grids by combining Ant Colony Optimization (ACO) with a Deep Q-Network (DQN) controller. In this hybrid approach, ACO optimizes voltage profiles while DQN adjusts reactive power support in real-time, allowing for quick responses to grid conditions. The proposed method significantly improves voltage stability and reduces system losses, showing strong performance under different renewable generation and load scenarios. The ACO and DQN framework also shows good computational efficiency, achieving convergence in about 45 to 50 iterations. By using historical and simulated data for DQN training, the controller predicts the best reactive power actions, ensuring scalable and reliable voltage regulation. This work provides a practical and flexible solution for modern power systems rich in renewables, supporting better grid stability and operational efficiency.

Avatrombopag (AVA), an oral thrombopoietin receptor agonist (TPO-RA), has demonstrated favorable efficacy in the treatment of pediatric immune thrombocytopenia (ITP). However, treatment-related thrombocytosis represents a clinically relevant adverse event that may compromise treatment safety and continuity. Currently, no validated tools are available to predict the risk of AVA-induced thrombocytosis before treatment initiation. In this real-world study, we aimed to develop and validate a predictive model for AVA-associated thrombocytosis in children with ITP. A total of 74 pediatric patients treated with AVA at the Hematology-Oncology Center of Beijing Children's Hospital between July 2021 and January 2024 were included. We compared the proposed model with established classical machine learning baselines, including Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and XGBoost, as well as state-of-the-art deep learning models for tabular data, including TabPFN, FT-Transformer, and HyperTab. Among the evaluated models, the FT-Transformer achieved the best performance, with an accuracy of 0.785&#x2009;&#xb1;&#x2009;0.023 and an area under the receiver operating characteristic curve (AUC) of 0.851&#x2009;&#xb1;&#x2009;0.021. Model interpretability was enhanced using Shapley Additive Explanations (SHAP), enabling visualization of individual feature contributions to thrombocytosis risk. This AI-driven prediction model, grounded in real-world clinical data, demonstrates robust predictive performance and offers clinically interpretable insights. It provides a reliable reference for individualized risk assessment and supports safer, more precise use of AVA in pediatric ITP management.

Accurate, timely diagnosis of postpartum hemorrhage (PPH) is critical for effective management. However, in low-resource settings, reliance on visual estimation leads to missed diagnoses in up to 50% of cases, underscoring the need for more objective and reliable methods. Evidence suggests that the use of tools for objective measurement of blood loss, such as calibrated drapes, can improve early detection and prompt treatment of PPH. This article shares learning from the Accelerating Measurable Progress and Leveraging Investments for Postpartum Hemorrhage Impact (AMPLI-PPHI) project's experience introducing calibrated drapes for PPH detection in coordination with governments of Nigeria and Zambia and supporting county government-led rollout in Kenya. Early implementation revealed key lessons including the importance of engagement of ministries of health and key stakeholders in decision-making on the type of calibrated tool to use. Care must be taken to prepare healthcare providers, communities, government authorities, and other stakeholders about the potential increase in reported PPH cases due to more accurate measurement and diagnosis. Persistent challenges remain concerning safe waste disposal and ensuring monitoring of blood loss and vital signs, including documentation of checks every 15&#x2009;min in the first hour, as recommended by 2025 WHO-FIGO-ICM guidelines. Additionally, context-specific considerations may be required for scale-up of objective measurement of postpartum blood loss, such as understanding the cultural dynamics related to blood measurement and afterbirth practices, and ensuring the workforce is sufficient in numbers and capacity to provide close monitoring and measurement of blood loss necessary for early PPH detection.

The transition toward sustainable cities requires integrated energy planning frameworks that coordinate multiple technologies, policy instruments, and social considerations. This study proposes a robust optimization framework for rich-renewables eco-sustainable urban communities, where multi-energy hubs including electricity, thermal, cooling, and hydrogen systems are jointly managed under uncertainty. A scenario-independent static robust model is developed to ensure reliable operation under renewable intermittency, supported by sensitivity analyses. The framework introduces hydrogen chemistry consortium processes, integrating electrolyzers, methanation, fuel cells, and carbon capture, utilization, and storage to enhance renewable utilization and reduce emissions. Both stationary storage systems and electric public transportation fleets are incorporated to provide distributed and mobile energy flexibility. Demand-side management and policy mechanisms, including carbon taxation and cap-and-trade, are embedded to align operations with environmental targets. A digital-social welfare layer evaluates affordability and equitable access. Simulation results across multiple scenarios demonstrate that the proposed framework reduces operational costs by over 45%, improves grid independence by more than 35%, and achieves emission reductions exceeding 90%. Welfare indicators also show significant improvement, confirming the effectiveness of the integrated approach.

Accurate identification of frontal sinus boundaries is a critical step in anterior skull base surgery, particularly in transsinusal approaches, as it allows optimization of the surgical bony window while minimizing unnecessary bone removal. To describe a simple and reproducible technique of external frontal bone transillumination for intraoperative frontal sinus mapping. After bicoronal exposure of the frontal bone, a standard fiber-optic light cable connected to the operating room light source is applied in direct contact with the frontal squama under low ambient light conditions. When properly applied, the frontal sinus appears as a brighter translucent area, allowing accurate delineation of its margins. Postoperative CT imaging was retrospectively reviewed to assess correspondence between the intraoperatively identified margins and the actual anatomical boundaries. Between 2015 and 2024, the technique was applied in 14 consecutive patients undergoing anterior skull base surgery, including olfactory groove meningiomas, post-traumatic anterior skull base cerebrospinal fluid fistulas, and one case of intrasinusal osteoma. In all cases, external transillumination enabled clear identification of frontal sinus boundaries and facilitated creation of an adequate surgical window. Postoperative imaging confirmed correspondence between the planned opening and the actual anatomical extent of the frontal sinus opening. No sinus-related complications or postoperative cerebrospinal fluid leaks were observed. External frontal bone transillumination is a simple, fast, and reliable method for intraoperative frontal sinus mapping during anterior skull base surgery. Its ease of adoption and use of routinely available equipment make it a useful adjunct to preoperative imaging for optimizing surgical exposure.

The detection of cancer cells not only facilitates the early diagnosis of cancers but also provides valuable support for formulating personalized treatment plans for patients. In this work, we develop a new aptamer-based electrochemical biosensing method for cancer cell detection, employing an electrochemically activated DNA circuit as a signal generation module. Specifically, target cells are captured onto the indium tin oxide electrodes through aptamer-mediated recognition of surface proteins. Then, a DNA circuit begins with the potential-driven selective bioconjugation of inherent tyrosine residues on the cell surface. This is subsequently followed by the incorporation of a facile click chemistry reaction and a netlike cascade assembly involving multiple hairpin probes. As a result, a large number of electroactive methylene blue molecules are recruited to the cell surface, thereby ensuring highly efficient electrochemical signal generation. Taking breast cancer cells MDA-MB-231 as a model, this method enables the detection of target cells in the linear range from 5 to 1&#x202f;&#xd7;&#x202f;104&#x202f;cells with a limit of detection down to 4.2&#x202f;cells, and also demonstrates high specificity and applicability to complex samples with good recoveries from 95.0% to 106.2%. Furthermore, the DNA circuit-based signal generation module exhibits excellent universality and signal stability, allowing the method to be adapted for detecting other cancer cells, such as HER-2-positive BT-474 and MDA-MB-453&#x202f;cells through simple aptamer replacement. Therefore, this work presents a reliable new tool for cancer cell detection, which holds great promise for playing an important role in future cancer diagnostic practices.

Organosilicon chemistry, a subsection of organic chemistry with unique chemical properties, is typically underrepresented in the training sets of (Q)SAR models and often outside the applicability domain. Using bacterial reverse mutation as an endpoint for a proof-of-concept study, we compiled a peer-reviewed reference dataset with publicly available reliable Ames tests of more than 100 organosilicon substances to assess the predictive performance of fourteen in silico methods, including mechanistic profilers, expert systems, hybrid and statistical models with varying underlying algorithms (Derek Nexus, Sarah Nexus, VEGA mutagenicity models, TEST mutagenicity models, OECD QSAR Toolbox profilers). The assessment showed that most, but not all, tools predict mutagenic potential of organosilicon chemistry as accurately as for other organic datasets, with six tools providing balanced accuracies &#x2265; 80%. The nature of the algorithm was identified as the key driver for accuracy. The presence of the silicon atom in a molecule sometimes resulted in substances being outside model-specific applicability domains, but this did not necessarily correlate with predictive performance. Furthermore, the assessment showed that organosilicon chemistry does not possess intrinsic gene mutation potential. All positive Ames test results in the dataset could be linked to organofunctional structural alerts known to be related to mutagenicity.

Workplace gender discrimination against female nurses is a critical issue requiring evidence-based investigation and documentation. This study aimed to validate the Persian version of the Scale to Measure the Perception of Workplace Gender Discrimination for Female Nurses and ensure its psychometric robustness for clinical and research applications. Employing a cross-sectional methodological design, the study recruited 535 female nurses in Iran through convenience sampling. The Persian translation of the scale was developed in accordance with World Health Organization (WHO) guidelines. Validity and reliability were assessed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and internal consistency measures. The EFA and CFA confirmed a five-factor structure with 29 items, accounting for 51.029% of the total variance. Model fit was excellent, as indicated by CFI, GFI, TLI, RMSEA, and SRMR values. The scale exhibited acceptable internal consistency, with Cronbach's alpha and McDonald's &#x3c9; coefficients of 0.905 and 0.916, respectively. These findings establish the Persian version of the scale as a valid and reliable instrument, addressing a significant gap in the assessment of workplace gender discrimination for female nurses.