Search Results (181)

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

The Lamb wave-based damage detection method shows great potential for composite impact failure assessments. However, the traditional single signal feature-based methods only depend on partial structural state monitoring information, without considering the inconsistency of damage sensitivity and detection capability for different signal features. Therefore, this paper proposes a damage fusion detection method based on distance analysis and evidence theory for composite laminate panels. Firstly, the signal features of different dimensions are extracted from time–frequency domain perspectives. Correlational analysis and cluster analysis are applied to achieve feature reduction and retain highly sensitive signal features. Secondly, the damage detection results of highly sensitive features and the corresponding basic probability assignments (BPAs) are acquired using distance analysis. Finally, the consistent damage detection result can be acquired by applying evidence theory to the decision level to fuse detection results for highly sensitive signal features. Impact tests on ten composite laminate panels are implemented to validate the proposed fusion detection method. The results show that the proposed method can accurately identify the delamination damage with different locations and different areas. In addition, the classification accuracy is above 85%, the false alarm rate is below 25% and the missing alarm rate is below 15%. Full article

Background/Objectives: The Wavelia Microwave Breast Imaging (MWBI) technology aims to increase sensitivity in dense breasts, where X-ray mammography is of limited value. Its potential contribution to the reduction in the false positives in breast cancer diagnosis, by developing MWBI image descriptors supporting malignant-to-benign lesion discrimination, is also being investigated. After a First-In-Human (FiH) study with interesting findings on a small dataset of 24 symptomatic breast lesions, an upgraded 2nd prototype of Wavelia was manufactured and tested on a larger and more diverse dataset, including 62 patients and a balanced distribution of malignant and benign symptomatic breast lesions. Methods: A set of technological and methodological evolutions, outlined in this article, was implemented in Wavelia#2 to handle the diversity in larger patient datasets. Multi-modal MWBI imaging is employed to parameterize the interaction mechanisms between the microwaves and the imaged breast at varying geometrical and tissue consistency conditions. MWBI Region-Of-Interest (ROI) extraction and characterization based on multidimensional radiomic feature vectors is implemented to expand the malignant-to-benign lesion diagnostics potential of MWBI compared to the limited scope of the FiH study with Wavelia#1, which employed three specific preselected features. Results: This study demonstrates significant diagnostic accuracy of multiple texture-based and intensity-based features to discriminate between malignant and benign breast lesions with Wavelia#2 MWBI. A phenomenological qualitative assessment of the false positive rate on healthy breasts is also presented for the MWBI technology for the first time. Conclusions: The analysis contributes to the rationalization of the MWBI imaging and image analysis outputs towards standardization, objective interpretability, and ultimate clinical acceptance. Full article

Current consumer trends for meat production with reduced antibiotic use constitute huge challenges in animal farming. Using indigenous raw materials such as aromatic or medicinal plants or their extracts could positively affect or retain animals’ health. The present study aimed to evaluate the effects of medicinal plant extracts and essential oils on pig performance parameters, health indices and meat quality. A phytobiotic mixture (PM) consisting of oregano (Origanum vulgare subsp. hirtum) essential oil, rock samphire (Crithmum maritimum L.) essential oil, garlic flour (Allium sativum L.) and false flax flour (Camelina sativa L. Crantz) was used in pig diets, containing in the experimental trials two different proportions of the oregano essential oil (200 mL/t of feed vs. 400 mL/t of feed). Three groups of weaned pigs were fed either the control diet (CONT) or one of the enriched diets (PM-A or PM-B, 2 g/kg). After a 43-day feeding period, at 77 days of age, blood was taken from the jugular vein for biochemical and hematological tests, and eight pigs were humanely slaughtered. A microbiological analysis of intestinal digesta from the ileum and caecum was conducted. Additionally, meat tissue cuts (biceps femoris, external abdominal and triceps brachii) were collected for a chemical analysis, fatty acid lipid profile and oxidative stability testing. The statistical analysis revealed no differences (p > 0.05) in the body weights and growth rates among the groups. An increase (p < 0.05) in total aerobic bacteria was detected in the ileum of group PM-A, while Escherichia coli (E. coli) counts were reduced (p < 0.05) in group PM-B. In the caecum, reductions in Enterobacteriaceae and Lactobacillaceae counts were observed in groups PM-A and PM-B. Concentrations of malondialdehyde (MDA) as an indicator of lipid peroxidation were significantly reduced (p < 0.05) in triceps brachii and biceps femoris for both groups PM-A and PM-B (day 0). A reduction (p < 0.05) in MDA was noticed in triceps brachii and external abdominal meat samples (day 7) for groups PM-A and PM-B. In addition, the fatty acid profile of the meat lipids (ΣPUFA, h/H and PUFA/SFA ratios) was positively modified (p < 0.05) in the ham and belly cuts. The addition of the PM significantly (p < 0.05) affected the redness of the ham and shoulder meat (a* value increased), the yellowness of only the ham (b* value decreased) and the lightness of both belly (L* value increased) and ham samples (L* value decreased). The meat proximate analysis, as well as hematological and biochemical parameters, did not identify any differences (p > 0.05) between the groups. In conclusion, the two investigated mixtures could be used in weaned pigs’ diets, with positive results in intestinal microbial modulation, oxidative stability, fatty acid profile and color characteristics of the pork meat produced. Full article

The joint detection of multiple features significantly enhances radar’s ability to detect weak targets on the sea surface. However, issues such as large data requirements and the lack of robustness in high-dimensional decision spaces severely constrain the detection performance and applicability of such methods. In response to this, this paper proposes a radar target detection method based on linear fusion of two features from the perspective of feature dimension reduction. Firstly, a two-feature linear dimensionality reduction method based on distribution compactness is designed to form a fused feature. Then, the generalized extreme value (GEV) distribution is used to model the tail of the probability density function (PDF) of the fused feature, thereby designing an asymptotic constant false alarm rate (CFAR) detector. Finally, the detection performance of this detector is comparatively analyzed using measured data. Full article

Rainfall-induced slope failures are among the most frequent and destructive natural hazards in Japan’s mountainous regions, often causing severe loss of life and damage to infrastructure. This study presents an integrated statistical framework for regional-scale landslide hazard mapping, with a focus on 0-order basins. To enhance spatial prediction accuracy, both bivariate and multivariate statistical models are employed. Bivariate models efficiently assess the relationship between individual conditioning factors and landslide occurrences but assume variable independence. Conversely, multivariate models account for multicollinearity and the combined effects of interacting factors, although they often require more complex data processing and may lack spatial clarity. To leverage the strengths of both approaches, two hybrid models were developed and applied to a 242.94 km² area in Fukuoka Prefecture, Japan. Model validation was performed using a matrix-based evaluation supported by a threshold optimization algorithm. Among the models tested, the hybrid Frequency Ratio–Logistic Regression (FR + LR) model demonstrated the highest predictive performance, achieving a success rate of 84.30%, a false alarm rate of 17.88%, and a miss rate of 12.30%. It effectively identified critical slip surfaces within zones classified as ‘High’ to ‘Very High’ susceptibility. This integrated approach offers a statistically robust, scalable, and interpretable solution for landslide hazard assessment in geomorphologically complex terrains. It provides valuable support for regional disaster risk reduction and contributes directly to achieving the Sustainable Development Goals (SDGs). Full article

Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically integrates normalization, feature selection, adaptive oversampling, and classifier optimization to enhance detection performance while minimizing computational overhead. The evaluation is conducted using three distinct Canadian forest fire datasets: Alberta Forest Fire (AFF), British Columbia Forest Fire (BCFF), and Saskatchewan Forest Fire (SFF). Initial classifier benchmarking identified the best-performing tree-based model, followed by normalization and feature selection optimization. Next, four oversampling methods were evaluated to address class imbalance. An ablation study quantified the contribution of each module to overall performance. Our targeted, stepwise strategy eliminated the need for exhaustive model searches, reducing computational cost by 97.75% without compromising accuracy. Experimental results demonstrate substantial improvements in F1-score, AFF (from 69.12% to 82.75%), BCFF (61.95% to 77.91%), and SFF (90.03% to 96.18%) alongside notable reductions in False Negative Rates compared to baseline models. Full article

Interstitial lung diseases (ILD) significantly impact health and mortality, affecting millions of individuals worldwide. During the COVID-19 pandemic, lung ultrasonography (LUS) became an indispensable diagnostic and management tool for lung disorders. However, utilising LUS to diagnose ILD requires significant expertise. This research aims to develop an automated and efficient approach for diagnosing ILD from LUS videos using AI to support clinicians in their diagnostic procedures. We developed a binary classifier based on a state-of-the-art CSwin Transformer to discriminate between LUS videos from healthy and non-healthy patients. We used a multi-centric dataset from the Royal Melbourne Hospital (Australia) and the ULTRa Lab at the University of Trento (Italy), comprising 60 LUS videos. Each video corresponds to a single patient, comprising 30 healthy individuals and 30 patients with ILD, with frame counts ranging from 96 to 300 per video. Each video is annotated using the corresponding medical report as ground truth. The datasets used for training the model underwent selective frame filtering, including reduction in frame numbers to eliminate potentially misleading frames in non-healthy videos. This step was crucial because some ILD videos included segments of normal frames, which could be mixed with the pathological features and mislead the model. To address this, we eliminated frames with a healthy appearance, such as frames without B-lines, thereby ensuring that training focused on diagnostically relevant features. The trained model was assessed on an unseen, separate dataset of 12 videos (3 healthy and 9 ILD) with frame counts ranging from 96 to 300 per video. The model achieved an average classification accuracy of 91%, calculated as the mean of three testing methods: Random Sampling (92%), Key Featuring (92%), and Chunk Averaging (89%). In RS, 32 frames were randomly selected from each of the 12 videos, resulting in a classification with 92% accuracy, with specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. Similarly, KF, which involved manually selecting 32 key frames based on representative frames from each of the 12 videos, achieved 92% accuracy with a specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. In contrast, the CA method, where the 12 videos were divided into video segments (chunks) of 32 consecutive frames, with 82 video segments, achieved an 89% classification accuracy (73 out of 82 video segments). Among the 9 misclassified segments in the CA method, 6 were false positives and 3 were false negatives, corresponding to an 11% misclassification rate. The accuracy differences observed between the three training scenarios were confirmed to be statistically significant via inferential analysis. A one-way ANOVA conducted on the 10-fold cross-validation accuracies yielded a large F-statistic of 2135.67 and a small p-value of 6.7 × 10⁻²⁶, indicating highly significant differences in model performance. The proposed approach is a valid solution for fully automating LUS disease detection, aligning with clinical diagnostic practices that integrate dynamic LUS videos. In conclusion, introducing the selective frame filtering technique to refine the dataset training reduced the effort required for labelling. Full article

Seasonal and environmental factors, including temperature, humidity, and storage conditions, significantly impact the stability of biochemical markers in dried blood spot (DBS) samples. This study investigates these influences specifically for adenosine (ADO) levels, a critical biomarker for neonatal screening of adenosine deaminase (ADA) deficiency. This study analyzed seasonal fluctuations in ADO concentrations across three regions in China (Ningbo, Nanjing, and Changsha) over 11 months, and evaluated ADO stability under different storage conditions (4 °C, 20 °C, and 40 °C). ADO levels demonstrated significant seasonal variability, peaking in July–August. Median concentrations increased by 111–189% in warmer months compared to winter across all sites. Storage experiments showed that ADO was most stable at 4 °C (fluctuations < 5% over 7 days), while levels at 40 °C increased by 18%. Re-adjusting the ADO reference range based on seasonal data reduced false positive rates from 2.48% to 0.15%, a 94% reduction. This study underscores the necessity of implementing seasonally dynamic reference ranges and strict cold-chain storage (4 °C) to enhance screening accuracy for ADA deficiency. The findings provide a robust foundation for optimizing neonatal screening protocols globally, especially in regions with distinct seasonal climates. Full article

Between 2019 and 2023, left-turn crashes accounted for 4.5% of traffic accidents in Japan, with 36% of injuries involving cyclists and 66% at signalized intersections. This study quantifies conflict situations between left-turning vehicles and straight-moving bicycles in real-world traffic environments and provides a foundation for determining appropriate timing of future in-vehicle early warning systems. Trajectories reconstructed from seven hours of camera footage yielded six spatio-temporal and behavioral indicators for 37 events with a post-encroachment time (PET) ≤ 3 s. Indicators—PET, time-to-crossing (TTC), right-of-way, urgent braking, deceleration to avoid a crash, and Kalman-based trajectory variance—were statistically related to a composite risk index, R. Approximately 80% of events fell within PETs of 2–3 s, while urgent braking occurred in 50% of cases with PETs of ≤2 s. Each 1 s reduction in PET increased R by 0.18 (R² = 0.55). PETs ≤ 2.5 s or TTCs ≤ 1.5 s flagged 95% of high-risk events 0.5 s in advance. Joint thresholds involving urgent braking and high variance raised coverage to 100%, with lead times of 0–1.4 s and a false alarm rate of 8%. These findings provide an innovative multi-indicator framework based on real-world trajectories, offering quantitative scenario-specific thresholds for effective in-vehicle warnings at urban intersections. Full article

This study investigates the feasibility of using a two-channel subcutaneous EEG device (SubQ) to detect and monitor PGES. The SubQ device, developed by UNEEG Medical A/S, offers a minimally invasive alternative to scalp EEG, enabling ultra-long-term monitoring and remote data analysis. We used annotated scalp EEG data and data from the SubQ device. The pre-processing pipeline included channel reduction, resampling, filtering, and feature extraction. A Variational Auto-Encoder (VAE) was employed for anomaly detection, trained to identify PGES instances, and post-processing was applied to predict their duration. The VAE achieved a 100% detection rate for PGES in both scalp and SubQ datasets. However, the predicted durations had an average offset of 35.67 s for scalp EEG and 26.42 s for SubQ data. The model’s false positive rate (FPR) was 59% for scalp EEG and 56% for SubQ data, indicating a need for further refinement to reduce false alarms. This study demonstrates the potential of subcutaneous EEG as a valuable tool in the study of epilepsy and the monitoring of PGES, ultimately contributing to a better understanding and management of SUDEP risk. Full article

High-grade sarcomas often lack typical morphological features and exhibit no clear differentiation, often leading to a diagnosis of undifferentiated sarcoma (US). Pleomorphic leiomyosarcoma (PLMS) is a high-grade sarcoma consisting of a typical leiomyosarcoma (LMS) component alongside dedifferentiated high-grade areas. A few decades ago, PLMS was regarded as a subtype of high-grade sarcoma previously referred to as malignant fibrous histiocytoma; it is now classified as a variant of LMS. The mechanisms underlying myogenic differentiation and their relevance to the pathological diagnosis of high-grade sarcomas remain poorly understood. To investigate the gene expression networks associated with myogenic differentiation, we employed Cap Analysis of Gene Expression (CAGE) to distinguish PLMS from other high-grade sarcoma subtypes. We analyzed 27 frozen high-grade sarcoma samples, comprising 10 PLMSs, 11 high-grade myxofibrosarcomas, 3 dedifferentiated liposarcomas, 2 USs, and 1 high-grade sarcoma not otherwise specified, using CAGE profiling. Hierarchical clustering based on differentially expressed genes identified by CAGE separated 7 of the 10 PLMSs from other high-grade sarcomas, while the remaining 3 PLMSs clustered with a single US case. CAGE analysis also revealed that the myostatin (MSTN) promoter (false discovery rate [FDR] < 0.05) was more strongly activated in the high-grade sarcoma group lacking morphological and immunohistochemical smooth muscle differentiation than in the PLMS group, whereas the alpha smooth muscle actin (ACTA2) promoter (FDR < 0.05) was more prominently activated in the PLMS group. Immunohistochemical analysis showed reduced or absent myostatin expression in PLMSs, in contrast to diffuse myostatin expression in other high-grade sarcomas. Smooth muscle actin, encoded by ACTA2, was expressed in all 10 PLMS cases but only in 11 of 17 other high-grade sarcomas. Furthermore, both conventional immunohistochemistry and double immunostaining revealed that myostatin and myogenic markers exhibited largely mutually exclusive expression patterns within these tumors. A validation study was performed using 59 soft tissue sarcoma cases, including 27 PLMSs and 16 LMSs. Loss or reduction in myostatin expression was confirmed in both LMS and PLMS, and the ratio of myostatin loss was comparable (62.5% in LMS vs. 63% in PLMS). Collectively, these findings suggest that myostatin contributes to smooth muscle differentiation in high-grade sarcomas and has potential utility as a diagnostic marker. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

Exploring the action mechanisms and enhancement pathways of the resilience of agricultural product green supply chains is conducive to strengthening the system’s risk resistance capacity and providing decision support for achieving the “dual carbon” goals. Based on theories such as dynamic capability theory and complex adaptive systems, this paper constructs a resilience framework covering the three stages of “steady-state maintenance–dynamic adjustment–continuous evolution” from both single and multiple perspectives. Combined with 768 units of multi-agent questionnaire data, it adopts Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the influencing factors of resilience and reveal the nonlinear mechanisms of resilience formation. Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. It reduces false negative risk omission by 50% and improves the ability to capture high-risk samples by three times, which verifies the feasibility and applicability of the fsQCA-XGBoost prediction method in the field of resilience prediction for agricultural product green supply chains. This research provides a risk prevention and control paradigm with both theoretical explanatory power and practical operability for agricultural product green supply chains, and promotes collaborative realization of the “carbon reduction–supply stability–efficiency improvement” goals, transforming them from policy vision to operational reality. Full article

The underground coal mine environment is complex and dynamic, making the application of visual algorithms for object detection a crucial component of underground safety management as well as a key factor in ensuring the safe operation of workers. We look at this in the context of helmet-wearing detection in underground mines, where over 25% of the targets are small objects. To address challenges such as the lack of effective samples for unworn helmets, significant background interference, and the difficulty of detecting small helmet targets, this paper proposes a novel underground helmet-wearing detection algorithm that combines dynamic background awareness with a limited number of valid samples to improve accuracy for underground workers. The algorithm begins by analyzing the distribution of visual surveillance data and spatial biases in underground environments. By using data augmentation techniques, it then effectively expands the number of training samples by introducing positive and negative samples for helmet-wearing detection from ordinary scenes. Thereafter, based on YOLOv10, the algorithm incorporates a background awareness module with region masks to reduce the adverse effects of complex underground backgrounds on helmet-wearing detection. Specifically, it adds a convolution and attention fusion module in the detection head to enhance the model’s perception of small helmet-wearing objects by enlarging the detection receptive field. By analyzing the aspect ratio distribution of helmet wearing data, the algorithm improves the aspect ratio constraints in the loss function, further enhancing detection accuracy. Consequently, it achieves precise detection of helmet-wearing in underground coal mines. Experimental results demonstrate that the proposed algorithm can detect small helmet-wearing objects in complex underground scenes, with a 14% reduction in background false detection rates, and thereby achieving accuracy, recall, and average precision rates of 94.4%, 89%, and 95.4%, respectively. Compared to other mainstream object detection algorithms, the proposed algorithm shows improvements in detection accuracy of 6.7%, 5.1%, and 11.8% over YOLOv9, YOLOv10, and RT-DETR, respectively. The algorithm proposed in this paper can be applied to real-time helmet-wearing detection in underground coal mine scenes, providing safety alerts for standardized worker operations and enhancing the level of underground security intelligence. Full article