Search Results (1,568)

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

X-ray security screening is a well-established technology used in public spaces. The traditional method for detecting prohibited items in X-ray images relies on manual inspection, necessitating security personnel with extensive experience and focused attention to achieve satisfactory detection accuracy. However, the high-intensity and long-duration nature of the work leads to security personnel fatigue, which in turn reduces the accuracy of prohibited items detection and results in false alarms or missed detections. In response to the challenges posed by the coexistence of multiple prohibited items, incomplete identification information due to overlapping items, variable distribution positions in typical scenarios, and the need for portable detection equipment, this study proposes a lightweight automatic detection method for prohibited items. Based on establishment the sample database for prohibited items, a new backbone network with a residual structure and attention mechanism is introduced to form a deep learning algorithm. Additionally, a dilated convolutional spatial pyramid module and a depthwise separable convolution algorithm are added to fuse multi-scale features, to improve the accuracy of prohibited items detection. This study developed a lightweight automatic detection method for prohibited items, and its highest detection rate is 95.59%, which demonstrates a 1.86% mAP improvement over the YOLOv4-tiny baseline with 122 FPS. The study achieved high accurate detection of typical prohibited items, providing support for the assurance of public safety. Full article

This paper investigates federated learning (FL) for cross-site scripting (XSS) detection under out-of-distribution (OOD) drift. Real-world XSS traffic involves fragmented attacks, heterogeneous benign inputs, and client imbalance, which erode conventional detectors. To simulate this, we construct two structurally divergent datasets: one with obfuscated, mixed-structure samples and another with syntactically regular examples, inducing structural OOD in both classes. We evaluate GloVe, GraphCodeBERT, and CodeT5 in both centralised and federated settings, tracking embedding drift and client variance. FL consistently improves OOD robustness by averaging decision boundaries from cleaner clients. Under FL scenarios, CodeT5 achieves the best aggregated performance (97.6% accuracy, 3.5% FPR), followed by GraphCodeBERT (96.8%, 4.7%), but is more stable on convergence. GloVe reaches a competitive final accuracy (96.2%) but exhibits a high instability across rounds, with a higher false positive rate (5.5%) and pronounced variance under FedProx. These results highlight the value and limits of structure-aware embeddings and support FL as a practical, privacy-preserving defence within OOD XSS scenarios. Full article

This paper presents a comparative analysis of ten state-of-the-art robotic de-mining systems, grouped into (i) sensor-centric platforms for high-precision detection and (ii) rapid mechanical-contact vehicles for clearance. Building on these findings, we propose a lightweight tracked platform (~1.9 T) equipped with a four-channel sensing suite-RGB/IR camera, 32-layer LiDAR, pulsed-induction metal detector, and 2.45 GHz microwave thermography—integrated in an adaptive Bayesian “detect → confirm → neutralize” loop. The modular end-effector permits either pinpoint mechanical intervention or deployment of a linear charge. Modelling indicates an expected detection sensitivity ≥ 95% with a false-positive rate ≤ 5% in humanitarian demining mode and a clearance throughput above 1.5 ha·h⁻¹ in breaching mode. Ongoing work includes CFD analysis of the thermal front, fabrication of a prototype, and performance testing in accordance with IMAS 10.20. Full article

Intelligent detection of tomato seedling transplant quality represents a core technology for advancing agricultural automation. However, in practical applications, existing algorithms still face numerous technical challenges, particularly with prominent issues of false detections and missed detections during recognition. To address these challenges, we developed the ESG-YOLO object detection model and successfully deployed it on edge devices, enabling real-time assessment of tomato seedling transplanting quality. Our methodology integrates three key innovations: First, an EMA (Efficient Multi-scale Attention) module is embedded within the YOLOv8 neck network to suppress interference from redundant information and enhance morphological focus on seedlings. Second, the feature fusion network is reconstructed using a GSConv-based Slim-neck architecture, achieving a lightweight neck structure compatible with edge deployment. Finally, optimization employs the GIoU (Generalized Intersection over Union) loss function to precisely localize seedling position and morphology, thereby reducing false detection and missed detection. The experimental results demonstrate that our ESG-YOLO model achieves a mean average precision mAP of 97.4%, surpassing lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n in precision, with improvements of 9.3, 7.2, 5.7, and 2.2%, respectively. Notably, for detecting key yield-impacting categories such as “exposed seedlings” and “missed hills”, the average precision (AP) values reach 98.8 and 94.0%, respectively. To validate the model’s effectiveness on edge devices, the ESG-YOLO model was deployed on an NVIDIA Jetson TX2 NX platform, achieving a frame rate of 18.0 FPS for efficient detection of tomato seedling transplanting quality. This model provides technical support for transplanting performance assessment, enabling quality control and enhanced vegetable yield, thus actively contributing to smart agriculture initiatives. Full article

Identifying homologous vulnerabilities across diverse IoT firmware images is critical for large-scale vulnerability auditing and risk assessment. However, existing approaches often rely on coarse-grained components or single-dimensional metrics, lacking the semantic granularity needed to capture cross-firmware vulnerability relationships. To address this gap, we propose FirmVulLinker, a semantic profiling framework that holistically models firmware images across five dimensions: unpacking signature sequences, filesystem semantics, interface exposure, boundary binary symbols, and sensitive parameter call chains. These multi-dimensional profiles enable interpretable similarity analysis without requiring prior vulnerability labels. We construct an evaluation dataset comprising 54 Known Defective Firmware (KDF) images with 74 verified vulnerabilities and assess FirmVulLinker across multiple correlation tasks. Compared to state-of-the-art techniques, FirmVulLinker achieves higher precision with substantially lower false-positive and false-negative rates. Notably, it identifies and reproduces 53 previously undisclosed N-day vulnerabilities in firmware images not listed as affected at the time of public disclosure, effectively extending the known impact scope. Our results demonstrate that FirmVulLinker enables scalable, high-fidelity homologous vulnerability analysis, offering a new perspective on understanding cross-firmware vulnerability patterns in the IoT ecosystem. Full article

An unsupervised anomaly-detection framework is proposed and field validated for thrust-bearing monitoring in the impulse turbine of a shoreline oscillating water-column (OWC) wave energy converter (WEC) off Jeju Island, Korea. Operational monitoring is constrained by nonstationary sea states, scarce fault labels, and low-rate supervisory logging at 20 Hz. To address these conditions, a 24 h period of normal operation was median-filtered to suppress outliers, and six physically motivated time-domain features were computed from triaxial vibration at 10 s intervals: absolute mean; standard deviation (STD); root mean square (RMS); skewness; shape factor (SF); and crest factor (CF, peak divided by RMS). A feature-based autoencoder was trained to reconstruct the feature vectors, and reconstruction error was evaluated with an adaptive threshold derived from the moving mean and moving standard deviation to accommodate baseline drift. Performance was assessed on a 2 h test segment that includes a 40 min simulated fault window created by doubling the triaxial vibration amplitudes prior to preprocessing and feature extraction. The detector achieved accuracy of 0.99, precision of 1.00, recall of 0.98, and F1 score of 0.99, with no false positives and five false negatives. These results indicate dependable detection at low sampling rates with modest computational cost. The chosen feature set provides physical interpretability under the 20 Hz constraint, and denoising stabilizes indicators against marine transients, supporting applicability in operational settings. Limitations associated with simulated faults are acknowledged. Future work will incorporate long-term field observations with verified fault progressions, cross-site validation, and integration with digital-twin-enabled maintenance. Full article

The implementation of newborn screening (NBS) has revolutionized the diagnostic landscape of cystic fibrosis (CF). In Portugal, NBS was initiated in October 2013 through a pilot study and was subsequently fully integrated into a nationwide program by December 2018. Infants with positive screening results are referred to a specialized CF reference center for diagnostic confirmation, employing Sweat Chloride Testing (SCT) and genetic testing for CFTR variants. We aimed to analyze infants with a positive CF screening and determine the false positive and false negative rates, as well as to calculate the positive predictive value and sensitivity of our NBS program. A retrospective nationwide analysis was conducted on infants with a positive NBS for CF between October 2013 and February 2023. Two hundred and forty infants were referred from the NBS program; 74 (30.8%) were confirmed to have CF through SCT and genetic testing. Sensitivity was 93.2%, and the positive predictive value (PPV) was 30.8%. In addition, 48.5% were homozygous for F508del variants, and 87.8% had at least one F508del variant. Guidelines set forth by the European Cystic Fibrosis Society advise NBS programs to achieve a minimum PPV of 30% and a minimum sensitivity of 95%. Our report demonstrated good compliance with these recommendations. Full article

Pedestrian accidents involving heavy vehicles such as trucks and buses remain a critical safety issue, primarily due to structural blind spots. While existing systems like radar-based FCW and BSD have been adopted, they are not fully optimized for pedestrian detection, particularly under adverse weather conditions. This study focused on the empirical validation of a 360-degree pedestrian collision avoidance system using multichannel ultrasonic sensors specifically designed for heavy-duty vehicles. Eight sensors were strategically positioned to ensure full spatial coverage, and scenario-based field experiments were conducted under controlled rain (50 mm/h) and fog (visibility <30 m) conditions. Pedestrian detection performance was evaluated across six distance intervals (50–300 cm) using indicators such as mean absolute error (MAE), coefficient of variation (CV), and false-negative rate (FNR). The results demonstrated that the system maintained average accuracy of 97.5% even under adverse weather. Although rain affected near-range detection (FNR up to 17.5% at 100 cm), performance remained robust at mid-to-long ranges. Fog conditions led to lower variance and fewer detection failures. These empirical findings demonstrate the system’s effectiveness and robustness in real-world conditions and emphasize the importance of evaluating both distance accuracy and detection reliability in pedestrian safety applications. Full article

Credit card fraud remains a major cause of financial loss around the world. Traditional fraud detection methods that rely on supervised learning often struggle because fraudulent transactions are rare compared to legitimate ones, leading to imbalanced datasets. Additionally, the models must be retrained frequently, as fraud patterns change over time and require new labeled data for retraining. To address these challenges, this paper proposes an ensemble unsupervised learning approach for credit card fraud detection that combines Autoencoders (AEs), Self-Organizing Maps (SOMs), and Restricted Boltzmann Machines (RBMs), integrated with an Adaptive Reconstruction Threshold (ART) mechanism. The ART dynamically adjusts anomaly detection thresholds by leveraging the clustering properties of SOMs, effectively overcoming the limitations of static threshold approaches in machine learning and deep learning models. The proposed models, AE-ASOMs (Autoencoder—Adaptive Self-Organizing Maps) and RBM-ASOMs (Restricted Boltzmann Machines—Adaptive Self-Organizing Maps), were evaluated on the Kaggle Credit Card Fraud Detection and IEEE-CIS datasets. Our AE-ASOM model achieved an accuracy of 0.980 and an F1-score of 0.967, while the RBM-ASOM model achieved an accuracy of 0.975 and an F1-score of 0.955. Compared to models such as One-Class SVM and Isolation Forest, our approach demonstrates higher detection accuracy and significantly reduces false positive rates. In addition to its performance, the model offers considerable computational efficiency with a training time of 200.52 s and memory usage of 3.02 megabytes. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. Full article

Aim: In sub-Saharan Africa, approximately 40 million pregnant women are exposed to parasitic diseases such as malaria caused by Plasmodium falciparum, Schistosome parasites, and soil-transmitted helminths (STHs). When parasitic diseases share the same habitat and overlap in distribution, then high co-infection rates occur. The co-infection can lead to consequences for the child, such as intrauterine growth retardation, low birth weight, pre-term delivery, and neonatal mortality. Methods: The objective of the study was to determine the nature and extent of coinfection from 100 samples collected from the Battor (50) and Adidome (50) towns of Ghana in collaboration with the Noguchi Memorial Institute for Medical Research, University of Ghana. Results: Out of 50 for the Adidome towns determined for P. falciparum by Rapid Diagnostic Test (RDT), Malaria Pan-specific Antigen (PAN), and Malaria Pf kit, 39 were true positive (TP), 8 were true negative (TN), and 30 were false negative (FN). For Battor, 19 were TP, 12 TN, and 20 FN. For S. mansoni in Adidome via polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP), 21 tested positive, and 29 were negative, with 52.5% sensitivity and 100% specificity. For S. haematobium, 28 were positive and 22 negative using PCR with 70% sensitivity and 100% specificity. In LAMP, 28 were positive, and 22 negatives, with 70% sensitivity and 100% specificity. In Battor PCR for S. mansoni, 28 positives and 22 negatives with 68.3% sensitivity and 100% specificity. In LAMP, 32 were positive, and 18 were negative, with 80% sensitivity and 100% specificity. For S. haematobium, PCR showed 30 positive and 20 negative, with 73.2% sensitivity and 100% specificity. With LAMP, 21 were positive, and 29 negatives, with 51% sensitivity and 100% specificity. In both towns, 20–30 years had the highest infection prevalence for P. falciparum, S. mansoni, S. haematobium, and Strongyloides stercoralis. Conclusion: The results will be utilized as a part of the continuous surveillance for future research aiming at gathering nationally representative data in Ghana on the prevalence of coinfection and proposing interventions based on that for the vulnerable pregnant women population. Full article

The escalating global threat of antimicrobial resistance (AMR) underscores the urgent need for innovative therapeutics. Bacteriophages (phages), natural bacterial predators, offer promising solutions, especially when harnessed through advances in artificial intelligence (AI). This review explores how AI-driven innovations are transforming phage biology, with an emphasis on three pivotal areas: (1) AI-enhanced structural prediction (e.g., AlphaFold); (2) deep learning functional annotation; (3) bioengineering strategies, including CRISPR-Cas. We further discuss applications extending to medical therapy, biosensing, agricultural biocontrol, and environmental remediation. Despite progress, critical challenges persist—including high false-positive rates, difficulties in modeling disordered protein regions, and biosafety concerns remain. Overcoming these requires experimental validation, robust computational frameworks, and global regulatory oversight. AI integration in phage research is accelerating the development of next-generation therapeutics to combat AMR and advance engineered living therapeutics. Full article

Monitoring and tracking small moving objects in cluttered environments remain a major challenge for artificial-intelligence-based motion vision systems. This difficulty is not only due to the limited features presented by small objects themselves but also because of the numerous fake features present in complex dynamic environments. Drawing inspiration from the efficient small target motion detection mechanisms in insects’ brains, researchers have developed various visual networks for detecting tiny moving objects within complex natural environments. Although these networks perform well in detecting small-object motion by leveraging motion information, their ability to distinguish true targets from background noise remains severely limited under low-light conditions, where the contrast of small targets drops sharply and they are more easily overwhelmed by false motion in the background. To resolve the aforementioned limitation, this research proposes a new visual neural network. The network achieves effective discrimination between small moving targets and false targets in the background in low-light environments by leveraging the motion information for the targets and the differences in the response gradients between real moving targets and fake objects from the background. The designed network is composed of two main components: a motion perception module and a response gradient analysis module. The motion information perception module is responsible for acquiring the motion and position information for small targets, while the response gradient detection module extracts the response gradients between a tiny object and a background object and integrates the motion information, thereby effectively distinguishing small targets from fake background objects. The experimental results demonstrate that the proposed model can effectively distinguish small targets and suppress background false alarms in low-light environments. Comparisons of the experimental performance show that under a fixed false alarm rate, our model achieved a detection rate of 0.8. In addition, the proposed method recorded an average precision of 0.1 and an average F1-score of 0.1888. In contrast, the highest average precision achieved by the other methods was only 0.0075, and the highest F1-score was 0.0151. These results clearly indicate that our method substantially outperforms previous approaches in both its average precision and F1-score. These results collectively validate the effectiveness and competitiveness of the proposed model in small target detection tasks under low-light conditions. Full article

Dempster–Shafer theory (DST), a generalization of probability theory, is well suited for managing uncertainty and integrating information from diverse sources. In recent years, DST has gained attention in cybersecurity research. However, despite the growing interest, there is still a lack of systematic comparisons of DST implementation strategies for malware detection. In this paper, we present a comprehensive evaluation of DST-based ensemble mechanisms for malware detection, addressing critical methodological questions regarding optimal mass function construction and combination rules. Our systematic analysis was tested with 630,504 benign and malicious samples collected from four public datasets (BODMAS, DREBIN, AndroZoo, and BMPD) to train malware detection models. We explored three approaches for converting classifier outputs into probability mass functions: global confidence using fixed values derived from performance metrics, class-specific confidence with separate values for each class, and computationally optimized confidence values. The results establish that all approaches yield comparable performance, although fixed values offer significant computational and interpretability advantages. Additionally, we introduced a novel linear combination rule for evidence fusion, which delivers results on par with conventional DST rules while enhancing interpretability. Our experiments show consistently low false positive rates—ranging from 0.16% to 3.19%. This comprehensive study provides the first systematic methodology comparison for DST-based malware detection, establishing evidence-based guidelines for practitioners on optimal implementation strategies. Full article