Search Results (1,948)

Technology-critical elements are increasingly used in electronics, renewable-energy systems, high-performance alloys and other strategic technologies, yet their occurrence in wild fungi remains poorly characterised. This study evaluated gallium (Ga), germanium (Ge), niobium (Nb), tantalum (Ta) and tungsten (W) in wild mushrooms collected from Leicester and Leicestershire, UK. The dataset was reconstructed at fruiting-body level to avoid pseudo-replication from paired cap and stem records, yielding 121 analytical units. Results were corrected using digestion-date acid blanks, interpreted against analytical-portion-specific working limits of detection and classified as fully quantified (FQ), partially censored (PC) or fully censored (FC). Ga was FQ in 106/121 units and was the only element with sufficient quantitative coverage for continuous distributional estimation; its regression-on-order-statistics median was 475.48 ng g⁻¹ dw (IQR: 278.70–941.63). The corresponding FQ counts were 16 for Ge, 33 for Nb, 10 for Ta and 24 for W, with 7, 4 and 4 additional PC observations for Nb, Ta and W, respectively. In 22 paired Agaricus bitorquis fruiting bodies, Ga concentrations were higher in stems than caps (Wilcoxon p = 5.25 × 10⁻⁵; BH-FDR = 1.05 × 10⁻⁴). Composite topsoils from 26 monitored sites provided park-scale geochemical context only and were not used to infer bioaccumulation or direct soil-to-fungus transfer. These findings establish a blank-corrected and censoring-aware occurrence baseline and suggest exploratory, site-specific potential for wild mushrooms as complementary biomonitoring matrices for selected technology-critical elements, while demonstrating that their usefulness is element-, taxon- and design-dependent. Full article

Aortitis—inflammation of the aortic wall—presents at the interface of vasculitis, infection, structural aortic disease, and cardiovascular risk. It may occur in giant cell arteritis (GCA), Takayasu arteritis, immunoglobulin G4 (IgG4)-related disease, drug-induced injury, infection, or as an isolated finding after aortic surgery. Modern imaging detects aortic inflammation more frequently, but the main challenge is classification rather than detection: determining whether disease is infectious or immune-mediated, active or dominated by fixed structural damage, systemic or isolated, and whether the dominant threat is aneurysm, dissection, undertreated infection, or avoidable immunosuppression. This review considers aortitis as a high-risk vascular syndrome requiring aetiology-first classification rather than descriptive labelling. Before escalating immunosuppression, infection must be actively excluded and inflammatory activity distinguished from fixed vascular damage. Treatment should be individualised according to phenotype, age, vascular territory, comorbidity, and toxicity risk, with surveillance continuing even after symptoms and inflammatory markers improve. Optimal care depends on multidisciplinary assessment integrating rheumatology, infectious diseases, vascular surgery, radiology, and cardiology expertise. Progress will require standardised imaging definitions, registries linking inflammatory control with structural vascular outcomes, and validation of artificial intelligence (AI) tools before clinical adoption. Full article

Belt conveyors are essential systems for the continuous transport of various materials in many sectors and applications, including heavy industry or mining, which are characterized by demanding environmental and operational conditions. Our research is focused on the development of the system based on magnetic sensors for the detection of ferromagnetic objects. These detection systems are designed to prevent damage to conveyor belts and the downstream vehicles, machines, and processing equipment involved in material transport and processing. By detecting hazardous foreign objects, they help avoid belt damage or tearing, thereby reducing operational disruptions and the associated maintenance and repair costs. Our study confirmed that in addition to the development of the hardware and software solutions, it is also necessary to develop methods for the processing and evaluation of the data recorded by the detection system, as the data represent a valuable source of information not only for the operational workers but also for the managers and are very helpful in the creation of the sustainable transportation system. The article describes an innovative application of the Weibull distribution for the operational enhancement of the system and its comparison to the conventionally used histograms. In addition to that, the utilization possibilities of the obtained statistical data to evaluate the belt conveyor loading for a better planning of the process, to monitor the work of the operational or other employees’ quality of the supported material, or to reveal failures of the detection system or even of the belt conveyor are overviewed and discussed. Full article

Mobile Ad Hoc Networks (MANETs) or Wireless Sensor Networks (WSNs) have a “decentralized” architecture and are very susceptible to sophisticated attacks, such as identity forgery attacks using deep learning (DL) methods. Secure routing and intrusion detection systems have been developed but they are usually not scalable, consume too much power, introduce too much communication overhead and do not provide enough privacy protection, or are not resilient to changes in adversarial behavior. This paper introduces a Federated Pyramid Swin Vision Transformer (FPSViT) framework that enhances the routing optimization and energy-efficient communication for MANET–WSN networks with the support of Generative AI (GAI), addressing these challenges. The proposed framework incorporates three modules: federated averaging for privacy-preserving distributed learning, Pyramid Swin Vision Transformer (PSViT) for extracting Sybil attack characteristics at multiple scales, and a GAI-based adversarial pattern generation module to boost the robustness of the detection process in the presence of evolving attack patterns. Energy-aware routing optimization: It takes into account the energy level of the nodes, power consumption of the links, link reliability and trust values to optimize the routing for minimum power consumption with secure communication. Results of experimental evaluations on various Sybil attack scenarios show that the proposed FPSViT is able to achieve 98.84%, 98.52%, 98.21%, and 98.36% detection accuracy, precision, recall, and F1-score, respectively, and consume 0.381 J/node on average and increase the lifetime of the network to 2876 rounds. The power consumption analysis demonstrates that FPSViT consumes 12–21% less energy than other methods such as Federated CNN, FL-LSTM, Lightweight Standalone Swin Detector, and RL-based Secure Routing, thanks to optimized routing decisions and avoiding unnecessary transmissions, as well as adaptive trust-based communication. Moreover, the proposed framework achieves an improvement in the packet delivery ratio to 98.24%, decreases communication overhead by 9–17% and increases network lifetime by 10–19%. The results have also validated that FPSViT is a scalable, privacy-preserving, and power-saving security solution for dynamic MANET–WSN environments and is able to successfully resist advanced DL-driven Sybil attacks. Full article

Safe human–robot collaboration remains a critical challenge in manufacturing. Traditional safety approaches, such as cages and proximity sensors, are often insufficient for dynamic human interaction. This paper presents a digital twin-based collision avoidance framework for industrial collaborative robot manipulation. The system integrates RGB-D sensing, human pose estimation using Ultralytics YOLO26s-pose, Kalman-filter-based 3D arm tracking, short-term motion prediction, and QP-based reactive motion control. Human arm keypoints detected from RGB-D images are reconstructed in 3D, transformed into the robot base frame, and tracked during temporary occlusion using Kalman filtering with kinematic constraints. Predicted human–robot clearance is evaluated to trigger speed reduction, stopping, or collision avoidance commands. The framework was implemented with a UR10e robot, an Intel RealSense D435 camera, a Unity3D digital twin, and ROS communication. Controlled laboratory experiments demonstrated the proof-of-concept feasibility of the integrated framework for tracking human arm motion, anticipating proximity risk, and triggering protective robot responses. The results do not establish deployment readiness in complex industrial or multi-participant environments. Full article

Bus-mounted vision sensing provides a practical and complementary perspective for intelligent transportation systems, but reliable traffic object detection from bus front-view cameras remains challenging because elevated viewpoints induce severe scale skewness, dense interactions around bus stops and intersections, and frequent heterogeneous occlusion. To support this sensing scenario while avoiding ambiguity with previously used dataset acronyms, we construct the Bus Front-view Traffic Dataset (BFTD), a high-resolution benchmark collected from forward-facing cameras mounted on multiple buses operating on urban routes during real-world service. The BFTD contains 8131 images and 56,137 annotated instances across five traffic-participant categories, covering dense pedestrians, mixed-traffic flow, illumination variation, rain, fog, and occlusion-prone scenes. Based on the visual characteristics of bus-mounted cameras, we propose YOLO-M2LA, a local–global attention detection framework in which CBS-SPD preserves fine-grained information during early downsampling and M2LA couples multi-scale local context modeling with efficient global dependency aggregation. Extensive experiments on BFTD and public benchmarks show that the proposed framework improves detection accuracy, particularly for small and visually crowded traffic participants, while maintaining a practical accuracy–efficiency trade-off. Dataset statistics, condition-specific evaluation, ablation analysis, and qualitative visualization further support the effectiveness of BFTD and YOLO-M2LA for vision-based traffic sensing. The dataset and implementation are publicly available online. Full article

This article reviews soft clipping, a non-linear amplitude response of IEPE/ICP sensors, which occurs when the internal electronics of an IEPE/ICP sensor exceed their specified linear operating range, typically corresponding to an output of $\pm 5 \mathrm{V}$. Because most measurement systems allow a wider voltage range of $\pm 10 \mathrm{V}$, this non-linear behavior can occur without triggering system-level warnings. Shaker tests indicate that prolonged operation beyond the rated measurement range results in significant signal distortion, particularly attenuating negative acceleration values and producing asymmetric signals. Even after returning to nominal operating conditions, a recovery time of several seconds is required. While the soft clipping effect appears largely frequency independent up to 5 kHz, it may be difficult to detect in real-world vibration tests with complex, broadband signals. Impact tests further reveal that soft clipping subtly affects shock responses, primarily altering initial peaks and potentially obscuring high-frequency modes that decay rapidly. In contrast, frequency-domain analyses are less affected. Overall, the findings emphasize that avoiding operation outside the rated measurement range and implementing appropriate monitoring or warning strategies are essential for reliable IEPE/ICP measurements, especially for time-domain analysis methods. Full article

Financial fraud keeps rising these days. Organizations attempt to stop this trend by using various methods, such as distributing guides on how to avoid scams and frauds and automatically generating alerts when suspicious activities occur. However, this passive approach does not mitigate the problem, as the trend is worsening, and it is usually too late when victims realize they have been scammed. Therefore, active approaches must be employed before scams reach the victims. A wide variety of preventive methods, such as neural networks and data mining, have been used to detect financial fraud data, but none have proven entirely effective in combating scams. Each method has its pros and cons. This research takes advantage of multiple machine learning techniques, such as k-nearest neighbors (kNN) and decision trees, by utilizing data fusion to detect financial fraud accurately. The data fusion function used here is self-adjusting through learning. During the training phase, the system is repeatedly applied to the dataset until an optimal detection rate is achieved. Experimental results from credit card transactions show that the proposed method outperforms each individual method. Parameter or threshold values for the data fusion are set heuristically. Future research will focus on developing reconfigurable data fusion by automatically adjusting the values. Full article

Sickle cell disease (SCD) represents one of the most complex hematological diseases leading to a lifetime of physiological crises characterized by chronic hemolysis, inflammation, hypoxia, and anemia with changes to body systems that result in patients having shorter lifespans. Transfusions, either simple or as part of red cell exchanges, are often needed to lower the hemoglobin S levels to minimize the possibility of sickling of red blood cells (RBCs) and provide patients with greater oxygen carrying capacity. However, hemolysis in SCD patients is a common finding/presentation of the disease, especially during acute crises. One of the ensuing complications of a life of transfusions is the development of alloantibodies to RBC antigens despite partial or extended matching. This is further complicated by formation of autoantibodies even in the setting of RBC matching, suggesting that a hyperactive immune response in these patients is primed to respond with formation of antibodies. In a sub-cohort of patients, no antibodies are detected despite extensive investigation, and the ensuing hemolysis requires minimizing exposure to RBC transfusions to avoid developing a greater hemolytic process. Instead, immunosuppression or monoclonals that target complement or cytokines shown to be involved in this type of hemolysis are necessary. In this context, this narrative review will present antibody-dependent and antibody-independent mechanisms of hemolysis in SCD, including therapeutic approaches that target specific areas of the immune response that are possibly involved in the destruction of RBCs. Full article

Nitrogen is integral to all living systems, but its diverse forms are interconverted dynamically through reductions and oxidations that constitute the nitrogen cycle. Ecosystems gain combined nitrogen by bacterial and archaeal reduction in atmospheric N₂, while combined nitrogen is lost to the atmosphere as nitrogenous gases through denitrification. We sought to characterize the diversity and activity of free-living nitrogen-fixing bacteria or diazotrophs in natural prairie grasslands and the activity of the other steps of the nitrogen cycle through metatranscriptomics. DNA and mRNA were obtained from prairie sites that did not contain any leguminous plants to avoid symbiotic nitrogen fixation. Both 16S rRNA gene and nifH amplicon pools were sequenced to characterize the diversity of diazotrophs, and the expression levels of N-cycle genes were quantified by RNAseq. Nitrogen fixation without and with added carbohydrates was quantified by measuring the incorporation of ¹⁵N₂. The soil samples contained a diversity of diazotrophs, as reflected both by 16S rRNA gene and nifH gene sequences. Carbohydrate amendment of soil samples led to substantial ¹⁵N₂ incorporation, showing that members of the resident microbiota were able to fix nitrogen. However, we did not detect ¹⁵N₂ incorporation in unamended soil samples, pointing to a lack of in situ fixation. The very low levels of nitrogenase gene transcripts supported this finding. In contrast, transcripts for nitrification and denitrification genes were expressed, pointing to a gap in the nitrogen cycle. The prevalence of diazotrophs with undetectable nitrogen fixing activity suggested the absence of available carbohydrates to provide the energy needed. The apparent imbalance in the nitrogen cycle would not be sustainable, so other possible mechanisms of acquiring combined nitrogen should be explored. Full article

In military confrontations, intentional suppression jamming can significantly degrade the reliability of friendly communication systems. By integrating conventional frequency hopping with spectrum sensing, cognitive frequency hopping technology can proactively avoid jammed frequency bands, thereby enhancing anti-jamming performance. This paper proposes a jamming detection and parameter estimation algorithm based on an improved YOLOv8 model. The proposed method extracts the time–frequency features of jamming signals and predicts their bounding boxes in time–frequency images. Based on the coordinates of the predicted bounding boxes, the center frequency, bandwidth, and temporal parameters of the jamming signals are estimated, thereby supporting spectrum sensing. Simulation results under MATLAB-generated signal conditions show that the proposed method achieves high detection accuracy and low mean squared relative error in the considered simulation scenarios. In addition, the proposed method maintains effective detection and parameter-estimation performance in composite jamming scenarios. This study provides a useful simulation-based reference for spectrum sensing in cognitive frequency hopping systems. Full article

Quantum cyber-physical systems can involve computation-based strategies like quantum algorithms, physical parts, and communication-based infrastructures such as physical quantum bits (qubits), measurement units, and communication links. A photonics-driven quantum cyber-physical system can rely on physical parameters of the environment or communication links such as the refractive index. The injection of false data into the actual value of the refractive index can cause the use of an incorrect value of the refractive index in the system if needed, and as a result, an incorrect interpretation of information or even access to non-real information instead of the actual information. The first attempt to avoid this issue can be the detection of the existence of the cyber-attacks in the system. This paper will address this challenge using artificial intelligence for binary classification to detect the cyber-attacks in the system. For the proof of concept, the proposed strategy is examined deploying the real and imaginary parts of the refractive index value corresponding to a semiconductor, i.e., GaAs. Different scenarios are performed, including a single evaluation, an analysis of several training runs, a comparison considering two types of normalization techniques, variations in the size of artificial intelligence, and a comparison among different machine learning techniques, including artificial neural networks, decision tree models, a logistic regression model, and support vector machine classifiers. Based on the obtained results, for the single evaluation, the class of 95.24 % of the testing samples could be classified successfully. In addition, for the case of the analysis of several training runs, a total of 9000 runs were run for 60 shallow artificial neural networks with different sizes. For 58 neural networks, the maximum achieved accuracy was 100 %. Besides, for the case of the comparison between the normalization techniques, two methods were evaluated, i.e., min-max and z-score normalization. The results were very close to each other, but, more accurately, z-score normalization indicated a better performance and a higher accuracy. Finally, among the mentioned machine learning models, artificial neural networks mostly showed higher accuracies. Full article

Background/Objectives: Deep learning approaches have demonstrated strong performance in breast histopathology image classification; however, reliable generalization across heterogeneous acquisition environments remains challenging due to domain shift. In clinical practice, missed malignant cases are particularly critical because they may directly affect diagnostic decisions and patient outcomes. This study systematically investigates the behavior of modern deep learning architectures and adaptation strategies under realistic cross-domain conditions, with particular emphasis on malignant case detection and false-negative reduction. Methods: Three modern architectures—ConvNeXt-Tiny, Swin-Tiny, and MaxViT-Tiny—were initially trained on a large-scale breast histopathology dataset and subsequently evaluated on the BreaKHis dataset using strict patient-level separation to avoid information leakage. Three transfer settings were investigated: direct zero-shot transfer, head-only adaptation, and full fine-tuning. Performance was evaluated independently across four magnification levels (40×, 100×, 200×, and 400×) using accuracy, precision, sensitivity, F1-score, ROC–AUC, PR–AUC, and false-negative rates. Results: Direct zero-shot transfer produced substantial performance degradation across all architectures, with mean false-negative rates ranging from 75.85% to 90.11%, highlighting the limited transferability of source-domain representations under heterogeneous acquisition conditions. Both adaptation strategies substantially improved performance and reduced missed malignant cases to below 10%. Swin-Tiny under head-only adaptation achieved the most favorable malignant detection profile, reaching a mean sensitivity of 97.36% while reducing the average false-negative rate to 2.64%. In contrast, MaxViT-Tiny achieved the highest mean ROC–AUC value (0.849) after full fine-tuning, although this did not correspond to the lowest false-negative burden. Conclusions: The findings demonstrate that maximizing global discrimination performance does not necessarily correspond to optimal malignant detection under cross-domain conditions. Sensitivity and missed-case analysis provide complementary information beyond conventional discrimination metrics and may support more informed model assessment. Furthermore, the proposed methodology provides a reproducible framework for investigating adaptation performance in AI-assisted breast histopathology systems. Full article

Microplastics (MPs) are emerging environmental contaminants increasingly detected in foods, beverages, and food-contact materials, making dietary intake a relevant route of human exposure. In sports nutrition, this issue may be particularly important because athletes often have high food and fluid consumption, frequent use of packaged sports nutrition products, dietary supplements, bottled beverages, and sport-specific hydration strategies. This narrative review, supported by a structured literature search, examines dietary MP exposure and its potential relevance to gastrointestinal function, gut microbiota, oxidative stress, inflammation, mitochondrial activity, endocrine regulation, metabolism, recovery, adaptation, and performance-related outcomes in athletes. Current evidence suggests that MPs and nanoplastics may interact with biological systems through mechanisms involving intestinal barrier disruption, microbiota alterations, inflammatory activation, oxidative damage, mitochondrial perturbation, endocrine-disrupting chemicals, and metabolic dysregulation. However, most available data derive from in vitro studies, animal models, food contamination analyses, exposure-estimation studies, and indirect human biomonitoring evidence. Direct studies in athletic populations are currently lacking. Therefore, the possible implications of MP exposure on recovery, adaptation, and exercise performance should be interpreted as biologically plausible but unproven. From a practical perspective, evidence-informed strategies may include reducing avoidable plastic-related exposure while maintaining adequate hydration, energy availability, nutrient timing, supplement quality, and dietary patterns that support antioxidant defenses, inflammatory balance, gut health, and physiological resilience. Future research should prioritize standardized exposure assessment, validated biomarkers, human biomonitoring, and sport-specific studies evaluating MP exposure in relation to physiological and performance-related outcomes. Full article

This study presents two workflows for leveraging longitudinal computed tomography (CT) datasets when developing deep learning-based detection systems for gradually developing postharvest disorders. Workflow 1 (Longitudinal Benchmarking) benchmarks neural networks by training and testing them on images from different stages of disorder progression. It examines the trade-off between detecting a disorder early or accurately and evaluates whether neural networks can generalize across time points. Workflow 2 (Longitudinal eXplainable Artificial Intelligence (XAI) Heatmaps) provides heatmaps that indicate how changes over time affect the outcomes of neural networks. It uses image registration to align an earlier-acquired image and then uses it as a baseline when calculating the heatmap. The workflows are demonstrated on a dataset of ‘Braeburn’ apples that were CT-scanned multiple times while developing internal browning during controlled-atmosphere (CA) storage and shelf life. The Longitudinal Benchmarking workflow was used to investigate whether images acquired immediately after CA storage can be used to predict the eventual browning after a shelf-life period, which is highly relevant in industrial practice. Moreover, the longitudinal XAI heatmaps avoided artifacts caused by out-of-distribution baselines or identical baseline regions, which occurred with conventional black or zero baselines. Full article