Search Results (4,071)

This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We conduct an in-depth literature search and quantitative analysis of papers published in this area and focus independently of the application (drivers, office and wheelchair users, etc.). Quantitative information about the number of subjects, investigated scenarios, sensor types, machine learning usage, and laboratory vs. real-world works is extracted. In posture recognition, most works recognize at least forward, backward, left and right movements on a seat. The remaining works use the pressure sensing mat for bedridden people. In physiological parameters measurement, most works detect the heart rate and often also add respiration rate recognition. Machine learning algorithms are used in most cases and are taking on an ever-greater importance for classification and regression problems. Although all solutions use different techniques, returning satisfactory results, none of them try to detect small movements, which can pose challenges in determining the optimal sensor topology and sampling frequency required to detect fine movements. For physiological measurements, there are lots of challenges to overcome in noisy environments, notably the detection of heart rate, blood pressure, and respiratory rate at very low signal-to-noise levels. Full article

DNA binding proteins play a crucial role in regulating gene expression, DNA replication, and chromatin organization. While many DNA-binding proteins have been identified, many unique DNA-binding proteins in non-model organisms and recently evolved lineage- or species-specific proteins remain uncharacterized or often lack experimental validation. In addition, genetic variants may alter previously known DNA-binding proteins, leading to loss of binding ability. To address this gap, various computational tools have been developed to predict DNA-binding proteins from protein sequences or structures. Yet, their real-world utility in biological research remains uncertain. To evaluate their effectiveness, we assessed the availability and predictive performance of existing tools using five real-world case studies. We found that most tools were web-based, offering accessibility to researchers without computational expertise. However, many suffered from poor maintenance, including frequent server connection problems, input errors, and long processing times. Among the ten tools that were functional and practical, we found that prediction scores often failed to reflect incorrect outputs, and multiple methods frequently produced the same erroneous predictions. Overall, even a small number of misclassifications can significantly distort biological interpretation, indicating that current DNA-binding prediction tools are not yet sufficiently reliable for empirical research. Full article

Object detection has a vital impact on the analysis and interpretation of visual scenes. It is widely utilized in various fields, including healthcare, autonomous driving, and vehicle surveillance. However, complex scenes containing small, occluded, and multiscale objects present significant difficulties for object detection. This paper introduces a lightweight object detection algorithm, utilizing YOLOv8n as the baseline model, to address these problems. Our method focuses on four steps. Firstly, we add a layer for small object detection to enhance the feature expression capability of small objects. Secondly, to handle complex forms and appearances, we employ the C2f-DCNv2 module. This module integrates advanced DCNv2 (Deformable Convolutional Networks v2) by substituting the final C2f module in the backbone. Thirdly, we designed the CBAM, a lightweight attention module. We integrate it into the neck section to address missed detections. Finally, we use Ghost Convolution (GhostConv) as a light convolutional layer. This alternates with ordinary convolution in the neck. It ensures good detection performance while decreasing the number of parameters. Experimental performance on the PASCAL VOC dataset demonstrates that our approach lowers the number of model parameters by approximately 9.37%. The mAP@0.5:0.95 increased by 0.9%, recall (R) increased by 0.8%, mAP@0.5 increased by 0.3%, and precision (P) increased by 0.1% compared to the baseline model. To better evaluate the model’s generalization performance in real-world driving scenarios, we conducted additional experiments using the KITTI dataset. Compared to the baseline model, our approach yielded a 0.8% improvement in mAP@0.5 and 1.3% in mAP@0.5:0.95. This result indicates strong performance in more dynamic and challenging conditions. Full article

Background: Advanced non-small cell lung cancer (NSCLC) has limited curative options and poor survival. Racotumomab, an anti-idiotype monoclonal antibody vaccine targeting tumor gangliosides, has shown efficacy in clinical trials. This study evaluated its real-world effectiveness as maintenance therapy following first-line chemotherapy. Materials and Methods: A multi-center observational study was conducted on 162 patients with advanced NSCLC who received racotumomab from 2012 to 2024. Effectiveness was evaluated in the intention-to-treat (ITT) cohort. Overall survival (OS) was estimated, with subgroup analyses conducted according to clinical and demographic factors. Results: The median OS was 14.9 months (95% CI: 11.7–18.1), and the 5-year survival rate reached 20%. Patients diagnosed with stage III disease, those with better Eastern Cooperative Oncology Group (ECOG) performance status, and individuals younger than 65 years experienced significantly longer survival. Racotumomab demonstrated a favorable hazard ratio compared to historical controls (HR 0.44 vs. supportive care; HR 0.55 vs. docetaxel). Conclusions: In the era of immune checkpoint inhibitors, these real-world results indicate a promising role for racotumomab in the maintenance setting for advanced NSCLC. These findings provide a strong rationale for further investigation of racotumomab in the context of modern immunotherapy, particularly in combination trials with other immunomodulatory antibodies, along with the validation of clinical and biologic predictive biomarkers. Full article

The deployment of airborne hyperspectral sensors has expanded rapidly, driven by their ability to capture spectral information beyond the visual range and to reveal objects that remain obscured in conventional imaging. In scenarios where prior target signatures are unavailable, anomaly detection provides an effective alternative by identifying deviations from the spectral background. However, real-world reconnaissance and monitoring missions frequently take place in complex and dynamic environments, requiring anomaly detectors to demonstrate robustness and adaptability. These requirements have rarely been met in current research, as evaluations are still predominantly based on small, context-restricted datasets, offering only limited insights into detector performance under varying conditions. To address this gap, we propose a context-adaptive hyperspectral sensor and perception management (hSPM) architecture that integrates sensor context extraction, band selection, and detector management into a single adaptive processing pipeline. The architecture is systematically evaluated on a new, large-scale airborne hyperspectral dataset comprising more than 1100 annotated samples from two diverse test environments, which we publicly release to support future research. Comparative experiments against state-of-the-art anomaly detectors demonstrate that conventional methods often lack robustness and efficiency, while hSPM consistently achieves superior detection accuracy and faster processing. Depending on evaluation conditions, hSPM improves anomaly detection performance by 28–204% while reducing computation time by 70–99%. These results highlight the advantages of adaptive sensor processing architectures and underscore the importance of large, openly available datasets for advancing robust airborne hyperspectral anomaly detection. Full article

This work analyses what the systematic effect of public awareness on domestic water consumption is. In some parts of the world, the availability of water is continually decreasing, mainly due to reduced rainfall, so it is of paramount importance to raise awareness among the population. We conducted an experiment on a large sample of participating units located in urban areas of Italy, mainly in the central portion of the country. Approximately 750 people participated, belonging to 250 buildings, mainly domestic residences, but also professional offices, small companies, and student residences. In the first phase, lasting three weeks, normal per capita water consumption was quantified. Subsequently, instructions were given on how to save water during various uses in the household (showers, cleaning hands, use of water in toilets and in the kitchen, watering small green areas, use of water in the kitchen, and so on), and small visual messages conveyed through stickers were posted on water dispensers to remind users to behave properly. Finally, household consumption was assessed again during a further 3-week period. An average water-saving (WS) rate of +17.20% was found, in line with results obtained from a previous similar experiment involving a much smaller sample. Higher WS rates were recorded for buildings with less inhabitants. This experiment enabled us to quantify the significant effect of the awareness-raising action on the reduction in water consumption, without the use of any structural action (e.g., replacement of dispensers, improvement of the water system, realization of recycling systems). Moreover, the simplicity of the proposed methodology makes it suitable for implementation in other regions worldwide, thus promoting a step forward towards more sustainable use of water. Full article

Surgical site infections (SSIs) remain a significant complication in spine surgery, especially in instrumented procedures with long operative times. Although guidelines recommend cefazolin as the first-line agent due to its efficacy against Staphylococcus aureus, predictable pharmacokinetics, and safety, its real-world practice is highly variable, with inappropriate and prolonged regimens reported across Europe. Vancomycin is often used as the first choice of therapy empirically and without screening, exposing patients to risks such as delayed infusion, nephrotoxicity, and the emergence of vancomycin-resistant enterococci (VRE).This review assesses the present function of vancomycin in relation to cefazolin for spinal prophylaxis and examines wider trends in the misuse of surgical antibiotic prophylaxis, which were identified through PubMed and Scopus searches. Evidence from randomized and prospective studies consistently supports cefazolin as the preferred prophylactic agent in clean spinal surgery. Observational data suggest that adjunctive or topical vancomycin may reduce infection rates in selected high-risk or revision cases, though the results are inconsistent and frequently limited by retrospective designs and heterogeneous outcome reporting. Importantly, the most rigorous randomized controlled trial found no benefit of intrawound vancomycin over the placebo. A small number of available investigations in vancomycin use with major design limitations have resulted in no significant VRE emergency. Unexpectedly, widespread use of vancomycin was followed by a notable transition toward Gram-negative and opportunistic organisms. In summary, vancomycin may only be considered in patients with documented MRSA colonization, β-lactam allergy, or selected revision procedures, but its widespread empirical use as a first-choice therapy is not supported. Full article

Current deep learning-based dense target detection models face dual challenges in industrial scenarios: high computational complexity leading to insufficient inference efficiency on mobile devices, and missed/false detections caused by dense small targets, high inter-class similarity, and complex background interference. To address these issues, this paper proposes DBA-YOLO, a lightweight model based on YOLOv10, which significantly reduces computational complexity through model compression and algorithm optimization while maintaining high accuracy. Key improvements include the following: (1) a C2f PA module for enhanced feature extraction, (2) a parameter-refined BIMAFPN neck structure to improve small target detection, and (3) a DyDHead module integrating scale, space, and task awareness for spatial feature weighting. To validate DBA-YOLO, we constructed a real-world dataset from cigarette package images. Experiments on SKU-110K and our dataset show that DBA-YOLO achieves 91.3% detection accuracy (1.4% higher than baseline), with mAP and mAP75 improvements of 2–3%. Additionally, the model reduces parameters by 3.6%, balancing efficiency and performance for resource-constrained devices. Full article

Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R² of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms. Full article

Cannabidiol (CBD) and other phytocannabinoids are gaining attention for their therapeutic potential in cardiovascular disease (CVD), the world’s leading cause of death. This review highlights advances in understanding the endocannabinoid system, including CB1 and CB2 receptors, and the mechanisms by which CBD exerts anti-inflammatory, antioxidative, vasoprotective, and immunomodulatory effects. Preclinical and translational studies indicate that selective activation of CB2 receptors may attenuate atherogenesis, limit infarct size in ischemia–reperfusion injury, decrease oxidative stress, and lessen chronic inflammation, while avoiding the psychotropic effects linked to CB1. CBD also acts on multiple molecular targets beyond the CB receptors, affecting redox-sensitive transcription factors, vascular tone, immune function, and endothelial integrity. Early clinical trials and observational studies suggest that CBD may lower blood pressure, improve endothelial function, and reduce sympatho-excitatory peptides such as catestatin, with a favorable safety profile. However, limited bioavailability, small sample sizes, short study durations, and uncertainty about long-term safety present challenges to its clinical use. Further research is needed to standardize dosing, refine receptor targeting, and clarify the role of the endocannabinoid system in cardiovascular health. Overall, current evidence supports CBD’s promise as an adjunct in CVD treatment, but broader clinical use requires more rigorous, large-scale studies. Full article

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds, $t_k^{\mathrm{*}}$ at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse. Full article

Background: Small-bowel cancers (SBCs) are rare, histologically diverse malignancies with limited data from Asian populations. This study aimed to describe histological subtype distribution, clinical features, survival outcomes, and prognostic factors in SBCs over a 20-year period. Methods: We retrospectively reviewed patients diagnosed with SBC at a tertiary referral center in Southern Thailand (2005–2024). Clinical, pathological, and radiological data were analyzed by histologic subtype. Results: A total of 158 patients were included: adenocarcinoma (81.0%), gastrointestinal stromal tumor (GIST, 5.7%), well-differentiated neuroendocrine tumor (NET, 5.7%), other sarcomas (5.1%), and poorly differentiated neuroendocrine carcinoma (NEC, 2.5%). Adenocarcinoma predominantly affected older patients and frequently presented with advanced-stage disease and poor performance status, whereas NET and NEC occurred in younger patients typically at early NET and metastatic NEC stages. Median overall survival (OS) varied by subtype: adenocarcinoma (8.3 months), GIST (63.6 months), NEC (8.9 months), NET (not reached), and other sarcomas (9.8 months). Five-year OS rates were 14.0%, 55.6%, 0%, 88.9%, and 18.8%, respectively. Eastern Cooperative Oncology Group performance status ≥2, duodenal location, and metastatic disease were independently associated with worse OS. Conclusions: SBCs display distinct clinical and prognostic profiles by subtype. Overall prognosis remained poor, underscoring the need for earlier detection and subtype-specific management. Full article

Background: Uncontrolled asthma remains a significant clinical challenge, often linked to impaired lung function and increased diaphragmatic workload. Recent studies have shown promising results using a triple inhaled therapy comprising beclomethasone dipropionate/formoterol fumarate/glycopyrronium (BDP/FF/G). This study assessed the real-world efficacy of BDP/FF/G on lung function and diaphragmatic workload in patients with uncontrolled asthma. Methods: A prospective observational study enrolled 21 adult patients diagnosed with uncontrolled asthma despite high-dose ICS/LABA therapy. Patients underwent lung function tests and right diaphragmatic ultrasound assessments at baseline and after three months of treatment with BDP/FF/G (172/5/9 mcg, administered as two inhalations every 12 h). Results: After three months, significant improvements were observed in FEV₁ (from 57.75 ± 12.30% to 75.10 ± 18.94%, p < 0.001) and FEF_25–75 (from 47.80 ± 19.23% to 75.10 ± 36.06%, p < 0.001). Additionally, during the same period, we recorded significant reductions in residual volume (from 130.10 ± 28.20% to 92.55 ± 21.18%, p < 0.001) and total airway resistance (R_tot) (from 164.60 ± 83.21% to 140.70 ± 83.25%, p < 0.05). The mean asthma control test (ACT) score increased by 5.6 points (p < 0.001), surpassing the established minimal clinically important difference (MCID) of 3 points and raising the cohort mean above the well-controlled threshold. The right diaphragmatic workload was significantly decreased, as shown by a reduction in thickening fraction (TF) (from 63.86 ± 17.67% to 40.29 ± 16.65%, p < 0.01). Correlation analysis indicated significant associations between diaphragmatic function and some lung function parameters (FEV₁, FEF_25–75, and R_tot). Conclusions: In this real-world pilot, triple BDP/FF/G was linked to improvements in airflow, hyperinflation, symptoms, and a reduction in diaphragmatic thickening fraction, indicating potential physiological benefit. Due to the small sample size, single-centre design, and 3-month follow-up, these results should be viewed as hypothesis-generating and need to be confirmed in larger, controlled, multicentre studies with longer follow-up. Full article

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

Posture recognition is critical in modern educational and office environments for preventing musculoskeletal disorders and maintaining cognitive performance. Existing methods based on human keypoint detection typically rely on convolutional neural networks (CNNs) and single-scale features, which limit representation capacity and suffer from overfitting under small-sample conditions. To address these issues, we propose MSBN-SPose, a Multi-Scale Bayesian Neuro-Symbolic Posture Recognition framework that integrates geometric features at multiple levels—including global body structure, local regions, facial landmarks, distances, and angles—extracted from OpenPose keypoints. These features are processed by a multi-branch Bayesian neural architecture that models epistemic uncertainty, enabling improved generalization and robustness. Furthermore, a lightweight neuro-symbolic reasoning module incorporates human-understandable rules into the inference process, enhancing transparency and interpretability. To support real-world evaluation, we construct the USSP dataset, a diverse, classroom-representative collection of student postures under varying conditions. Experimental results show that MSBN-SPose achieves 96.01% accuracy on USSP, outperforming baseline and traditional methods under data-limited scenarios. Full article