Search Results (1,029)

A hybrid modelling framework is proposed to predict endpoint sulphur content in the ladle furnace (LF) refining process by embedding metallurgical expert knowledge into interpretable machine learning (ML). Industrial process data were extracted from the Level-2 (L2) system of a steel plant, and a desulphurisation dataset comprising 5169 heats with 29 process variables was constructed using a knowledge-guided time window from the joint satisfaction of refining conditions to the final argon-blowing stage. After data cleaning, normalisation and correlation-based feature selection, four algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Artificial Neural Network (ANN)—were trained and compared on a representative cluster of steel grades identified by K-means. The ANN model achieved a coefficient of determination (R²) of 0.7752, a root mean square error (RMSE) of 0.0027 wt%, a mean absolute error (MAE) of 0.0017 wt% and a hit rate (HR, ±0.0025 wt% for S) of 76.40% on the test set. SHapley Additive exPlanations (SHAP) indicate that limestone addition, slag basicity, argon flow rate, refining time and initial sulphur content dominantly govern sulphur removal. The expert-knowledge-guided, interpretable framework provides quantitative support for specification-conforming endpoint sulphur control while mitigating over-desulphurisation and reagent consumption. Full article

Shooting activities offer educational and recreational value; however, their application in school physical education and recreational settings remains limited due to safety concerns, high costs, and restricted access to specialized facilities and equipment. To address these constraints, this study designed and implemented a low-cost laser shooting system suitable for school physical education and recreational use. The proposed system comprises a laser-gun module, a physical electronic target providing immediate on-site feedback using an illuminance sensor, a Fresnel lens, and RGB LEDs, and a web-based electronic target that enables real-time scoring, logging, and visualization via smartphone or tablet cameras and browser-based processing. By adopting a low-power, projectile-free laser structure with pulse-limited emission, the system enhances operational safety, while the use of general-purpose components and web standards reduces cost and lowers barriers to adoption. Technical verification conducted under controlled indoor conditions demonstrated stable single-shot operation, reliable hit detection, and accurate score calculation for both the physical and web-based targets. Expert validation involving specialists in physical education, educational technology, and sports technology yielded consistently high evaluations across safety, cost efficiency, functional completeness, and field applicability. These findings suggest that the proposed system represents a practical and scalable alternative for school physical education classes and recreational programs. Future research should examine user-level usability, learning outcomes, system robustness under diverse environmental conditions, and structured expert consensus processes. Full article

Helicobacter pylori (H. pylori) is the primary etiological agent for chronic gastritis, peptic ulcers, and gastric adenocarcinoma. The alarming rise in multidrug-resistant (MDR) strains, particularly against clarithromycin (CLR), metronidazole (MNZ), and levofloxacin (LVX), has severely compromised standard therapies. Thus, there is an urgent clinical need for novel antimicrobial agents that operate through distinct mechanisms to bypass resistance pathways and mitigate gastric cancer risk. We designed and synthesized a series of antimicrobial peptides, focusing on the proteolytically stable all-D-amino acid enantiomer, DT7-3, derived from a probiotic-sourced template. Minimum inhibitory concentrations (MICs) were determined against standard strains and 11 clinical MDR isolates via the broth microdilution method. Antimicrobial mechanisms were elucidated using scanning electron microscopy (SEM) for morphology, fluorescence-based assays for anti-adhesion activity, and real-time qPCR to quantify virulence gene expression (babA, ureA, and vacA). Biocompatibility was assessed using defibrinated sheep erythrocytes, gastric epithelial cells (GES-1), and representative beneficial gut microbiota. Analysis of the clinical isolates revealed resistance rates of 63.6% for CLR/LVX and 81.8% for MNZ, with 54.5% identified as MDR. DT7-3 exhibited superior potency (MIC 1–32 µg/mL) against all strains, significantly outperforming its L-enantiomer counterparts. Mechanistic studies unveiled a “triple-hit” mechanism: (1) rapid membrane disruption; (2) potent inhibition of bacterial adhesion to host cells (~60% reduction at 0.5 × MIC); (3) significant downregulation of critical virulence factors (babA, ureA, and vacA). Furthermore, DT7-3 showed an excellent safety profile, with negligible hemolysis (<5% at 32 µg/mL) and minimal cytotoxicity toward GES-1 cells, yielding a high selectivity index (SI, MHC/MIC) > 32 relative to mammalian cells. Crucially, DT7-3 showed high selectivity for the pathogen over beneficial gut microbiota (MIC > 128 µg/mL, SI > 16). Crucially, DT7-3 maintained potent bactericidal activity (MIC ≤ 16 µg/mL) even under cholesterol-enriched conditions. The engineered D-peptide DT7-3 is a potent candidate for combating MDR H. pylori. Its multifaceted mechanism, targeting bacterial viability while suppressing core virulence factors, positions it as a robust lead compound for next-generation eradication therapies aimed at reducing the burden of H. pylori-associated diseases. Full article

(1) Background: The scarcity and imbalance of real fault data significantly limit the development of data-driven fault diagnosis methods for liquid rocket engines (LREs), especially under few-shot conditions. (2) Methods: To address this issue, this study proposes a GAN-based fault data augmentation framework for multivariate LRE time-series signals and a hybrid diagnostic classifier combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and multi-head attention (MHA). The GAN component is introduced to alleviate fault-data scarcity and class imbalance by generating additional fault-like samples, while the classifier is designed to capture local features, long-range temporal dependencies, and diagnostically informative temporal regions. (3) Results: A multidimensional evaluation based on temporal similarity, statistical consistency, and global distribution discrepancy indicates that the generated samples preserve important characteristics of the original signals under the current evaluation protocol. On the augmented LRE dataset, the proposed classifier achieved strong diagnostic performance. In addition, supplementary experiments on the public HIT aero-engine dataset further support the effectiveness of the classifier architecture, its component-wise contribution, and its behavior under imbalanced few-shot settings, while also demonstrating the value of uncertainty-aware prediction. (4) Conclusions: The results provide encouraging evidence that the proposed framework can improve LRE fault diagnosis under data-scarce conditions. However, the present findings should be interpreted within the scope of the available data and evaluation setting. More comprehensive generator-side ablation, broader external validation, and physics-oriented assessment of the generated signals are still needed before stronger conclusions can be made. Full article

Context. The grocery sector is undergoing a massive shift in consumer behavior, with global chatbot usage projected to reach 8.4 billion units by 2024—surpassing the total human population—and online grocery revenue per shopper expected to hit USD 449.00 by 2023. In this competitive landscape, small grocery stores must adopt AI-driven tools to modernize their operations. However, these businesses often face significant inefficiencies in manual inventory management, resulting in errors and reduced competitiveness. Objective. This research aims to develop and validate a chatbot application using Large Language Models and Retrieval-Augmented Generation (RAG) for operational management of grocery stores. Method. The method employed a quantitative experimental approach with a five-component system architecture: a web interface, a FastAPI API, a Mistral-7B-Instruct-v0.2 model, a dynamic SQL generator, and a custom RAG application with an FAISS vector database, all integrated through SQLAlchemy 2.0.40. Results. The results demonstrate that a chatbot achieves an average response time of 0.08 s with 80% overall accuracy, showing a 96.2% improvement in information query time and a 92.9% reduction in operational errors. Conclusions. Major conclusions suggest that the chatbot system is effective for retail environments and has the potential to enhance the operational efficiency of grocery stores, serving as a foundation for future research in applied conversational assistance. Full article

Background: Turner syndrome is a genotypic disorder in females characterized by the total or partial absence of an X chromosome. While cardiovascular issues and sensorineural hearing loss are well-documented, vestibular system involvement remains understudied. This study aims to examine vestibular system involvement in patients with Turner syndrome and assess if they exhibit a higher prevalence of peripheral vestibular pathology compared to the general population. Methods: A retrospective longitudinal study was conducted with 21 Turner syndrome patients and 21 age-matched controls. Evaluations included clinical history, otoscopy, pure tone audiometry, the Video Head Impulse Test (vHIT) to measure vestibulo-ocular reflex gain, and computerized dynamic posturography, specifically the Sensory Organization Test (SOT) and Stability Limits Analysis. Results: Turner syndrome patients showed significantly higher hearing thresholds across all frequencies compared to controls (p < 0.001). In the vHIT, 30% of the Turner group presented pathological results, with significant gain reductions in the right horizontal and left posterior semicircular canals. Posturography revealed a significant reduction in overall stability (p = 0.006) and a significantly lower vestibular index (p = 0.011) in the Turner group. Additionally, patients with Turner syndrome demonstrated significant impairments in directional control, reaction time, and excursion points during Stability Limits Analysis. Conclusions: Patients with Turner syndrome are more likely to experience vestibular disorders, a finding likely associated with estrogen deficiency and the loss of its protective effect on the inner ear. These results highlight the necessity of including vestibular and posturographic assessments in the routine clinical follow-up of these patients to facilitate early detection and rehabilitation, even in the absence of overt symptoms like vertigo. Full article

Objective: To investigate how the high-altitude environment modifies severe pertussis in young infants and analyze its pathophysiological mechanisms and clinical management implications. Methods: Clinical data of two young infants with severe pertussis residing at 3650 m were retrospectively analyzed, including presentation, laboratory findings, pathogen detection, treatment, and outcomes. A literature review explored synergistic interactions between high-altitude factors and pertussis pathophysiology. Results: Case 1 had macrolide-resistant Bordetella pertussis (MRBP, 23S rRNA A2047G) with peak WBC 52.25 × 10⁹/L, and received cefoperazone-sulbactam, piperacillin-tazobactam and azithromycin, and was successfully treated with trimethoprim-sulfamethoxazole combined with exchange transfusion. Case 2 had Bordetella pertussis confirmed by PCR with peak WBC 36.55 × 10⁹/L, receiving cefoperazone-sulbactam and azithromycin, and recovered. Both developed respiratory failure requiring non-invasive ventilation and survived without pulmonary hypertension. High-altitude stressors—hypoxia, enhanced pulmonary vascular reactivity, and hypercoagulability—synergize with pertussis-induced hyperleukocytosis as a “dual hit,” accelerating cardiopulmonary deterioration and elevating thrombotic risks. Conclusions: High altitude is an independent risk modifier in infantile pertussis, demanding heightened vigilance and proactive interventions: early non-invasive ventilation, prophylactic anticoagulation, and timely exchange transfusion before pulmonary hypertension develops. This is the first high-altitude case series that provides essential insights for clinicians in similar environments globally, guiding early recognition and proactive management strategies to improve outcomes in this vulnerable population. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article

Epstein–Barr virus (EBV) is a human pathogenic and oncogenic herpesvirus, with worldwide importance, at times associated with serious to life-threatening symptoms, especially in immunocompromised hosts. The available preventive options against EBV disease are limited to medically elaborate and cost-intensive measures of cell-based immunotherapy. The development of novel options of anti-EBV drug targeting is currently a matter of intense international efforts. A putative target of the antiviral therapy approach is the EBV-encoded protein kinase BGLF4, which fulfills a multifaceted role in productive viral replication. So far, viral BGLF4 interactor proteins and phosphorylated substrates have occasionally been reported, but in particular cellular interactors await further characterization concerning both, their relevance for BGLF4 functionality and their accessibility to antiviral drugs. In this study, we have analyzed host cell–BGLF4 interaction, BGLF4 kinase properties, and BGLF4-directed small molecules. The main results are as follows: (i) a mass spectrometry-based interactomic study was performed with EBV-producing Akata-BX1 cells, thereby identifying the human pyruvate dehydrogenase (PDH) as a relevant BGLF4 interactor; (ii) BGLF4–PDH interaction was confirmed by protein coimmunoprecipitation, subcellular cofractionation, and confocal imaging; (iii) the BGLF4-mediated phosphorylation of PDH was demonstrated by an in vitro kinase assay (IVKA); (iv) a reduction in PDH phosphorylation was shown for selected kinase inhibitors, which also exerted BGLF4-directed inhibitory potential in a quantitative qSox-IVKA, and (v) these hit compounds showed anti-EBV activity in lytically induced P3HR-1 cells using qPCR measurement, as well as PDH-inhibitory activity using standardized PDH assays. These data lead to an improved understanding of EBV–host interaction that may open novel anti-EBV preventive opportunities. Combined, the findings point to PDH as a new cellular interactor of the EBV kinase BGLF4. Also, notably, the data on pharmacological intervention with kinase activity or substrate phosphorylation may possibly provide as yet untapped options of antiviral drug targeting. Full article

Heparin remains a foundational parenteral anticoagulant across both acute and chronic care settings. This narrative review summarizes clinical indications and dosing of unfractionated (UFH) and low-molecular-weight heparin (LMWH). It also details laboratory monitoring using activated partial thromboplastin (APTT), anti-factor Xa (anti-Xa), activated clotting time (ACT) and viscoelastic testing (VET), including common pitfalls and interferences. We provide considerations for specific populations as well as complications including heparin resistance, heparin-induced thrombocytopenia (HIT) and heparin reversal strategies. Future research directions include harmonization of therapeutic ranges, mitigation of assay interference and prospective evaluation on monitoring, particular in extracorporeal membrane oxygenation (ECMO), pregnancy and cardiac surgical settings. Full article

As the number of electric vehicles (EV) increases, the demand for recommending the best charging location when using a large-scale charge network to charge is also increasing. A successful recommendation will utilize the user’s preference and the operational constraints of the charging network to make sure that it also takes into account the real-time operational requirements of the network. Most current papers focus on optimizing individual algorithmic components in isolation; consequently, many of these papers neglect to provide a holistic view of an integrated system. In addition, there are many operational requirements that current research does not consider, such as cold-start personalization for new users and enforcing real-time operational constraints like station availability, power capacity, maintenance windows, etc. This paper describes a deployable multi-stage recommendation system that creates a candidate list based on location and ranks preferences based on user, station and context features. The recommendation system also adds a configurable rule-based re-ranking layer to ensure that both hard constraints (i.e., charger availability and power-cap limits) and soft objectives (i.e., load balancing and operator priority) are enforced. A method for enabling mixed use between stable Bayesian and adaptive Bayesian methods was developed to provide users starting with cold-start performance that do not have adequate histories. Evaluation of this method using 100k+ real charging sessions showed that the fraction of sessions where the ground-truth station appears in the top-two recommendations (Hit@2) for the recommendation system was 0.82, representing a 37% increase in performance compared to proximity-based recommendation methods. The online deployed recommendation system has a 99th-percentile serving latency (P99) of less than 200 ms. The findings of this paper provide a framework for the implementation of operationally-relevant user-centric recommendation systems for EV services at scale. Full article

Instrumented mouthguards (iMGs) enable in vivo monitoring of head-impact exposure by reporting event-level peak linear acceleration (PLA) and peak angular acceleration (PAA) in contact sports. This case study describes head impacts in a world-class Muay Thai fighter during routine sparring sessions over a two-week period leading into a competitive bout. Seven sparring sessions were monitored using an iMG (PROTeQT, HitIQ), and only manufacturer (in-mouth)-flagged events above the device’s 8 g trigger threshold were analyzed. Event-level data were exported from the manufacturer portal; raw time-series signals and proprietary signal-processing parameters were not accessible, and no independent video verification was performed. Across the camp, 590 impacts were recorded. Mean PLA values were modest across sessions (7.6 to 19.5 g), with one event exceeding 106 g (max PLA 162.2 g). In contrast, PAA exhibited greater variability, with multiple device-flagged events exceeding 7900 rad/s², particularly in Sessions 4, 6, and 7, where maximum PAA values reached 19,862 to 26,850 rad/s². Overall, these data indicate that sparring was predominantly low in translational loading, while occasionally producing high recorded rotational peaks. Because outputs are device- and processing-pipeline-specific and were not independently verified, threshold-based severity banding and extreme peaks should be interpreted cautiously. This case demonstrates the potential utility of iMG monitoring to characterize session-to-session variability in sparring exposure and to inform practical sparring load management strategies aimed at reducing cumulative head-impact burden. Full article

Accurate picking of microseismic P-wave arrival times is essential for the localization and monitoring of mining-induced seismic events. Conventional Short-Term Average/Long-Term Average (STA/LTA) detectors, while computationally efficient, are highly susceptible to noise interference. Conversely, deep learning approaches exhibit superior noise robustness but often involve substantial computational redundancy and compromised real-time performance. To address these limitations, we propose a novel two-stage picking framework that integrates STA/LTA with a lightweight U-Net, enabling rapid preliminary detection followed by fine-grained refinement. In the first stage, STA/LTA rapidly scans continuous waveforms to identify candidate windows potentially containing P-wave arrivals. In the second stage, a lightweight U-Net performs sample-level regression within each candidate window to refine arrival-time estimates with high precision. This coarse-to-fine paradigm effectively balances computational efficiency and picking accuracy. Experimental validation on 500 Hz microseismic data acquired from a coal mine in Gansu Province demonstrates that the proposed method achieves a hit rate of 63.21% within a tolerance window of ±0.01 s. This represents performance improvements of 25.42% and 40.47% over convolutional neural network (CNN) and STA/LTA methods, respectively, while reducing the mean absolute error to 0.0130 s. Furthermore, the model exhibits consistent performance on independent test sets, confirming its generalization capability and noise robustness. By combining the computational efficiency of STA/LTA with the representational power of deep learning, the proposed approach demonstrates significant potential for real-time industrial deployment. Full article

We say a finite simple connected undirected graph is equiresistant if all its edges have the same effective resistance when the graph is considered as an electric circuit where the edges are unit resistors. Using simple properties of electric circuits, we identify some new families of graphs that are equiresistant and then apply this knowledge to find upper bounds for some molecular indices that improve; in the case of equiresistant graphs, similar upper bounds are found in the literature. Full article

Unmanned Aerial Vehicle (UAV) remote sensing has become essential for real-time earth observation applications, including precision agriculture, traffic monitoring, and disaster response. However, small-target detection in UAV aerial imagery still faces critical challenges: extreme scale variation due to variable flight altitudes, background interference from complex terrain, and insufficient pixel information for tiny objects. To address these issues, this work proposes FKIFM-DETR, a real-time transformer-based detection framework leveraging multi-domain information fusion. First, a Spatial-Frequency Fusion Module (SFM) is designed to integrate spatial and frequency-domain features for capturing fine-grained target details while suppressing background noise; second, a High–Low Frequency Block (HL-Block) is introduced to separately process high-frequency local details and low-frequency global context, balancing detail retention and semantic awareness; finally, a Channel Feature Recalibration-Enhanced Feature Pyramid Network (SPCR-FPN) is employed to strengthen the interaction between shallow spatial features and deep semantic features. On the VisDrone2019 dataset, FKIFM-DETR achieves 6.3% and 5.3% improvements in mAP@0.5 and mAP@0.5:0.95 over the RT-DETR baseline, respectively; evaluations on TinyPerson and HIT-UAV datasets further demonstrate its cross-scenario applicability. These results demonstrate the potential of FKIFM-DETR for practical UAV remote sensing applications such as crowd surveillance, vehicle tracking, and emergency rescue. Full article