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Study on the fast response characteristics and mechanical reliability of high-voltage circuit breaker solenoid valves

The effect of time–space compression in the Yangtze River Delta region under transportation integration: An accessibility-based analysis

The Yangtze River Delta (YRD) region is one of the areas in China with the highest level of transportation integration, featuring a well-developed high-speed rail network, multimodal transport system, interconnected intercity bus services, and intelligent transportation platforms. These developments have brought about significant effects of time-space compression in the region. Under the effects of time-space compression, the changes in the regional socio-economic spatial structure are worth further exploration. To this end, from the perspective of accessibility, this study explores the time–space compression effect in the YRD region using models such as economic linkage strength (ELS), central city isochronous-ring, daily accessibility (DA), and employment accessibility (EA) indicators. The results show: (1) The time distance between central cities and prefectural-level and lower-tier cities has greatly decreased, resulting in a network-based structure and a trend towards urban system flattening; within 1–2 hours of central cities, the scale of the population, number of cities, and regional coverage area have rapidly expanded, promoting regional integration and urban agglomeration. (2) Intercity time-space compression has significantly increased the total ELS between cities, intensifying the “strong center” economic linkage pattern. The radiation and driving capacity of central cities for the region has notably increased, and the equilibrium of urban economic linkage capabilities has improved. (3) Time-space compression strengthens the “competitive-cooperative effect” of central cities, facilitating the convenient sharing and equalization of high-quality urban public services, but also leading to tensions in the supply and demand of these resources. (4) A high-accessibility region has formed in the “Shanghai-Nanjing-Hangzhou” triangle, where high-accessibility cities possess larger market hinterlands and more employment opportunities, providing significant opportunities for the rise of small and medium-sized cities in peripheral areas. Based on these findings, in the context of time-space compression, these regions should actively build convenient, efficient, and resilient commuting and transfer networks. A networked spatial development strategy should be implemented, establishing efficient regional collaboration mechanisms and public service cooperation systems to promote coordinated regional development.

Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings

Abstract Timely stroke diagnosis is essential for delivering life-saving treatments, yet current prehospital stroke assessment tools often lead to missed or delayed diagnoses. Emergency Medical Services (EMS) increasingly collect detailed data during transport, offering an opportunity to develop AI-based tools to support early stroke detection. In this retrospective study, we evaluated the availability and reliability of prehospital data compared to Emergency Department (ED) records. We tested the potential of machine learning to support EMS stroke triage. Our cohort included 4,333 patients across 8,221 ambulance encounters from 2015 to 2020 with stroke rate of 2.0% (64% severe strokes). Vital signs such as heart rate, respiratory rate, blood pressure, oxygen saturation, and GCS scores, recorded in over 88%, were generally higher than their ED counterparts. We trained and evaluated random forest, XGBoost, and sequential neural networks for detecting stroke and severe stroke. The XGBoost model performed best for stroke detection (ROC-AUC 0.843 [0.77–0.89], PR-AUC 0.293 [0.16–0.45]), while Random Forest performed best for severe strokes (ROC-AUC 0.826 [0.75–0.90], PR-AUC 0.186 [0.07–0.35]). Models were calibrated to improve reliability, and feature importance was assessed using SHAP to enhance interpretability. These findings highlight the promise of AI-based tools in improving prehospital stroke triage with real-time EMS data.

Indistinguishable mitochondrial phenotypes after exposure of healthy myoblasts to myalgic encephalomyelitis/chronic fatigue syndrome or control serum

Myalgic Encephalomyelitis (ME) / Chronic Fatigue Syndrome is a disease of uncertain aetiology that affects up to 400,000 individuals in the UK. Exposure of cultured cells to the sera of people with ME has been proposed to cause phenotypic changes in these cells in vitro when compared to sera from healthy controls. ME serum factors causing these changes could inform the development of diagnostic tests. In this study, we performed a large-scale, pre-registered replication of an experiment from Fluge et al (2016) that reported an increase in maximal respiratory capacity in healthy myoblasts after treatment with serum from people with ME compared to serum from healthy controls. We replicated the original experiment with a larger sample size, using sera from 67 people with ME and 53 controls to treat healthy cultured myoblasts, and generated results from over 1,700 mitochondrial stress tests performed with a Seahorse Bioanalyser. We observed no significant differences between treatment with ME or healthy control sera for our primary outcome of interest, oxygen consumption rate at maximal respiratory capacity. Results from our study provide strong evidence against the hypothesis that ME blood factors differentially affect healthy myoblast mitochondrial phenotypes in vitro .

A hybrid machine learning framework for offline signature verification using gray wolf optimization

Fluoxetine minimally affects hearing loss but induces gene expression changes in the cochlear nuclei after noise exposure

Purpose This study investigated the effects of fluoxetine on noise-induced injuries to the cochlea and auditory nerve, with a focus on its impact on perineuronal nets (PNNs) and gene expression changes in the ventral cochlear nuclei (VCN). Methods Sprague-Dawley rats were exposed to white noise at 115 dB SPL for 3 hours per day over 3 weeks. After measuring auditory brainstem response (ABR) thresholds, rats were treated with fluoxetine (10 mg/kg) for 19 days. Four groups were included (vehicle, fluoxetine, noise + vehicle, and noise + fluoxetine; n = 10 per group). ABR measurements, analysis of extracellular baskets in cochlear ribbon synapses and PNNs, and RNA sequencing of the VCN were performed. Results Following fluoxetine treatment, noise-exposed rats (noise + fluoxetine group) showed hearing thresholds comparable to those in the noise + vehicle group. Both noise-exposed groups exhibited cochlear hair cell loss and disorganization. Extracellular baskets surrounding cochlear ribbon synapses were significantly reduced in noise + vehicle rats and were not restored in noise + fluoxetine rats. Aggrecan expression in the VCN was reduced in the noise + fluoxetine group. RNA sequencing revealed upregulation of genes including Mal, Fos, Rapgef3, Papss2, Adamts4, and Heph, and downregulation of genes such as Pde5a, Kcnma1, Nr4a1, Dlgap3, Slc18a2, and Dgkg . Conclusion Fluoxetine exerted only modest, 4 kHz‑restricted improvements in ABR thresholds and did not restore cochlear structure or normal hearing following noise‑induced hearing loss. However, it induced distinct transcriptional alterations in the VCN and modulated the extracellular environment, suggesting a potential role in neural remodeling rather than direct auditory restoration.

Advanced natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA

Psychosocial correlates of adherence self-efficacy and HIV viral suppression among adolescents and young adults in Western Kenya

Objective To measure adherence self-efficacy (ASE) to antiretroviral therapies (ART) and evaluate the relationship between ASE, depression, perceived social support, and HIV viral suppression among adolescents and young adults living with HIV (ALHIV) in Kenya. Design Cross-sectional analysis of baseline data from a longitudinal cohort of Kenyan ALHIV. Methods ALHIV were recruited from nine health facilities in western Kenya. Participants completed behavioral surveys at enrollment, and study team members extracted viral load data from a national database. ASE was assessed using a modified HIV-Adherence Self-efficacy Assessment Survey (HIV-ASES), and depression was assessed as a score of ≥ 10 using the PHQ-9. Linear mixed effects regression modeling and general linear mixed effects regression modeling, clustering by facility, were used to determine associations between ASE scores, viral suppression, and correlates of interest. Results Overall, 987 ALHIV age 15 and older were included in this study, 70% were female, 58% ages 15−19, and 57% were attending or had completed at least secondary school. Ninety-six percent had ASE data, 73% (517/703) were virally suppressed, 90% (888/987) scored 9 or below on the PHQ-9, 47% (460/987) reported high perceived social support, and 65% (645/987) were classified as having orphan status. ALHIV who had moderate-to-severe depression had a mean ASE score that was 13.41 points lower (95% CI: −20.12 – −6.52, p  < 0.001) than those with none-or-mild depression. Female ALHIV had higher odds of viral suppression (adjusted OR: 1.55, 95%CI: 1.07–2.25, p  = 0.02) as did ALHIV with higher social support (adjusted OR: 1.67, 95%CI: 1.17–2.40, p  = 0.005). There was no significant association identified between ASE and viral suppression. Conclusions Emotional well-being and social support are essential to improve adherence self-efficacy and viral suppression among youth living with HIV.

Distribution patterns and formation mechanisms of stable landslide dams in the Qinghai-Tibet plateau: a preliminary study

Performance evaluation of GPU-based parallel sorting algorithms

Sorting can be approached in two main ways: sequentially and in parallel. In sequential sorting, data is processed in a single-threaded manner, which can be slow for large datasets. However, parallel sorting divides the task across multiple processing units, enabling faster results by processing data simultaneously. Furthermore, Compute Unified Device Architecture (CUDA) technology enables developers to leverage GPU power for general-purpose parallel computing, significantly accelerating tasks like sorting. This paper investigates the GPU-based parallelization of merge sort (MS), quick sort (QS), bubble sort (BS), radix top-k selection sort (RS), and slow sort (SS) presenting optimized algorithms designed for efficient sorting of large datasets using modern GPUs. The primary objective is to evaluate the performance of these algorithms on GPUs utilizing CUDA, with a focus on analyzing both parallel time complexity and space complexity across various data types. Experiments are conducted on four dataset scenarios: randomly generated data, reverse-sorted data, already-sorted data, and nearly-sorted data. Also, the performance of GPU-accelerated implementations is compared with their sequential counterparts to assess improvements in computational efficiency and scalability. Earlier GPU-based generations of this type typically achieved acceleration rates between 2× and 9× over scalar CPU code. With newer GPU enhancements, including parallel-aware primitives and radix- or merge-optimized operations, acceleration rates have seen significant improvement. Our experiments indicate that Radix Sort based on GPUs achieves a significant speedup of approximately 50× (sequential: 240.8 ms, parallel: 4.83 ms) on 10 million random sort elements. Quick Sort and Merge Sort have 97× and 103× speedups, respectively (Quick: 1461.97 ms vs. 15.1 ms; Merge: 2212.33 ms vs. 21.4 ms). Bubble Sort, while significantly improving in parallel (123,321.9 ms to 7377.8 ms for an ≈17× improvement), is considerably worse overall. Slow Sort demonstrates a moderate but consistent acceleration, reducing execution time from 74.07 ms in the sequential version to 3.99 ms on the GPU, yielding an ≈18.6× speedup. These experimental findings confirm that the new single-GPU implementations can get speedups ranging from 17× to over 100×, surpassing the typical gains reported in previous generations and comparable to or over rates of acceleration reported for cutting-edge parallel sorting algorithms in recent studies.

Cafestol-derivatives as potential FXR agonists and CYP7A1 inhibitors and their impact on hypercholesterolemia: an in silico study

Abstract Cafestol is a hypercholesterolemic ent -kaurane diterpene found in unfiltered coffee, a regular component of the human diet. This diterpene is reported to modulate macromolecules involved in cholesterol metabolism, such as the farnesoid X receptor (FXR) and the cytochrome P450 7A1 enzyme (CYP7A1) in both in vitro and in vivo models. However, there are no studies on the hypercholesterolemic potential of cafestol roasting derivatives and phase I metabolites of cafestol, which share the same ent -kaurane scaffolds. In the present work, we compared binding modes of cafestol roasting derivatives and phase I metabolites of cafestol identified in a zebrafish model with two human protein-targets, FXR and CYP7A1, using molecular docking and molecular dynamics simulations (MDS) guided by cavity detection studies. We also performed a pharmacophore map focusing on the steroidal skeleton of co-crystallized ligands of these two protein targets and compared it with the ent -kaurane nucleus, along with an in silico ADME analysis of cafestol roasting derivatives to predict their physicochemical and pharmacokinetic properties. Based on their structural similarity to cafestol, this study preliminarily supports that these compounds may contribute to increased serum cholesterol, through interaction with these targets, thereby intensifying the cardiovascular risk associated with the consumption of unfiltered coffee beverages.

Augmented reality in engineering education: Strategic design and evidence-based results

The primary objective of this study is to define and empirically validate a novel procedural methodological framework, named SEBAS (Selection, Establishment, Blueprint, Application, Synthesis), for the strategic integration of Augmented Reality (AR) learning activities in higher engineering education. The research addresses the lack of specialized methodologies for using AR in the complex and technical discipline of mining engineering and evaluates the impact of the implementation on student educational outcomes. A mixed-method quasi-experimental design was employed, involving 136 undergraduate students enrolled in mining-related programs. A specific group of these students participated in augmented reality (AR) workshops developed using the SEBAS framework. Quantitative data on academic performance history were collected, along with qualitative data from pre- and post-intervention surveys to assess students’ perceptions and motivation. The results demonstrated a substantial positive impact on academic performance. Participating students achieved a higher average final grade (4.70, SD = 1.75) compared to the overall group (4.30, SD = 1.98). Welch’s t-tests confirmed statistically significant differences in the courses with higher AR integration (Course 1, t(36)=3.30, p = 0.002; Course 2, t(38)=10.03, p < 0.001). A thorough qualitative analysis was conducted, which yielded substantial evidence of a considerable positive shift in student perceptions, levels of interest, and practical applicability. The primary contribution of this research is the SEBAS framework, which offers a structured, pedagogically sound, and replicable model for incorporating immersive technologies. This framework facilitates the development of strategically designed AR activities that enhance academic performance, promote cognitive engagement, and prepare students for the challenges of the industry.

Oxysophoridine promotes osteoarthritis repair via GSH system activation and ROS suppression

Deep learning framework for RNA 5hmC prediction using RNA language model embeddings

By influencing gene expression and contributing to epigenetic modifications, Ribonucleic Acid (RNA) 5-Hydroxymethylcytosine (5hmC) modification significantly affects cellular pathways. It plays an important role in complex regulatory networks and gene expression. Moreover, 5hmC modifications are linked to a variety of human diseases, including diabetes, cancer, and cardiovascular conditions. However, experimental methods to identify RNA 5hmC modifications, such as chromatography and Polymerase Chain Reaction (PCR) amplification, are costly and time-consuming. So, computational methods are necessary to predict these modifications. In this study, several feature descriptors were analyzed and compared to finalize the best ones. Different deep-learning models were explored to design the proposed model architecture. Neighbourhood analysis was conducted on the dataset to provide insights into a deeper understanding of RNA 5hmC modifications. The proposed model, InTrans-RNA5hmC, is a dual-branch deep learning model that has two branches: the Inception branch and the Transformer branch. Word embeddings having the contextual information and language model embeddings from the RiboNucleic Acid Language Model (RiNALMo) were used as the finalized feature descriptors. InTrans-RNA5hmC outperformed existing SOTA methods, achieving 0.97 sensitivity, 0.985 balanced accuracy, and 0.985 F1 score on the Independent test set.

Exploring the proprioceptive potential of joint receptors using a biomimetic robotic joint

Abstract In neuroscience, joint receptors have traditionally been viewed as limit detectors, providing positional information only at extreme joint angles, while muscle spindles are considered the primary sensors of joint angle position. However, joint receptors are widely distributed throughout the joint capsule, and their full role in proprioception remains unclear. In this study, we specifically focused on mimicking Type I joint receptors, which respond to slow and sustained movements, and quantified their proprioceptive potential using a biomimetic joint developed with robotics technology. Results showed that Type I-like joint receptors alone enabled proprioceptive sensing with an average error of less than 2 degrees in both bending and twisting motions. These findings suggest that joint receptors may play a greater role in proprioception than previously recognized and that the relative contributions of muscle spindles and joint receptors are differentially weighted within neural networks during development and evolution. Furthermore, this work may prompt new discussions on the differential proprioceptive deficits observed between the elbows and knees in patients with hereditary sensory and autonomic neuropathy type III. Together, these findings highlight the potential of biomimetics-based robotic approaches for advancing interdisciplinary research bridging neuroscience, medicine, and robotics.

Utility of inhaled β2-agonists in reducing serum potassium levels in adult patients with hyperkalemia: A scoping review

Introduction Hyperkalemia is a potentially life-threatening condition that requires prompt intervention to prevent cardiac complications. While insulin and glucose administration remains a cornerstone of treatment, inhaled β2-adrenergic agonists have been proposed as a complementary or alternative strategy, particularly in emergency settings. This scoping review aimed to describe the utility of inhaled β2-agonists in lowering serum potassium levels in adult patients. Methods A scoping review was conducted following the PRISMA-ScR guidelines and the Joanna Briggs Institute methodology. Experimental studies published within the past ten years evaluating the use of inhaled β2-agonists in adult patients with hyperkalemia were included. Five studies were analyzed (three randomized controlled trials and three quasi-experimental studies), assessing dosage, route of administration, magnitude of potassium reduction, and reported adverse events. Results Most studies used nebulized salbutamol at a dose of 10 mg, with observed reductions in serum potassium ranging from 0.62 to 1.636 mEq/L, and a peak effect between 1 and 4 hours post-administration. One study also reported the use of levalbuterol. The most common adverse effects were tachycardia, dizziness, and mild hyperglycemia, all of which were clinically manageable. Efficacy was demonstrated in both patients with chronic kidney disease and in individuals with normal renal function. Conclusion Inhaled β2-agonists, particularly nebulized salbutamol, represent an effective and safe therapeutic option for the acute reduction of serum potassium in adults with hyperkalemia. Their rapid onset of action and applicability across various patient profiles make them a valuable tool in emergency settings, especially where immediate access to advanced therapies such as dialysis is limited. Further research is warranted to evaluate long-term outcomes, safety in patients with cardiovascular comorbidities, and optimal dosing strategies.

Role of Arabian Sea warm pool and atmospheric instability in triggering a monsoonal MCC over Peninsular India

Reselling or agency selling? Sales mode selection for a manufacturer in private label competition under information asymmetry

Big data systems enhance retailers’ predictive capabilities, providing them with a significant advantage in understanding end-market demand, increasing the competitiveness of retailers’ private labels, and significantly impacting manufacturers’ sales strategies. This paper applies dynamic game-theoretic models to explore the interaction between the retailer’s information-sharing strategies and the manufacturer’s sales mode selections when the retailer introduces a private label. Through backward induction, the equilibrium strategies of both parties can be derived. The results show that the manufacturer consistently benefits from information-sharing under both sales modes, potentially achieving a win-win outcome under the agency selling mode. Improving the retailer’s information forecast accuracy encourages a shift from reselling to agency selling mode. The retailer’s lower information forecast accuracy or the smaller demand variance offers no first-mover advantage to either the manufacturer or the retailer. Additionally, an Information Compensation Mechanism (ICM) for revenue-sharing is proposed, and contract conditions across different sales modes are discussed.

An efficient deep CNN based BiLSTM framework with RanA optimization for accurate cardiac arrhythmia detection

Remote electrophysiological cardiotocography (eCTG), evaluation of feasibility in complicated pregnancies from 32 until 37 weeks gestational age in a home@hospital setting (HASTA): A prospective cohort study protocol

Introduction High-risk pregnancies, including those complicated by pre-eclampsia, fetal growth restriction or preterm pre-labor rupture of membranes, often require hospitalization for fetal and maternal monitoring. Home monitoring may reduce the psychological and family burden, improve patient satisfaction, and lower health care costs. The primary diagnostic tool for fetal home monitoring is cardiotocography (CTG). However, conventional CTG can suffer from poor signal quality due to maternal obesity or movements. Since electrophysiological cardiotocography (eCTG) relies on electrical activity in cardiac and uterine muscles, signal quality is less impacted by obesity and motion artefacts than with conventional CTG, making eCTG also interesting for home monitoring. As remote eCTG is self-administered, it eliminates the need for professional support at home, potentially increasing autonomy and satisfaction. Nevertheless, signal quality of self-administered eCTG in the preterm period is unknown. This prospective cohort study will assess the feasibility of self-administered eCTG, using NemoRemote in a hospital setting between 32–37 weeks of gestation, simulating home use. Methods In this single center prospective cohort study, 60 pregnant patients (≥18 years) with a singleton high-risk pregnancy from 32 until 37 weeks of gestational age will be recruited. Remote self-administered eCTG will be performed either daily for patients admitted to the hospital, or at least twice a week at the outpatient clinic. The primary outcome is the proportion of successful eCTG recordings. Secondary outcomes include maternal and perinatal outcomes, patient and healthcare professional satisfaction, and a cost analysis. Conclusion This study will provide valuable insights into the technical and logistical feasibility of self-administered eCTG monitoring in high-risk pregnancies, with potential implications for the implementation of remote fetal monitoring. A hospital-based feasibility study was chosen over an implementation study to safely assess signal quality of remote eCTG before broader application. Trial registration This trial is registered on the Dutch trial register “Onderzoeksportaal” (NO. NL-OMON57084) https://onderzoekmetmensen.nl/en/trial/57084 . Date registered: 05/11/2024 and the international trial register “ClinicalTrials.gov” (NO. NCT06859177); http://clinicaltrials.gov/study/NCT06859177 . Date registered: 05/03/2025