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Automated cause classification of fire accident reports (FIREAR) is crucial for enhancing public safety and developing data-driven prevention strategies. However, existing deep learning models often struggle with the unique challenges these documents present—namely their extreme length, high semantic noise, and fragmented causal information. To overcome these limitations, we propose the Fire Accident Reports Attention Mechanism (FAR-AM), a novel hybrid deep learning framework. FAR-AM first uses a large language model (LLM) to preprocess lengthy raw reports into concise, high-signal summaries. Its core architecture then employs an inter-layer self-attention mechanism to dynamically fuse hierarchical features across all encoder layers of BERT. The fused features are subsequently processed by a TextCNN for final classification. We evaluate FAR-AM on AGNews(title), AGNews(content), THUCNews, and our real-world FIREAR corpus. FAR-AM outperforms strong transformer baselines, including RoBERTa. On the FIREAR dataset, it achieves 73.58% accuracy and 70.65% F1. A comprehensive ablation study further validates the contribution of each component in the multi-stage framework. These results indicate that, for complex domain-specific tasks, specialized hybrid architectures can be more effective and robust than monolithic, general-purpose models.

Cardiovascular responses to diving are characterized by two opposing responses: tachycardia resulting from exercise and bradycardia resulting from the apnea. The convergence of bradycardia and tachycardia may determine the cardiovascular responses to diving. The purpose of this study was to investigate the interaction of breath holding and muscle mechanoreflex on cardiovascular responses in breath-hold divers (BHDs) and non-BHDs. We compared the cardiovascular responses to combined apnea and the mechanoreflex in BHDs and non-BHDs. All participants undertook three trials—apnea, passive leg cycling (PLC), and combined trials—for 30 s after rest. Cardiovascular variables were measured continuously. Nine BHD (male:female, 4:5; [means ± SD] age, 35 ± 6 years; height, 168.6 ± 4.6 cm; body mass, 58.4 ± 5.9 kg) and eight non-BHD (male:female, 4:4; [means ± SD] age, 35 ± 7 years; height, 163.9 ± 9.1 cm; body mass, 55.6 ± 7.2 kg) participants were included. Compared to the resting baseline, heart rate (HR) and cardiac output (CO) significantly decreased during the combined trial in the BHD group, while they significantly increased during the combined trials in the non-BHD group (P < 0.05). Changes in the HR and CO were significantly lower in the BHD group than in the non-BHD group in the combined trial (P < 0.05). These results suggest that bradycardia with apnea in BHDs is prioritized over tachycardia with the mechanoreflex, whereas that in non-BHDs is not. This finding implies that diving training changes the interaction between apnea and the mechanoreflex in cardiovascular control.

The potential role of hydrogen sulfide (H 2 S) in the modulation of neuropathic pain is increasingly recognized. This study investigated the therapeutic effect of intraperitoneal injection of the H 2 S donor sodium hydrosulfide (NaHS) on neuropathic pain. Utilizing the spared nerve injury (SNI) model in mice, the research investigates the role of astrocytes and the excitatory neurotransmitter glutamate in chronic pain. The findings reveal that sodium hydrosulfide (NaHS), an H 2 S donor, effectively enhances the mechanical pain threshold and thermal pain escape latency in SNI mice. The study further demonstrates NaHS’s potential in reducing glutamate levels in the spinal cord and the discharge frequency of neurons in the primary somatosensory cortex hindlimb region (S1HL) brain area, suggesting a novel therapeutic approach for neuropathic pain through the modulation of astrocyte function and EAAT2 expression.

This paper addresses the trajectory tracking and obstacle avoidance control problems for a class of mobile robot systems. Two classes of controllers are designed for the mobile robot system in the free motion, respectively. A new hybrid position virtual-force controller is designed to adjust the distance between the mobile robot and the obstacles. Since the uncertainties between the mobile robot dynamics model and obstacles degrade the performance of the obstacle avoidance system, a multi-layer feed-forward neural networks (NNs) deep learning method with hybrid position and virtual-force is proposed, such that the distance between the mobile robot and the obstacles converges to an adjustable bounded region. It is shown that the proposed controller in this paper is smooth, effective, and only uses the system output. The control design conditions are relaxed because of the developed multi-layer feed-forward NNs deep learning compensator. The simulation results and obstacle avoidance cases are performed to show the effectiveness of the proposed method.

A class of variable selection procedures for parametric models via non-concave penalized likelihood was proposed by Fan and Li to simultaneously estimate parameters and select important variables. They demonstrated that this class of procedures has an oracle property when the number of parameters is finite. However, in most model selection problems the number of parameters should be large and grow with the sample size. In this paper some asymptotic properties of the nonconcave penalized likelihood are established for situations in which the number of parameters tends to ∞ as the sample size increases. Under regularity conditions we have established an oracle property and the asymptotic normality of the penalized likelihood estimators. Furthermore, the consistency of the sandwich formula of the covariance matrix is demonstrated. Nonconcave penalized likelihood ratio statistics are discussed, and their asymptotic distributions under the null hypothesis are obtained by imposing some mild conditions on the penalty functions. The asymptotic results are augmented by a simulation study, and the newly developed methodology is illustrated by an analysis of a court case on the sexual discrimination of salary.

PurposeAlthough the muscle mechanoreflex is an important mediator to cardiovascular regulation during exercise, its modulation factors remain relatively unknown. Therefore, the purpose of this study was to investigate the effect of muscle stiffness on the muscle mechanoreflex.MethodsParticipants were divided based on their median muscle stiffness (2.00 Nm/mm) into a low group (n = 15) and a high group (n = 15), and the muscle mechanoreflex was compared between the groups. After a 15-min rest in the supine position, heart rate (HR), blood pressure (BP), stroke volume (SV), and cardiac output (CO) were measured at rest for 3 min and during static passive dorsiflexion (SPD) at 20° for 1 min. Following a 15-min re-rest, muscle stiffness and passive resistive torque were evaluated in the distal end of the muscle belly of the medial gastrocnemius.ResultsPeak relative changes in HR (low group: 6 ± 4% and high group: 12 ± 4%) and CO (low group: 8 ± 10% and high group: 13 ± 9%) were greater in the high group than in the low group (both, P < 0.05). A significant positive correlation was found between resistive torque during SPD and muscle stiffness and peak relative changes in HR (r = 0.51 and 0.61, both P < 0.05). However, there was no correlation between muscle elongation during SPD and peak relative changes in HR (r = − 0.23, P = 0.20).ConclusionThese findings suggest that muscle stiffness may be modulatory factor of muscle mechanoreflex.

Beam-switching technology is critical for fifth-generation (5G) Frequency Range 1 (FR1) base stations, yet existing odd-number Butler matrix designs often struggle to achieve compact size, low complexity, and efficient performance. Although a few studies have investigated 5 × 5 Butler matrices, their reliance on waveguide structures or multilayer implementations leads to larger footprints and greater fabrication complexity. This work introduces a novel 5 × 5 Butler matrix integrated with a five-element dipole antenna array for 3.3–3.7 GHz applications, offering notable improvements in beam-switching efficiency and overall system design. The proposed matrix generates five distinct beams at −144°, −72°, 0°, 72°, and 144° by employing precise phase progression, while eliminating crossovers and power dividers to simplify the layout. With a compact footprint of 2.67 × 0.80 × 0.02 cubic wavelength—94.4% smaller than waveguide-based designs—the matrix achieves a bandwidth of 3.32–3.62 GHz and consistently covers the target beams. The integrated system attains measured gains up to 11.4 dBi and half-power beamwidths ranging from 7.96° to 23.66°, with sidelobe levels comparable to those of state-of-the-art configurations. By employing a low-loss substrate, the gain can be further enhanced by as much as 6.81 dB, highlighting the potential for additional performance gains. These innovations establish the proposed design as a compact, low-complexity, and high-performance solution for 5G base station applications.

Estimation and hypothesis test for partial linear single-index multiplicative models are considered in this paper. To estimate unknown single-index parameter, we propose a profile least product relative error estimator coupled with a leave-one-component-out method. To test a hypothesis on the parametric components, a Wald-type test statistic is proposed. We employ the smoothly clipped absolute deviation penalty to select relevant variables. To study model checking problem, we propose a variant of the integrated conditional moment test statistic by using linear projection weighting function, and we also suggest a bootstrap procedure for calculating critical values. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real example is analyzed for illustration.

Background Rare diseases, though individually uncommon, collectively affect a significant portion of the population. However, their epidemiology in China remains underexplored. A population-based rare disease registry comprising 14.31 million individuals was conducted between 2012 and 2023 by the Beijing Municipal Health Big Data and Policy Research Center. Rare disease cases were identified via ICD-10 codes mapped to China’s national rare disease lists (2018 and 2023) and international databases. Age-standardized incidence rates (ASIR) were calculated per 100,000 person-years with 95% confidence intervals. Results Our analysis identified 12,371 rare disease cases, with the overall ASIR increasing from 6.109 in 2012 to 7.394 in 2023. Rare neurologic diseases accounted for 52.12% of cases, followed by systemic and rheumatologic diseases (16.89%) and rare neoplastic diseases (9.99%). The most frequently diagnosed rare diseases included generalized myasthenia gravis, ANCA-associated vasculitis, and malignant melanoma. Significant sex-based differences were observed, with female patients more affected by systemic and rheumatologic conditions, while male patients showed a higher incidence of respiratory disorders. Pediatric patients predominantly presented with inborn errors of metabolism and rare immune diseases. Comparisons with global data revealed notable disparities, such as a higher prevalence of Wilson’s disease and a lower incidence of amyotrophic lateral sclerosis (ALS) in China. Conclusions This study represents the first large-scale, population-based analysis of rare diseases in China, revealing distinct epidemiological patterns. These findings underscore the critical need for healthcare policies that address the unique challenges posed by rare diseases in China.

Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for finding a sparse semivarying coefficient model, which is widely accepted for longitudinal data analysis. Our proposed method first reduces the number of covariates to a moderate order by employing a screening procedure, and then identifies both the varying and constant coefficients using a group SCAD estimator, which is subsequently refined by accounting for the within-subject correlation. The screening procedure is based on working independence and B-spline marginal models. Under weaker conditions than those in the literature, we show that with high probability only irrelevant variables will be screened out, and the number of selected variables can be bounded by a moderate order. This allows the desirable sparsity and oracle properties of the subsequent structure identification step. Note that existing methods require some kind of iterative screening in order to achieve this, thus they demand heavy computational effort and consistency is not guaranteed. The refined semivarying coefficient model employs profile least squares, local linear smoothing and nonparametric covariance estimation, and is semiparametric efficient. We also suggest ways to implement the proposed methods, and to select the tuning parameters. An extensive simulation study is summarized to demonstrate its finite sample performance and the yeast cell cycle data is analyzed.