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Currently, underground coal mine target detection algorithms based on deep learning show poor performance in detecting complex small targets under conditions of uneven light intensity distribution, complex target environments, and imbalanced multi-class target scale distribution, often resulting in missed detection and false detection. To address these issues, based on the single-stage target detection algorithm YOLOv8n, this study proposed an underground coal mine multi-target detection algorithm based on feature extraction by dynamic snake convolution (FEDSC)-feature fusion by bi-directional feature pyramid network and semantic and detail fusion (FFBD). FEDSC replaced the backbone network of YOLOv8n to expand the receptive field, while FFBD acted as the neck network to reduce target false detection and missed detection. Additionally, a decoupling detection head of SIoU was used as the detection layer to improve the model's adaptability to small targets and the convergence speed. The results showed that: ① The mAP@0.5 of the FEDSC-FFBD algorithm was 97.00%, the number of model parameters was 4.22×106, and the number of floating point operations per second was 21.7×109. ② The mAP@0.5 of the FEDSC-FFBD alorithm was 3.40% higher than the YOLOv8n algorithm, and the recognition accuracy of the helmet small target was 90.90%, 11% higher than the YOLOv8n algorithm. ③ Compared with other YOLO series algorithms, the FEDSC-FFBD algorithm achieved the highest mAP@0.5, which was 3.60%, 1%, 10.50%, and 6.40% higher than YOLOv5s, YOLOv9c, YOLOv10n, and YOLOv11n algorithms, respectively. ④ The FEDSC-FFBD algorithm improved the detection accuracy of multi-class targets and reduced missed detection and false detection of small targets under conditions of uneven light intensity distribution, complex target environments, and imbalanced target scale distribution in underground coal mine. The underground coal mine multi-target detection algorithm based on FEDSC-FFBD overcame the challenge of small-scale target detection caused by uneven light intensity distribution without relying on image quality enhancement algorithms.

BACKGROUND: Black-White disparities in heart disease treatment may be attributable to differences in physician referral networks. We mapped physician networks for Medicare patients and examined within-physician Black-White differences in patient sharing between primary care physicians and cardiologists. METHODS AND RESULTS: Using Medicare fee-for-service files for 2016 to 2017, we identified a cohort of Black and White patients with heart disease and the primary care physicians and cardiologists treating them. To ensure the robustness of within-physician comparisons, we restricted the sample to regional health care markets (ie, hospital referral regions) with at least 10 physicians sharing ≥3 Black and White patients. We used claims to construct 2 race-specific physician network measures: degree (number of cardiologists with whom a primary care physician shares patients) and transitivity (network tightness). Measures were adjusted for Black-White differences in physician panel size and calculated for all settings (hospital and office) and for office settings only. Of 306 US hospital referral regions, 226 and 145 met study criteria for all settings and office setting analyses, respectively. Black patients had more cardiology encounters overall (6.9 versus 6.6; P

The occurrence of sarcopenia is closely related to chronic kidney disease. In recent years, the incidence rate of sarcopenia in chronic kidney disease patients has increased significantly, affecting the prognosis of the disease. In recent years, studies have found that the occurrence of sarcopenia in chronic kidney disease is related to factors such as chronic inflammation, oxidative stress, mitochondrial dysfunction, abnormal hormone levels, insulin resistance, excessive angiotensin Ⅱ, vitamin D deficiency, hemodialysis, and movement disorders. This article provides a review of its pathogenesis.

Verbal praise is frequently used as motivation by teachers in class and truly effective verbal praise plays an important role in maintaining classroom order and stimulating positive student behavior. In this study, students from three classes of X Primary School of Jiangxi Province were interviewed and 328 verbal praise expressions collected. The subsequent empirical study on the effectiveness of verbal praise in four dimensions, namely content, spatiality, subjectivity and time, found that verbal praise generates both shallow and deep effects. The shallow effects act as a foundation for deep effects but do not necessarily result in deep effects. Other conditions are required to actualize a deep effect.

The univariate non-stationary growth model (UNGM) is widely used in the verification of nonlinear filters, and the unscented Kalman filter (UKF) is often used as the reference filter for comparative analysis when using this model to evaluate the filter performance. However, due to the strong nonlinearity of UNGM and the change of model properties with different parameter settings, the estimation misalignment problem due to different reasons will occur when UKF is used for filtering. To solve these problems, this paper analyzes the complex characteristics of UNGM in filtering process, and proposes an UKF with sliding sampling module(SSUKF). The algorithm is optimized on the basis of UKF, and can effectively deal with the complex characteristics of UNGM by sampling and analyzing the filtering information in the filtering process and correcting the distribution of Sigma points in real time. SSUKF is applied to UNGM under different parameters and compared with UKF and bootstrap particle filter(BPF). The simulation results show that SSUKF can effectively solve the misalignment problem when UKF is applied to UNGM, and the calculation speed is better than BPF. Compared with UKF, SSUKF is suitable as a benchmark filter for evaluating the performance of nonlinear filters using UNGM.

Feline chaphamaparvovirus (FeChPV) is a novel parvovirus previously reported in Canadian cats and Chinese dogs with diarrhea in 2019 and 2020, respectively. Herein, we aimed to establish a simple detection method for FeChPV in field clinics. The primers and probes for the multienzyme isothermal rapid amplification and lateral flow dipstick (MIRA-LFD) assay were designed to target the conserved regions of the FeChPV genome and determine the optimal reaction temperature and time. Without relying on precision instruments, FeChPV detection using the MIRA-LFD assay was completed within 20 min at 37°C, without any cross-reaction with other reference viruses. The newly established MIRA-LFD assay had a detection limit of 32.3 copies/μL, which was 10-fold lower than that of the nested polymerase chain reaction (PCR) assay. Furthermore, the MIRA-LFD assay detected 29 FeChPV-positive samples among 417 cats with diarrhea, providing a slightly higher positivity rate than the nested PCR assay. These results indicate that the newly developed MIRA-LFD assay for FeChPV detection is an efficient, economical, reliable, and simple method that can help in the early prevention and control of FeChPV infection. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. Existing prediction methods focus on dynamic modeling of the spatiotemporal dependencies of traffic flow, capturing the periodicity and spatial heterogeneity in traffic data. However, they still suffer from a lack of focus on the important local information in long-term predictions, leading to overly smooth results that fail to effectively capture sudden changes in traffic patterns. To address these limitations, we propose the sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network (sAMDGCN) model. Specifically, we extend sLSTM and introduce temporal trend-aware multi-head attention to jointly capture the complex temporal dependencies. We propose a multi-head dynamic graph convolutional network to capture a wider range of dynamic spatial dependencies. To validate the effectiveness of sAMDGCN, we perform extensive experiments on four real-world traffic flow datasets. Experimental results show that our proposed sAMDGCN model outperforms the advanced baseline methods in long-term traffic flow prediction tasks, demonstrating its superior performance in capturing complex and dynamic traffic patterns. [ABSTRACT FROM AUTHOR]

With the development of system-on-chip (SoC) and chiplet technology in the post-Moore era, an increasing number of chiplets are being integrated into a single chip. Consequently, the functions and complexity that can be realized are growing daily. Simultaneously, the volume of test data required for ultra-large-scale integrated circuits (ULSIs) has risen significantly. However, traditional automatic test equipment (ATE) is constrained by its data storage and bandwidth limitations, and its long technology iteration cycle. These cannot keep pace with the rapid development of design technology. This discrepancy leads to challenges in ULSI testing, such as excessively long test time and difficulties in completing the tests. Test compression technology can effectively address these issues by reducing the performance requirements of the test equipment, which in turn can lower test costs. This paper summarizes the classifications of chip test compression technology and, based on their current development, provides a detailed analysis of key technologies. It includes test compression-oriented coding methods, optimization of scan chain structures, and enhancements in coding for compression efficiency. Finally, a forward-looking perspective on the development of chip test compression technology is presented. The aim is to offer a reference for subsequent research in this field and related areas, as well as to provide technical support for the advancement of ULSI testing in the post-Moore era. [ABSTRACT FROM AUTHOR]

Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal's complexity and anti-jamming capability. Furthermore, a method for optimizing the processing of echo signals based on de-chirp and delay matching is proposed. The locally generated LFM signal is used to de-chirp the received echoes, resulting in a narrowband difference frequency noise signal. Subsequently, delay matching is performed in the fast time domain using the locally generated NFM signal according to the number of sampling points in the traversal processing period, allowing for the acquisition of target delay information. While reducing the analog-to-digital (A/D) sampling rate, the detection performance for wideband echo signals under high sampling rates is still maintained, with sidelobe levels and range resolution preserved. Accumulating this information in the slow time domain enables accurate target detection. The effectiveness of the proposed method is validated through simulation experiments. [ABSTRACT FROM AUTHOR]

Canine parvovirus type-2 (CPV-2), the primary causative agent of serious canine enteric diseases, is highly contagious and associated with high fatality rates worldwide. To comprehend the current emergence of CPV-2 in central and eastern China, 130 rectal swabs from domestic or stray dogs with gastroenteritis symptoms were collected during 2020–2023. A total of 118 positive samples were detected via polymerase chain reaction, and further used to amplify and sequence the VP2 gene. Sequence analysis of the deduced amino acids of VP2 protein indicated that CPV-2c was the most prevalent variant (n = 106, 89.83%), followed by the novel CPV-2a (n = 10, 8.47%) and CPV-2b (n = 2, 1.69%) variants. The VP2 protein from the obtained and reference strains showed 86.95% (AH2103 and HB2108) to 99.94% identity. Based on the nine predicted recombination events, some prevalent CPV-2c strains were highly similar to previously isolated strains, indicating their complex evolution and recombination. The predicted analysis suggested that mutations in the antigen epitope (Val219Ile, Phe267Tyr, and Asn426Glu) and other mutations (Met87Leu, Ile101Thr, and Ser297Ala) affect the tertiary structure of the VP2 protein. This research will help us understand the recent evolution and mutation of Chinese CPV-2 and provide suggestions for updating the CPV-2 vaccine. [ABSTRACT FROM AUTHOR]

Detecting the quantity and diversity of diatoms is of great significance in areas such as climate change, water quality assessment, and oil exploration. Here, an efficient and accurate object detection model, named SC-DiatomNet, is proposed for diatom detection in complex environments. This model is based on the YOLOv3 architecture and uses the K-means++ algorithm for anchor box clustering on the diatom dataset. A convolutional block attention module is incorporated in the feature extraction network to enhance the model's ability to recognize important regions. A spatial pyramid pooling module and adaptive anchor boxes are added to the encoder to improve detection accuracy for diatoms of different sizes. Experimental results show that SC-DiatomNet can successfully detect and classify diatoms accurately without reducing detection speed. The recall, precision, and F1 score were 94.96%, 94.21%, and 0.94, respectively. It further improved the mean average precision (mAP) of YOLOv3 by 9.52% on the diatom dataset. Meanwhile, the detection accuracy was improved compared with those of other advanced deep learning algorithms. SC-DiatomNet has potential applications in water quality analysis and monitoring of harmful algal blooms. [ABSTRACT FROM AUTHOR]

Purpose: The current investigation is to assess FOXP3 expression in breast cancer patients and evaluate the predictive significance of FOXP3. Patients and Methods: A cohort of 313 cases between January 2015 and November 2015 were enrolled this research. Immunohistochemistry (IHC) assay was utilized to detect the expression levels of FOXP3 in primary breast carcinoma specimens. These patients were separated into two groups by semiquantitative scoring approach. Chi-square test and Fisher's exact test were conducted to investigate the correlations between FOXP3 expression in tumors and clinicopathological variables. Kaplan–Meier method and Log rank test were utilized to generate survival curves for disease-free survival (DFS) and overall survival (OS). The independent factors were examined using Cox regression analysis. Nomogram models were created for assessing DFS and OS rates. Results: Depending on the levels of FOXP3 expression in tumors, these patients were categorized into two groups: low FOXP3 expression (174 cases) and high FOXP3 expression (139 cases). The patients exhibiting low levels of FOXP3 expression in tumors demonstrated a longer survival duration contrasted with those with high expression (DFS: 88.75 vs 65.87 months, χ2=36.1100, P< 0.0001; OS: 89.70 vs 78.37 months, χ2=32.4900, P< 0.0001). Multivariate analysis revealed that FOXP3 was a significant prognostic factor [DFS: hazard ratio (HR): 2.822, 95% CI: 1.595– 4.992, P< 0.0001; OS: HR: 3.232, 95% CI: 1.812– 5.763, P< 0.0001]. The good predictive clinical utility of FOXP3-based nomograms within the threshold probability range for different survival rates was demonstrated by calibration curve and decision curve analyses. Conclusion: FOXP3 expression serves as a crucial prognostic indicator in breast cancer patients, and may aid preoperative evaluation in clinical practice. [ABSTRACT FROM AUTHOR]