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Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries.

Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample’s relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

In this paper, the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is adopted to monitor the Line of Sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in northeast China using Sentinel-1 Synthetic Aperture Radar (SAR) images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross wavelet transform with monthly precipitation data are also conducted, to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding during 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in vicinity of the Liushan paleochannel. The results of this paper have fully taken in account for the complications of large topographic relief, geological conditions, spatial distribution and temporal evolution characteristics of surface displacements in opencast mining area. The wide range and long time series dynamic monitoring of opencast mine are of great significance to ensure mine safety production and geological disaster prevention in the investigated mining area. [ABSTRACT FROM AUTHOR]

With the increasing demand for decentralized systems and the widespread usage of blockchain, low throughput and high latency have become the biggest stumbling blocks in the development of blockchain systems. This problem seriously hinders the expansion of blockchain and its application in production. Most existing smart contract scheduling solutions use static feature analysis to prevent contract conflicts during parallel execution. However, the conflicts between transactions are complex; static feature analysis is not accurate enough. In this paper, we first build the dependency between smart contracts by analyzing the features. After numerous experiments, we propose a conflict model to adjust the relationship between threads and conflict to achieve high throughput and low latency. Based on these works, we propose adaptive parallel scheduling for smart contracts on the blockchain. Our adaptive parallel scheduling can distinguish conflicts between smart contracts and dynamically adjust the execution strategy of smart contracts based on the conflict factors we define. We implement our scheme on ChainMaker, one of the most popular open-source permissioned blockchains, and build experiments to verify our solution. Regarding latency, our solution demonstrates remarkable efficiency compared with the fully parallel scheme, particularly in high-conflict transaction scenarios, where our solution achieves latency levels just one-twentieth of the fully parallel scheme. Regarding throughput, our solution significantly outperforms the fully parallel scheme, achieving 30 times higher throughput in high-conflict transaction scenarios. These results highlight the superior performance and effectiveness of our solution in addressing latency and throughput challenges, particularly in environments with high transaction conflicts. [ABSTRACT FROM AUTHOR]

Objectives: Investigate the biomechanical characteristics in tracheostomized patients with aspiration following acquired brain injury (ABI) and further explore the relationship between the biomechanical characteristics and aspiration. Methods: This is a single-center cross-sectional study. The tracheostomized patients with aspiration following ABI and age-matched healthy controls were recruited. The biomechanical characteristics, including velopharynx (VP) maximal pressure, tongue base (TB) maximal pressure, upper esophageal sphincter (UES) residual pressure, UES relaxation duration, and subglottic pressure, were examined by high-resolution manometry and computational fluid dynamics simulation analysis. The penetration–aspiration scale (PAS) score was evaluated by a videofluoroscopic swallowing study. Results: Fifteen healthy subjects and fifteen tracheostomized patients with aspiration following ABI were included. The decreased VP maximal pressure, increased UES residual pressure, and shortened UES relaxation duration were found in the patient group compared with the control group (p < 0.05). Furthermore, the subglottic pressure significantly decreased in patients (p < 0.05), while no significant difference was found in TB maximal pressure between groups (p > 0.05). In addition, in the patient group, VP maximal pressure (rs = −0.439; p = 0.015), UES relaxation duration (rs = −0.532; p = 0.002), and the subglottic pressure (rs = −0.775; p < 0.001) were negatively correlated with the PAS score, while UES residual pressure (rs = 0.807; p < 0.001) was positively correlated with the PAS score (p < 0.05), the correlation between TB maximal pressure and PAS score (rs = −0.315; p = 0.090) did not reach statistical significance. Conclusions: The biomechanical characteristics in tracheostomized patients with aspiration following ABI might manifest as decreased VP maximal pressure and subglottic pressure, increased UES residual pressure, and shortened UES relaxation duration, in which VP maximal pressure, UES relaxation duration, subglottic pressure, and UES residual pressure were correlated with aspiration.

Stroke incidence is the most severe complication associated with atrial fibrillation (AF), and the most common site of thrombus formation in AF patients is the left atrial appendage (LAA). This study was developed to use two-dimensional speckle tracking imaging (2D-STI) to explore associations between LAA strain/strain rate and stroke incidence and to evaluate the value of utilizing LAA strain and strain rate values to support the stratification of nonvalvular AF (NVAF) patients based on stroke risk. A total of 486 AF patients who had undergone transesophageal echocardiography to exclude potential intracardiac thrombosis between March 2021 and November 2022 were consecutively enrolled. Patients meeting the inclusion criteria were separated into two groups according to their history of stroke/transient ischemic attack (TIA). LAA strain and strain rate values in these patients were measured via 2D-STI. Multivariable logistic regression analysis was employed to determine independent risk factors for the construction of a combined predictive model. Of the 333 analyzed patients (134 females, aged 65 (56,72) years), 39 (11.71%, 39/333) had a history of stroke at the time of evaluation. Multivariate logistic regression analysis demonstrated that nonparoxysmal AF, CHA2DS2VASc score, LAA thrombus/spontaneous echo contrast (SEC), LAA strain, and strain rate were all predictors of stroke incidence among NVAF patients. The combined predictive model demonstrated excellent discriminative ability, with an AUC of 0.91 (95%CI 0.87–0.95, P < 0.001), and a sensitivity and specificity of 79.49% and 89.46%, respectively. The Hosmer-Lemeshow test confirmed good calibration, yielding a value of 0.98. Comparative decision curve analysis showed that the model provided superior net benefits compared to the CHA2DS2VASc score. Furthermore, the model exhibited improved predictive performance and reclassification for stroke when compared to the CHA2DS2VASc score (AUC 0.91 vs. 0.88, Z = 2.32, P = 0.02), accompanied by a significant increase in the net reclassification index (+ 5.44%, P < 0.001) and integrated discrimination improvement (8.21%, P < 0.001). These data demonstrate that LAA strain and strain rate, as measured via 2D-STI, can offer value when assessing LAA function in AF patients, potentially providing further predictive value to extant clinical risk scoring strategies. [ABSTRACT FROM AUTHOR]

In the theory of traditional Chinese medicine, acupoints refer to special points and areas on the meridian line of the human body. Traditional Chinese medicine believes that the application of unique techniques such as pressing, kneading, rubbing, pushing, and patting to acupoints or massage with the help of specific tools has the effects of promoting blood circulation, dredging meridians, and eliminating fatigue. At present, most automatic massage devices are for large-area massage of the trunk, and few are specifically for acupoint massage of the limbs. First, this paper analyzes the characteristics of traditional Chinese medical acupoint massage and then obtains the design index of an automatic acupoint massage device. After that, based on the principle of a series elastic actuating mechanism, a flexible uni-acupoint massage device and control system, imitating the acupoint massage technique of traditional Chinese medicine, were designed. In order to analyze the massage force of the massage device, the man–machine contact dynamic model of the massage device was established, and the force of the massage device was simulated and analyzed. Finally, an experimental platform was built to verify the massage force and massage process of the massage device. The experimental results show that the massage device designed in this paper meets the indexes of traditional Chinese medical massage, in terms of the massage process and massage force, and verify the rationality of the design.

The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that DeepagriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries. [ABSTRACT FROM AUTHOR]

We aimed to study the regulatory roles and mechanism of circular nuclear factor IX (circNFIX) in cancer growth and stemness properties of ovarian cancer (OC). CircNFIX and SH3RF3 levels in OC tissues and cells were tested by quantitative real‐time PCR. RNase R treatment quantified circNFIX RNA stability. Molecular interaction among circNFIX, LIN28B, and SH3RF3 was predicted by bioinformatics software and validated through RNA immunoprecipitation (RIP) assay. The gain‐ or loss‐experiments of circNFIX on capabilities of metastasis and stemness in vitro were assessed using Cell Counting Kit‐8, Transwell, western blot, and sphere‐formation assays. CircNFIX and SH3RF3 were markedly elevated in OC tissues and OC cells. Knocking down circNFIX repressed the proliferation, migration, invasion, and stemness properties of A2780 and SKOV3 cells. The RIP assay verified the direct binding relationship between LIN28B, circNFIX, and SH3RF3. Additionally, overexpression of circNFIX elevated the SH3RF3 expression, while this effect was reversed by LIN28B silence. Rescue experiments demonstrated that the overexpression of SH3RF3 reversed the knockdown of circNFIX on OC cells' proliferation, metastasis, and stemness properties. CircNFIX improved the mRNA stability and translation of SH3RF3 via recruiting LIN28B, thus promoting the proliferation, invasion, and stemness properties of OC cells in vitro. [ABSTRACT FROM AUTHOR]

Southern California, USA, has been suffering severe surface deformation due to its active crustal movement under the north–south compression of the Pacific Plate and North American Plate. Meanwhile, affected by groundwater extraction and recharge, oil exploitation, surface subsidence, uplift, and seasonal deformation occur commonly in this region. In this paper, multi-path SAR datasets were collected to investigate and monitor surface deformation in Southern California. The unified simultaneous least squares (USLS) approach is applied to remove the deformation discontinuity between adjacent SAR image paths. Multiple deformation patterns of structural faults, groundwater withdrawal, and oil exploitation are observed with the interferometric synthetic aperture radar (InSAR) technique. The InSAR-derived results were validated with GPS monitoring data. The correlations between land deformation and groundwater withdrawal, faults, and precipitation were intensively analyzed, finding out and mastering the magnitude and characteristics of ground deformation in Southern California. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is a gynecological cancer with high mortality. OC‐derived exosomal circRNAs can regulate angiogenesis. This study aims to explore the role and mechanism of exosomal circRNA nuclear factor I X (CircNFIX) derived from OC cells in angiogenesis. Quantitative real‐time polymerase chain reaction was employed to evaluate the levels of circNFIX, miR‐518a‐3p, and tripartite motif protein 44 (TRIM44) in OC and adjacent tissues. Exosomes from the ovarian surface epithelial cell (HOSEpiC) and OC cells (SKOV3 or OVCAR3) were isolated by differential centrifugation. Exosomes were cocultured with the human umbilical vein endothelial cells (HUVECs). The angiogenesis capacity was analyzed by Tube formation assay. 3‐(4,5‐dimethyl‐2‐thiazolyl)‐2,5‐diphenyl‐2‐H‐tetrazolium bromide (MTT) and Transwell assays were used to determine the cell viability and migration ability. The dual‐luciferase report, RNA immunoprecipitation (RIP), and RNA pull‐down assays were applied to validate the gene's interaction. CircNFIX and TRIM44 expression were higher and miR‐518a‐3p was lower in OC tissues than in the adjacent tissues. Upregulated circNFIX and TRIM44 were significantly correlated with the tumor size and International Federation of Gynecology and Obstetrics (FIGO) stage of OC patients. HUVECs treated OC‐derived exosomes had higher proliferation, migration, and angiogenesis capacities than the control group. While OC‐derived exosomal circNFIX silencing restrained HUVECs' proliferation, migration, and angiogenesis, compared with the OC‐derived exosomes group. OC‐derived exosomal circNFIX positively regulated TRIM44 expression by targeting miR‐518a‐3p in HUVECs. OC‐derived exosomal circNFIX promoted angiogenesis by regulating the Janus‐activated kinase/signal transducer and activator of transcription 1 (JAK/STAT1) pathway via miR‐518a‐3p/TRIM44 axis in HUVECs. [ABSTRACT FROM AUTHOR]