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Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Shijiazhuang, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

Anthropometric parameters are widely used in the clinical assessment of hypertension, type 2 diabetes, and cardiovascular disease. However, few studies have compared the association between different anthropometric parameters and insulin resistance (IR). This study was aimed at investigating the relationship between 6 indicators, including body mass index (BMI), calf circumference (CC), arm circumference (AC), thigh circumference (TC), waist circumference (WC), waist-height ratio (WHtR), and IR. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was used to measure IR. Weighted linear regression was used to assess the relationship between different parameters and IR. The receiver operating characteristic curve (ROC) was employed to compare the strength of the relationship between different anthropometric parameters and IR. A total of 8069 participants were enrolled in our study, including 4873 without IR and 3196 with IR. The weighted linear regression results showed that BMI, CC, AC, TC and WC were significantly correlated with IR, except WHtR. After adjusting for multiple confounding factors, we found that BMI, AC and WC were significantly positively correlated with IR, while TC was significantly negatively correlated with IR. Logistic regression results showed that a larger TC was associated with a decreased risk of IR. In addition, BMI and WC had similar areas under the curve (AUC: 0.780, 95% CI 0.770–0.790; AUC: 0.774, 95% CI 0.763–0.784, respectively), which were higher than TC and AC (AUC: 0.698, 95% CI 0.687–0.710, AUC: 0.746, 95% CI 0.735–0.757, respectively). To our knowledge, this is the first study to report a negative correlation between TC and IR among patients without diabetes mellitus. Therefore, TC may be a new tool to guide public health and a clinical predictor of IR in non-diabetic patients.

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network’s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.

Multiple autonomous underwater vehicles (AUVs) have gradually become the trend in underwater operations. Identifying and detecting these new underwater multi-targets is difficult when studying underwater moving targets. A 28-element transducer is used to test the echo of multiple AUVs with different layouts in a lake. The characteristics of the wideband echo signals are studied. Under the condition that the direction of arrival (DOA) is not known, an autofocus coherent signal subspace (ACCSM) method is proposed. The focusing matrix is constructed based on the received data. The spatial spectrum of the array signal of multiple AUVs at different attitudes is calculated. The algorithm estimates the DOA of the echo signals to overcome the shortcomings of traditional wideband DOA estimation and improve its accuracy. The results show that the highlights are not only related to the number of AUVs, but are also modified by scale and attitude. The contribution of the microstructure of the target in the overall echo cannot be ignored. Different parts of the target affect the number of highlights, thus resulting in varying numbers of highlights at different attitude angle intervals. The results have significant implications for underwater multi-target recognition.

Layered vanadium-based materials are considered to be great potential electrode materials for aqueous Zn-ion batteries (AZIBs). The improvement of the electrochemical properties of vanadium-based materials is a hot research topic but still a challenge. Herein, a composite of Zn-ion pre-intercalated V2O5·nH2O combined with reduced graphene oxide (ZnVOH/rGO) is synthesized by a facile hydrothermal method and it shows improved Zn-ion storage. ZnVOH/rGO delivers a capacity of 325 mAh·g−1 at 0.1 A·g−1, and this value can still reach 210 mAh·g−1 after 100 cycles. Additionally, it exhibits 196 mAh·g−1 and keeps 161 mAh·g−1 after 1200 cycles at 4 A·g−1. The achieved performances are much higher than that of ZnVOH and VOH. All results reveal that Zn2+ as “pillars” expands the interlayer distance of VOH and facilitates the fast kinetics, and rGO improves the electron flow. They both stabilize the structure and enhance efficient Zn2+ migration. All findings demonstrate ZnVOH/rGO’s potential as a perspective cathode material for AZIBs.

The literature has shown that mobile phone addiction is an important risk factor for depression. However, the internal mechanisms of mobile phone addiction leading to depression are still not clear. This study examined the mediating role of sleep quality and moderating role of peer relationships in the association between mobile phone addiction and depression. A sample of 450 Chinese medical students were recruited to complete measures of mobile phone addiction, depression, sleep quality and peer relationships. In this study, SPSS 25.0 and macro PROCESS were used to conduct statistical analysis on the collected data. The results showed that sleep quality partially mediated the association between mobile phone addiction and depression. Moreover, the effect of sleep quality on depression was moderated by peer relationships. The present study can advance our understanding of how and when mobile phone addiction leads to depression. Limitations and implications of this study are discussed.

Many approaches have been developed to analyze remote sensing images. However, for the classification of large-scale problems, most algorithms showed low computational efficiency and low accuracy. In this paper, the newly developed semi-supervised extreme learning machine (SS-ELM) framework with k-means clustering algorithm for image segmentation and co-training algorithm to enlarge the sample sets was used to classify the agricultural planting structure at large-scale areas. Data sets collected from a small-scale area within the Hetao Irrigation District (HID) at the upper reaches of the Yellow River basin were used to evaluate the SS-ELM framework. The results of the SS-ELM algorithm were compared with those of the random forest (RF), ELM, support vector machine (SVM) and semi-supervised support vector machine (S-SVM) algorithms. Then the SS-ELM algorithm was applied to analyze the complex planting structure of HID in 1986–2010 by comparing the remote sensing estimated results with the statistical data. In the small-scale case, the SS-ELM algorithm performed better than the RF, ELM, SVM, and S-SVM algorithms. For the SS-ELM algorithm, the average overall accuracy (OA) was in a range of 83.00–92.17%. On the contrary, for the other four algorithms, their average OA values ranged from 56.97% to 92.84%. Whereas, in the classification of planting structure in HID, the SS-ELM algorithm had an excellent performance in classification accuracy and computational efficiency for three major planting crops including maize, wheat, and sunflowers. The estimated areas by using the SS-ELM algorithm based on the remote sensing images were consistent with the statistical data, and their difference was within a range of 3–25%. This implied that the SS-ELM framework could be served as an effective method for the classification of complex planting structures with relatively fast training, good generalization, universal approximation capability, and reasonable learning accuracy.

Background: Inducing mesenchymal stem cells (MSCs) osteogenesis may be beneficial in a number of clinical applications. The aim of this study is to identify key novel biomarkers of this process and to analyze the possible regulatory effects on inflammation and immunity. Results: Seven datasets (GSE159137, GSE159138, GSE114117, GSE88865, GSE153829, GSE63754, GSE73087) were obtained from the Gene Expression Omnibus database and were assigned to either the training or the validation dataset. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied to the training data to select biomarkers of osteogenesis, which were then confirmed using the validation dataset. FK506 binding protein 5 (FKBP5), insulin-like growth factor binding protein (IGFBP2), prostaglandin E receptor 2 (PTGER2), SAM domain and HD domain-containing protein 1 (SAMHD1), and transmembrane tetratricopeptide 1 (TMTC1) were highlighted as potential biomarkers. In addition, the differential expressions of immunity and inflammation-related genes were examined and their correlations with the five identified biomarkers were analyzed. The results from performing RT-qPCR and Western blots confirmed that the levels of each of these biomarkers were all significantly increased following osteogenic differentiation of MSCs. Conclusions: Our results identify five biomarkers related to MSCs osteogenesis and allow us to identify their potential roles in immunoregulation and inflammation. Each biomarker was verified by in vitro experiments.

Polyploid rice, first discovered by Japanese scientist Eiiti Nakamori in 1933, has a history of nearly 90 years. In the following years, polyploid rice studies have mainly focused on innovations in breeding theory, induction technology and the creation of new germplasm, the analysis of agronomic traits and nutritional components, the study of gametophyte development and reproduction characteristics, DNA methylation modification and gene expression regulation, distant hybridization and utilization among subspecies, species and genomes. In recent years, PMeS lines and neo-tetraploid rice lines with stable high seed setting rate characteristics have been successively selected, breaking through the bottleneck of low seed setting rate of polyploid rice. Following, a series of theoretical and applied studies on high seed setting rate tetraploid rice were carried out. This has pushed research on polyploid rice to a new stage, opening new prospects for polyploid rice breeding.

Defect engineering is an effective strategy for modifying the energy storage materials to improve their electrochemical performance. However, the impact of oxygen defect and its content on the electrochemical performances in the burgeoning aqueous NH4+ storage field remains explored. Therefore, for the first time in this work, an oxygen-defective ammonium vanadate [(NH4)2V10O25·8H2O, denoted as Od-NHVO] with a novel 3D porous flower-like architecture was achieved via the reduction of thiourea in a mild reaction condition, which is a facile method that can realize the intention to regulate the oxygen defect content, with the capability of mass-production. The as-prepared OdM-NHVO with moderate oxygen defect content can deliver a stable specific capacitance output (505 F g−1, 252 mAh g−1 at 0.5 A g−1 with ∼80% capacitance retention after 10,000 cycles), which benefits from extra active sites, unimpeded NH4+-migration path and relatively high structure integrity. In contrast, low oxygen defect content will lead to the torpid electrochemical reaction kinetics while too high content of it will reduce the charge-storage capability and induce structural disintegration. The superior NH4+-storage behavior is achieved with the reversible intercalation/de-intercalation process of NH4+ accompanied by forming/breaking of hydrogen bond. As expected, the assembled flexible OdM-NHVO//PTCDI quasi-solid-state hybrid supercapacitor (FQSS HSC) also exhibits high areal capacitance, energy density and reliable flexibility. This work provides a new avenue for developing materials with oxygen-deficient structure for application in various aqueous non-metal cation storage systems. [Display omitted] •Defect engineering is employed to improve the NH4+-storage ability of the ammonium vanadate.•The oxygen defect content in oxygen-defective ammonium vanadate (Od-NHVO) can be controlled.•OdM-NHVO cathode with moderate oxygen defect content shows a high specific capacitance of 505 F g−1.•Reversible storage process with forming/breaking of hydrogen bond is demonstrated.•Flexible OdM-NHVO//PTCDI quasi-solid-state HSC possesses prominent electrochemical performance and flexibility.