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In recent years, visual-based sign language recognition (SLR) has become an active research area with the advancement of deep learning. However, it is difficult to collect sign language data, and many datasets suffer from data lack and imbalance, leading to overfitting and reduced accuracy in machine learning. In general, data augmentation is used as a solution to the problems but model training and data augmentation are performed independently, and it is difficult to adjust the parameters for data augmentation. Therefore, we focus on visual-based SLR using skeletal data and propose an adversarial learning SLR model called Adversarial Vulnerability-Seeking Networks (AVSN), which jointly trains two independent processes, data augmentation, and machine learning. The AVSN is particularly applicable in scenarios where diverse and extensive sign language datasets are not available. For example, when developing SLR systems for lesser-known sign languages or specialized vocabularies used in specific professional contexts, AVSN can improve model performance by generating high-quality, diverse training data. The generator produces hard adversarial data intended to mislead the machine learning model, which acts as a discriminator. On the other hand, the discriminator learns from both raw and adversarial data. In other words, the generator exposes the vulnerabilities of the discriminator, and the discriminator improves its performance by learning from both types of data. We evaluated the proposed method from two aspects, the performance of the model trained by data augmentation and the quality of the data generated by data augmentation. First, we evaluated the performance of the model trained using data augmentation using common evaluation metrics such as accuracy and F-score. The proposed method achieved an improvement of 0.58% in accuracy and 0.017 in F-score compared to the model without data augmentation. Next, we quantitatively evaluated the quality of the augmented data by data augmentation using evaluation metrics such as FID, variety, multimodality, affinity, and diversity. The proposed method was successful in enhancing the novelty of the data, which contributed to the generalization improvement of the model.

In this study, a composite wall material was prepared by blending whey protein isolate-D-xylose mixture and their Maillard reaction product with sodium alginate, which were then used for encapsulating proanthocyanidin to fabricate proanthocyanidin microcapsules. By characterizing the morphology, investigating stability, and assessing the in vitro antioxidant activity of different proanthocyanidin microcapsules, the advantages of microencapsulation of proanthocyanidin and the superiority of using Maillard reaction product as a wall material were elucidated. The results indicated that the microcapsules formed using the Maillard reaction product achieved an encapsulation efficiency of 98.60%±0.09%, with a proanthocyanidin content of 49.97±0.87 mg/g and a particle size of 1219.61±388.24 μm, exhibiting good granular morphology. Compared to microcapsules without the Maillard reaction, those formed with it demonstrated enhanced stability under conditions of 90 ℃, pH≤4, pH=10, and in gastric digestion fluid. Furthermore, when the proanthocyanidin concentration reached 30 μg/mL, the microcapsules formed with the Maillard reaction product exhibited stronger in vitro antioxidant activity. This study lays the groundwork for further development and utilization of proanthocyanidin microcapsules, offering new insights into the development and advancement of proanthocyanidin microcapsule food products.

Objective: To investigate the protective effects of flavonomaside on HT22 mouse hippocampal neuronal cells by inhibiting the inflammatory response induced by lipopolysaccharide (LPS) in mouse microglia (BV2) and elucidate the underlying mechanisms. Methods: BV2 cells were categorized into control (Con), LPS model (1 μg/mL), flavonomaside low, medium, and high dose groups (50, 100, 200 μmol/L respectively), TLR4 inhibitor group (TAK, 10 μmol/L), and TAK+flavonomaside group (10 μmol/L+200 μmol/L). Nitric oxide (NO), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) levels in supernatants and cell viability were quantified. Western blot assessed the protein levels of TLR4, Myd88, NF-κB p65, and phosphorylated NF-κB p65 in BV2 cells. The BV2 cell supernatant from each group acted as conditioned medium (CM) on HT22 cells to measure cell viability and observe morphological changes. Western blot also detected the protein levels of Bax, Bcl-2, cleaved Caspase-3, and Caspase-3 in HT22 cells. Results: No significant difference in BV2 cell viability was observed with varying flavonomaside concentrations compared to the Con group. The flavonomaside (200 μmol/L) group showed a significant reduction in NO, TNF-α, and IL-6 secretion compared to the LPS group (P

The rapid development of wireless communications has put forward higher requirements for the design of security architectures for future communications. The broadcast and openness of wireless systems make information transmission vulnerable to security threats such as interception, eavesdropping and interference. Wireless endogenous security utilizes the intrinsic characteristics of the wireless channel and communication-related technologies to complement existing security mechanisms and improve transmission security. According to the development trend of wireless security, the key technologies of endogenous security in the physical layer of wireless communication were discussed. Research overviews of wireless key generation technology, wireless authentication technology, and wireless encrypted transmission technology were given. The wireless communication physical layer endogenous security advantages, i.e., uniqueness, compatibility, heterogeneity, and reproducibility, were analyzed. Explore how to realize the de-redundancy of the physical layer architecture and the reuse of physical layer functions. Future work on key technologies for endogenous security in the physical layer of wireless communications is outlined.

This paper first provided an overview of the current status of the Starlink constellation development, then studied the Starlink system from aspects such as system composition, satellite iteration versions, constellation configuration, and system development characteristics. Finally, through simulation, a comparison of end-to-end transmission latency based on the Starlink constellation and terrestrial fiber optics was made, and the the relationship between the end-to-end transmission distance and the delay under different transmission paths was obtained. Moreover, through link estimation, the service level of Starlink satellites for different services could be evaluated from the perspective of transmission rate. Further, an analysis of the coverage capabilities of a single satellite and the constellation was conducted.

Improving the degradation performance and enhancing the biocompatibility are the main challenges of Mg-based biodegradable implants. In this study, a nano-hydroxyapatite-enhanced (nHA) Mg matrix composite was fabricated via friction stir processing and characterised, including microstructure, mechanical, in vitro degradation properties, and cytocompatibility. Hydroxyapatite is renowned for its superior bone compatibility, promoting healing responses and tissue growth. Friction stirring created a gradient grain structure in the alloy, with the stir zone exhibiting the highest grain refinement. The stir zone also contained most of the incorporated nHA and exhibited a strong texture with grains preferentially oriented along the [0001] direction. Immersion and polarisation experiments showed an increase in the FSPed WE43-nHA's corrosion resistance due to the refined microstructure. The treatment also caused a shift in the corrosion mode of the alloy from localized to uniform corrosion despite some localized corrosion associated with the nHA. Cytocompatibility tests in human osteoblast (HOB) cell lines indicated good biocompatibility in the Mg-nHA alloy, with cells exhibiting relatively healthy morphology and increased live cell count. Friction stir processing is a viable manufacturing option for creating Mg-based metal matrix composites with improved corrosion resistance and good biocompatibility.

The accumulation of coal gangue (CG), fly ash (FA) and the burning of corn straw (CS) cause severe environmental issues. The study was carried out to prepare CG–FA–CS porous aggregate (CFCA) by sintering. The physical properties of CFCA were tested, XRD, FTIR, SEM, TEM, and MIP were used to characterize the microstructure and pore structure of CFCA. Furthermore, the skeleton and pore structure formation mechanisms of CFCA were revealed. Results showed that the maximum cylinder compressive strength of CFCA is 5.89 MPa, and the minimum thermal conductivity of CFCA is 0.086 W/(m·K). The main crystal phase of CFCA is anorthite (CaAl2Si2O8), and the secondary crystal phase is mullite (Al6Si2O13). Moreover, crystal phase accumulates to form the skeleton structure, providing the strength of aggregate. The pore volume of medium pores (103–104 nm) of CFCA accounted for the largest percentage, which accounted for 66.65 %–92.29 % of the total pore volume. The CFCA prepared in this study exhibits a multi-type pore structure. The study provides a feasible way for the comprehensive utilization of solid waste, which can also reduce the environmental problems caused by exploitation of natural resources.

Under the background of the “carbon peaking and carbon neutrality goals”, the salt cavern is an important place for geological storage and carbon sequestration. The limited knowledge of the quantity and quality of the salt cavern and immature investigation technology constrains the development and utilization of salt cavern. After analyzing the geological exploration reports on major salt mines, reserves reports, enterprise annual reports, and research articles, a scientific understanding and basic judgment of the amount and utilizability of salt caverns in China are obtained; a technical system of salt cavern resource survey is established through a test with comprehensive detection technology in the concentrated area of salt cavern. The results show that salt cavern resources in China are characterized by abundant reserves, wide distribution, feasible burial depth, and convenient utilization on large scale. The most abundant salt cavern resources occur in the east and central regions. The national salt cavern resources are divided into levels I, II, and III by using an availability evaluation method system of salt cavern resource. Level I accounting for about 29.4%, is mainly distributed in Jiangsu, Hubei, Henan, and other provinces in east and central China. Salt cavern resources in level I are preferred for development and utilization. Level II accounting for about 65.4%, is widely distributed in southwest, northwest, and centra China, and can be used as a prospective area for development and utilization. Salt cavern resources in level III accounting for about 5.2%, are not suitable for development and utilization. Ground geophysical exploration, which can identify the characteristics of the regional geology, salt layer geology, hydrogeology, and salt cavity distribution of the salt mining area, guides the site selection on the salt cavern construction; geophysical exploration of high precision, which can obtain the information of salt mine levels and cavity structure, benefit the construction and operation detection of storage. This study provides the basic data and technical support for the planning and development of salt cavern resources in China.

Human activity recognition (HAR) plays a crucial role in human-computer interaction, smart home, health monitoring and elderly care. However, existing methods typically utilize camera, radio frequency (RF) signals or wearable devices for activity recognition. Each single-sensor modality has its inherent limitations, like camera-based methods having blind spots, Wi-Fi-based methods depending on the environment and the inconvenience of wearing Inertial Measurement Unit (IMU) devices. In this paper, we propose a HAR system that leverages three types of sensor combinations: Wi-Fi, IMU and a hybrid of Wi-Fi+IMU. We utilize the Channel State Information (CSI) provided by Wi-Fi and the accelerometer and gyroscope data from IMU devices to capture activity characteristics. Then, we employ six machine learning algorithms to recognize eight types of daily activities. These algorithms include Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Decision Tree, Random Forest, Logistic Regression and k-Nearest Neighbors (kNN). Additionally, we investigate the accuracy of hand gesture recognition using different sensor combinations and analyze the calculation speed of each combination. We conduct a survey to collect user feedback on the performance of various sensor combinations in our HAR system. The results show that the combination of CSI+IMU yields the best HAR recognition accuracy, with a accuracy of 89.38%. The SVM algorithm consistently performs well across all systems, especially excelling in the CSI+IMU system supported by energy and average Fast Fourier Transform (FFT) features. However, we also find that the success of sensor fusion depends on specific algorithms and features. Fusion of CSI and IMU does not universally enhance recognition accuracy for all features and algorithms and can, in some cases, actually reduce accuracy.

Learning how to effectively respond to ethical dilemma can affect nurses’ physical and mental health, which is not conducive to developing a nursing career. Nursing students’ ethical behavior warrants attention as professionals about to begin clinical work. We aim to understand the current situation and influencing factors of Chinese nursing students’ ethical behavior. A cross-sectional descriptive study was conducted. Descriptive statistics and multiple linear regression were used to analyze the data. Full-time nursing students were recruited from an undergraduate medical university in Jinan through convenient sampling from November to December 2021. Research ethics approval (No. 2022-0018) was obtained from the Ethics Committee of Affiliated Hospital of Shandong University of Traditional Chinese Medicine. Informed consent was also received from participants. The EBT scores of the nursing students were 95.14 ± 11.37, which was not high compared with the total score. Gender, year level, and professional values had a significant impact on participants’ ethical behavior. A positive correlation was found between nursing professional values and ethical behavior. A gap still exists between the moral development and maturity of undergraduate nursing students. To further cultivate their ethical behavior and improve their confidence and ability to respond to ethical dilemmas, more innovative methods must be employed in teaching ethics courses, and continuity in the ethics education system must be maintained. For male and third- and fourth-year nursing students who showed lower ethical behavior scores, nursing educators can develop their ethical behaviors by helping them establish positive professional values.

Hydraulic pumps are the core components that provide power for hydraulic transmission systems, which are widely used in aerospace, marine engineering, and mechanical engineering, and their failure affects the normal operation of the entire system. This paper takes a single axial piston pump as the research object and proposes a small-sample fault diagnosis method based on the model migration strategy for the situation in which only a small number of training samples are available for axial piston pump fault diagnosis. To achieve end-to-end fault diagnosis, a 1D Squeeze-and-Excitation Networks (1D-SENets) model was constructed based on a one-dimensional convolutional neural network and combined with the channel domain attention mechanism. The model was first pre-trained with sufficient labeled fault data from the source conditions, and then, based on the model migration strategy, some of the underlying network parameters were fixed, and a small amount of labeled fault data from the target conditions was used to fine-tune the rest of the parameters of the pre-trained model. In this paper, the proposed method was validated using an axial piston pump fault dataset, and the experimental results show that the method can effectively improve the overfitting problem in the small sample fault diagnosis of axial piston pumps and improve the recognition accuracy.

As the editor of the Special Issue on “Exploring IoT Sensors and Their Applications: Advancements, Challenges, and Opportunities in Smart Environments”, I am delighted to present this collection of groundbreaking research that addresses the emerging needs and challenges in the field of IoT sensors and smart environments [...]

This paper presents a gesture-controlled robotic arm system designed for agricultural harvesting, utilizing a data glove equipped with bending sensors and OptiTrack systems. The system aims to address the challenges of labor-intensive fruit harvesting by providing a user-friendly and efficient solution. The data glove captures hand gestures and movements using bending sensors and reflective markers, while the OptiTrack system ensures high-precision spatial tracking. Machine learning algorithms, specifically a CNN+BiLSTM model, are employed to accurately recognize hand gestures and control the robotic arm. Experimental results demonstrate the system’s high precision in replicating hand movements, with a Euclidean Distance of 0.0131 m and a Root Mean Square Error (RMSE) of 0.0095 m, in addition to robust gesture recognition accuracy, with an overall accuracy of 96.43%. This hybrid approach combines the adaptability and speed of semi-automated systems with the precision and usability of fully automated systems, offering a promising solution for sustainable and labor-efficient agricultural practices.

We propose and demonstrate a cost-effective half-duplex transceiver for Fiber-to-the-Room (FTTR) network by using a homemade uncooled and isolator-free distributed feedback (DFB) laser as both receiver (Rx) and transmitter (Tx). AlGaInAs multiple quantum wells (MQWs) and a partially corrugated grating are utilized for uncooled and isolator-free operation of the DFB lasers. Tx/Rx mode switching is implemented by a specially designed complementary metal-oxide-semiconductor (CMOS) chip including biasing circuits, a trans-impedance amplifier (TIA) and a single pole double throw (SPDT) radio frequency (RF) switch. The transceiver can work up to 5 Gbps in both Tx and Rx modes with 17 ns Rx to Tx switching time and 94.4 ns Tx to Rx switching time, respectively. The Tx output optical power of the transceiver is 11 dBm, while the Rx sensitivity is −18 dBm at a bit error rate (BER) of 10−3. This integration method proves that the transceiver is a cost-effective high speed light source for the future FTTR network.

The goal of this study is to help people to monitor brushing process to maintain their oral quality by providing real-time feedback. In this research, a low-cost toothbrushing monitoring system of brushing regions and brushing force using multimodal sensors and Unity is proposed for toothbrushing quality monitoring. An inertial sensor attached to the handle of a toothbrush and Random Forester Classifier (RFC) model were used to estimate brushing regions; five force sensors clipped on the toothbrush and Random Forest Regression (RFR) model were used to estimate brushing force; a visual interface based on Unity was designed to display detection results in real-time. For brushing region detection, the results show that offline verification accuracy is 97.6%, and average accuracy of online detection method is 74.0%. For brushing force detection, 5 subjects were invited to participate in experiment on both User Dependent (UD) and User Independent (UI). The results show that average Root Mean Squared Error (RMSE) is 22.08g for UD experiment; average RMSE is 37.06g for UI experiment. For this 3D brushing monitoring system, 20 subjects were invited to participate in usability experiment. The results show that 3D brushing monitoring system of this research has good usability, performance, and user satisfaction.

Abstract Insight into the mechanism of docetaxel resistance in breast cancer may help to improve prognosis. We aimed to investigate the role of N6-methyladenosine (m6A) and the METTL3/LINC00662/miR-186-5p pathway in regulating docetaxel resistance in triple negative breast cancer (TNBC). We have recruited 193 pathologically diagnosed TNBC patients from 2016 to 2017 in our hospital. Quantitative real-time PCR was used to evaluate the expression of LINC00662 and miR-186-5p both in vivo and in vitro. CCK8 tests were used to assess cell viability. ELISA was used for protein expression evaluation. Dual luciferase reporter gene assay and RNA pull-down were used to evaluate the interaction between LINC00662 and miR-186-5p. m6A levels were enhanced in breast cancer tissues and cells. LINC00662, miR-186-5p and METTL3 were differentially expressed in vivo, and METTL3 expression was associated with LINC00662 and miR-186-5p expression. LINC00662 and miR-186-5p were differentially expressed in vitro; LINC00662 promoted cell viability and decreased the apoptosis rate, whereas miR-186-5p inhibited cell viability and increased the apoptosis rate. Furthermore, we found that METTL3 regulated m6A levels in docetaxel-resistant breast cancer cells by regulating the expression of LINC00662. Moreover, LINC00662 and miR-186-5p regulated the cell viability rate of docetaxel-resistant breast cancer cells. Further experiments showed that LINC00662 directly interacted with miR-186-5p to exert biological functions; besides miR-186-5p could regulate the expression of METTL3. METTL3 promotes m6A levels and docetaxel resistance in breast cancer by regulating the expression of LINC00662 and miR-186-5p; more experiments are needed to clarify the role of m6A regulation in drug resistance.

Objective According to the diagnosis and treatment process of a case of spontaneous intracranial hypotension (SIH) with cerebral venous sinus thrombosis (CVST), the etiology, diagnosis and treatment were analyzed in order to improve the clinicians' understanding. Methods and Results A 34⁃year⁃old male patient developed persistent headache in the occipital region accompanied by intermittent throb⁃like pain. The onset was independent of body position or circadian rhythm, and the symptoms were progressively aggravated. The patient was initially diagnosed with CVST, and gained a poor curative effect of endovascular treatment and anticoagulant therapy. The subsequent diagnosis of SIH with CVST was corrected by head MRI and magnetic resonance myelography (MRM). After bedrest, hydration, steroids, anticoagulation and defibration therapy, the headache symptoms disappeared and the laboratory test results returned to the normal range. There was no recurrence after discharge. Conclusions For CVST patients without typical orthostatic headache, more attentions should be paid to the analysis of imaging findings and clinical data, in order to avoid the possibility of missed diagnosis of intracranial hypotension syndrome (IHS).

Global lakes play an active role in releasing carbon into the atmosphere. However, previous research was less focused on shallow tropical and sub-tropical lakes, especially ecosystem respiration during the drawdown period. This study was designed to determine the environmental factors that determine ecosystem respiration during the drawdown period in a typical shallow sub-tropical lake, Dongting Lake in China. Ecosystem respiration from the exposed mudflat and a newly colonized meadow were investigated using a Li-8100 soil CO2 flux system in situ. The soil water content soil organic carbon (SOC), dissolved organic carbon (DOC), total nitrogen and soil C/N ratio were measured at 0–30 cm soil depth layers. No difference was found among different soil depth layers for soil properties, while the dissolved organic carbon value varied significantly among different levels of the cumulative days of the mudflat exposed to the air (CDE). Carex colonizing significantly increased soil organic carbon and DOC at the surface soil layer. Exposure to the air and Carex colonizing together strengthened the intensity of carbon dioxide (CO2) emission in the mudflat, achieving 0.716 ± 0.114 μmol m-2s-1 and 2.240 ± 0.375 μmol m-2s-1, respectively. Exposure to the air led mudflat to exceed other landscapes or different vegetation types in Dongting Lake, becoming the most active area releasing CO2 into the atmosphere, with the respiration flux reaching a peak period at around 60 days after exposure, which was enormously reinforced by Carex colonizing. Reducing the area and duration of mudflat exposure to the air during the drawdown period might be useful in reducing CO2 emissions to the atmosphere in shallow sub-tropical lakes.

Abstract. Background:. Acute kidney injury (AKI) is a common and serious complication following lung transplantation (LTx), and it is associated with high mortality and morbidity. This study assessed the incidence of AKI after LTx and analyzed the associated perioperative factors and clinical outcomes. Methods:. This retrospective study included all adult LTx recipients at the China-Japan Friendship Hospital in Beijing between March 2017 and December 2019. The outcomes were AKI incidence, risk factors, mortality, and kidney recovery. Multivariate analysis was performed to identify independent risk factors. Survival analysis was presented using the Kaplan–Meier curves. Results:. AKI occurred in 137 of the 191 patients (71.7%), with transient AKI in 43 (22.5%) and persistent AKI in 94 (49.2%). AKI stage 1 occurred in 27/191 (14.1%), stage 2 in 46/191 (24.1%), and stage 3 in 64/191 (33.5%) of the AKI patients. Renal replacement therapy (RRT) was administered to 35/191 (18.3%) of the patients. Male sex, older age, mechanical ventilation (MV), severe hypotension, septic shock, multiple organ dysfunction (MODS), prolonged extracorporeal membrane oxygenation (ECMO), reintubation, and nephrotoxic agents were associated with AKI (P

The spatial position and dip feature of the density boundary are significant to the study of fault and tectonic frameworks. Edge detection methods generally attach importance to the horizontal position of the boundary, but it is difficult to determine the dip feature expressly. A density gradient inversion method was proposed based on the corresponding relationship among the gravity forward field, forward kernel matrix, and model attributes. The inversion result of this method is that the density gradient value is different from the conventional gravity inversion. It can directly display the 3D distribution features integrated with 3D inversion results of the density gradient in different directions. The theoretical model means that the inversion results can not only identify the horizontal position of the boundary but also qualitatively determine the dip feature of faults. It has been widely applied to fault identification in the Songliao Basin. According to the joint inversion results, the strike feature and the dip feature can be quantitatively and qualitatively identified, respectively, making up for the shortcomings of sparse distribution and poor lateral resolution of existing seismic data.

In skeleton-based human action recognition domain, the methods based on graph convolution networks have great success recently. However, most graphical neural networks consider the skeleton as a spatiotemporally uncorrelated graph and rely on a predetermined adjacency matrix, ignoring the spatiotemporal relevance of human actions and taking up significant computational costs. Meanwhile, the methods use graph convolution to focus too much on the neighboring nodes of the joints and ignore the totality of the action. In this work, we propose a lightweight but efficient neural network called NLB-ACSE based on the Graph Convolutional Network (GCN). Our model consists of two large branches: non-local block branch that focuses on the long distance features and adaptive cross-spacetime edge branch that focuses on the short distance features. Both branches extract information across time and space, and focus on long and short information. Some simple but effective strategies also are applied to our model, such as semantics, maxpooling, and fusion inputs, which have small parameter burden but obtain a higher accuracy on ablation study. The proposed method with an order of magnitude smaller size than most previous papers is evaluated on three large datasets, NTU60, NTU120, and Northwesten-UCLA. The experimental results show that our method achieves the state-of-the-art performance.

Background: Obesity is closely related to osteoporosis. Lycium barbarum polysaccharides (LBPs) have anti-osteoporosis activity. Objective: This study aimed to explore the role of LBPs in palmitic acid (PA)-induced osteoblast apoptosis. Methods: The microarray data set GSE37676 was downloaded from Gene Expression Ominibus (GEO) database. Top 300 differentially expressed genes (DEGs) were used to construct a protein–protein interaction (PPI) network based on STRING database, and significant modules were analyzed and their key genes were screened by using Cytoscape software. COEXPEDIA database showed that there was co-expression between Chrdl1 and peroxisome proliferator-activated receptor (PPARγ). MC3T3-E1 cells were treated with 100–500 μg/mL of PA. Reverse transcription polymerase chain reaction (RT-PCR) and western blot assays were used to detect mRNA and protein levels. Cell Counting Kit-8 (CCK-8) assay and flow cytometry were used to detect cell viability and cell apoptosis. Results: Chrdl1 was the key gene from the most significant module and downregulation in MC3T3-E1 cells treated with PA. MicroRNA miR-200b-3p and PPARγ were significantly upregulated among PA-treated MC3T3-E1 cells. The results of luciferase reporter gene assay showed that miR-200b-3p targeted Chrdl1 3’-UTR. Over-expressing miR-200b-3p promoted PA-induced cell apoptosis and inhibited cell viability. After pre-treating cells with PA and LBP, MC3T3-E1 cell apoptosis rate was relatively lower than that of mimics+PA200 group. Chrdl1 inhibition partly reversed miR-200b-3p effect on inhibiting apoptosis among MC3T3-E1 cells pre-treated with LBP and PA. Decreased C CASP3, PPARγ and increased Chrdl1 by miR-200b-3p inhibition were partly reversed by Chrdl1 inhibition. Conclusions: LBPs inhibit PA-induced MC3T3-E1 cell apoptosis by mainly decreasing miR-200b-3p to upregulate Chrdl1, but miR-200b-3p/Chrdl1/PPARγ is not the only mechanism for LBPs protecting osteoblasts from PA.

Abstract High strength SiC whisker-reinforced Ti3SiC2 composites (SiCw/Ti3SiC2) with an improved thermal conductivity and mechanical properties were fabricated by spark plasma sintering. The bending strength of 10 wt% SiCw/Ti3SiC2 was 635 MPa, which was approximately 50% higher than that of the monolithic Ti3SiC2 (428 MPa). The Vickers hardness and thermal conductivity (k) also increased by 36% and 25%, respectively, from the monolithic Ti3SiC2 by the incorporation of 10 wt% SiCw. This remarkable improvement both in mechanical and thermal properties was attributed to the fine-grained uniform composite microstructure along with the effects of incorporated SiCw. The SiCw/Ti3SiC2 can be a feasible candidate for the in-core structural application in nuclear reactors due to the excellent mechanical and thermal properties.

BackgroundVisceral fat has been considered an important risk factor of elevated serum uric acid (SUA). Perirenal fat is a unique visceral fat around the kidneys that has special morphological and physiological features while its relationship with SUA remains incompletely elucidated. This study aimed to assess the association between perirenal fat volume (PrFV) and SUA.MethodsA cross-sectional study was conducted in 102 subjects aged ≥ 18 years old recruited from Nanjing,China. The clinical characteristics including age, sex, drinking behavior, history of hypertension, body mass index, waist circumference, total cholesterol, fast plasma glucose, urea, serum creatinine, C-reactive protein, and SUA were recorded. PrFV was measured by ultrasonography. Multivariate linear models and the restricted cubic spline were used to investigate the association between PrFV and SUA.ResultsThe median age of this study population was 52.5 (42.0-60.0) years and 56.9% were female. The median value of SUA was 5.73 mg/dL (4.58-6.80 mg/dL). The subjects were divided by PrFV tertiles and we found that the subjects in the highest PrFV tertile had a higher level of SUA compared to those in the lowest tertile (β=1.86, 95%CI 1.23–2.48, P for trend

Floodplain wetlands are among the most dynamic ecosystems on Earth, featuring high biodiversity and productivity. They are also sensitive to anthropogenic disturbances and are globally threatened. Understanding how flow regime drives the spatiotemporal dynamics of wetland habitats is fundamental to effective conservation practices. In this study, using Landsat imagery and the random forest (RF) machine learning algorithm, we mapped the winter distribution of four wetland habitats (i.e., Carex meadow, reedbed, mudflat, and shallow water) in East Dongting Lake, a Ramsar wetland in the middle to lower Yangtze Basin of China, for 34 years (1988–2021). The dynamics of wetland habitats were explored through pixel-by-pixel comparisons. Further, the response of wetland habitats to flow regime variations was investigated using generalized additive mixed models (GAMM). Our results demonstrated the constant expansion of reedbeds and shrinkage of mudflats, and that there were three processes contributing to the reduction in mudflat: (1) permanent replacement by reedbed; (2) irreversible loss to water; and (3) transitional swapping with Carex meadow. These changes in the relative extent of wetland habitats may degrade the conservation function of the Ramsar wetland. Moreover, the duration of the dry season and the date of water level withdrawal were identified as the key flow regime parameters shaping the size of wetland habitats. However, different wetland vegetation showed distinct responses to variations in flow regime: while Carex meadow increased with earlier water withdrawal and a longer dry season, reedbed continuously expanded independent of the flow regime corresponding to the increase in winter rainfall. Our findings suggested that flow regime acts in concert with other factors, such as climate change and sand mining in river channels, driving wetland habitat transition in a floodplain landscape. Therefore, effective conservation can only be achieved through diverse restoration strategies addressing all drivers.

Ecosystem services (ES) are directly affected by land use and land cover changes (LUCC); however, the impacts of extended period LUCC on ES are poorly explored. Here, we mapped the 1998–2019 annual land use and land cover in the Dongting Lake Region (China) and explored the spatiotemporal evolution of LUCC and landscape patterns (i.e., composition, shape, and aggregation) and their relationship with ES, including carbon storage, gross primary production (GPP), water conservation capacity, and crop yield in the region. The results showed a significant increase in forest areas and impervious surfaces and a decrease in croplands and bare lands with spatial heterogeneity. Carbon storage was strongly correlated with forest, cropland, waterbody, impervious surface, and bare land, and there was a nonlinear relationship between landscape patterns and ES. The trade-offs and synergies (correlations) among ES varied considerably, with crop yield being significantly synergistic with carbon stocks, GPP, or GPP with carbon stocks. This study revealed the nonlinear relationship between landscape patterns and ES, and the mechanism of landscape characteristics on ES. The findings can provide scientific support for regional land use planning, ES regulation, and landscape optimization in the lake region.

In this paper, the influence of data imbalance on neural networks is discussed, and an improved learning algorithm to solve this problem is proposed. The experimental results show that in the case of imbalanced data, the training error of neural network converges slowly and the generalization ability is poor. Our theoretical analysis shows that in the process of training, the gradient descent direction of the weights is dominated by the major-classes, which accounts for the slow convergence of the training error. Based on these results, we propose the Equilibration Mini-batch Stochastic Gradient Descent (EMSGD) method to ensure the equilibrium of the data in the mini-batch. The advantage of this technique is that it makes full use of the existing random sampling step of MSGD without increasing the computational complexity. In addition, by over-sampling of minor-classes in the mini-batch, duplicated instances would be greatly reduced, thus preventing the model from overfitting. The experimental results show that under the condition of the imbalanced training data, EMSGD can make the neural network training error converge rapidly.

In this paper, seven LED lamps are selected to investigate how the junction temperature (Tj) of LED varies during the step-stress temperature accelerated aging test. The ambient temperatures are 80 °C and 70 °C for the first and second step of the aging test, respectively, and the total aging time is 2180 h. A non-contact method for monitoring junction temperatures of samples, by the ratio of white light energy to blue light energy (W/B), is applied. Experimental measurements show that the W/B ratio and Tj satisfy good linear relationship during the aging for all samples. Then, the junction temperatures are acquired at eight typical points of aging time which are used to eliminate the impact of Tj variation on the estimation of the decay rate and accelerated lifetime. It is shown that the estimated lifetime is longer than that acquired by the traditional method. The estimation errors of the accelerated lifetimes by the traditional method are in a range from 12.8% to 18.6% under the aging temperature of 80 °C and from 12.5% to 20.3% under the aging temperature of 70 °C for the seven samples. The estimation accuracy of the decay rates and accelerated lifetimes of LED lamps in the step-stress accelerated aging test is improved by the proposed methodology to a certain extent.

Abstract In this paper, we study the following fractional Schrödinger–Poisson system with superlinear terms {(−Δ)su+V(x)u+K(x)ϕu=f(x,u),x∈R3,(−Δ)tϕ=K(x)u2,x∈R3, $$ \textstyle\begin{cases} (-\Delta )^{s}u+V(x)u+K(x)\phi u=f(x,u), & x \in \mathbb{R}^{3}, \\ (-\Delta )^{t}\phi =K(x)u^{2}, & x \in \mathbb{R}^{3}, \end{cases} $$ where s,t∈(0,1) $s,t\in (0,1)$, 4s+2t>3 $4s+2t>3$. Under certain assumptions of external potential V(x) $V(x)$, nonnegative density charge K(x) $K(x)$ and superlinear term f(x,u) $f(x,u)$, using the symmetric mountain pass theorem, we obtain the existence and multiplicity of non-trivial solutions.

In order to preserve the local structure of high dimension fault feature better, locality preserving projections（LPP） are improved and a fault diagnosis method based mixed label information locality preserving projections（MLILPP） for dimension reduction is proposed. MLILPP constructed similarity matrix and divergence matrix based on label information. The similarity matrix is used to preserve the local structure of the same-label samples before and after dimension reduction, whereas the divergence matrix is used to enlarge the distance between the different-label samples after dimension reduction. As a result, data structure is retained effectively, better low dimension structure can be obtained, and higher fault diagnosis accuracy can achieved. The experiment results of hydraulic pump fault diagnosis verified the effectiveness of the method.

In this paper, we researched Pedestrian Dead Reckoning (PDR) with one foot-mounted IMU sensor. The issues of PDR are magnetism noise and accumulated error due to the noise included in acceleration and gyro data. Two methods are proposed in this paper. First is the gait-phase-estimation method with pitch angle for the Zero Velocity Update algorithm. Second is a method for avoiding accumulated errors by updating the roll and pitch angles with acceleration. The two experiments were conducted to examine the error of gait-phase estimation and distance estimations. The relative error of distance was about 7.40% in the case of walking straight and about 12.27% in the case of a shifting travel direction.

Objective To investigate the expression of long non-coding RNA AC009336.24 (LncRNA AC009336.24) in small cell lung cancer (SCLC) tissues and its clinical significance. Methods The expressions of LncRNA AC009336.24 in 120 cases of SCLC tissues. 35 cases of para-cancerous tissues and 40 cases of normal lung tissues were detected by real-time fluorescent quantitative PCR. The relationship between the expression of LncRNA AC009336.24 and clinicopathological characteristics and chemosensitivity in SCLC patients was analyzed. Results The expression level of LncRNA AC009336.24 in SCLC tissues and chemoresistance patients were higher than those in para-cancerous tissues, normal lung tissues and chemosensitive patients (all P < 0.001). LncRNA AC009336.24 expression was positively correlated with disease stage, lymph node metastasis, distant metastasis and chemosensitivity (all P < 0.05). The overall survival time and progression-free survival of the patients with high expression level of LncRNA AC009336.24 were shorter than those with low expression level of LncRNA AC009336.24 (all P < 0.001). LncRNA AC009336.24 expression, disease stage and distant metastasis were independent prognostic factors (all P < 0.05). Conclusion LncRNA AC009336.24 is involved in the development of SCLC and may be used as a potential prognostic marker for SCLC.

The automation and intelligentization of fault diagnosis system for satellite ground system directly affect the success of tasks and the reliability of the system. This paper designed a fault diagnosis system based on rule base, which contained satellite ground system failure rule base, failure model, abnormal and alarm mechanism. Software implementation has been verified by actual project, it shows that the fault diagnosis system based on rule base can improve the capacity of fault management functions, real-time monitoring and automatic fault diagnosis support system. In addition, fault analysis and location can enhance the automation level and efficiency of satellite fault diagnosis, make efficient and reliable diagnosis of remote sensing satellite receiving system, raise the success rate of satellite data receiving, and have good practicability and popularization.

In view of problems that coal and rock identification systems use single sensor to monitor data and have low precision, reliability and stability, an identification method of coal and rock based on information fusion and neural network was proposed. A variety of necessary sensors are added to the existing shearer, which are used to collect current, pressure, vibration frequency, acceleration and other signals of the shearer under different situations. Wavelet packet is used for characteristics extraction, and BP neural network is used for data fusion, so as to achieve coal and rock identification. The test results of the real machine show that the identification error of the proposed method is within ±0.5, which verifies its validity.

The emergence of houses is a social revolution around the world. Over the past several decades, Chinese archaeologists have excavated many Neolithic to Bronze Age houses, but there is still a great amount of uncertainty about the social and environmental factors driving the differences between these house structures in the Yellow River Basin. In this paper, we summarize data from excavation reports on the shape and size of Neolithic-Bronze Age houses in the upper, middle and lower reaches of the Yellow River, respectively, to identify some social and environmental factors that may have affected the development of house structures across northern China. Our results show that the shape and size of the houses developed at a different pace, but in general followed a similar developmental sequence: (1) 10–8 ka BP, the bud of settlements emerged in the middle and lower reaches of the Yellow River; (2) 8–7 ka BP, people started to construct small pithouses without walls; (3) 7–6 ka BP, people made medium-sized pithouses with low walls, and surface buildings were made with a wood skeleton and mud walls; (4) 6–5 ka BP, ultra-large houses emerged; (5) 5–4 ka BP, house form became more varied, including pithouses, cave dwellings and surface buildings with a wood skeleton mud wall, rammed earth wall, piled mud-grass mixed walls and adobe walls; and (6) 4–3 ka BP, original palaces emerged. Our analyses indicate that the environment played an essential role in determining the house changes over time and that the early to middle Holocene’s warm and humid climate provided excellent conditions for the emergence of settlements throughout the region. Due to the shortage of trees, people chose to change their house construction methods to accommodate the growing lumber shortage. In conclusion, the rapid shift in house construction methods reflects the changing ecological condition as well as a feedback cycle between the environment and social practices driven by resource limitations.

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.