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At present, the physical condition of college students is declining day by day, and the relevant sports departments pay more and more attention to the daily physical exercise of college students. To deeply understand the physical health status of college students and further adjust the health intervention measures, this paper constructs a neural network reliability evaluation model and carries out the physical health standard test on 50 college students from the sports department of Nanchang University and analyzes the physical health test data by using the neural network error reverse evaluation method; to optimize the physical health test system, the accuracy of physical health test is fitted in parallel. Then, according to the fitting degree of the test results, the direction of health intervention is predicted, and some specific suggestions are made for college students’ health indicators such as running and vital capacity. The research shows that the fitting degree of the neural network reliability evaluation model is 86%, and the accuracy of the neural network model is high. In the 50 college students’ physical health project, the fitting degree of 50 m running and vital capacity is higher than that of long distance running. Therefore, the neural network reliability evaluation model is feasible for college students’ physical health tests and can rank the intervention items, which has great practical significance for the improvement of college students’ physical health.

The tumor suppressor protein B-cell translocation gene 2 (BTG2) is downexpressed in lung adenocarcinoma (LUAD); however, its role in LUAD survival remains unknown. This investigation is aimed at exploring the activity of BTG2 in LUAD. We analyzed BTG2 expression in LUAD datasets of the TCGA database and examined that BTG2 was markedly downregulated in comparison with adjacent normal tissues. The prognostic analysis suggested that higher expression of BTG2 protein correlates with prolonged survival in patients. Vectors expressing BTG2 were stably transduced into lung adenocarcinoma A549 cells. The overexpression of BTG2 in A549 cells causes cellular G1 phase arrest but did not affect cell proliferation, accompanied by increased activation of NF-κB. Our data indicate that BTG2 overexpression may trigger an autoregulatory prosurvival NF-κB pathway, which is resistant to environmental intervention owing to an increased level of BTG2.

Basketball is one of the most popular sports, but apart from a small number of sports specialties, ordinary people rarely have the opportunity to receive professional basketball training, let alone coaches who provide one-on-one dribbling posture guidance. Dribbling is a very basic and important technique in basketball. Mastering the correct dribbling posture can help people further improve their basketball skills. In response to this problem, this article designed a smart wearable product to monitor the user’s posture in basketball dribbling training. If the user has a wrong dribble posture, the product will automatically prompt and give relevant suggestions. This article focuses on user demand research, product conceptual design, prototype development, and dribbling posture determination experiments and elaborates the design and development process of the product. Based on the experimental data, this article believes that the optimal parameters of the monitoring standard for the “head down too long” during basketball dribbling are the x -axis angle value of the motion sensor is critical for -120°, and the duration of exceeding the critical value is 1 second. The optimal parameters of the “dribbling wrist flip” monitoring standard are the x -axis angle value of the motion sensor has a critical value of 105°, and the duration of exceeding the critical value is 0.4 seconds. Judging from the end user’s experience and rating of the product’s trial experience, it can be seen that the smart product can indeed play a very good auxiliary effect in the field of dribbling posture monitoring. Popularizing it in the daily training of basketball players can effectively promote the full informatization and intelligent development of sports.

Objective. The aim is to study the different roles of single and joint application of magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) in prostate malignant tumors. Methods. 72 patients with prostate masses who underwent CEUS and MRI examination in our hospital from October 2021 and March 2022 were enrolled in this research. The differentially diagnostic roles of CEUS, MRI, and CEUS combined MRI for prostate cancer was assessed on basis of pathological findings as the reference standard. The specificity and sensitivity of the joint application for prostate malignant tumors with various prostate-specific antigen (PSA) levels were also evaluated. Results. The sensitivity of CEUS, MRI, and the joint application for prostate cancer were 72.1%, 74.4%, and 90.7%, respectively. Compared with single application, the sensitivity of CEUS combined with MRI was significantly higher. The specificity of MRI, CEUS, and the combination of the two for prostate cancer were 82.8%, 79.3%, and 89.7%, respectively, and the statistical differences for specificity were not found. The area under ROC curve (AUC) of CEUS combined with MRI in prostate malignant tumor diagnosis was obviously more than that of CEUS and MRI ( P < 0.05 ). CEUS combined with MRI has relative high sensitivity in these patients with different levels of PSA. Conclusions. Contrast-enhanced ultrasound combined with MRI can significantly improve the sensitivity and specificity of prostate cancer diagnosis so that patients can be better diagnosed in advance.

Adaptive signal control (ASC) is a well-researched topic that offers an efficient way for traffic management. It possesses a powerful ability to accommodate complex and constantly changing urban transportation networks. With the development of vehicular communication, CV-based ASC shows remarkable advantages compared with the traditional ASC system. Though the existing CV-based ASC strategies were proposed in the past few years, however, there are still issues to overcome. Most of the studies on CV-based ASC are based on the assumption of high CV penetration rate, which often result in poor performance when applied to low CV penetration environments. Besides, the lack of consideration for mixed traffic flow, which is in terms of both the vehicle types and CV penetration of different types of vehicles. To solve these issues, this paper developed an Occupancy-Based ASC strategy for a congested signalized intersection to optimize signal timing and reduce total passenger delay in the low CV penetration environment. Focused on the issues existing in the low CV penetration environment, a Maximum Likelihood Estimation (MLE) model was proposed to estimate vehicle arrivals, and two traffic models, MicroDM and MacroDM, were developed to model the mixed traffic flow and estimate passenger delay. With the purpose of offering fair treatment to passengers approaching the intersection, we proposed an Occupancy-Based Adaptive Signal Control strategy. By transforming the complex signal control problem into a mixed-integer linear programming problem, we found the optimal solution for minimizing total passenger delay. We then evaluated the proposed Occupancy-Based ASC strategy using simulation case studies. The results show that changing traffic status could be captured and estimated with the real-time CV trajectory data as input. Applying the Occupancy-Based ASC control strategy, phases with HOVs or more vehicles will be allocated more travel time. In particular, optimization results show that the proposed Occupancy-Based ASC strategy effectively balances passenger travel demands during peak volume periods.

Bladder cancer is the most prevalent tumor of the urinary tract, ranking seventh in males and seventeenth in women. The gold standard for the definitive diagnosis and initial treatment of non-muscle-invasive bladder cancer is transurethral resection (TUR) of the bladder tumor. The ability to accurately detect disease, typically in the presence of hematuria as well as to detect early recurrent tumors in patients with a history of NMIBC, is critical to the successful treatment of non-muscle-invasive bladder cancer (NMIBC). Unfortunately, the current biomarker landscape for NMIBC is still evolving. Cystoscopy remains the gold standard, but it can still miss 10% of tumors. As a result, physicians frequently employ additional diagnostic tools to aid in the diagnosis of bladder cancer. The efficacy of transurethral bipolar plasma needle electrodes and ring electrodes in the treatment of non-muscle-invasive bladder cancer was compared and analyzed in this study. During our study, 100 patients with non-muscle-invasive bladder cancer admitted to our hospital between June 2019 and June 2020 were randomly assigned to a control group and an observation group, with 50 cases in each group. The observation group was given a bipolar plasma needle electrode, while the control group was given a bipolar plasma ring. Patients continued to receive bladder irrigation chemotherapy as well as traditional Chinese medicine (TCM) treatment as part of our treatment plan, while the control group received only bladder irrigation chemotherapy. Clinical factors such as operational blood loss, catheter indention time, length of hospital stay, and others were compared between the two groups. When the risk grades in the two groups were compared, the observation group had fewer medium- and high-risk grades than the control group, but the control group had more low-risk grades, with statistical significance (P

Exosomes are nanosized extracellular vesicles which are emerging as novel therapeutic nanoparticles. This paper reports a novel concept of engineering exosomes using nanomaterial inside the vascular endothelial cells (ECs). Gold nanorods (GNRs) could inhibit EC division and internalized GNRs located in endosomes of binucleated ECs. The GNRs could alter the composition of bioactive molecules loaded in exosomes. The engineered EC-derived exosomes could inhibit tumor cell proliferation, migration, and invasion in vitro and suppress tumor growth in vivo. miRNA sequencing showed that the engineered exosomes contained various miRNAs that could disrupt the TGFβ pathway. Further data suggested that the engineered exosomes could suppress the expression of TGF β 1 and TGF β 2, thus inhibiting the activation of SMAD2 and SMAD3. These data highlighted the therapeutic potential of engineering exosomes using nanomaterials.

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of bridge cranes based on its online physical states and control model. Due to the swing of the payload, the safe braking distance cannot be a constant value. Therefore, we here propose a model prediction control (MPC)-based anti-swing method for non-zero initial states, where a new reference trajectory and a new cost function for optimization are proposed, such that the proposed MPC method can control the crane to follow the proposed reference trajectory and achieve a stable stop state with anti-swing. Furthermore, an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance achieved by using the proposed MPC braking control method. In this way, we can predict how far the crane would require to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. Experiments using both a simulated crane and a real crane demonstrate that the proposed safe braking distance prediction method is effective for safe braking control of the bridge cranes.

The tripolarized MIMO system can provide one more degree of freedom and have a more compacted size over a dual-polarized MIMO system, which is attractive for high-capacity wireless communication systems. In this paper, we analyze and model channel properties for tripolarized MIMO systems based on experimental channel measurements in typical indoor and outdoor scenarios. Firstly, channel measurement campaigns in the laboratory and the Urban Micro (UMi) scenarios on sub-6 GHz bands are presented. Then, based on measured data, path loss, delay spread (DS), and cross-polarization discrimination (XPD) for 9 polarization combinations are analyzed and modeled in a statistical way. Statistical results of these channel properties are also given. It is observed that channel properties of both large-scale fading and small-scale fading depend strongly on the polarization direction. Furthermore, we evaluate the performance of tripolarized MIMO systems by analyzing the Demmel condition number and channel capacity gain (CG). For both the indoor and the outdoor scenarios, it is found that colocated tripolarized antenna can bring a nearly threefold CG with respect to the unipolarized one. These results can give good insights into the design and evaluation of tripolarized MIMO systems.

The study on the subsidence of backfill mining block has been a concern of many scholars. A mechanical model of plate subsidence is established by studying the roof of a filling mining area in Luo Iron Mine in this paper. The boundary conditions are given, and the Navier method is used to solve the problem. Based on the thin plate model, the subsidence distribution map of the roof of the underground plate area is obtained. Based on the basic calculation parameters, the influence of a different foundation coefficient, mining depth, length-width ratio of plate area, elastic modulus of roof rock, and thickness of roof on the subsidence of roof is studied. According to the deflection calculation formula obtained, the expression of the internal force and stress is deduced, and the distribution of stress and shear stress on the upper and lower surfaces of the roof is analyzed. The dangerous area of the roof can be obtained, which provides a theoretical basis for the daily maintenance of mine safety.

One goal for large-scale deployment of connected and autonomous vehicles is to achieve the traffic safety benefit since connected and autonomous vehicles (CAVs) could reduce the collision risk by enhancing the driver’s situation perception ability. Previous studies have analyzed the safety impact of CAVs involved in traffic, but only few studies examined the safety benefits brought by CAVs when approaching high-collision-risk road segments such as the freeway crash hotspots. This study chooses one freeway crash hotspot in Wuhan, China, as an instance and attempts to estimate the safety benefits for differential penetration rates (PRs) of CAVs using the surrogate safety assessment model (SSAM). First, the freeway crash hotspot is identified with kernel density estimation and simulated by VISSIM. Then, the intelligent driver model (IDM) and Wiedemann 99 (a car-following model) are adopted and calibrated to control the driving behaviors of CAVs and human-driven vehicles (HVs) in this study, respectively. The impact that rather CAVs are constrained with or without managed lanes on traffic safety is also discussed, and the PR of CAVs is set from 10% to 90%. The results of this study show that when the PR of CAVs is lower than 50%, there is no significant improvement on the safety measures such as conflicts, acceleration, and velocity difference, which are extracted from the vehicle trajectory data using SSAM. When the penetration rate is over 70%, the experiment results demonstrate that the traffic flow passing the freeway hotspot is with fewer conflicts, smaller acceleration, and smaller velocity difference in the scenario where CAVs are constrained with managed lane compared with the scenario without managed lane control. The safety benefit that CAVs bring needs to be discussed. The lane management of CAVs will also lead to distinct safety impact.

Elastic waves cause energy loss during the transmission of coal measures. These losses include propagation loss, dielectric absorption loss, scattering loss, and frequency migration loss. The absorption loss is mainly caused by the inelastic absorption. The scattering loss is caused by the uneven heat absorption in the formation. The frequency shift loss is caused by the piezoelectric effect of coal-bearing formations and the intermodulation of different frequency signals. After considering the influence factors of the coal seam structure, this paper presents a model of low-frequency elastic waves loss coefficient. The paper proposed the loss coefficient of the elastic wave in the coal measure strata by considering two main attenuation mechanisms: intrinsic absorption and scattering. This paper theoretically studied the effects of the model parameters such as density, porosity, particle size, and wave frequency on the loss of wave energy using COMSOL simulation. Besides, the comparison of MATLAB simulation results shows that the simulation results produced by the model proposed in this paper are similar to the models embedded in COMSOL. This work can be applied to coal, oil, and gas exploration and is also helpful to study the mechanisms of wave attention on the low-frequency band.

The upper echelons theory is utilized to establish how CEO’s attributes affect firm’s technological innovation decisions. The extant literature has largely ignored the impacts of CEO media exposure. An unbalanced panel data analysis is used to examine the effects of CEO media exposure on Chinese polluting firm’s green technological innovation. It is illustrated that CEO media exposure generally enhances Chinese polluting firms’ green technological innovation decisions. In addition, we find that firms with state ownership and environmental regulations all moderate positively the relationship between CEO media exposure and green technological innovation. The research suggests that CEO media exposure appears to be a stimulus to firm’s green technological innovation decisions.

The reliability of high-power light-emitting-diode (LED) devices strongly depends on the die-attach quality because voids may increase junction temperature and total thermal resistance of LED devices. Die-attach material has a key role in the thermal management of high-power LED package by providing low-contact thermal resistance. Thermal and mechanical analyses were carried out by experiments and thermal simulation. The quantitative analysis results show that thermal resistance of die-attach layer (thermal resistance caused by die-attach material and voids in die-attach layer) plays an important role in total thermal resistance of high-power LED packaging according to the differential structure function of thermal transient characteristics. The increase of void fraction in die-attach layer causes the increases of thermal resistance of die-attach layer; the thermal resistance increased by 1.95 K/W when the void fraction increased to 62.45%. The voids also make an obvious influence on thermal stress and thermal strain of chip; the biggest thermal stress of chip was as high as 847.1 MPa compared to the 565.2 MPa when the void fraction increases from being void-free to 30% in the die-attach layer.

This study investigates the mechanism of channel wave multipath propagation to determine features of a channel wave in a coal seam. For this purpose, deduction formulas were used for different time delays and propagation paths and, subsequently, a multipath fading channel wave pattern was built. MATLAB software was used to simulate the model. To this end, the characteristics of the coal seam and the surrounding rock were considered. The simulation results were compared with results obtained from the model contained in COMSOL software to verify the accuracy of the model. According to the results obtained from the simulation, the proposed model shows a good match with the COMSOL model, which is a common simulation comparison standard. In addition, the channel wave energy approximates to exponential decay at a constant distance from the source, and the multipath propagation considerably affects its energy attenuation. Furthermore, the multipath reflections’ travel time derivation is accomplished properly in the coal seam.

The 3-dimensional (3D) channel model gives a better understanding of statistical characteristics for practical channels than the 2-dimensional (2D) channel model, by taking the elevation domain into consideration. As different organizations and researchers have agreed to a standard 3D channel model, we attempt to measure the 3D channel and determine the parameters of the standard model. In this paper, we present the statistical propagation results of the 3D multiple-input and multiple-output (MIMO) channel measurement campaign performed in China and New Zealand (NZ). The measurements are done for an outdoor-to-indoor (O2I) urban scenario. The dense indoor terminals at different floors in a building form a typical 3D propagation environment. The key parameters of the channel are estimated from the measured channel impulse response (CIR) using the spatial-alternating generalized expectation-maximization (SAGE) algorithm. Till now there is abundant research performed on the azimuth domain; this paper mainly considers the statistical characteristics of the elevation domain. A statistical analysis of 3D MIMO channel results for both China and NZ measurements is presented for the following parameters: power delay profile (PDP), root mean square (rms), delay spread (DS), elevation angle-of-arrival (EAoA) distribution, elevation angle-of-departure (EAoD) distribution, elevation angular spread (AS), and cross-polarization discrimination (XPD).

This paper uses 2-norm degree and coefficient of variation on degree to analyze the basic characteristics and to discuss the robustness of scale-free networks. And we design two optimal nonlinear mixed integer programming schemes to investigate the optimal robustness and analyze the characteristic parameters of different schemes. In this paper, we can obtain the optimal values of the corresponding parameters of optimal designs, and we find that coefficient of variation is a better measure than 2-norm degree and two-step degree to study the robustness of scale-free networks. Meanwhile, we discover that there is a tradeoff among the robustness, the degree, and the cost of scale-free networks, and we find that when average degree equals 6, this point is a tradeoff point between the robustness and cost of scale-free networks.

In the last decade, the nonstationary properties of channel models have attracted more and more attention for many scenarios, that is, vehicle-to-vehicle (V2V), mobile-to-mobile (M2M), and high-speed train (HST). However, little research has been done on the real-physical channel model. In this paper, we propose a generalized three-dimensional (3D) nonstationary channel model, in which the scatterers are assumed to be distributed around the transmitter (Tx) and receiver (Rx) on a two-sphere model. By employing the von Mises-Fisher distribution, the mean values of the azimuth angle of departure (AAoD) and elevation angle of departure (EAoD) and the azimuth angle of arrival (AAoA) and elevation angle of arrival (EAoA) are tracked by time-variant (TV) Brownian Markov (BM) motion paths, which ensure the nonstationarity of the proposed channel model. Moreover, the TV autocorrelation function (ACF) and Doppler power spectrum density (DPSD) of the proposed nonstationary channel model are calculated by using signal processing tools, for example, fast Fourier transform (FFT) and short-time Fourier transform (STFT). In addition, the simulation results show that the TV scatterer distribution results in a nonstationary nonisotropic channel model, and the proposed model can be employed to simulate the 3D nonstationary channel model.

We focus on the interference statistical metrics of matched filter transmission over line-of-sight (LoS) downlink channels. The exact moments of the interference are derived, which can be used to make approximations for user rates and gain insights into the effects of system parameters such as angle spread (AS), antenna numbers, and array spacings. As the interference decreases with larger AS, we propose a user scheduling scheme by grouping users into different subspaces with corresponding orthogonal fraction of resource blocks, aiming to maximize the 3D angular separation among multiusers within each group; thus, a lower interference and higher sum rate would be expected. By performing this user scheduling scheme with 3D angular separation, a large sum rate gain is observed, which provides us a promising method in interference control and management.

Feature analysis and classification detection of abnormal cells from images for pathological analysis are an important issue for the realization of computer assisted disease diagnosis. This paper studies a method for cervical squamous epithelial cells. Based on cervical cytological classification standard and expert diagnostic experience, expressive descriptors are extracted according to morphology, color, and texture features of cervical scales epithelial cells. Further, quantificational descriptors related to cytopathology are derived as well, including morphological difference degree, cell hyperkeratosis, and deeply stained degree. The relationship between quantified value and pathological feature can be established by these descriptors. Finally, an effective method is proposed for detecting abnormal cells based on feature quantification. Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.

This paper uses a time-delay dependentH∞control model to analyze the effect of manufacturing decisions on the process of transmission from resources to capability. We establish a theoretical framework of manufacturing management process based on three terms: resource, manufacturing decision, and capability. Then we build a time-delayH∞robust control model to analyze the robustness of manufacturing management. With the state feedback controller between manufacturing resources and decision, we find that there is an optimal decision to adjust the process of transmission from resources to capability under uncertain environment. Finally, we provide an example to prove the robustness of this model.

The immune system is critical in preventing infection and cancer, and malnutrition can weaken different aspects of the immune system to undermine immunity. Previous studies suggested that vitamin B6 deficiency could decrease serum antibody production with concomitant increase in IL4 expression. However, evidence on whether vitamin B6 deficiency would impair immune cell differentiation, cytokines secretion, and signal molecule expression involved in JAK/STAT signaling pathway to regulate immune response remains largely unknown. The aim of this study is to investigate the effects of vitamin B6 deficiency on the immune system through analysis of T lymphocyte differentiation, IL-2, IL-4, and INF-γsecretion, andSOCS-1andT-betgene transcription. We generated a vitamin B6-deficient mouse model via vitamin B6-depletion diet. The results showed that vitamin B6 deficiency retards growth, inhibits lymphocyte proliferation, and interferes with its differentiation. After ConA stimulation, vitamin B6 deficiency led to decrease in IL-2 and increase in IL-4 but had no influence on IFN-γ. Real-time PCR analysis showed that vitamin B6 deficiency downregulatedT-betand upregulatedSOCS-1transcription. This study suggested that vitamin B6 deficiency influenced the immunity in organisms. Meanwhile, the appropriate supplement of vitamin B6 could benefit immunity of the organism.

The process of drilling mud filtrate invading into a reservoir is time dependant. It causes dynamic invasion profiles of formation parameters such as water saturation, salinity, and formation resistivity. Thus, the responses of a high-definition induction log (HDIL) tool are time dependent. The logging time should be considered as an important parameter during logging interpretation for the purposes of determining true formation resistivity, estimating initial water saturation, and evaluating a reservoir. The time-dependent HDIL responses are helpful for log analysts to understand the invasion process physically. Field examples were illustrated for the application of present method.

This paper proposes a fast and accurate calibration method to calibrate multiple multimodal sensors using a novel photogrammetry system for fast localization of EEG sensors. The EEG sensors are placed on human head and multimodal sensors are installed around the head to simultaneously obtain all EEG sensor positions. A multiple views’ calibration process is implemented to obtain the transformations of multiple views. We first develop an efficient local repair algorithm to improve the depth map, and then a special calibration body is designed. Based on them, accurate and robust calibration results can be achieved. We evaluate the proposed method by corners of a chessboard calibration plate. Experimental results demonstrate that the proposed method can achieve good performance, which can be further applied to EEG source localization applications on human brain.

Purpose. Detecting brain regions characterizing mild traumatic brain injury (mTBI) by combining Tract-Based Spatial Statistics (TBSS) and Graphical-model-based Multivariate Analysis (GAMMA).Materials and Methods. This study included 39 mTBI patients and 28 normal controls. Local research ethics committee approved this prospective study. Diffusion-tensor imaging was performed in mTBI patients within 7 days of injury. Skeletonized fractional anisotropy (FA) maps were generated by using TBSS. Brain regions characterizing mTBI were detected by GAMMA.Results. Two clusters of lower frontal white matter FA were present in mTBI patients. We constructed classifiers based on FA values in these two clusters to differentiate mTBI and controls. The mean accuracy, sensitivity, and specificity, across five different classifiers, were 0.80, 0.94, and 0.61, respectively.Conclusions. Combining TBSS and GAMMA can detect neuroimaging biomarkers characterizing mTBI.

Location problems exist in the real world and they mainly deal with finding optimal locations for facilities in a network, such as net servers, hospitals, and shopping centers. The inverse location problem is also often met in practice and has been intensively investigated in the literature. As a typical inverse location problem, the inverse 1-median problem on tree networks with variable real edge lengths is discussed in this paper, which is to modify the edge lengths at minimum total cost such that a given vertex becomes a 1-median of the tree network with respect to the new edge lengths. First, this problem is shown to be solvable in linear time with variable nonnegative edge lengths. For the case when negative edge lengths are allowable, the NP-hardness is proved under Hamming distance, and strongly polynomial time algorithms are presented underl1andl∞norms, respectively.