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In this study, the physical and mathematical models of wind supercharging solar chimney power plant combined with seawater desalination by indirect condensation freshwater production (WSSCPPSDIC) were proposed. The numerical simulation was carried out for analyzing the flow field characteristics of WSSCPPSDIC. Subsequently, a comparison of solar chimney power plant (SCPP), solar chimney power plant combined with seawater desalination by indirect condensation freshwater production (SCPPSDIC) and WSSCPPSDIC was conducted in different rotational speed. It was found that the ingenious and special condenser combined in the integrated system could preheat the seawater and realize the full utilization of the latent heat of condensation released by water vapor. Besides, the negative pressure generated by the wind supercharging system installed at chimney exit remarkably increased the electricity production. Generally, WSSCPPSDIC could effectively improve the freshwater output and the solar energy comprehensive utilization efficiency, specifically 9.805 ton/h and 16.73%.

Under the influence of electromagnetic excitation and terminal loads disturbance, the motor rotor shaft of the semi-direct cutting section of a shearer may produce electromechanical coupling resonance. Against this background, this work investigates the vibration properties of a motor rotor and the effect of electromechanical couplings. According to the electric machine theory and the Maxwell equation, the electromechanically coupled nonlinear torsional vibration model for the rotor system is first established under the influence of electromagnetic excitation and terminal load disturbance. Besides, its approximate solution is derived through the multi-scale method in the case of main parametric resonance, and the steady-state solution is determined through the numerical method. Moreover, the electromagnetic parameters including internal power factor angle, permanent magnet coefficient, number of pole pairs, and magnetic saturation coefficient and the mechanical parameters such as torsional stiffness of rotor and terminal load, which are responsible for the torsional vibration of rotor system, are studied, respectively. The results show that the stiffness failure and load mutation could cause chaotic motions, and a reasonable selection of electromagnetic and mechanical parameters for the permanent magnet synchronous motor (PMSM) could prevent the jumping and bifurcation from happening. This work is expected to benefit, both theoretically and practically, the design, optimization, and diagnosis of high-performance shearers that are operated in a complex working environment.

Wireless communication is playing an important role in benefiting our daily lives. Fifth generation (5G) advanced wireless communication is anticipated to support various upcoming services. Moreover, machine learning (ML) is expected to optimize wireless systems by tackling complex problems which cannot be solved using traditional mathematical models. In this paper, applications of ML for 5G advanced wireless communication are introduced in conjunction with its research advances. Furthermore, this paper explores the challenges and future research directions of applying ML in 5G advanced wireless communication.

With the development of wireless communication technologies and explosive increases number of UEs, data traffic grows rapidly so that much denser deployment is essential. More frequent handover cause higher latency and throughput reduction, which has a negative impact on the network performance and users’ satisfaction. For the applications of 5G network including resource allocation and mobility management, it is essential to predict the positions of mobile users in the future so as to make preparation in advance. In this paper, we propose a Long Short-term Memory (LSTM) model based location prediction considering the wireless measurement reports from the serving base station and the neighbour base stations, and introduce orientation loss function in order to enable the model to acknowledge the information on the direction of the UE movement. Extensive numerical experiments demonstrated that the proposed LSTM model based on multiple features and orientation information can achieve better performance on the location prediction.

Objective: High-temperature environments induce menstrual disorders, which can seriously damage women's reproductive health and workability. The regulation mechanism underlying the induction of these disorders at high temperature is yet to be elucidated. Saliva is an information-rich biological fluid that can reflect systemic diseases. Blood metabolites can enter the saliva through extracellular, intracellular, or paracellular pathways. Therefore, saliva metabolites may provide a window for the early detection of diseases. Methods: Here, we investigated the characteristics of menstrual cycle disorders and saliva metabolomics. Women from high and normal temperature areas of China were selected and divided into two groups—high-temperature (H group) and control (C group). A questionnaire survey was conducted in summer (July) to investigate the incidence rate of menstrual disorders, characteristics of the disorders, and factors influencing the risk of these disorders in different regions. Metabolomics was applied to analyze the characteristics of the salivary metabolites and neurotransmitters in the two groups of women with menstrual disorders.Results: The incidence rate of menstrual disorders was significantly higher in the H group than in the C group (P < 0.05). High-temperature environment, anxiety, pressure, and sleep quality were identified as critical factors associated with menstrual disorders. Non-targeted saliva metabolomics identified 64 significantly different metabolites, which mainly enriched in metabolic pathways such as carbohydrate metabolism, membrane transport, digestive system, and nucleotide metabolism(P < 0.05). Targeted metabolomics of neurotransmitters revealed increased expression of histamine (HA) and glutamine and decreased expression of 5-hydroxyindole acetic acid (5-HIAA) (P < 0.05).Conclusions: The incidence rate of menstrual disorder was increased in the H group, which is significantly related to factors such as anxiety and pressure. Changes in metabolites and neurotransmitters indicated that besides mood, endocrine regulation mechanism, inflammatory reactions that affect the formation of the follicular, cell membrane might contribute to the development of menstrual disorders.

In order to explore the problems in technology path, social value and key technologies of intelligent teaching system in the era of artificial intelligence 2.0, this paper takes four modules of intelligent teaching system in the era of artificial intelligence 2.0 as objects , and discusses the application path and social value of intelligent teaching system, and carries out in-depth research on the key technologies of natural language processing, question answering system, expression recognition and educational data mining corresponding to the four modules. This paper also analyzes the status, role and development trend of four key technologies in the application and development process of intelligent teaching system in the era of artificial intelligence 2.0. The research in this paper can provide the basis for the development and application of intelligent teaching system in the era of artificial intelligence 2.0, and promote the better integration and development of new information technology and education & teaching.

The working environment of divers under water is complex and changeable, which is full of danger and needs effective monitoring measures. This paper presents an Autonomous Diving Buddy to assist divers and ensure their safety. In this paper, considering the limitations of the previous underwater interaction, we proposed an algorithm based on gesture recognition, assisted by underwater image enhancement and target tracking, which can effectively complete the task of underwater gesture recognition. Then we experimented on the algorithm and the system. Experiments show that the algorithm performs well and the proposed ADB can make corresponding actions correctly according to gestures.

In order to explore the problems of the key technology of intelligent teaching system in the era of artificial intelligence 2.0 and promote the development of intelligent teaching system better, we study the status and role of the four key technologies in intelligent teaching system. This paper takes four modules of intelligent teaching system as the objects, and discusses four key technologies supporting the application of intelligent teaching system, namely natural language processing, question answering system, expression recognition, educational data mining, etc. This paper discusses the status and the role of key technologies in the intelligent teaching system in the era of artificial intelligence 2.0, and the problems, causes and the necessity of solving in the application and development process of intelligent teaching system. The research results of this paper can provide a theoretical basis for the application of intelligent teaching system in the era of artificial intelligence 2.0, and standardize the orderly development of intelligent teaching system.

With the deployment of the 5G, lower latency and higher frequency brings not only the users' satisfaction but also the dense deployment of base stations which causes large energy consumption. Thus, energy saving is a challenging issue. Machine learning techniques, which could extract features from raw data in the wireless access network and predict the future state by the previous features, are useful to energy saving. In this paper, we proposed the energy saving scheme based on machine learning techniques to achieve the balance between system performance and energy efficiency. Cell switching-off by load prediction using different algorithms, and dynamic threshold adjustment improve the energy efficiency and thereby reduces the operation cost. By demonstrating the results of experiments in wireless environment, the switch-off duration could reach up to 176% increase, and the energy scheme can save up to 2, 223 RMB electricity saving per week per cell.

In this study, a model of wind supercharged solar chimney power plant combined with seawater desalination (WSSCPPCSD) was proposed. The integrated system was divided into wind pressure ventilator and solar chimney power generation system for numerical simulation. Three-dimensional unsteady state numerical investigation on wind pressure ventilator was carried out as well as steady state numerical study on the solar chimney power generation system. The flow field characteristics and performance parameters of the two subsystems were analyzed. Subsequently, the comparison between solar chimney power plant (SCPP), solar chimney power plant combined with seawater desalination (SCPPCSD) and WSSCPPCSD was conducted. The results show that, although SCPPCSD can greatly improve the solar energy comprehensive utilization, the power generation is reduced. The wind pressure ventilator proposed in this paper could not only significantly increase the power generation but also increase the freshwater production by providing a negative pressure of 64.5 Pa at the chimney outlet. It was found that at 800 W/m2, an increase in power output of about 14.7 kW and increase in hourly freshwater production of 30 g/h were obtained.

In vehicular communications, intracell interference and the stringent latency requirement are challenging issues. In this paper, a joint spectrum reuse and power allocation problem is formulated for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Recognizing the high capacity and low-latency requirements for V2I and V2V links, respectively, we aim to maximize the weighted sum of the capacities and latency requirement. By decomposing the original problem into a classification subproblem and a regression sub-problem, a convolutional neural network (CNN) based approach is developed to obtain real-time decisions on spectrum reuse and power allocation. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 3.62% of its CPU runtime., arXiv admin note: text overlap with arXiv:1903.00165

This article proposes to develop a WiFi-controlled portable atomic force microscope (AFM). The AFM consists of a horizontal probe, controlling circuits, digital to analog (D/A) and analog to digital (A/D) interfaces, a microcomputer (Raspberry Pi, RPi), and a laptop. The proposed AFM uses a pocket-size power supply to drive the controlling circuits, the D/A and A/D interfaces, as well as the RPi that constructs network hotspots and generates scanning signals. With special design and integration of the whole system, both of the AFM probe and electronic controlling system are portable. At a distance of 50 m from the proposed AFM, experiments in the constant height mode and the constant force mode are conducted to evaluate its performance. The results show that this WiFi-controlled AFM has a maximum scan range of 3.6 × 3.6 μm2 with nanometer order resolution. Meanwhile, it achieves satisfactory image contrast, stability, and repeatability. Compared with conventional AFMs, the AFM proposed in this paper no longer relies on commercial AC mains supply or high-voltage DC power supply, and realizes WiFi-controlled AFM scanning and imaging in 50 m or farther without wire or network cable connection to a laptop or a desktop computer. Given credits to these features, WiFi-controlled AFMs are expected to own a wider range of application, especially in isolated environments, outdoor researches, or even fieldwork investigations.

Objective to investigate the effect of Huoxue Jiedu Jiangtang Recipe (HJJR) on cardiac remodeling and function prognosis in diabetic patients complicated with coronary heart disease (CHD) after percutaneous coronary intervention (PCI), and to explore its possible mechanism. Methods 92 diabetic patients with CHD who had received PCI therapy, were equally randomized into the treatment group (46) and the control group (46) from January 2016 to August 2016. The control group were given standardized western medicine, while treatment group received HJJR besides standardized treatment. The treating course were 2 months. Before and after treatment, the left ventricular morphology index [left ventricular end diastolic diameter (LVEDD), left ventricular end systolic diameter (LVESD), left ventricular posterior wall thickness (LVPWT), left ventricular end-diastolic ventricular septal thickness (LVEST)] and the cardiac function data [stroke volume (SV), left ventricular fractional shortening (FS), the cardiac index (CI), cardiac output (CO), maximum blood flow velocity of early diastolic/ atrium late diastolic (E/A), left ventricle ejection fraction (EF)] were detected with echocardiography. The serum levels of C-reactive protein (CRP), tumor necrosis factor-a (TNF-α), interleukin-6 (IL-6) and Angiotensin II (Ang II) were tested with enzyme linked immunosorbent assay (ELISA), and the clinical efficacy and 5-year survival rate were evaluated. Results The total clinical effective rate and 5-year survival ratio in treatment group were higher than those in control group (P

The permanent magnet semi-direct drive cutting transmission system of shearer is taken as the research object, the non-linear dynamic model of the system is established and the Runge-Kutta method is used to solve it. The statistical processing is carried out to gear stress by rain-flow counting method, and an eight-stage random loading model is established. Combining the nonlinear fatigue damage theory, the dynamic reliability of gear is predicted through the modified stress-strength interference model. Finally, the influence of some parameters on gear reliability is further studied, which provides a reference for the reliability optimization of gear in shearer cutting unit.

Urinary incontinence and Erectile function are two hot issues after radical prostatectomy. This study aims to explore the application and curative effect of “pubovesical complex preserving technique for intrafascial laparoscopic radical prostatectomy (PPLRP)”. Methods: This was a retrospective study of 200 patients who underwent laparoscopic radical prostatectomy (LRP) from January 2011 to May 2018. Results: During the study period, we collected 68 patients with PPLRP in group A, 46 patients with conventional intrafascial LRP in group B and 86 patients with interfascial LRP in group C, respectively. Three groups had no difference in terms of Preoperative age, Clinical stage, Prostate-specific antigen (PSA) value, Gleason score at biopsy and Preoperative good potency (IIEF-5 score) (P>0.05). There was no difference between the three groups in terms of operative time, intraoperative blood loss, time of catheter retain, postoperative stay and histologic status. All groups had similar PSM (positive surgical margin, PSM) rate. Continence rates at immediately after catheter removal, 1,3 and 6 months: 68% (46/68),85% (58/68),93% (63/68) and 100% (68/68) in group A; and 63% (29/46),80% (37/46),89% (41/46) and 96% (44/46) in group B; and 22% (19/63),49% (42/63),72% (62/63) and 84% (72/63) in group C. Regarding sexual function: No differences were found in the three groups in potency (IIEF-5 score). Baseline IIEF-score reached 50%,35% and 21% respectively at postoperative 6 months. Biochemical recurrence-free survival at 3 years was 79.3%,76.3% and 76.4% in A, B and C group, respectively. Conclusions: The PVC preserving technique for LRP provides early recovery from incontinence, faster recovery of sexual function preoperative levels. There is no adverse effect on the oncological outcome in the selective prostate cancer. It is an ideal way of nerve-sparing LRP.

In this paper we researched the vehicle braking sense in three aspects of human, vehicle and environment and analysed their impacts on brake feeling. Through the real vehicle test we analysed the relationship among pedal force, pedal travel and deceleration. We used dynamometer test method to study the brake noise question. We designed a fixture which could imitate the suspension and made the test more close to the true level. Moreover we discussed how to establish the evaluation system of vehicle braking condition. Through real vehicle test of braking, we can test and record the brake system parameters in the braking process under urban working conditions. We recorded the brake frequency, the change of brake speed and brake disc temperature. Meanwhile, based on the analysis of braking condition, we put forward the index of brake load to reflect the city’s traffic conditions. Experiment show that the braking condition and brake feel are related, braking condition also provides theoretical support for the design of brake system.

Cloud computing provides a flexible and cost-effective solution to big data applications. Data privacy, however, is a main concern. To avoid information leakage to a cloud service provider, a user may store portions of encoded data in multiple clouds and perform computing tasks in a distributed way. In this work, nested MDS codes and the criterion of perfect secrecy are adopted. The allocation of encoded data to be stored in heterogeneous clouds with different computing capability is formulated as an optimization problem, subject to data reliability and security constraints. The problem is shown to be feasible if and only if the storage budget is above a certain level. The tradeoff between minimum computation time and storage budget is analytically characterized, and the former is proved to be a piecewise-linear decreasing function of the latter. When the storage budget increases beyond a certain threshold, the optimal computation time levels off. Closed-form expressions of minimum computation time and optimal storage allocation are obtained. Numerical results show that the optimized allocation outperforms equal allocation significantly if the computing rates of different clouds have large variation. Refereed/Peer-reviewed