ALBERT

Description: In the present study, we employ fluorescence spectroscopy, dynamic light scattering, and molecular docking methods. Binding of anticancer drug anastrozole with human lysozyme (HL) is studied. Binding of anastrozole to HL is moderate but spontaneous. There is anastrozole persuaded hydrodynamic change in HL, leading to molecular compaction. Binding of anastrozole to HL also decreased in vitro lytic activity of HL. Molecular docking results suggest the electrostatic interactions and van der Waals forces played key role in binding interaction of anastrozole near the catalytic site. Binding interaction of anastrozole to proteins other than major transport proteins in blood can significantly affect pharmacokinetics of this molecule. Hence, rationalizing drug dosage is important. This study also points to unrelated effects that small molecules bring in the body that are considerable and need thorough investigation.

Description: Controlling and maintaining the orientation of the balloon-borne gondola for high-altitude flight is a prerequisite for ensuring the pointing control of observation instruments. When the balloon-borne gondola is flying in the stratosphere of the atmosphere, the existing external interferences will be converted into the coupling moment to the azimuth control system. Meanwhile, those uncertain factors and the frictional nonlinearity of the control system will also cause a certain magnitude of coupling moment. The existence of such coupling moment largely impacts on the accuracy and stability of the orientation control for the angular momentum exchange devices of the balloon-borne gondola. To address such an issue, this paper proposes and implements a novel type of integrated decoupler device. With this decoupler adopted, the aziDmuth control system could sense the existence of coupling torque and azimuth fluctuations quickly and suppress the influences of external interference, uncertain factors, and system structure nonlinearity on the azimuth control effectively, thereby improving the control accuracy of the azimuth control system. Both simulations and experiments are conducted to verify the effectiveness of the proposed device. The results show that the integration of the decoupler and the controller of the azimuth control system provide the azimuth control of the balloon-borne gondola with high accuracy and stability. Such a decoupler device design has a broad potential and could not only be used for balloon-borne gondola control but also could be applied onto other control systems using angular momentum exchange devices as actuators.

Description: Gates are important operating facilities and resources in civil airports. It is a core task in the airport operation management to select reasonable gates for inbound and outbound flights. We present a continuous time formulation with second-order cone programming (SOCP) for the gate assignment problem which allocates flights to available gates to optimize both the transfer time of passengers and the robustness of the airport operations schedules. The problem is formulated as a mixed integer nonlinear program, and then, the quadratic objective that minimizes the walking distance of transferring passengers is linearized, and the objective that minimizes the variance of idle time at the gates is transformed to a second-order cone constraint with a linear objective function. Then, a Lagrangian relaxation algorithm is developed by exploiting the problem structure. Computational tests are carried out to illustrate the efficiency of the model and the algorithms. It is shown that the continuous time formulation is more efficient than the existing model, and the Lagrangian relaxation algorithm can obtain better solutions faster than a commercial solver.

Description: Background. Young people are challenged with sexual and reproductive health (SRH) problems due to limited access to services and information. It contributes a high burden of sexually transmitted disease, unsafe abortion, and premature mortality, especially in low-income countries like Ethiopia. Hence, this study aimed at assessing the SRH service utilization and associated factors among college students at West Arsi Zone in Oromia region, Ethiopia. Methods. Institutional-based cross-sectional study was conducted among 519 randomly selected college students from January 01, 2019, up to April 30, 2019. Data were collected through self-administered pretested questionnaires. Data entry and analysis for descriptive and logistic regression models were performed by using SPSS (version 23). The result was declared as statistically significant at p 

Description: Generating relations involving the special functions have already proved their important role in mathematics and other fields of sciences. In this paper, we aim to provide some presumably new generating relations in connection with the generalized multi-index Bessel–Maitland function Jνjm,qλjm,γ.. The main results presented here, being very general, can yield a number of particular or equivalent identities, some of which are explicitly demonstrated.

Description: With the rapid development of e-commerce, logistic enterprises must better predict customer demand to improve distribution efficiency, so as to deliver goods in advance, which makes logistics stochastic and dynamic. In order to deal with this challenge and respond to the concept of “green logistics,” an electric vehicle routing problem with stochastic demands (EVRPSD) and proactive remedial measures is investigated, and an EVRPSD model with probability constraints is established. At the same time, a hybrid heuristic algorithm, combining a saving method and an improved Tabu search algorithm, is proposed to solve the model. Moreover, two insertion strategies with the greedy algorithm for charging stations and dynamic nodes are introduced. Finally, a large number of experimental data show that the heuristic algorithm proposed in this paper is feasible and effective.

Description: Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. The existence of a mixed pixel affects the accuracy of the ground object identification and classification and hinders the application and development of hyperspectral technology. For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well. Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing.

Description: Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the candidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse coefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to adapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent frames information. Thirdly, we employ an inverse sparse representation method to model the relationship between target candidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating scheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our algorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and severe occlusion.

Description: Many people use traditional methods such as quasi-Newton method and Gauss–Newton-based BFGS to solve nonlinear equations. In this paper, we present an improved particle swarm optimization algorithm to solve nonlinear equations. The novel algorithm introduces the historical and local optimum information of particles to update a particle’s velocity. Five sets of typical nonlinear equations are employed to test the quality and reliability of the novel algorithm search comparing with the PSO algorithm. Numerical results show that the proposed method is effective for the given test problems. The new algorithm can be used as a new tool to solve nonlinear equations, continuous function optimization, etc., and the combinatorial optimization problem. The global convergence of the given method is established.

Description: According to the waste type identification requirement in waste classification, a waste type identification method based on a bird flock neural network (BFNN) was proposed. The problem of obtaining the feature dataset of waste images was considered, and color histogram and texture feature extraction techniques were used. The local optimum problem of a typical backpropagation neural network (BPNN) was considered, and a bird flock optimization (BFO) algorithm was proposed. The accuracy problem of the typical BPNN was considered, and a new online weight adjustment method of neurons was proposed. The number of hidden layer neurons (nodes) of the typical BPNN was considered, and an online adjustment method was proposed. The experimental results show that the recyclables (paper, plastic, glass, and cloth) and nonrecyclables can effectively be identified by the waste type identification method based on the BFNN, and the recognition accuracy is 81% which meets actual needs.

Description: A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic algorithm with a coarse-grained architecture island model. The original population is divided into several subpopulations to simulate different living environments, thus increasing species richness. For each subpopulation, different mutation rates were adopted, and the crossover results were optimized by combining the crossover method based on distance. The adaptive mutation strategy based on the number of generations was adopted to prevent the algorithm from falling into the local optimal solution. An elite strategy was adopted for outstanding individuals to retain their superior genes. The algorithm was implemented with CUDA/C, combined with the powerful parallel computing capabilities of GPUs, which greatly improved the computing efficiency. It provided a new solution to the TSP problem.

Description: A common challenge faced by liner operators in practice is to effectively allocate empty containers now in a way that minimizes the expectation of costs and reduces inefficiencies in the future with uncertainty. To incorporate uncertainties in the operational model, we formulate a two-stage stochastic programming model for the stochastic empty container repositioning (ECR) problem. This paper proposes a separable piecewise linear learning algorithm (SPELL) to approximate the expected cost function. The core of SPELL involves learning steps that provide information for updating the expected cost function adaptively through a sequence of piecewise linear separable approximations. Moreover, SPELL can utilize the network structure of the ECR problem and does not require any information about the distribution of the uncertain parameters. For the two-stage stochastic programs, we prove the convergence of SPELL. Computational results show that SPELL performs well in terms of operating costs. When the scale of the problem is very large and the dimensionality of the problem is increased, SPELL continues to provide consistent performance very efficiently and exhibits excellent convergence performance.

Description: In this paper, we propose a finite-time sliding mode trajectory tracking control methodology for the vertical takeoff and landing unmanned aerial vehicle (VTOL UAV). Firstly, a system error model of trajectory tracking task is established based on Rodrigues parameters by considering both external and internal uncertainties. According to the cascade property, the system model is divided into translational and rotational subsystems, and a hierarchical control structure is hence proposed. Then, a finite-time generalized nonlinear disturbance observer (NDOB) is proposed, based on which the finite-time convergence result of equivalent disturbance estimation can be acquired. Finally, by introducing a tan-type compensator into the traditional terminal sliding mode control (SMC), the finite-time convergence result of the closed-loop control system is acquired based on Lyapunov stability analysis. Simulation results show the effectiveness of the proposed methodology.

Description: Compared with single consensus, the multiconsensus of multiagent systems with nonlinear dynamics can reflect some real-world cases. This paper proposes a novel distributed law based only on intermittent relative information to achieve the multiconsensus. By constructing an appropriate Lyapunov function, sufficient conditions on control parameters are derived to undertake the reliability of closed-loop dynamics. Ultimately, the availability of results is completely validated by these numerical examples.

Description: Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.

Description: Spring linkage can be applied to in-pipe robots for connecting different modules together and can make it pass through elbows more easily. However, its stiffness cannot be set to be too hard or too soft. This paper tries to make a balance between the compressive stiffness and the bending stiffness of the spring. After a brief introduction to the construction mechanism and some assumptions, the mathematical representation of the spring bending stiffness was deduced based on the Kirchhoff theory which describes the spatial curve with displacement rather than time. Then, some simulations aiming at verifying the correctness of the deduced bending stiffness expression were carried out. Finally, the relationship between the two rigidities was found out, which helps to find a way to decrease the bending stiffness of spring while keeping its compressive stiffness strong enough.

Description: Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits.

Description: This paper proposes a novel predictive strategy based on a model predictive control (MPC) for the interior permanent magnet synchronous motors (IPMSMs) driven by a three-level simplified neutral-point clamped inverter (3L-SNPC) for electric vehicle applications (EVAs). Based on the prediction of the future behavior of the controlled variables, a predefined multiobjective cost function incorporates the control objectives which are evaluated for every sampling period to generate the optimal switching state applied directly to the inverter without the modulation stage. The control objectives in this paper are tracking current capacity, neutral-point voltage balancing, common-mode voltage control, and switching frequency reduction. The principal concepts of the novel scheme are summarized as follows: first, the delay compensation based on the long horizon of prediction is adopted by a multilevel power converter structure. Second, based on the modified Lyapunov candidate function, both stability and recursive feasibility are ensured of the proposed predictive scheme. Third, the practicability of the real-time implementation is improved by the proposed “static voltage vector” (SVV) and “single state variation” (SSV) principles. Finally, the proposed concepts are implemented in the novel predictive control formulation as additional constraints without compromising the complexity and the good performances of the predictive controller. Therefore, only the switching states that guarantee the stability and the reduction of calculation burden criteria are considered in the evaluation of cost function. The proposed predictive scheme based on the “SVV” principle has demonstrated superior performance in simulation compared with the proposed scheme with the “SSV” principle. The computational burden and switching frequency rates are reduced by 35% and 56.22%, respectively.

Description: To study the seismic response law of the immersed tunnel under different structural stiffnesses, three groups of shaking table models of immersed tunnel are carried out, including free-field model, flexible tunnel model, and rigid tunnel model. The similarities and differences of the pore water pressure and acceleration time-history between the free site and liquefiable soil layer around the flexible and rigid immersed tube tunnel are analyzed. The results show that, compared with the soil layer at the same position in the free-field, both the amplitude of acceleration and frequency component in the surrounding soil layer are affected by the stiffness of tunnel, and the influence comes greater with the larger stiffness of tunnel. When the input amplitude of seismic ground motion is small, the soil layer in the free field and the flexible tunnels share the same acceleration amplification law. The development law of pore pressure in the soil layer of test 1 was similar to test 2 but was quite different from test 3. Specifically, when the tunnel stiffness is smaller, the surrounding soil layer is easier to liquefy, with greater influence of the tunnel stiffness on the development law of pore pressure in the surrounding soil layer. The lower the soil buried depth is, the faster the pore pressure dissipates. The growth rate of tunnel strain is related to the stiffness of the structure. Generally speaking, the strain growth rate of the structure with smaller stiffness is higher under moderate earthquakes. The smaller the tunnel stiffness is, the more adaptable the tunnel is to the movement of the surrounding soil layer.

Description: Human motion prediction aims at predicting the future poses according to the motion dynamics given by the sequence of history poses. We present a new hierarchical static-dynamic encoder-decoder structure to predict the human motion with residual CNNs. Specifically, to better mine the law of the motion, a new residual CNN-based structure, v-CMU, is proposed to encode not only the static information but also the dynamic information. Based on v-CMU, a hierarchical structure is proposed to model different correlations between the different given poses and the predicted pose. Moreover, a new loss function combining the static and dynamic information is introduced in the decoder to guide the prediction of the future poses. Our framework features two-folds: (1) more effective dynamics mined due to the fusion of information of the poses and the dynamic information between poses and the hierarchical structure; (2) better decoding or prediction performance, thanks to the mid-level supervision introduced by the new loss function considering both the static and dynamic losses. Extensive experiments show that our algorithm can achieve state-of-the-art performance on the challenging G3D and FNTU datasets. The code is available at https://github.com/liujin0/SDnet.

Description: It is of great significance to comprehensively and reasonably identify the key nodes in the WeChat epidemic information dissemination system. First, the recognition results can be used to guide the spread of epidemic-related information in WeChat, such as accelerating the spread of valuable information or monitoring the spread of rumors. Secondly, the analysis of key nodes helps us understand the evolution of the epidemic information dissemination network in WeChat, and the analysis of key nodes also helps us understand the modes and methods of epidemic-related information dissemination in WeChat. Finally, the results of these studies may be generalized to other fields of social life. This paper analyzes the composition of and relationship between epidemic-related information dissemination systems in WeChat and proposes a Supernetwork model for WeChat epidemic information dissemination on this basis. In this study, a comprehensive identification method of key nodes of the WeChat epidemic information dissemination system under the Supernetwork vision was constructed, and the method is analyzed and verified through examples in this paper.

Description: Aiming at the problem that the stability of the quadruped robot is decreased as its leg momentum is too high, a stable balance adjustment structure of the quadruped robot based on the bionic lateral swing posture is proposed. First, the leg structure of the quadruped robot is improved and designed by using the mechanism of the lateral swing posture of the leg of the hoof animal. Then, the D-H method is used to construct the corresponding leg kinematics model and determine the generalized coordinates of the leg joints in the lateral swing posture. The torque expression of the quadruped robot when it is tilted is established. Based on the differential equation of momentum of the hip joint and its static stability analysis, the static stability conditions in the upright posture and the bionic lateral swing posture are given. Finally, the experimental simulation and comparative analysis of the upright posture and the lateral swing posture of the quadruped robot are proposed by using the Adams virtual prototype technology. The simulation results show that as the angle of lateral swing increases, the peak value of the positive flip torque of the quadruped robot body increases accordingly, while the degree of tilt decreases accordingly, which shows that the bionic lateral swing posture of the quadruped robot has higher static stability than the traditional upright posture. This research provides a technical reference for the design and optimization of the offline continuous gait of the robot and the improvement of stability.

Description: The flavonoids are compounds synthesized by plants, and they have properties such as antioxidant, anticancer, anti-inflammatory, and antibacterial, among others. One of the most important bioactive properties of flavonoids is their antioxidant effect. Synthetic antioxidants have side toxic effects whilst natural antioxidants, such as flavonoids from natural sources, have relatively low toxicity. Therefore, it is important to incorporate flavonoids derived from natural sources in several products such as foods, cosmetics, and drugs. For this reason, there is currently a need to extract flavonoids from plant resources. In this review are described the most important parameters involved in the extraction of flavonoids by unconventional methods such as ultrasound, pressurized liquid extraction, mechanochemical, high hydrostatic pressure, supercritical fluid, negative pressure cavitation, intensification of vaporization by decompression to the vacuum, microwave, infrared, pulsed electric field, high-voltage electrical discharges, and enzyme-assisted extraction. There are no unified operation conditions to achieve high yields and purity. Notwithstanding, progress has been achieved in the development of more advanced and environmentally friendly methods of extraction. Although in literature are found important advances, a complete understanding of the extraction process in each of the unconventional techniques is needed to determine the thermodynamic and kinetic mechanisms that govern each of the techniques.

Description: The main emphasis on this paper is to analyze the axisymmetric flow and heat transfer in a liquid film over an unsteady radially stretching surface in the presence of a transverse magnetic field. The similarity transformations are used to reduce the highly nonlinear governing partial differential equations for momentum and energy into a set of ordinary differential equations. A numerical scheme is developed for the reduced nonlinear differential equations for the velocity and temperature fields. The literature survey shows that the present problem of thin film flow over a radially stretching sheet has not been studied before. The features of the flow and heat transfer characteristic for different values of governing parameters such as unsteadiness parameter, Prandtl number, Eckert number, and magnetic parameter are thoroughly examined. This study noticed that, by increasing the magnetic parameter and unsteadiness parameter, film thickness decreases.

Description: In order to extract more information that affects customer arrears behavior, the feature extraction method is used to extend the low-dimensional features to the high-dimensional features for the warning problem of user arrears risk model of electric charge recovery (ECR). However, there are many irrelevant or redundant features in data, which affect prediction accuracy. In order to reduce the dimension of the feature and improve the prediction result, an improved hybrid feature selection algorithm is proposed, integrating nonlinear inertia weight binary particle swarm optimization with shrinking encircling and exploration mechanism (NBPSOSEE) with sequential backward selection (SBS), namely, NBPSOSEE-SBS, for selecting the optimal feature subset. NBPSOSEE-SBS can not only effectively reduce the redundant or irrelevant features from the feature subset selected by NBPSOSEE but also improve the accuracy of classification. The experimental results show that the proposed NBPSOSEE-SBS can effectively reduce a large number of redundant features and stably improve the prediction results in the case of low execution time, compared with one state-of-the-art optimization algorithm, and seven well-known wrapper-based feature selection approaches for the risk prediction of ECR for power customers.

Description: Immersed boundary-lattice Boltzmann method (IB-LBM) has become a popular method for studying fluid-structure interaction (FSI) problems. However, the performance issues of the IB-LBM have to be considered when simulating the practical problems. The Graphics Processing Units (GPUs) from NVIDIA offer a possible solution for the parallel computing, while the CPU is a multicore processor that can also improve the parallel performance. This paper proposes a parallel algorithm for IB-LBM on a CPU-GPU heterogeneous platform, in which the CPU not only controls the launch of the kernel function but also performs calculations. According to the relatively local calculation characteristics of IB-LBM and the features of the heterogeneous platform, the flow field is divided into two parts: GPU computing domain and CPU computing domain. CUDA and OpenMP are used for parallel computing on the two computing domains, respectively. Since the calculation time is less than the data transmission time, a buffer is set at the junction of two computational domains. The size of the buffer determines the number of the evolution of the flow field before the data exchange. Therefore, the number of communications can be reduced by increasing buffer size. The performance of the method was investigated and analyzed using the traditional metric MFLUPS. The new algorithm is applied to the computational simulation of red blood cells (RBCs) in Poiseuille flow and through a microchannel.

Description: The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

Description: This research is conducted on the characters and trends of traffic flow in highway maintenance work areas under typical maintenance work forms. In order to improve the safety of the highway maintenance work area, a data monitoring method based on the combination of mixed speed measurement and background difference method were developed. During the on-site detection, the starting point of the warning zone, the starting point of the upstream transition zone, the starting point of the working zone, the midpoint of the working zone, and the six speed measurement sections of the working zone were collected at the end point and the end zone. In the video detection, the background subtraction was used, and the morphological denoting method and the connected domain analysis method were used to retain the vehicle foreground. After analyzing the connection domain and removing the wrong target, the vehicle target area is extracted from research. The research finally obtained the traffic flow characteristics of the start point of the warning zone, the start point of the upstream transition zone, the start point of the work zone, the midpoint of the work zone, the end point of the work zone, and the end point of the downstream transition zone. The study also obtained the traffic volume and the change trend of headway. The combination of mixed velocity method and background difference method is helpful for data monitoring in typical highway maintenance work areas. The measured data results are helpful for studying the distribution characteristics and trends of traffic flow in typical highway maintenance work areas.

Description: We quantify the strength and the directionality of information transfer between the Ghana stock market index and its component stocks as well as observe the same among the individual stocks on the market using transfer entropy. The information flow between the market index and its components and among individual stocks is measured by the effective transfer entropy of the daily logarithm returns generated from the daily market index and stock prices of 32 stocks ranging from 2nd January 2009 to 16th February 2018. We find a bidirectional and unidirectional flow of information between the GSE index and its component stocks, and the stocks dominate the information exchange. Among the individual stocks, SCB is the most active stock in the information exchange as it is the stock that receives the highest amount of information, but the most informative source is EGL (an insurance company) that has the highest net information outflow while the most information sink is PBC that has the highest net information inflow. We further categorize the stocks into 9 stock market sectors and find the insurance sector to be the largest source of information which confirms our earlier findings. Surprisingly, the oil and gas sector is the information sink. Our results confirm the fact that other sectors including oil and gas mitigate their risk exposures through insurance companies and are always expectant of information originating from the insurance sector in relation to regulatory compliance issues. It is our firm conviction that this study would allow stakeholders of the market to make informed buy, sell, or hold decisions.

Description: Facing more and more severe global warming problems, renewable energy, as an alternative to traditional fossil fuels, is attracting more and more attentions due to its capability of reducing carbon emission. This paper considers two-tier HetNets with orthogonal frequency division multiple access (OFDMA), where the macro base station (MBS) is powered by power grid and small base stations (SBSs) have hybrid energy supplies. Through smart grid, SBSs can share their renewable energy with each other. We consider the problem of cross-layer interference caused by spectrum reuse, the burst of user data, and the randomness of renewable energy arrivals. Through energy cooperation, this paper investigates maximizing the time-average energy efficiency of SBSs. Based on user data queue and SBS energy queue, the optimal problem is decoupled into two subproblems by Lyapunov optimization: resource allocation subproblem and energy scheduling and energy cooperation subproblem. By solving two subproblems, the online solution to the optimization problem is obtained. Through theoretical analysis and simulations, both user data queues and energy queues have an upper bound, the network is stable, and the proposed algorithm performs better than the basic algorithm without energy cooperation.

Description: This work introduces a computational method for solving the linear two-dimensional fuzzy Fredholm integral equation of the second form (2D-FFIE-2) based on triangular basis functions. We have used the parametric form of fuzzy functions and transformed a 2D-FFIE-2 with three variables in crisp case to a linear Fredholm integral equation of the second kind. First, a method based on the use of two m-sets of orthogonal functions of triangular form is implemented on the integral equation under study to be changed to coupled algebraic equation system. In order to solve these two schemes, a finite iterative algorithm is then applied to evaluate the coefficients that provided the approximate solution of the integral problems. Three examples are given to clarify the efficiency and accuracy of the method. The obtained numerical results are compared with other direct and exact solutions.

Description: Background. The rational use of drugs requires that patients receive medications appropriate to their clinical needs, in doses that meet their own individual requirements, for an adequate period of time, and at the lowest cost to them and their community with full information and with the lowest possible cost. If one of these is not met, it is referred to as irrational drug use. Many drugs have been sold or prescribed inappropriately in the world, and a significant part of the world population lacks access to essential medicine. The aim of this study was to assess practice towards rational drug use at Finoteselam and Asirade Zewudie hospitals. Methods. A cross-sectional study design was used for this study, and the study was conducted from October 11 to November 30, 2019. A total of 770 prescriptions were selected as per WHO criteria by using systematic random sampling and reviewed with the help of an observational checklist. The data were entered and processed with SPSS version 25 and evaluated using the WHO criteria. Result. The average number of drugs per prescription was 1.8 and 2.05, antibiotics encountered were 77.7% and 72.5%, injections encountered were 5.97% and 7.01%, percentage of drugs prescribed by generic names was 97% and 93.4%, counseling time was 1.6 minutes and 2.25 minutes, and dispensing time was 51.3 seconds and 62.72 seconds at Finoteselam and Asirade Zewudie hospitals, respectively. Conclusion. The majority of WHO core drug use indicators were not met in the two hospitals. The average number of drugs encountered in Asirade Zewudie hospital was slightly higher than the WHO recommended range, whereas the average number of drugs encountered in Finoteselam hospital was exactly equal. The percentage of antibiotics encountered was very high compared with WHO recommendation, but the percentage of injections encountered was below the WHO recommended range and time spent on counseling and dispensing was too short when compared with WHO recommendation. In addition to this, both hospitals had no essential drug list or formulary as well as a key drug list.

Description: Recently, wide applications of fuzzy set theory have attracted the attention of both researchers and practitioners. Fuzzy portfolio develops as a new area in the research field of investment portfolio. This paper investigates the major research hotspots, development trend, and evolution of fuzzy portfolio, which provides a systematic review of the current fuzzy portfolio literature. CiteSpace, the most commonly used bibliometrics software, is used in this article. According to the 602 articles with 15132 references, several conclusions can be summarized as follows: (1) Fuzzy portfolio becomes increasingly interdisciplinary with the connections among “Computer Science” and so on. (2) Most contributive authors are Markowitz and Zadeh. (3) South China University of Technology makes excellent performance in this research area and China is the most influential country. (4) European Journal of Operations Research is the cradle of plenty of crucial fuzzy portfolio investigations. (5) We find some research hotspots helpful to make scientific predictions of future trends by analyzing the keywords. By utilizing the effective bibliometric methods, we provide a comprehensive analysis and deep insights into the fuzzy portfolio research, enabling the individuals, especially the new beginners who are interested in this area to learn fuzzy portfolio, which will be of great help for their future explorations.

Description: In this work, a general geometric nonlinear model of straight thin-walled box beams (STBBs) under combined eccentric and axial loads is established. In order to accurately reflect the behavior of STBB, the additional shear lag warping is added to enrich the displacement field. It is necessary to define the section shape function to describe the local section deformation. Therefore, extension, bending, torsion, distortion, and shear lag effects are expressed by the generalized coordinate method. Based on the stability of transverse unconstrained box beam theory, meaningful higher-order solutions can be obtained by defining a set of coupled deformation modes. The equilibrium equation is discretized by the Galerkin method, and the Newton–Raphson incremental method is used to derive and solve the nonlinear governing equations. On this basis, the analytical expression of stiffness matrix is established. For solving the stability problem, the effectiveness of the proposed method is verified by comparing the calculation results of shell element (Ansys) with other theories. Numerical examples even show that the proposed method can not only get the influence of shear lag but also obtain the variation of lateral buckling of the beam model.

Description: In this article, a modified Polak-Ribière-Polyak (PRP) conjugate gradient method is proposed for image restoration. The presented method can generate sufficient descent directions without any line search conditions. Under some mild conditions, this method is globally convergent with the Armijo line search. Moreover, the linear convergence rate of the modified PRP method is established. The experimental results of unconstrained optimization, image restoration, and compressive sensing show that the proposed method is promising and competitive with other conjugate gradient methods.

Description: EFA (extended factored approach) algorithm is the main method of space-time adaptive processing technology (STAP) for airborne phased array radar, but it is faced with many problems, such as large number of samples and large amount of calculation. Therefore, this paper uses a method of spatial data dimensionality reduction processing based on cyclic iterative calculation to optimize its STAP. The final experimental results show that, after spatial data dimensionality reduction processing optimization, the STAP performance of EFA algorithm is further expanded in the range of sample number adaptation; especially in the case of small sample number, the optimized STAP performance has been basically close to the ideal compared with other optimization schemes; tap performance also proves that the optimization scheme in this paper has better convergence speed and STAP performance.

Description: This paper reviews the progress of the multiple population mortality model and the defects in parameter estimation and proposes an effective method to improve the performance of the mortality model. We set up a multiple population group, using the data of mainland China, Hong Kong (China), and Japan, to test fitting performance and forecasting performance. Using the TSWLS and TSSVD methods in a multiple population stochastic mortality model has advantages in fitting performance and robustness. In addition, the forecasting value of mortality ratio between any two populations can converge to a fixed constant in a certain time period which obeys the regular of human biological characteristics.

Description: Scientific evaluation of the competitiveness of China’s dairy industry is of great significance to the development of regional dairy industry. In view of the fact that the existing evaluation model cannot solve the dynamic nonlinear optimization problem, this paper established a projection pursuit model based on accelerating genetic algorithm and carried on the comprehensive evaluation and dynamic analysis on the competitiveness of dairy industry in China’s 31 provinces and cities (not including Hong Kong, Macao, and Taiwan) from five dimensions, namely, basic factors of production, the industry demand conditions, the enterprise competitiveness and growth force, related and supporting industries, and the natural environmental elements. It was found that firstly, from the perspective of time, the development trend of China’s dairy industry basically shows a positive trend. With the improvement of national living standards, the competitiveness of the dairy industry is also constantly improved. Secondly, from the perspective of space, regional differences in the competitiveness of the dairy industry are significant. Inner Mongolia, Heilongjiang, Shandong, and Hebei are at the forefront of the development of the industry, while the development of the dairy industry in the rest of the region has a big gap with those areas. From the perspective of regional layout, the advantageous region of China’s dairy industry has been preliminarily formed, and the competitive advantage is mainly concentrated in the northern region. Accordingly, the countermeasures and suggestions to enhance the competitiveness of China’s dairy industry were put forward.

Description: In this paper, we consider the problem of investment and reinsurance with time delay under the compound Poisson model of two-dimensional dependent claims. Suppose an insurance company controls the claim risk of two kinds of dependent insurance businesses by purchasing proportional reinsurance and invests its wealth in a financial market composed of a risk-free asset and a risk asset. The risk asset price process obeys the geometric Brownian motion. By introducing the capital flow related to the historical performance of the insurer, the wealth process described by stochastic delay differential equation (SDDE) is obtained. The extended HJB equation is obtained by using the stochastic control theory under the framework of game theory. Under the reinsurance expected premium principle, optimal time-consistent investment and reinsurance strategy and the corresponding value function are obtained. Finally, the influence of model parameters on the optimal strategy is explained by numerical analysis.

Description: Rice husk biochars were prepared by carbonization at 400–600°C. The products were analyzed by FTIR, SEM-EDS, BET, and approximate analysis in order to find final products with the best properties and the lowest carbonization temperature. It has been found that the biochar prepared at 500°C, which has 37.86 ± 0.11% yield, 341.0776 m2/g of BET surface area, and 0.136639 cm3/g of micropore volume, is suitable for use as a root supplement in the aquaponic system. The aquaponic systems consist of aquaculture and a hydroponic system with and without biochar supplement. The control experiment consists of an aquaculture and planting panel with biochar supplement disconnected from each other. Tilapia and Chinese morning glory were used for growth studies. The water quality from all aquaculture ponds has also been analyzed at an interval of 10 days for 47 days. The results showed that the growth rates of Tilapia and Chinese morning glory in the aquaponic system with biochar were clearly higher than in the control experiment, which is in accordance with the water quality in each aquaculture pond. However, the growth rates of Tilapia (23.5 g/body vs. 22.7 g/body) and morning glory (3.907 g/stem vs. 2.609 g/stem) in supplemented biochar system tend to be higher than the nonsupplemented biochar system. It has been shown that rice husk biochar can help in treating water in the aquaponic system by increasing the amount of dissolved oxygen in the aquaculture water and conversion of toxic compounds to those beneficial for plant growth.

Description: For the long-term sustainable development, the modern enterprises should consider both competition and cooperation. In the current studies of corporate competition strategies and games, the quantification of cooperation-competition (coopetition) between enterprises is not deeply investigated. In this paper, we establish a coopetition game model of oligarchic enterprises in the industry by using the quantitative altruistic factor and nonlinear cost function, analyze the influence of altruistic factor on equilibrium variables, and then validate it in the generation side market. The following conclusions are drawn: (1) the coopetition of any form will increase the market equilibrium price and the total equilibrium profit of the industry, which induces the motivation and intention of cooperation between oligarchic enterprises. (2) The increased unilateral altruism is instable and unsustainable because it will produce an altruistic threshold that makes the total equilibrium profit of the industry increase and then decrease. The unilateral altruism of high-cost generation companies is more beneficial for increasing the total equilibrium profit of the industry, but it is realized in a difficult way. Due to a higher initial altruism level, there is lack of motivation for the increased unilateral altruism. (3) The mutually altruistic coopetition is the most effective way for improving the total equilibrium profit of the industry, but it is hard to finally achieve the complete monopoly because of cost differentiation. (4) The established game model of generation market is more universal and provides a certain quantitative interpretation for electricity crisis.

Description: In this paper, we further study the dynamic characteristics of the Yu–Wang chaotic system obtained by Yu and Wang in 2012. The system can show a four-wing chaotic attractor in any direction, including all 3D spaces and 2D planes. For this reason, our interest is focused on multistability generation and chaotic FPGA implementation. The stability analysis, bifurcation diagram, basin of attraction, and Lyapunov exponent spectrum are given as the methods to analyze the dynamic behavior of this system. The analyses show that each system parameter has different coexistence phenomena including coexisting chaotic, coexisting stable node, and coexisting limit cycle. Some remarkable features of the system are that it can generate transient one-wing chaos, transient two-wing chaos, and offset boosting. These phenomena have not been found in previous studies of the Yu–Wang chaotic system, so they are worth sharing. Then, the RK4 algorithm of the Verilog 32-bit floating-point standard format is used to realize the autonomous multistable 4D Yu–Wang chaotic system on FPGA, so that it can be applied in embedded engineering based on chaos. Experiments show that the maximum operating frequency of the Yu–Wang chaotic oscillator designed based on FPGA is 161.212 MHz.

Description: To achieve numerical simulation of large deformation evolution processes in underground engineering, the barycentric interpolation test function is established in this paper based on the manifold cover idea. A large-deformation numerical simulation method is proposed by the double discrete method with the fixed Euler background mesh and moving material points, with discontinuous damage processes implemented by continuous simulation. The material particles are also the integration points. This method is called the manifold cover Lagrangian integral point method based on barycentric interpolation. The method uses the Euler mesh as the background integral mesh and describes the deformation behavior of macroscopic objects through the motion of particles between meshes. Therefore, this method can avoid the problem of computation termination caused by the distortion of the mesh in the calculation process. In addition, this method can keep material particles moving without limits in the set region, which makes it suitable for simulating large deformation and collapse problems in geotechnical engineering. Taking a typical slope as an example, the results of a slope slip surface obtained using the manifold cover Lagrangian integral point method based on barycentric interpolation proposed in this paper were basically consistent with the theoretical analytical method. Hence, the correctness of the method was verified. The method was then applied for simulating the collapse process of the side slope, thereby confirming the feasibility of the method for computing large deformations.

Description: Viscoelastic artificial boundary elements are one of the most commonly used artificial boundaries when solving dynamic soil-structure interactions or near-field wave propagation problems. However, due to the lack of clear and practical stability criteria for the explicit algorithm that considers the influence of viscoelastic artificial boundary elements, the determination of the stable time increment in such numerical analyses is still a challenge. In this study, we proposed a numerical stability analysis method for the explicit algorithm with a 3D viscoelastic artificial boundary element based on the idea of a subsystem. Through this method, the artificial boundary subsystem that controls the stability of the overall numerical system is determined, and the analytical solution for the stability condition of the explicit integration algorithm with 3D viscoelastic artificial boundary elements is obtained. On this basis, the maximum time increment for solving dynamic problems with viscoelastic artificial boundary elements can be determined.

Description: This paper proposes effective evidence on the correlation between trend and self-organized criticality (SOC) of the power outage sequence in China. Taking the data series of blackouts from 1981 to 2014 in the China power grid as the research object, the method of V/S is introduced into the analysis of the power system blackout sequence to demonstrate their prominent long-time correlations. It also verifies the probability distribution of load loss about blackout size in the China power grid has a tail feature, which shows that the time series of blackouts in the China power grid is consistent with SOC. Meanwhile, a kind of mathematical statistics analysis is presented to prove that there is a seasonal trend of blackouts, and the blackout frequency and blackout size have not decreased over time but have an upward trend in the China power grid, thereby indicating that blackout risk may be increasing with time. The last 34 years’ data samples of power failure accidents in the China power grid are used to test the proposed method, and the numerical results show that the proposed self-organized criticality and trend analysis method can pave the way for further exploration of the mechanism of power failure in the China power grid.

Description: In this paper, an extended car-following model with consideration of the driver’s desire for smooth driving and the self-stabilizing control in historical velocity data is constructed. Moreover, for better reflecting the reality, we also integrate the velocity uncertainty into the new model to analyze the internal characteristics of traffic flow in situation where the historical velocity data are uncertain. Then, the model’s linear stability condition is inferred by utilizing linear stability analysis, and the modified Korteweg-de Vries (mKdV) equation is also obtained to depict the evolution properties of traffic congestion. According to the theoretical analysis, we observe that the degree of traffic congestion is alleviated when the control signal is considered, and the historical time gap and the velocity uncertainty also play a role in affecting the stability of traffic flow. Finally, some numerical simulation experiments are implemented and the experiments’ results demonstrate that the control signals including the self-stabilizing control, the driver’s desire for smooth driving, the historical time gap, and the velocity uncertainty are of avail to improve the traffic jam, which are consistent with the theoretical analytical results.

Description: In the present study, the current control method of the model predictive control is applied to the field-oriented control induction motor. The augmentation model of the motor is initially established based on the stator current equation, which performs the current predictive control and formulates the new cost function by means of tracking error. Then, the influence of parameter error on the current control stability in the prediction model is analysed, and the current static error is corrected according to the correlation between the input and feedback. Finally, a simple and effective three-vector control strategy is proposed. Moreover, three adjacent basic voltage vectors are utilized, and then six candidate voltage vectors are synthesized in each sector to replace eight basic voltage vectors in the conventional model predictive control (MPC). The obtained results show that synthesized vectors, which have arbitrary amplitude and direction, significantly expand the coverage of the system’s control set, reduce the torque and flux pulsation in the conventional MPC, and improve the steady-state performance of the system. Finally, the dSPACE platform is employed to validate the performed experiment. It is concluded that the proposed method can reduce the torque and flux pulse, perform the induction motor current control, and improve the steady-state performance of the system.

Description: Predicting vehicle carbon emissions on vertical curve sections can provide guidance for low-carbon vertical profile designs. Given that the influence of vertical curve design indicators on the fuel consumption and CO2 emissions of vehicles are underexplored, this study filled this research gap by establishing a theoretical carbon emission model of vehicles on vertical curve sections. The carbon emission model was established based on Xu’s vehicle energy conversion model, the conversion model of energy, fuel consumption, and CO2 emissions. The accuracy of the theoretical carbon emission model and the CO2 emission rules on vertical curve sections were verified by field test results. Field tests were carried out on flat sections, longitudinal slope sections, and various types of vertical curve sections, with five common types of vehicles maintaining cruising speed. The carbon emission rate effects on the vertical curve are closely related to the gradient and irrelevant of the radius. On the vertical profile composed with downhill/asymmetric/symmetrical vertical curve with a gradient greater than the balance gradient, the carbon emission rate is determined by the gradient and radius. The influence of the gradient on carbon emissions of vehicle on these vertical profiles was more significant than the radius. The radius is irrelevant to the carbon emission rate on the other forms of vertical profile. These results may benefit highway designers and engineers by providing guidelines regarding the environmental effects of highway vertical curve indexes.

Description: In recent years, fuzzy set theory and possibility theory have been widely used to deal with an uncertain decision environment characterized by vagueness and ambiguity in the financial market. Considering that the expected return rate of investors may not be a fixed real number but can be an interval number, this paper establishes an interval-valued possibilistic mean-variance portfolio selection model. In this model, the return rate of assets is regarded as a fuzzy number, and the expected return rate of assets is measured by the interval-valued possibilistic mean of fuzzy numbers. Therefore, the possibilistic portfolio selection model is transformed into an interval-valued optimization model. The optimal solution of the model is obtained by using the order relations of interval numbers. Finally, a numerical example is given. Through the numerical example, it is shown that, when compared with the traditional possibilistic model, the proposed model has more constraints and can better reflect investor psychology. It is an extension of the traditional possibilistic model and offers greater flexibility in reflecting investor expectations.

Description: In this study, the design of a novel model based on nonlinear third-order Emden–Fowler delay differential (EF-DD) equations is presented along with two types using the sense of delay differential and standard form of the second-order EF equation. The singularity at ξ = 0 at single or multiple points of each type of the designed EF-DD model are discussed. The detail of shape factors and delayed points is provided for both types of the designed third-order EF-DD model. For the verification and validation of the model, two numerical examples are presented of each case and numerical results have been performed using the artificial neural network along with the hybrid of global and local capabilities. The comparison of the obtained numerical results with the exact solutions shows the perfection and correctness of the designed third-order EF-DD model.

Description: The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.

Description: The research on autonomous recognition mechanism for survivability has vigorously been growing up. A method of autonomous cognitive model and quantitative analysis for survivable system was proposed based on cognitive computing technology. Firstly, a cognitive model for survivable system with cross-layer perception ability was established, a self-feedback evolution mode of cognitive unit based on monitor-decide-execute loop structure was improved, and a self-configuration of cognitive unit is realized. Then, combined with the cognitive state transition graph, the analysis of cognitive performance for survivable systems based on dynamic cognitive behavioral changes was constructed. Finally, the cognitive processes of survivable system were described by using formal modeling. Simulation validated the influence degree of test parameters on system survivability from two perspectives of the probability of intrusion detection systems vulnerability and attacks detected. Results show that enhancing the rate of monitoring actions change and the rate of performing actions change obviously improved the cognitive performance of survivable system.

Description: Dilute ensembles of granular matter (so-called granular gases) are nonlinear systems which exhibit fascinating dynamical behavior far from equilibrium, including non-Gaussian distributions of velocities and rotational velocities, clustering, and violation of energy equipartition. In order to understand their dynamic properties, microgravity experiments were performed in suborbital flights and drop tower experiments. Up to now, the experimental images were evaluated mostly manually. Here, we introduce an approach for automatic 3D tracking of positions and orientations of rod-like particles in a dilute ensemble, based on two-view video data analysis. A two-dimensional (2D) localization of particles is performed using a Mask R-CNN neural network trained on a custom data set. The problem of 3D matching of the particles is solved by minimization of the total reprojection error, and finally, particle trajectories are tracked so that ensemble statistics are extracted. Depending on the required accuracy, the software can work fully self-sustainingly or serve as a base for subsequent manual corrections. The approach can be extended to other 3D and 2D particle tracking problems.

Description: This paper proposes a novel decoupled approach of a regenerative braking system for an electric city bus, aiming at improving the utilization of the kinetic energy for rear axle during a braking process. Three contributions are added to distinguish from the previous research. Firstly, an energy-flow model of the electric bus is established to identify the characteristic parameters which affect the energy-saving efficiency of the vehicle, while the key parameters (e.g., driving cycles and the recovery rate of braking energy) are also analyzed. Secondly, a decoupled braking energy recovery scheme together with the control strategy is developed based on the characteristics of the power assistance for electric city bus which equips an air braking system, as well as the regulatory requirements of ECE R13. At last, the energy consumption of the electric city bus is analyzed by both the simulation and vehicle tests, when the superimposed and the decoupled regenerative braking system are, respectively, employed for the vehicle. The simulation and actual road test results show that compared with the superposition braking system of the basic vehicle, the decoupled braking energy recovery system after the reform can improve the braking energy recovery rate and vehicle energy-saving degree. The decoupled energy recovery system scheme and control strategy proposed in this paper can be adopted by bus factories to reduce the energy consumption of pure-electric buses.

Description: This study addresses one of the most essential distributed control problems in multiagent systems, called the average consensus issue, using a new event-triggered sampling control perspective. Although the continuous-time sampling for average consensus has provided good results currently, a systematic investigation into the continuous-time agent dynamics with sampled-data control inputs under an event-triggered mechanism is critically lacking. The problem considered in this paper can be formulated into an average consensus problem of hybrid systems. The method considers three types of control schemes, among which periodic sampling is integrant. The first scheme is a classical sampling controller reinvestigated through a lemma. The second scheme realizes aperiodic control update as well as periodic communication, while the third scheme achieves both aspects aperiodically. Corresponding sufficient conditions of the aforementioned three schemes are derived such that the asymptotic stability of systems is ensured by using algebraic graph theory, matrix analysis, and Lyapunov theory. The constraints for the allowed sampling period, event parameter, and maximum eigenvalue of graph Laplacian are explicitly derived. Moreover, the potential Zeno behavior of agents due to the sampling control theory is avoided. Thus, a digitally implementable technique is provided. Finally, some numerical examples are provided to verify the effectiveness of the proposed theoretical analysis.

Description: This research intends to construct a signless Laplacian spectrum of the complement of any k-regular graph G with order n. Through application of the join of two arbitrary graphs, a new class of Q-borderenergetic graphs is determined with proof. As indicated in the research, with a regular Q-borderenergetic graph, sequences of regular Q-borderenergetic graphs can be constructed. The procedures for such a construction are determined and demonstrated. Significantly, all the possible regular Q-borderenergetic graphs of order 7

Description: Despite the achievements reported from using rainwater harvesting systems, the contribution and drawbacks that affect their usage in mountainous landscapes have received little attention. The uptake and usage of domestic rooftop rainwater harvesting systems (RRWHS) in developing countries is on the increase due to increasing water scarcities. We explored the effect of rainfall variability on water supply and the downsides of using the systems by rural households in Uganda. The objectives were to assess the variability of rainfall (1985–2018), categorise RRWHS used, and examine the influence of slope ranges on the placement of systems and also to quantify the harvested and saved rainwater and establish the factors that affected system usage. Rainfall variability was assessed using a Mann–Kendall test, while system contributions and drawbacks were examined using socioeconomic data. A representative of 444 households were selected using a multicluster sampling procedure and interviewed using semistructured questionnaires. Findings revealed that the months of March, April, September, August, and October experienced an upward trend of rainfall with a monthly coefficient of variation between 41 and 126%. With this, households responded by employing fixed (reinforced concrete tanks, corrugated iron tanks, and plastic tanks) and mobile RRWHS (saucepans, metallic drums/plastic drums, jerrycans, and clay pots). At the high altitude, households deployed mostly plastic jerrycans and industrial plastic/metallic drums to harvest and save water. Overall, the mean annual volume of rainwater harvested on the slopes of Mt. Elgon was 163,063 m3/yr, while the potential to save water ranged from 4% to 7% of the annual household water demand. The factors that hindered the deployment of RRWHS to harvest and save water were high operational costs, price fluctuations, unreliable rainfall pattern, inadequate funds, and limited accessibility. The rainfall received if well-harvested and saved can redeem households of water insecurity, though there is an urgent need of subsidies from the government to increase accessibility of the systems.

Description: Current research in biometrics aims to develop high-performance tools, which would make it possible to better extract the traits specific to each individual and to grasp their discriminating characteristics. This research is based on high-level analyses of images, captured from the candidate to identify, for a better understanding and interpretation of these signals. Several biometric identification systems exist. The recognition systems based on the iris have many advantages and they are among the most reliable. In this paper, we propose a new approach based on biometric iris authentication. A new scheme was made in this work that consists of calculating a three-dimensional head pose to capture a good iris image from a video sequence which affects the identification results. From this image, we were able to locate the iris and analyse its texture by intelligent use of Meyer wavelets. Our approach was evaluated and approved through two databases CASIA Iris Distance and MiraclHB. The comparative study showed its effectiveness compared to those in the literature.

Description: Adequate mechanical stimulation is essential for cellular health and tissue maintenance, including articular cartilage, which lines the articulating bones in joints. Chondrocytes, which are the sole cells found in articular cartilage, are responsible for matrix synthesis, maintenance and degradation. It is generally believed that chondrocytes require mechanical stimuli through daily physical activity for adequate cartilage homeostasis. However, to date, the molecular mechanisms of cellular force sensing (mechanotransduction) are not fully understood. Among other mechanisms, the cytoskeleton is thought to play a key role. Despite that gravity is a very small force at the cellular level, cytoskeletal adaptations have been observed under altered gravity conditions of a parabolic flight in multiple cell types. In this study, we developed a novel hardware which allowed to chemically fix primary bovine chondrocytes at 7 time points over the course of a 31-parabola flight. The samples were subsequently stained for the microtubules and vimentin network and microscopic images were acquired. The images showed a large heterogeneity among the cells in morphology as well as in the structure of both networks. In all, no changes or adaptions in cytoskeleton structure could be detected over the course of the parabolic flight.

Description: Odor-baited devices are increasingly needed to compliment long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) for control of residual malaria transmission. However, the odor-baited devices developed so far are bulky, dependent on the source of electricity and carbon dioxide (CO2), and they are logistically unsuitable for scaling up in surveillance and control of malaria vectors. We designed a passive and portable outdoor host seeking device (POHD) and preliminarily evaluated suitable components against Anopheles arabiensis that maintains residual malaria transmission. Experiments were conducted using semifield reared An. arabiensis within the semifield system at Ifakara Health Institute (IHI) in southeastern Tanzania. These mosquitoes were exposed to Suna traps® baited with BG lures or source of light and augmented with carbon dioxide (CO2) in view of identifying best attractants necessary to improve attractiveness of designed POHD. Two Suna traps® were hanged at the corner but outside the experimental hut in a diagonal line and rotated between four corners to control for the effect of position and wind direction on mosquito catches. Furthermore, mosquitoes were also exposed to either a bendiocarb-treated or bendiocarb-untreated POHD baited with Mbita blend, Ifakara blend, and worn socks and augmented with warmth (i.e., 1.5 liter bottle of warm water) inside an experimental hut or a screened rectangular box. This study demonstrated that mosquitoes were more strongly attracted to Suna trap® baited with BG lures and CO2 relative to those traps baited with a source of light and CO2. The POHD baited with synthetic blends attracted and killed greater proportion of An. arabiensis compared with POHD baited with worn socks. Efficacy of the POHD was unaffected by source of warmth, and it was reduced by about 50% when the device was tested inside a screened rectangular box relative to closed experimental hut. Overall, this study demonstrates that the POHD baited with synthetic blends (Mbita and Ifakara blends) and bendiocarb can effectively attract and kill outdoor biting malaria vector species. Such POHD baited with synthetic blends may require the source of CO2 to enhance attractiveness to mosquitoes. Further trials are, therefore, ongoing to evaluate attractiveness of improved design of POHD baited with slow-release formulation of synthetic blends and sustainable source of CO2 to malaria vectors under semifield and natural environments.

Description: Gastric diseases are increasing with the infection of Campylobacter jejuni. Late stages of infection lead to peptic ulcer and gastric carcinoma. C. jejuni infects people within different stages of their life, especially childhood, causing severe diarrhea; it infects around two-thirds of the world population. Due to bacterial resistance against standard antibiotic, a new strategy is needed to impede Campylobacter infections. Plants provide highly varied structures with antimicrobial use which are unlikely to be synthesized in laboratories. A special feature of higher plants is their ability to produce a great number of organic chemicals of high structural diversity, the so-called secondary metabolites. Twenty plants were screened to detect their antibacterial activities. Screening results showed that Rheum officinalis was the most efficient against C. jejuni. Fractionation pattern was obtained by column chromatography, while the purity test was done by thin-layer chromatography (TLC). The chemical composition of bioactive compound was characterized using GC-MS, nuclear magnetic resonance, and infrared analysis. Minimal inhibitory concentration (MIC) of the purified compound was 31.25 µg/ml. Cytotoxicity assay on Vero cells was evaluated to be 497 µg/ml. Furthermore, the purified bioactive compound activated human lymphocytes in vitro. The data presented here show that Rheum officinalis could potentially be used in modern applications aimed at the treatment or prevention of foodborne diseases.

Description: Stability of a high-speed craft is an essential matter, and porpoising is one of the most critical instabilities that could occur in some planing hulls due to inappropriate design. In this paper, the porpoising phenomenon and variation of step location yielding resistance reduction are studied through experimental and numerical methods. The investigated models include a single-step model and a nonstep model with the same general shape, but with different step location. The nonstep model is previously tested, but the single-step model is examined in the present study. The nonstep model experiences porpoising at 8 m/s speed, but the single-step model remains stable at the same speed. A three-dimensional CFD analysis is conducted using the finite volume method (FVM). On the contrary, the volume of fluid (VOF) scheme is used for free surface modeling, and the overset mesh technique is implemented within StarCCM+ software. The CFD results of total hydrodynamic resistance and dynamic trim angle are compared against the experimental data. The numerical results are in good agreement with the experimental data. Subsequently, ten different stepped models are simulated to examine their effects. The longitudinal distance between steps and aft of these models are in the range of 19 to 50 percent of the length of models. The obtained results show that as steps are located farther than aft, the models become more stable, and resistance increases due to trim reduction. Finally, the optimum location of the step is extracted with the aim of minimizing the resistance through the design of experiment (DOE) method. Based on the DOE method, it is observed that the sensitivity of the drag value to the step location is higher than the speed.

Description: The pseudopotential lattice Boltzmann method (LBM) with a tunable surface tension term is applied to study a droplet impact on a moving thin film. The Re effects of dimensionless parameters on the upstream and downstream crown evolution are studied, including Reynolds number (Re), Weber number (We), liquid film thickness, and horizontal velocity of the liquid film. The movement of the liquid film causes the asymmetry development of the upstream and downstream crown. Both the instability of upstream and downstream crowns increases with the increase of Re and We, and the upstream crown becomes more prone to break up. And a critical value of film thickness exists with the height of the upstream and downstream liquid crowns reaches the maximum value. And the velocity of liquid film restrains the development of the height of the upstream and downstream crowns, but it promotes the growth of the crown radius.

Description: In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

Description: In this study, the effect of the roughness parameter during the peristaltic transport of a Newtonian fluid in a nonuniform channel has been explored. The motivation of this study comes from various research studies in the area of life sciences and engineering, which reveal that the wall of living beings’ arteries and all other surfaces have roughness to some extent. As peristalsis is a major mode of transporting biological fluids in various organs, the effect of surface roughness during peristaltic flow becomes very significant. The problem of peristaltic motion of a Newtonian fluid through a rough nonuniform channel having sinusoidal-shaped roughness has been investigated in the current work. To analyze the flow, analytic formulation of pressure rise, friction force, velocity, and pressure gradient has been carried out under the low Reynolds number and long-wavelength approximation. Results obtained for zero surface roughness from the current model are in complete agreement with previous studies available in the literature that have been carried out without considering the surface roughness of the wall. Numerical outcomes for the properties mentioned above have been plotted for analyzing the impact of roughness on the physical and flow parameters.

Description: In view of the poor performance of the original mathematical model of assembly construction project precost budget, a mathematical model of assembly construction project precost budget based on improved neural network algorithm is proposed. This paper investigates the cost content of assembly construction project and analyzes its early cost. It finds that the early cost of assembly construction project includes component production cost, transportation component cost, and installation component cost. Based on the improved neural network algorithm to build an improved neural network model, the improved neural network model to mine the cost data in the early stage of assembly construction project is used. In this paper, the earned value variable is introduced to transform the project duration and project cost in the early stage of the prefabricated construction project into quantifiable cost data, and the earned value analysis method is used to estimate the implementation cost of the prefabricated construction project. According to the result of cost estimation, the mathematical model of precost budget of prefabricated construction project is built based on the project parameters. In order to prove that the cost budget performance of the mathematical model based on the improved neural network algorithm in the early stage of assembly construction project is better, the original mathematical model is compared with the mathematical model, the experimental results show that the cost budget performance of the model is better than the original model, and the cost budget performance is improved.

Description: The role of green supplier evaluation and selection (GSES) in supply chain management is increasingly appreciated due to the intensification of competition, raising public consciousness, and environmental issues. To improve GSES, a large number of approaches have been proposed in the past decades. However, few attempts have been made to systematically review and classify the literature in this field. The objective of this study is to afford a comprehensive review of the studies which aim to develop models and methods in helping enterprises to assess and select the right green suppliers. To achieve this goal, a total of 193 journal articles extracted from the Scopus database over the period of 2009 to 2020 were chosen and reviewed. These publications were classified into ten categories based on their adopted GSES models and analyzed concerning the evaluation criteria, criteria weighting methods, and performance evaluation methods. Moreover, a bibliometric analysis was conducted according to the frequency of supplier selection methods, citation number, publication year, journal, country, and application area. This study supports practitioners, managers, and researchers in effectively recognizing and applying the GSES models to enhance organizational competitiveness and provides an insight into its state of the art.

Description: In recent years, with the expansion of university enrollment in China, the cost of running a school is getting higher and higher. Under the circumstance of insufficient state investment in higher education, the education charge paid by students has become an important source of the university income. It has become a hot topic of social concern to formulate a reasonable charging model to enable more students to enter higher education institutions. In this paper, we mainly put forward the basic differential equation models describing the problem of higher education charges in China. Through the qualitative analysis of these two basic models, we draw several conditions for universities to maintain or stabilize their education charges and give some suggestions on macrocontrol of university education charges in China.

Description: Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection task and CRNN is employed to deal with the text recognition task. We combine the datasets of general objects and texts together to train the networks. At test time, the detection network detects various objects in an image. Then, the text images are passed to the text recognition network to derive the text contents. The experiments show that the proposed method achieves 78.3 mAP (mean Average Precision) for general objects and 72.8 AP (Average Precision) for texts in regard to detection performance. Furthermore, the proposed method is able to detect and recognize affine transformed or occluded texts with robustness. In addition, for the texts detected around general objects, the text contents can be used as the identifier to distinguish the object.

Description: The estimation of continuous and simultaneous multijoint angle based on surface electromyography (sEMG) signal is of considerable significance in rehabilitation practice. However, there are few studies on the continuous joint angle of multiple joints at present. In this paper, the wavelet packet energy entropy (WPEE) of the special subspace was investigated as a feature of the sEMG signal. An Elman neural network optimized by genetic algorithm (GA) was established to estimate the joint angle of shoulder and elbow. First, the accuracy of the method is verified by estimating the angle of the shoulder joint. Then, this method was used to simultaneously and continuously estimate the shoulder and elbow joint angle. Six subjects flexed and extended the upper limbs according to the intended movements of the experiment. The results show that this method can obtain a decent performance with a RMSE of 3.4717 and R2 of 0.8283 in shoulder movement and with a RMSE of 4.1582 and R2 of 0.8114 in continuous synchronous movement of the shoulder and elbow.

Description: The horses were trotted in a straight line, and the acceleration data were recorded from the movement of the horse’s head. Inertial sensors were used to collect the information. The sample entropy was used to characterize the dynamics of horse gait. The gait of sound and lame horses was compared, and the sound horses had smaller values of the sample entropy. The effects of sedative detomidine on the gait of lame horses were also examined. The values of sample entropy for sedated horses were smaller than the values of the sample entropy for untreated horses. The sample entropy can provide important information about the dynamics of the equine gait.

Description: Helicobacter pylori (H. pylori) is the most common cause of gastric ulcer; however, its association with gastric cancer has been proved through a variety of studies. Importantly, H. pylori infection affects around half of the world’s population leading to a variety of gastric problems and is mostly present in asymptomatic form. Although about 20% of people infected with H. pylori develop preneoplastic gastric lesions in later stages of their life, around 2% of infected individuals develop gastric cancer. Nevertheless, the outcome of H. pylori infection is determined by complex interaction between the host genetics, its environment, and virulence factors of infecting strain. There are several biomarkers/traits of H. pylori that have been linked with the onset of cancer. Among these, presence of certain major virulence factors including cytotoxin-associated gene A (CagA), vacuolating cytotoxin (VacA), and outer inflammatory protein A (OipA) plays a significant role in triggering gastric cancer. These factors of H. pylori make it a potent carcinogen. Therefore, eradication of H. pylori infection has shown positive effects on decreasing the risk of gastric cancer, but this has become a challenge due to the development of antibiotic resistance in H. pylori against the antibiotics of choice. Thus, the unmet need is to develop new and effective treatments for H. pylori infection, considering the antimicrobial resistance in different regions of the world. This review discusses the properties of H. pylori associated with increased risk of gastric cancer, antibiotic resistance pattern, and the possible role of eradication of H. pylori in preventing gastric cancer.

Description: Natural products are used as alternative drugs in traditional medicine to treat infection and inflammation and relieve pain. Heartwood of Cassia garettiana Craib has been investigated as an ingredient in Thai traditional medicine for anti-HIV protease, but there is no report on its antibacterial and anti-inflammatory activities. The objectives of this study were to investigate the anti-inflammatory and antibacterial activities, time-kill profile, and main active constituents of an ethanolic extract of C. garettiana heartwood. The study followed the generally accepted experimental design. All tests were investigated in triplicate. The heartwood of C. garettiana was extracted by maceration with 95% EtOH. The antibacterial activity of the extract and its chemical constituents were determined by their MIC values using resazurin as an indicator. Time-kill profile was determined at 0, 2, 4, 6, 8, 10, 12, and 24 hrs and expressed as log CFU/mL. The anti-inflammatory activity of the extract and its chemical components was investigated by their inhibiting effect on IL-6 and TNF-α production by ELISA. The ethanolic extract was analyzed for its chemical constituents by HPLC technique. The ethanolic extract showed both dose- and time-dependent bactericidal effects against Staphylococcus aureus, methicillin-resistance Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, Pseudomonas aeruginosa, Salmonella Typhi, Salmonella Typhimurium, Klebsiella pneumoniae, and Shigella dysenteriae with MIC values of 312.5, 312.5, 312.5, 1,250, 2,500, 625, 625, 2,500, and 625 μg/mL, respectively. It showed an inhibiting effect on IL-6 production at concentrations of 12.5 to 100 μg/mL. The main active chemical constituent of C. garettiana was piceatannol that showed antibacterial activity against all test bacteria except P. aeruginosa. C. garettiana showed a broad spectrum of antibacterial activity against both Gram-negative and Gram-positive bacteria. Piceatannol and resveratrol from the plant strongly inhibited IL-6 production. Based on these results, we concluded that the ethanolic extract of C. garettiana showed both an antibacterial activity and inhibition of IL-6. Piceatannol is the active constituent of the extract and showed anti-inflammatory and antibacterial activities against Gram-negative and Gram-positive bacteria.

Description: Data Envelopment Analysis is one of the paramount mathematical methods to compute the general performance of organizations, which utilizes similar sources to produce similar outputs. Original DEA schemes involve crisp information of inputs and outputs that may not always be accessible in real-world applications. Nevertheless, in some cases, the values of the data are information with indeterminacy, impreciseness, vagueness, inconsistent, and incompleteness. Furthermore, the conventional DEA models have been originally formulated solely for desirable outputs. However, undesirable outputs may additionally be present in the manufacturing system, which wishes to be minimized. To tackle the mentioned issues and in order to obtain a reliable measurement that keeps original advantage of DEA and considers the influence of undesirable factors under the indeterminate environments, this paper presents a neutrosophic DEA model with undesirable outputs. The recommended technique is based on the aggregation operator and has a simple construction. Finally, an example is given to illustrate the new model and ranking approach in details.

Description: The virtual joint method (VJM) cannot calculate the strain energy stored in each rod. In order to solve the problem, a modeling method of the elastostatic stiffness was investigated for the UP/UPS parallel manipulators (PMs), taking the example of the 6-SPS PM. The modeling method was based on screw theory, Castigliano’s theorem, and strain energy (where U, P, and S, respectively, denote universal, prismatic, and spherical joints). First, the actuator and constraint wrenches of the mechanism were obtained by screw theory. Second, compact limb stiffness matrices were obtained in terms of strain energy and Castigliano’s second theorem. Finally, analytic expressions for the overall stiffness matrix of the mechanism and the amplitudes of the actuator force were obtained by adopting the virtual work principle and the balance equation for the mobile platform. All relative errors between the results of the analytical model and the finite element model are below 2%, which validates the effectiveness of the elastostatic stiffness model. The virtual work index was adopted to evaluate the stiffness performance of the mechanism, and the results show that the stiffness is not only related to position and orientation but also closely related to the directions of external loads. It is also demonstrated that the method has general adaptability for the stiffness analysis for the US/UPS PMs, laying the foundation for further reasonable dynamic design and optimization of such manipulators.

Description: In this paper, by using the partial order method, the existence and uniqueness of a solution for systems of a class of abstract operator equations in Banach spaces are discussed. The result obtained in this paper improves and unifies many recent results. Two applications to the system of nonlinear differential equations and the systems of nonlinear differential equations in Banach spaces are given, and the unique solution and interactive sequences which converge the unique solution and the error estimation are obtained.

Description: Deep borehole heat exchanger (DBHE) technology does not depend on the existence of hot water reservoir and can be used in various regions. However, the heat extraction from DBHE can hardly be improved due to poor thermal conductivity of rocks. Here, a single-well enhanced geothermal system (SWEGS) is proposed, which has a larger heat-exchange area of artificial reservoir created by fracturing hydrothermal technology. We find that, due to heat convection between rocks and fluid, the extracted thermal output for SWEGS is 4772.73 kW, which is 10.64 times of that of DBHE. By changing the injection water temperature, volume flow rate, and artificial reservoir volume, it is easy to adjust the extracted thermal output to meet the requirement of building thermal loads varying with outdoor air temperature. Understanding these will enable us to better apply SWEGS technology and solve the fog and haze problem easily and efficiently.

Description: Unmanned aerial vehicle- (UAV-) assisted communication has great potential to provide on-demand wireless services and improve the outdoor link throughput. In this paper, a UAV-based cognitive radio network (CRN) is investigated in which the UAV works as a secondary user (SU). Considering the overlay spectrum sensing mode, the UAV can operate on the licensed spectrum bands of primary user (PU) only when PU is idle. In each working frame structure, both sensing time slot and transmission time slot are analysed in radians. Specifically, our objective is to maximize the spectrum efficiency (SE) of the UAV by jointly optimizing the sensing radian and the number of radians. For the single-radian and multiradian schemes, the dichotomy and alternative iterative optimization (AIO) algorithm are proposed to solve the SE optimization problem. Simulation results show that the proposed multiradian cooperative spectrum sensing (CSS) scheme can achieve better performance on ensuring the quality-of-service (QoS) of the PU, and it can significantly enhance the SE of the UAV especially in the severe channel environments.

Description: This study investigates the pavement network maintenance and rehabilitation (M&R) programming problem, over a certain planning horizon and in the context of limited funding. We designed an integer programming model to fulfill three purposes, namely, optimize the road conditions, minimize user disturbance costs, and minimize agency costs. We present a case study in which this model is applied to the pavement network of Shanghai. We investigate the results through the use of five M&R strategies, to identify the Pareto-optimal trade-offs inherent in developing pavement network M&R planning. The results demonstrate that there is a positive relationship between PCI improvement and user disturbance costs and between PCI improvement and agency costs. Additionally, we conduct a comparative analysis between agency and government-oriented strategies to evaluate the effectiveness and equity consideration. The findings suggest that the government-oriented strategy improves the pavement condition effectively with low user disturbance costs, and the agency-oriented strategy accounts for more equity consideration. Finally, we formulate an extension model that considers multiple road types, for application to pavement network M&R programming. The results show that light rehabilitation and preventive maintenance are the most frequently implemented treatments on arterial roads and secondary trunk roads from the case network implementation. This study helps decision-makers identify the trade-offs made when developing a pavement network M&R program.

Description: This paper proposes a fuzzy PI controller to control the speed of a permanent magnet synchronous motor (PMSM). The structure of the system includes the speed loop controller (SLC) and the current loop controller (CLC). The speed loop controller is the fuzzy PI and standard model (SM). The CLC includes vector control and the space vector pulse width modulation (SVPWM). It compiles two closed-loop control systems for the PMSM. This research uses a very high-speed integrated circuit hardware description language (VHDL) to implement the proposed algorithm and embed it into Matlab/Simulink for simulation. Based on the PMSM parameter, this article tests the controller’s correctness with some of the load cases by changing the combined inertia and viscous friction of rotor and load. After success in simulation, the system is tested again by experiment on the FPGA kit. The simulation and experiment results show that when the load changes, the PMSM speed is still stable. The novelty of this research is that it compares two kinds of controllers between simulation and experiment results.

Description: Generative adversarial networks are currently used to solve various problems and are one of the most popular models. Generator and discriminator are characteristics of continuous game process in training. While improving the quality of generated pictures, it will also make it difficult for the loss function to be stable, and the training speed will be extremely slow compared with other methods. In addition, since the generative adversarial networks directly learns the data distribution of samples, the model will become uncontrollable and the freedom of the model will become too large when the original data distribution is constantly approximated. A new transfer learning training idea for the unsupervised generation model is proposed based on the generation network. The decoder of trained variational autoencoders is used as the network architecture and parameters to generative adversarial network generator. In addition, the standard normal distribution is obtained by sampling and then input into the model to control the degree of freedom of the model. Finally, we evaluated our method on using the MNIST, CIFAR10, and LSUN datasets. The experiment shows that our proposed method can make the loss function converge as quickly as possible and increase the model accuracy.

Description: In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.

Description: 3D hand pose estimation can provide basic information about gestures, which has an important significance in the fields of Human-Machine Interaction (HMI) and Virtual Reality (VR). In recent years, 3D hand pose estimation from a single depth image has made great research achievements due to the development of depth cameras. However, 3D hand pose estimation from a single RGB image is still a highly challenging problem. In this work, we propose a novel four-stage cascaded hierarchical CNN (4CHNet), which leverages hierarchical network to decompose hand pose estimation into finger pose estimation and palm pose estimation, extracts separately finger features and palm features, and finally fuses them to estimate 3D hand pose. Compared with direct estimation methods, the hand feature information extracted by the hierarchical network is more representative. Furthermore, concatenating various stages of the network for end-to-end training can make each stage mutually beneficial and progress. The experimental results on two public datasets demonstrate that our 4CHNet can significantly improve the accuracy of 3D hand pose estimation from a single RGB image.

Description: This paper aims to study stable portfolios with mean-variance-CVaR criteria for high-dimensional data. Combining different estimators of covariance matrix, computational methods of CVaR, and regularization methods, we construct five progressive optimization problems with short selling allowed. The impacts of different methods on out-of-sample performance of portfolios are compared. Results show that the optimization model with well-conditioned and sparse covariance estimator, quantile regression computational method for CVaR, and reweighted L1 norm performs best, which serves for stabilizing the out-of-sample performance of the solution and also encourages a sparse portfolio.

Description: Since there is negative coverage of some western media on the business activities of Chinese overseas enterprises, which has adverse impact on the image of Chinese enterprises and even the national image of China, this study aims to detect the corporate social responsibility image (hereafter CSR image) of Chinese construction enterprises in Africa (hereafter CCEA) through analyzing the coverage of Financial Times (hereafter FT) from the UK and The Wall Street Journal (hereafter WSJ) from the US and dig up the motives behind their coverage. Octopus is first applied to mine and collect the reports data on CCEA from 2011 to 2019 by the two media. Two small corpora including the reports are then built. NVivo is next used to do the statistical analysis and clustering analysis of the keywords in two corpora as a whole and AntConc is finally utilized to do the statistics of high-frequency evaluative adjectives and nouns modified by evaluative adjectives as well as the concordance of the low-frequency words but closely relevant to corporate social responsibility (hereafter CSR) in two corpora, respectively. The results of the detailed analyses of the keywords are combined to unveil the CSR image of CCEA, which is followed by a discussion about the motives behind the coverage and finally some suggestions are put forward to improve the CSR image of CCEA. Theoretically, the present study promotes the interaction among data science, management, communications, and linguistics; practically it offers some advice to CCEA to elevate their CSR image.