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Objective: The study draws attention to designing a dyskinesia assessment system using a Kinect sensor to improve the efficiency of rehabilitation training. Methods: The login page design. Step 2: System functions setting. Relevant movement guidance content and rehabilitation evaluation content are incorporated in the system to make rehabilitation training efficient and orderly. Comprehensive data processing, evaluation, and export functions are necessary to reference rehabilitation physicians in diagnosis and treatment. Step 3: System modules design. Based on the system settings, corresponding functional modules have been designed and developed. With each module realizing its specific functions, it must be ensured that there is a certain degree of correlation between the modules. Step 4: The system function framework design. Results: A simple and comfortable login page is designed; 2. The system is capable of rehabilitation training and data management; 3. Specifically designed modules include sports collection module, rehabilitation training module, rehabilitation evaluation module, and information management module; 4. After logging in, the patient should first search for the rehabilitation plan in the rehabilitation training function module and then perform rehabilitation training regarding the rehabilitation plan. Kinect synchronously obtains patient sports information throughout the training process, and patients can obtain rehabilitation assessment information and automatically save it in the information management module. Conclusions: The Kinect-based dyskinesia assessment and training system designed in this study can heighten the efficiency of rehabilitation training for patients with dyskinesia, and it is highly suggested in clinical practice. Level of evidence II; Therapeutic studies - investigation of treatment results. RESUMO Objetivo: O estudo chama a atenção para o projeto de um sistema de avaliação de discinesia usando sensor Kinect para melhorar a eficiência do treinamento de reabilitação. Métodos: O design da página de login. Etapa 2: configuração das funções do sistema. Conteúdo de orientação de movimento relevante e conteúdo de avaliação de reabilitação são incorporados ao sistema para tornar o treinamento de reabilitação eficiente e ordenado. Funções abrangentes de processamento, avaliação e exportação de dados são necessárias para fornecer referência para médicos de reabilitação em diagnóstico e tratamento. Etapa 3: Projeto dos módulos do sistema. Com base nas configurações do sistema, os módulos funcionais correspondentes foram projetados e desenvolvidos. Com cada módulo realizando suas funções específicas, deve-se garantir que haja um certo grau de correlação entre os módulos. Etapa 4: O design da estrutura de funções do sistema. Resultados: Uma página de login simples e confortável é projetada; 2. O sistema é capaz de treinamento de reabilitação e gerenciamento de dados; 3. Módulos especificamente projetados incluem módulo de coleta de esportes, módulo de treinamento de reabilitação, módulo de avaliação de reabilitação e módulo de gerenciamento de informações; 4. Após o login, o paciente deve primeiro pesquisar o plano de reabilitação no módulo de função de treinamento de reabilitação e, em seguida, realizar o treinamento de reabilitação com referência ao plano de reabilitação. O Kinect obtém de forma síncrona informações sobre esportes do paciente durante o processo de treinamento, e os pacientes podem obter informações de avaliação de reabilitação e salvá-las automaticamente no módulo de gerenciamento de informações. Conclusões: O sistema de avaliação e treinamento de discinesia baseado no Kinect projetado neste estudo pode aumentar a eficiência do treinamento de reabilitação para pacientes com discinesia, e é altamente sugerido na prática clínica. Nível de evidência II; Estudos terapêuticos- investigação dos resultados do tratamento. RESUMEN Objetivo: El estudio llama la atención para el diseño de un sistema de evaluación de la discinesia utilizando el sensor Kinect para mejorar la eficiencia del entrenamiento de rehabilitación. Métodos: Diseño de la página de inicio de sesión. Paso 2: Configuración de las funciones del sistema. El contenido de la guía de movimiento relevante y el contenido de evaluación de la rehabilitación se incorporan en el sistema para hacer que la capacitación en rehabilitación sea eficiente y ordenada. Las funciones integrales de procesamiento, evaluación y exportación de datos son necesarias para proporcionar referencia a los médicos rehabilitadores en el diagnóstico y el tratamiento. Paso 3: Diseño de los módulos del sistema. Sobre la base de la configuración del sistema, se han diseñado y desarrollado los módulos funcionales correspondientes. Con cada módulo realizando sus funciones específicas, debe asegurarse que existe un cierto grado de correlación entre los módulos. Paso 4: El diseño del marco de la función del sistema. Resultados: Se diseña una página de inicio de sesión simple y cómoda; 2. El sistema es capaz de entrenamiento en rehabilitación y manejo de datos; 3. Los módulos específicamente diseñados incluyen el módulo de recolección de deportes, el módulo de capacitación en rehabilitación, el módulo de evaluación de la rehabilitación y el módulo de gestión de la información; 4. Después de iniciar sesión, el paciente debe buscar primero el plan de rehabilitación en el módulo de función de entrenamiento de rehabilitación y luego realizar el entrenamiento de rehabilitación con referencia al plan de rehabilitación. Kinect obtiene de forma sincrónica la información deportiva del paciente durante todo el proceso de entrenamiento, y los pacientes pueden obtener información sobre la evaluación de la rehabilitación y guardarla automáticamente en el módulo de gestión de información. Conclusiones: El sistema de evaluación y entrenamiento de discinesia basado en Kinect diseñado en este estudio puede aumentar la eficiencia del entrenamiento de rehabilitación para pacientes con discinesia, y está altamente recomendado en la práctica clínica. Nivel de evidencia II; Estudios terapéuticos- investigación de los resultados del tratamiento.

Based on above background, quinolinium, 8-hydroxy-quinolinium, and benzothiazolium ionic liquids, containing the acidic anions of methanesulfonate ([CH3SO3]−), phosphate ([H2PO4]−), p-toluenesulfonate ([p-TSA]−), and bisulfate ([HSO4]−) were synthesized. After comparison, the aqueous solution of benzothiazole bisulfate [HBth][HSO4] was selected as the most ideal extractant for removing pyridine and aniline. Meanwhile, benzothiazole bisulfate [HBth][HSO4] solution was found as the best one for removing quinoline from simulated oil. Then, the single stage extraction and two-step extraction were used in the extraction for the simulated oil containing pyridine, quinoline or aniline, and their mixture, respectively. Their denitrogenation performance on their N-removal effect was compared on the basis of structural features, and main extraction conditions were further investigated, including mass ratio of IL to water, mass ratio of IL to oil, and temperature. Furthermore, the extraction process was described by two kinetic equations. Recovery and reuse of IL were realized by back-extraction and liquid-liquid separation, and a related mechanism was speculated, according to all the experimental results. Finally, based on the developed method for preparing complex adsorbent tablets, corresponding immobilized IL was used to remove target objects, by solid phase extraction, in order to extend separation ways, which was more easily recovered after extraction.

The rise of e-commerce mode and platform brings new ideas to the marketing innovation of modern agriculture represented by fresh agri-products. However, the standardization production and management of fresh agri-products has brought great labor force. Based on this, this paper first analyzes the background and ideas of agricultural supply side reform, and then studies the e-commerce mode innovation of fresh agri-products under the agricultural supply side reform.

The driverless technology has developed rapidly in recent years. Unmanned vehicles need to learn to observe the road from the visual point of view if they want to achieve automatic driving, which specifically is the detection of lane lines. This includes identifying the positional relationship between the lane line and the car, whether it is a solid line or a dotted line. The detection of lanes is an important part of the vehicle-aided driving system. In view of this feature, this paper proposes the use of improved Hough transform to achieve straight-track detection of lane detection, while for the detection of curved sections, the tracking algorithm is studied. By controlling the slope of the lane lines in the two frames before and after comparison, a limitation is made near the previously detected lane line area, ie, a region of interest (ROI) is set, and a search for a corner pixel is performed in the direction, for the corner portion Rebuild. The experimental results show that the algorithm has the characteristics of fast operation speed, high accuracy and good robustness.

The band structures and electronic transport properties of AA-P2-doped armchair silicene nanoribbons (ASiNRs), with two phosphorus atoms substituting two adjacent silicon atoms in the same sublattice A, were investigated by applying density-functional theory in combination with the non-equilibrium Green’s function method. The results proved that the adjustment of the location of AA-P2 dopants in 7-ASiNRs gives rise to semiconducting and metallic characteristics of systems. The low-bias negative differential resistance behaviors appeared to be symmetrical in AA-P2-doped ASiNR devices. However, the symmetry of negative differential resistance behaviors gradually declined with doping AA-P2 from the center to the edge of the nanoribbons. In addition, a striking rectifying behavior can be found. These outstanding properties indicate the potential application of SiNRs in nanodevices.

As the traffic flow has the characteristics of non-linear and strong interference, it has different features in different time-frequency domain. The traditional short-term traffic flow forecasting methods have the disadvantages of lower prediction accuracy, harder parameter determination and poorer adaptability. Aiming at above problems, we proposed a short — term traffic flow forecasting algorithm based on the wavelet function and the Extreme Learning Machine (ELM) to optimize the short — term traffic flow forecasting method. Firstly, the activation function of hidden layer neurons in the prediction model of the ELM is optimized according to the denoising principle of the wavelet function. Secondly, the short-term traffic volume prediction model of the ELM is established, and the traffic volume during the evening peak hours of the Canadian Whitemud Drive highway is forecasted. Finally, the results of this paper are compared with ones that predicted by BP neural network model Compared the R2 value of 0.7 in this method with the one of 0.5331 in BP neural network, the results show that the proposed method in this paper has better generalization ability and more proper stability than BP neural network has. The prediction results are in good agreement with the desired short — term traffic volume, and the short-term traffic flow can be predicted more efficiently.

The deep learning is a growing multi-layer neural network learning algorithm in the field of machine learning in recent years. Firstly, this paper analyzes the superiority of the deep learning at the aspect of feature extraction. Aimed at the lack of feature expression capacity and curse of dimensionality results from excessive feature dimensions of shallow learning, this paper proposes that using deep learning can extract high-lever features from low-lever features though its given layer structure. Secondly, the deep learning algorithm is applied in the case of road vehicle detection. Based on the traditional method, such as neural network the deep learning structure is further studied to increase the performance of feature extraction and classification recognition. Also, some tests are run in the Matlab software. The tests results show that with the increasing the amount of the data, the mean error and misclassification rate gradually decrease, so this algorithm based on the neural network has good superiority and adaptability of the deep learning. Finally, this paper proposes some suggestions for the improvement of the algorithm and prospects the development direction of the deep learning in the field of machine learning and artificial intelligence.

By applying density functional theory (DFT) combined with nonequilibrium Green's function method, we have investigated the electronic structures and quantum transport properties of (6, 3) spiral chiral single-walled carbon nanotubes (SCSWCNTs) in the presence of carboxyl ( COOH )-containing defect complexes. COOH - B - MV complex defect in (6, 3) single-walled carbon nanotubes (SWCNT) was energetically favorable than COOH - B - SW and COOH - B - Per complexes. Our calculated results proved that the complex partially improved the transport properties of (6, 3) SWCNTs with COOH - B - MV complex, but reduced the efficient transmission channels of (6, 3) SWCNTs with COOH - B - SW and COOH - B - Per complexes. It is anticipated that metallic-like (6, 3) SWCNT with COOH -containing complex defects can exhibit large range variations in transport behaviors, which are strongly dependent on the coupling between COOH group and B - MV complex defect. These tremendous properties suggest potential application of COOH -containing B -doped complexes in CNTs-based nanoelectronic devices.

By adopting density functional theory in combination with nonequilibrium Green's functions, we investigated the electronic structure and transport properties of silicon/nitrogen ( Si / N ) co-doping armchair graphene nanoribbons (AGNRs) with SiN x co-dopant incorporated in neighboring carbon atoms. The results demonstrate that the electronic structure can be modulated by introducing SiN x co-dopants in AGNRs. The striking negative differential resistance behaviors in the range of low bias can be observed in Si / N co-doped AGNR devices. These remarkable properties suggest the potential application of Si / N co-doping AGNRs in molectronics.

By applying nonequilibrium Green's functions in combination with density-functional theory, the effects of nitrogen-vacancy complex on electronic transport properties are investigated in spiral chirality single walled carbon nanotube device. The results show that rectifying behaviors can be tuned by introducing the complex defects with vacancy and nitrogen atoms. Moreover, current-voltage characteristics and negative differential conductance behavior can also be observed in this model. The mechanisms for these interesting phenomena are suggested.