Miracle-dz/UniFAR-FTUs huggingface dataset:
Section 5(4) of the Mental Health Act 1983: a review of local policy and guidance in England and Wales

Purpose – The purpose of this paper is to review policy or guidance on the implementation of Section 5(4) written by NHS mental health trusts in England and health boards in Wales. Design/methodology/approach – A Freedom of Information request was submitted to all trusts in England (n=57) and health boards in Wales (n=7) asking them to provide a copy of any policy or guidance on the implementation of Section 5(4). Documents were analysed using content analysis. Specific attention was given to any deviations from the national Mental Health Act Codes of Practice. Findings – In total, 41 (67.2 per cent) organisations had a policy on the implementation of Section 5(4). There was a high level of consistency between local guidance and the Mental Health Act Codes of Practice. There were however; different interpretations of the guidance and errors that could lead to misuse of the section. Some policies contained useful guidance that could be adopted by future versions of the national Codes of Practice. Research .
Quality weighting in gridding Aquarius sea surface salinity

Aquarius is a satellite designed for measuring sea surface salinity (SSS). The Aquarius measurements may be influenced by marine environmental factors. The result is the inconsistency of data quality under different conditions. Although the data qualities have been considered in some previous studies, they have only been used for data screening. Base on this, a quality weighting method is proposed in this paper. The key differences between our method and traditional method, is way of weighting the data. In the present paper, both distance and data quality are considered in the weighting process. After the weight is determined, the weighted average fitting (WAF) method is used to calculate the grid SSS value. Then weekly 0.25°×0.25°gridded SSS fields between 40°S and 40°N are generated, covering the period from September 2011 through May 2015. The error statistics are calculated and the result shows that the root-mean squared difference (RMSD) is about 0.18 psu, which can improve the accuracy by about 31%. Therefore the method proposed in this paper could improve the precise of SSS filed with higher temporal and spatial resolution significantly.
A research on generative adversarial network algorithm based on GPU parallel acceleration

In recent years, Generative Adversarial Network (GAN) has received much attention in the field of machine learning. It is an unsupervised learning model which is widely used in image, video, voice, etc. Based on GAN's two-man zero-sum game theory, the researchers proposed excellent variant algorithms such as deep convolutional GAN(DCGAN), Conditional GAN(CGAN), Least Squares GAN(LSGAN), and Boundary Equilibrium GAN (BEGAN), which has gradually overcome the problem of training imbalance and model collapse. However, the time efficiency of model training has always been a challenging problem. This paper proposes a GAN algorithm based on GPU parallel acceleration, which utilizes the powerful computing power of GPU and the advantages of multi-parallel computing, greatly reduces the time of model training, improves the training efficiency of GAN model, and achieves better modeling performance. Finally, we used the LSUN public scene dataset and the TIMIT public voice dataset to evaluate the proposed algorithm and compare it with the traditional GAN, DCGAN, LSGAN, and BEGAN algorithms. The experiment has fully proved the time advantage of the model training of the algorithm introduced in this paper.
Surveillance of abnormal behavior in elevators based on edge computing

Edge computing is an extension of the cloud computing paradigm that shifts part of computing data, applications and services from the cloud server to the network edge, providing low-latency, mobility and location-aware support for delaysensitive applications. The elevators in the high-rise buildings are geographically distributed and movable. Safety and reliability of elevators have attracted people’s attention. Security problem in the elevator is a key issue, especially in emergencies requiring fast response and low latency. In this paper, an elevator abnormal behavior video surveillance system is designed and developed using edge computing paradigm. The recognition of abnormal image sequences and the evaluation of abnormal behavior are realized. Collecting, processing, and analyzing video images are completed at the network edge in real time. The Edge computing nodes are distributed and deployed according to the geographic location of the elevator. The edge nodes are based on mobile embedded devices, and use the computing resources of the embedded devices to implement edge computing at the network edge. Through the edge network, there are several edge nodes based clusters being built to perform distributed computation tasks.
Sheep delivery scene detection based on faster-RCNN

Sheep delivery scene detection is one of the important applications of object detection technology in the field of animal delivery detection. At present, there are reports on the detection of delivery scenarios of pigs and dairy cows at home and abroad, but the research on the behavior of sheep delivery is still in its infancy. This paper aims to apply the Faster- RCNN model for the detection of ewes and newborn lambs in a sheep delivery scenario; Training the Faster-RCNN model based on the ZF, VGG16 feature extraction networks and the Soft-NMS algorithm respectively by using the selfmade sheep delivery scene data set, and the experimental results were compared; The comparison of experimental results show that the Faster-RCNN model based on Soft-NMS algorithm and VGG16 feature extraction network has better effect in sheep delivery scene detection. The method can effectively complete the detection of the ewes and the newborn lambs in the sheep delivery scene, expand the application range of artificial intelligence in the animal husbandry, and has certain popularization and application value for promoting the development of the wisdom animal husbandry.
WOA with adaptive mutation operator to estimate parameters of heavy oil thermal cracking model

This paper proposes an enhanced whale optimization algorithm with adaptive mutation operator (amWOA). In amWOA, the adaptive mutation operator is designed to balance the global search and local search abilities. The population sequencing strategy is added to the mutation operator to help the algorithm jump out of the local optimum. The numerical results of three test functions show that the amWOA has better performance. The amWOA is adopted for parameter estimation of the heavy oil thermal cracking model. The simulation results show that the amWOA has the smallest modeling error.
Analysis of structure characteristics and internal electron diffusion characteristics of GaAs

GaAs material has excellent photoelectric properties and is the most sensitive vacuum semiconductor material in the visible light band. GaAs photocathode has become the core component of low-light-level night vision device and been widely used in the field of low-light-level night vision. We systematically analyzed the structural characteristics of the low-light image intensifier and defined the boundary conditions of GaAs electron emission. It provided calculation basis for further analysis of the photoelectric effect of GaAs photocathode. We established GaAs crystal model on first-principle, calculated the energy band structure, and analyzed the mechanism of surface electrons escaping. After photon energy transferring to the electronic, electrons were excited and went out of its orbit, becoming free electrons and gaining initial kinetic energy. According to the experience, we assumed the collision energy loss rate after free electron diffusion process, and calculated number of electron collision in crystal model and displacement distance. Linear displacement distance is electron diffusion length. The initial kinetic energy of electrons excited by GaAs material depends on the energy of incident photons, as well as on the cathode's own temperature. We analyzed the relationship between the electron diffusion length of the material and the temperature. The electron emission characteristics of GaAs material were summarized, which provided technical support for the subsequent process research of this cathode material. GaAs low light image intensifier is made of the following parts: photocathode、 MCP、 screen and high voltage power. Using the elastic collision model, we calculated the energy of the photon transported to the electron transfer. Assuming the collision energy loss rate of electronic diffusion is 20% every time, free electrons crash until photon energy losses. The collision frequency and the moving distance are GaAs material properties. We analyzed the relationship between the temperature and electron diffusion length of GaAs in this paper.
The role of detail spatial data resulted from unmanned aerial vehicle for tourism area planning in Karst Area, Gunungkidul

Karst area has a fascinating tourist attraction especially special interest tourism such as cave tubing in Kalisuci turism area, Gunungkidul, Yogyakarta. The presence of visitor restrictions has an impact on tourism waiting times. However, it also spurs the development potential besides cave exploration. Planning and development of tourist sites that include locations and consider the existing conditions of the region can only be done optimally with data and spatial analysis. On the other hand, the specific terrain condition, steeply hilly karst, is a great challenge in planning and developing karst area. This research aims to built basic spatial data in the Kalisuci Tourism area and provide a reference about the role of spatial data produced for planning special interest tourism in Kalisuci karst area, Gunungkidul. Basic spatial data is built from the results of data acquisition using Unmanned Aerial Vehicle (UAV) technology. This data is very important in the planning inputs for the development of the Kalisuci special interest tourism area in supporting the planning of tourist areas in accordance with terrain conditions. In addition, the data obtained from acquisition with UAVs are expected to provide detail spatial data collection, which is currently very limited. The resulting data are small format aerial photographs, aerial photo imagery (orthorectification results), and digital elevation models in the form of Digital Surface Model (DSM). This paper discusses how far the data generated can contribute to overall field observation and assist in planning special interest tourism in karst area.
Video-based traffic sign detection and recognition

For driverless cars, the detection and identification of traffic signs is critical to the understanding of the environment around the vehicle. There are many smaller traffic signs in the pictures taken during driving, which are difficult to detect by existing object detection methods.In view of the real-time and accuracy requirements of the traffic sign recognition problem of driverless cars, this paper improves the VGG convolutional neural network, proposes the VGG-8 model, and improves the VGG-16 structure. The network is optimized through SGD and Nesterov Momentum. Training and testing in the GTSRB traffic dataset. By comparing a small-scale four-layer convolutional neural network VGG-6 with a 13-layer convolutional neural network VGG-16, a six-layer convolutional neural network is proposed. VGG-8. Inspecting the ten traffic signs in the video, VGG8 has higher accuracy and running speed, and has certain feasibility in practical applications.
A sparsity-relaxed algorithm for the under-determined convolutive blind source separation

Convolutive blind source separation (CBSS) is a kind of signal processing method by separating multiple sources from a convolutive mixing model. The concept of CBSS is to recover the latent sources in a reverberant environment. Usually, a two-stage scheme including the mixing matrix estimation and the source recovery are proposed to fulfill this target. In this paper, we mainly discuss the source recovery problem based on the knowledge of estimated mixing matrix. Specifically, this problem can be categorized as a sparse source construction optimization model, especially for the under-determined case where the number of sources is greater than the number of microphones. Inspirited by the fact that only few source components are active at each time-frequency slot, a new augmented Lagrange method is proposed to find the optimal sparse solution of sources with the ℓp norm (0<p<1) based measurement function. The proposed method relaxes the strict sparse assumption on sources, hence improve the source separation performance. The experiment results demonstrate that the proposed algorithm is superior than the state-of-the-art methods.
An exploration of the experience of accessing primary health care for people with schizophrenia

People with schizophrenia have been found to experience health inequities that lead to early morbidity and mortality. Primary health care is an approach to reducing health inequities for individuals with schizophrenia. The purpose of this study was to examine what it is like for individuals with schizophrenia when they access primary health care. Exploring these experiences from the client perspective highlighted areas in which change is needed to improve access. This study used a qualitative methodology, specifically interpretive description, to analyze the data from 6 in-depth interviews with individuals with schizophrenia. The results indicate a need to address the multiple effects of stigma, the importance of considering physical and mental health care holistically, and the need to acknowledge and address the multiple losses that individuals with mental illness experience. EXPERIENCE OF ACCESSING PRIMARY HEALTH CARE iii Preface For the purpose of this study, ethics approval was obtained from The University of British Columbia (Vancouver), Behavioral Research Ethics Board. The Ethics Certificate Number obtained was H12-01308. EXPERIENCE OF ACCESSING PRIMARY HEALTH CARE iv Table of
Community conflict prediction method based on spliced BiLSTM

Existing community conflict prediction models usually use a single unidirectional LSTM network to process graph and word embeddings simultaneously. However，there is no temporal coherence between graph and word embeddings. And their importance for prediction is different. A community conflict prediction method based on spliced bidirectional LSTM is proposed. Firstly, two bidirectional LSTMs are utilized to process graph and word embeddings respectively to break temporal dependency. Secondly, the hidden states of the two bidirectional LSTMs are weighted. Finally, the weighted hidden states are spliced and fed into subsequent layers of the neural network to predict conflicts. Experimental results show that this method can improve the AUC value to 0.733 on the Reddit dataset, and reduce the number of iterations of training.
Real-time lane detection, fitting and navigation for unstructured environments

In the outdoor environment, robot perception is a challenging task encompassing several layers of abstractions like lane detection, object detection and avoidance, and way-point navigation. Intelligent Ground Vehicles are becoming popular and having an efficient perception stack is quintessential to its scaling for different tasks. Several issues like illumination variance, shadows, occlusions, etc. cause researchers to adopt computationally heavy approaches for improving generalization. We present a novel, real-time approach for combined lane detection, obstacle detection, and way-point navigation using features from a 2D-LiDAR and camera. A robust curve fitting algorithm has been implemented, adhering to the minimization of computation. The overall processing pipeline has been tested and validated to work well in outdoor conditions.
Analysis on Factors Driving Growth in China's Fishery Industry, Based on a Noncompetitive Input-Output Model

ABSTRACT Qin, G. ; Zhang, S. ; Liu, X. , and Cheng, B. , 2019. Analysis on factors driving growth in China's fishery industry, based on a noncompetitive input-output model. In: Li, L. ; Wan, X. ; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 367–370. Coconut Creek (Florida), ISSN 0749-0208. Based on the input-output (IO) table data from 2002 to 2012, a noncompetitive IO model was used to calculate the factors driving growth and change in the final demand of China's fishery industry. The results show that the intermediate input, initial input, consumption, capital formation, export, and other final demands of the fishery industry all showed significant growth, and the added value increased first and then decreased. The role of consumption is the most outstanding, capital formation is second, and export is lowest, with a contribution rate of 66.22%, 20.51%, and 11.31%, respectively. Compared with final demand, the contribution rates of intermediate input and increased rate of gross domestic product growth by fisheries are relatively small, with the contribution of the value-added rate of only 8.63%, and intermediate input structure's contribution to growth of 6.67%. From 2002 to 2007, consumption contributed the most at 48.88%, followed by export, with its contribution rate being up to 21.91%. From 2007 to 2012, consumption contributed the most at 76.68%, capital formation contributed 22.20%, and export contributed 7.21%.
Smart Bioimpedance Spectroscopy Device for Body Composition Estimation

The purpose of this work is to describe a first approach to a smart bioimpedance spectroscopy device for its application to the estimation of body composition. The proposed device is capable of carrying out bioimpedance measurements in multiple configurable frequencies, processing the data to obtain the modulus and the bioimpedance phase in each of the frequencies, and transmitting the processed information wirelessly. Another novelty of this work is a new algorithm for the identification of Cole model parameters, which is the basis of body composition estimation through bioimpedance spectroscopy analysis. Against other proposals, the main advantages of the proposed method are its robustness against parasitic effects by employing an extended version of Cole model with phase delay and three dispersions, its simplicity and low computational load. The results obtained in a validation study with respiratory patients show the accuracy and feasibility of the proposed technology for bioimpedance measurements. The precision and validity of the algorithm was also proven in a validation study with peritoneal dialysis patients. The proposed method was the most accurate compared with other existing algorithms. Moreover, in those cases affected by parasitic effects the proposed algorithm provided better approximations to the bioimpedance values than a reference device.
Depressive and Anxiety Symptoms among Pediatric In-Patients with Dengue Fever: A Case-Control Study

Background: Psychiatric symptoms have been reported in adult patients with dengue fever (DF); however, information on pediatric patients remains inadequate. We sought to identify the prevalence and predictors of depressive and anxiety symptoms and identify other psychiatric symptoms among pediatric patients with DF. This case-control study involved pediatric in-patients (n = 225) who had clinical or serologic-confirmed DF and healthy school-based controls (n = 260). Participants completed the Revised Child Anxiety and Depression Scale (RCADS). Results: The prevalence of depressive (13.3%) and anxiety (34.2%) symptoms among pediatric patients with DF was significantly (p < 0.001) higher than that among controls (3.5% and 16.2%, respectively). Multiple linear regression analysis found that age, family history of DF, ≤2 days of hospitalization, myalgia, and arthralgia were predictors of increased depressive and anxiety symptoms among the patients. Further, 26.7% of pediatric patients reported irritability, agitation, visual hallucinations, and aggressiveness. Conclusion: Pediatric patients present depressive and anxiety symptoms whose levels were associated with social and clinical factors. However, whether these symptoms are present only during the infection or may still persist after recovery or are brought by children’s adverse reactions to hospitalization are unknown, and thus, further studies are needed.
CXCL5 Modified Nanoparticle Surface Improves CXCR2+ Cell Selective Internalization

Driving nanomaterials to specific cell populations is still a major challenge for different biomedical applications. Several strategies to improve cell binding and uptake have been tried thus far by intrinsic material modifications or decoration with active molecules onto their surface. In the present work, we covalently bound the chemokine CXCL5 on fluorescently labeled amino-functionalized SiO2 nanoparticles to precisely targeting CXCR2+ immune cells. We synthesized and precisely characterized the physicochemical features of the modified particles. The presence of CXCL5 on the surface was detected by z-potential variation and CXCL5-specific electron microscopy immunogold labeling. CXCL5-amino SiO2 nanoparticle cell binding and internalization performances were analyzed in CXCR2+ THP-1 cells by flow cytometry and confocal microscopy. We showed improved internalization of the chemokine modified particles in the absence or the presence of serum. This internalization was reduced by cell pre-treatment with free CXCL5. Furthermore, we demonstrated CXCR2+ cell preferential targeting by comparing particle uptake in THP-1 vs. low-CXCR2 expressing HeLa cells. Our results provide the proof of principle that chemokine decorated nanomaterials enhance uptake and allow precise cell subset localization. The possibility to aim at selective chemokine receptor-expressing cells can be beneficial for the diverse pathological conditions involving immune reactions.
A novel model for malaria prediction based on ensemble algorithms

Background and objective Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction. Methods The ARIMA, STL+ARIMA, BP-ANN and LSTM network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017. We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. In addition, gradient-boosting regression trees (GBRTs) were used to combine the above four models. We also determined whether stacking structure improved the model prediction performance. Results The root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. After using the stacking architecture combined with the above four models, the RMSE, MASE and MAD values of the ensemble model decreased to 6.810, 0.224 and 4.625, respectively. Conclusions A novel ensemble model based on the robustness of structured prediction and model combination through stacking was developed. The findings suggest that the predictive performance of the final model is superior to that of the other four sub-models, indicating that stacking architecture may have significant implications in infectious disease prediction.