Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical and personal recovery for people with schizophrenia in China: prevalence and predictors

Abstract Background Recovery from schizophrenia is a multidimensional construct that includes two categories: clinical recovery (symptomatic and functional remission) and personal recovery. Aims To investigate the overlap between clinical and personal recovery and identify correlates of each. Methods A cross-sectional study was conducted with 356 people living with schizophrenia and randomly selected from 12 communities in China. Clinical recovery was assessed using both the Brief Psychiatric Rating Scale (BPRS) and the Global Assessment of Functioning (GAF). Personal recovery was assessed using the 8-item Recovery Assessment Scale (RAS). Disability and quality of life were assessed using the WHODAS 2.0 and WHOQOL-BREF-2, respectively. Results Our results showed a recovery proportion of 36.52% for clinical recovery (66.57% for symptomatic remission and 40.73% for functional remission), and 17.42% for personal recovery. Only 8.99% of individuals achieved overall recovery (i.e. they met criteria for both clinical and personal recovery), and there was only a modest correlation (r = 0.26) between these two types of recovery. Overall recovery was only correlated with the quality of life (OR = 1.46, 95% CI: 1.05, 2.03), but there were various correlates for clinical recovery and personal recovery separately. Conclusions Recovery from schizophrenia involves both clinical and personal recovery, but when examined in the same sample, personal recovery, and thus overall recovery, is less common, particularly among people with schizophrenia in China.
Probing Perovskite Carrier Dynamics under Sunlight

Understanding the nature of photogenerated carriers and their subsequent dynamics in perovskites is important for the development of related materials and devices. Most ultrafast dynamic measurements on the perovskite materials were conducted under high carrier densities, which likely obscures the genuine dynamics at low carrier densities under solar illumination conditions. In this study, we presented a detailed experimental study of the carrier density-dependent dynamics in hybrid lead iodide perovskites using a highly sensitive transient absorption spectrometer. We found that the carrier lifetime was about a hundred nanosecond in the linear response range, representing sunlight excitation, which was much longer than under high carrier densities. We also elucidated that the fast carrier decay (<1 ps) and the medium decay processes (tens of ps) occurred via the defect state trapping, and we determined its effects on the utilization percentage of photogenerated carriers through quantitative analysis. Furthermore, we obtained the Shockley-Queisser limit that took into account the carrier trapping effect, which directly reflected the material performance.
Towards Non-Linear Social Recommendation Using Gaussian Process

Recent research on recommender systems has proved that by leveraging social network information, the quality of recommendations can be evidently improved. Traditional social recommendation models typically linearly combine social network information. For instance, matrix factorization based models linearly combine latent factors of relevant users and items. However, in practice, the multifaceted social relations are so complex that simple linear combination may not be able to reasonably organize such information for accurate social recommendation. On the other hand, existing deep learning based non-linear methods lack systematic modeling of user-item-friend relations. To handle these issues, we propose a novel, non-linear latent factor model for social recommendations leveraging Gaussian process. By introducing a social-aware covariance function, we organize individual users’ past feedback, as well as the associated social information (e.g., friends’ feedback to the same items) into a covariance matrix, which non-linearly and systematically learns the complex interactions among users, their interacted items and their friends’ opinions. A stochastic gradient descent based optimization algorithm is developed to fit the model. Extensive experiments conducted on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art social recommendation models and Gaussian process based models.
The Health Status of Labor Force in East Kalimantan Province: Effect of Individual Characteristics and Household Conditions

The aims of this research are first, to explore the factors that are associated with health status. Second, to address the individual characteristics and household conditions that affect the health status of labor force, taking into account the urban-rural differences. This research utilized data from SUSENAS Cor 2018, and employed a cross sectional - multinomial logistic regression analysis method. The dependent variable is the health conditions of the labor force. The findings show that, age and number of household members were significant for both sick and very sick health status in all two type of areas analyzed. In urban and rural areas simultaneously gender, expenditure per capita, and drinking water were only significant for sick health group while marital status, education, employment status, sanitation, house area was only significant for very sick health status group. In urban areas, the results indicated that gender, employment status, and drinking water were only significant for sick health status, while marital status, education, sanitation, and house area significant for very sick health status. Moreover, in rural areas, marital status was only significant for sick health status whereas, sanitation and house area were significant for the very sick health status.
DISTRIBUTED PROPERTIES OF MULTI-CONDUCTOR LOW VOLTAGE CABLES AT 2–500 kHz: NETWORK ANALYSIS BY FOUR-TERMINAL SENSING, TIME-DOMAIN REFLECTOMETRY AND IMPEDANCE MEASUREMENTS

The first step in modelling a real grid in the frequency range 2–500 kHz is discussed in this paper. The distributed parameters of the equivalent transmission line (TL) model are determined combining the benefits of three techniques: Four-terminal (4T) sensing is used to evaluate resistances, time-domain reflectometry (TDR) to determine lengths of cables, and impedance (Z) measurements to estimate inductances and capacitances. Finally, a measurement of the network impedance is performed in order to have a more complete view of the behaviour of the network.
Soft-start control strategy for the three-phase grid-connected inverter with LCL filter

To solve the start-up inrush current existing in the three-phase grid-connected inverter, a soft-start control strategy for the three-phase grid-connected inverter with LCL filter is proposed. The analysis of start-up inrush current is illustrated in detail. The capacitor current of the LCL filter is used to detect the closure of the grid-connected switch, so the closing time of the grid-connected switch does not need to be known. The detailed procedure and flowchart of the proposed soft-start control method are provided. Finally, experimental results of a LCL-type three-phase grid-connected inverter verify the theoretical analysis. The control strategy is easy to implement. The start-up inrush current does not exist in the grid-connected inverter.
Vibration serviceability analysis of buildings near the area of blasting operation

Blasting operation results in ground vibration,which can further excite the building vibration.An evaluation of the vibration effects on surrounding buildings is often required before and/or after a blasting operation.A typical case was analyzed to explain the evaluation procedure.Through observing the cracks on the building and recording the histories of ground vibration velocities and building vibration accelerations at the nearest site,it is found that the vibrations have no damage effects on the buildings but they can seriously annoy the occupants.It's a typical vibration serviceability problem so that human vibration standard and vibration dose value(VDV) were introduced to analyze the impact level and impact range of blasting induced vibration.
Connecting Embeddings Based on Multiplex Relational Graph Attention Networks for Knowledge Graph Entity Typing

Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, we observe that it still faces two non-trivial challenges: (i) most existing KGET methods extract features by encoding the existing entity typing tuples, while underutilizing or even ignoring rich relational knowledge. (ii) they typically treat each entity typing tuple in KGs independently, and thus inevitably fail to take account of the inherent and valuable neighborhood information surrounding a tuple. To address these challenges, we build a novel Heterogeneous Relational Graph (HRG), and propose a Multiplex Relational Graph Attention Networks (MRGAT) to learn on HRG, and then utilize a Connecting Embeddings model (ConnectE) to make entity type inference. Specifically, the overall framework contains three significant components. First, to effectively integrate the heterogeneous structural information including the entity typing tuples and entity relation triples in KGs, we construct a heterogeneous relational graph that consists of three semantic subgraphs. Second, we employ MRGAT to learn embeddings on HRG. In MRGAT, each subgraph of HRG is fed to its corresponding model that is capable of capturing neighborhood information by aggregating the surrounding nodes’ features. Finally, given the learned embeddings, we make entity type prediction by the connecting embeddings method ConnectE. Experimental results demonstrate the effectiveness of our proposed model against various state-of-the-art baselines.
Mental and Behavioural Responses to Bahá’í Fasting: Looking behind the Scenes of a Religiously Motivated Intermittent Fast Using a Mixed Methods Approach

Background/Objective: Historically, fasting has been practiced not only for medical but also for religious reasons. Bahá’ís follow an annual religious intermittent dry fast of 19 days. We inquired into motivation behind and subjective health impacts of Bahá’í fasting. Methods: A convergent parallel mixed methods design was embedded in a clinical single arm observational study. Semi-structured individual interviews were conducted before (n = 7), during (n = 8), and after fasting (n = 8). Three months after the fasting period, two focus group interviews were conducted (n = 5/n = 3). A total of 146 Bahá’í volunteers answered an online survey at five time points before, during, and after fasting. Results: Fasting was found to play a central role for the religiosity of interviewees, implying changes in daily structures, spending time alone, engaging in religious practices, and experiencing social belonging. Results show an increase in mindfulness and well-being, which were accompanied by behavioural changes and experiences of self-efficacy and inner freedom. Survey scores point to an increase in mindfulness and well-being during fasting, while stress, anxiety, and fatigue decreased. Mindfulness remained elevated even three months after the fast. Conclusion: Bahá’í fasting seems to enhance participants’ mindfulness and well-being, lowering stress levels and reducing fatigue. Some of these effects lasted more than three months after fasting.
The Exercise of Mandate - How Mandatory Service Implementation Promoted the Use of E-Government Services in Denmark

Danish e-government has for two decades been considered a global leader. Among the various reasons for this Danish success, this article explores the mandatory online self-service and digital post initiative (2012-2015) as one of the effective, strategic contributions in increasing the wider penetration and use of digital public services in Danish society. Although the mandatory shift to digital service could have caused negative reactions from public servants and citizens, this was not the case in Denmark. By reviewing a set of mandatory digital public services in the Danish context, four key aspects are identified as essential drivers. That is: pride as citizens, high levels of trust and privacy, usability and accessibility of e-government services, and informal support by family and community. The article discusses the four supportive prerequisites as keys for enforcing the implementations, which could otherwise be seen as national coercive dirigisme.
AUTOMATIC FISH DETECTION FROM DIFFERENT MARINE ENVIRONMENTS VIDEO USING DEEP LEARNING

Abstract. The marine environment provides many ecosystems that support habitats biodiversity. Benthic habitats and fish species associations are investigated using underwater gears to secure and manage these marine ecosystems in a sustainable manner. The current study evaluates the possibility of using deep learning methods in particular the You Only Look Once version 3 algorithm to detect fish in different environments such as; different shading, low light, and high noise within images and by each frame within an underwater video, recorded in the Atlantic Coast of Morocco. The training dataset was collected from Open Images Dataset V6, a total of 1295 Fish images were captured and split into a training set and a test set. An optimization approach was applied to the YOLOv3 algorithm which is data augmentation transformation to provide more learning samples. The mean average precision (mAP) metric was applied to measure the YOLOv3 model’s performance. Results of this study revealed with a mAP of 91,3% the proposed method is proved to have the capability of detecting fish species in different natural marine environments also it has the potential to be applied to detect other underwater species and substratum.
A False alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.
Brushless DC motor control system based on DSP

To improve the control effect of BLDCM,a novel design of high performance BLDCM control system based on DSP(Digital Signal Processor)TMS320LF2407A has been put forward.It establis hes a double closed loop system of BLDCM,designs the relevant hardware circuit,and the software program as well.The experiment result shows that the digital control system based on TMS320LF240 7A is reliable and free from load disturbance,not only can meet the requirement of real time control,be convenient to realize any advanced control strategy,but also is simple,high reliable and has high application worth.
DESIGN AND OPERATION OF SOLAR-HYDROGEN-STORAGE INTEGRATED ELECTRIC VEHICLE CHARGING STATION IN SMART CITY

This paper proposes the novel design and operation of solar-hydrogen-storage (SHS) integrated electric vehicle (EV) charging station in future smart cities, with two key functionalities: 1. super-fast and off-grid charging; 2. multi-energy charging system using solar, hydrogen and energy storage. The integrated system design and modelling of SHS-EV charging station include hydrogen fuel cell generator to conduct off-grid and high-density power generation, a local solar power generation facility, a power-to-gas electrolysis for hydrogen production from power grid and solar power, and hydrogen and battery storage facilities to conduct local energy balancing. The operation model of SHS-EV charging station is established through a nonlinear optimization problem with complex objective function. Objective function of SHS-EV charging station is to minimize the operation costs including hydrogen fuel and electricity purchase costs. The system constraints are the technical limits of individual energy devices, as well as the system-level balancing of energy supply with the EV charging demand. The simulation results show that the proposed SHS-EV charging station can meet the EV charging demand by super-fast and off-grid charging from multi-energy sources. The SHS-EV charging station can also provide CO2 free power generation at point of charging. By using particle swarm optimization (PSO) to solve the SHS-EV charging system optimization model in MATLAB, the operation costs of SHS-EV charging station are greatly reduced.
Temperature dependence of low-frequency noise in AlAl2O3Al single-electron transistors

We have measured the temperature dependence of the charge noise power spectral density Sq in two-junction Al–Al2O3–Al single-electron transistors at temperatures from 85 mK to 4 K. Although individual Lorentzians are often visible, the noise spectra are dominated by excess low-frequency noise with a power-law dependence on frequency f where Sq∝1/fβ and β≃1. Below about 0.5 K, Sq is weakly dependent on the temperature T. Above 1 K, the charge noise Sq increases with T, and at 4 K Sq≈10−4 e2/Hz at 1 Hz, about a factor of 100 greater than at 85 mK.
Optimization of Physicochemical Conditions for the Phenanthrene Degrading Consortium NS-PAH-2015-PNP-5

Abstract Polycyclic aromatic hydrocarbons (PAHs) are widespread ecological contaminants. These are produced as a result of diverse natural and anthropogenic activities. Phenanthrene is a low molecular weight PAH and is found prominently around the petrochemical contaminated sites. The increasing environmental concentration of phenanthrene is a threat to the ecosystems and their natural functioning. The present study developed a phenanthrene degrading bacterial consortium NS-PAH-2015-PNP-5 from the petrochemical contaminated soil near the Indian Oil Corporation Limited (IOCL) refinery, Panipat (India). The sample soil's pH, EC, TOC, and phenanthrene concentrations were 7.9, 1.81dS/m, 4.2%, and 19.7 mg/kg, respectively. Results from the 16S rRNA metagenome analysis of the consortium NS-PAH-2015-PNP-5 have shown an overwhelming population of Proteobacteria and Pseudomonas spp. at the respective phylum and species diversity. Response surface methodology (RSM) of statistical optimization tool ‘Design-Expert’ was used to optimize the physicochemical conditions for the phenanthrene biodegradation study. In the experiment, three factors, including temperature, pH, and concentration of phenanthrene, were optimized against the single response phenanthrene degrading efficiency (%). The gas chromatographic analysis results showed that a phenanthrene degrading efficiency of 89% was obtained at optimized physicochemical conditions (i.e., 35 °C temperature, pH 7.3, and 869 ppm concentration of phenanthrene) after 7 days of incubation of NS-PAH-2015-PNP-5 at a constant shaking rate of 100 rpm. Highlights: Bacterial consortium NS-PAH-2015-PNP-5 having a foremost population of phylum Proteobacteria, was developed from the petrochemical contaminated soil. A synergistically improved rate of phenanthrene biodegradation by NS-PAH-2015-PNP-5 was achieved at optimized physicochemical conditions using the Design-expert.
Sage-Husa Random Weighting UKF and Its Application in Navigation

On the basis of absorbing the advantage of Sage-Husa filtering and unscented Kalman filtering,this paper uses random weighting estimation to extend the traditional Sage-Husa noise estimation defined in linear system to nonlinear system and a nonlinear Sage-Husa random weighting unscented Kalman filtering method is presented.This method adopts Sage filtering window smoothing method to obtain the covariance matrix of observation residual vector and innovation(prediction residual) vector.Then,observation residual and prediction residual are adjusted by using random weighting adaptive factor.Finally,the covariance matrix of state prediction vector is estimated by using adaptive random weighting estimation to control the effect of observation residual and prediction residual on navigation accuracy.Experimental results demonstrate that the proposed Sage-Husa random weighting UKF is obviously better than unscented Kalman filtering and adaptive unscented Kalman filtering and the proposed method can improve the accuracy of integrated navigation.
Depletions of Multi‐MeV Electrons and Their Association to Minima in Phase Space Density

Fast‐localized electron loss, resulting from interactions with electromagnetic ion cyclotron (EMIC) waves, can produce deepening minima in phase space density (PSD) radial profiles. Here, we perform a statistical analysis of local PSD minima to quantify how readily these are associated with radiation belt depletions. The statistics of PSD minima observed over a year are compared to the Versatile Electron Radiation Belts (VERB) simulations, both including and excluding EMIC waves. The observed minima distribution can only be achieved in the simulation including EMIC waves, indicating their importance in the dynamics of the radiation belts. By analyzing electron flux depletions in conjunction with the observed PSD minima, we show that, in the heart of the outer radiation belt (L* < 5), on average, 53% of multi‐MeV electron depletions are associated with PSD minima, demonstrating that fast localized loss by interactions with EMIC waves are a common and crucial process for ultra‐relativistic electron populations.
Case-Crossover Spatial Lag Grid Differences Between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Inpatient Hospitalizations in Baltimore, Maryland, USA: Identification of Homogeneous Spatial Areas

Optimal use of aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytic method is currently available to evaluate the spatial dimension. The temporal case-crossover design was modified to assess the association between Community Multiscale Air Quality (CMAQ) lag grids and four respiratory-cardiovascular hospital events. The maximum number of adjacent lag grids with the expo-sure-health outcome association determined the size of the homogeneous spatial area. The largest homogeneous spatial area included 5 grids (720 km2) and the smallest 2 grids (288 km2). PMC and PMCK analyses of ED asthma, IP asthma, IP MI, and IP HF were significantly higher in rural grids without air monitors than in urban with air monitors at lag grids 0, 1, and 01. Grids without air monitors had higher AOD-PM2.5 concentration levels, poverty percent, population density, and environmental hazards than grids with air monitors. ED asthma, IP MI, and HF PMCK ORs were significantly higher during the warm season than during the cold season at lag grids 0, 1, 01, and 04. The possibility of elevated fine PM and other demographic and environmental risk factors contributing to elevated respiratory-cardiovascular diseases in persons residing in rural areas was discussed.