Miracle-dz/UniFAR-FTUs huggingface dataset:
How Challenge Stress Affects Mental Health among College Students during the COVID-19 Pandemic: The Moderating Role of Self-Efficacy

While a plethora of studies has been conducted to examine stress and its impact on mental health in western countries, research is scarce investigating the relationship between student challenge stress and health illness in the context of Chinese colleges. No studies examined the moderating effect of self-efficacy on the relationship between challenge stress and health illness. This study attempted to investigate the relationships between these three variables among Chinese college students. Especially, this study focused on examining whether self-efficacy moderated the effect of perceived challenge stress on students' mental health. Also, the differences were tested between male and female students in terms of these three variables. A sample of 578 Chinese college students was recruited over an approximately 12-week period from 7 Chinese universities. An online survey link was distributed through WeChat. The SPSS version 26 software was used to analyze the data. Results showed that there is no significant difference between genders in terms of perceived challenge stress, self-efficacy, and students' mental health. In addition, challenge stress was positively related to the students' mental health (beta = 0.35, p < 0.01) while there was a negative association between self-efficacy and mental health (beta = -0.41, p < 0.01). Furthermore, self-efficacy plays a moderating role in the relationship between challenge stress and mental health (beta = -0.11, p = 0.02). Students with low self-efficacy tend to experience more mental health issues. It is suggested that Chinese colleges and universities pay more attention to students with low self-efficacy, either through faculty/staff interventions or peer counseling. Professors consider reducing students' academic stress to improve their mental health.
ServiceRank: Root Cause Identification of Anomaly in Large-Scale Microservice Architectures

Nowadays, increasing business applications running in the cloud are embracing the microservice architecture. This article presents the challenges and implications of diagnosing root causes of anomalies in large-scale microservice architecture using real incidents in IBM Bluemix. We propose ServiceRank, a novel framework for anomaly detection and root cause identification in the microservice architecture to tackle these challenges. ServiceRank introduces an anomaly detector followed by a root cause analysis module, which detects the suspected abnormal service without pre-defined thresholds. To generalize our approach, we design a causal relationship extraction approach to construct impact graphs for root cause investigation according to specific anomalies. To eliminate cloud design-patterns' impact on anomaly diagnosis, we propose a correlation calibration mechanism in ServiceRank and present a calibration algorithm for the circuit breaker - A typical protection pattern in the microservice architecture. Finally, we design a heuristic investigation algorithm based on the second-order random walk to identify the anomaly's root cause. Experimental results in a simulated environment and the IBM Bluemix platform show that ServiceRank outperforms selected approaches in accuracy and offers fast identification of root cause service when an anomaly occurs. Moreover, we can deploy ServiceRank rapidly and easily in various systems without any pre-defined knowledge.
Design of the Economic Mechanism of Sustainable Development of the Socio-Economic System of the Region

The significance of the influence of exogenous factors on the stability of the state of the socio-economic system (SES) necessitates the development of a methodological framework for regulating regional systems. One of the most important criteria for assessing the sustainability of SES is the state of human capital, which presupposes, first of all, the sufficiency of medical services in accordance with the necessary dynamics of natural reproduction of labour resources. The results of the research presented in the article summarize the author's ideas in the possibility of modelling the choice of an economic mechanism for financing the regional health care subsystem that provides the required level of sustainability based on the adequacy of financing of medical services. The basic theory of formalization of the problem of the design of the economic mechanism was the mathematical theory of games. The model for choosing the economic mechanism of the regional health care subsystem is formulated in terms of the design problem for economic mechanism.
Construction and Analysis of Variable Wall Thickness Profile of Double Scroll Teeth of Scroll Compressor

Based on the changing trend of the geometric parameters of the scroll profile of the involute of circle with variable radii, a new type of double scroll teeth profile of scroll compressor composed of involute of circle with variable radii is constructed. The construction process of the baseline is discussed, and the baseline equations are deduced. According to the principle of normal equidistance, the geometric model of variable wall thickness double scroll teeth is established, and the suction volume, tooth thickness, and scroll area of double scroll teeth are compared with those of the double scroll teeth composed of ordinary involute of circle. The results show that: compared with ordinary double scroll teeth with constant thickness, the scroll diameter when two scrolls mesh is reduced and the revolutions of orbiting scroll required in the working process are reduced and it is easy to select better scroll profile through adjusting the changing coefficient of the base circle radius k, which provides a reference for scroll machinery using environment-friendly working fluids.
Scope and Challenges of Self-Directed Learning in Undergraduate Medical Education: A Systematic Review

Context: Self-directed learning (SDL) is an individual’s ability to effectively use various strategies to reach his/her learning goals. We conducted this systematic review to explore the different methods, advantages, and challenges in SDL. Method: A systematic and comprehensive literature search on PubMed, Scopus, and Google Scholar databases was carried out using keywords such as “self-directed learning”, “self-regulated learning”, and “medical undergraduate curriculum” among articles published between 2010 and 2020. Original articles having methods, advantages, and challenges were included. The data were summarized and analyzed thematically. Results: Out of the total number of 1781 screened articles, sixteen eligible studies were included in the systematic review. Flipped classrooms, problem-based learning, case-based scenarios, video lectures, and e-learning were the commonly used methods. Self-assessment, time management, motivation, teamwork, and critical thinking were the advantages. Faculty guidance, time constraints, distraction due to technology, lack of organizational skills, and difficulty to learn complex topics were the common challenges of SDL. Conclusions: Most of the articles in recent years explain the growing interest in SDL in the medical curriculum. Despite numerous advantages of SDL, there are several challenges and limitations due to the lack of specific and defined guidelines for its implementation. Further research is required for structured methods and strategies for SDL effective implementation.
Effect of Drought Stress on Photosynthetic Parameters and Gene Expression in Brassica juncea L. (Czern. and Coss.)

Brassica juncea L. Czern & Coss. (Indian mustard) is a crucifer, used as a source of food spice and folk medicine all over the world (Tian and Deng, 2020). It is the most widely cultivated oilseed crop in the Indian subcontinent (Akhtar et al., 2020), with very high acerage (6.9 million hectares) and production (7.2 million metric tonnes) (USDA, 2018-19). B. juncea also known as Oriental mustard, brown mustard, chinese mustard or leaf mustard is an annual herb (Lin et al., 2011) and is an allotetraploid (AABB, 2n = 36). It has evolved through multiple hybridization events between B. rapa (AA, 2n International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 02 (2021) Journal homepage: http://www.ijcmas. com
Analysis of energy consumption structure based on K-means clustering algorithm

In order to solve the problem of huge and messy data in the process of analyzing energy consumption structure in different regions, an energy consumption structure analysis method based on K-means clustering algorithm is proposed, and the elbow method and contour coefficient method are used to analyze the data in Qinghai Province. The consumption structure was analyzed and the algorithm was verified. The results show that the algorithm can efficiently and quickly perform data mining and clustering based on local economic and environmental characteristics, which greatly improve the convenience of energy consumption structure analysis.
Promoting self-determined motivation for physical activity: From theory to intervention work

Please cite as: Quested, E. , Kritz, M. , Hancox, J. E. , Ntoumanis, N. , & Thøgersen-Ntoumani, C. (2021). Promoting self-determined motivation for physical activity: From theory to intervention work. In Z. Zenko & L. Jones (Eds.), Essentials of exercise and sport psychology: An open access textbook (pp. 37–61). Society for Transparency, Openness, and Replication in Kinesiology. https://doi.org/10.51224/B1003
How to reduce uncertainties in a coupled and spatialized water quality model using data assimilation?

<p>Intensive use of pesticides in agricultural catchments leads to a widespread contamination of rivers and groundwater. Pesticides applied on fields are transferred at surface and subsurface to waterbodies, resulting from the interaction of various physical processes. They are also highly influenced by landscape elements that can accelerate or slow down and dissipate water and contaminant flows. The PESHMELBA model has been developed to simulate pesticide fate on small agricultural catchments and to represent the landscape elements in an explicit way. It is characterized by a process-oriented approach and a modular structure that couples different models. </p><p>In the long run, we aim at setting up and comparing different landscape organization scenarios for decision-making support. However, before considering such operationnal use of PESHMELBA, uncertainties must be quantified and reduced. Additionally, the model is physically-based, fully-spatialized which leads to a large set of parameters that must be carefully estimated. To tackle both objectives, we set up a data assimilation framework based on satellite images and <em>in situ</em> data and we evaluate the potential of Ensemble Smoother for joint variable-parameter assimilation. Assimilating surface moisture images allows for direct correction of variables and parameters on the top part of the soil. However, due to the PESHMELBA structure based on a dynamic parallel code coupler (OpenPALM), the impact of such correction on other compartments and other physical processes has to be finely assessed. </p><p>In this preliminary study, a fairly simple virtual hillslope inspired from a realistic catchment is set up and data assimilation is performed on twin experiments, <em>i.e.</em>, using virtual surface moisture images. The potential of such technique for improving the global performances of the model is scrutinized and the sensitivity to the assimilation framework (ensemble size, frequency of observations, errors, etc.) is assessed. Valuable information on the coupling functioning are obtained allowing for anticipating performances in a real case. Identified limitations of surface moisture assimilation also give precious indications about existing gaps and pave the way for multi-source data assimilation. </p><p>&#160;</p>
AlN FBAR Particle Sensor With a Thermophoretic Sampling Mechanism

Pollution by particulate matter (PM) poses a serious and growing risk to human health. Film Bulk Acoustic Resonators (FBARs) have been proposed as a low-cost way to monitor particle concentration. This paper presents particle detection by a 1.1 GHz aluminium nitride based FBAR designed and fabricated by SilTerra Malaysia. The FBARs are used in a differential configuration with one sensing and one reference device in a system comprising a microchannel with an electric microfan and a thermophoretic precipitator microhotplate array for improved sampling. A custom-built test chamber was designed to characterise the device at different levels of particulate matter concentration in air. The particle feed rate into the test chamber was varied between 9.5, 19, 43, 66 and <inline-formula> <tex-math notation="LaTeX">$94~\mu \text{g}/\text{m}^{3}\text{s}$ </tex-math></inline-formula>. The FBAR frequency was found to decrease with increasing particulate matter concentration in the test chamber air. The experiments were conducted with and without the microchannel and it was found that the sampling channel increased sensitivity of FBAR resonant frequency to particle concentration per cubic meter of air from 5 Hz/<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>gm<sup>−3</sup> to 20 Hz/<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>gm<sup>−3</sup>. The detection limit in this test was estimated at ca. <inline-formula> <tex-math notation="LaTeX">$50~\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula>, which is around current European limits for PM10 pollution. In addition, the use of the sampling microfan to aid cleaning of FBARs after particle measurement was also investigated and found to be feasible especially for lower particle concentration.
Application of deep neural networks for fault diagnosis in a hybrid AC/DC power grid

As the AC/DC hybrid power grids expand and the distance of transmission increases, the environment becomes more complex, and the plug-in power electronics equipment is more diversified. In this situation, the possibility of grid faults is increased inevitably. In this paper, a feasible and effective method of fault diagnosis in an AC/DC transmission system based on deep neural network technique is proposed, inspired by data-driven concept. By applying convolutional neural network (CNN), an improved CNN architecture is designed to solve the fault phase selection problem. After that, a series of techniques are leveraged to improve the model performance. Finally, this improved model is trained and tested on labelled dataset, which is obtained from a designed AC/DC hybrid power grid. The simulation results demonstrate the validity of the proposed method, and the results of ablation experiment show that these techniques are very effective and can be applied generally to similar problems. On the other hand, this proposed CNN model can effectively utilize big data, and further improve the generalization of model.
Evaluation of Clinical and Hematological Parameters of Acute Febrile Illness Patients: A Dengue Predictive Model

Objectives: In tropical and subtropical nations dengue is a main public health matter. We try to find to analyze the clinical and hematological factors from a Complete Blood Count (CBC) which differentiate dengue infection. The purpose of the study was to categorize clinical features and hematological parameters and develop predictive model of high fever patients was treated as early marker and possible prognosticator factors of dengue. Methods: Demographic data analysis with variables like gender, place, age and clinical data analysis of clinical parameters with dengue confirmation test have been done develop predictive model factors to differentiate Dengue Infection (DI) from CBC data of Acute Febrile Illness (AFI) patients in Delhi-NCR, Sonepat region from 2015 to 2018. Results: Among 223 patients, 167 were confirmed with 100 primary and 67 secondary DI of maximum number male patients in the age group of 10-30 years from 2015 to 2018 while 56 had negative results. Badhkhalsa, Jakholi, Sewli and Rai were high dengue reported area in Delhi-NCR, Sonepat. There was a statistically significant value (p<0.05) of Total Leukocytes Count (TLC) cells/cmm during AFI phase from 2015 to 2018 using logistic regression and ROC graph. TLC (cells/cmm) had a higher area ± SE value from 2015 to 2018 (0.66 ± 0.07, 0.76 ± 0.10, 0.68 ± 0.07 and 0.79 ± 0.06) respectively which were statistically significant (p<0.05). Dengue diagnosis test of mean value of TLC (<4000 cells/cmm) from 2015 to 2018 were evaluated with a prevalence of dengue disease of 35.09%- 58.06%, sensitivity of 41.03%-100%, specificity of 24.10%-93.10% and accuracy rate of diagnosis evaluation of 62.07%-70.97% were related to danger sign DI in Delhi-NCR, Sonepat area. Conclusion: As per our study we can conclude that due to non-specific clinical features and delayed of confirmation test, among the clinical parameters TLC could be the useful feature for quick finding of DI which is unique, simple, easily available, cost effective approach mainly in rural area.
Production and optimization of extracellular alkaline protease from halotolerant chromate resistant Bacillus circulans isolated from tannery solid waste.

Halotolerant alkaline proteases are of great interest because of their high proteolytic activity and stability under alkaline and high saline environment. These enzymes have extensive applications in industries like laundry detergents, pharmaceutical, food, leather and feather processing and proteinaceous waste bioremediation. Keeping it in view, the study was aimed to isolate chromate resistant, haloalkliphilic protease producing Bacillus circulans bacteria from the discharged tannery solid waste. A total of seven bacterial strains were isolated on selective milk agar plates (pH 8.0-9.0) from tannery solid waste on the basis of different colony morphology and higher tolerance capacity for chromate and NaCl. These strains exhibited variable alkaline protease activity and were tolerant to different concentration of both chromate (200-1300 g/ml) and NaCl (1-9%). Out of seven, one strain TVD-5 was interestingly tolerant to high concentration of Cr(VI) (1300 g/ml) and NaCl (8.0%) and exhibited vibrant clear zone diameter between (13-30 mm) on 1.0 per cent skim milk agar medium at pH 9.0 after 28 h incubation. This strain produced maximum protease of 390 Units/ ml during early stationary phase after 36 h of growth. The enzyme exhibited its optimal activity at pH 9.0, temperature 35°C and 8.0 per cent salinity, whereas, significantly active and stable in broad pH (7.5-11.0) and temperature (25-45°C) range and at NaCl concentrations ranging from 7.0 to 13.0 per cent. This bacterium may potentially be useful for simultaneous bioremediation of Cr(VI) containing wastes in the environment. Also, the proteases of this study may have many applications in different industries and environmental bioremediation. How to view point the article : Verma, Tuhina and Agarwal, Swati (2016). Production and optimization of extracellular alkaline protease from halotolerant chromate resistant Bacillus circulans isolated from Tannery solid waste. Internat. J. Plant Protec. , 9(1) : 211-218.
Mass transfer modeling with and without evaporation for iodine chemistry in the case of a severe accident

Iodine is a fission product of major importance in a severe reactor accident because volatile species exist under reactor containment conditions. Radiolytic oxidation of iodide ions is an important source of volatile iodine species. The SISYPHE tests provide an experimental database of prime importance for the study of the mass transfer between the sump and the atmosphere of a containment building under natural convection and in an evaporating flow regime. This phenomenon greatly impacts the airborne iodine concentrations. The two main effects of evaporating conditions are to increase the kinetics of transfer from the liquid to the gaseous phase and to change the steady-state iodine concentrations. The well-known two-film model has been modified to extend to these types of conditions. The agreement between the experimental results and modeling is satisfactory. However, when applied to typical reactor conditions, the impact of this improved modeling on gaseous iodine concentration is not as strong as other phenomena; for example, uncertainties remain concerning organic iodide production mechanisms. Correlations enabling the calculation of individual mass transfer coefficients for the liquid and the gas phases are proposed. The values resulting from these correlations agree well with those obtained from the test interpretations.
Chandra Observations of the AS0295 Cluster

We present deep (205 ks) Chandra observations of the AS0295 binary merging cluster (z = 0.30). In the X-ray image, the secondary component is clearly visible as a surface brightness peak, while the primary cluster has a flatter distribution. We found cool gas (∼6 keV) associated with the secondary, while the central temperature of the primary does not deviate significantly from the mean temperature of the cluster of ∼9.5 keV. In the vicinity of the primary’s core we found the hottest region in the cluster accompanied by a surface brightness discontinuity. We propose that this region corresponds to a shock, for which we estimate a Mach number of . We found other merger signatures such as a plume of cool gas emerging from the primary cluster and a cold front and a possible bow shock (Mach number of ) leading the secondary cluster. Based on the observed properties in comparison to binary merger simulations from the literature we propose for AS0295 a low-mass ratio, off-axis merging scenario, with the secondary close to the first apocenter. Comparison of our results with strong-lensing observations of AS0295 from Cibirka et al. shows an offset between the total mass peak and the bulk of the gas distribution in the primary cluster. The properties of the merger and the existence of the offset between mass peak and gas make AS0295 a promising candidate for the study of mergers involving non-cool core clusters and the nature of dark matter.
Development and validation of multi-residue method for determination of 412 pesticide residues in cotton fiber using GC-MS/MS and LC-MS/MS

Abstract The new global concept is to care about textiles and clothes safety to improve the protection of the human health and the environment from the harmful pesticide residues. Very few articles have been published for determination of several pesticide classes in cotton fibers in one multi-residue method. A simple, efficient, sensitive, accurate, and reliable multi-residue method was developed for the determination of 412 residual pesticides in cotton fibers using modified QuEChERS method with Liquid and Gas Chromatography coupled to Triple Quadrupole Mass Spectrometer (LC-MS/MS & GC-MS/MS) for qualitative and quantitative analysis according to the international standards concepts. The developed method covered several pesticide classes, including 43 carbamates, 16 pyrethroids, 27 organochlorines (OCs), 54 organophosphorus (Ops), 31 urea derivatives, 7 Polychlorinated biphenyl (PCBs), 6 Neonicotinoid, and 228 other pesticides. Most of the target pesticides were listed in Oeko-Tex Standards, the EU Ecolabel for textile products, and the Egyptian recommendations of the Agricultural Pesticide Committee (APC-Egypt). The method optimization and validation were carried out according to the EU guidelines. The results were shown to be reliable where the corresponding average recoveries within the acceptable range of 70–120%; the relative standard deviations were less than 20%. The limit of quantitation (LOQ) of this method is 0.01 mg kg−1 all pesticides except for 3 GC-compounds and 19 LC-compounds which have LOQ of 0.05 mg kg−1.