Miracle-dz/UniFAR-FTUs huggingface dataset:
New Insights on the Diurnal Mechanism of Calcification in the Stony Coral, Stylophora pistillata

Scleractinian corals are evolutionary-successful calcifying marine organisms, which utilize an endo-symbiotic relationship with photosynthetic dinoflagellate algae that supply energy products to their coral hosts. This energy further supports a higher calcification rate during the day in a process known as light enhanced calcification. Although this process has been studied for decades, the mechanisms behind it are still unknown. However, photosynthesis and respiration also cause daily fluctuations in oxygen and pH levels, resulting in the coral facing highly variable conditions. Here we correlated gene expression patterns with the physiological differences along the diel cycle to provide new insights on the daily dynamic processes, including circadian rhythm, calcification, symbiosis, cellular arrangement, metabolism, and energy budget. During daytime, when solar radiation levels are highest, we observed increased calcification rate combined with an extensive up-regulation of genes associated with reactive oxygen species, redox, metabolism, ion transporters, skeletal organic matrix, and mineral formation. During the night, we observed a vast shift toward up-regulation of genes associated with cilia movement, tissue development, cellular movement, antioxidants, protein synthesis, and skeletal organic matrix formation. Our results suggest that light enhanced calcification is related to several processes that occur across the diel cycle; during nighttime, tissue might elevate away from the skeleton, extending the calcifying space area to enable the formation of a new organic framework template. During daytime, the combination of synthesis of acid-rich proteins and a greater flux of ions to the sites of calcification facilitate the conditions for extensive mineral growth.
Resource Development of Entrepreneurs of Small and Medium Enterprise (SME) of Wooden Batik Center in Putat Village and Sendangsari Village

Covid 19 Pandemic that hit many countries has led to changes in consumer behavior as in shifting target products as well as consumption patterns with wider digital use. This situation leads to the use of information technology by entrepreneurs in small and medium-scale businesses but the encouragement to make good use of it goes slow. It is caused by a lack of entrepreneurial mindset development owned by entrepreneurs. Therefore, this research aims to find out how far entrepreneurial mindset development is implemented in the development target community, by conducting a study on capacity building activity of workshop owners’ resource in the wooden batik center of Putat Village and Sendangsari Village. The qualitative research method was used with a multiple case approach. The research result shows that in the last six years, there has been no more entrepreneurial mindset training held by the government and private institutions. The psychological approach that is still done is to encourage workshop owners of wooden batik centers. Empowerment activities are necessary to be considered so that the activities are not only aimed at the target community but also the actors who need to be empowered first. Therefore, disorientation will not happen and the objectives of development activities can be implemented more efficiently and effectively.
Using Machine Learning to Predict Heart Disease

Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (KNN), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. In our research we used two separate datasets. The first heart disease dataset we used was collected from very famous UCI machine learning repository which has 303 record instances with 14 different attributes (13 features and one target) and the second dataset that we used was collected from Kaggle website which contained 1190 patient’s record instances with 11 features and one target. This dataset is a combination of 5 popular datasets for heart disease. This study compares the accuracy of various machine learning techniques. In our research, for the first dataset we got the highest accuracy of 92% by Support Vector Machine (SVM). And for the second dataset, Random Forest gave us the highest accuracy of 94.12%. Then, we combined both the datasets which we used in our research for which we got the highest accuracy of 93.31% using Random Forest.
Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

Background: Macromolecule structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, the Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle. Methods: In this study, we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling. Moreover, to achieve better prediction accuracy and completeness, we judged and processed the modeling results. Results: A benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining–based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high. Conclusion: SMCP is an ab initio modeling algorithm that substantially outperforms previous algorithms in the Rosetta framework, especially in improving the accuracy and completeness of the model. It is expected that the work will provide new research ideas for macromolecular structure prediction in the future. In addition, this work will provide theoretical basis for the development of the biomedical field.
Operators’ Ease and Satisfaction in Restoring Class II Cavities With Sectional Matrix Versus Circumferential Matrix System at Qassim University Dental Clinics

Objectives: This study aimed to investigate the operator’s ease, satisfaction, and comfort of using a circumferential matrix system and sectional matrix system on the proximal contact points and contours when restoring class II cavities in posterior teeth. Materials and methods: This cross-sectional study was performed at the clinics in the Department of Conservative Dental Sciences, College of Dentistry, Qassim University. A total of 105 dental students randomly participated in this study to restore a class II cavity with direct composite resin restoration. Operators' comfort and satisfaction were evaluated according to their assessment of the contact points they reproduced and the emergence profiles of restorations, using a circumferential matrix system and sectional matrix system. Results: Out of 105 operators, 57 were satisfied with using circumferential matrix bands for most of their cases while 78 of the operators were satisfied with sectional matrix bands. There were no significant differences between operator satisfaction and the use of circumferential matrix band system (P > 0.05) and sectional matrix band system (P = 0.134) but there was a significant difference between difficulty levels in the use of both matrix band systems (P < 0.05; P = 0.000). Conclusion: Circumferential and sectional matrix band systems showed no significant differences with operators' satisfaction during restoring class II cavities in posterior teeth but using a sectional matrix band system was considered easier than using a circumferential matrix band system.
Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data

Abstract. Significance: EDITED FOR TESTING Although emerging evidence suggests that the hemodynamic response function (HRF) can vary by brain region and species, a single, canonical, human-based HRF is widely used in animal studies. Therefore, the development of flexible, accessible, brain-region specific HRF calculation approaches is paramount as hemodynamic animal studies become increasingly popular. Aim: To establish an fMRI-compatible, spectral, fiber-photometry platform for HRF calculation and validation in any rat brain region. Approach: We used our platform to simultaneously measure (a) neuronal activity via genetically encoded calcium indicators (GCaMP6f), (b) local cerebral blood volume (CBV) from intravenous Rhodamine B dye, and (c) whole brain CBV via fMRI with the Feraheme contrast agent. Empirical HRFs were calculated with GCaMP6f and Rhodamine B recordings from rat brain regions during resting-state and task-based paradigms. Results: We calculated empirical HRFs for the rat primary somatosensory, anterior cingulate, prelimbic, retrosplenial, and anterior insular cortical areas. Each HRF was faster and narrower than the canonical HRF and no significant difference was observed between these cortical regions. When used in general linear model analyses of corresponding fMRI data, the empirical HRFs showed better detection performance than the canonical HRF. Conclusions: Our findings demonstrate the viability and utility of fiber-photometry-based HRF calculations. This platform is readily scalable to multiple simultaneous recording sites, and adaptable to study transfer functions between stimulation events, neuronal activity, neurotransmitter release, and hemodynamic responses.
Foreign Direct Investment and Its Impact on The Regional Level: A Prospective Analysis

Some countries in development like China, the Philippines, Nigeria, Pakistan, Bangladesh, Vietnam and Ukraine do an active promotion in order to raise foreign direct investment (FDI) under the proposal of a positive effect in economic growth while implementing this type of fundraising. Thus, it constitutes an important source of external financing, allowing increases in productivity through technologic transfer as well as rises in competitiveness, efficiency in the managerial models, and expand the countries’ exporting capabilities. After the economic crisis experienced in the 80’s, Latin America, specifically countries like Argentina, Brazil and Mexico, that have based their financing in loans, stopped to raise money by these means when the crisis appeared, arising as an alternative the FDI, also on account of the foreign creditors demanding the payment of their issued resources and the warning of not giving any more financing until these countries restructure their economies, it was established the capital stock of the recipient economy. In this context, it was necessary to implement structural reforms, which were contemplated in the “Washington Consensus”, such as price stabilization and fiscal deficit control with the purpose of recovering the trust to investors and reactivating the capital flow through loans or foreign direct investment aimed at Latin America. In 1990, foreign direct investment became the primary source of external financing to peripheral countries (Aitken y Harrison, 1999:1).
Simulation of Effect of Varistors on Output Characteristics of Impulse Current Circuit

The simulation model was set up to study the impulse current characteristics of varistors,the influencing laws of dynamic resistance of varistors on 8/20 μs impulse current waveform parameters for different source resistance were presented. The following results were obtained. Firstly,the dynamic resistance of varistors r has notable impact on the 8/20 μs impulse current waveform parameters,with the decreasing of the dynamic impedance r,the front time tf of impulse current waveform increases and reaches to 8 μs,the half peak time tt of impulse current waveform decreases and reaches to 20 μs,the ratio of tt and tf decrease and reach to 2.5. Secondly,the smaller is the source impedance of the impulse current circuit,the greater influence on the 8/20 μs impulse current waveform parameters. According to the impulse current amplitude changing between 40 A and 10 kA,variation range of front time tf is 4.75 μs and 19.537 μs for half peak time tt and 9.75 for the ratio of tt and tf respectively when the source impedance is 0.3 Ω. Whereas variation range of front time tf is 2.85 μs only and 3.54 μs for half peak time tt and 2.093 for the ratio of tt and tf respectively with the impulse current circuit of 0.9 Ω source impedance. Thirdly,the correlative law between the minimum impulse current amplitude and dynamic resistance of varistors is elicited,and according to requirements of test impulse current,simultaneously improving the output efficiency of the impulse current circuit,the different source impedance impulse current circuits should be adopted.
Quaternary Heusler alloy CoZrMnAs competent candidate for spintronics and thermoelectric technologies

We examined the quaternary Heusler alloy CoZrMnAs using Wein2k simulation code based on density functional theory. The structural results prove that the CoZrMnAs alloy is stable in the Y1‐type structure with a relaxed lattice constant of 0.62390 nm, which is ferromagnetic in nature. The electronic properties indicate that CoZrMnAs exhibits a half‐metallic nature with energy of 0.56 eV and the states at the Fermi level are 100% spin polarized. The magnetic moment of CoZrMnAs was found to be 1.0 μB per formula unit (fu) and is in accordance to Slater‐Pauling rule Mt = Zt − 24. The existence of gap is elaborated with viable d‐d hybridization among the transition metals Co, Zr, and Mn. Thermodynamic parameters like specific heat capacity, Debye temperature, and thermal expansion coefficient had been analyzed with the aid of Gibbs2 code. Furthermore, the thermoelectric parameters have been elucidated by the BoltzTraP2 code. The utmost values of total Seebeck and power factor are 64.11 μV/K and 36.75 (μW K−2 cm−1) at 900 K. These parameters assure the applications of alloy in waste heat recovery.
Sleep Stage Classification For Medical Purposes: Machine Learning Evaluation For Imbalanced Data

Background: Sleep is commonly associated with physical and mental health status. Sleep quality can be determined from the dynamic of sleep stages during the night. Data from the wearable device can potentially be used as predictors to classify the sleep stage. Robust Machine Learning (ML) model is needed to learn the pattern within wearable data to be associated with the sleep-wake classification, especially to handle the imbalanced proportion between wake and sleep stages. In this study, we incorporated a publicy available dataset consists of three features captured from a consumer wearable device and the labelled sleep stages from a polysomnogram. We implemented Random Forest, Support Vector Machine , Extreme Gradiet Boosting Tree, Densed Neural Network (DNN), and Long Short-Term Memory (LSTM), complemented by three strategies to handle the imbalanced data problem. Results: In total, we included more than 24,815 rows of preprocessed data from 31 samples. The proportion of minority-majority data is 1:10. In classifying this extreme imbalanced data, the DNN model was found to have the best performance compared to the previous best model, which is based on basic Multi-Layer Perceptron. Our best model successfully achieved a 12% higher specificity score (prediction score for minority class) and 1% improvement on the sensitivity score (prediction score for majority class) by including all features in the model. This achievement was affected by the implementation of custom class weight and oversampling strategy. In contrast, when we only used two features, XGB achieved a specificity improvement only by 1%, while keeping the sensitivity at the same level.Conclusions: The non-linear operation within the DNN model could successfully learn the hidden pattern from the combination of three features. Additionally, the class weight parameter avoided the model ignoring the minority class by giving more weight for this class in the loss function. The feature engineering process seemed to obscure the time-series characteristics within the data. This is why LSTM, as one of the best methods for time-series data, failed to perform well in this classification task.
The Effect of COVID on Child Maltreatment: A Review

This article addresses child maltreatment during the period where COVID-19 entered our lives in 2020. Repeated lockdowns kept children at home, away from school, from their support systems, and from their routine. Economic difficulties and having to look after the children who were at home were quite onerous on parents. The article explores the reasons that ACE increased during that time, and highlights what can parents, teachers, and the educational system do about it.
Sizing an open-channel woodchip bioreactor to treat nitrate from agricultural tile-drainage and achieve water quality targets

Woodchip bioreactors are capable of removing nitrate from agricultural runoff and subsurface tile drain water, alleviating human health hazards and harmful discharge to the environment. Water pumped from agricultural tile drain sumps to nearby ditches or channels could be cost effectively diverted through a woodchip bioreactor to remove nitrate prior to discharge into local waterways. Sizing the bioreactor to achieve targeted outlet concentrations within a minimum footprint is important to minimizing cost. Determining the necessary bioreactor size should involve a hydrological component as well as reaction type and rates. We measured inflow and outflow nitrate concentrations in a pumped open-channel woodchip bioreactor over a 13-month period and used a tanks-in-series approach to model hydrology and estimate parameter values for reaction kinetics. Both zero-order and first-order reaction kinetics incorporating the Arrhenius equation for temperature dependence were modeled. The zero-order model fit the data better. The rate coefficients (k = 17.5 g N m−3 day−1 and theta = 1.12 against Tref = 20 °C) can be used for estimating the size of a woodchip bioreactor to treat nitrate in agricultural runoff from farm blocks on California's central coast. We present an Excel model for our tanks-in-series hydrology to aid in estimating bioreactor size.
Exploring the Impact of Environmental Regulation
and Economic Agglomeration on Ecological
Efficiency in China

The objective of this paper was to explore the impact of environmental regulation and economic agglomeration on regional ecological efficiency in China. First, this paper adds energy factor and pollutant emission output to the production function and proves by the derivation of the mathematical model that environmental regulation and economic agglomeration can affect eco-efficiency. Second, based on the panel data of 30 province-level regions in China from 2003 to 2017, the dynamic spatial panel Durbin model and the mediating effect test model are used to empirically analyze the relationship between the three. The results show that economic agglomeration and eco-efficiency present a typical N-shaped curve relationship. Environmental regulation can enhance eco-efficiency and economic agglomeration, and economic agglomeration can be a mediator of environmental regulation to affect eco-efficiency. The joint implementation of environmental regulation and agglomeration economic is helpful to the improvement of ecological efficiency. Both eco-efficiency and economic agglomeration have significant time dependence and spatial spillover effects. From a regional perspective, the marginal role of environmental regulation in promoting eco-efficiency in the central and western regions is greater than that in the eastern regions. The impact of economic agglomeration on eco-efficiency still shows significant N-type characteristics in the central region, while it shows an inverted N-type characteristic in the eastern region, but it is not significant in the western region.
A Novel E3-ubiquitin Ligase-Related LncRNA Signature for Predicting Prognosis in Patients with Lung Adenocarcinoma

Background: E3 ubiquitin ligase mRNA plays an important role in mediating tumor microenvironment, and is involved in tumor initiation and progression. However, few studies have realized the value of E3 ubiquitin ligase-related lncRNAs in lung adenocarcinoma (LUAD). Methods: Herein, we comprehensively evaluated the E3-ubiquitination patterns including multiple tumor-related molecular phenotypes in LUAD samples using lncRNA profiling from GEO and TCGA database, identified a survival-related risk signature consisting of E3-ubiquitin ligase-related lncRNAs via LASSO and multivariate stepwise Cox regression analysis. Based on the risk score calculated for each sample, LUAD patients were divided into high- and low-risk groups. The predictive value of the signature in overall survival was explored, and a nomogram integrating the risk signature and clinical characteristics was identified and tested. Results: A risk signature consisting of 7 specific E3-ubiquitin ligase-related lncRNAs was screened, and can be viewed as a reliable independent predictor of prognosis. We performed consensus clustering analysis and successfully identified 4 molecular subtypes significantly linked to the OS of LUAD, which validates the prognostic and predictive value of this signature to some extent. The ssGESA analysis revealed that the high-risk group was bound up closely with epithelial-mesenchymal transition, hypoxia, and PI3K/AKT/mTOR pathways, and had a worse outcome. Moreover, we created a nomogram consisting of pathological staging and risk score. Validation analysis demonstrated high conformity of nomogram predictive probability and actual overall survival in LUAD of TCGA and GEO datasets.Conclusion: The model consisting of specific E3-ubiquitin ligase-related lncRNAs contributes to predicting the prognosis of LUAD patients.
Construction and Application of Manufacturing Field Strength Model based on Field Theory

Introduced field theory into automoblile assembly system under mass customization enviroment,proposed concept and properties of manufacturing field,constructed field strength model combined with the characteristics of automobile assembly system,and evaluated the influence of different parts feeding on manufacturing from manufacturing,logistics and information activity.Conducted example analysis by the field strength model based on ten general parts data of some product family assembly shop in enterprise A,analyzed key factors which impacted on strength difference,and provided basis for optimizing manufacturing logistics system and promoting smooth manufacturing integrally.