Miracle-dz/UniFAR-FTUs huggingface dataset:
The Comparison of the Equated Tests Scores by Using Various Covariates using Bayesian Nonparametric Model

This research is based on obtaining equated scores by using covariates in the Bayesian nonparametric model. As covariates in the study, gender, mathematics self-efficacy scores, and common item scores were used. The distributions were obtained for all score groups. Hellinger Distance was calculated to obtain the distances between the distributions of equated scores by using covariates and the distribution of the target test scores. These distances were compared with the distributions of equated scores obtained from methods based on Item Response Theory. The study was conducted on Canadian and Italian samples of Programme for International Student Assessment (PISA) 2012. PARSCALE and IRTEQ were used for classical methods, and R was used for Bayesian nonparametric model. When gender, mathematics self-efficacy scores, and common item scores were used as covariates in the model, distance values of obtained equated scores to target test scores were close to each other, but their distributions were different. The closest distribution to target test scores was achieved when gender and mathematics self-efficacy scores were used together as covariates in the model, and the farthest distributions were obtained from item response theory methods. As a result of the research, it was determined that the model is more informative than the classical methods.
PHARMACOPHORE SCREENING AND MOLECULAR DOCKING OF PHYTOCONSTITUENTS IN POLYGONUM SAGITTATUM FOR CYCLOOXYGENASE-2 INHIBITORS DISCOVERY.

Objective: The objective of this study is to discover cyclooxygenase (COX-2) inhibitors from Polygonum sagittatum (Polygonaceae), by screening the pharmacophores based on the interaction of mefenamic acid with COX-2, followed by molecular docking with COX-2. Methods: The protein crystal structure of human COX-2 in complex with mefenamic acid (PDB code: 5IKR) was selected, its ligand-protein interaction was studied by employing LigandScout to obtain the pharmacophore features. The features were validated against PGH2 database provided at http://dude.docking. org/targets/pgh2, and the result was used to screen the pharmacophores of the phytoconstituents isolated from P. sagittatum. Furthermore, a molecular docking of the phytoconstituents into COX-2 binding pocket was performed. The compounds were generated using MarvinSketch, and the energy was optimized by employing LigandScout MMFF94. Celecoxib and mefenamic acid, selective COX-2 inhibitors, were used as the standard drugs. Results: The pharmacophore features obtained were aromatic ring (hydrophobicity) and two hydrogen bond acceptors, which are proved valid against PGH2 training set (GH score = 0.78; AUC100% receiver operating characteristic curve = 0.97). There are four phytoconstituents (quercetin, protocatechuic acid, vanicoside A, and vanicoside B) that fit the features, and therefore, are predicted to be active in inhibiting COX-2. The docking reveals that three phytoconstituents (methyl-4-hydroxycinnamate, quercetin, and methyl gallate) interact with Tyr385, an important amino acid residue in COX-2 binding pocket. Quercetin is the best in inhibiting the enzyme (docking score −8.60 kcal/mol; inhibition constant 0.5 μM), compared to mefenamic acid (docking score −8.90 kcal/mol; inhibition constant 0.3 μM) and celecoxib (docking score −10.00 kcal/mol; inhibition constant 0.05 μM). Conclusions : Phytoconstituents in P. sagittatum fit the pharmacophore features generated from mefenamic acid and COX-2 complex; therefore, they might be potential in inhibiting COX-2 enzyme. Their binding modes are more similar to that of mefenamic acid than of celecoxib. Of those, quercetin is the best in inhibiting the enzyme. Its inhibitory activity is equal to mefenamic acid but is weaker than celecoxib.
Gendered clientelism and corruption: are women less corrupt than men in China?

ABSTRACT Among civil servants, are women less corrupt than men? The established literature suggests that there is no gender gap in officials’ attitudes toward corruption when they are exposed to ample opportunities and networks for corruption. Drawing on an original survey of Chinese civil servants, this study explores the gender gap in tolerance for corruption and the mediating effect of clientelism on the gender–corruption link. The findings indicate that women’s tolerance for corruption is significantly lower than that of men in China, a country consistently plagued by corruption. The study reveals that clientelism is a crucial explanatory variable in the relationship between gender and tolerance for corruption. Women are more likely to be excluded from clientelist networks that facilitate corruption, meaning that they are less likely to be exposed to a culture of corruption or to take advantage of corruption. Women’s lower acceptance of clientelism partly contributes to their stronger inclination to reject corruption.
Development of a three‐phase interleaved converter based on SEPIC DC–DC converter operating in discontinuous conduction mode for ultra‐fast electric vehicle charging stations

One of the main challenges that impact transportation systems electrification is their batteries' charging process. This work presents the development of a three-phase ultra-fast Electric Vehicle (EV) charger based on the SEPIC converter. Since SEPIC operating in Discontinuous Conduction Mode (DCM) is usually recommended for low-power applications, this work proposes a scheme for its employment in high-power EV chargers. This is achieved through three single-phase modules of interleaved SEPIC converters. The presented scheme ensures reducing the stresses on the semiconductor devices since the power is divided over the interleaved modules. The design addresses DCM operation in terms of both capacitor voltage and inductor current (DCVM and DICM, respectively). This paper examines the analysis of the proposed converter and the small-signal modelling. Also, the converter efficiency is assessed. A Constant Current (CC) charging approach is deployed for charging the EV battery. The validation of the designs is explored through simulation results using MATLAB/Simulink platform. A 4 kW experimental prototype for the interleaved SEPIC DC–DC converter is built to verify the claimed contributions with 92% efficiency.
Development of computational linguistic resources for automated detection of textual cyberbullying threats in Roman Urdu language

Automatic Cyberbullying detection has remained very challenging task since social media content and conversations are usually posted in unstructured free-text form leaving behind the language norms. The major concern and gap in formulating cyberbullying detection strategies is scarcity of available linguistic resources typically for newly evolved languages. Roman Urdu has recently emerged and hence is a resource poor language. Urdu has been widely known as the national language of Pakistan. However, because of socio-cultural and multilingual aspects, Roman Urdu is used widely on the Internet by Asians and more specifically Pakistanis. To fulfil the above stated gap, this research work presents guidelines for data annotation process and developed two linguistic resources: (i) Annotated corpus in Roman Urdu Language for cyberaggression and offensive language detection. The process of data annotation involved bilingual annotators instead of crowdsourcing. It has the benefit of correctly annotating instances that constitute clear cases of cyberbullying without compromising data quality. The developed corpus is highly balanced (with almost negligible skew) unlike most of the existing corpuses even in mature languages. (ii) Processing textual information for NLP tasks involves Stop-word elimination as a sub phase. Stop words carry least semantic information and increase feature space as compared to the other tokens and index terms in corpora. We have developed domain specific stop words for Roman Urdu Language considering all the lexical variants and typically in the context of aggression detection and collected data. The work has been carried out using python programming language and Pycharm IDE.
Removal of Levofloxacin from aqueous solution by Magnesium-impregnated Biochar: batch and column experiments

ABSTRACT Adsorption of levofloxacin (LEV) onto four types of magnesium (Mg)-impregnated biochars, fabricated via thermal pyrolysis of wood chips pretreated with MgSO4 was investigated. The Mg-impregnated biochars were characterized with various tools and techniques. Batch sorption experiments were conducted to determine the sorption kinetics and isotherms of LEV onto the Mg-impregnated biochars. The pseudo-second order kinetic model described the adsorption kinetic data better than the pseudo-first order kinetic model and the Elovich equation. Due to multi-mechanisms, the Freundlich model described the experimental isotherms better than the Langmuir model. The Langmuir maximum adsorption capacities of the Mg-impregnated biochars to LEV ranged from 7.38 to 25.2 mg g−1. In the fixed-bed column experiment, higher bed height and lower flow rate led to greater LEV removal. Findings from this work indicate that Mg-impregnated biochars can be used as an alternative adsorbent to effectively remove LEV from aqueous solutions.
Risk on Dental Porphyromonas with Esophageal Cancer: A Guangdong Elderly Cohort Study

Objective: To investigate the relationship between Dental Porphyromonas Concentration and Esophageal Cancer in Guangdong, China. Methods: A total of 2031 research objects were included from 2016 and follow-up for 6 years, greater than 65 years old, all are from outpatient department with esophageal foreign body sense. Data are retrospectively analyzing in year 2021. There were 43 cases found to developed esophageal cancer, 7 of them are from exposure group, (high dental porphyromonas group) and 36 from control. Two groups of individuals were retrieved from the Guangdong Provincial Hospital database between year 2016 June to 2021 June. Age, gender, race and region are strictly matched. Both of case and the controls are processing through: a. Specimen collection, b. DNA extraction, c. High-throughput sequencing (screening stage), d. Quantitative determination of target bacteria in the samples of the experimental group by Q-PCR, e. Statistical analysis of experimental data. Two sample t-test are using to detect whether have difference on concentration of Dental Porphyromonas between cases and control group. SAS 15 windows version is used as analytic tools. Results: We found that the odds on having Esophageal Cancer from high dental porphyromonas group is 1.23 times greater than controls (OR=1.23). Also, mean of concentration on Dental Porphyromonas in Esophageal Cancer Group (Cases) are statistically higher than Healthy controls, with a mean of 0.0157 in cases versus 0.0025 in controls, which is 6.54 times higher in Esophageal Group, compare with control group, with P<0.001. Where Hot Tea Drinking Habit are found associated with Esophageal Cancer, with the Odds Ratio (OR) on 1.30, as well as current smoking on an 1.13 odds ratio. Conclusion: The cohort study had found some significant associations on high Dental Porphyromonas with Esophageal Cancer Patient, where this association still lacks of proof to be a determinate causal relationship. Further greater populational longitudinal studies are need to carry out to find out more causal association, as well as finding out Dental Porphyromonas relevant Social Determinant on Esophageal Cancer.
Driver Safety Alert System using Machine Learning

Road accidents majorly occur due to driver fatigue and drowsiness or lack of concentration. A Driver Safety Alert System Using Machine Learning has been introduced for reducing the number of accidents. This system has been developed to support three major modules such as Drowsiness Detection, Lack of Concentration Detection and Mobile Usage Detection. It monitors driver performance by analyzing facial expressions, driver movements and his overall behavior. This system is based on application of Image Processing. Driver's live video is decomposed into frames and image processing is performed on each frame. The main image processing tool used is OpenCV. Python has been used as coding language. Drowsiness Detection has been carried out by estimating eye aspect ratio. Detection of lack of concentration has been done by checking head position. Mobile Usage Detection is based on detection of object near the ear. A driver's unusual behavior while driving that leads to road accidents has been detected and approaches are proposed to overcome these constraints which include Drowsiness Detection, Lack of Concentration Detection and Mobile Usage Detection.
Presepsin and (1,3)-β-D-Glucan as a Tool for Predicting Candida Sepsis and Monitoring the Effectiveness of Treatment in Critically Ill Patients

Background: To improve the accuracy in invasive candidiasis diagnostics, a new biomarker panel has been developed and validated on a 165-patient cohort of critically ill adults.Methods: The serum levels of inflammatory biomarkers, C-reactive protein, presepsin (PSEP), procalcitonin (PCT), and of panfungal (1,3)-β-D-glucan (BDG), were correlated with culture-confirmed candidemia or bacteremia in 58 or 107 patients, respectively. The diagnostic effect of the host and pathogen biomarkers was expressed using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: For invasive candidiasis, the best performing BDG exhibited 96.6% sensitivity, 97.2% specificity, 94.9% PPV and 98.1% NPV at a cut-off of 200 pg/mL (P ≤ 0.001). PSEP exhibited 100% sensitivity and 100% NPV at a cut-off of 700 pg/mL. Furthermore, PSEP was more accurate for 28-day mortality prediction (AUC = 0.74) than PCT (AUC = 0.31; PCT cut-off 0.5 ng/mL). Finally, PSEP showed a significant serum decrease as early as 14 days after echinocandin therapy initiation (P = 0.0012). Conclusions: At concentrations of BDG > 200 pg/mL and PSEP > 700 pg/mL, the probability of invasive candidiasis in critically ill adults is close to 100% defining a borderline between non-invasive superficial Candida colonization and invasive candidiasis.Trial registration: The study was approved by the University Hospital Ostrava Ethics Committee for Multicenter Clinical Trials (no. 448/2018) and registered in ClinicalTrials.gov (ID: NCT03584594), date of registration June 28,2018.
Design and Analysis of MIMO Dual Band Patch Antenna for 5G New Radio Applications in Mobile Terminals

This paper presents a dual-band MIMO antenna design with compact size for 5G communication under 6 GHz band frequency. The metallic monopole stub structure is used to miniaturization of antenna. The L-shape monopole antenna is modified by adding semi-circular element in radiating structure of monopole to obtain dual-band resonance. The High isolation is achieved by employing T-shaped stub in ground plane.It has compact size is 45 mm × 25 mm × 1.6 mm3. The proposed Dual Band MIMO antenna has been design on FR4 material with ɛr = 4.4 with 1.6 thickness. The proposed antenna has 5G application in the bands of 2.5 GHz (2.34 GHz-2.62 GHz) and 3.5 GHz (3.20 GHz-5.20 GHz). The bandwidth of antenna getting 320MHz and 2500MHz at 2.5GHz and 3.5GHz respectively. The Isolation (S21) of proposed antenna is -31.2 dB at 2.5 GHz and -19.5 dB at 3.5 GHz. VSWR is less than 1.06 for both the bands. The designed dual band MIMO antenna covers 5G bands of 2.3-2.4GHz (n30/n40), 2.4-2.5GHz (n7/n38/n41/n90), and 3.2-5.2GHz (n77/n78/n80). The experimental and simulated results observed good matching except some slight variation. This proposed dual band MIMO antenna is suitable for 5G mobile Communications.
Underwhelming force: Evaluating the neuropsychological evidence for higher‐order theories of consciousness

Proponents of the higher-order (HO) theory of consciousness (e.g., Lau and Rosenthal) have recently appealed to brain lesion evidence to support their thesis that mental states are conscious when and only when represented by other mental states. This article argues that this evidence fails to support HO theory, doing this by first determining what kinds of conscious deficit should result when HO state-producing areas are damaged, then arguing that these kinds of deficit do not occur in the studies to which HO theorists appeal. The article also develops an apparatus that can be used to evaluate whether other lesion evidence confirms or disconfirms HO theory.
KIF15 is An Oncogene of Prostate Cancer and is A Key Prognostic Factor in Patients with Prostate Cancer

Background KIF15, a member of kinesin superfamily proteins, has been found that play a of vital role in the carcinogenesis of various malignant tumor. But whether KIF15 can facilitate the evolution of prostate cancer (PCa) is still unknown. This study aims to explore its biological function in PCa cells and its relevance to prognosis and clinical features in PCa patients. Material and Methods KIF15 expression at mRNA and protein level in tumor and normal tissues was detected by quantitative real-time PCR (RT-PCR) and immunohistochemistry. Then the correlations between KIF15 expression and PCa patients’ clinical characteristics was analyzed. After inhibiting the expression of KIF15 by shRNA, the role of KIF15 on proliferative capacity of PCa was evaluated by using MTT assay. The function of KIF15 on metastatic potential of PCa was determined by using transwell assay. The prognostic value of KIF15 was determined by using bioinformatics analysis. Results Compared with normal tissues, KIF15 was overexpressed in PCa tissues. After knocking down KIF15 in C4-2 and Lncap cell lines, the proliferation (P < 0.001) and invasion (P < 0.001) capabilities of tumor cells are significantly reduced compared to the shCON group. The proliferation marker Ki67 and the metastasis-related marker MMP9 were also significantly reduced in two cell lines after silencing KIF15. Except that, increased KIF15 in tumor tissue is associated with clinical stage (P = 0.004), seminal vesicle invasion (P = 0.02), lymph node metastasis (P = 0.03), and poor disease-free survival (P < 0.05) in PCa patients. Conclusions The results proved that KIF15 might served as a prognostic factor and therapeutic target in prostate cancer, and play as a vital regulatory factor in tumorigenesis and cancer development of prostate cancer.
Dynamic simulation of fire propagation in forests and rangelands using a GIS-based cellular automata model

Prediction of the way wildfires propagate in forests and rangelands is one of the critical issues in environmental protection and disaster management. This research aims to simulate wildfire propagation using a geographical information system (GIS)-based cellular automata (CA) model. The model considers the most effective spatial and temporal drivers of wildfire propagation, including wind speed and direction, type and density of vegetation, and topographic conditions. Wind speed and direction were considered changeable over the simulation process. A genetic algorithm (GA) was used to calibrate the model developed. Validation of the model was performed using an independent fire case assessed by the overall accuracy (OA) criterion and the Kappa coefficient index. The mean values obtained for the OA and the Kappa coefficient in 100 runs for two wildfire cases indicated that the proposed model could be effectively used in the simulation of wildfire propagation. The results of this model can assist fire managers in predicting fire propagation to better control wildfires.
A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor’s learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20 000 aerial image pairs of size $256\times256$ . Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset.
Influence Analysis Of Forced Distribution Rating Performance Appraisal And Merit Pay To Performance Of Directorate General Of Taxes’s Employees With Job Satisfaction As Intervening Variable At Blora Tax Service Office

This study aims to analyze and determine influence of forced distribution rating performance appraisal and merit pay toward performance of Directorate General of Taxes’s employees with job satisfaction as intervening variable at Blora Tax Service Office. Technique used for this study is census then data analyzed with SmartPLS. This research’s subjects are 80 low management employees at Blora Tax Service Office. The result of this research shows that forced distribution rating performance appraisal and merit pay don’t affect directly on employee’s performance. But forced distribution rating performance appraisal and merit pay have positive and significant effect on job satisfaction while job satisfaction has positive and significant effect on performance. Then indirectly through job satisfaction, forced distribution rating performance appraisal and merit pay have positive and significant effect to employee’s performance. This result shows that job satisfaction is a suitable intervening variable for this research. Based on this research, it is suggested for future managerial policy with goal to increase performance, job satisfaction should be one of deciding factors.
A comparison between ruptured and unruptured ectopic pregnancy and its association with the risk factors: A single center study

Background: Rupture of ectopic pregnancy (EP) occurs mainly in the first trimester and is a life-threatening condition. It is a leading cause of maternal morbidity and mortality. Many risk factors had shown significant association with EP. This study aims to find the risk factors associated with ruptured and unruptured EP. Methodology: This prospective study was conducted among 60 ectopic pregnancy cases during a study period of one and half years. The study population was classified according to the rupture status. The variables assessed were baseline, investigative and operative findings, and risk factors of ectopic pregnancy. Analysis was done to find the association of these factors with rupture status. Results: Out of the 60 cases, the rupture was present in 35 patients and was absent in 25. Age, parity, and gestational age were not associated with rupture status. Investigative and operative findings are significantly associated with rupture status (Free fluid: p=0.001, Uterine size: p=0.029, Size of sac: p= 0.013, β-hCG: p=0.023, Site of ectopic pregnancy: p=0.001), i.e., abnormalities in these factors shows more chance of rupture in ectopic pregnancy cases. Risk factors such as the history of contraceptive use and sterilization show significance to rupture status (p=0.009 & p=0.006), respectively. Conclusion: Any women in the reproductive age group who presents with amenorrhea, lower abdominal pain, or bleeding per vaginum should be evaluated for EP, irrespective of the sterilization status and the history of contraceptive use to prevent the rupture and morbidity/mortality associated with the same.