Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow routing aiming load balancing and path length in multi-hop networks with different link qualities

This paper considers an exact bi-objective approach for simultaneously minimizing the total cost of flow routing and the network bottleneck when link qualities and flow weights are relevant. This is useful for wireless multi-hop networks with long-term TCP (Transmission Control Protocol) flows. The introduced proposal can generate a minimal complete set of Pareto-optimal solutions. Our proposal is evaluated through simulation in which are explored different parameter settings and metrics. Mathematics Subject Classification. 90C10, 90C29, 90C35, 90C90. Received December 17, 2020. Accepted August 14, 2021.
Forward Cycle Bases and Periodic Timetabling

Periodic timetable optimization problems in public transport can be modeled as mixed-integer linear programs by means of the Periodic Event Scheduling Problem (PESP). In order to keep the branch-and-bound tree small, minimum integral cycle bases have been proven successful. We examine forward cycle bases, where no cycle is allowed to contain a backward arc. After reviewing the theory of these bases, we describe the construction of an integral forward cycle basis on a line-based event-activity network. Adding turnarounds to the instance R1L1 of the benchmark library PESPlib, we computationally evaluate three types of forward cycle bases in the Pareto sense
Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK

Objectives To evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies. Design A geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19. Participants All hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020. Outcome measures Predictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates. Results Peak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%. Conclusions Local demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.
Identification of Potent Angiotensin Converting Enzyme 2 Inhibitors through Virtual Screening and Structure-Based Pharmacophore Design

International Journal of Advanced Science and Engineering www.mahendrapublications. com *Corresponding Author: science@gmail.com & asm2032@gmail.com Received: 12.05. 2017 Accepted: 10.06. 2017 Published on: 27.07. 2017 ABSTRACT: Angiotensin Converting Enzyme (ACE), a metallo-peptidase is the best known important drug target in the treatment of hypertension and responds to broad range ACE inhibitors such as Captopril. Whilst, many phytochemical compounds including alkaloids and flavonoids were also reported with anti-hypertensive activity. On the other hand, ACE2 is considered as an interesting new cardio-renal disease target as it is close and unique ACE homologue. In this scenario, the anti-hypertensive activities of 17 phytochemical compounds were analyzed through docking studies with ACE2. Also, the other ACE inhibitors with reported IC50 values were considered for docking interactions and used as training set. Further, the best docked phytochemical compound Rosemarinic acid and the training set compounds with ACE inhibitor activity were used to design the pharmacophore and validated. The generated 3D pharmacophore is subjected to screen the compounds with the significant chemical features against May bridged database consisting of more than one lakh compounds and subsequently, the hit compounds were screened using various filters such as estimated activity, Lipinski’s rule of five, and ADMET properties and resulted Eight compounds. The anti-hypertensive activities of these 5 compounds with good fit values were selected for further docking studies with ACE2. The five compounds PD 00533, CD 01374, CD 04888, CD 01278 and BTB 04932 exhibited the best docking scores and also favors the necessary hydrogen bond interactions with in the activity site of ACE and thus identified as novel leads with antihypertensive activity.
Operationalization of tactical thinking of football players by main game roles

The article considers the main characteristics of the types of tactical thinking of football players. The aim of the article is empirical research and theoretical substantiation the operational elements of tactical thinking of football players in the main game roles. Differences in the main indicators of tactical thinking of football players of different game roles have been determined. Participants of the research are: 72 football players were involved as subjects. The footballers represented four teams from different regions of Ukraine: “Naftovyk-Okhtyrka” Football Club (Okhtyrka, Ukraine), “TSK Agribusiness” Football Club (Romny, Ukraine), “Romny” Football Club (Romny, Ukraine), “Crystal Football Club” (Kherson, Ukraine). The following psychodiagnostic techniques with experimental scales were used: “Study of the profile of athletes’ thinking”, “Prediction ability” by L. Regush. Empirical results of the research of tactical thinking are presented. According to the results of empirical research, we can state there are significant differences between the answers of football players of different game roles. The level of tactical thinking of attackers is much higher than that of other players. Defenders differ significantly from goalkeepers by more pronounced general predictability (t = 2.08) and flexibility (t = 4.51); from attackers by less analyticity (t = 2.72) and flexibility (t = 2.61). Midfielders have significantly more developed awareness (t = 2.76) and profoundness (t = 4.85) than goalkeepers, but, at the same time, less developed than the attacker’s analyticity (t = 5.05), flexibility (t = 5.2), profoundness t = 5.2) and prospectiveness (t = 2.67). The attackers are significantly ahead of the defenders by the indicators of analyticity (t = 2.72) and flexibility (t = 2.61). Attackers and goalkeepers differ the most among the studied game roles, namely concerning general predictability (t = 3.28), analyticity (t = 5.67), flexibility (t = 5.26) and prospectiveness (t = 3.07). It has been found that the level of manifestation of the main properties of tactical thinking of defense is significantly lower than in other game roles.
Acquisition and onward transmission of a SARS-CoV-2 E484K variant among household contacts of a bamlanivimab-treated patient

The implementation of monoclonal antibody therapeutics during the COVID19 pandemic has altered the selective pressures encountered by SARS-CoV-2, raising the possibility of selection for variants resistant to one or more monoclonal antibodies and subsequent transmission into the wider population. Early studies indicated that monoclonal antibody treatment in immunocompromised individuals could result in within-host viral evolution preferentially affecting epitopes recognized by these antibodies, although whether this signifies a real risk of transmissible antibody resistant virus is unclear. In this study we have taken advantage of a regional SARS-CoV-2 genomic surveillance program encompassing regions in Wisconsin, Minnesota and Iowa to monitor the introduction or de novo emergence of SARS-Cov-2 lineages with clinically relevant variants. Here we describe a newly acquired E484K mutation in the SARS-CoV-2 spike protein detected within the B.1. 311 lineage. Multiple individuals in two related households were infected. The timing and patterns of subsequent spread were consistent with de novo emergence of this E484K variant in the initially affected individual who had been treated with bamlanivimab monotherapy. The subsequent transmission to close contacts occurred several days after the resolution of symptoms and the end of this patient's quarantine period. Our study suggest that the selective pressures introduced by the now widespread administration of these antibodies may warrant increased genomic surveillance to identify and mitigate spread of therapy-induced variants.
Causal meditation analysis to understand how different components of a complex psychological intervention improved symptoms (or not) of depression in Goa, India

Background: Understanding how and under what circumstances a highly effective psychological intervention, improved symptoms of depression is important to bring this evidence-informed intervention to scale, particularly in resource-poor settings. We aim to estimate the indirect effects of potentially important mediators to improve symptoms of depression in the Healthy Activity Program (HAP) trial. Methods: Interventional in(direct) effects were used to simultaneously decompose the total effect of the intervention on depression symptoms measured through the Patient Health Questionnaire (PHQ-9). The following indirect effects were considered: characteristics of sessions including the number of sessions and homework completed; behavioural activation according to an adapted version of the Behavioural Activation for Depression Short Form (BADS-SF), and extra sessions offered to participants who did not respond to the intervention. Results: Of the total effect of the intervention measured through the difference in PHQ-9 scores between treatment arms (mean difference: -2.2, 95% bias-corrected CI: -3.2, -0.8), 45% was mediated through improved levels of behavioural activation (-1.0, -1.3, -0.6). There was no evidence to support the mediating role of characteristics of the sessions nor the extra sessions offered to participants who did not respond to the treatment. Conclusions Findings from our robust mediation analyses, confirmed the importance of behavioural activation in improving depression symptoms. Contrary to published literature, our findings suggest that neither the number of sessions nor proportion of homework completed, improved outcomes. Moreover, results indicate that the extra sessions were insufficient to improve symptoms of depression for participants who did not respond to the intervention.
Circulating proteins to predict adverse COVID-19 outcomes

Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.
Patch2Self denoising of diffusion MRI in the cervical spinal cord improves intra-cord contrast, signal modelling, repeatability, and feature conspicuity

Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and quantification of these images. The purpose of this study is to evaluate a denoising approach, Patch2Self, to improve the quality, reliability, and accuracy of quantitative diffusion MRI of the spinal cord. Patch2Self is a self-supervised learning-based denoising method that leverages statistical independence of noise to suppress signal components strictly originating from random fluctuations. We conduct three experiments to validate the denoising performance of Patch2Self on clinical-quality, single-shell dMRI acquisitions with a small number of gradient directions: 1) inter-session scan-rescan in healthy volunteers to evaluate enhancements in image contrast and model fitting; 2) repeated intra-session scans in a healthy volunteer to compare signal averaging to Patch2Self; and 3) assessment of spinal cord lesion conspicuity in a multiple sclerosis group. We find that Patch2Self improves intra-cord contrast, signal modeling, SNR, and lesion conspicuity within the spinal cord. This denoising approach holds promise for facilitating reliable diffusion measurements in the spinal cord to investigate biological and pathological processes.
Classification of Rock Mineral in Field X based on Spectral Data (SWIR & TIR) using Supervised Machine Learning Methods

The massive development of science and technology in the industrial era 4.0 includes artificial intelligence, which is purposed to produce research output in the field of geology more accurately and can be completed in a short time using large amounts of data. Machine learning is a part of artificial intelligence that can provide learning processes on computers independently without explicit programming. The process of identifying rocks through classification can be done using machine learning. The study area is in the Manjimup region, Western Australia which consists of Volcanogenic Massive Sulphide (VMS) deposits. This study purposed to determine the classification of rock minerals using accuracy values from the evaluation of models generated using supervised machine learning based on spectral data, namely Short-Wavelength Infrared (SWIR), and Mid or Thermal Infrared (TIR) acquired from electromagnetic spectrum measurements to identify rock mineral features. The spectral data comes from five rock drilling data in the study area. The supervised machine learning method used to determine the best accuracy consists of 5 types of methods, which are K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multi-layer Perceptron (MLP). Machine learning is completed by supervised method because the research data contains information about label data, which is the type of rock mineral so that it can produce a classification based on the level of accuracy for each type of rock mineral data. The SVM method produces the best accuracy on SWIR data with 82.5% accuracy and the MLP method produces the best accuracy on TIR data with 82% accuracy for rock mineral classification.
Implementation of Big Data to Develop Origin-Destination Matrix Estimation Model

Big data is a huge collection of electronic data which is currently popular to be developed in various scientific fields, including the transportation sector. Several data categories that can be used for transportation infrastructure planning are real-time location/position and timestamp of road users. This technological sophistication addresses a very crucial transportation planning problem that has been faced for many years, that is the inaccurate and unrepresentative prediction of the Origin-Destination Matrix. Up to this point, the Origin-Destination Matrix has been obtained using conventional and unconventional methods. Data was collected through manual traffic counts survey and direct interview in the study area. This results in unreliable data and inadequate sampling rates, which are only able to cover 20% of the population. In addition, the application of these techniques is expensive, time-consuming, and require a lot of human resources. Therefore, this study tries to develop a model capable of predicting the origin and destination of mobility, which in this case is represented by the sub-district boundary as a zone. As a result, an Origin-Destination Matrix can be obtained with a high degree of accuracy. The case study used is Bandung city. In the future, this research is expected to become a milestone in elaborating a validated Origin-Destination Matrix estimation model that can be used in various areas.
Extending technology acceptance model (TAM) to measure the students’ acceptance of using digital tools during open and distance learning (ODL)

Ever since the Covid-19 pandemic outbreak, we are taught to live in the new norm. Higher learning institutions are forced to adopt a new norm of open and distance learning (ODL) immediately. Since that, students are facing issues, especially in courses that need a conventional setting. During ODL, students and lecturers rely on digital tools, social media, pre-recorded video, and video conference to assist the learning process. However, there are unexplored topics on the students’ intention to use digital tools during ODL. A sample of 367 students who responded from different diploma and degree programs of Universiti Teknologi MARA (UiTM) Terengganu was collected via an online survey. Data collected are analysed on its correlation and coefficient determinant, regression, and analysis of actual use. Technology Acceptance Model (TAM) is expanded to include external factors of technology access and online learning skills to be measured with perceived usefulness, perceived ease of use, attitude, behaviour intention to use and actual system use. Findings on the study will help uncover significant understanding of students’ acceptance of ODL in using digital tools for a better decision-making and students’ support assistance. Hence, this study benefits us to foresee the possible future need for courses in conducting ODL successfully.
Determination of the complex permittivity of cherry, pulped, green, and roasted coffee using a planar dielectric platform and a coaxial probe between 0.3 and 6 GHz

ABSTRACT The complex permittivity for four processing stages of coffee: coffee cherries, pulped coffee, green coffee, and roasted coffee was obtained and divided into two groups. The wet group is constituted by cherry and pulped coffee, and the dry group by green and roasted coffee. The wet group presented a dielectric constant between 64 and 43, and a loss factor between 34 and 12. The dry group has values oscillating between 1.55 and 1.1 for the dielectric constant, and 0.037 and 0.005 for the loss factor. The permittivity difference between the wet and dry groups suggests the dependence of the coffee permittivity on the water content, along with the presence of other polar compounds. A decaying response for the dielectric constant was seen, with a proportion of 30:1 from the wet to the dry group. The loss factor presents a convex response for the wet group and a constant response for the dry group with a factor of 500 to a 100:1 from the wet to the dry group. A set of 18 samples at every stage was used to estimate the moisture content by partial least squares. The Meyer and Schilz coefficient was used for these models, that resulted in prediction errors of 2.8% and 3.8% when compared to the stage mean moisture content for the wet group. The dry group resulted in errors of 21% and 89.7%. These results indicate that the complex permittivity of coffee is capable of estimating physicochemical variables as the moisture content of the beans at distinct processing stages.
Using Movie to Increase Students’ Vocabulary in Politeknik Harapan Bersama

This research aims to describe the implementation of Movie for Mechanical Engineering students of Politeknik Harapan Bersama Tegal. The method used is class action research using 2 cycles. The subject of this study is class 6C with 26 people. The student's improvement in vocabulary understanding is 60% in the first cycle. However, the results were categorized as failed because only 10 students received a minimum score of 73 or above 73. So the second cycle is implemented. In the second cycle there is an increase in the percentage understanding vocabulary 75% of the average value of 69-86. So it can be concluded that the student's understanding increases using movie.
Efficient and Reusable Metal–Organic Framework Catalysts for Carboxylative Cyclization of Propargylamines with Carbon Dioxide

Carbon dioxide (CO2) capture and transformation are important for decreasing the concentration of atmospheric CO2. To effectively capture CO2 and further fix it into valuable chemical products, functionalized dynamic metal–organic frameworks (MOFs) have been utilized not only because of their inherent cavity for accommodating CO2 but also owing to their reversible structural transformations in response to external stimuli for regulating the reaction. Herein, we report a dynamic and functional MOF [Cd3(L)2(BDC)3]2⋅16 DMF (MOF‐1 a; DMF=N,N‐dimethylformamide) achieved by reaction of the amino tripodal imidazole ligand N1‐(4‐(1 H‐imidazol‐1‐yl)benzyl)‐N1‐ (2‐aminoethyl)ethane‐1,2‐diamine (L) and 1,4‐benzenedicarboxylic acid (H2BDC) with cadmium salt. MOF‐1 a not only shows unprecedented high catalytic activity [initial turnover number (TON) up to 9300] and broad substrate scope for the carboxylative cyclization of propargylamines with CO2, but also can be switched on and off upon reversible structural transformation owing to its dynamic five‐fold interpenetrating structure. Further studies demonstrate that MOF‐1 a shows selective catalytic properties depending on the size of substrates, similarly to sophisticated biological systems.