Miracle-dz/UniFAR-FTUs huggingface dataset:
possibil-Energy balance of dairy cows predicted by mid-infra-red spectra data of milk using Bayesian approaches

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infra-red (FT-MIR) spectroscopy is a non-invasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp us-ing the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype r y y ,ˆ ( ) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5 to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, predicted DMI (DMIp) and predicted EB (EBp), and 0.42 for BW. The ge-netic correlation between EnM and BW was −0.17, with DMIp was 0.40 and with EBp was −0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance
Geomorphic influence on small glacier response to post‐Little Ice Age climate warming: Julian Alps, Europe

The evolution of glaciers and ice patches, as well as the equilibrium‐line altitude (ELA) since the Little Ice Age (LIA) maximum were investigated in the Julian Alps (south‐eastern European Alps) including ice masses that were previously unreported. Twenty‐three permanent firn and ice bodies have been recognized in the 1853 km2 of this alpine sector, covering a total area in 2012 of 0.385 km2, about one‐fifth of the area covered during the LIA (2.350 km2). These features were classified as very small glaciers, glacierets or ice patches, with major contribution to the mass balance from avalanches and wind‐blown snow. Localized snow accumulation is also enhanced in the area due to the irregular karst topography. The ice masses in the region are at the lowest elevations of any glaciers in the Alpine Chain, and are characterized by low dynamics. The ELAs of the two major LIA glaciers (Canin and Triglav) have been established at 2275 ± 10 m and 2486 ± 10 m, respectively, by considering the reconstructed area and digital elevation model (DEM) and using an accumulation area ratio (AAR) of 0.44 ± 0.07, typical of small cirque glaciers. Changes in the ELA and glaciers extension indicate a decoupling from climate. This is most evident in the smallest avalanche‐dominated ice bodies, which are currently controlled mainly by precipitation. The damming effect of moraine ridges and pronival ramparts at the snout of small ice bodies in the Julian Alps represents a further geomorphological control on the evolution of such ice masses, which seem to be resilient to recent climate warming instead of rapidly disappearing as should be expected. Copyright © 2016 John Wiley & Sons, Ltd.
Neural Mechanisms of Selection and Control of Visually Guided Eye Movements

The selection and control of action is a critical problem for both biological and machine animated systems that must operate in complex real world situations. Visually guided eye movements provide a fruitful and important domain in which to investigate mechanisms of selection and control. Our work has focused on the neural processes that select the target for an eye movement and the neural processes that regulate the production of eye movements. We have investigated primarily an area in the frontal cortex that plays a central role in the production of purposive eye movements which is called the frontal eye field. A fundamental property of biological nervous systems is variability in the time to respond to stimuli. Thus, we have been particularly interested in examining whether the time occupied by perceptual and motor decisions explains the duration and variability of behavioral reaction times. Current evidence indicates that salient visual targets are located through a temporal evolution of retinotopically mapped visually evoked activation. The responses to non-target stimuli become suppressed, leaving the activation representing the target maximal. The selection of the target leads to growth of movement-related activity at a stochastic rate toward a fixed threshold to generate the gaze shift. For a given image, the neural concomitants of perceptual processing occupy a relatively constant interval so that stochastic variability in response preparation introduces additional variability in reaction times. Neural processes in another cortical area, the supplementary eye field, do not participate in the control of eye movements but seem to monitor performance. The signals and processes that have been observed in the cerebral cortex of behaving monkeys may provide useful examples for the engineering problems of robotics. ᭧ 1998 Elsevier Science Ltd. All rights reserved.
Comparison of three coarsening methods of gridded digital elevation models by ParFlow.CLM in a typical karstic basin of Central China

Extraction of critical hydrologic features from high-resolution topographic data is challenging using existing grid coarsening approaches, such as surface ﬂow path, river network, and slope, which limits the application of hydrological models. In this research, the inﬂuence of various grid coarsening techniques on the prediction outcomes was measured by a numerical experiment based on the integrated hydrological model ParFlow-Common Land Model (ParFlow.CLM). Three grid coarsening methods (Nearest Neighbor Coarsening, Majority Coarsening, Hydrography-Driven Coarsening) were applied to simulate evapotranspiration(E), soil temperature(ST), streamﬂow, soil moisture(SM) and latent(LE) heat ﬂuxes in central China’s Sixi Valley, a classic example of a karstic basin. As a result, the three grid coarsening methods perform uniform in simulating latent heat ﬂuxes and soil temperature. However, their ability to predict soil moisture surface ﬂow and evapotranspiration are more di-verging. The hydrography-driven coarsening extracts signiﬁcantly more accurate valleys, rivers network, and slopes closer to the actual terrain than existing coarsening strategies. Slopes derived from hydrography-driven coarsening methods can be used to predict more accurately the top soil moisture, evapotranspiration
COVseq is a cost-effective work�ow for mass-scale SARS-CoV-2 genomic surveillance

Genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to monitor the spread and evolution of the virus across different populations, geographical regions and species. Here, we present a streamlined work�ow—COVseq—based on the CUTseq method that we previously described, which can be used to generate highly multiplexed sequencing libraries compatible with Illumina platforms, from hundreds of SARS-CoV-2 samples in parallel, in a rapid and cost-effective manner. We validated COVseq on RNA extracted from the supernatant of a SARS-CoV-2 culture as well as from 85 RNA samples from nasopharyngeal swabs, demonstrating the ability of COVseq to achieve near complete genome coverage, including the S region encoding the spike protein. A cost analysis showed that COVseq could be used to sequence thousands of samples per week at less than 10 USD per sample, including library preparation and sequencing costs. COVseq is a versatile and scalable method that can be readily applied for genomic surveillance of the ongoing pandemic and easily adapted to other pathogens such as in�uenza viruses.
A SYSTEMATIC LITERATURE REVIEW OF DEEP AND MACHINE LEARNING ALGORITHMS IN CARDIOVASCULAR DISEASES DIAGNOSIS

During the whole cardiac cycle, heart sounds are created, and blood enters the heart chambers as the cardiac regulators open and close. Blood flow produces aural noises; the more turbulent the blood flow, the more ambiances are created. Two common cardiac sounds occur in sequence with each heartbeat in healthy adults. These are the first heart sound (S1) and second heart sound (S2), which are caused by the closure of the atrioventricular and semilunar valves, respectively. The current systematic review depends on “the Preferred Reporting Items for Systematic reviews and Meta-Analysis statement” and 40 appropriate studies. The search of the literature employed search engines similar to: IEEE Xplore, Google Scholar, Hindawi, PubMed, SCOPUS, Wiley Online, Web of Science, Taylor and Francis, ScienceDirect, and Ebscohost. This study concentrated on four characteristics: Algorithms of Machine and Deep Learning, best-algorithm performance, datasets, and application used in Cardiovascular Diseases predictions. The experimental articles did not use Reinforcement Learning, Semi-supervised learning, promising aspects of Deep and Machine Learning. Algorithms based on ensemble technique exhibited sensible rates of accuracy nonetheless were not frequent, whereas Convolutional Neural Network (CNN) were well epitomized. A few studies smeared main datasets (13 of 37). Recurrent Neural Network (RNN), boosting algorithms, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), were the best performing algorithms. This review will be beneficial for investigators predicting Cardiovascular Diseases using machine and deep learning methods.
High Prevalence of Chronic Musculoskeletal Pain and Analgesics Use Habits in the Geriatric Population with Chronic Kidney Disease Kronik Böbrek Hastalığı Olan Geriyatrik Popülasyonda Kronik Muskuloskeletal Ağrının

Objective: Individuals with chronic kidney disease (CKD) are at a risk of unsuccessful pain management and kidney injury using analgesics inappropriately. In this study, the prevalence of chronic musculoskeletal pain (CMP) and analgesics use habits was investigated in a geriatric population with non-dialysis dependent CKD, as well as the frequency of non-steroidal anti-inflammatory drugs (NSAIDs) and/or paracetamol use. Method: This single-center cross-sectional study recruited patients over 65 years under follow-up for non-dialysis dependent CKD in nephrology
Decontamination of 2, 4-D and Atrazine through Adsorption on Soil under Different Modifications and Its Kinetic and Equilibrium Studies

Current agricultural practices involve the use of different kinds of herbicides and other agrochemicals. Organochlorine herbicides, such as 2,4-D (2,4-dichlorophenoxyacetic acid) and atrazine are used irrationally to increase the yield of agricultural products in Pakistan. Thus the intensive use leads to diffuse contamination of non-target environments. For instance the contamination of groundwater and surface water threatens human health and the environment, due to the toxic properties of these compounds. The primary objective of study is to devise remediation and control of 2,4-D and Atrazine. For this purpose a natural adsorbent i.e., soil is used with different modifications. In order to determine whether and to what degree different soil properties affect the process of decontamination of selected herbicides, three soils (agricultural, garden and barren) with different organic matter contents were used. Adsorption-desorption performance of each soil towards herbicides was experimentally studied to investigate the influence of certain modifications i.e. different pH, organic content and dosage of pesticide. Soil characteristics are important in influencing the chemicalgeosorbent interactions. Equilibrium and Kinetic Parameters have also been investigated to develop a relationship between adsorbate and adsorbent. The results showed that the adsorbent soil exhibited a maximum removal potential for both herbicides at pH 4 followed by increase in desorption under alkaline conditions. Atrazine and 2,4-D showed removal of 90% and 35%, respectively by use of agricultural and garden soils, respectively, exhibiting a significantly high decontamination efficiency for the former herbicide especially with adsorbate having higher organic content. Equilibrium was attained after contact of 5 hours and 96 hours for 2,4-D and atrazine, respectively. Atrazine and 2,4-D is retained against leaching losses in soils principally by sorption to organic matter as a result of hydrophobic bonding. The kinetic model of Lagergren-first order provided a good description of the experimental data. The results indicate that soils with different physico-chemical properties have different effects on the adsorption of the two herbicides, especially at higher concentration levels.
Analysis of Interest in Using E-commerce Shopee during the Covid-19 Pandemic

The increasing number of people using the internet encourages a bigger online market. Some marketplaces dominate the Indonesian market, one of which is Shopee. In the current pandemic situation, almost everything the community needs, both primary and secondary needs, can be met from online services. Therefore, there is a demand for shopee companies to know and understand the needs or preferences of consumers. If the company can know and understand the needs or preferences of consumers, the company can win people's hearts to buy on the shopee application. This study aims to find out what factors influence the interest in using e-commerce by customers during the covid 19 pandemic. The method used is SEMPLS analysis, tests carried out by the inner and outer models, which help test the TAM Factor variables, Social Factors, and E-Service Quality. The results obtained Factors that influence attitudes towards the use of e-commerce by customers during the pandemic are factors;Perceived usefulness, Perceived ease of use, Peer Influence Customer Service on attitude toward using in the use of mobile e-commerce applications during the covid 19 pandemic. © 2022 Little Lion Scientific.
FINETUNED ROBERTA ARCHITECTURE FOR MOOCS EVALUATION USING ADVERSARIAL TRAINING

Massive Open Online Course is an online learning platform that can provide the opportunity to the learners to gain knowledge in a particular domain or subject. During recent days, MOOCs grabbed the attention of learners and provide quality education for free. As many MOOC providers are offering same courses, Choosing the best one out of all available MOOCs become very challenging task. MOOCs Evaluation plays an important role in finding the sentiment of the learners through the reviews and helps the MOOC Providers to improve the curricular quality. This paper proposed MOOC-RoBERTa , a sentiment analysis architecture that can evaluate MOOCs using Student reviews. Firstly, we prepared a balanced MOOC Reviews Dataset containing 13200 reviews. Secondly finetuned a RoBERTa Model for sentiment analysis on MOOCs. Next, trained the proposed model by applying Adversarial attacks approach to gain domain specific knowledge and test the model to find the sentiment of the learners. Finally, we compared the performance of the proposed model with different variants of other transfer learning models like BERT, Albert, XLNet. The experimental results demonstrate that the proposed model outshines the state-of-the-art methods by achieving the accuracy of 96.6% on MOOC Reviews Dataset.
Denitrification and oxygen consumption in the sediments of four New Zealand estuaries

Rates of sediment denitrification were determined under illuminated conditions by the N Isotope Pairing Method (IPM) in four New Zealand estuaries (Avon-Heathcote, Waikouaiti, Tokomairiro, and Tautuku), which ranged in size, nutrient loading, and catchment land use. The proportion of the gaseous end products (N20 produced relative to N2) and differentiation of denitrification supported by N03from the water column (Dw) or N03produced via nitrification (Dn) were determined with the overall aims of quantifying the rates of denitrification and identifying which environmental factors influenced the rates. Denitrification rates ranged from 0-12 ! lmol m-2 hin the Waikouaiti River estuary, from 0118 ! lmol m-2 hin the Avon-Heathcote and Tautuku River estuaries, and from 43-347 ! lmol m-2 hin the Tokomairiro River estuary. The size of the sediment particles, temperature, water column pH, chlorophyll a content of the sediment, and biomass of annelids and arthropods were related to the rates of denitrification. At most sites, the concentration ofN03and N02in the water column was <30 ! lmol r and Dn comprised >86 % of the total denitrification. The rate of Dw and the ratio of N20:N2 produced increased with higher DIN concentration in the water column. Denitrification was measured temporally in the A vonHeathcote estuary. Although there were some significant differences in the rates of denitrification among the sampling events, no seasonal differences in the rates of denitrification were detected. The IPM is based on several assumptions that must be met in order to achieve accurate rates of denitrification; thus, these assumptions were tested by adding three concentrations of N03to the incubation chambers and measuring the isotope production ratios of N20 and N2 over time. Fluxes of oxygen across the sediment-water interface (determined under dark and illuminated conditions by sediment core incubations and microelectrode profiles) and the potential for anaerobic ammonium oxidation (anammox) in the sediment were quantified and discussed in the context of their influences on sediment denitrification. Ladderane lipids, a bioindicator of bacteria capable of anammox, were not detected at any of the study sites where it was measured. Oxygen uptake by the sediments ranged from 146-1597 ! lmol m-2 hand was much higher than oxygen release, indicating low benthic primary production and a high oxygen demand by these sediments. The sediment oxygen penetration depth at the sites ranged from 2.7-9.6 mm. The results indicate that maintaining an oxic layer in the sediment is essential for the maintenance of denitrification.
REAL-TIME INFORMATION SYSTEM OF RAW WATER SALT LEVELS PDAM TIRTA KHATULISTIWA PONTIANAK BASED ON LORA GATEWAY TECHNOLOGY

Quality clean water is essential for humans to meet their daily needs. In Pontianak City, one way to obtain clean water is by subscribing to the Perusahaan Daerah Air Minum (PDAM). PDAM obtains raw water in large volumes during the rainy and dry seasons from the Kapuas River. Water taken from rivers without going through treatment is commonly referred to as raw water. Previously raw water was still in poor condition, such as cloudy, high salt content, and smelled. It raises concerns in maintaining water quality which will later be distributed to customers after going through the sterilization process by the PDAM. The quality of raw water from the Pontianak Kapuas River is very vulnerable to saltwater intrusion from the high seas, especially in the dry season. Based on information from the media which contained an expert statement from the University of Tanjungpura Pontianak, seawater intrusion into the Kapuas River could reach 2000 ppm (salt content in water), which means it is the same as saltwater and goes as far as 50 km. Information about seawater intrusion into the Kapuas River that is conveyed occurs periodically or only when it occurs during the dry season. A breakthrough (innovation) is needed so that information about the water quality of the Kapuas River can be carried out continuously and in real-time and can be accessed by related parties and monitored remotely. In addition, variations in early detection of water quality in the Kapuas River can be an alternative in finding alternative locations for raw water sources for PDAMs. They become the right solution in distributing clean water to customers. The results of this study are an information system on the condition of raw water in the Kapuas River, which PDAM Tirta Khatuliswa Pontianak can monitor in real-time. This technology utilizes LoRa as data communication between nodes to the gateway. The monitoring process is carried out using a website that is connected by a microcontroller through the IoT process.
AN EXPERIMENTAL STUDY: PERSONALIZED GAMIFED LEARNING BASED ON LEARNING STYLE

Numerous game related elements such as points, badges, leaderboard, and ranking are utilized in educational context when motivation and engagement need to be boosted. Undoubtedly, influencing students’ engagement has been reported as one of the gamification’s pros. However, study the effects of certain game elements on dissimilar types of students is recommended by several researchers. Accordingly, a learning style is a vital factor in human learning process which is considered as an important personalization parameter in several eLearning tools. This research has used Felder-Silverman Learning Style Model (FSLSM) as personalization parameter along with 10 game elements to propose a personalized gamified learning model. A Design Science Research Approach (DSRA) has been undertaken to examine the proposed model in improving the students’ scores in Data Flow Diagram (DFD) lesson during class learning process. For validating the proposed model, 50 Multiple Choice Questions for DFD web-based gamified application has been developed. An experimental study using the application has been conducted with 71 undergraduate students from School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM). Participants were divided into two groups: experimental and control. Additionally, the gamification application has two different modes: personalized mode (for experimental group) and non-personalized mode (for control group). Data was collected from the application database and perceived usefulness questionnaire. An independent t-test has been used to compare means of scores between the groups. Result shows that personalized gamified learning is an effective method in learning process, as well as in boosting student perceived usefulness of the application.