Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDIES ON NEONATAL CALF DIARRHEA IN KUWAIT

Neonatal calf diarrhea, also known as calf scours, is a common disease affecting newborn calves. It causes significant economic losses due to morbidity, mortality, and therapy cost and decreased rates of development in affected calves. Multiple enteric pathogens are known to cause this disease including viruses, bacteria and parasites. Cryptosporidium spp. is a protozoan parasite usually responsible for gastrointestinal tract disease in human and calves. E. coli K99 was also in-criminated as one of the important causes of neonatal diarrhea. Corona viral infection could also cause diarrhea in newborn calves. Onehundredninety-eightfecalsamplescollectedfromdiarrhe-ic calves were tested with timmunochromatographic rapid test. Sixty one (30.8%) were po-sitive for different pathogens either as single infection or co-infection with two or more enteric pathogens. Cryptosporidium was the highest 20.2% (40/198), followed by E. coli K99 at 17.2% (34/198), then Rotavirus 9.1% (18/198) and only 2 samples were positive for Coronavirus (1%).
T-S Fuzzy Model Based Large-Signal Stability Analysis of DC Microgrid With Various Loads

DC microgrid offers high efficiency, simple control, and reduced conversion stages advantages, but suffers from induced instability caused by its low inertia. Due to the strong nonlinearity of the DC microgrid, the analysis of its large-signal stability (LSS) is a very challenging task. This paper uses the T-S fuzzy model (TSFM) method to investigate the LSS of the DC microgrid with various loads, which is connected to the AC grid. The TSFM of the DC microgrid with grid-connected voltage source converter (VSC) as the main power supply is established. The analysis of the LSS of the DC microgrid is carried out using an estimate of the domain of attraction (DOA) of the working point. On the basis of the DOA, the effects of different types of loads and system parameters on the LSS are researched. The research results can serve as a guideline for adjusting the physical and control parameters to solve the DC microgrid instability problem. Finally, the large-signal stability analysis (LSSA) of the DC microgrid is performed using the MATLAB/Simulink simulation platform. The results of the simulation confirm the rightness of the proposed LSSA approach. They indicate that constant power load (CPL) has the greatest effect on the LSS of the DC microgrid compared to the constant impedance load (CIL) and the constant current load (CCL). In addition, the results of the time-domain simulation also validate the effectiveness of this method, which is a way to enhance the LSS of the DC microgrid through the adjustment of system parameters.
Multiscale Fracture Behaviour of Ice

The paper discusses the evidences of fractality in fracture of ice at different scale. Starting from the grain scale at which the physical properties of ice material are described by the fractal geometry, afterwards the paper analyses the formation of crevasses on the glaciers of the Italian Alps. At the geological scale, the application of the box-counting method permits to analyse the distribution of crevasses network within the glacier continuum. This confirms the selfsimilar fractal properties of ice fracture networks. Finally, the ice behavior at sub-continental scale is detected analyzing the collision between the glacial ice tongue (the Drygalski Ice Tongue) and a massive iceberg (the B-15A). Studying the evolution of the phenomenon, the comparison between the data obtained at different scales very large scale of Antarctic icebergs; medium scale of Alpine glaciers [1] and laboratory and slab scale [2] – is made and the scaling of the fracture properties of ice is discussed [3, 4].
Beyond Calculations: A Course in Statistical Thinking

ABSTRACT Statisticians are in general agreement that there are flaws in how science is currently practiced; there is less agreement in how to make repairs. Our prescription for a Post-p < 0.05 Era is to develop and teach courses that expand our view of what constitutes the domain of statistics and thereby bridge undergraduate statistics coursework and the graduate student experience of applying statistics in research. Such courses can speed up the process of gaining statistical wisdom by giving students insight into the human propensity to make statistical errors, the meaning of a single test within a research project, ways in which p-values work and don't work as expected, the role of statistics in the lifecycle of science, and best practices for statistical communication. The course we have developed follows the story of how we use data to understand the world, leveraging simulation-based approaches to perform customized analyses and evaluate the behavior of statistical procedures. We provide ideas for expanding beyond the traditional classroom, two example activities, and a course syllabus as well as the set of statistical best practices for creating and consuming scientific information that we develop during the course.
A Distinctive Binary Descriptor and Two-Point RANSACWC for Point Cloud Registration

Point cloud registration is a fundamental problem in many applications. The point cloud registration based on local shape descriptor has been widely researched. In order to further improve the performance of registration, a novel registration method is proposed in this article. First, a binary descriptor is designed to establish correspondences between two point clouds. The descriptor has high descriptiveness. Thus, more correct correspondences are established. Then, a 3-D transformation estimation technique is developed, in which multiple constraints are used to accelerate the computation. When the randomly selected correspondences do not satisfy the constraints, the iteration is skipped. Finally, the experiments are performed to analyze the descriptor and 3-D transformation estimation technique. The comparison with the existing descriptors is implemented on three datasets. The results demonstrate that our descriptor has better matching performance. As for the 3-D transformation estimation technique, the combinations of the constraints are first analyzed. The performance of different constraints is presented and the best combination is chose. The comparative results with the existing techniques demonstrate that the proposed 3-D transformation estimation technique can obtain better registration accuracy and computation efficiency.
Solution to Scalability and Sparsity Problems in Collaborative Filtering using K-Means Clustering and Weight Point Rank (WP-Rank

Collaborative Filtering is a method to be used in recommendation systems. Collaborative Filtering works by analyzing rating data patterns. It is also used to make predictions of user interest. This process begins with collecting data and analyzing large amounts of information about the behavior, activities, and tendencies of users. The results of the analysis are used to predict what users like based on similarities with other users. In addition, Collaborative Filtering is able to produce recommendations with better quality than recommendation systems based on content and demographics. However, Collaborative Filtering still faces scalability and sparsity problems. It is because the data is always evolving so that it becomes big data, besides that there are many data with incomplete conditions or many vacancies are found. Therefore, the purpose of this study proposed a clustering and ranking based approach. The cluster algorithm used K-Means. Meanwhile, the WP-Rank method was used for ranking based. The experimental results showed that the running time was faster with an average execution time of 0.15 second by clustering. In addition, it was able to improve the quality of recommendations as indicated by an increase in the value of NDCG at k=22, the average value of NDCG was 0.82, so that the recommendations produced had more quality and more appropriate with user interests.
A MODIFIED LOGIT ROUTE CHOICE MODEL OVERCOMING PATH OVERLAPPING PROBLEMS. SPECIFICATION AND SOME CALIBRATION RESULTS FOR INTERURBAN NETWORKS

Random utility route choice models play a central role in stochastic assignment, to road networks. The usually adopted specifications for such models are Multinomial Logit (MNL) and Probit, both having pros and cons. In this paper a modified specification of the Logit model, named C-Logit, is proposed. The C-Logit overcomes the main shortcoming of MNL, ie unrealistic choice probabilities for paths sharing a number of links, while keeping a closed analytical structure allowing calibration on disaggregate data and efficient path flow computations when paths are explicitly enumerated. A behavioural interpretation of the C-Logit model is proposed by presenting it as a particular case of a wider class of implicit joint models of route perception-choice. In the second part of the paper a procedure for path enumeration is proposed, and MNL and C-Logit models are specified and calibrated from a sample of 1471 paths chosen by truck drivers on the Italian inter-city road network. The preliminary calibration results are promising and show significant improvements induced by both the commonality factor characterizing the C-Logit model and by label variables which can be seen as attributes explaining path perception. (A) For the covering abstract see IRRD 886400.
Salt Stress-Induced Modulation of Porphyrin Biosynthesis, Photoprotection, and Antioxidant Properties in Rice Plants (Oryza sativa)

Salt stress disrupts cellular ion homeostasis and adversely impacts plant growth and productivity. We examined the regulatory mechanisms of porphyrin biosynthesis, photoprotection, and antioxidant properties in salt-stressed rice seedlings. In response to 150 mM NaCl, the rice seedlings exhibited dehydration, reduced relative water content, and increased levels of conductivity, malondialdehyde, and H2O2. The expression levels of the salt-stress-responsive genes NHX1, SOS1, and MYB drastically increased after NaCl treatment. The seedlings grown under NaCl stress displayed declines in Fv/Fm, ΦPSII, rETRmax, and photochemical quenching but increases in nonphotochemical quenching (NPQ) and the expression of genes involved in zeaxanthin formation, BCH, and VDE. Under salt stress conditions, levels of chlorophyll precursors significantly decreased compared to controls, matching the downregulation of CHLD, CHLH, CHLI, and PORB. By contrast, NaCl treatment led to increased heme content at 24 h of treatment and significant upregulations of FC2, HO1, and HO2 compared to controls. Salt-stressed seedlings also increased their expression of CATs (catalases) and APXs (ascorbate peroxidases) as well as the activities of superoxide dismutase, CAT, APX, and peroxidase. Our results indicate that chlorophyll and heme biosynthesis involve the protective strategies for salt stress alleviation through photoprotection by the scavenging of chlorophyll precursors and NPQ as well as activating antioxidant enzymes.
Deep Neural Networks for Spectrum Sensing: A Review

As we advance towards 6G communication systems, the number of network devices continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS), Cognitive Radio (CR) exploits the frequency spectrum dynamically by detecting and transmitting in underutilized bands. The performance of 6G networks can be enhanced by utilizing Deep Neural Networks (DNNs) to perform SS. This paper provides a detailed survey of several Deep Learning (DL) algorithms used for SS by classifying them as Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, combined CNN-LSTM architectures and Autoencoders (AEs). The works are discussed in terms of the input provided to the DL algorithm, data acquisition technique used, data pre-processing technique used, architecture of each algorithm, evaluation metrics used, results obtained, and comparison with standard SS detectors. This survey further provides an overview of traditional Machine Learning (ML) algorithms and simple Artificial Neural Networks (ANNs) while highlighting the drawbacks of conventional SS approaches for completeness. A description of some publicly available Radio Frequency (RF) datasets is included and the need for comprehensive RF datasets and Transfer Learning (TL) is discussed. Furthermore, the research challenges related to the use of DL for SS are highlighted along with potential solutions.
The Burden of Epstein–Barr Virus (EBV) and Its Determinants among Adult HIV-Positive Individuals in Ethiopia

Epstein–Barr virus (EBV) is a well-known risk factor for the development of nasopharyngeal carcinoma, Hodgkin’s lymphoma (HL), and Non-Hodgkin’s lymphoma (NHL). People with HIV infection (PWH) are at increased risk for EBV-associated malignancies such as HL and NHL. Nevertheless, there are limited data on the burden of EBV among this population group in Ethiopia. Hence, this study aimed to determine the burden of EBV infection among adult HIV-positive individuals in Ethiopia and assess the determinants of EBV DNA positivity. We conducted a cross-sectional study at the Tikur Anbessa Specialised Hospital from March 2020 to March 2021. Two hundred and sixty individuals were enrolled in this study, including 179 HIV-positive and 81 HIV-negative individuals. A structured questionnaire was used to capture demographic and individual attributes. In addition, the clinical data of patients were also retrieved from clinical records. EBV viral capsid antigen (VCA) IgG antibody was measured by multiplex flow immunoassay, and EBV DNA levels were tested by quantitative real-time polymerase chain reaction (q-PCR) assays targeting the EBNA-1 open reading frame (ORF). Descriptive statistics were conducted to assess each study variable. A multivariable logistic regression model was applied to evaluate the determinants of EBV infection. Statistical significance was determined at a p-value < 0.05. Two hundred and fifty-three (97.7%) study participants were seropositive for the EBV VCA IgG antibody. Disaggregated by HIV status, 99.4% of HIV-positive and 93.8% of HIV-negative participants were EBV seropositive. In this study, 49.7% of HIV-positive and 24.7% of HIV-negative individuals were EBV DNA positive. PWH had a higher risk of EBV DNA positivity at 3.05 times (AOR: 3.05, 95% CI: 1.40–6.67). Moreover, among PWH, those with an HIV viral load greater than 1000 RNA copies/mL (AOR = 5.81, 95% CI = 1.40, 24.13) had a higher likelihood of EBV DNA positivity. The prevalence of EBV among PWH was significantly higher than among HIV-negative individuals. Higher HIV viral loads in PWH were associated with an increased risk of EBV DNA positivity. Since the increases in the viral load of EBV DNA among PWH could be related to the risk of developing EBV-associated cancers, it is necessary for more research on the role of EBV in EBV-associated cancer in this population group to be carried out.
DC‐link voltage control of small capacitor PMSM drive system based on MPC algorithm

The permanent magnet synchronous motor (PMSM) drive system with small dc‐link capacitor will have the problem of unstable drive system due to the small damping of the LC filter and the negative impedance characteristics of the constant power load. The input LC filter can suppress interference and is essential for the normal operation of the drive, but it may not match the impedance of the post‐stage inverter, causing the drive system to become unstable. In this paper, by constructing the dc‐link voltage prediction model, combined with the PMSM current predictive control, the weighted cost function of the model predictive control (MPC) strategy is designed to realize the motor optimal control and the dc‐link voltage fluctuation suppression control. To avoid introducing additional sensors when constructing the dc‐link voltage model, a source state observer that can estimate the dc‐link voltage and input current is constructed. Simulation and experimental results are provided to verify the effectiveness of the proposed method.
Leaching column method for predicting effluent quality from surface mines

A leaching column technique that produces effluent similar in quality to coal mine drainage in the Eastern Interior Basin has been developed. The columns are charged with 1000 grams of crushed overburden and cycled through the leaching process once a week for twelve weeks. Relationships among the important chemical constituents are very similar to those present in actual mine drainage waters, but the overall concentrations of ions are more dilute in the leachates. Multiplying the leachate concentrations by a factor of 4.5 gives values similar to mine waters. Available data suggests that each of the leaching cycles represents about three years of natural weathering, so that the leaching technique can predict the evolution of the mine drainage water over a time span of about 36 years. Use of the leaching technique with drill core samples should provide a method of approximating mine drainage quality in advance of mining and should be quite useful in identifying potentially toxic units in the overburden. However, the results should be considered to be a worst case situation, because the technique does not consider the effects of reclamation.
Exploring the phylogenetic signal in the cranial variation of European populations of grayling (Actinopterygii, Salmonidae)

This is a preliminary and exploratory study of cranial variation in European populations of grayling. We investigated the correspondence between size/shape variation of the dorsal (dc), ventral (vc) and occipital (oc) cranium and phylogenetic relationships (inferred from mitochondrial control region – mtDNA cr and microsatellite dna data) of six grayling populations: three from Balkan phylogenetic clade and two from Caspian phylogenetic clade of the European grayling Thymallus thymallus and one population of the Adriatic grayling Thymallus aeliani, which until recently was considered the Adriatic phylogenetic clade of T. thymallus. Significant size and shape differences were found between populations in all three cranial views. However, significant size-related shape variation (allometry) was found for dc and vc, but not for oc. The size variation of each cranial view does not contain phylogenetic signal, but size variation of oc is consistent with genetic variation inferred from microsatellite dna. Regarding shape variation, a significant phylogenetic signal was detected only for oc, and only the shape variation of oc is consistent with the genetic variation inferred from the mtDNA cr. Moreover, the Adriatic grayling T. aeliani (Soča population) was clearly separated from the three T. thymallus populations of the Balkan phylogenetic clade and the two T. thymallus populations of the Caspian phylogenetic clade only at the level of oc. Thus, our results suggest that different cranial regions differ in allometry, reflect phylo(genetic) relationships differently, and exhibit differences in ecophenotypic plasticity, with oc seeming best suited to represent the phylogenetic relationships of the grayling populations studied.
Phenomics as an approach to Comparative Developmental Physiology

The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. “Phenomics”, a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.
Technical Negotiations and Phenomena: Capturing Social Media, and Enchantment in Indonesian Tourism

This study aims to measure the influence of Instagram influencers and their relationship in giving the image of a destination to the interest of tourists visiting tourist destinations in Indonesia. Furthermore, it identifies photo-taking techniques in framing phenomena, events, or the ambience of tourist objects. This is related to the role of influencers and social media that dominates the tourism aspect in the era of the Internet of Things. This study uses a quantitative approach accompanied by a descriptive description. The primary data were 29 Instagram influencers who shared their travels, which were obtained by distributing questionnaires from August to September 2021. The data obtained were then processed and analyzed using SPSS26 modelling software. The results showed that 1) most of the influencers chose to upload photos or videos of the location along with the influencer himself (41,365), 2) influencers chose to use long shot camera shot size (46.46%) to be able to show the location of tourist destinations better, 3) in terms of using angle, 2,356 respondents (54.16%) used eye level shots, and 4) 2,255 (51.84%) used to post photos with cold colour schemes when taking pictures. Based on the statistical data, social media influencers affect purchase intention. Therefore, these image indicators of tourist attractions in quality and track record need to be considered by managers to prepare places that cater to influencers as tourism promotion agents. Keywords: Influencer, social media, phenomena, tourism, Indonesia, photography technique