Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Serum Sodium, Potassium and Calcium Levels as Prognostic Markers in Cirrhosis of Liver: A Study Protocol

Background: Cirrhosis of the liver is final stage of chronic liver disease [1], which can lead to complications associated with portal hypertension and liver failure. In a much remunerated state, the cirrhotic patient maintains close association to traditional solution and acid–base standing, however this delicate balance may be discontinuous by malady progression, infection, dietary indiscretion/deprivation, or medicine intervention. Many mineral metabolism disorders are represented in relation with hepatic diseases. This study aims to estimate the levels of Sodium, Potassium and Calcium in Cirrhosis of Liver and correlate them with severity of cirrhosis and in-hospital morbidity and mortality. Materials and Methods: This cross-sectional study is planned to be carried out in Medicine department, AVBRH, Wardha. Total 50 patients with liver diseases will be enrolled. The lab. investigations will include different parameters of liver function tests along with assessment of sodium, potassium and calcium level. Child-Pugh, end-stage liver disease scores model will be determined using required formulas. All such patients’ in-hospital death records will be collected. Data will be collected and analyzed. The mean and standard deviations of the measurements per cluster will be used for applied analysis (SPSS twenty-two.00 for windows; SPSS INC, Chicago, USA). For every assessment purpose, data will be statistically analyzed using factorial ANOVA. Distinction between 2 groups determined using student t-test similarly as chi sq. test (p <.05). Expected Results: We expect significant correlation between the level of Na, K and Calcium with in-hospital morbidity and mortality in patients of cirrhosis. Conclusion: Serum levels of calcium, potassium and sodium should be integrated into model for predicting in-hospital mortality in patients of cirrhosis. Considering that prognostic accuracy of elements may well be comparatively modest, any studies ought to confirm clinical quality of liquid body substance sodium, potassium, and Ca concentrations.
3D Printed RFID Tag Antenna Miniaturized Through Volumetric Folding and Slow-Wave Structures

Radio-frequency identification (RFID) is becoming important with emerging applications for smart cities. RFID Tags are required to be small in size, low in cost and must exhibit as long read range as possible. There is a direct tradeoff between Tag antenna size and its read range. In this work, we study this tradeoff through the use of a relatively higher dielectric constant substrate, volumetric folding, and slow-wave structures (SWS). A 3D antenna design is chosen due to two reasons, 1) it can be folded on a 3D structure, 2), the 3D structure can be used as a package for electronics (in case of active RFID implementation). To enable a low-cost realization, the antenna substrate (package) has been 3D printed with filaments of ${\varepsilon }_{r}={5}. {3}$ . A dipole antenna has been folded on this 3D substrate, in a way that the electromagnetic fields radiating from various segments of the antenna do not cancel with each other. Finally, the antenna is loaded with specially designed SWS, whose values have been estimated using artificial transmission line theory. The final antenna design, operating at 866.9 MHz, has a ${ka}$ of 0.26 and demonstrates a radiation efficiency of 32%. The antenna is integrated with a commercial RFID chip (Monza R6) through a silver paste and the measured read range is 2.73 m, while the corrected read range is 4.05 m when the impedance mismatch is considered. Despite being one of the smallest and the lowest cost design (involving 3D printing), the Tag demonstrates one of the highest read range.
Do Differences in Analyst Quality Matter for Investors Relying on Consensus Information?

This study investigates whether investors can reap economic benefits from analyzing differences in analyst quality. Although high-quality analysts’ average forecast is more accurate than the consensus forecast for firms with a large analyst following, the benefits of using high-quality analysts’ average forecasts are not economically significant. In contrast, the value of analyst quality differentiation exists in the second moment of forecasts. High-quality analysts’ forecast dispersion gives investors an advantage in dealing with uncertainty by predicting return volatility and providing opportunities for economically significant returns using option straddle and post-earnings announcement drift investment strategies. This paper was accepted by Suraj Srinivassan, accounting. Funding: A. Rubin and A. Vedrashko thank the financial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Supplemental Material: The data are available at https://doi.org/10.1287/mnsc.2023. 4699 .
Secretory protein beta‐lactoglobulin in cattle stable dust may contribute to the allergy‐protective farm effect

Abstract Background Growing up on a cattle farm and consuming raw cow's milk protects against asthma and allergies. We expect a cattle‐specific protein as active component in this farm effect. Methods Dust was collected from cattle and poultry stables and from mattresses of households. Urine was obtained from cattle, and ambient aerosols were sampled. Samples were analysed for BLG by SDS PAGE/immunoblot and mass spectrometry, and for association with metals by SEC‐ICP‐MS. PBMC of healthy donors were incubated with BLG +/− zinc, and proliferation and cytokines determined. BALB/c mice were pre‐treated intranasally with stable dust extract containing BLG or depleted of BLG, and subsequent allergy response after sensitization was evaluated on antibody and symptom level. Results A major protein in dust from cattle farms and ambient air was identified as BLG. Urine from female and male cattle is a major source of BLG. In dust samples, BLG was associated with zinc. In vitro, zinc‐BLG provoked significantly lower proliferation of CD4+ and CD8+ cells while inducing significantly higher levels of IFN‐γ and IL‐6 than the apo‐BLG devoid of zinc. In vivo, pre‐treatment of mice with dust extract containing BLG resulted in lower allergy symptom scores to BLG and unrelated Bet v 1 than pre‐treatment with extract depleted of BLG. These in vitro and in vivo effects were independent of endotoxin. Conclusion The lipocalin BLG is found in large amounts in cattle urine, accumulates in bovine dust samples and is aerosolized around farms. Its association with zinc favorably shapes the human cellular immune response towards Th1‐cytokines in vitro. BLG together with zinc in stable dust protects mice from allergic sensitization. BLG with its associated ligands may in an innate manner contribute to the allergy‐protective farm effect.
Knowledge, attitude, and prevention COVID-19 outbreak of parental children aged 1-6 years

Background: Attitude, knowledge and prevention regarding coronavirus disease 2019 (COVID-19) of parents is rarely been found. Therefore, this study aimed to explore attitude, knowledge and prevention regarding COVID-19 of parents of children aged between 1-6 years.Methods: A telephone-based cross-sectional survey was conducted in the northeast of Thailand during August 2021 to September 2021. The survey contained questions on knowledge, attitudes and prevention (KAP) regarding COVID-19. Participants’ demographic characteristics were recorded and analyzed. Multiple linear regression was used to test factors related to attitude, knowledge, and practice towards COVID-19.Results: The result from multiple linear regression analysis showed that females had higher scores of prevention than did males (difference: 3.21; 95% CI: 1.58, 4.83). In addition, parents who were more than 30 years old had lower scores for prevention of COVID-19 (difference: -0.78; 95% CI: -1.46, -0.10). Conclusions: We found associations between parent’s prevention and gender, prevention and age, attitude and occupation, and prevention and occupation towards COVID-19.
Enhanced Bioactivity of Pomegranate Peel Extract following Controlled Release from CaCO3 Nanocrystals

Pomegranate peel extract is rich of interesting bioactive chemicals, principally phenolic compounds, which have shown antimicrobial, anticancer, and antioxidative properties. The aim of this work was to improve extract' bioactivity through the adsorption on calcium carbonate nanocrystals. Nanocrystals revealed as efficient tools for extract adsorption reaching 50% of loading efficiency. Controlled release of the contained metabolites under acidic pH has been found, as it was confirmed by quantitative assay and qualitative study through NMR analysis. Specific functionality of inorganic nanocarriers could be also tuned by biopolymeric coating. The resulting coated nanoformulations showed a great antimicrobial activity against B. cinerea fungus preventing strawberries disease better than a commercial fungicide. Furthermore, nanoformulations demonstrated a good antiproliferative activity in neuroblastoma and breast cancer cells carrying out a higher cytotoxic effect respect to free extract, confirming a crucial role of nanocarriers. Finally, pomegranate peel extract showed a very high radical scavenging ability, equal to ascorbic acid. Antioxidant activity, measured also in intracellular environment, highlighted a protective action of extract-adsorbed nanocrystals twice than free extract, providing a possible application for new nutraceutical formulations.
Thinking responsibly about responsible AI and ‘the dark side’ of AI

ABSTRACT Artificial Intelligence (AI) has been argued to offer a myriad of improvements in how we work and live. The notion of AI comprises a wide-ranging set of technologies that allow individuals and organizations to integrate and analyze data and use that insight to improve or automate decision-making. While most attention has been placed on the positive aspects companies realize by the adoption by the adoption and use of AI, there is a growing concern around the negative and unintended consequences of such technologies. In this special issue we have made a call for research papers that help us explore the dark side of AI use. By adopting a dark side lens, we aimed to expand our understanding of how AI should be implemented in practice, and how to minimize or avoid negative outcomes. In this editorial, we build on the notion of responsible AI, to highlight the different ways in which AI can potentially produce unintended consequences, as well as to suggest alternative paths future IS research can follow to improve our knowledge about how to mitigate such occurrences. We further expand on dark side theorizing in order to uncover hidden assumptions of current literature as well as to propose other prominent themes that can guide future IS research on AI adoption and use.
Extreme-ultraviolet Stellar Characterization for Atmospheric Physics and Evolution mission: motivation and overview

Abstract. The Extreme-ultraviolet Stellar Characterization for Atmospheric Physics and Evolution (ESCAPE) mission is an astrophysics Small Explorer employing ultraviolet spectroscopy (EUV: 80 to 825 Å and FUV: 1280 to 1650 Å) to explore the high-energy radiation environment in the habitable zones around nearby stars. ESCAPE provides the first comprehensive study of the stellar EUV and coronal mass ejection environments that directly impact the habitability of rocky exoplanets. In a 20-month science mission, ESCAPE will provide the essential stellar characterization to identify exoplanetary systems most conducive to habitability and provide a roadmap for NASA’s future life-finder missions. ESCAPE accomplishes this goal with roughly two-order-of-magnitude gains in EUV efficiency over previous missions. ESCAPE employs a grazing incidence telescope that feeds an EUV and FUV spectrograph. The ESCAPE science instrument builds on previous ultraviolet and x-ray instrumentation, grazing incidence optical systems, and photon-counting ultraviolet detectors used on NASA astrophysics, heliophysics, and planetary science missions. The ESCAPE spacecraft bus is the versatile and high-heritage Ball Aerospace BCP-Small spacecraft. Data archives will be housed at the Mikulski Archive for Space Telescopes.
Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.
PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS: A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET

For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to find an approach which works the best. In this paper, we make a thorough analysis of the predictive accuracy of different machine learning and econometric approaches for predicting the returns and volatilities on the OMX Baltic Benchmark price index, which is a relatively less researched stock market. Our results show that the machine learning methods, namely the support vector regression and k-nearest neighbours, predict the returns better than autoregressive moving average models for most of the metrics, while for the other approaches, the results were not conclusive. Our analysis also highlighted that training and testing sample size plays an important role on the outcome of machine learning approaches.
The nature and chromosomal landscape of endogenous retroviruses (ERVs) integrated in the sheep nuclear genome

Endogenous retroviruses (ERVs) represent genomic components of retroviral origin that are found integrated in the genomes of various species of vertebrates. These genomic elements have been widely characterized in model organisms and humans. However, composition and abundances of ERVs have not been categorized fully in all domestic animals. The advent of next generation sequencing technologies, development of bioinformatics tools, availability of genomic databases and molecular cytogenetic techniques have revolutionized the exploration of the genome structure. Here, we investigated the nature, abundance, organization and assembly of ERVs and complete genomes of Jaagsiekte sheep retrovirus (JSRV) from high-throughput sequencing (HTS) data from two Iraqi domestic sheep breeds. We used graph-based read clustering (RepeatExplorer), frequency analysis of short motifs (k-mers), alignment to reference genome assemblies and fluorescent in situ hybridization (FISH). Three classes of ERVs were identified with the total genomic proportions of 0.55% from all analyzed whole genome sequencing raw reads, while FISH to ovine metaphase chromosomes exhibited abundant centromeric to dispersed distribution of these ERVs. Furthermore, the complete genomes of JSRV of two Iraqi sheep breeds were assembled and phylogenetically clustered with the known enJSRV proviruses in sheep worldwide. Characterization of partial and complete sequences of mammalian ERVs is valuable to provide insights into the genome landscape, to help with future genome assemblies and to identify potential sources of disease when ERVs become active.
Studies of Abrasive Water Jet Machining (AWJM) Parameters on Banana/Polyester Composites Using Robust Design Concept

In this present work the banana/polyester was fabricated using hand layup method followed by compression moulding technique. The moulded composite was studied with special references to the Taguchi’s method for optimizing the cutting parameters of AWJM for minimizing the kerf width and kerf taper. An L9 (34) orthogonal array was selected to conduct the experiment with the identified significant parameters such as Feed rate, Pressure, Standoff distance and Abrasive Method. The significance of cutting parameters was analysed with the help of ANOVA. The optimum process parameters were identified with the help of signal to noise ratio (SN). The decreasing value of hardness was observed after the machining of composite using shore-D hardness tester. The influence of abrasive particle was found to be predominant affecting the performance of the output quality measures.
EFFECT OF VIBRATION ON THE HEALTH OF DRIVER OF THREE WHEELER USING ISO 2631

The majority population in India depends on vehicle- 3 wheeler for transportation due to their poor economic condition. These vehicles are exposed to vibrations due to irregular surface of road or soil profile, engine vibration and condition of vehicle which affects the health as well as discomfort of the driver and passengers. These oscillations are transferred into the body of driver and passenger through the body tissues, organs and systems of the individual causing various effects on the structures within the body before it is dampened and dissipated. The literature review reveals that the vibrations are most hazardous to the health if it exceeds the limit. The experimental analysis is carried out to measure the magnitude of the vibrations acting on driver as well as passenger for the different road profile at different speed. The methodology adopted is as per the International Organization for Standardization (ISO) guidelines for whole body vibration (WBV) exposure having frequency ranges from 0 to 100 Hz. The study emphasis on vibration tests for different road and speed conditions by referring, ISO 2631 and human comfort charts.
Tremo: an inspection climbing inchworm based on magnetic switchable device

TREMO is a compact magnetic climbing inchworm with the capability to move in complex ferromagnetic environments. The robot fits inside a cylinder of 500 mm in height and 120 mm in diameter for an approximate weight of 800 g. It has a modular configuration based on three energy autonomous sub-systems: an arm and two feet. The arm has five degrees of freedom. It has a central articulation with a stroke of 270°. Both ends of the arm have an innovative articulation based on a differential system. This allows unlimited rotation of 360° in the heading angles and 180° in the elevation angle. At both ends of the arm a camera is mounted. The feet are donut-shaped and are mounted around the camera. The climbing ability is based on the advantageous properties of Magnetic Switchable Devices (MSD) [1]. Each feet of the robot embeds three MSD for better compliance and stability of the adhesion to the surface. The three MSD of one feet together with their motors weight 32 g and can hold above 90 N.
Deep Learning-Based Probabilistic Autoencoder for Residential Energy Disaggregation: An Adversarial Approach

Energy disaggregation is the process of disaggregating a household's total energy consumption into its appliance-level components. One of the limitations of energy disaggregation is its generalization capacity, which can be defined as the ability of the model to analyze new households. In this article, a new energy disaggregation approach based on adversarial autoencoder (AAE) is proposed to create a generative model and enhance the generalization capacity. The proposed method has a probabilistic structure to handle uncertainties in the unseen data. By transforming the latent space from a deterministic structure to a Gaussian prior distribution, AAEs decoder transforms into a generative model. The proposed approach is validated through experimental tests using two different datasets. The experimental results exhibit a 55% MAE performance increase compared to deterministic models and 7% compared to probabilistic models. In addition, considering the predictions made when the appliances are on, the AAE improves the performance by 16% for UKDALE and 36% for REDD dataset compared to the state-of-art models. Moreover, the online analysis performance of AAE is examined in detail, and the disadvantages of instant predictions and the possible solutions are extensively discussed.
Who votes after a coup? Theory and evidence from Egypt

ABSTRACT More than half of leaders who come to power through military coups hold elections to legitimate their regimes, yet there is extensive subnational variation in how citizens accept or reject this process. In this paper, we examine district-by-district voting patterns in Egyptian presidential elections a few months following the July 2013 military coup to identify the ecological correlates of three district-level measures of citizen engagement with the electoral process: voter turnout, valid (non-spoilt) ballots, and votes cast for the regime-affiliated candidate. Controlling for baseline measures of these outcomes from the free and fair presidential elections prior to the coup, we find support for the enduring effect of partisanship: districts with higher support for the deposed candidate in pre-coup elections featured systematically lower turnout and rates of valid voting in post-coup elections.
PEDIATRIC HODGKIN LYMPHOMA IN IRAQ-KRG-SULAIMANI

Background: Hodgkin lymphoma (HL) is a highly curable malignancy. It is a unique neoplasm, in which the malignant cell is the Reed-Stenberg cell (RSC); it constitutes approximately 40% of all lymphomas that present during childhood. Objective: To obtain local data on the pattern of childhood and adolescent HL in our community at presentation and then compare it with the international figure. Methods: Eighty-five patients with newly diagnosed pediatric HL were admitted to Hiwa Hematology/Oncology Hospital in Sulaimaniya province of Iraqi Kurdistan were included in this study, they were studied prospectively from March 2006 to March 2014. The patients staged by Ann Arbor Staging system and categories into three risk group depending on the Stage and Number of Nodal Sites and the presence of Bulky Disease. Data analyzed using SPSS software; version 13 and P-value obtained by Chi-square test. Results:The median age at diagnosis was about 13.7 years with peak age of incidence was between 15-18 year and with male predominance. Most common site of the primary tumor was cervical lymph nodes; nodular sclerosis was the most common histopathological subtype and the majorities were presented in stage IIA. Conclusion: The stage in this study was age dependent but neither there was relation between the site of the primary tumor and the risk categories nor relation between risk categories and sex. Keywords: Hodgkin lymphoma, stage, sex distribution, risk category Citation: Abdallah BK, Rashid NG, Tawfiq SA. Pediatric Hodgkin lymphoma in Iraq-KRG-Sulaimani. Iraqi JMS. 2019; 17(1): 12-17. doi: 10.22578/IJMS.17. 1.3.