Miracle-dz/UniFAR-FTUs huggingface dataset:
Neonatal Sepsis and Associated Factors Among Newborns in Woldia and Dessie Comprehensive Specialized Hospitals, North-East Ethiopia, 2021

Introduction Neonatal sepsis is a clinical illness characterized by infection-related signs and symptoms in the first month of life, with or without bacteremia. Septicemia, meningitis, pneumonia, arthritis, osteomyelitis, and urinary tract infections are all examples of systemic illnesses that can affect newborns. Hence, the main aim of this study was to assess the prevalence and factors associated with neonatal sepsis among newborns in Woldia and Dessie Comprehensive Specialized Hospitals, northeast Ethiopia, from January 1 to July 30, 2021. Methods This institution-based cross-sectional study was conducted from January 1 to July 30, 2021, on 344 randomly selected neonates who visited the hospital. A systematic random sampling technique was used to select samples, and data were collected using a pre-tested standardized questionnaire. For data entry and analysis, Epi Data version 4.1 and SPSS version 24 applications were used, respectively. The goodness-of-fit was tested by the Hosmer–Lemeshow statistic test. Bivariable and multivariable binary logistic regressions were used to identify associated factors at a 95% confidence interval. Significance was considered at p-value <0.05. Results In this study, the prevalence of neonatal sepsis was 79.4% (95% CI: 75.2–83.6%). Maternal UTI/STI history [AOR: 3.1; 95% CI (1.5–7.1)], gestational age <37 weeks [AOR: 4.4; 95% CI (1.0–8.9)], PROM [AOR: 4.9; 95% CI (2.5–6.8)], and new-born resuscitation history [AOR: 2.3; 95% CI (1.5–4.3)] were all significantly associated with neonatal sepsis. Conclusion This study indicates that the proportion of neonatal sepsis is high. A history of maternal PROM, being a preterm neonate, a history of maternal UTI/STIs, and having received resuscitation at birth were identified as risk factors for neonatal sepsis.
Fundamental number theory with applications

An update of the most accessible introductory number theory text available, Fundamental Number Theory with Applications, Second Edition presents a mathematically rigorous yet easy-to-follow treatment of the fundamentals and applications of the subject. The substantial amount of reorganizing makes this edition clearer and more elementary in its coverage. New to the Second Edition Removal of all advanced material to be even more accessible in scope New fundamental material, including partition theory, generating functions, and combinatorial number theory Expanded coverage of random number generation, Diophantine analysis, and additive number theory More applications to cryptography, primality testing, and factoring An appendix on the recently discovered unconditional deterministic polynomial-time algorithm for primality testing Taking a truly elementary approach to number theory, this text supplies the essential material for a first course on the subject. Placed in highlighted boxes to reduce distraction from the main text, nearly 70 biographies focus on major contributors to the field. The presentation of over 1,300 entries in the index maximizes cross-referencing so students can find data with ease.
Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science

Although causal inference has shown great value in estimating effect sizes in, for instance, physics, medical studies, and economics, it is rarely used in sports science. Targeted Maximum Likelihood Estimation (TMLE) is a modern method for performing causal inference. TMLE is forgiving in the misspecification of the causal model and improves the estimation of effect sizes using machine-learning methods. We demonstrate the advantage of TMLE in sports science by comparing the calculated effect size with a Generalized Linear Model (GLM). In this study, we introduce TMLE and provide a roadmap for making causal inference and apply the roadmap along with the methods mentioned above in a simulation study and case study investigating the influence of substitutions on the physical performance of the entire soccer team (i.e., the effect size of substitutions on the total physical performance). We construct a causal model, a misspecified causal model, a simulation dataset, and an observed tracking dataset of individual players from 302 elite soccer matches. The simulation dataset results show that TMLE outperforms GLM in estimating the effect size of the substitutions on the total physical performance. Furthermore, TMLE is most robust against model misspecification in both the simulation and the tracking dataset. However, independent of the method used in the tracking dataset, it was found that substitutes increase the physical performance of the entire soccer team.
Recapsoma®: A Novel Mixture Based on Bergamot, Ipomoea Batatas, Policosanol Extracts and Liposomal Berberine for the Treatment of Hypercholesterolemia

Cardiovascular disease (CVD) is considered one of the major causes of mortality worldwide. Epidemiological studies have shown that regular consumption of phenols is inversely associated with cardiovascular disease, and the use of nutraceuticals and functional foods can provide protective, preventive, and possibly curative effects in CVD. A novel mixture of different natural substances named Recapsoma® (bergamot, liposomal berberine, Ipomoea batatas, oleuropein, polycosanols, and vitamin E) has been produced, and its anti-dyslipidaemic efficacy has been tested, specifically studying the in vitro effects on the mechanisms of action underlying cholesterol synthesis, triglycerides, and LDL-cholesterol oxidation. The work has demonstrated the ability of this herbal extract mixture to inhibit the action of PCSK, ACAT, PAP, and HMGR and to increase the LDL receptor (LDLR), underlying the synergistic effect of the mixture over the single components. Such results suggest that the Recapsoma® mixture could be used as a tool for controlling hypercholesterolemia, and an alternative to statins, especially for those patients with metabolic syndrome.
The Contribution of Cognitive Factors to Compulsive Buying Behaviour: Insights from Shopping Habit Changes during the COVID-19 Pandemic

The last decade has seen an increase in compulsive behaviours among young adults worldwide, particularly in 2020, during restrictions due to the COVID-19 pandemic. Importantly, even if shopping is an ordinary activity in everyday life, it can become a compulsive behaviour for certain individuals. The aim of this study was to investigate the role of working memory and decision-making style in compulsive behaviour. A total of 105 participants (65 F, 40 M) were recruited online from May 2020 to December 2020. They completed a series of questionnaires to measure shopping compulsive behaviour, decision-making styles, deficits in working memory and online shopping habits. The results show that during the COVID-19 pandemic, people spent much more time shopping online, made more purchases and spent more money than prior to the pandemic. Moreover, both higher working memory deficits and spontaneous decision-making style predicted a greater tendency to engage in compulsive buying. These results suggest the need to develop specific training programs to improve cognitive aspects related to compulsive shopping behaviour.
Determinants of wasting among under-five children in Ethiopia: Multilevel ordinal logistic regression analysis of EDHS 2016

Background: In Ethiopia, wasting is still a serious public health problem in under-5 children regardless of the efforts done within the country. Although studies have been conducted in Ethiopia, multilevel analysis has rarely been used to identify the factors associated with wasting among children. Therefore, this study aimed to identify the factors of wasting among under-5 children in Ethiopia using multilevel ordinal logistic regression analysis. Methods: The data were obtained from the 2016 Ethiopia Demographic and Health Survey, conducted from January to June 2016. A sample of 8,919 children under-5 years was included. Data were analyzed using STATA version 14. A multilevel logistic regression model was fitted and an adjusted odds ratio (AOR) with a 95% confidence interval (CI) was obtained. Results: The prevalence of wasting among children under-5 years was 10.1% (901), of which 8.1% (632) had moderate wasting and 3.0% (269) had severe wasting. Children aged 36–47 months (AOR = 0.5; 95% CI: 0.4, 0.63), 48–59 (AOR = 0.5; 95% CI: 0.4–0.63), girls (AOR = 0.75; 95% CI: 0.65, 0.87), smaller-than-average birth weight (AOR = 1.94; 95% CI: 1.44, 2.61), very small birth weight (AOR = 1.75; 95% CI: 1.34, 2.30) were the individual-level factors associated with wasting, whereas husband’s educational status (AOR = 0.37; 95% CI: 0.29, 0.69) was the household-level factor. Somalia (AOR = 1.72; 95% CI: 1.08, 2.74), Sothern Nations Nationalities and People (SNNP) (AOR = 0.39; 95% CI: 0.24, 0.64), and Addis Ababa (AOR = 0.43; 95% CI: 0.21, 0.88) regions were the community-level factors associated with child wasting. Conclusion: This study shows that wasting among children under-5 years is affected by individual, household, and community-level factors. Interventions should be strengthened to give attention to the child’s early age, male sex, low birth weight, and husband’s educational status. In addition, interventions should target the regions identified to have a high risk of wasting.
Long COVID – respiratory symptoms in non-hospitalised subjects – a cross-sectional study

Abstract Background The aim of this study was to describe and analyse the variety of respiratory appearances in Long COVID subjects who were not hospitalised during the acute phase of the infection. Methods A consecutive series of 60 subjects participated 10.8 months (SD 4.5) after the acute phase of the infection. Respiratory function was tested concerning lung volumes, expiratory flow, muscle strength, physical capacity including concurrent oxygen saturation, chest expansion, lung sounds, pain and breathing pattern. Differences between those with or without positive test and duration of symptoms more or less than 6 months were analysed with T-test, Chi-square test and Fisher’s exact test. Results Decreased forced vital capacity was found in 6/60 (10%), and forced expiratory volume in 1 s and 7/60 (12%), low maximal inspiratory pressure in 38/58 (54%) and low maximal expiratory pressure in 10/58 (17%). Decreased physical capacity was registered in 36/52 (69%), and thoracic expansion in 26/46 (56%). Pathologic lung sounds had 15/58 (26%) and six patients desaturated during the test of physical capacity. A majority (36/58, 67%) presented pain in the ribcage. All but three patients (95%) showed a dysfunctional breathing pattern in sitting and standing. Only poor and fair correlations were found between age, duration and level of physical capacity compared to spirometry, respiratory muscle strength and thoracic expansion. Conclusion Abnormal breathing pattern and respiratory movements as well as pain, and reduced lung volumes, flow, respiratory muscle strength, physical capacity and thoracic expansion may be involved in Long COVID. The breathing symptoms should therefore be looked for in a wider picture beyond spirometry and oximetry.
Distributed cohesive configuration controller for a swarm with low‐cost platforms

This study presents a cohesive configuration controller for distributed space coverage by a swarm of robots. The goal is to build a dense, convex network that is robust against disconnection while robots are flocking with only incomplete knowledge about the network. The controller is an integrated framework of two different algorithms. First, we present a boundary force algorithm: physics‐based swarm intelligence that borrows the concept of surface tension force between liquid molecules. The combination of such a force with conventional flocking produces a convex and dense configuration without knowledge of the complete geometry of a robot network. Second, robots distributively determine when a configuration is on the verge of disconnection by identifying a local articulation point—a region where the removal of a single robot will change the local topology. When such a point is detected, robots switch their behavior to clustering, which aggregates them around the vulnerable region to remove every articulation point and retain a connected configuration. Finally, we introduced an index that objectively represents the level of risk of a robot configuration against the massive fragmentation, called vulnerability index. We provide theoretical performance analyses of each algorithm and validate the results with simulations and experiments using a set of low‐cost robots.
Determination of Theoretical Fracture Criteria of Layered Elastic Composite Material by ANFIS Method from Artificial Intelligence

The theoretical breaking boundary of the composite elastic composite material consisting of two is otropichomogeneous layers was investigated [6,7]. Three-dimensional theory of elasticity and a fragment edhomogeneous body model were used to find the theoretical breaking limit [3,6,7]. The defects of this material, which has local defects, are of the same length hand is changing with the angle β. The purpose of the articles [10,11] was to examine the effect of theoretical refraction criterion limit values on the change of the angle β. It has been observed that the theoretical refraction limit values obtained for four different composite materials increase as the angle art increases. In this study, the change values with the β angle of the theoretical refraction limit obtained in [11] article were recovered from the artificial intelligence of the MATLAB platform by ANFIS (Sugeno) method. It was seen that the values obtained with the Anfis method are the same with the theoretical results.
Quantum search algorithms

In this thesis two quantum search algorithms on two different graphs, a hypercube and a d-dimensional square lattice, are analysed and some applications of the lattice search are discussed. The approach in this thesis generalises a picture drawn by Shenvi, Kempe and Whaley, which was later adapted by Ambainis, Kempe and Rivosh. It defines a one parameter family of unitary operators U_λ with parameter λ. It will be shown that two eigenvalues of U_λ form an avoided crossing at the λ-value where U_λ is equal to the old search operator. This generalised picture opens the way for a construction of two approximate eigen- vectors at the crossing and gives rise to a 2×2 model Hamiltonian that is used to approximate the operator U_λ near the crossing. The thus defined Hamiltonian can be used to calculate the leading order of search time and success probability for the search. To the best of my knowledge only the scaling of these quantities has been known. For the algorithm searching the regular lattice, a generalisation of the model Hamiltonian for m target vertices is constructed. This algorithm can be used to send a signal from one vertex of the graph to a set of vertices. The signal is transmitted between these vertices exclusively and is localised only on the sender and the receiving vertices while the probability to measure the signal at one of the remaining vertices is significantly smaller. However, this effect can be used to introduce an additional sender to search settings and send a continuous signal to all target vertices where the signal will localise. This effect is an improvement compared to the original search algorithm as it does not need to know the number of target vertices.
Information Processing Using Networks of Chemical Oscillators

I believe the computing potential of systems with chemical reactions has not yet been fully explored. The most common approach to chemical computing is based on implementation of logic gates. However, it does not seem practical because the lifetime of such gates is short, and communication between gates requires precise adjustment. The maximum computational efficiency of a chemical medium is achieved if the information is processed in parallel by different parts of it. In this paper, I review the idea of computing with coupled chemical oscillators and give arguments for the efficiency of such an approach. I discuss how to input information and how to read out the result of network computation. I describe the idea of top-down optimization of computing networks. As an example, I consider a small network of three coupled chemical oscillators designed to differentiate the white from the red points of the Japanese flag. My results are based on computer simulations with the standard two-variable Oregonator model of the oscillatory Belousov–Zhabotinsky reaction. An optimized network of three interacting oscillators can recognize the color of a randomly selected point with >98% accuracy. The presented ideas can be helpful for the experimental realization of fully functional chemical computing networks.
Neutrophil Immunomodulatory Activity of (−)-Borneol, a Major Component of Essential Oils Extracted from Grindelia squarrosa

Grindelia squarrosa (Pursh) Dunal is used in traditional medicine for treating various diseases; however, little is known about the immunomodulatory activity of essential oils from this plant. Thus, we isolated essential oils from the flowers (GEOFl) and leaves (GEOLv) of G. squarrosa and evaluated the chemical composition and innate immunomodulatory activity of these essential oils. Compositional analysis of these essential oils revealed that the main components were α-pinene (24.7 and 23.2% in GEOFl and GEOLv, respectively), limonene (10.0 and 14.7%), borneol (23.4 and 16.6%), p-cymen-8-ol (6.1 and 5.8%), β-pinene (4.0 and 3.8%), bornyl acetate (3.0 and 5.1%), trans-pinocarveol (4.2 and 3.7%), spathulenol (3.0 and 2.0%), myrtenol (2.5 and 1.7%), and terpinolene (1.7 and 2.0%). Enantiomer analysis showed that α-pinene, β-pinene, and borneol were present primarily as (−)-enantiomers (100% enantiomeric excess (ee) for (−)-α-pinene and (−)-borneol in both GEOFl and GEOLv; 82 and 78% ee for (−)-β-pinene in GEOFl and GEOLv), while limonene was present primarily as the (+)-enantiomer (94 and 96 ee in GEOFl and GEOLv). Grindelia essential oils activated human neutrophils, resulting in increased [Ca2+]i (EC50 = 22.3 µg/mL for GEOFl and 19.4 µg/mL for GEOLv). In addition, one of the major enantiomeric components, (−)-borneol, activated human neutrophil [Ca2+]i (EC50 = 28.7 ± 2.6), whereas (+)-borneol was inactive. Since these treatments activated neutrophils, we also evaluated if they were able to down-regulate neutrophil responses to subsequent agonist activation and found that treatment with Grindelia essential oils inhibited activation of these cells by the N-formyl peptide receptor 1 (FPR1) agonist fMLF and the FPR2 agonist WKYMVM. Likewise, (−)-borneol inhibited FPR-agonist-induced Ca2+ influx in neutrophils. Grindelia leaf and flower essential oils, as well as (−)-borneol, also inhibited fMLF-induced chemotaxis of human neutrophils (IC50 = 4.1 ± 0.8 µg/mL, 5.0 ± 1.6 µg/mL, and 5.8 ± 1.4 µM, respectively). Thus, we identified (−)-borneol as a novel modulator of human neutrophil function.
Students' Responses Towards Online Learning English Language Through Google Classroom

The advancement of data and innovation proceeds to develop in this transformation industry 4.0, particularly within promoting innovation with the web. It impacts the world of instruction, particularly in learning strategies. One of the improvements in instruction and learning strategies that are right now in utilize is Google classroom. This is to recognize how students' reactions towards learning English Calling utilizing google classroom are connected to the instructing and learning preparation at the Mataram College of Tourism. This investigation is s study inquiry. The number of tests this considers was 135 understudies from Mataram College of Tourism. The factors inspected in this think incorporate angles of ease in getting to, value, communication, and interaction, and students’ fulfillment in learning utilizing google classroom. The result of this investigation is that students in Mataram Tourism College impression of Google Classroom is simple, valuable, and simple for communication and interaction, and feel fulfilled with Google Classroom. In any case, from the learning activity, it was found that the respondents thought that the quality of the learning handling was not flexible in the first meeting and they were awkward amid the learning in learning while using the google classroom
THE DYNAMICAL BEHAVIOR AND PERIODIC SOLUTION IN DELAYED NONAUTONOMOUS CHEMOSTAT MODELS

In this paper, the global dynamics and existence of positive periodic solutions in a general delayed nonautonomous chemostat model are investigated. The positivity and ultimate boundedness of solutions are firstly obtained. The sufficient conditions on the uniform persistence and strong persistence of solutions are established. Furthermore, the criterion on the global attractivity of trivial solution is also established. As the applications of main results, the periodic delayed chemostat model is discussed, and the necessary and sufficient criteria on the existence of positive periodic solutions, and uniform persistence and extinction of microorganism species are obtained. Lastly, the numerical examples are presented to illustrate the main conclusions.
Are Lockdowns Effective in Managing Pandemics?

The present coronavirus crisis caused a major worldwide disruption which has not been experienced for decades. The lockdown-based crisis management was implemented by nearly all the countries, and studies confirming lockdown effectiveness can be found alongside the studies questioning it. In this work, we performed a narrative review of the works studying the above effectiveness, as well as the historic experience of previous pandemics and risk-benefit analysis based on the connection of health and wealth. Our aim was to learn lessons and analyze ways to improve the management of similar events in the future. The comparative analysis of different countries showed that the assumption of lockdowns’ effectiveness cannot be supported by evidence—neither regarding the present COVID-19 pandemic, nor regarding the 1918–1920 Spanish Flu and other less-severe pandemics in the past. The price tag of lockdowns in terms of public health is high: by using the known connection between health and wealth, we estimate that lockdowns may claim 20 times more life years than they save. It is suggested therefore that a thorough cost-benefit analysis should be performed before imposing any lockdown for either COVID-19 or any future pandemic.