Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGARUH USIA, TINGGI BADAN DAN RIWAYAT PEMBERIAN ASI EKSKLUSIF TERHADAP STUNTING PADA BALITA

Background: Stunting is a chronic condition that describes stunted growth due to long-term malnutrition . Stunting can be influenced by the lack of nutrition, low birth weight exclusive breastfeeding, duration of exclusive breastfeeding. Objective : to analyze the effect of age, height, and history of exclusive breastfeeding on stunting using multiple logistic regression tests and the presentation of the regression equation model followed by the probability of stunting in toddlers. Methods: The study population was toddlers who had medical record data at the public health center "X" in Banyuanyar Surakarta in October 2019. By using a consecutive sampling technique, a sample of 30 children was obtained. Results: age, height, and history of exclusive breastfeeding together affected stunting (F = 25,676, p = 0,000), with a joint effect of 71.9%. Age, height, and history of exclusive breastfeeding individually also affected stunting, respectively age (t = 3,656, p = 0.001), height (t = -4,499, p = 0,000), and history of breastfeeding exclusive (t = 3.207, p = 0.004). Conclusion: exclusive breastfeeding for toddlers can improve nutritional status, thereby reducing the chances of toddlers experiencing stunting .
Some physical characteristics of dietary fibres and their influence on the microbial ecology of the human colon

Current thinking on the subject of dietary fibre tends to emphasize an inclusive definition that overlooks disparate characteristics of individual fibres and their components. The current definition includes all carbohydrate and some noncarbohydrate components that are resistant to mammalian enzymes. The analytical approach proposes to resolve the differences between sources of fibre by characterizing the different proportions of cellulose, hemicellulose, pectin, etc. and in turn resolving these into their respective component sugars. Forage analysis, largely applied to nutrition of ruminants, has emphasized the nutritional non-uniformity of cellulose and hemicellulose despite similarities in chemical linkage and structure of the respective components among different species of forage plants. This has led to the suggestion that intrinsic differences among celluloses are due to macromolecular interactions rather than to their intrinsic structure and composition (Van Soest, I 982). This paper applies the same view to dietary fibre in human nutrition and provides some evidence that physical properties are major determinants of the biological properties of dietary fibre in the human digestive tract.
Estimation and Assessment of Plasma Reduced Glutathione Status among Sudanese Females with Breast Cancer Undergoing Chemotherapy

Original Research Article Glutathione is considered as a major molecule that plays an essential role in the antioxidant defense system. The mechanism of chemotherapeutic agents are act based on oxidative stress to kill the cancer cell, so the antioxidant activity of glutathione is thought to be interfering with chemotherapeutic action.The aim of the recent study is Estimation of Plasma Reduced Glutathione among Sudanese Females with Breast Cancer Undergoing Chemotherapy . The current study was conducted on 100 Sudanese females with Breast Cancer who attended the Institute of Nuclear Medicine – Madani – SUDAN between August to October / 2018. ELISA 96 well plates (OD 412 nm) and quantiChrom Glutathione assay kit were used to measure the level of reduced glutathione in Plasma. The outcome of the recent study, the level of plasma reduced glutathione in patients was significantly higher than control (MEAN 92.03±46.6; 13.87±9.27 respectively; P-value 0.001). Our finding suggests that the status of reduced glutathione levels might be play a role as a prognostic factor for poor response of chemotherapy in Sudanese female with Breast Cancer.
Barriers to adapt eCommerce by rural Microenterprises in Sweden: A case study

Research has shown that small and medium-sized enterprises (SMEs) are rapidly adopting the e-commerce. However, there is nearly no research into how microenterprises are adopting eCommerce. Present paper focus on microenterprise adaption of eCommerce in terms of barriers in relation to already known research on SMEs. A case study, carried out by 12 microenterprises to find out barriers to adapt eCommerce had been done. The empirical results show that the microenterprises share most of the barriers to adapt the eCommerce with studies of SMEs, but also reveal additional factors affecting adaptation option of eCommerce; supplier agreement, communication and customer strategy. Conclusions are that microenterprises need additional support and communication and customer strategy to adapt eCommerce, depending of their requirement and needs of eCommerce.
Dispersion and Slip Casting of Hydroxyapatite

The dispersibility in deionized water of hydroxyapatite (HA) synthesized by a high-temperature (1000°C) solid-state reaction between tricalcium phosphate and calcium hydroxide was investigated as a function of the pH of the medium and the quantity of two dispersing agents (A 5 inorganic, B 5 organic)added to the slips. Although pH modification had a negligible effect on dispersibility, both of the dispersing agents produced a good dispersion at considerably higher concentrations (>2 wt% of HA). At optimum amounts (2–4 wt%) of the dispersing agents, the slips showed near-Newtonian flow behavior up to 45 wt% solids loading and non-Newtonian behavior at >50 wt%. By the optimal addition of dispersing agents and conditioning by ball milling, 60–67 wt% (32–39 vol%) solids-loaded HA slips could be cast into plaster molds to produce 50%–58% dense green bodies, which, in turn, sintered to 90%–94% density in the temperature range 1300°–1400°C. The sintered HA exhibited a three-point flexural strength of 40–60 MPa and a homogeneous microstructure, with interspersed microporosities.
Subacute ruminal acidosis in dairy cows - physiological background, risk factors and diagnostic methods

According to the latest studies, the prevalence of subacute ruminal acidosis (SARA) is around 20% in early and mid- lactation dairy cows, generating annual losses in the United States of approximately USD 500 million to 1 billion. The diagnosis of SARA is still difficult due to lack of pathognomonic clues and the delayed appearance of certain clinical signs. Therefore, SARA remains neglected or even unrecognized in many dairy herds. SARA is characterized by daily episodes of low ruminal pH, when the pH remains in the range of 5.2 to 6 for a prolonged period due to the accumulation of short-chain fatty acids and insufficient rumen buffering. The causes of SARA are related to high-grain diets, such as feeding excessive amounts of non-structural carbohydrates and highly fermentable forages, and insufficient dietary coarse fibre. SARA is associated with the inflammation of several organs and tissues in dairy cows, and its main long-term health and economic consequences are the fluctuation of feed intake, reduced fibre digestion, depression of milk yield and milk fat content, gastrointestinal damage, diarrhoea, laminitis, liver abscesses, and lameness. The aim of this review is to summarize the information available on the physiological aspects, risk factors, prevalence and possible indicators of SARA in dairy cattle. Basedon the existing literature, rumenocentesis and the use of an oral stomach tube are reliable field techniques to detect SARA. Nowadays, improved field techniques allowing the continuous measurement of reticuloruminal pH are also available for better diagnosis of SARA. Wireless indwelling pH probes may become important tools for the continuous measurement of ruminal pH in the coming years.
Determination of Antimicrobial Activity of Extracts of Indigenous Wild Mushrooms against Pathogenic Organisms

Objective This study has investigated the antimicrobial activity of extracts of indigenous wild mushrooms against selected organisms. Methods Thirty-five (35) indigenous wild mushrooms were collected from Arabuko-Sokoke and Kakamega National Reserve Forests, Kenya. All mushrooms were identified and their contents were extracted and screened for their antimicrobial activities against Escherichia coli (clinical isolate), Klebsiella pneumoniae (ATCC 13883), Pseudomonas aeruginosa (clinical isolate), Pseudomonas aeruginosa (ATCC 27853), Staphylococcus aureus (ATCC 25923), MRSA (ATCC 33591), Candida albicans (clinical isolate), and Candida parapsilosis (ATCC 90018) using tetrazolium microtiter plate bioassay method. Results Of the 35 tested mushroom extracts, extracts of three (3) mushrooms, namely, Trametes spp. (Arabuko-Sokoke forest), Trametes, and Microporus spp. (Kakamega forest), have shown promising antimicrobial activities against the tested organisms. The S. aureus (ATCC 25923), P. aeruginosa (ATCC 27853), and Methicillin-Resistant Staphylococcus aureus (MRSA) (ATCC 33591) were the most susceptible to chloroform extract of Trametes spp. collected from Arabuko-Sokoke forest. Of the tested organisms, S. aureus (ATCC 25923) was the most susceptible whereas E. coli was the most resistant organism to the hot water extract of Trametes spp. collected from Arabuko-Sokoke forest. Chloroform extract of Microporus spp. has shown the highest antibacterial activity against S. aureus (ATCC 25923), MRSA (ATCC 33591), and K. pneumoniae (ATCC 13883) but limited activity against E.coli. All extracts of the three wild mushrooms have shown the most antibacterial activities against S. aureus (ATCC 25923). Conclusion The present study has shown that the extracts of the three wild mushrooms have shown promising antimicrobial activities against the tested organisms.
Probing ISM Structure in Trumpler 14 and Carina I Using the Stratospheric Terahertz Observatory 2

We present observations of the Trumpler 14/Carina I region carried out using the Stratospheric Terahertz Observatory 2. The Trumpler 14/Carina I region is in the western part of the Carina Nebula Complex (CNC), which is one of the most extreme star-forming regions in the Milky Way. We observed Trumpler 14/Carina I in the 158 μm transition of [C ii] with a spatial resolution of 48″ and a velocity resolution of 0.17 km s−1. The observations cover a 0. °25 by 0. °28 area with central position l = 297. °34, b = −0. °60. The kinematics show that bright [C ii] structures are spatially and spectrally correlated with the surfaces of CO clouds, tracing the photodissociation region (PDR) and ionization front of each molecular cloud. Along seven lines of sight (LOSs) that traverse Tr 14 into the dark ridge to the southwest, we find that the [C ii] luminosity from the H ii region is 3.7 times that from the PDR. In the same LOS, we find in the PDRs an average ratio of 1 : 4.1 : 5.6 for the mass in atomic gas : dark CO gas : molecular gas traced by CO. Comparing multiple gas tracers, including H i 21 cm, [C ii], CO, and radio recombination lines, we find that the H ii regions of the CNC are well described as H ii regions with one side freely expanding toward us, consistent with the Champagne model of ionized gas evolution. The dispersal of the GMC in this region is dominated by EUV photoevaporation; the dispersal timescale is 20–30 Myr.
Nonmodular Architectures of Cognitive Systems based on Active Inference

In psychology and neuroscience it is common to describe cognitive systems as input/output devices where perceptual and motor functions are implemented in a purely feedforward, open-loop fashion. On this view, perception and action are often seen as encapsulated modules with limited interaction between them. While embodied and enactive approaches to cognitive science have challenged the idealisation of the brain as an input/output device, we argue that even the more recent attempts to model systems using closed-loop architectures still heavily rely on a strong separation between motor and perceptual functions. Previously, we have suggested that the mainstream notion of modularity strongly resonates with the separation principle of control theory. In this work we present a minimal model of a sensorimotor loop implementing an architecture based on the separation principle. We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent. These forces can be seen as variables that an agent cannot directly control, i.e., a perturbation from the environment or an interference caused by other agents. As an alternative approach inspired by embodied cognitive science, we then propose a nonmodular architecture based on active inference. We demonstrate the robustness of this architecture to unknown external inputs and show that the mechanism with which this is achieved in linear models is equivalent to integral control.
Hybrid Precoder and Combiner for Imperfect Beam Alignment in mmWave MIMO Systems

In this letter, we aim to design a robust hybrid precoder and combiner against beam misalignment in millimeter-wave (mmWave) communication systems. We consider the inclusion of the `error statistics' into the precoder and combiner design, where the array response that incorporates the distribution of the misalignment error is first derived. An iterative algorithm is then proposed to design the robust hybrid precoder and combiner to maximize the array gain in the presence of beam misalignment. To further enhance the spectral efficiency, a second-stage digital precoder and combiner are included to mitigate the inter-stream interference. Numerical results show that the proposed robust hybrid precoder and combiner design can effectively alleviate the performance degradation incurred by beam misalignment.
On Testing of Data-Intensive Software Systems

Today's software systems like cyber-physical production systems or big data systems have to process large volumes and diverse types of data which heavily influences the quality of these so-called data-intensive systems. However, traditional software testing approaches rather focus on functional behavior than on data aspects. Therefore, the role of data in testing has to be rethought and specific testing approaches for data-intensive software systems are required. Thus, the aim of this chapter is to contribute to this area by (1) providing basic terminology and background on data-intensive software systems and their testing, and (2) presenting the state of the research and the hot topics in the area. Finally, the directions of research and the new frontiers on testing data-intensive software systems are discussed.
Effect of Indium Nanocrystals on the Structural and Optical Properties of Gallium Arsenid

In this study, the structural and optical properties of indium (In) nanocrystals doped GaAs using Sol-gel method at different concentrations were investigated via X-ray diffraction technique, Transmission electron microscope and ultra violet spectroscopy. From the results, it can be shown that the InGaAs films possess polycrystalline structure with (220) crystallographic direction for InAs and (002) crystallographic direction for GaAS. In the absorbance-wavelength spectra, it is shown that absorbance increases with increase in concentration of dopant with corresponding wavelength except at 0.2% concentration which shows a blue shift of the first excitons absorption peak at 260 nm. Correspondingly the transmittance spectra decrease with the increase in the concentration except for 0.2% of InNCs doped GaAs that shows a decrease due to the increase in the scattering of the photon by the crystals. Also, there was a decrease in band gap energy as a result of introducing InNCs into the host material.
Was ist eine Professur fuer Kuenstliche Intelligenz?

The Federal Government of Germany aims to boost the research in the field of Artificial Intelligence (AI). For instance, 100 new professorships are said to be established. However, the white paper of the government does not answer what an AI professorship is at all. In order to give colleagues, politicians, and citizens an idea, we present a view that is often followed when appointing professors for AI at German and international universities. We hope that it will help to establish a guideline with internationally accepted measures and thus make the public debate more informed.
Deep learning as closure for irreversible processes: A data-driven generalized Langevin equation

The ultimate goal of physics is finding a unique equation capable of describing the evolution of any observable quantity in a self-consistent way. Within the field of statistical physics, such an equation is known as the generalized Langevin equation (GLE). Nevertheless, the formal and exact GLE is not particularly useful, since it depends on the complete history of the observable at hand, and on hidden degrees of freedom typically inaccessible from a theoretical point of view. In this work, we propose the use of deep neural networks as a new avenue for learning the intricacies of the unknowns mentioned above. By using machine learning to eliminate the unknowns from GLEs, our methodology outperforms previous approaches (in terms of efficiency and robustness) where general fitting functions were postulated. Finally, our work is tested against several prototypical examples, from a colloidal systems and particle chains immersed in a thermal bath, to climatology and financial models. In all cases, our methodology exhibits an excellent agreement with the actual dynamics of the observables under consideration.
Fast Bayesian Uncertainty Estimation of Batch Normalized Single Image Super-Resolution Network

In recent years, deep convolutional neural network (CNN) has achieved unprecedented success in image super-resolution (SR) task. But the black-box nature of the neural network and due to its lack of transparency, it is hard to trust the outcome. In this regards, we introduce a Bayesian approach for uncertainty estimation in super-resolution network. We generate Monte Carlo (MC) samples from a posterior distribution by using batch mean and variance as a stochastic parameter in the batch-normalization layer during test time. Those MC samples not only reconstruct the image from its low-resolution counterpart but also provides a confidence map of reconstruction which will be very impactful for practical use. We also introduce a faster approach for estimating the uncertainty, and it can be useful for real-time applications. We validate our results using standard datasets for performance analysis and also for different domain-specific super-resolution task. We also estimate uncertainty quality using standard statistical metrics and also provides a qualitative evaluation of uncertainty for SR applications.
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models*

We consider Mixed Linear Regression (MLR), where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the corresponding coefficient vectors. We introduce a Mixed Integer Programming (MIP) formulation for MLR subject to regularization constraints on the coefficient vectors. We establish that as the number of training samples grows large, the MIP solution converges to the true coefficient vectors in the absence of noise. Subject to slightly stronger assumptions, we also establish that the MIP identifies the clusters from which the training samples were generated. In the special case where training data come from a single cluster, we establish that the corresponding MIP yields a solution that converges to the true coefficient vector even when training data are perturbed by (martingale difference) noise. We provide a counterexample indicating that in the presence of noise, the MIP may fail to produce the true coefficient vectors for more than one clusters. We also provide numerical results testing the MIP solutions in synthetic examples with noise.
heSRPT: Optimal Parallel Scheduling of Jobs With Known Sizes

When parallelizing a set of jobs across many servers, one must balance a trade-off between granting priority to short jobs and maintaining the overall efficiency of the system. When the goal is to minimize the mean flow time of a set of jobs, it is usually the case that one wants to complete short jobs before long jobs. However, since jobs usually cannot be parallelized with perfect efficiency, granting strict priority to the short jobs can result in very low system efficiency which in turn hurts the mean flow time across jobs. In this paper, we derive the optimal policy for allocating servers to jobs at every moment in time in order to minimize mean flow time across jobs. We assume that jobs follow a sublinear, concave speedup function, and hence jobs experience diminishing returns from being allocated additional servers. We show that the optimal policy, heSRPT, will complete jobs according to their size order, but maintains overall system efficiency by allocating some servers to each job at every moment in time. We compare heSRPT with state-of-the-art allocation policies from the literature and show that heSRPT outperforms its competitors by at least 30%, and often by much more.
Coalescence preference and droplet size inequality during fluid phase segregation

Using molecular dynamics simulations and scaling arguments, we investigate the coalescence preference dynamics of liquid droplets in a phase-segregating off-critical, single-component fluid. It is observed that the preferential distance of the product drop from its larger parent, during a coalescence event, gets smaller for large parent size inequality. The relative coalescence position exhibits a power-law dependence on the parent size ratio with an exponent . This value of q is in strong contrast with earlier reports 2.1 and 5.1 in the literature. The dissimilarity is explained by considering the underlying coalescence mechanisms.
Best-of-Three Voting on Dense Graphs

Given a graph G of n vertices, where each vertex is initially attached an opinion of either red or blue. We investigate a random process known as the Best-of-three voting. In this process, at each time step, every vertex chooses three neighbours at random and adopts the majority colour. We study this process for a class of graphs with minimum degree d = nα, where α = Ømega((łog łog n)-1). We prove that if initially each vertex is red with probability greater than $1/2+δ, and blue otherwise, where δ ≥ (łog d)-C for some C>0, then with high probability this dynamic reaches a final state where all vertices are red within O(łog łog n) + O(łog(δ-1)) steps.
Three-dimensional Simulations of Massive Stars. I. Wave Generation and Propagation

We present the first three-dimensional (3D), hydrodynamic simulations of the core convection zone (CZ) and extended radiative zone spanning from 1% to 90% of the stellar radius of an intermediate-mass ( ) star. This allows us to self-consistently follow the generation of internal gravity waves (IGWs) at the convective boundary and their propagation to the surface. We find that convection in the core is dominated by plumes. The frequency spectrum in the CZ and that of IGW generation is a double power law, as seen in previous two-dimensional (2D) simulations. The spectrum is significantly flatter than theoretical predictions using excitation through Reynolds stresses induced by convective eddies alone. It is compatible with excitation through plume penetration. An empirically determined distribution of plume frequencies generally matches the one necessary to explain a large part of the observed spectrum. We observe waves propagating in the radiation zone and excited standing modes, which can be identified as gravity and fundamental modes. They show similar frequencies and node patterns to those predicted by the stellar oscillation code GYRE. The continuous part of the spectrum fulfills the IGW dispersion relation. A spectrum of tangential velocity and temperature fluctuations close to the surface is extracted, which is directly related to observable brightness variations in stars. Unlike 2D simulations, we do not see the high frequencies associated with wave breaking, likely because the 3D simulations presented in this paper are more heavily damped.