Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of FODMAPs and gluten on irritable bowel syndrome- from self-reported symptoms to molecular profiling

Irritable bowel syndrome (IBS) is a complex disorder of gut-brain interactions. The diagnosis of IBS is based on subjective reporting of abdominal pain and altered bowel habits in the absence of any clinical alterations of the gut or other pathological conditions. Dietary regimens for symptom management include a low fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) diet and a gluten-free diet. However, scientific evidence supporting these dietary recommendations for managing IBS symptoms is weak: trials have been non-blinded and underpowered. While mechanistic understanding and objective markers of response remain scarce. Therefore, the aim of this thesis was to conduct a large double-blind study to investigate the effect of FODMAPs and gluten on symptomatic and molecular data including 16S rRNA analysis of the gut microbiota and metabolomics analyses, both at a group and subgroup (differential response) level. The resulting data served also to assess the accuracy of the Bristol Stool Form Scale (BSFS) used in IBS subtype diagnosis, and thus overcome the lack of objective evaluation of IBS symptoms. Trial data revealed that gluten caused no symptoms and FODMAPs triggered only modest symptoms of IBS, albeit with large inter-individual differences. Subjective reporting according to the BSFS conformed only modestly with stool water content in IBS, warranting caution towards IBS subtyping. FODMAPs increased saccharolytic microbial genera, phenolic-derived metabolites and 3indolepropionate, but decreased bile acids. The genera Agathobacter, Anaerostipes, Fusicatenibacter, and Bifidobacterium correlated with increased plasma concentrations of phenolic-derived metabolites and 3-indolepropionate, i.e, metabolites related to decreased risk of incident type 2 diabetes and inflammation. Indeed, among FODMAP-related metabolites, only weak correlations to IBS symptoms were detected, as in the case of 3-indolepropionate to abdominal pain and interference with quality of life, warranting further investigation. Gluten displayed a modest effect on metabolites involved in lipid metabolism, including carnitine derivates, an acyl-CoA derivate, a medium-chain fatty acid, and an unknown lipid, but with no interpretable link to health. No molecular markers of a differential response were found, despite a comprehensive exploration with multiple analytical approaches. This could be explained by the absence of baseline variables, such as other omics layers or psychological factors, that could have determined the difference. In summary, the results indicate that gluten does not cause IBS symptoms. Moreover, the minor effect of FODMAPs on IBS symptoms must be weighed against their potential beneficial health effects. While the complexity of IBS likely explains the absence of molecular evidence for differential responses, such data analytical approach has potential where clear benefits of dietary interventions exist. Finally, the use of BSFS should include training for self-assessment, as a tool for subtyping IBS.
Fast Convergence of Random Reshuffling under Interpolation and the Polyak-Łojasiewicz Condition

Modern machine learning models are often over-parameterized and as a result they can interpolate the training data. Under such a scenario, we study the convergence properties of a sampling-without-replacement variant of Stochastic Gradient Descent (SGD), known as Random Reshuffling ( RR ). Unlike SGD that samples data with replacement at every iteration, RR chooses a random permutation of data at the beginning of each epoch. For under-parameterized models, it has been recently shown that RR converges faster than SGD when the number of epochs is larger than the condition number ( κ ) of the problem under standard assumptions like strong convexity. However, previous works do not show that RR outperforms SGD under interpolation for strongly convex objectives. Here, we show that for the class of Polyak-Łojasiewicz (PL) functions that generalizes strong convexity, RR can outperform SGD as long as the number of samples ( n ) is less than the parameter ( ρ ) of a strong growth condition (SGC).
Hollow-Pass: Hide Your Pattern Password From Shoulder Surfers

This paper introduces Hollow-Pass, a new approach to combat shoulder surfing attacks on pattern passwords, a type of graphical password (GP) schema. Hollow-Pass does not necessitate external devices and renders the grid and pattern imperceptible to distant shoulder surfers. The usability and security of Hollow-Pass were evaluated through two types of user tests, online and onsite. Results showed that Hollow-Pass is successful in resisting shoulder surfing for simple patterns at various viewing angles (front, left-front, and right-front) and distances (1.0m, 1.5m, and 2.0m).
Multiobjective fault‐tolerant fixed‐order/PID control of multivariable discrete‐time linear systems with unmeasured disturbances

In this paper, a multiobjective fault‐tolerant fixed‐order output feedback controller design technique is proposed for multivariable discrete‐time linear systems with unmeasured disturbances. Initially, a multiobjective fixed‐order controller is designed for the system by transforming the problem of tuning the parameters of the controller into a static output feedback problem and solving a mixed H2/H∞ optimization problem with bilinear matrix inequalities. Subsequently, the fixed‐order controller is used to construct the closed‐loop system and an active fault‐tolerant control scheme is applied using the input/output data collected from the controlled system. Motivated by its popularity in industry, the proposed method is also used to tune the parameters of proportional‐integral‐derivative controllers as a special case of structured controllers with the fixed order. Two numerical simulations are provided to demonstrate the design procedure and the flexibility of the proposed technique.
PRINCIPLE OF SENTIMENT ANALYSIS USING CNN AND ONLINE SOCIAL NETWORK DATA, A TECHNOLOGICAL METHOD TO STRESS DETECTION

According to the research, stress is often a human's response to various dangers or wants. When working properly, this reaction may help us stay focused, motivated, and cognitively engaged, but if it becomes out of control, it can be hazardous and result in depression, anxiety, hypertension, and a variety of other life-threatening conditions. Cyberspace is a huge platform for individuals to communicate everything and everything they experience in their daily lives. Then, depending on the posts and status updates the person provides, it may be utilized as a very effective approach to determine that person's degree of stress. This is a suggestion for a website that accepts the subject's Twitter username as an input, scans and analyses the topic's profile using sentiment analysis, and then displays the findings. These findings show the subject's overall stress levels and provide an overview of its mental and emotional condition. Here in this project we are aiming the CNN deep learning architecture for the stress of evolution in online social networks such as Twitter.
Daily Surface Temperature Prediction using Artificial Neural Network : A Case study at Urban Area and Coastal Area of Jakarta, Indonesia

The weather forecast is significant to protect life and property. A forecast of temperature is important to the agriculture sector because when high temperatures can cause the soil to dry out faster and reduce the availability of water for plants. Furthermore, understanding the temperature condition can help meteorologists forecast the other atmosphere condition. The purpose of this research is to make a modeling prediction of temperature conditions the next day using an artificial neural network model (ANN). To make the ANN model, the daily average temperature measured in the meteorological stations in the urban and the coastal areas of Jakarta over 2010 – 2019 was used as training data. The testing data using surface temperature during January – December 2020. This model uses the various number of neurons
A highly stable spectrophotometric capability for the Origins Space Telescope (OST) mid-infrared imager, spectrometer, coronagraph (MISC)

This paper describes the transit spectrograph designed for the Origins Space Telescope mid-infrared imager, spectrometer, coronagraph (MISC) and its performance derived through analytical formulation and numerical simulation. The transit spectrograph is designed based on a densified pupil spectroscopy design that forms multiple independent spectra on the detector plane and minimizes the systematic noise in the optical system. This design can also block any thermal light incoming into pixels around the transit spectra. The gain fluctuations occurring in the detector and readout electronics are accurately corrected by use of a number of blanked-off pixels. We found that the transit spectrograph for the OST concept 1 with a diameter of 9.3m potentially achieves the photon-noise-limited performance and allows detection of biosignature gases through transmission spectroscopy of transiting planets orbiting late- and middle-M type stars at 10 pc with 60 transit observations.
Remarques sur les propriétés métriques des ensembles ordonnés

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A comparison of numerical integration schemes for particle filter-based estimation of gas flow dynamics

In this paper, we endeavor to find a fast and robust time integration solver for estimating gas flow transients within the framework of particle filtering. We examined both stiff and nonstiff solvers, namely embedded explicit Runge–Kutta (ERK) schemes based on Dormand–Prince, a second-order diagonally implicit RK (DIRK) scheme, a Krylov–Rosenbrock method using a multiple Arnoldi process and an implicit RK scheme with coefficients from Radau IIA quadrature formulas. Issues of accuracy, robustness and computation time, but also the stiffness of the problem were addressed. As well as comparing the performance of the time-stepping schemes for multiple model realizations in the particle filter, the analysis was also completed for a single noise-free model integration. Numerical experiments showed that the DIRK scheme is most robust whereas the ERK schemes were superior in terms of efficiency if the grid density and tolerance were properly selected. Under certain circumstances, nonphysical oscillations might appear in the solution domain. Stiffness measures and superior efficiency of the ERK schemes indicate that the problem can be considered as nonstiff.
The unique solution on the Neumann B.C. of diffusion equation with energy method

For this paper, we study the diffusion equation under a homogeneous Neumann boundary condition. We show that the energy function is a non-increasing function, and it can be used to show that our solution is at most one solution to the problem. The separation of variables is used to solve the partial differential equation. Mathematics Subject Classification: 34B05, 35J05, 35J25, 35L05
Mapping education 4.0 research trends: a bibliometrics analysis on African publications

Education systems have not been spared the disruptions occurring because of the Fourth Industrial Revolution (4IR). While there have been publications on Education 4.0 in Africa, there is a scarcity of research that attempt to unpack research trends, gaps, and patterns. A bibliometric analysis was conducted using data extracted from the Scopus database. Data were analysed using VOSviewer. African research outputs on Education 4.0 have increased linearly between 2015 and 2022. These research outputs are mostly published outside Africa. There are limited collaboration networks among African institutions and academics. African authors tend to cite more academics outside Africa than they cite fellow academics affiliated at African institutions. Research funding appears limited. Research areas that emerged most frequently included e-learning, machine learning, cloud computing and artificial intelligence. Unpacking research trends on Education 4.0 provides researchers and policy makers with critical information to map out future research and policies, respectively. © 2023 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Statistical Analysis of the Costs of OPEC Crude Oil

- In this study, Seasonal Naïve, ETS (Error, Trend, Seasonal), Neural Networks (NN), and ARIMA methods are applied for forecasting the Time series data and out of these NN is found to be the most efficient method. The present study employs measures, ME, RMSE, MAE, MASE and ACF1 to evaluate the performance of the model. These are used to compare the performance of the proposed technique and that of ARIMA and other methods. The study adopts the data of the costs of crude oil from the Organization of the Petroleum Exporting Countries (OPEC). Linear and non-Linear methods of forecasting the prices of crude oil among the OPEC are discussed, and their advantages and disadvantages are also illustrated for improving the performance and prediction. The revealing results of the proposed i.e. NN method outperforms the others in forecasting accurate results.
Hemophilia and patient quality of life assessment in Latin America

Hemophilia is a coagulation disease, generally hereditary, in which the patient's coagulation cascade is variably compromised, either by mutations in the proteins of this cascade, or by the presence of antibodies that aﬀect the coagulation process. Its severity depends on the altered protein, and in the most severe cases can trigger life-threatening hemorrhagic events. Three types of hemophilia have been described (A, B, C), and their classiﬁcation depends on the speciﬁc protein aﬀected or mutated in the patient. In this review of the literature in our region, we will be reviewing the pathophysiology of this disease, its eﬀect on the quality of life of patients, and the tools available for its assessment.
Swallowing difficulties in the different forms of food supply in congenital heart disease infants: a systematic review

Introduction: The safest way of feeding babies with heart disease can be a challenge for the mul-tidisciplinary team to choose. Objective: To identify the main swallowing difficulties in the different forms of feeding in infants with congenital heart disease. Methods: The guiding question was: “What are the main swallowing difficulties in the different forms of feeding in infants with congenital heart disea - se? ”. The population was defined as infants with heart disease, considering breastfeeding as exposure of interest and bottle feeding considered a comparison group. Swallowing difficulties were considered the outcome. Articles without language restriction were selected
From Semantic Web to Wisdom Web: A Retrospective on the Journey to 2043

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UNDERSTANDING THE CHAOTIC BEHAVIOR OF PARTICULATE MATTER CONCENTRATIONS USING NONLINEAR TECHNIQUES

We have made a comparative study about the nonlinear behaviour of PM2.5 hourly average concentrations, which were measured at some of the most polluted mid-sized cities located in the South of Chile. The chosen cities were Chillán, Coyhaique and Temuco where high PM2.5 concentrations concentrated in the winter season are caused by the intensive use of wood for heating. The city of Cochabamba, Bolivia, has also been included in this study, due to its very high level of atmospheric pollution by PM10 (especially in the winter season). This city is at a greater height compared to the Chilean cities. Using nonlinear tools, as Wavelet, Recurrence Plots, and Phase Portrait we have investigated the behaviour of PM2.5 and PM10 (hourly) concentrations. Wavelet spectrum and global amplitude for the more polluted cities in study was calculated. Spectral descomposition was performed in time-frecuency through Morlet’s wavelet transform and their global amplitud in time and energy, concentrated around the most importants peaks. On the other hand, a graphical tool that shows typical patterns of dynamic behaviour is the recurrence graph allowing extraction of qualitative characteristics from time series. This method was applied for all cities in study showing patterns that differ from a noisy or random signal. Also the technique of phase-portrait analysis was implemented, showing typical dynamical patterns of non-linear time series, different to a noisy signal pattern. Finally, it was found that hourly airborne particle concentrations exhibit a possible chaotic behaviour, related to short-term predictability some hours ahead.
EFFECT OF MASTITIS ON MILK COMPOSITION OF CROSS-BRED (HOLSTEIN FRIESIAN X CHOLISTANI) AND SAHIWAL CATTLE

The present study was designed to investigate the effect of mastitis on various components including fat percentage, protein, total solids, solid not fat, pH, specific gravity and lactose content in milk of cross-bred (Holstein Friesian × Cholistani) and Sahiwal cattle. A total of 376 milk samples (excluding blind and nonfunctional teats) from 100 cows (50 cross-bred and 50 sahiwal) were collected aseptically. Animals of different ages, parity and stages of lactation were selected under field condition. The diagnosis of subclinical mastitis was done on basis of Surf Field Mastitis Test. For bacterial examination of milk, the samples were grown in laboratory and various biochemical tests were performed. The results of the present study indicated that solid not fat (SNF) of mastitic sahiwal and cross-bred cattle and total solids percentage of normal and mastitic cattle were significantly reduced as the severity of mastitis increased in animals. The milk pH of normal and mastitic cattle including both sahiwal and cross-bred was significantly increased in severe cases of mastitis. The specific gravity of normal and mastitic milk samples showed that there was no significant difference in specific gravity as compared to normal. The protein percentage of normal and mastitic cattle including both sahiwal and cross-bred cattle indicated that protein concentration was significantly reduced in severe cases of mastitic animals. The findings of present study highlighted that mastitis cause major alterations in composition of milk leading to poor quality of the milk.
INTRODUCTION TO GENERATING FUNCTIONS AND THEIR APPLICATIONS

Generating functions are functional representations of sequences of numbers. This expository paper aims to provide a detailed account of the power of the generating functions. After a walk-through of the definition of and operations on generating functions, I will show applications of generating functions to four mathematical scenarios in multiple branches of mathematics including combinatorics and number theory. By the end of this paper, readers are expected to learn both the phenomenon and the explanation: not only will they understand how generating functions are used to tackle tricky problems, but also why they are suitable tools for those problems
An overview of material removal processes and its industrial application

This paper provided an overview of material removal processes and their industrial applications. Materials perform very important functions industrially owing to their mechanical properties and nature. The essence of material removal processes is to formulate the structural features required for a product to perform to the specification of its intended applications. Unfinished castings can be modified through the removal of materials to make them meet design requirements and also a group of shaping processes that involve removing unwanted surplus material from a starting workpiece to bring the remaining piece closer to the desired shape. Additionally, selective material removal can be used to transform generic castings into one-of-a-kind finished goods. Common castings can have unique hold patterns, flanges eliminated, wall thicknesses decreased, surfaces flattened, and other design characteristics added by removing unneeded material, resulting in distinctively diverse final products. In contrast, casting and molding operations are frequently substituted with material removal techniques. Materials removal may end up being the sole viable option when the issues related to cast form processing concerns become overwhelming (often based on cost-effectiveness).
Total and correlation energy of the uniform polarized electron gas at finite temperature: Direct path integral simulations

The uniform electron gas (UEG) at finite temperature has recently attracted substantial interest due to the experimental progress in the field of warm dense matter. To explain the experimental data accurate theoretical models for high density plasmas are needed which crucially depend on treatment of quantum effects in electron-electron interaction as well as in the interaction of electrons with uniform positive background. To comply with these requirements we have developed the new quantum path integral model of the UEG and present the results of related direct path integral Monte-Carlo (DPIMC) simulations. Contrary to the known in literature approaches treating the electron-background interaction classically our simulations take into account the quantum effects in this interaction. We have observed very good agreement with known in literature results only up to moderate densities when the ratio of the average interparticle distance to the Bohr radius is of order four (rs ≥ 4) and observe deviations for higher densities. At very high electron density (rs ≈ 1) presented in literature approaches as well as our simulations are problematic due to the strong degeneracy of electrons and increasing fermion sign problem.
ADOPTION STATUS OF PEARL MILLET PRODUCTS BY RURAL WOMEN

Pearl millets are the most important among millet species, accounting for approximately half of the total worldwide production of millets. It is mainly cultivated in India and Africa and is uniquely tolerant of hot and dry conditions. The present study on adoption status of pearl millets product by rural women was conducted in Hisar district on 200 women from four villages (Sundawas, Bichpari, Shikarpur and Shapur), adopted last four years under IAHS programme of College of Home Science. The overall adoption status was measured in stages of adoption (knowledge, persuasion, decision, implementation, confirmation). The high majority of the rural women had acquired the knowledge and interest in pearl millet productsMore than half (61.5%) respondents took the decision for adoption of pearl millet products and around 51.0% respondents adopted these products and only 44.0% finally adopted the pearl millet products. 7% respondents rejected this technology. The major constraints reported in adoption of pearl millet products were lack of material, time and taste not preferred by family members.