Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term energy drink consumption influences plasma glucose, apolipoprotein B, body mass index and pulse rate among students

OBJECTIVE: Energy drinks are becoming more popular every year, particularly among young adults such as college students, despite evidence that they have harmful health effects. The effect of energy drink consumption on plasma glucose, serum apolipoproteins, and triglyceride levels in students was investigated. METHODS: In order to test this, we chose two representative types of energy drinks in Nigeria, namely fearless and predator. These energy drinks are brand names of non-alcoholic beverages aimed to provide energy. 30 students, apparently healthy male human subjects aged 18 to 30 years who gave informed consent to the research work were randomly selected and divided into two groups: Group A (fearless energy drink consumers, n=15) and Group B (predator energy drink consumers, n=15). RESULTS: The results demonstrated significant reductions in pulse rate (86.00±41.32 vs. 78.87±27.72; p=0.03) and BMI (21.41±1.93 vs. 21.7±12.02; p=0.00) as compared to baseline values after one month of “fearless energy drink” consumption. Plasma glucose levels were significantly higher (97.53±10.62 vs. 88.80±11.33; p=0.01) and Apo B levels were significantly lower (21.41±1.93 vs. 21.71±2.02; p=0.00) following two weeks of fearless energy drink consumption than in baseline. In addition, BMI and Apo B levels were significantly lower after two weeks of predator energy drink consumption, but plasma glucose levels were significantly higher after two weeks and one month of predator energy drink consumption, respectively (p<0.05). SBP, DBP, TG and Apo A levels did not differ significantly in both fearless and predator energy drink consumers at baseline and after the study period respectively (p>0.05). CONCLUSION: This study has shown that the consumption of energy drinks causes significant alterations in BMI, pulse rate, plasma glucose and apolipoprotein B levels which may have important clinical consequences for energy drink consumers
Comparativa entre classification trees, random forest y gradient boosting; en la predicción de la satisfacción laboral en Ecuador.

Con el objetivo de encontrar un modelo adecuado para predecir el Nivel de Satisfaccion Laboral en Ecuador, se compararon tres modelos de prediccion basados en arboles. Los modelos “Random Forest” y “Gradient Boosting” se consideran mas complejos que el modelo “Classification Trees” y suponen mejores resultados; sin embargo, al aplicarlos sobre una base de datos obtenida a partir de la Encuesta Nacional de Empleo, Desempleo y Subempleo; se encontro que los criterios y eficiencia de prediccion son similares para los tres modelos, alcanzando aproximadamente un 30% de error en la clasificacion. Se concluyo que no necesariamente un modelo mas complejo obtiene resultados mas precisos.
Numerical solution for option pricing with stochastic volatility model

The option pricing equations derived from stochatic volatility models in finance are often cast in the form of nonlinear partial differential equations. To solve the equations, we used the upwind finite difference scheme for the spatial discretisation and a fully implicit time-stepping scheme. The result of this scheme is a matrix system in the form of an M-Matrix and we proof that the approximate solution converges to the viscosity solution to the equation by showing that the scheme is monotone, consistent and stable. Numerical experiments are implemented to show that the behavior and the order of convergence of upwind finite difference method.
The concept of information architecture in the context of enterprise architecture

PurposeInformation architecture (IA) is often understood as a comprehensive master plan for organizational data assets and is widely considered as an essential component of broader enterprise architecture (EA). However, the status and practical operationalization of IA still remain largely unclear. In order to clarify these questions, this paper investigates what instruments related to IA are actually employed in organizations.Design/methodology/approachThis study builds on the analysis of architecture practices in 27 diverse organizations. Based on the semi-structured interviews with architects and the examination of utilized architectural documents, we explore IA-related instruments with their usage scenarios that have been adopted in the studied organizations.FindingsThe authors identify 12 distinct instruments used in the industry and analyze in detail their features, properties and relationships. This paper analysis shows that these instruments are rather diverse and largely inconsistent across different organizations. The study findings also suggest that IA cannot be considered as a comprehensive plan for information, but rather as a variable set of loosely related instruments and practices that help organizations manage information.Originality/valueThe study offers a unique perspective on the concept of IA, as it is practiced in the industry today, as well as a critical scrutiny of the respective prescriptions abundant in the existing literature. Although this study does not attempt to theorize on the findings, it makes a significant empirical contribution by offering a solid evidence-based view of IA and its key instruments currently missing in the available literature.
FEATURE ENHANCEMENT OF IOT BASED SMART HEALTH MONITORING AND MANAGEMENT USING CLOUD COMPUTING

Smart and linked social insurance is one of the wide range of applications made possible by the Web of Things (IoT) that is particularly important. Organized sensors enable the social occasion of rich data demonstrating our physical and mental wellbeing, whether they are worn on the body or embedded in our living spaces. Such data can actualize a positive revolutionary shift in the medical services scene if it is constantly collected, totaled, and profitably mined. In particular, the availability of data at previously unimaginable scales and transient longitudes combined with a new era of astute handling calculations can: (an) encourage an improvement in the routine with regard to medication, from the current post facto analyze and treat responsive worldview to a proactive system for visualization of illnesses at an early stage, combined with aversion, fix, also, by and large administration of wellbeing rather than malady, (b) empower personalization of treatment and the board alternatives focused on especially to the explicit conditions and needs of the individual, and (c) help diminish the expense of medicinal services while at the same time enhancing results. In this paper, we feature the chances and difficulties for IoT in understanding this vision of things to come of medicinal services.
A New Algorithm for Robust Aﬃne-Invariant Clustering

Cluster analysis is an unsupervised machine learning technique commonly employed to partition a dataset into distinct categories referred to as clusters. The k -means algorithm is a prominent distance-based clustering method. Despite overwhelming popularity, the algorithm is neither invariant under non-singular linear transformations nor robust, i.e., can be unduly inﬂuenced by outliers. To address these deﬁciencies, we adopt an alternative clustering procedure based on minimizing a “trimmed” variant of the negative log-likelihood function and develop a novel “concentration step” (C-step) that can iteratively reduce the objective function. A simulation study over multiple synthetic scenarios and a real-world example are analyzed to assess the performance of our algorithm. Compared to k -means, empirical studies indicate competitiveness and oftentimes superiority of our algorithm.
First Test Results of the 150 mm Aperture IR Quadrupole Models for the High Luminosity LHC

The High Luminosity upgrade of the LHC at CERN will use large aperture (150 mm) quadrupole magnets to focus the beams at the interaction points. The high field in the coils requires Nb3Sn superconductor technology, which has been brought to maturity by the LHC Accelerator Research Program (LARP) over the last 10 years. The key design targets for the new IR quadrupoles were established in 2012, and fabrication of model magnets started in 2014. This paper discusses the results from the first single short coil test and from the first short quadrupole model test. Remaining challenges and plans to address them are also presented and discussed.
Internal control, marketization process and enterprise non-efficiency investment-analysis based on property rights heterogeneity

: Investigate whether internal control has an inhibiting influence on non-efficiency investment. Is the degree of marketization of firms and the form of property rights influencing this negative effect? This research examines the influence and disparities of internal control on the non-efficient investment of organizations with varying property rights and degrees of marketization using panel data from 2,133 Chinese listed A-share companies from 2015 to 2020. Based on existing research, this paper introduces the marketization process and the nature of property rights, which provides a new perspective for the study of the influence of internal control on the investment efficiency of enterprises. The study of this paper also hopes to provide reference for government regulatory authorities.
One bottleneck is not enough

Rate-distortion theory has been used to model the relationship between the capacity of a resource-limited agent and learning performance. However, most of these models define a single bottleneck on either the representational or policy complexity of the agent, but not both. Here we explicitly model representational capacity and policy capacity separately, and show that they make independent and non-interchangeable impacts on learning performance and efficiency of learned representations. This preliminary work has the potential to provide normative guidance about how to design more efficient RL agents, while also informing better descriptive models of human behavior by capturing different forms of cognitive constraints.
Who are the hyper prolific authors in environmental sciences?

Hyper prolific scientists are individuals who produce an exceptionally large number of scientific papers, often at a rate that is much higher than their peers. While productivity is generally a positive attribute in the scientific community, hyper prolific scientists may raise concerns about the quality and impact of their research. It is important to carefully evaluate the work of hyper prolific scientists to be mindful of the potential risks and negative consequences of producing an excessively large number of papers. Therefore this paper investigates hyper prolific authors in environmental sciences using bibliometric data from 2013-2021 to identify and characterize their patterns. The results reveal some hyper prolific authors that publish, on average, 50-100 papers per year. A network analysis further uncovers a close-connected network of scientists who gain an incredible number of highly cited papers. It is concluded that the scientific community has the need to establish ethical guidelines and practices on the number of publications to ensure that publishing a paper is conducted with integrity and that personal interests do not compromise the pursuit of knowledge.
Model Order Reduction for Parameterized Nonlinear Evolution Equations

Model order reduction (MOR) has emerged as an important tool in reducing the computational burden of large-scale systems, particularly in real-time or many-query contexts, e.g., optimization, control, and uncertainty quantification. In this thesis, we study projectionbased MOR methods for parameterized nonlinear evolution equations. The nonlinearity and parametrization pose many difficulties in MOR, which call for efficient and sharp error bounds and effective parameter sampling strategies for constructing simulation-efficient parametric reduced-order models (ROMs) at low cost. We propose two output error bounds to estimate the output error of the ROM in the vector space for parameterized evolution equations. One is a primal-only output error bound, and the other is a primal-dual output error bound. The former is based on the analysis of the residual of the original system, while the latter is derived by introducing and using a novel dual system. In particular, the primal-dual output error bound successfully avoids the accumulation of the residual over time, which is a common drawback in the existing error estimations for time-stepping schemes. Both error bounds are independent of the MOR methods and the spatial discretization approach employed, and they are applicable to a broad class of nonlinear and linear evolution equations. In addition to the error bound, we pursue efficient construction of ROMs. A technique that we call adaptive snapshot selection is proposed to collect the snapshots adaptively so that the ROM can be constructed more efficiently. This technique is applicable to snapshot-based MOR methods, e.g., the reduced basis method and the proper orthogonal decomposition method. For multi-stage systems, we propose to accelerate full-order model simulation by using (intermediate) ROMs as predictors during the basis construction process. Numerical experiments are carried out to show the performance of the proposed output error bounds and the acceleration techniques. Two academic examples are employed to test the derived error bounds, and applications to real-life models in chemical engineering are also explored. ROM-based optimization and/or uncertainty quantification of batch and simulated moving bed chromatography are successfully implemented, and the results show that the ROMs are very efficient in reducing the computational cost.
GERMINATION AND SECONDARY DORMANCY OF HYDROCHARIS DUBIA SEED

The germination of freshly harvested, dry-stored and wet-stored Hydrocharis dubia seeds were treated with exogenous GA 3 , cold stratification, puncture, different temperatures and combined treatments under controlled laboratory conditions. The results showed that: The optimal temperature for H. dubia seed germination is 23℃ and fresh seed has conditional dormancy, which can be released by puncture. Seed germination percentage decreased significantly after dry seed storage and the main reason for the reduction of "seed germination ability" by dry storage was that dry storage. caused secondary dormancy but a reduction of seed vigor. The secondary dormancy is non- deep physiological dormancy, which can be released by cold stratification, seed puncture and combination treatments. The findings provided further insight into how germination strategy contributes to the seed bank formation and species reproduction.
NEW MODELING OF GALACTIC PROTON MODULATION DURING THE MINIMUM OF SOLAR CYCLE 23/24

During the recent prolonged solar minimum of cycle 23/24, the PAMELA detector measured 27-day averaged Galactic proton energy spectra over the energy range that is important for solar modulation. By comparing these spectra to computed spectra from a three-dimensional model that contains all of the important heliospheric modulation processes, the recent minimum can be studied in detail from a modulation perspective. This was done by setting up a realistic heliosphere in the model, and reproducing a representative selection of seven intermittent PAMELA spectra, separated by approximately six months, from 2006 July to 2009 December. Additionally, a new very local interstellar proton spectrum was constructed using measurements below 600 ? > MeV from Voyager 1, taken beyond the heliopause, combined with PAMELA and AMS-02 measurements above 30 ? > GeV at the Earth. As a result of the extreme minimum modulation conditions that governed the recent solar minimum, the highest ever Galactic cosmic ray spectrum at Earth was observed by PAMELA at the end of 2009. It was found that, apart from the self-consistent changes in the heliospheric current sheet and the heliospheric magnetic field over time, additional increases in the mean free paths during this period were required below ∼ 4 ? > GV in order to reproduce the intensities observed by PAMELA.
Research on Keyframe Animation Generation Method of Elastic Body Based on Inverse Finite Element Optimization Analysis

This paper proposes a keyframe animation method of elasitc body based on inverse Finite Element Method (FEM) analysis, which constructs a multi-objective optimization process and makes a space-time optimization analysis on the keyframes in deformation space to reconstruct a user-specified deformation space of target model. By introducing signed distance field, we translate user-specified keyframe shapes into implicit surfaces, which makes our method suitable for various data format. We define an objective function measuring the distance from displacement field to implicit surface, and meanwhile introduce inverse FEM into iterative optimization process to obtain optimal control force field and model material parameter. And all experimental results show the validity of our method in accuracy and efficiency.
Benchmarking Travel Time and Demand Prediction Methods Using Large-scale Metro Smart Card Data

Urban mass transit systems generate large volumes of data via automated systems established for ticketing, signalling, and other operational processes. This study is motivated by the observation that despite the availability of sophisticated quantitative methods, most public transport operators are constrained in exploiting the information their datasets contain. This paper intends to address this gap in the context of real-time demand and travel time prediction with smart card data. We comparatively benchmark the predictive performance of four quantitative prediction methods: multivariate linear regression (MVLR) and semiparametric regression (SPR) widely used in the econometric literature, and random forest regression (RFR) and support vector machine regression (SVMR) from machine learning. We find that the SVMR and RFR methods are the most accurate in travel flow and travel time prediction, respectively. However, we also find that the SPR technique offers lower computation time at the expense of minor inefficiency in predictive power in comparison with the two machine learning methods.
Image thresholding using measures of fuzziness

In image analysis, image thresholding which is used for separating the object from the background is one of the most common application. For the preprocessing purposes of an image, thresholding is a necessary tool. Here, a new method based on minimizing measures of fuzziness is presented. Every pixel in the image and the averaged image have a membership value. Using these membership values entropy measures of fuzziness of image set and averaged image set are calculated. Then, threshold is founded by optimally minimizing calculated measures. The experimental results indicated that the new method has good performance but takes a long time in averaging.
Service and Energy Management in Fog Computing: A Taxonomy Approaches, and Future Directions

Background and Objectives: Today, the increased number of Internet-connected smart devices require powerful computer processing servers such as cloud and fog and necessitate fulfilling requests and services more than ever before. The geographical distance of IoT devices to fog and cloud servers have turned issues such as delay and energy consumption into major challenges. However, fog computing technology has emerged as a promising technology in this field. Methods: In this paper, service/energy management approaches are generally surveyed. Then, we explain our motivation for the systematic literature review procedure (SLR) and how to select the related works. Results: This paper introduces four domains of service management and energy management, including Architecture, Resource Management, Scheduling management, and Service Management. Scheduling management has been used in 38% of the papers. Therefore, they have the highest service management and energy management. Also, Resource Management is the second domain that has been able to attract about 26% of the papers in service management and energy management. Conclusion: About 81% of the fog computing papers simulated their approaches, and the others implemented their schemes using a testbed in the real environment. Furthermore, 30% of the papers presented an architecture or framework for their research, along with their research. In this systematic literature review, papers have been extracted from five valid databases, including IEEE Xplore, Wiley, Science Direct (Elsevier), Springer Link, and Taylor & Francis, from 2013 to 2022. We obtained 1596 papers related to the discussed subject. We filtered them and achieved 47 distinct studies. In the following, we analyze and discuss these studies; then we review the parameters of service quality in the papers, and ultimately, we present the benefits, drawbacks, and innovations of each study.
Instituting Modest Therapeutic Lifestyle Changes for Those at High Cardiometabolic Risk in Primary Care

I am Ralph La Forge, MSc, Physiologist and Clinical Lipid Specialist at Duke University Division of Endocrinology in Durham North Carolina and I am also a Consultant to IHS Division of Diabetes Treatment and Prevention in Albuquerque NM. This podcast is in two parts. The longer part, which is this first part, will be the justification and scientific support including recent clinical trial for instituting modest life style changes. And the second shorter part of this podcast will be examples of these modest life style changes.
Neutral delay differential equation Kerr cavity model

A neutral delay differential equation (NDDE) model of a Kerr cavity with external coherent injection is developed that can be considered as a generalization of the Ikeda map with second and higher order dispersion being taken into account. It is shown that this model has solutions in the form of dissipative solitons both in the limit, where the model can be reduced to the Lugiato-Lefever equation (LLE), and beyond this limit, where the soliton is eventually destroyed by the Cherenkov radiation. Unlike the standard LLE the NDDE model is able to describe the overlap of multiple resonances associated with different cavity modes.
Post-harvest Management for Sustainable Agriculture

Agriculture is a major source of livelihoods in developing countries in the Asia-Pacific region. On average, the sector accounts for 36 per cent of total employment but only 10 per cent of the GDP in these countries [1], indicating low productivity in the sector relative to other sectors of the economy. Low productivity and lack of infrastructure are constraints to agricultural production as well as trade. According to ESCAP estimates, up to 218 million people in the region could be pulled out of poverty by an improvement in agricultural productivity [2].