Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Resistant Starch (RS) Rich Sorghum Food Consumption on Lipids and Glucose Levels of Diabetic Subjects

Resistant starches are an important class, which gives benefits of fiber without affecting the sensory characteristics. Few studies reported the beneficial effect of resistant starch supplementation in reduction of lipid and glucose levels in diabetic subjects. In the present study we investigated the Resistant Starch (RS) rich millet food on lipid and glucose levels in diabetic subjects. Supplementation of 65 g of RS rich rawa (broken sorghum) for 90 days, significantly reduced Body Mass Index (BMI), Fasting Glucose (FG), TC (Total Cholesterol) and LDL-C (Low Density Lipo protein) (p,0.05) in diabetic subjects (n=15), while a non-significant reduction was found in HbA1c, eAG (estimated Average Glucose), TG, HDL-C and VLDL-C. The study indicated that regular consumption of RS rich foods might be beneficial for the diabetic population. Studies in larger population further strengthen the present understanding. Original Research Article Hymavathi et al.; JPRI, 32(30): 86-92, 2020; Article no.JPRI. 62480 87
MAJOR, TRACE AND RARE-EARTH ELEMENTS IN FOURTEEN GSJ REFERENCE SAMPLES. DETERMINATION BY X-RAY FLUORESCENCE SPECTROMETRY AND INDUCTIVELY COUPLED PLASMA OPTICAL EMISSION SPECTROMETRY

Major and trace elements have been determined by wavelength dispersive X-ray fluorescence and by inductively coupled plasma optical emission spectroscopy (ICP-OES), in the Geological Survey of Japan Igneous rock series, using a routine sample preparation technique including lithium borate fusion. Samples were analysed for 10 majors elements and the following trace elements: Ba, Co, Cr, Ga, Nb, Sc, Sr, V, Y, Zn and Zr. The rare-earth elements were determined only by ICP-OES following separation by ion exchange. In most cases, results compared well with published values.
Survey Based Study: Classification of Patients with Alzheimerâ€™s Disease

Neuroimaging is a description, whether in two-dimensions (2D) or threedimensions (3D), of the structure and functions of the brain. Neuroimaging provides a valuable diagnostic tool, in which a limited approach is used to create images of the focal sensory system by medicine professionals. For the clinical diagnosis of patients with Alzheimer's Disease (AD) or Mild Cognitive Impairs (MCI), the accurate identification of patients from normal control persons (NCs) is critical. Recently, numerous researches have been undertaken on the identification of AD based on neuroimaging data, including images with radiographs and algorithms for master learning. In the previous decade, these techniques were also used slowly to differentiate AD and MCI symptoms from structure classification methods. This review focuses on neuroimaging studies conducted to detect and classify AD, through a survey based on Google Scholar content. We explore the challenges of this field and evaluate the performance of these studies along with their negative aspects.
Can One Derive the Conformational Preference of a β-Peptide from Its CD Spectrum?

CD spectroscopy is often used to elucidate the secondary structure of peptides built from non-natural amino acids such as β-amino acids. The interpretation of such CD spectra is not always unambiguous. Here, we present a case where two β-hexapeptides, a dimethyl-β-hexapeptide indicated as DM-BHP (A) and its nonmethylated analogue indicated as BHP (B), exhibit similar CD spectra, whereas they are expected to differ in secondary structure. The structural properties of both peptides were studied by molecular dynamics simulation, and from the resulting trajectories, the corresponding CD spectra were calculated. Starting from a fully extended conformation, BHP is observed to form a 314-helix, while DM-BHP remains unfolded. However, even though these two peptides hardly share any conformations, their calculated CD spectra are alike and show the same features as the experimentally measured ones. Our results imply that a particular CD pattern can be induced by spatially different structures, which makes it diffic.
Nonlinear control with wave observer to maximize harvested power for point absorber wave energy converters

This paper designs control laws that maximize the harvested power for submerged point absorber wave energy converters (PAWECs) constrained to heave motion. First, a mathematical model of a submerged PAWEC driven by nonlinear hydrodynamic, friction, restoring, and harvested power forces is derived. Second, a new disturbance observer is designed that globally practically exponentially estimate the wave velocity and its time derivatives. Next, the optimal operating points are derived such that when the PAWEC operates at these points, the harvested power is maximized. Fourth, nonlinear control laws are designed to track the optimal operating points based on the proposed disturbance observer and Lyapunov's direct method. In addition to maximize the harvested power, these optimal control laws ensure that the harvested power is always nonnegative; i.e., no power is drawn from the network to the PAWEC at any time. For comparison, a control law that maximizes the hydrodynamic extraction power is also designed. Several simulations are carried out and illustrate that the maximizing harvested power control yields better results than others.
Seasonal Anomalies and Firms Financial Distress; Evidence from Nairobi Securities Exchange, Kenya

Stock market generally accepted as security exchange has immensely attracted so much interests from various stakeholders around the world as they endow exceedingly to the growth of the world economy. Kenya’s stock market, Nairobi Securities Exchange, being an emerging market, this study focused on seasonal anomalies as a type of market anomalies which is operationalized monthly. When there is seasonal anomalies, firms tend to exhibit unhealthy financial position which is financial distress. Financial distress takes a huge chunk of challenges which firms face in their day to day operations and is measured by Z-Score. This study takes a departure from the previous studies and assesses listed firms which are trading, under suspension or delisted from the stock markets while at the same time relating seasonal anomalies to financial distress which created a scholarly gap. This study adopts descriptive research design. It also embraced secondary data from 2007 to 2017 from a target population of 67 listed firms which had been licensed by the Capital Market Authority. It was found that there was indeed a relationship between seasonal anomalies and firms’ financial distress. The study recommends that the management must have well thought risk mitigants so as to avoid the harsh repercussions of the seasonal anomalies effects. Investors ought to engage finance experts on investment decisions so as to only invest in financially healthy firms. Policy makers and regulators should have the capability and capacity of unearthing the infamous practice of tax evasion or tax fraud by firms. They should also make sure that information dissemination is done in accordance and within the precincts of the law so as to benefit of all the stakeholders.
Geometric and magnetic properties of Co adatom decorated nitrogen-doped graphene

The potential for nitrogen doped graphene with dispersed Co metal atoms as a candidate material for future spintronic applications was studied using first-principles calculations. Among the three types of defective structures, the pyridinic and pyrrolic systems were demonstrated to be structurally stable since the binding energy between the Co adatom and these substrate layers exceeded the cohesive energy of Co metal alone. Also, Co adatom on these two structures showed high magnetic moment values, which surpassed that of the Co metal bulk. From these results, it is shown that the pyridinic and pyrrolic N defects on graphene have a positive effect on the geometric stability and magnetic property of decorated Co atoms.
Uncertainties and their interaction in flood risk assessment with
climate change

Abstract. The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local scale changes. Understanding and quantifying this cascade is essential to developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four Irish catchments, incorporating within our modelling chain uncertainties associated with 12 Global Climate Models contained in the Coupled Model Inter-comparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood risk in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding related to changes in annual maximum precipitation. We use a sensitivity test based on the analysis of variance (ANOVA) to decompose uncertainties and their interactions in estimating selected flood quantiles in the 2080s for each catchment. We find that the dominant sources of uncertainty vary between catchments, calling into question the ability to generalise about the importance of different components of the cascade of uncertainty in future flood risk. For two of our catchments, uncertainties associated with bias correction methods and extreme value distributions outweigh the uncertainty associated with the ensemble of climate models. For all catchments and flood quantiles examined, hydrological model parameter uncertainty is the least important component of our modelling chain, while the uncertainties derived from the interaction of components are substantial (>20 percent of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall runoff processes. Future work that seeks to further explore the dominant components of uncertainty as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be included in climate change impact assessments.
Estimation of coastal aquifer properties: A review of the tidal method based on theoretical solutions

Determination of the hydraulic properties of coastal aquifer systems has important implications that are related to the issues such as seawater intrusion, submarine groundwater discharge, migration of contaminants, assessment of water resources, and geotechnical engineering. Over recent decades, many analytical solutions that consider different types of coastal aquifer systems or models have been developed. These solutions can be used as a theoretical basis for the tidal method that characterizes the hydraulic properties of a coastal aquifer system using hydraulic response measurements in an observation well or wells induced by tidal waves in the ocean. The hydraulic properties of an aquifer can be estimated through fitting a series of time‐dependent changes of hydraulic head detected in an observation well to those calculated based on theoretical solutions. For simplicity, most theoretical solutions only consider one tidal component and idealized boundary conditions, although in reality multiple tidal components exist simultaneously and boundary conditions can be more complicated. For practical applications of the tidal method and to increase its reliability, multiple tidal components should be considered and models considering more complicated boundary conditions should be developed. In addition, methods that can determine both the hydraulic conductivity and storage coefficient, rather than only the hydraulic diffusivity, that is, the ratio of hydraulic conductivity to storage coefficient of coastal aquifer, should be developed. Cautions should be taken into account when using the tidal method because earth tides and changes in local atmospheric pressure may induce similar tidal fluctuations in the hydraulic head within inland observation wells.
An Integrated Model of Financial Literacy among B–School Graduates Using Fuzzy AHP and Factor Analysis

Financial literacy is a very relevant concept in today’s world, where the financial markets are growing each day. Therefore, the objective of this study is to identify the critical dimensions of financial literacy in the Indian context. In this cross–sectional research, the data are collected from 698 students who were studying in leading private and government B–schools in the Chandigarh Tri–City region. The exploratory factor analysis is used to extract the factors that affect the financial literacy of B–school graduates. The confirmatory factor analysis is used to validate these factors. Finally, fuzzy AHP is used to rank these factors. The results of this study identify the five critical financial literacy factors using exploratory factor analysis and confirmatory factor analysis. In addition to this, fuzzy AHP is used to rank the identified factors. Results show that the most important factor of financial literacy is financial or professional training followed by financial behavior, financial attitude, financial knowledge, and the least important factor is financial culture. Financial literacy is essential from the youth’s point of view. To our knowledge, no such study has been done that has tried to model financial literacy in the Indian context through exploratory factor analysis, confirmatory factor analysis, and fuzzy AHP. TOPIC: Wealth management Key Findings ▪ Financial literacy improves the financial decisions of an individual. The measurability of the construct improves when the construct is analyzed across different dimensions. ▪ The study makes use of exploratory factor analysis, confirmatory factor analysis, and fuzzy AHP to identify, validate, and prioritize critical dimensions of financial literacy. ▪ Financial training is the most important factor of financial literacy, followed by financial behavior, financial attitude, and financial culture.
Comparative Total Syntheses of Turkey Ovomucoid Third Domain by Both Stepwise Solid Phase Peptide Synthesis and Native Chemical Ligation

Turkey ovomucoid third domain, OMTKY3, is a potent protein inhibitor of most serine proteinases that prefer a neutral residue at the P1 position. It has been a target of increasingly intense scrutiny by site-directed mutagenesis in an attempt to elucidate the sequence-to-reactivity algorithm of the inhibitor. Here we report the total chemical synthesis of (6−56)OMTKY3 using both the stepwise solid phase peptide synthesis (SPPS) technique and the native chemical ligation approach. After refolding and affinity purification, the resultant products were characterized by electrospray ionization mass spectrometry, analytical ion exchange chromatography, measurement of association equilibrium constants with six different serine proteinases, and thermal denaturation studies. The two synthetic proteins were found to be functionally as well as structurally identical to their recombinant counterpart. Moreover, the native chemical ligation and stepwise SPPS techniques gave rise to comparable yields and similar produc.
Analysis of surface roughness during turning of AISI 52100 hardened alloy steel using minimal cutting fluid application

ABSTRACT Surface roughness is an important parameter for machined components. It affects their wear resistance and fatigue life. High-velocity pulsing jet minimal cutting fluid application (MCFA) is one of the emerging technique that helps to minimise the use of cutting fluid and reduce the adverse effects on human health and the environment. In this paper, an attempt was made to study the effects of cutting speed, feed rate and depth of cut on the surface roughness (Ra) in turning of AISI 52100 alloy steel of hardness 58 HRC with multilayer (TiN/TiCN/Al2O3) coated carbide tool insert under MCFA environment. The response surface methodology (RSM) based quadratic regression model for the surface roughness (Ra) was developed with 95% confidence level. The adequacy and validity of the developed model were checked based on P-value, F-value and R2 -value. The analysis of variance (ANOVA) was used to determine the relative significance of cutting parameters and their interactions on the surface roughness (Ra). The result showed that the feed rate had significant effect in reducing the surface roughness followed by cutting speed, whereas depth of cut did not show any significant effect. Experimental data were optimised using the desirability function approach. A good agreement was observed between the predicted output and the experimental result.
Adsorptive Removal of Strontium from Water by using Chemically Modified Orange Juice Residue

Adsorbent for strontium (Sr2+) ion removal was prepared from orange waste generated in an orange juice factory. The orange juice residue (OJR) was treated with lime water to prepare saponified orange juice residue (SOJR). The energy dispersive X-ray (EDX) spectroscopic analysis revealed that substitution of Ca2+ ions in SOJR by Sr2+ ions takes place during the adsorption process. The adsorption of Sr2+ ions onto SOJR was fast and strongly pH dependent. Maximum adsorption occurred at pH higher than 5. The adsorption isotherm study showed maximum adsorption capacity of SOJR for Sr2+ ion was 833.4 mmol/kg. Equimolar amount of Ca2+ was found to be released from SOJR during Sr2+ adsorption also indicating that adsorption of Sr2+ on SOJR takes place according to the Ca2+ substitution reaction by cation exchange mechanism. Thus, the adsorption gel prepared in this study can be a promising adsorbent to remove Sr2+ from aqueous solution.
Social Workers during COVID-19: Do Coping Strategies Differentially Mediate the Relationship between Job Demand and Psychological Distress?

Abstract The consequences of the coronavirus disease of 2019 (COVID-19) pandemic have raised many challenges in the social services workforce. The current study aimed to examine the associations between job demands, coping strategies (i.e. emotion-focused coping and problem-focused coping) and psychological distress exhibited by social workers during the COVID-19 pandemic. In addition, the mediating role of different coping strategies was investigated for the associations revealed. The participants were 615 social workers, working in various organisations and with diverse populations in Israel. Each completed a validated self-report questionnaire. Results revealed that job demands were significantly associated with higher psychological distress, and that coping strategies, especially emotion-focused coping was associated with higher psychological distress and with job demands. In addition, emotion-focused coping strategies were found to mediate the association between job demands and psychological distress, especially ventilation of emotion beyond other strategies, which relate to emotion-focused coping. These findings are discussed with relation to a stress and coping theory, suggesting that in social work practice there is a need for intervention measures to improve social workers’ coping strategies during crises such as the COVID-19 pandemic.
Turbidity of deuterated isobutyric acid and heavy water in the one‐phase region near the critical solution point

We have measured the total light scattered from a mixture of deuterated isobutyric acid and heavy water (deuterium oxide, D2O ) near its upper critical solution point. The resulting turbidity was measured in a reduced temperature region 10−6<t<10−2, where t=(T−Tc)/Tc and Tc is the critical solution temperature, in a sample close to the critical composition. This completely deuterated mixture near its critical point exhibits similar properties to the undeuterated mixture and the turbidity can be explained using an Ising model. When the critical exponents ν and γ were fixed at the values predicted from renormalization‐group theory, the amplitude ξ0 of the correlation length was determined to be 0.343±0.024 nm, while the amplitude of the turbidity τ0 was (3.51±0.03)×10−6 cm−1, which seem consistent with two‐scale‐factor universality predictions.
An Effort Worth Making: A Qualitative Study of How Swedes Respond to Antibiotic Resistance

Abstract Due to the alarming rise of antibiotic resistance, medically unwarranted use of antibiotics has assumed new moral significance. In this paper, a thematic content analysis of focus group discussions was conducted to explore lay people’s views on the moral challenges posed by antibiotic resistance. The most important finding is that lay people are morally sensitive to the problems entailed by antibiotic resistance. Participants saw the decreasing availability of effective antibiotics as a problem of justice. This involves individual as well as collective moral responsibility. Yet, holding agents responsible for their use of antibiotics involves varying degrees of demandingness. In our discussion, these findings are related to the contemporary ethical debate on antibiotic resistance and two proposals for the preservation of antibiotic effectiveness are compared to and evaluated against participants’ views.
A Cometabolic Biotransformation Model for Halogenated Aliphatic Compounds Exhibiting Product Toxicity

A model is proposed to describe the rate and extent of cometabolic transformation of halogenated aliphatic compounds by resting microbial cells. The finite transformation capacity (T c ) of resting cells, which appears to be associated with cometabolic oxidation of many halogenated aliphatic compounds, is used to incorporate the effects of product toxicity and reductant supply into a modified expression of Monod kinetics
Distance Learners’ Attributes in Optimizing Learning Achievement Using Learning Analytics

The distance learning operations and activities from Learning Management System (LMS) and various information system provide huge educational data. Data mining can be used as a tool to extract the huge educational raw data and turn them into useful information. Educational Data Mining (EDM) is the utilization of data mining systems on educational data, to analyse insight information and to determine educational strategic direction. This paper aims (1) to propose distance learners’ attributes in optimizing learning achievement using learning analytics and, (2) to identify the application areas of the attributes for Educational Data Mining and Learning Analytics. Data pre-processing is the first step in any data mining process and, Waikato Environment for Knowledge Analysis (WEKA) is one of the machine learning algorithms for educational data mining undertakings. It allows the transformation of raw educational data into a suitable format, ready to be used by a data mining algorithm for performing a specific educational data set. Therefore, a precise description of distance learners’ attributes views was developed and gathered. A framework was designed to demonstrate the proposal of distance learners’ attributes from distance learners’ profile, distance learners’ learning activities and distance learners’ learning behaviour; and it will shape the improvement of Distance Learners’ Learning Achievement. Hence, the specified educational dataset in the proposal can be adapted and visualize using any learning analytics tools for strategizing distance learning new strategies.