Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Intermittent Fasting and Exercise on Body Composition, Antioxidant Capacity, and Blood Parameters in Obese Rats

: This study aimed to examine the effects of exercise, intermittent fasting, and combining two methods on body weight, adipose tissue, lipids, and glucoregulator and antioxidant parameters. 90 day-old male rats were randomly divided into 5 groups of 6 rats each. The groups consist of rats with normal body weight-sedentary (C), obese-sedentary (O), obese-intermittent fasting only (IF), obese-exercise (swimming) only (E), and obese-intermittent fasting together with exercise (IFE). For 8 weeks (1. phase), 1 group was fed with standard pellets (2.7 cal/g). The other 4 groups were fed with a high-fat diet (4.496 cal/g) to develop obesity. Then for 8 weeks (2. phase), all rats feeding standard pellets were exercised and/or done intermittent fasting. There was no significant difference in body weight at the beginning of the study. The change in body weight was significantly higher in C than IFE, and E. Fasting blood glucose was higher in IF and IFE. Oxidative stress index (OSI) was lower in IF and IFE. Also, paraoxonase 1 (PON1) decreased in IFE. The fat cells' diameters of IF or E were lower than O but higher than C. The fat cell diameters of IFE were smaller than C. In conclusion, a combination of intermittent fasting and exercise in obese individuals caused reduced body weight, fat cell size, and oxidative stress. Besides, it caused increased fasting blood glucose, different effects on blood lipids, and decreased PON1. Thus intermittent fasting can be suggested by considering its positive and negative aspects and keeping it under follow-up.
DEEP LEARNING-BASED METHOD FOR PREDICTION OF SOFTWARE ENGINEERING PROJECT TEAMWORK ASSESSMENT IN HIGHER EDUCATION

One of the very recent applications of machine learning and deep learning is to predict student’s performance for a specific task. Particularly in software engineering projects in higher educational institutions, one of the major challenges is how to improve the overall software team performance with the ultimate goal of increasing software team productivity. This problem can be termed as Software Engineering Teamwork Assessment and Prediction (SETAP). To address this, we proposed a SETAP method that uses an effective deep-learning-based method for assessment and early prediction of student learning effectiveness in software engineering teamwork. Further, we identified critical features by using feature importance, which affects the performance of the student's team. For the experimental purpose, we used the freely available dataset from the UCI data repository for student learning effectiveness in software engineering teamwork. In the proposed work, we used Feed-forward deep neural network classifier for this purpose. Deep neural networks can produce high accuracy with complex data very quickly. This made them a perfect choice for our work. To compare and evaluate the performance of the proposed prediction method with others (RF, GLM, GBM), we used accuracy percentage, sensitivity, and specificity as performance measuring benchmarks. Also, we evaluated the method on our university datasets. Results proved that the proposed method performed better than the above-mentioned state-of-the-art classifiers.
Quantifying a relationship between place-based learning and environmental quality

The goal of this study was to investigate the degree to which school-based and nonformal education programs that focus on air quality (AQ) achieved measurable AQ improvements, and whether specific instructional methods were associated with those improvements. We completed a standardized telephone interview with representatives of 54 AQ education programs. Quantitative analysis of these interviews generated three key findings: (1) nearly half (46%) of the programs we studied reported evidence that AQ had actually improved over the course of their projects; (2) most (89%) of the programs we studied took some sort of action to improve AQ; and (3) programs reporting more place-based learning qualities and practices such as service-learning and community partnerships were more likely to report improvements in AQ (r = .40, p < .01).
Adoption of Digital Technologies and Decision Support Systems Adoption of Digital Technologies and Decision Support Systems in Horticulture Supply Chains in Horticulture Supply Chains

This paper presents key factors affecting the adoption of digital technologies and decision support technologies in horticulture supply chains. Using the case study method, in-depth interviews were conducted with decision-makers of three distinctive horticulture supply chains, complemented by site observations. The Technology Acceptance Model (TAM) is the theoretical foundation guiding this qualitative study. The findings indicate the constructs perceived ease of use, usefulness, attitudes, behavioural intentions, trust, social influence, and costs affecting adoption. The study contributes to the literature by presenting an integrated TAM model that can be used to understand adoption in horticulture supply chains. It strengthens TAM by providing evidence of its applicability to real-life industry settings and further creates a link to the unique adoption problems in industry. The practical contribution to ICT developers, industry peak bodies, and governments is that the inter-firms’ interactions need to be considered when designing decision-support technologies and implementing improvement programs.
MACHINE LEARNING PIPELINE APPROACH TO SECURE IOT-BASED SMART CITIES

The data stored and transmitted in IoT-based smart cities is often huge and highly sensitive. One of the major threats to data in IoT-based smart cities is network intrusions and attacks. Various algorithms have been proposed to mitigate these attacks with high accuracy. However, most of them have major flaws, such as high detection times and the limitation of only being able to perform binary classification of network traffic. In this paper, we propose a new Machine Learning (ML)-based approach for intrusion detection in IoT-based smart cities. The proposed approach performs the detection in a pipeline of three phases: (1) preprocessing of incoming network traffic; (2) binary classification of traffic data into normal or attack; and (3) perform a final multi class classification phase. The experiments were performed on the TON-IoT public dataset, and after a comparison study of several ML algorithms, we settled to use Decision Tree (DT) for the binary classification phase and Random Forest (RF) for multi class classification. In addition to high accuracy, our approach achieves better computation time, and more fine-grained detection of attacks, which are highly important for intrusion detection systems. This aspect should not be overlooked in the context of IOT devices, which usually have very limited computational resources.
Patients with Intracerebral Hemorrhage Early Rate of Contrast Extravasation in

BACKGROUND AND PURPOSE: For patients with ICH, knowing the rate of CT contrast extravasation may provide insight into the pathophysiology of hematoma expansion. This study assessed whether the PCT-derived PS can measure different rates of CT contrast extravasation for admission CTA spot signs, PCCT, PCL, and regions without extravasation in patients with ICH. MATERIALS AND METHODS: CT was performed at admission and at 24 hours for 16 patients with ICH with/without contrast extravasation seen on CTA and PCCT. PCT-PS was measured at admission. The Wilcoxon rank sum test with a Bonferroni correction was used to compare PS values from the following regions of interest: 1) spot sign lesions only (9 foci), 2) PCL lesions only (9 foci), 3) hematoma excluding extravasation, 4) regions contralateral to extravasation, 5) hematoma in patients without extravasation, and 6) an area contralateral to that in 5. Additionally, hematoma expansion was determined at 24 hours defined by NCCT. RESULTS: PS was
The effects of low protein during gestation on mouse pancreatic The effects of low protein during gestation on mouse pancreatic development and beta cell regeneration development and beta cell regeneration

: Beta cells are partially replaced in neonatal rodents after deletion with streptozotocin (STZ). Exposure of pregnant rats to a low protein (LP) diet impairs endocrine pancreas development in the offspring, leading to glucose intolerance in adulthood. Our ob-jective was to determine whether protein restriction has a similar effect on the offspring in mice, and if this alters the capacity for beta cell regeneration after STZ. Pregnant Balb/c mice were fed a control (C) (20% protein) or an isocaloric LP (8% protein) diet during gestation. Pups were given 35 mg/kg STZ (or vehicle) from d 1 to 5 for each dietary treatment. Histologic analysis showed that C-fed offspring had largely replaced beta cell mass (BCM) after STZ by d 30, but this was not sustained over time. Female LP-fed offspring showed an initial increase in BCM by d 14 but developed glucose intolerance by d 130. In contrast, male LP offspring showed no changes in BCM or glucose tolerance. However, LP exposure limited the capacity for recovery of BCM in both genders after STZ treat-ment. ( Pediatr Res 68: 16–22, 2010)
Long-term east-west asymmetry in monsoon rainfall on the Tibetan Plateau: COMMENT

[Hudson and Quade (2013)][1] suggest that an early Holocene increase in the size of lakes on the Tibetan Plateau ([Fig. 1A][2]) was caused by increased precipitation. They used the area within high paleoshorelines to reconstruct paleorainfall patterns, and found that paleolake area increased
Integrative Study of Hypothalamus–pituitary–thyroid–immune System Interaction: Thyroid Hormone-mediated Modulation of Lymphocyte Activity through the Protein Kinase C Signaling Pathway

Thyroid hormones play critical roles in differentiation, growth and metabolism, but their participation in immune system regulation has not been completely elucidated. Modulation of in vivo thyroid status was used to carry out an integrative analysis of the role of the hypothalamus–pituitary–thyroid (HPT) axis in T and B lymphocyte activity. The participation of the protein kinase C (PKC) signaling pathway and the release of some cytokines upon antigenic stimulation were analyzed. Lymphocytes from hyperthyroid mice displayed higher T-and B-cell mitogen-induced proliferation, and those from hypothyroid mice displayed lower T-and B-cell mitogen-induced proliferation, compared with euthyroid animals. Reversion of hypothyroid state by triiodothyronine (T3) administration recovered the proliferative responses. No differences were found in lymphoid subset balance. Both total PKC content and mitogen-induced PKC transloca-tion were higher in T and B cells from hyperthyroid mice, and lower in cells from hypothyroid mice, compared with controls. Levels of thyroid-stimulating (TSH) and TSH-releasing (TRH) hormones were not directly related to lymphocyte proliferative responses. After immunization with sheep red blood cells (SRBCs) and re-stimulation, in vitro spleen cells from hyper-or hypothyroid mice showed, respectively, increased or decreased production of interleukin (IL)-2 and interferon (IFN)-cytokines. Additionally, an increase in IL-6 and IFN-levels was found in hyperthyroid cells after in vivo injection and in vitro re-stimulation with lipopolysaccharide (LPS). Our results show for the first time a thyroid hormone-mediated regulation of PKC content and of cytokine production in lymphocytes; this regulation could be involved in the altered responsiveness to mitogen-induced proliferation of T and B cells. The results also confirm the important role that these hormones play in regulating lymphocyte reactivity.
Impact of covid-19 pandemic on the global economy and future prospects: A systematic review of global reports

Propose: This study aims to investigate the economic practices and fluctuations due to the outbreak of the Covid-19 Pandemic in 2020 and future impacts in international economies Method: This study is based on the variable "Low Probability - High Impact" that is studied by methods commonly used in future studies, namely: heuristic approach, deterministic approach, probabilistic approach In addition, systematic and deductive reasoning method by analyzing the reports received by some countries, institutions, international organizations and research centers interested in this regard Results: Reports showed that the economic crisis caused by the Covid-19 pandemic have major impacts on the global economy and significant changes will occur in the long run Where the epidemic differs economically because it continues to hit hard the major economic countries and the countries most affected include the G7, in addition to China These countries represent 60% of global supply and demand (GDP), 65% of global manufacturing, and 41% of global industrial exports Future implications: The COVID-19 will continue to spread even after the emergence of treatment, because the treatment goes through many stages, including discovery and testing, production and distribution, and this means that the virus will keep pace with the global economy until 2021 and perhaps more The world will try to move to repair the economic damage caused by the COVID-19 Economic reforms must be based on the current structures of globalization in order to succeed Developing countries must pay attention to the need to build regional blocks based on sound foundations because they will be a protective shield for them in the long term for the effects of all variables based on (low probability - high impact) There will be a major change in international relations, and this will appear in new organizational forms, services and economic, the social role will be the solid force of these alliances Originality/Value: This paper helps the world prepare to accompany the COVID-19 virus with a minimum for the next year 2021 Its effects will remain for many years, especially in developing countries that the Vaccine will be delayed receive Therefore, strict health and economic precautions must be taken to mitigate the effects of the virus on its citizens and economy © 2021 Little Lion Scientific All rights reserved
L. gasification in a bubbling fluidized bed: The effect of magnesite and olivine on product gas, tar and gasification

Gasiﬁcation of Cynara cardunculus L. was performed in a bubbling ﬂuidized bed (BFB) using air as gasifying agent and, magnesite and olivine as diﬀerent bed materials. Temperature was varied during the experiments (700-800 ° C) with ﬁxed biomass feeding and air ﬂow rate. The eﬀect of using the magnesite and olivine on gas and tar composition, carbon and biomass conversion, and cold gas eﬃciency was investigated. The product gas showed high hydrogen content (13-16 %v/v) for both magnesite and olivine in the studied temperature range. Higher heating value and gas yield were improved with increasing the temperature from 700 to 800 ° C. Biomass and carbon conversion were greater than 75 %, obtaining values higher than 90 % for both 700 and 800 ° C in magnesite and for 800 ° C in olivine. Small differences in total tar were observed between materials, although tar composition was very different. BTEX were higher for olivine and similar PAHs was obtained for both magnesite and olivine. Ahigher catalytic activity at 800 ° C was observed for magnesite. Gasiﬁcation performance was better with magnesite at 700 ° C while olivine showed better properties at 800 ° C.
The global geochemistry of bomb‐produced tritium: General circulation model compared to available observations and traditional interpretations

Observational evidence suggests that of the tritium produced during nuclear bomb tests that has already reached the ocean, more than twice as much arrived through vapor impact as through precipitation. In the present study, the Goddard Institute for Space Studies 8 x 10 deg atmospheric general circulation model is used to simulate tritium transport from the upper atmosphere to the ocean. The simulation indicates that tritium delivery to the ocean via vapor impact is about equal to that via precipitation. The model result is relatively insensitive to several imposed changes in tritium source location, in model parameterizations, and in model resolution. Possible reasons for the discrepancy are explored.
FPGA-Based Cosimulation of S-Box Constitution from Fractional Order Liu System

Substitution box(S-box) has an important duty in encryption. In recent years plenty of S-box designs by chaotic systems have been studied. In this paper a high speed S-box constitution from fractional order Liu system on FPGA is proposed. The design was implemented by using Xilinx System Generator(XSG) toolbox in MATLAB/Simulink software in single precision �oating point numbers format and a low cost Basys 3 FPGA trainer card was used. In addition, numerical fractional order equations was solved by using Grünwald-Letnikov(GL) method with 1024 elements length. The S-box table was constructed about 0.137s. After all performance analyzes as nonlinearity, strict avalanche criterion, differential probability, bits independent criterion, and equiprobable input/output XOR distribution, were performed. The performance analyzes shown that the design was satisfactory.
Geological Society of America Bulletin

The depositional age and stratigraphic correlations of metamorphosed and variably deformed rocks of Mount Everest are poorly known because of limited recovery of diagnostic fossils. Detailed study of Cambrian and Ordovician strata from along the length of the Himalaya has produced a coherent stratigraphy that stretches from northern India to Tibet. Our work also demonstrates that the North Col Formation rocks (= Everest series), between the Qomolangma and Lhotse detachments of the South Tibetan detachment system, still locally preserve sedimentary textures and primary stratigraphy that match those within Cambrian strata ~1100 km to the west in northern India. This demonstrates a coherency of depositional systems and stratigraphic architecture for Cambrian deposits along much of the Himalaya Tethyan margin. It also allows, for the fi rst time, identifi cation of precise depositional ages of several units in the Everest region, in particular, the Yellow Band carbonate and directly underlying siliciclastic strata, which are both shown to be late Middle Cambrian in age. Detrital zircon data presented herein for a sample from these siliciclastic strata contain a similar age spectrum to those from Middle Cambrian strata in northern India, as well as grains as young as ca. 526 Ma, both of which support the depositional age and continuity of depositional systems along the length of the Himalaya. Highly fractured rocks of the Ordovician lower Chiatsun Group in the hanging wall of the South Tibetan detachment system in Nyalam, 75 km to the west of Everest, correlate with Ordovician strata of the Mount Qomolangma Formation on Mount Everest. Our correlations indicate that the base of the summit pyramid of Everest, the foot of the “Third Step,” is composed of a 60-m-thick, white-weathering thrombolite bed. The top of this ancient microbial deposit crops out only 70 m below the summit of Mount Everest.
Crystal chemistry of natural layered double hydroxides. 1. Quintinite-2H-3c from the Kovdor alkaline massif, Kola peninsula, Russia

Abstract The crystal structure of quintinite-2H-3c, [Mg4Al2(OH)12](CO3)(H2O)3, from the Kovdor alkaline massif, Kola peninsula, Russia, was solved by direct methods and refined to an agreement index (R1) of 0.055 for 484 unique reflections with |Fo| ≥ 4σF. The mineral is rhombohedral, R32, a = 5.2745(7), c = 45.36(1) Å. The diffraction pattern of the crystal has strong and sharp Bragg reflections having h-k = 3n and l = 3n and lines of weak superstructure reflections extended parallel to c* and centred at h-k ≠ 3n. The structure contains six layers within the unit cell with the layer stacking sequence of . AC=CA=AC=CA=AC=CA. The Mg and Al atoms are ordered in metal hydroxide layers to form a honeycomb superstructure. The full superstructure is formed by the combination of two-layer stacking sequence and Mg-Al ordering. This is the first time that a long-range superstructure in carbonate-bearing layered double hydroxide (LDH) has been observed. Taking into account Mg-Al ordering, the unique layer sequence can be written as . =Ab1C=Cb1A=Ab2C=Cb2A=Ab3C=Cb3A=. The use of an additional suffix is proposed in order to distinguish between LDH polytypes having the same general stacking sequence but with different c parameters compared with the ‘standard’ polytype. According to this notation, the quintinite studied here can be described as quintinite-2H-3c or quintinite-2H-3c[6R], indicating the real symmetry.
Molecular analysis reveals a distinct subgenogroup of porcine epidemic diarrhea virus (PEDV) in Northern Vietnam in 2018–2019

Porcine epidemic diarrhea (PED) is known as a serious disease infecting all ages of pigs, with a mortality rate of up to 100% in piglets. The spike protein (S) of porcine epidemic diarrhea virus (PEDV), in particular, the S1-CTD (C-terminal domain), which contains the conservative neutralizing epitopes CO 26K equivalent (COE) (aa 499–638), exhibits a high degree of genetic and antigenic diversity. We obtained 26 Vietnamese PEDV-S1-CTD sequences (630 bp) from samples collected from seven northern provinces from June 2018 to January 2019 and analyzed them with 35 sequences from previous studies, representing the G1a and 1b, G2a and 2b, and recombinant (G1c) genotypes, and PEDV vaccines. The majority (73.1%) of strains (19/26) belonged to the subgroup G2b. With strong nodal support (98%), seven strains were clustered into an independent, “distinct subgenogroup” (named dis-subG), separate from both G1a and G1b and other G2a, 2b, and 1c (recombinant strains). When compared with the prototype CV777 strain (AF353511), sequence analysis revealed distinct changes of 513 T>S , 520 G>D , 527 V>(I/L/M) , 591 L>F , 669 A>(S/P) , 691 V>I in the COE and S1 D regions of the S1-CTD protein that were only identi�ed in these Vietnamese strains. This cluster is a new antigenic variant subgroup and further studies are required to investigate its anigenicity. The results in this study revealed the continuous evolution in the S1 region of the Vietnamese PEDV strains, which emphasizes the need for frequent updates of the vaccines for effective protection.
Stable Surface Modification by Cold Atmospheric-Pressure Plasma: Comparative Study on Cellulose-Based and Synthetic Polymers

This study’s aim is a comparison of the plasma-induced effects on polymers exposed in helium and argon gaseous environments in a pulsed dielectric barrier discharge at atmospheric pressure. Cellulose-based and synthetic polymers are tested with regard to a range of parameters, such as wettability, adhesion, surface energy and polarity, the oxygen amount in their structure, and surface morphology. The surface properties are analyzed by contact angle measurements, X-ray photoelectron spectroscopy, and scanning electron microscopy images. The results point to the efficient and remarkably stable modifications of the plasma-exposed surfaces, such as their enhanced adhesion, surface energy, and oxygen incorporation. Additionally, plasma provides significant oxygen uptake in cellulose-based materials that bear already prior to treatment a high amount of oxygen in their structure. The comparison between the properties of the non-permeable, homogeneous, smooth-surface synthetic polymer and those of the loosely packed, porous, heterogeneous cellulose-based polymers points to the different rates of plasma-induced modification, whereby a progressive alteration of cellulosic surface properties over much larger ranges of exposure durations is noted. Present experimental conditions ensure mild treatments on such sensitive material, such as paper, and this is without alterations of the surface morphology and the physical degradation of the material over a large range of treatment duration.