Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Mechanism For Enhanced Performance Of Shark Smell Optimization

— Various meta-heuristic approaches have been developed to find the optimal solution to optimization problems. However, different approach takes different amount of time and efficiency to achieve the optimal solution. Determination of high performance and lower computing time with simple algorithm is therefore continuously established. Shark Smell Optimization (SSO) algorithm has been proven to have high efficiency in many optimization applications. However, like the other swarm intelligence, SSO algorithm also has possibility to get trapped in local maxima or premature convergence. Thus, a new adaptive shark smell optimization (ASSO) is proposed to improve the convergence efficiency of standard SSO algorithm. An overview and performance comparison of six well-known meta-heuristic optimization algorithm is also presented in this paper. In order to verify the effectiveness of this newly developed method, the algorithm was tested on common benchmark functions used in the literature. Numerical results indicate that the ASSO algorithm strategy outperforms the basic SSO algorithm, Genertic Algorithm (GA), Particle Swarm Intelligence (PSO), Firefly Algorithm (FA), Artificial Bee Colony (ABC) and Teaching Learning Based Optimization (TBLO) in term of reaching for global solution.
Xenobiotic-dependent regulation of human karyopherin A2 (KPNA2) gene.

A common reaction to xenobiotic exposure is the modulation of genes that mediate the response to such insult, including the drug metabolising enzymes and drug transporters. This transcriptional response is typically mediated by the nuclear receptor family of ligand activated transcription factors, and the translocation of these key regulators into the nucleus, whether before or after ligand binding, is a pre-requisite for their activity. The karyopherin a family of adapter proteins (6 in man) form a molecular bridge between nuclear cargoes and the nuclear import machinery. Previous work demonstrated that the rat karyopherin a genes are themselves responsive to a number of xenobiotics, including classical nuclear receptor ligands. The aim of this study was to delineate the molecular mechanism underlying transcriptional regulation of human karyopherin a2 (KPNA2) gene. In silico analysis of the 2.6kb immediately upstream and including the first exon of human KPNA2 promoter revealed several putative transcription factor binding sites, most notably for the nuclear receptors vitamin D receptor (VDR), progesterone receptor (PR), retinoid related orphan receptor a (RORa), peroxisome proliferated activated receptor a (PPARa), glucocorticoid receptor a (GRa), pregnane X receptor (PXR) and hepatic nuclear factor 4 (HNF4) and the anti-oxidant response protein, Nrf2. Using a reporter gene assay in the human hepatoma cell line Huh7, preliminary experiments demonstrated that the KPNA2 gene was responsive to cognate ligands for many of these nuclear receptors, as well as oxidative stress-mediated activation of Nrf2. In-depth analysis including promoter deletion, site directed mutagenesis and electrophoretic mobility shift assay were carried out to determine the molecular mechanism underlying these transcriptional effect. The down-regulation of human KPNA2 expression by cyproterone acetate was mediated via GRa and not PXR whereas the down-regulation of human KPNA2 expression by Wy-14,643 was elicited not by its cognate nuclear receptor, PPARa but most likely via Nrf2 through the oxidative stress signaling pathway. In conclusion, these findings provide a rational explanation for the mechanistic basis of human KPNA2 gene regulation by a number of endobiotics and xenobiotics, and postulate the importance of this phenomenon for optimizing cellular response to these stimuli.
Solar light active plasmonic Au@TiO2 nanocomposite with superior photocatalytic performance for H2 production and pollutant degradation

Spherically shaped plasmonic Au nanoparticles (NPs) of size 10 nm (±4 nm) have been decorated on TiO2 NPs for the synthesis of Au@TiO2 composites via an aqueous sol–gel method. The effects of the Au concentration on the optical properties, structural surface properties and photocatalytic activity have been investigated. The optical study indicated characteristics of the surface plasmon resonance (SPR) of Au NPs, which greatly red shifted the photo-absorption from UV to visible light. A Tauc plot showed a downward shift in the band gap energy due to the creation of a metal–semiconductor Schottky junction at the nano Au@TiO2 surface. The well crystallized mixed (anatase and rutile) TiO2 phase formation was investigated using X-ray diffraction studies. The decrease in the photoluminescence (PL) intensity with Au loading indicated an increase in the charge carrier separation. The porous nature of the synthesized photocatalyst material was observed in both Field Emission Scanning Electron Microscopy (FESEM) and Field Emission Transmission Electron Microscopy (FETEM). The homogeneous distribution of Au NPs over the entire TiO2 surface was examined using FETEM and was also confirmed by elemental mapping with STEM. The broadening and shifting of the Raman peak from 143.2 cm−1 to 144.7 cm−1 indicated the generation of crystalline defects such as vacancies and interstitial rearrangement, which may act as trapping sites for electrons. BET surface measurements revealed that the surface area increased from 29.19 m2 g−1 for bare TiO2 to 60.05 m2 g−1 for 2% (w/w) Au loading on TiO2. The synthesized porous Au@TiO2 composites exhibited a high photocatalytic activity for the splitting of H2O under natural solar radiation with the H2 generation rate of 399 μmol/0.1 g h−1, which is much higher than 132 μmol/0.1 g h−1 demonstrated by pristine TiO2 NPs. Along with the hydrogen (H2) generation via water splitting, photocatalytic Rhodamine-B degradation and the kinetics of the reaction were investigated in solar light. The rate was observed to be due to a pseudo-first order reaction.
Improvement of Software-Defined Network Performance Using Queueing Theory: A Survey

Software-Defined Networking (SDN) is a growing network technology that has brought significant benefits to a wide range of disciplines, from science to technology to various fields. This structure can be used in network-based environments, data centers and various research sites. OpenFlow is one of the most widely used protocols for interaction between a controller and a switch in a Software-Defined Networking. Understanding the performance and limitations of the network defined by open source software, including bottlenecks and security vulnerabilities due to the centralized network structure, are important prerequisites for the efficient deployment of these systems. These points of view led researchers to examine various related mathematical models to address these issues. Queueing theory provides the most important and accurate model for evaluating the performance of the SDN networks affected by these restrictions, which has attracted the attention of researchers recently. Regarding to extensive mathematical modeling, this theory has also been used to improve network efficiency. Researchers have used this theory to investigate operational power and improve time consumption and control of data planes due to the nature of classification and storage of packets in SDN buffers. These methods overcome controller performance bottlenecks and increase SDN control capacity, especially for large distributed networks. In this paper, we examine the queueing models for different applications in different layers of SDN, in which researchers use these methods to monitor network loads, evaluate and predict performance changes due to diversity in network traffic. We introduce and review a collection of articles that explore, different applications of Queueing theory in SDN networks. In addition, in order to increase the efficiency of this research, detailed comparisons are performed in terms of structure, mathematical models and the final simulation results.
The regularized asymptotics of a solution of the Cauchy problem in the presence of a weak turning point of the limit operator

An asymptotic solution of the linear Cauchy problem in the presence of a ‘weak’ turning point of the limit operator is built using Lomov’s regularization method. The major singularities of the problem are written out in an explicit form. Estimates are given with respect to , which characterise the behaviour of the singularities as . The asymptotic convergence of the regularized series is proved. The results of the work are illustrated by an example. Bibliography: 8 titles.
Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach

The paper is focusing on the problem of price optimization in the area of accommodation services. The main aim is to propose a novel simulation-based methodology of price optimization based on the customer’s price acceptance. The authors create a model based on the known approaches but extended by the stochastic approach and optimization based on the coefficient of price elasticity. The whole model is created, the price is set and optimized in two steps. The first step makes segmentation and optimization (with the price elasticity approach). The second step then sets the price of the reservation—the final price for a customer. This reservation price is mainly determined by knowledge of the length of stay, occupancy and booking lead time. All those parameters are described in the text from the economic point of view and make the base for the whole and complex revenue management model.
Variable Frequency Control for Isolated, Nonresonant Single-Stage AC–DC Converter With a Constant DC-Link Voltage

This article proposes a system, which comprises an isolated, nonresonant single-stage ac–dc converter with variable frequency control. One of the main contributions of this article is that the proposed system ensures a stable dc-link voltage independent of power flow, which improves the reliability of the system. Moreover, it eliminates the capacitor imbalance issue when using pulsewidth modulation control in bridgeless, nonresonant single-stage converters. Due to this, a minimal output capacitance is needed to regulate the output current in low output voltage applications tightly. The proposed system shows high flexibility in controlling the current waveforms, which enables soft switching for all turn-on instants of the switches. A new equivalent circuit is introduced to study the steady-state operation of the proposed system accurately. Experimental and simulation results are provided for a $250 \, \mathrm{W}$ prototype to demonstrate the superior performance of the proposed system.
The Overall Consequence of Antiviral Drugs Given to Pregnant Women with COVID-19

During pregnancy, the anatomical structure of the respiratory system changes, and the virus transmitted by droplets and aerosols are more easily inhaled and difficult to remove by pregnant women. Women are generally more susceptible to various pregnancy-related complications and respiratory pathogens, increasing the risk of developing adverse pregnancy and neonatal outcomes. Anecdotal evidence suggests that pregnant women do not appear to differ from the general population in terms of disease transmission, and to date, there is no evidence of vertical transmission from mother to fetus. However, in another study, it is known that members of the coronavirus family are responsible for serious complications such as miscarriage, fetal growth restriction, and congenital anomalies during pregnancy. To date, only a few studies have reported relatively higher rates of adverse birth outcomes in women affected by SARS-CoV-2 infection in late pregnancy. This literature review presents the use of drugs used for the treatment of COVID-19 disease in pregnancy and pregnancy outcomes. It is also aimed to examine the effective control and management of SARS CoV-2 infection in the pregnancy, delivery, and postpartum period in line with the existing literature and guide health personals. Large-scale epidemiological studies are needed to evaluate the course of the infection during pregnancy and the effects of the drugs used on pregnancy and fetus.
Evaluation of Outdoor Particle Infiltration into Classrooms Considering Air Leakage and Other Building Characteristics in Korean Schools

We analyzed the effects of air leakage and other building characteristics on outdoor particle penetration in classrooms. The building characteristics including air leakage of 12 Korean schools were investigated, and onsite measurements were conducted to estimate the outdoor particle infiltration. The correlations among variables associated with air leakage and building characteristics and outdoor particle infiltration were analyzed using the Pearson correlation analysis and linear regression. The effective leakage area (ELA) of classrooms varied highly from 340.8–1566.9 cm2, and a significant disparity in the air leakage characteristics among the classrooms appeared. The results of onsite measurement revealed that the average ELA was larger in the corridor side with an ELAcorridor-side of 264.7 cm2 than in the outdoor side (ELAoutdoor-side of 93.1 cm2). Results of correlation analysis indicated a high correlation (r = 0.68~0.78, p-value < 0.05) between the size resolved outdoor particle source (P × λ) and specific ELA. Particularly, a strong linear relation (R2 = 0.69~0.71) with specific ELAcorridor-side was seen. Results suggest that cracks between windows and doors in the corridor side considerably affect outdoor particle penetration. These results indicate the importance of improving the airtightness of not only the building envelope but also the inter-zonal walls for effectively reducing the outdoor particle infiltration into classrooms.
Cash transfer and female labor supply: evidence from Brazil’s rural area

Abstract This paper analyzed the relationship between Bolsa Família Program (BFP) and labor supply of women in rural areas from Brazil, using the Propensity Score Matching (with the nearest-neighbor, kernel, and IPW criteria), Ordinary Least Squares on the treatment (beneficiaries), and control (non-beneficiaries) groups with the robustness analysis proposed by Oster (2015). The results showed that the BFP has an opposite effect to the one pronounced in the critiques of the program, the beneficiaries did not have more or fewer work hours than the non-beneficiaries, this fact can be explained by the value of the benefit not being able to meet the expenses of the household. From these results, we can conclude that both tests reinforce the importance of continuing the investigation of the effects on female labor supply in the rural environment in the light of different guidelines.
Generalized Normal Distribution Algorithm-Based Control of 3-Phase 4-Wire Grid-Tied PV-Hybrid Energy Storage System

The presented work employs the multiple random feature kernel mean p-power algorithm (MRFKMP) for the voltage source converter (VSC) control of a three-phase four-wire grid-tied dual-stage photovoltaic-hybrid energy storage system (HESS) to achieve multiple objectives during various induced dynamic conditions. The proposed control enables the VSC to accomplish manifold goals, i.e., reactive power compensation, power quality enhancement, load, power balancing at common coupling point and grid voltage balancing during unity power factor mode of operation. The proposed system is scrutinized under steady-state and numerous dynamic states such as irradiation variation, specified power mode, abnormal grid voltage, load, and grid voltage unbalancing. The seamless control facilitates the swift resynchronization of the grid as well as maintaining stability during islanding and re-synchronization operations while satisfying the necessary load requirements. The associated HESS consisting of battery and ultra-capacitor is competent enough in managing the interruptions occurring on the grid, load and photovoltaic side. The DC bus voltage is controlled by the PI controller, which is tuned by the generalized normal distribution algorithm and kept at the desired level during diverse operating conditions. The optimized DC bus generates an accurate loss component of current and further enhances the VSC performance. The proposed system is investigated by simulation and found acceptable as per IEEE 519 standards.
Identification of novel missense mutations (c.1156T>C and c.785T>C) in the PSEN1 gene in Chinese families with early-onset Alzheimer’s disease

Introduction: Mutations in the presenilin 1 ( PSEN1 ) gene are associated with the inherited form of early-onset Alzheimer’s disease (AD). Currently, more than 200 PSEN1 mutations have been identified. Material and methods: Two Chinese families with early-onset Alzheimer’s disease were enrolled in this study. Their clinical and radiological profiles were analyzed, and their genomic DNA was extracted from peripheral blood and amplified for sequencing. The sequences of all exons and intron/exon boundaries in the PSEN1 gene were analyzed. The potential pathogenicity of the identified mutations was predicted using the SIFT and PolyPhen-2 bioinformatics software tools. Results: In the first family, a novel heterozygous missense mutation (c.1156T>A) was identified in exon 11 of the PSEN1 gene. This nucleotide substitution led to an amino acid change from phenylalanine to leucine at codon 386 (p.F386L), and was predicted to be ‘deleterious’ by SIFT and ‘probably damaging’ by PolyPhen-2. This mutation was not consistent across all family members with the disease. In the second family, a novel heterozygous missense mutation (c.785T>C) was identified in exon 8 of the PSEN1 gene, which also caused an amino acid change (p.L262S). This mutation was also predicted to be ‘deleterious’ by SIFT and ‘probably damaging’ by PolyPhen-2. Conclusions: We identified two novel heterozygous missense mutations (c.1156T>C and c.785T>C) in the PSEN1 gene in separate Chinese families with early-onset Alzheimer’s disease. This study provides new PSEN1 gene mutation profiles, which help to elucidate the pathogenesis of early-onset Alzheimer’s disease.
Factors affecting the use of patient portals among chronically ill patients in an outpatient hospital setting, kingdom of Saudi Arabia

Background: In the light of the major healthcare transformations taking place in the Kingdom of Saudi Arabia (KSA), more focus is placed on promoting patient engagement to improve the quality of healthcare services provided. There is limited research that explores the factors related to the use of patient portals in KSA, which support patient engagement. We aimed to investigate factors that contribute to the use of a specific patient portal system, implemented at Prince Sultan Military Medical City, Riyadh, KSA among chronically ill patients in an outpatient setting. Method: A cross-sectional study was performed in the adult outpatient clinics. We included five main outcomes: self-reported portal use, participant characteristics, patient activation levels, e-Health literacy scores, and the availability of internet access at home. We used descriptive analyses, univariate and multivariate logistic regression to analyze the data. Results: A total of 403 patients (81% response rate) participated in our study. More than 50% of the participants were female, younger than 55 years, educated, with monthly family income less than 10, 000 SR. Most of the participants reported at least one form of internet connection. A total of 212 (52.6%) participants were users of the portal. Patient activation measure scores were at level 3 or below for the majority of participants (66%) and e-Health literacy scores were at a moderate to a high level for 276 (69%) of participants. Portal use was more likely among males (OR = 2.182, p = 0.00), with two or more forms of internet connections (OR = 5.586, p = 0.037), and with moderate to high levels of e-Health literacy (OR = 2.028, p = 0.003) (OR = 2.309, p = 0.022). There was a positive correlation between e-Health literacy scores and patient activation levels (R = 0.321, p = 0.000). Conclusion: Patient portal use was significantly influenced by certain factors, including gender, level of e-Health literacy and availability of internet. There is a tremendous opportunity to improve the use of patient portal by focusing on methods to increase the level of patient activation and e-Health literacy scores.
Extraction of toluene and n-heptane mixture using ionic liquid Aliquat 336 and mathematical modeling for solvent selection

ABSTRACT Liquid–liquid equilibrium data for the system consisting of toluene, n-heptane and Aliquat 336 ionic liquid was obtained at 303.15 K and atmospheric pressure. The values of distribution coefficient and selectivity were evaluated. Mathematical model relating distribution coefficient and dispersion, dipolar interaction and hydrogen bonding has been developed. Hansen’s solubility parameters were used to determine the parameters. The model was applied for various ionic liquids as solvents. The model predicted the trends similar to the trend reported in the literature. Thus model can predict better solvent for the given extraction application. This methodology has great potential to act as a knowledge-based framework to aid development of new tailor-made solvents. This will save cost and time in experimentation and analysis.
Dividend Payout and Firm Performance: Evidence from a Frontier Market

This study attempts to identify the impact of dividend payout on the market and financial performances of listed entities in Colombo Stock Exchange (CSE). We selected 25 listed entities for this study for 06 years from the year 2014 to 2019. Dividend payout used as a key independent variable while Earning volatility, Net working capital, Capital structure and firm size considered as control variables. Stock return and Market Value Added (MVA) are used as the proxies for firm’s market performance while return on equity, return on Assets and Economic Value Added (EVA) are used as proxies for financial performance. In terms of analysis, panel regression was applied in order to identify whether there is an impact of dividend payout on firm financial and market performance. The findings revealed that there is a significant positive influence of dividend payout on firm stock return, return on equity, return on assets and EVA. Our findings become original as the first study to employ market performance indicators in this phenomenon. Henceforth, these findings are valuable for the policy makers specially for corporate managers and investors for their decision making.
Gradient-Based Algorithm for Tracking the Activity of Neural Network Weights Changing

The research conducted in this paper is in the field of machine learning. The main object of the research is the learning process of an artificial neural network in order to increase its efficiency. The algorithm based on the analysis of retrospective learning data. The dynamics of changes in the values of the weights of an artificial neural network during training is an important indicator of training efficiency. The algorithm proposed in this work is based on changing the weight gradients values. Changing of the gradients weights makes it possible to understand how actively the network weights change during training. This knowledge helps to diagnose the training process and makes an adjusting the training parameters. The results of the algorithm can be used to train an artificial neural network. The network will help to determine the set of measures (actions) needed to optimize the learning process by the algorithm results.