Miracle-dz/UniFAR-FTUs huggingface dataset:
Ship Accident Prediction Based on Improved Quantum-Behaved PSO-LSSVM

Water transportation plays an important role in the comprehensive transportation system and regional logistics. The number of vessel accidents is an important indicator for evaluating vessel traffic safety and the efficiency of the maritime management strategy. The aim of this work is to provide an efficient way to predict the number of vessel accidents in China. Firstly, to weaken the randomness of the vessel accident number time series, the gray processing operation is adopted to generate a new sequence with exponential and approximate exponential rules. In addition, an extended least-squares support vector machine (LSSVM) model is applied in the forecasting of the new sequence, in which the parameters of the LSSVM are optimized by an improved quantum-behaved particle swarm (IQPSO). The proposed method is applied in the forecasting of the number of vessel accidents in China, and the efficiency is shown by comparing the prediction results with GM (1, 1), PSO-LSSVM, and QPSO-LSSVM.
Flame Driving of Longitudinal Instabilities in Dump Type Ramjet Combustors

Abstract Coaxial, dump type ramjet combustors are often prone to combustion instability problems that can seriously affect their performance. This study is concerned with the highly detrimental low frequency instabilities which often occur in such engines and investigates the coupling between wedge shaped flames and longitudinal acoustic fields in an experimental apparatus specifically developed for this purpose. The presence of burning vortical structures is observed in the flame region. These structures appear at frequencies close to the first natural acoustic frequency of the apparatus and are believed to be connected with a shear layer type of instability of the flame. Experiments conducted show that the unsteady combustion in these structures is capable of driving the acoustics at the fundamental mode frequency. with increase in fuel-air ratio, a spontaneous instability involving the fundamental mode is observed and explained in terms of increased driv ing associated with the higher, unsteady heat re.
Quality of Life and Mental Health in Iranian Transgender Women: Socio-Demographic Differences

Background: Mental health issues and quality of life are among the important items of the general health in all people, and especially in transgender people. The present study aimed to assess the quality of life, depression, anxiety, and stress in transgender women and determine the factors contributing to them. Methods: A cross-sectional study on 127 Iranian transgender women was performed from August 2019 to April 2020 using a convenience sampling method. The quality of life (QOL) was determined by using the WHO questionnaire (WHOQOL-BREF). The DASS-21 questionnaire was employed to evaluate the anxiety, depression, and stress in the subjects. Results: 48% of the individuals had a moderate QOL. The highest score achieved in the physical health dimension, and the lowest score in the social relationships dimension. Depression, anxiety, and stress were severe and extremely severe in 22%, 20.4%, and 17.3% of the subjects. A significant relationship was observed between the overall perception of QOL and depression, anxiety, and stress (p <0.001). A significant positive association between the subscales of QOL and education and favorable economic status were observed among transgender women. Also, a significant negative correlation existed between the subscales of QOL with age and sexual violence. Regarding mental health, education had a significant relationship with reduced stress and anxiety, and favorable economic status had a significant relationship with reduced depression, while sexual violence was associated with increased stress in the individuals.Conclusion: The study results show that change in some modifiable variables such as education can improve transgender women’s quality of life and mental health by improving economic status. Moreover, there is also a strong need for developing sexual violence prevention services regarding its considerable effects on mental health and quality of life.
AG+‐hPLC of conjugated linoleic acids on a silica‐based stationary phase. Part III: Model compounds

During the development of a method for the direct analysis of the conjugated linoleic acids (CLAs) in their free fatty acid form, it was necessary to include a competing acid (acetic acid) in the hexane-based mobile phase in order to achieve resolution of the isomers in a reasonable time. Thus it was clear that the CLAs were retained by two mechanisms. In this study we examine the relative retentions of the CLAs, their methyl esters (CLAMEs, which is the form generally analyzed), and oleic and stearic acids. The primary aim was to investigate the nature of CLA retention in terms of the Ag + -π bond interactions (that might be approximated by the retention of the CLAMEs) and the -COOH-surface silanol hydrogen bonding that should be the same as stearic acid, if the retention mechanisms are independent. Allowing for some retention of the ester group in the CLAMEs on the silica, it may have been expected that the sum of the retention of the CLAMEs and stearic acid would slightly exceed that of the CLAs. On the contrary, it was found that the retention of the CLAs far exceeded the sum for the CLAMEs and stearic acid. Some synergy is suggested, rather than a third retention mechanism.
Percepção de responsáveis e alunos do ensino fundamental de Bambuí, MG em relação aos estudos não presenciais em período de distanciamento social no Brasil

As medidas de distanciamento social adotadas após o início da pandemia da COVID-19 provocaram grandes impactos na educação. A substituição do ensino presencial por metodologias que contam com o apoio de tecnologias de informação e comunicação foi necessária para a manutenção das atividades educacionais de forma remota. Entretanto, inúmeras dificuldades foram encontradas para uma rápida adaptação das escolas, professores, pais ou responsáveis e alunos a essa nova realidade. A importância da inclusão digital tornou-se ainda mais evidente, e os reflexos no aprendizado e saúde mental dos estudantes podem trazer consequências a longo prazo. O presente estudo objetivou obter maiores informações sobre a percepção dos responsáveis e dos estudantes do ensino fundamental de escolas do município de Bambuí, MG, quanto às atividades educacionais não presenciais realizadas em período de distanciamento social. Foram aplicados 54 questionários virtuais, e constatou-se que os estudantes têm o celular como principal meio de acesso à internet. A limitação no acesso à internet também foi um aspecto elencado. Outro fator que se destacou foi a dificuldade de concentração dos estudantes durante a realização das atividades. Além disso, uma parcela importante dos estudantes encontra-se desmotivada e apresentou sintomas de ansiedade durante o período de distanciamento social. Portanto, conclui-se que o distanciamento social tem impactado negativamente na vida dos estudantes do ensino fundamental de acordo com a percepção dos responsáveis e dos próprios alunos, com reflexos educacionais e socioemocionais.
Plasma Lipids as Biomarkers for Alzheimer's Disease: A Systematic Review

Alzheimer's disease (AD) is caused by several risk factors leading to dementia. It’s diagnosis usually depends on clinical presentation and certain biomarkers in the cerebrospinal fluid (CSF). The brain has a high content of cholesterol and the metabolism of cholesterol in the brain can be associated with beta-amyloid plaques formation, which is seen in Alzheimer’s disease. Given these implications, we studied if plasma lipid levels can vary in Alzheimer's disease and if these can be used as biomarkers to diagnose and predict the progression of Alzheimer's disease. Certain mutations in the brain cholesterol transport receptors and proteins and their association with Alzheimer's were also studied. This systematic review abides by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched multiple databases, such as Pubmed, Google Scholar, Pubmed central, ScienceDirect, Web of Science, and Medline with the help of keywords like Alzheimer's disease, cognitive impairment, plasma lipid biomarkers, cholesterol, brain cholesterol metabolism separately and in combination with each other. We collected 49 quality appraised articles on the association between plasma lipids and Alzheimer's disease and the genetic mutations in alleles related to cholesterol metabolism and Alzheimer's disease by applying the inclusion and exclusion criteria. Based on the finding of the studies reviewed, we found an association between plasma lipids, polymorphisms in genes associated with cholesterol transport, and Alzheimer's disease. Increased serum low-density lipoprotein (LDL-C), triglycerides (TG), total cholesterol (TC), sphingolipids, 24S hydroxycholesterol (24S-HC), 27O hydroxycholesterol (27O-HC) was associated with Alzheimer's. Decreased high-density lipoprotein (HDL-C) and phospholipids were noticed. Genetic mutations in apolipoprotein E (ApoE), apolipoprotein B (ApoB), apolipoprotein A (ApoA), ATP binding cassette transporter 1 (ABCA1), ATP binding cassette transporter 7 (ABCA7), amyloid precursor protein (APP), cytochrome P450 family 46 subfamilies A member 1 (CYP46A1), presenilin 1 (PSEN1), presenilin 2 (PSEN2) are also associated with increased risk of Alzheimer's disease. This study found an association between plasma lipids and Alzheimer's, proving that plasma lipids can be used as biomarkers for early diagnosis of Alzheimer's disease. It may also help predict the prognosis and stage the disease severity. Further studies are needed to find out the exact mechanism behind these changes.
TIMOR LESTE�S ACCESSION TO ASEAN 2011: AN ANALYSIS OF INTERNATIONAL FACTORS

After gaining its independence in 2002, Timor Leste struggled for joining ASEAN’s membership. Timor Leste changed foreign policy orientation from PIF to ASEAN. Then, Timor Leste had put its decision in 2011 which officially enrolled as member of ASEAN. This article seeks to analyze Timor Leste’s decision to join ASEAN’s membership. The authors sought to find the answers by using foreign policy decision-making model. This article focused on international factors which are derived from two important variables namely international context and economic and military conditions. The authors found from international context that Timor Leste decided to join ASEAN due to ASEAN’s attractiveness as prospective regional organization and strong support emanating from Indonesia as an influential state regionally. Economic conditions comprise close economic relations of Timor Leste with ASEAN countries. Military conditions are analyzed from Timor Leste’s military capability amidst neighbor’s military capability and its need to collective security.
Chemotaxis behaviour of Steinernema carpocapsae in response to Galleria mellonella (L.) larvae infected by con- or hetero-specific entomopathogenic nematodes

ABSTRACT Entomopathogenic nematodes (EPNs) infect insects and reproduce within the host cadaver. The nematodes use chemotaxis to assist in host-finding. However, the chemotaxis of nematode species in response to insects previously infected by con- and hetero-specific EPN in a sand context has rarely been reported. Our aim was to address the response of Steinernema carpocapsae All (Sc) to Galleria mellonella cadavers infected by con- or hetero-specific EPNs (Heterorhabditis bacteriophora HB1, S. feltiae SN, S. rarum 17 C&E) in sand via chemotaxis assays. Additionally, the effect of different time points post-infection was also evaluated. Results indicated that previously infected G. mellonella larvae repelled infective juveniles (IJs) of Sc (p < 0.05) regardless of the time point post-infection and EPN species (con- or hetero-specific) and had a lower chemotaxis index as compared to that of no larvae addition in both sides. Moreover, it was found that for each of four tested EPN species, EPNs infected insect cadavers released dimethyl disulphide, which was identified by solid-phase microextraction (SPME) coupled to gas chromatography–mass spectrometry (GC–MS). We found that dimethyl disulphide acted as a repellent to Sc infective juveniles and reached a significant effect at the level of 0.106 mol/L. Our findings could provide a better understanding of movement behaviour of EPN infective juveniles in response to insect resources in the presence of con- or hetero-specific EPNs.
Systems Neuroscience of Natural Behaviors in Rodents

Animals evolved in complex environments, producing a wide range of behaviors, including navigation, foraging, prey capture, and conspecific interactions, which vary over timescales ranging from milliseconds to days. Historically, these behaviors have been the focus of study for ecology and ethology, while systems neuroscience has largely focused on short timescale behaviors that can be repeated thousands of times and occur in highly artificial environments. Thanks to recent advances in machine learning, miniaturization, and computation, it is newly possible to study freely moving animals in more natural conditions while applying systems techniques: performing temporally specific perturbations, modeling behavioral strategies, and recording from large numbers of neurons while animals are freely moving. The authors of this review are a group of scientists with deep appreciation for the common aims of systems neuroscience, ecology, and ethology. We believe it is an extremely exciting time to be a neuroscientist, as we have an opportunity to grow as a field, to embrace interdisciplinary, open, collaborative research to provide new insights and allow researchers to link knowledge across disciplines, species, and scales. Here we discuss the origins of ethology, ecology, and systems neuroscience in the context of our own work and highlight how combining approaches across these fields has provided fresh insights into our research. We hope this review facilitates some of these interactions and alliances and helps us all do even better science, together.
An Update on Self-Amplifying mRNA Vaccine Development

This review will explore the four major pillars required for design and development of an saRNA vaccine: Antigen design, vector design, non-viral delivery systems, and manufacturing (both saRNA and lipid nanoparticles (LNP)). We report on the major innovations, preclinical and clinical data reported in the last five years and will discuss future prospects.
Toward Smart Lockdown: A Novel Approach for COVID-19 Hotspots Prediction Using a Deep Hybrid Neural Network

COVID-19 caused the largest economic recession in the history by placing more than one third of world’s population in lockdown. The prolonged restrictions on economic and business activities caused huge economic turmoil that significantly affected the financial markets. To ease the growing pressure on the economy, scientists proposed intermittent lockdowns commonly known as “smart lockdowns”. Under smart lockdown, areas that contain infected clusters of population, namely hotspots, are placed on lockdown, while economic activities are allowed to operate in un-infected areas. In this study, we proposed a novel deep learning prediction framework for the accurate prediction of hotpots. We exploit the benefits of two deep learning models, i.e., Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) and propose a hybrid framework that has the ability to extract multi time-scale features from convolutional layers of CNN. The multi time-scale features are then concatenated and provide as input to 2-layers LSTM model. The LSTM model identifies short, medium and long-term dependencies by learning the representation of time-series data. We perform a series of experiments and compare the proposed framework with other state-of-the-art statistical and machine learning based prediction models. From the experimental results, we demonstrate that the proposed framework beats other existing methods with a clear margin.
Optimization of a High Temperature PEMFC micro‐CHP System by Formulation and Application of a Process Integration Methodology

A 1 kWe micro combined heat and power (CHP) system based on high temperature proton exchange membrane fuel cell (PEMFC) technology is modeled and optimized by formulation and application of a process integration methodology. The system can provide heat and electricity for a single‐family household. It consists of a fuel cell stack, a fuel processing subsystem, heat exchangers, and balance‐of‐plant components. The optimization methodology involves system optimization attempting to maximize the net electrical efficiency, and then by use of a mixed integer nonlinear programming (MINLP) problem formulation, the heat exchange network (HEN) annual cost is minimized. The results show the high potential of the proposed model since high efficiencies are accomplished. The net electrical efficiency and total system efficiency, based on lower heating value (LHV), are 35.2% and 91.1%, respectively. The minimized total annual cost of the HEN is $8,147 year–1.
Self-Assembly of Alkanethiol Molecules onto Platinum and Platinum Oxide Surfaces

We studied the self-assembly of n-alkanethiols CH3(CH2)mSH (m = 5, 7, 9, 11, 13, 15, 17, 19, 21) onto platinum thin films with two different surface properties: metallic and oxide. Water contact angle, ellipsometry, and reflection−absorption infrared spectroscopy (RAIRS) measurements suggested that well-organized, methyl-terminated monolayer films formed on the metallic platinum surface. On the other hand, the molecular films formed with the alkanethiol molecules on platinum oxide surfaces were loosely packed and poorly ordered. For the monolayer films formed with alkanethiols of different chain lengths on metallic Pt surfaces, a transition from slightly disordered to crystalline-like structure was observed with increasing chain length. Based on the intensities of the methylene and methyl C−H vibration modes in the RAIRS, the tilt angle of the alkyl chain in the monolayers on Pt surfaces was estimated to be less than 15° with respect to the surface normal. X-ray photoelectron spectroscopy revealed the fo.
The Relationship between Technological Capabilities and Organizational Impact: Direct and Indirect Routes for Employed and Self-Employed Personal Fitness Trainers

The purpose of this study was to investigate the relationships between technological capabilities, individual impact, market agility and organizational impact in employed and self-employed personal trainers hired by fitness centers. The study was performed with 259 personal trainers. Exploratory Factor Analysis, Confirmatory Factor Analysis and a Structural Equation Model were used. The results showed, for the employees, positive relationships between all variables, except for the relationship between individual impact and organizational impact. In contrast, for self-employed personal trainers, only the relationships of technological capabilities with market agility and individual impact, and between the latter and organizational impact, were positive. While the individual variables of the employees show positive relationships with the organizational variables analyzed, this impact could not be determined in the performance of the self-employed. These results are discussed in relation to the previous literature, and possible causes for the differences found between employees and self-employed personal trainers are pointed out. This study’s findings indicate that the promotion and consolidation of personal trainers’ technological capabilities (TCs), whether they are self-employed or employees, will be key for the fitness centers where they work, by directly influencing the individual impact of the trainers on them and in the organizational performance.
Airborne Virus Survivability During Long-Term Sampling Using a Non-Viable Andersen Cascade Impactor in an Environmental Chamber

In order to evaluate the survivability of airborne viruses and the sampling performance of an eight-stage non-viable Andersen impactor in typical indoor environments featuring low viral aerosol concentrations, aerosols of a male-specific bacteriophage (MS2), human adenovirus type 1 (HAdV-1), and avian influenza virus (AIV) were sampled size-selectively using the impactor in an environmental chamber. Live virus titer, total virus RNA or DNA concentration, and intensity of a fluorescein tracer were measured to calculate relative virus recovery and virus survival. Viral aerosols were first sampled for 1 and 6 h at 25°C and 50% relative humidity (RH). Virus inactivation and plate overloading were found to be significant in the impactor. Viral aerosols were then sampled at different temperature and humidity levels. MS2 and AIV showed higher survival at lower temperature. Absolute humidity (AH) was found to be a better predictor of virus survival than RH, and the interaction between AH and temperature was not significant. For the tested AH range of 8.8 to 15.2 g/m3, MS2 and HAdV-1 had the highest survival at the lowest AH while AIV had the highest survival at the highest AH. More than 95% of mass collected was for particles smaller than 4.7 m, with the mass median diameter of 1.5 m. In the nebulizer, virus inactivation was not significant at 10 psi (69 kPa) compressed air pressure for up to 6 h of nebulization. Nebulizer analysis also reveals that the use of fluorescein tracer may not always accurately predict the physical loss of virus. Copyright 2014 American Association for Aerosol Research
Surface properties of water clusters: a molecular dynamics study

Radial local densities, local energies per molecule, orientational distribution functions, normal component of the pressure tensor and other surface properties of water are calculated, based on molecular dynamics simulations of water clusters at 300 K. Three different water models are evaluated: the rigid five-site ST2 and four-site TIP4P models; and the three-site SPC/E model, which is made flexible with respect to the angle bending. The size of the clusters is varied from 64 to 1000 water molecules. It is concluded that surface properties are highly sensitive to the choice of potential model. On the basis of the dependence of the work of cluster formation on the cluster size, the influence of the water model on the surface tension of the plane surface is discussed. None of the three models considered gives a proper value for the surface tension of water at room temperature.
Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms

Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.