Search results for: prediction factors

Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

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Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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Authors: Rofem Inyang Bassey, Pfarelo Matshidze, Dolphin Mabale, Ishmael Iwara

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This article strove to establish the factors responsible for the activities of the transitional rites of passage on youth’s morality. South Africa, among other countries, has serious effects on the moral systems of youths. Factors responsible for the activities of the transitional rites of passage on youth’s morality has been brought to the fore by on-going transitional rites depicted by weekend’s social activities with the practice of substance use and abuse among the youths at various recreational spots. The transitional rites are structured under the guise of “freaks” as an evolving culture among the youths. The freaks culture is a counterculture of the usual initiation’s schools for transitional rites of passage which instil morality among youths. Qualitative design was used to obtain data from custodians, parents of the youths, and the youths themselves. The findings widely show that the factors responsible for the activities of the transitional rites is the act of the current on-going transitional rites effect as an inappropriate behaviour towards youth morality. This study concluded that to establish the factors responsible for the activities of the transitional rites of passage on youth’s morality, activities and practices evolved as a current socialization standard for quick maturity status, as a result of that, it will be harder to provide a complete change of this evolving culture. However, what will work is to build on the existing transitional rites of passage to moderate appropriate youths’ morality in Thulamela communities.

Keywords: behaviour, morality, transitional rites, youths, socials activities, freak culture

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Authors: Anurag Tripathi, Sanad Khandelwal

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Age and gender determination are required in forensic for victim identification. There is secondary dentine deposition throughout life, resulting in decreased pulp volume and size. Evaluation of pulp volume using Cone Beam Computed Tomography (CBCT)is a noninvasive method to evaluate the age and gender of an individual. The study was done to evaluate the efficacy of pulp volume method in the determination of age and gender.Aims/Objectives: The study was conducted to estimate age and determine sex by measuring tooth pulp volume with the help of CBCT. An observational study of one year duration on CBCT data of individuals was conducted in Lucknow. Maxillary central incisors (CI) and maxillary canine (C) of the randomly selected samples were assessed for measurement of pulp volume using a software. Statistical analysis: Chi Square Test, Arithmetic Mean, Standard deviation, Pearson’s Correlation, Linear & Logistic regression analysis. Results: The CBCT data of Ninety individuals with age range between 18-70 years was evaluated for pulp volume of central incisor and canine (CI & C). The Pearson correlation coefficient between the tooth pulp volume (CI & C) and chronological age suggested that pulp volume decreased with age. The validation of the equations for sex determination showed higher prediction accuracy for CI (56.70%) and lower for C (53.30%).Conclusion: Pulp volume obtained from CBCT is a reliable indicator for age estimation and gender prediction.

Keywords: forensic, dental age, pulp volume, cone beam computed tomography

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Authors: A. Al-Wandi, A. Al-Ali, R. Dali, Y. Al-Karaghouli

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Background: Non-communicable diseases (NCDs) have become an alarming health problem across the globe. The risk of developing those diseases begins in childhood and develops gradually under the influence of risk factors including obesity, hypertension, dyslipidemia, cigarette smoking and decreased physical activity. Therefore, this study aims to determine the level of knowledge, attitudes, and practices of the risk factors of lifestyle induced chronic diseases (non-communicable diseases) among secondary school students in Sharjah city. Methods: Five hundred and ninety-one school children, from grades 10 to 12, formed the study sample, using the multistage stratified cluster sampling method. Four governmental schools were chosen, for each gender. Data was collected through a pretested, close-ended questionnaire consisting of five sections; demographics, physical activity, diet, smoking and sleeping patterns. Frequencies and descriptive statistics were used to analyze data through SPSS 23. Results: The data showed 64.6% of students had low knowledge of risk factors of non-communicable diseases. Concerning physical activity, 58.2 % were physically inactive and females being less active than males. More than 2/3 of students didn’t fulfill the recommended daily intake of fruits and vegetables (75.9%). 8% reported to be smokers with cigarettes being the most encountered tobacco product. Conclusion: Our study has demonstrated a low level of knowledge and practices yet, positive attitudes towards risk factors of chronic diseases. We recommend implementation of thorough awareness campaigns through public health education about the risk factors of non-communicable diseases.

Keywords: non-communicable diseases, physical activity, diet, knowledge, attitudes, practices, smoking

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Authors: Nitin Dewangan, Nitin Kattula, Megha Anawat

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Hydraulic reservoir is the key component in the mobile construction vehicles; most of the off-road earth moving construction machinery requires bigger side hydraulic reservoirs. Their reservoir construction is very much non-uniform and designers used such design to utilize the space available under the vehicle. There is no way to find out the space utilization of the reservoir by oil and validity of design except virtual simulation. Computational fluid dynamics (CFD) helps to predict the reservoir space utilization by vortex mapping, path line plots and dwell time prediction to make sure the design is valid and efficient for the vehicle. The dwell time acceptance criteria for effective reservoir design is 15 seconds. The paper will describe the hydraulic reservoir simulation which is carried out using CFD tool acuSolve using automated mesh strategy. The free surface flow and moving reference mesh is used to define the oil flow level inside the reservoir. The first baseline design is not able to meet the acceptance criteria, i.e., dwell time below 15 seconds because the oil entry and exit ports were very close. CFD is used to redefine the port locations for the reservoir so that oil dwell time increases in the reservoir. CFD also proposed baffle design the effective space utilization. The final design proposed through CFD analysis is used for physical validation on the machine.

Keywords: reservoir, turbulence model, transient model, level set, free-surface flow, moving frame of reference

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Authors: Georges Ghazi, Magali Gelhaye, Ruxandra Botez

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Over the years, the Flight Management System (FMS) has experienced a continuous improvement of its many features, to the point of becoming the pilot’s primary interface for flight planning operation on the airplane. With the assistance of the FMS, the concept of distance and time has been completely revolutionized, providing the crew members with the determination of the optimized route (or flight plan) from the departure airport to the arrival airport. To accomplish this function, the FMS needs an accurate Aircraft Performance Model (APM) of the aircraft. In general, APMs that equipped most modern FMSs are established before the entry into service of an individual aircraft, and results from the combination of a set of ordinary differential equations and a set of performance databases. Unfortunately, an aircraft in service is constantly exposed to dynamic loads that degrade its flight characteristics. These degradations endow two main origins: airframe deterioration (control surfaces rigging, seals missing or damaged, etc.) and engine performance degradation (fuel consumption increase for a given thrust). Thus, after several years of service, the performance databases and the APM associated to a specific aircraft are no longer representative enough of the actual aircraft performance. It is important to monitor the trend of the performance deterioration and correct the uncertainties of the aircraft model in order to improve the accuracy the flight management system predictions. The basis of this research lies in the new ability to continuously update an Aircraft Performance Model (APM) during flight using an adaptive lookup table technique. This methodology was developed and applied to the well-known Cessna Citation X business aircraft. For the purpose of this study, a level D Research Aircraft Flight Simulator (RAFS) was used as a test aircraft. According to Federal Aviation Administration the level D is the highest certification level for the flight dynamics modeling. Basically, using data available in the Flight Crew Operating Manual (FCOM), a first APM describing the variation of the engine fan speed and aircraft fuel flow w.r.t flight conditions was derived. This model was next improved using the proposed methodology. To do that, several cruise flights were performed using the RAFS. An algorithm was developed to frequently sample the aircraft sensors measurements during the flight and compare the model prediction with the actual measurements. Based on these comparisons, a correction was performed on the actual APM in order to minimize the error between the predicted data and the measured data. In this way, as the aircraft flies, the APM will be continuously enhanced, making the FMS more and more precise and the prediction of trajectories more realistic and more reliable. The results obtained are very encouraging. Indeed, using the tables initialized with the FCOM data, only a few iterations were needed to reduce the fuel flow prediction error from an average relative error of 12% to 0.3%. Similarly, the FCOM prediction regarding the engine fan speed was reduced from a maximum error deviation of 5.0% to 0.2% after only ten flights.

Keywords: aircraft performance, cruise, trajectory optimization, adaptive lookup tables, Cessna Citation X

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Authors: Ifigenia Stylianou

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As more than three-quarters of students going online daily via computers, tablets, and smartphones, the phenomenon of cyberbullying is growing rapidly. Knowing that victims of online bullying are often also victims of traditional bullying and that traditional bullying is considered as an extension of cyberbullying. In this study, we aim to identify (1) whether cyberbullying lead to more intense forms of school bullying, and (2) whether some biological and environmental factors mediate between this relation, and act protectively to bullying and inappropriate behaviour in school. To answer this questions, a sample of X students, aged X, were asked to complete eight questionnaires (Personal Experiences Checklist, Inventory of Peers Attachment, Questionnaire on Teacher Interaction, School Climate Survey for Bullying, Strengths and Difficulties Questionnaire, Youth Psychopathic Traits Inventory-Short Form, Barratt Impulsiveness Scale-11) in X time periods. Results can provide us important information to improve understanding the factors that are related to bullying. In addition, the results can assist in developing intervention programs to tangle the issue of bullying at schools. All data have been collected and are currently being processed for statistical analyses.

Keywords: cyberbullying, bullying, school climate, psychopathy traits, attachment, mediation factors

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Wan-Ling Jiang, Hsin Chi, Jia-Lu Cheng, Ming-Fang Cheng

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Background: Investigating the risk factors for the fecal carriage of third-generation cephalosporin-resistant E.coli could contribute to further disease prevention. Previous research on third-generation cephalosporin-resistant prevalence in children in different regions of Taiwan is limited. This project aims to explore the risk factors and regional differences in the prevalence of third-generation cephalosporin-resistant and other antibiotic-resistant E. coli in the northern, southern, and eastern regions of Taiwan. Methods: We collected data from children aged 0 to 18 from community or outpatient clinics from July 2022 to May 2023 in southern, northern, and eastern Taiwan. The questionnaire was designed to survey the characteristics of participants and possible risk factors, such as clinical information, household environment, drinking water, and food habits. After collecting fecal samples and isolating stool culture with E.coli, antibiotic sensitivity tests and MLST typing were performed. Questionnaires were used to analyze the risk factors of third-generation cephalosporin-resistant E. coli in the three different regions of Taiwan. Results: In the total 246 stool samples, third-generation cephalosporin-resistant E.coli accounted for 37.4% (97/246) of all isolates. Among the three different regions of Taiwan, the highest prevalence of fecal carriage with third-generation cephalosporin-resistant E.coli was observed in southern Taiwan (42.7%), followed by northern Taiwan (35.5%) and eastern Taiwan (28.4%). Multi-drug resistant E. coli had prevalence rates of 51.9%, 66.3%, and 37.1% in the northern, southern, and eastern regions, respectively. MLST typing revealed that ST131 was the most prevalent type (11.8%). The prevalence of ST131 in northern, southern, and eastern Taiwan was 10.1%, 12.3%, and 13.2%, respectively. Risk factors analysis identified lower paternal education, overweight status, and non-vegetarian diet as statistical significance risk factors for third-generation cephalosporin-resistant E.coli. Conclusion: The fecal carriage rates of antibiotic-resistant E. coli among Taiwanese children were on the rise. This study found regional disparities in the prevalence of third-generation cephalosporin-resistant and multi-drug-resistant E. coli, with southern Taiwan having the highest prevalence. Lower paternal education, overweight, and non-vegetarian diet were the potential risk factors of third-generation cephalosporin-resistant E. coli in this study.

Keywords: Escherichia coli, fecal carriage, antimicrobial resistance, risk factors, prevalence

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Authors: C. A. Bradley, D. Patsios, D. Berridge

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This paper presents evidence to better understand the risk and protective factors related to sibling conflict and the patterns of association between sibling conflict and negative adjustment outcomes by incorporating additional familial and societal factors within statistical models of risk and adjustment. It was conducted through the secondary analysis of a large representative cross-sectional dataset of children in the UK. The original study includes proxy interviews for young children and self-report interviews for adolescents. The study applies an ecological systems framework for the analyses. Hierarchical regression models assess risk and protective factors and adjustment outcomes associated with sibling conflict. Interactions reveal differential effect between contextual risk factors and the social context of influence. The general pattern of findings suggested that, although factors affecting likelihood of experiencing sibling conflict were often determined by child age, some remained consistent across childhood. These factors were often conditional on each other, reinforcing the importance of an ecological framework. Across both age-groups, sibling conflict was associated with siblings closer in age; male sibling groups; most advantaged socio-economic group; and exposure to community violence, such as witnessing violent assault or robbery. The study develops the evidence base on the influence of ethnicity and socio-economic group on sibling conflict by exploring interactions between social context. It also identifies key new areas of influence – such as family structure, disability, and community violence in exacerbating or reducing risk of conflict. The study found negative associations between sibling conflict and young children’s mental well-being and adolescents' mental well-being and anti-social behaviour, but also more context specific associations – such as sibling conflict moderating the negative impact of adversity and high risk experiences for young children such as parental violence toward the child.

Keywords: adjustment, conflict, ecological systems, family systems, risk and protective factors, sibling

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Redie Kidane Ghebrehawariat, Betiel Habte Hadgu, Filmon Berhane Kahsay, Rim Berhane Fisehaye, Samuel Haile Kahsay, Saron Yemane Yosief, Selemawit Mosazghi Gilazghi

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A cross-sectional study was conducted from 22 February to 9 April 2022 on small, medium, and large holder dairy farms to determine the bovine mastitis prevalence and associated risk factors in the Anseba region, Eritrea. A total of 34 dairy farms and 193 dairy cows were randomly selected. Dairy cows were physically examined for any change on the udder and milk; a California mastitis test was performed to check sub-clinical mastitis; a closed-ended semi-structured questionnaire composed of 28 variables/risk factors (21 management risk factors and 7 animal-level risk factors) was used to determine the risk factors responsible for clinical and sub-clinical mastitis in the dairy cows. The overall cow-level prevalence of mastitis was 147 (76.2%). The animal level prevalence rate of clinical and sub-clinical mastitis was found to be 22 (11.4%) and 125 (64.8%), respectively, while herd level prevalence both for clinical and subclinical mastitis was found to be 14 (41.2%) and 26 (76.5%) respectively. Based on the already set P-value, which is <0.05, a number of risk factors were found to have a significant relationship with the occurrence of clinical and sub-clinical mastitis. Generally, animal risk factors such as animal age, parity, injury on the udder or teat, and previous history of mastitis presence of injury on the udder and lactation stage were risk factors with a significant relationship with the occurrence of clinical and sub-clinical mastitis. On the other hand, management risk factors with a significant relationship to the occurrence of clinical and sub-clinical mastitis were herd size, failure to milk mastitic cow, at last, educational level, floor type, failure to use a towel, using one towel for more than one cow and failure to practice mastitis test. From a total of 772 quarters, 280 (36.3%) were found positive for sub-clinical mastitis using the California mastitis test; of these, 70 (9%) were weakly positive, 90 (11.7%) were distinct positive, and 120 (15.5%) were strongly positive. Furthermore, 13 (1.7%) quarters were blocked. Quarter level prevalence was right front 80 (41.5%), left front 64 (33.3%), right hind 69 (35.8%) and left hind 67 (34.7%). The study has shown that mastitis is a major problem for dairy farms and the findings suggested that mastitis is one of the limiting factors in increasing milk production. Subclinical mastitis was found to be a devastating problem, and it occurred in all three breeds of lactating dairy cattle. Therefore, farmers should work hard to avoid the above-mentioned risk factors to minimize the infection of their dairy cattle by mastitis and thereby increase their profit. On the other hand, the Ministry of Agriculture, through the extension unit, should work in close contact with the farmers to increase awareness of the economic importance of the disease and associated risk factors.

Keywords: mastitis, prevalence, dairy cattle, Anseba, Eritrea

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Nebila Lichiheb, LaToya Myles, William Pendergrass, Bruce Hicks, Dawson Cagle

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Atmospheric transport of hazardous materials in urban areas is increasingly under investigation due to the potential impact on human health and the environment. In response to health and safety concerns, several dispersion models have been developed to analyze and predict the dispersion of hazardous contaminants. The models of interest usually rely on meteorological information obtained from the meteorological models of NOAA’s National Weather Service (NWS). However, due to the complexity of the urban environment, NWS forecasts provide an inadequate basis for dispersion computation in urban areas. A dense meteorological network in Washington, DC, called DCNet, has been operated by NOAA since 2003 to support the development of urban monitoring methodologies and provide the driving meteorological observations for atmospheric transport and dispersion models. This study focuses on the comparison of wind observations from the DCNet station on the U.S. Department of Commerce Herbert C. Hoover Building against the North American Mesoscale (NAM) model outputs for the period 2017-2019. The goal is to develop a simple methodology for modifying NAM outputs so that the dispersion requirements of the city and its urban area can be satisfied. This methodology will allow us to quantify the prediction errors of the NAM model and propose adjustments of key variables controlling dispersion model calculation.

Keywords: meteorological data, Washington D.C., DCNet data, NAM model

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Authors: Shivam Kushwaha, Veena Bansal

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Timeshare refers to real estate that is owned simultaneously by many, for a specified time in a year, for a specified numbers of years and is maintained and managed by an agency. Timeshare falls under the umbrella of tourism and is often used for vacation. Timeshare industry has attracted significantly less number of customers in India as compared to the US and Europe. In more than 40 years of existence of timeshare industry, it has not been able to grow its roots among Indian customers. The purpose of the study: To explore perception of Indian customers towards the adoption of timeshare segment of the hospitality industry and identify the factors. Source of data: Survey has been done on existing owners of holidays memberships, resorts or those who at least tourism experience in their past purchases. Methodology: Logistic Regression is used to predict binary responses of the customers based on identified critical factors which might influence timeshare purchases. Result: The study identified four factors: discretionary income, exchange options, ownership pride, risk, and measured their influence on intention to purchases in India. It is recognized that is all four variables are statistically significant while explaining in purchase intentions of customers in India.

Keywords: timeshare, holiday, tourism, customer perception, intent to use, Indian tourism

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Authors: Ali Bakhtiari Gharibdosti, Mohammad Hosein Bijeh Keshavarzi, Samira Alijani

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To understand internal characteristics relationships and determine factors which explain under consideration characteristics in rapeseed varieties, 10 rapeseed genotypes were implemented in complete accidental plot with three-time repetitions under drought stress in 2009-2010 in research field of agriculture college, Islamic Azad University, Karaj branch. In this research, 11 characteristics include of characteristics related to growth, production and functions stages was considered. Variance analysis results showed that there is a significant difference among rapeseed varieties characteristics. By calculating simple correlation coefficient under both conditions, normal and drought stress indicate that seed function characteristics in plant and pod number have positive and significant correlation in 1% probable level with seed function and selection on the base of these characteristics was effective for improving this function. Under normal and drought stress, analyzing the main factors showed that numbers of factors which have more than one amount, had five factors under normal conditions which were 82.72% of total variance totally, but under drought stress four factors diagnosed which were 76.78% of total variance. By considering total results of this research and by assessing effective characteristics for factor analysis and selecting different components of these characteristics, they can be used for modifying works to select applicable and tolerant genotypes in drought stress conditions.

Keywords: correlation, drought stress, factor analysis, rapeseed

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Authors: Fatemeh Abdollahi, Mehran Zarghami, Jamshid Yazdani Jarati, Mun-Sunn Lye

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Fathers may be at risk of depression during the postpartum period. Some studies have been reported maternal depression is the key predictor of paternal postpartum depression (PPD). This study aimed to explore this association. Using a cross-sectional study design, 591 couples referring to primary health centers at 2-8 weeks postpartum (during 2017) were recruited. Couples screened for depression using Edinburgh Postnatal Depression Scale (EPDS). Data on socio-demographic characteristics and psychosocial factors was also gathered. Paternal PPD was analyzed in relation to maternal PPD and other related factors using multiple regressions. The prevalence of Paternal and maternal postpartum depression was 15.7% (93) and 31.8% (188), respectively. The regression model showed that there was increased risk of PPD in fathers whose wives experienced PPD [OR=1.15, (95%CI: 1.04-1.27)], who had a lower state of general health [OR=1.21, (95%CI: 1.11-1.33)], who experienced increased number of life events [OR=1.42, (95%CI: 1.01-1.2.00)], and who were at older age [OR=1.20, (95%CI: 1.05- 1.36)]. Also, there was a decreased risk of depression in fathers with more children compared with those with fewer children [OR=0.20, (95%CI: 0.07-0.53)]. Maternal PPD and psychosocial risk factors were the strong predictors of parental PPD. Being grown up in a family with two depressed parents are an important issue for children and needs futher research and attention.

Keywords: Father, Mother, Postpartum depression, Risk factors

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Authors: Murat Gunduz, Mohanad H. A. AbuHassan

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Construction industry mainly focuses on the superstructure, infrastructure, and oil and gas industry. The development of infrastructure projects in developing countries attracted a lot of foreign construction contractors, consultants, suppliers and diversified workforce to interfere and to be evolved in such huge investment. Reducing worksite delays in such projects require knowledge and attention. Therefore, it is important to identify the influencing delay attributes affecting construction projects. The significant project factors affecting construction delays were investigated. Data collection was carried out through an online web survey system to capture significant factors. Significant factors were determined with importance index and relevant recommendations are made. The output of the data analysis would lead the industry experts better assess the impact of construction delays on construction projects.

Keywords: construction industry, delays, importance index, frequency index

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Authors: Sara Bouzekri, Said Madani

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In many Mediterranean cities such as Algiers, the human activity, the strong mobility the urban sprawl, the air pollution, the problems of waste management, the wasting of the resources and the degradation of the environment weaken in an unquestionable way the farming. The question of sustainable action vis-a-vis these threats arises then in order to maintain a level of desired local development. The methodology is based on a multi-criteria method based on the AFOM diagnosis, which classifies agricultural strength indicators and those of threat, according to an analytical approach. In a sustainable development perspective, it will be appropriate to link the threat factors of the case study with the factors of climate change to see their impact on the future of agriculture. This will be accompanied by a SWOT analysis, which crosses the most significant criteria to arrive at the necessary recommendations based on future projects for urban agriculture.

Keywords: Algiers, environment, urban agriculture, threat factors

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Authors: Mohammad Reza Bagherzadeh, Mohammad Hossein Taheri

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Knowledge management serves as one of the great breakthroughs in information and knowledge era and given its outstanding features, successful organizations tends to adopt it. Therefore, to deal with knowledge management establishment in universities is of special importance. In this regard, the present research aims to shed lights on factors background knowledge management establishment at Islamic Azad University, Behshahr Branch (Northern Iran). Considering three factors information technology system, knowledge process system and organizational culture as a fundamental of knowledge management infrastructure, foregoing factors were evaluated individually. The present research was conducted in descriptive-survey manner and participants included all staffs and faculty members, so that according to Krejcie & Morgan table a sample size proportional to the population size was considered. The measurement tools included survey questionnaire whose reliability was calculated to 0.83 according to Cronbachs alpha. To data analysis, descriptive statistics such as frequency and its percentage tables, column charts, mean, standard deviation and as for inferential statistics Kolomogrov- Smirnov test and single T-test were used. The findings show that despite the good corporate culture as one of the three factors background the establishment of the knowledge management at Islamic Azad University Behshahr Branch, other two ones, including IT systems, and knowledge processes systems are characterized with adverse status. As a result, these factors have caused no necessary conditions for the establishment of Knowledge Management in the university provided.

Keywords: knowledge management, information technology, knowledge processes, organizational culture, educational institutions

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Authors: Mariam Ismail, Kazeem Rufai, Jeremiah Balogun

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A study was conducted to apply fuzzy logic to the development of a monitoring model for child pornography based on associated risk factors, which can be used by forensic experts or integrated into forensic systems for the early detection of child pornographic activities. A number of methods were adopted in the study, which includes an extensive review of related works was done in order to identify the factors that are associated with child pornography following which they were validated by an expert sex psychologist and guidance counselor, and relevant data was collected. Fuzzy membership functions were used to fuzzify the associated variables identified alongside the risk of the occurrence of child pornography based on the inference rules that were provided by the experts consulted, and the fuzzy logic expert system was simulated using the Fuzzy Logic Toolbox available in the MATLAB Software Release 2016. The results of the study showed that there were 4 categories of risk factors required for assessing the risk of a suspect committing child pornography offenses. The results of the study showed that 2 and 3 triangular membership functions were used to formulate the risk factors based on the 2 and 3 number of labels assigned, respectively. The results of the study showed that 5 fuzzy logic models were formulated such that the first 4 was used to assess the impact of each category on child pornography while the last one takes the 4 outputs from the 4 fuzzy logic models as inputs required for assessing the risk of child pornography. The following conclusion was made; there were factors that were related to personal traits, social traits, history of child pornography crimes, and self-regulatory deficiency traits by the suspects required for the assessment of the risk of child pornography crimes committed by a suspect. Using the values of the identified risk factors selected for this study, the risk of child pornography can be easily assessed from their values in order to determine the likelihood of a suspect perpetuating the crime.

Keywords: fuzzy, membership functions, pornography, risk factors

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Authors: Vasujeet Singh, Pruthiviraj Nemalipuri, Vivek Vitankar, Harish Chandra Das

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For the correct prediction of thermal and hydraulic performance (bed voidage, suspension density, pressure drop, heat transfer, and combustion kinetics), one should incorporate the correct parameters in the computational fluid dynamics simulation of a fluidized bed gasifier. Scarcity of fossil fuels, and to fulfill the energy demand of the increasing population, researchers need to shift their attention to the alternative to fossil fuels. The current research work focuses on hydrodynamics behavior and gasification of sawdust inside a 2D industrial scale FBG using the Eulerian-Eulerian multifluid model. The present numerical model is validated with experimental data. Further, this model extended for the prediction of gasification characteristics of sawdust by incorporating eight heterogeneous moisture release, volatile cracking, tar cracking, tar oxidation, char combustion, CO₂ gasification, steam gasification, methanation reaction, and five homogeneous oxidation of CO, CH₄, H₂, forward and backward water gas shift (WGS) reactions. In the result section, composition of gasification products is analyzed, along with the hydrodynamics of sawdust and sand phase, heat transfer between the gas, sand and sawdust, reaction rates of different homogeneous and heterogeneous reactions is being analyzed along the height of the domain.

Keywords: devolatilization, Eulerian-Eulerian, fluidized bed gasifier, mathematical modelling, sawdust gasification

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Wallace Wong, Julia Toews, Kirsten Graham, Teija Yli-Renko, Dorcas Chau, Judith Lai, Momoka Nabatame

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Many transgender individuals report recognizing their non-cisgender identity in early childhood. However, numerous transgender adolescents choose to delay coming out to their family or friends until after puberty. This study explored factors influencing this decision by analyzing data from over 100 cases of transgender adolescents who disclosed their identity post-puberty. Using a qualitative thematic analysis approach, we identified key factors affecting this choice, including fear of rejection, lack of affirming resources, internalized transphobia, and safety concerns. The study also revealed the positive impact of increased transgender visibility and online support communities in facilitating later coming-out experiences. Our findings highlight the intersection of personal, social, and environmental factors contributing to disclosure timing for transgender adolescents. These insights can inform supportive interventions and policies to assist transgender youth throughout their coming out process, regardless of when it occurs. This research contributes to a deeper understanding of the challenges faced by transgender adolescents and the support they need during this critical period.

Keywords: transgender, gender identity, coming out, adolescents

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Authors: Rajiv Kumar, Amit Sachan, Arindam Mukherjee

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Government agencies around the world, including India, are incorporating digital technologies and processes into their day-to-day operations to become more efficient. Despite low internet penetration (around 34.8% of total population) in India, Government of India has made some public services mandatory to access online (e.g. passport, tax filing).This is insisting citizens to access mandatory public services online. However, due to digital divide, all citizens do not have equal access to internet. In light of this, it is an interesting topic to explore how citizens are able to access mandatory online public services. It is important to understand how citizens are adopting these mandatory e-government services and how the adoption behavior of these mandatory e-government services is different or similar to adoption behavior of non-mandatory e-government services. The purpose of this research is to investigate the factors that influence adoption of mandatory and non-mandatory e-government services in India. A quantitative technique is employed in this study. A conceptual model has been proposed by integrating the influencing factors to adopt e-government services from previous studies. The proposed conceptual model highlights a comprehensive set of potential factors influencing the adoption of e-government services. The proposed model has been validated by keeping in view the local context of Indian society. Online and paper based survey was administered, collected data was analyzed and results have been discussed. A total of 463 valid responses were received and further the responses were analyzed. The research reveals that the influencing factors to adopt e-government services are not same for both mandatory and non-mandatory e-government services. There are some factors that influence adoption of both mandatory and non-mandatory e-government services but there are some which are relevant for either of mandatory and non-mandatory e-government services. The research findings may help government or concerned agencies in successfully implementing e-government services.

Keywords: adoption, e-government, India, mandatory, non-mandatory

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Authors: Sang-Woo Kim, Young-Seon Lee

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An important concern in metal forming, even at elevated temperatures, is whether a desired deformation can be accomplished without any failure of the material. A detailed understanding of the critical condition for crack initiation provides not only the workability limit of a material but also a guide-line for process design. This paper describes the utilization of ductile fracture criteria in conjunction with the finite element method (FEM) for predicting the onset of fracture in warm metal working processes of magnesium alloy sheets. Critical damage values for various ductile fracture criteria were determined from uniaxial tensile tests and were expressed as the function of strain rate and temperature. In order to find the best criterion for failure prediction, Erichsen cupping tests under isothermal conditions and FE simulations combined with ductile fracture criteria were carried out. Based on the plastic deformation histories obtained from the FE analyses of the Erichsen cupping tests and the critical damage value curves, the initiation time and location of fracture were predicted under a bi-axial tensile condition. The results were compared with experimental results and the best criterion was recommended. In addition, the proposed methodology was used to predict the onset of fracture in non-isothermal deep drawing processes using an irregular shaped blank, and the results were verified experimentally.

Keywords: magnesium, AZ31 alloy, ductile fracture, FEM, sheet forming, Erichsen cupping test

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Authors: Almontas Vilutis, Vytenis Jankauskas

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The paper presents the results of the investigation of the dry sliding friction of wood-plastic composites (WPCs) against WC-Co cemented carbide. The dependence of the dynamic coefficient of friction on the main influencing factors (vertical load, temperature, and sliding distance) was investigated by evaluating their mutual interaction. Multiple regression analysis showed a high polynomial dependence (adjusted R2 > 0.98). The resistance of the composite to thermo-mechanical effects determines how temperature and force factors affect the magnitude of the coefficient of friction. WPC-B composite has the lowest friction and highest resistance compared to WPC-A, while composite and cemented carbide materials wear the least. Energy dispersive spectroscopy (EDS), based on elemental composition, provided important insights into the friction process.

Keywords: friction, composite, carbide, factors

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Authors: Osman Zorbaz, Selen Demirtas-Zorbaz, Ozlem Ulas

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The factors that cause adolescents to drop out school were several. One of the frameworks about school dropout focuses on the contextual factors around the adolescents whereas the other one focuses on individual factors. It can be said that both factors are important equally. In this study, both adolescent’s individual factors (anti-social behaviors, academic success) and contextual factors (parent academic involvement, parent academic support, number of siblings, living with parent) were examined in the term of school dropout. The study sample consisted of 346 high school students in the public schools in Ankara who continued their education in 2015-2016 academic year. One hundred eighty-five the students (53.5%) were girls and 161 (46.5%) were boys. In addition to this 118 of them were in ninth grade, 122 of them in tenth grade and 106 of them were in eleventh grade. Multiple regression and one-way ANOVA statistical methods were used. First, it was examined if the data meet the assumptions and conditions that are required for regression analysis. After controlling the assumptions, regression analysis was conducted. Parent academic involvement, parent academic support, number of siblings, anti-social behaviors, academic success variables were taken into the regression model and it was seen that parent academic involvement (t=-3.023, p < .01), anti-social behaviors (t=7.038, p < .001), and academic success (t=-3.718, p < .001) predicted school dropout whereas parent academic support (t=-1.403, p > .05) and number of siblings (t=-1.908, p > .05) didn’t. The model explained 30% of the variance (R=.557, R2=.300, F5,345=30.626, p < .001). In addition to this the variance, results showed there was no significant difference on high school students school dropout levels according to living with parents or not (F2;345=1.183, p > .05). Results discussed in the light of the literature and suggestion were made. As a result, academic involvement, academic success and anti-social behaviors will be considered as an important factors for preventing school drop-out.

Keywords: adolescents, anti-social behavior, parent academic involvement, parent academic support, school dropout

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Authors: E. Vlachopapadopoulou, E. Dikaiakou, E. Anagnostou, I. Panagiotopoulos, E. Kaloumenou, M. Kafetzi, A. Fotinou, S. Michalacos

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Aim: To investigate the relation and impact of Body Mass Index (BMI), Waist Circumference (WC) and Body Fat Weight (BFW) on insulin resistance (MATSUDA INDEX < 2.5) in children and adolescents. Methods: Data from 95 overweight and obese children (47 boys and 48 girls) with mean age 10.7 ± 2.2 years were analyzed. ROC analysis was used to investigate the predictive ability of BMI, WC and BFW for insulin resistance and find the optimal cut-offs. The overall performance of the ROC analysis was quantified by computing area under the curve (AUC). Results: ROC curve analysis indicated that the optimal-cut off of WC for the prediction of insulin resistance was 97 cm with sensitivity equal to 75% and specificity equal to 73.1%. AUC was 0.78 (95% CI: 0.63-0.92, p=0.001). The sensitivity and specificity of obesity for the discrimination of participants with insulin resistance from those without insulin resistance were equal to 58.3% and 75%, respectively (AUC=0.67). BFW had a borderline predictive ability for insulin resistance (AUC=0.58, 95% CI: 0.43-0.74, p=0.101). The predictive ability of WC was equivalent with the correspondence predictive ability of BMI (p=0.891). Obese subjects had 4.2 times greater odds for having insulin resistance (95% CI: 1.71-10.30, p < 0.001), while subjects with WC more than 97 had 8.1 times greater odds for having insulin resistance (95% CI: 2.14-30.86, p=0.002). Conclusion: BMI and WC are important clinical factors that have significant clinical relation with insulin resistance in children and adolescents. The cut off of 97 cm for WC can identify children with greater likelihood for insulin resistance.

Keywords: body fat weight, body mass index, insulin resistance, obese children, waist circumference

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Authors: Esam El Gohary

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— The companies’ ability to survive and compete in the fierce competition is determined by its competitiveness level. With the spread of information technology use and appearance of online shopping, it became crucial for companies to adopt e-commerce system to increase its competitiveness. This paper was conducted with the purpose of determine how increasing the service value through e-commerce factors (competitive strategy, ICT infrastructures, logistics, security, human resources and innovation) can enhance companies' competitiveness. The problem of this paper is summarized in the absence of the thorough awareness of e-commerce benefits for business owners and customers, as well as how to reduce the intangibility attributes of e-commerce. For this purpose this paper describes the e-commerce in Egypt and its success factors (infrastructures, legal and regulatory environment, human resources and innovation), as well as displays the barriers of such factor, to investigate the significant of these factors on increasing service value and enhance companies' competitiveness. This paper revealed that e-commerce companies have many opportunities to enhance its competitiveness in Egypt, which is enhanced by several factors. The most important factors are “strong ICT infrastructure, qualified and skilled human resources, in addition to the distinctive logistics that distinguish Egypt due to its location, strong legal and regulatory environment and Innovation, as well as the competitive strategy. As well as, companies encounter several threats such as; the lack of infrastructures and logistics in rural areas, the absence of the inclusive understanding and awareness of e-commerce, fear from e-payment transactions and fraud, the ambiguity and burdensome of customs. Through the research findings several recommendations were introduced to both government and companies to overcome threats and exploit opportunities to improve performance and enhance companies' competitiveness.

Keywords: e-commerce competitiveness, e-commerce factors, e-commerce in Egypt, information technology

Procedia PDF Downloads 101