Search results for: predicting factors

Authors: Zhengyi Kong, Xueqing Wang, Quang-Viet Vu

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In this study, the structural behavior of high strength steel (HSS) hot-finished elliptical hollow section (EHS) subjected to uniaxial eccentric compression is investigated. A finite element method for predicting the cross-section resistance of HSS hot-finished EHS is developed using ABAQUS software, which is then verified by comparison with previous experiments. The validated finite element method is employed to carry out parametric studies for investigating the structural behavior of HSS hot-finished EHS under uniaxial eccentric compression and evaluate the current design guidance for HSS hot-finished EHS. Different parameters, such as the radius of the larger and smaller outer diameter of EHS, thickness of EHS, eccentricity, and material property, are considered. The resulting data from 84 finite element models are used to obtain the relationship between the cross-section resistance of HSS hot-finished EHS and cross-section slenderness. It is concluded that current design provisions, such as EN 1993-1-1, BS 5950-1, AS4100, and Gardner et al., are conservative for predicting the HSS hot-finished EHS under uniaxial eccentric compression.

Keywords: hot-finished, elliptical hollow section, uniaxial eccentric compression, finite element method

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Authors: Samir K. Safi

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The purpose of this study was to compare the performance of two types of models, namely MIDAS and MIDAS-GARCH, in predicting the volatility of crude oil returns based on gold price returns and the COVID-19 pandemic. The study aimed to identify which model would provide more accurate short-term and long-term predictions and which model would perform better in handling the increased volatility caused by the pandemic. The findings of the study revealed that the MIDAS model performed better in predicting short-term and long-term volatility before the pandemic, while the MIDAS-GARCH model performed significantly better in handling the increased volatility caused by the pandemic. The study highlights the importance of selecting appropriate models to handle the complexities of real-world data and shows that the choice of model can significantly impact the accuracy of predictions. The practical implications of model selection and exploring potential methodological adjustments for future research will be highlighted and discussed.

Keywords: GARCH-MIDAS, MIDAS, crude oil, gold, COVID-19, volatility

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Authors: Amirmehdi Dokhanchi, Babak Ziyae

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Studying and prioritizing effective factors on productivity of human resources through TOPSIS method is the main aim of the present research study. For this reason, while reviewing concepts existing in productivity, effective factors were studied. Managers, supervisors, staff and personnel of Tabriz Tractor Manufacturing Company are considered subject of this study. Of total individuals, 160 of them were selected through the application of random sampling method as 'subject'. Two questionnaires were used for collecting data in this study. The factors, which had the highest effect on productivity, were recognized through the application of software packages. TOPSIS method was used for prioritizing recognized factors. For this reason, the second questionnaire was put available to statistics sample for studying effect of each of factors towards predetermined indicators. Therefore, decision-making matrix was obtained. The result of prioritizing factors shows that existence of accurate organizational strategy, high level of occupational skill, application of partnership and contribution system, on-the-job-training services, high quality of occupational life, dissemination of appropriate organizational culture, encouraging to creativity and innovation, and environmental factors are prioritized respectively.

Keywords: productivity of human resources, productivity indicators, TOPSIS, prioritizing factors

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Authors: Yas Barzegar, Atrin Barzegar

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Drinking water quality assessment is a major issue today; technology and practices are continuously improving; Artificial Intelligence (AI) methods prove their efficiency in this domain. The current research seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system (FIS) is applied with different defuzzification methods. The Proposed Model includes three fuzzy intermediate models and one fuzzy final model. Each fuzzy model consists of three input parameters and 27 fuzzy rules. The model is developed for water quality assessment with a dataset considering nine parameters (Alkalinity, Hardness, pH, Ca, Mg, Fluoride, Sulphate, Nitrates, and Iron). Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; it is an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The FIS method can provide an effective solution to complex systems; this method can be modified easily to improve performance.

Keywords: water quality, fuzzy logic, smart cities, water attribute, fuzzy inference system, membership function

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Authors: N. Sena, C. Vasconcelos

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Portugal has a lower rate of people who exercise. Fitness centers are a widely recognized context for the performance of an exercise. Thus, the objective of this study is to analyze the motivational factors for the practice of exercise in a sample of Portuguese fitness center users. The sample consists of 34 users (23 men and 11 women), aged between 16 and 60 years old (24.7 ± 11,5 years old). The instrument used for data collection was the Motivation Questionnaire for Exercise (version translated and validated into Portuguese), consisting of forty-nine items grouped into ten motivational factors. Responses to the Exercise Motivation Questionnaire are given on a 6-point Likert scale (0="not at all true for me" to 5="completely true for me"). With regard to the results, it is possible to verify that the motivational factors considered most relevant by the sample of our study were “Well-being” (4.44 ± 0.28), followed by “Health” (4.29 ± 0.57) and “Stress Management” (4.06 ± 0.54). The factors “Affiliation” (3.11 ± 0.49) “Personal Appreciation” (2.26 ± 0.59) and “Medical History” (1.71 ± 0.74) were considered by the respondents to be the least important factors for performing the exercise. The conclusion of this study is that in the sample of this study, the factors that most motivated the practice of exercise were “Well-being”, “Health” and “Stress Management”. In the opposite direction, the factors that least motivated the individuals in this sample to practice exercise were “Affiliation”, “Personal Appreciation” and “Medical History”.

Keywords: exercise, fitness center users, motivational factors, Portugal

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Authors: Linda Smail, Zineb Azouz

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Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks.

Keywords: Bayesian networks, D-Separation, level two Bayesian networks, factorization of computation

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Authors: Katyana A. Vert-Pre, James T. Thorson, Thomas Trancart, Eric Feunteun

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In marine ecosystems, spatial and temporal species structure is an important component of ecosystems’ response to anthropological and environmental factors. Although spatial distribution patterns and fish temporal series of abundance have been studied in the past, little research has been allocated to the joint dynamic spatio-temporal functional patterns in marine ecosystems and their use in multispecies management and conservation. Each species represents a function to the ecosystem, and the distribution of these species might not be random. A heterogeneous functional distribution will lead to a more resilient ecosystem to external factors. Applying a Vector-Autoregressive Spatio-Temporal (VAST) model for count data, we estimate the spatio-temporal distribution, shift in time, and abundance of 140 species of the Eastern English Chanel, Bay of Biscay and Mediterranean Sea. From the model outputs, we determined spatio-temporal clusters, calculating p-values for hierarchical clustering via multiscale bootstrap resampling. Then, we designed a functional map given the defined cluster. We found that the species distribution within the ecosystem was not random. Indeed, species evolved in space and time in clusters. Moreover, these clusters remained similar over time deriving from the fact that species of a same cluster often shifted in sync, keeping the overall structure of the ecosystem similar overtime. Knowing the co-existing species within these clusters could help with predicting data-poor species distribution and abundance. Further analysis is being performed to assess the ecological functions represented in each cluster.

Keywords: cluster distribution shift, European marine ecosystems, functional distribution, spatio-temporal model

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Authors: Ehsan Mehryaar, Reza Bushehri

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One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

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Authors: Khalid Alasim

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The reading comprehension levels of students who are deaf or hard of hearing (DHH) are low compared to those of their hearing peers. One possible reason for this low reading levels is related to the students’ prior knowledge. This study investigated the potential factors that might affected DHH students’ prior knowledge, including their degree of hearing loss, the presence or absence of family members with a hearing loss, and educational stage (elementary–middle school). The study also examined the contribution of prior knowledge in predicting DHH students’ reading comprehension levels, and investigated the differences in the students’ scores based on the type of questions, including text-explicit (TE), text-implicit (TI), and script-implicit (SI) questions. Thirty-one elementary and middle-school students completed a demographic form and assessment, and descriptive statistics and multiple and simple linear regressions were used to answer the research questions. The findings indicated that the independent variables—degree of hearing loss, presence or absence of family members with hearing loss, and educational stage—explained little of the variance in DHH students’ prior knowledge. Further, the results showed that the DHH students’ prior knowledge affected their reading comprehension. Finally, the result demonstrated that the participants were able to answer more of the TI questions correctly than the TE and SI questions. The study concluded that prior knowledge is important in these students’ reading comprehension, and it is also important for teachers and parents of DHH children to use effective ways to increase their students’ and children’s prior knowledge.

Keywords: reading comprehension, prior knowledge, metacognition, elementary, self-contained classrooms

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Authors: Belalia Douma Omar, Bakhta Boukhatem, Mohamed Ghrici

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Self-compacting concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work condition and also reduce the impact of environment by elimination of the need for compaction. Fuzzy logic (FL) approaches has recently been used to model some of the human activities in many areas of civil engineering applications. Especially from these systems in the model experimental studies, very good results have been obtained. In the present study, a model for predicting compressive strength of SCC containing various proportions of fly ash, as partial replacement of cement has been developed by using Adaptive Neuro-Fuzzy Inference System (ANFIS). For the purpose of building this model, a database of experimental data were gathered from the literature and used for training and testing the model. The used data as the inputs of fuzzy logic models are arranged in a format of five parameters that cover the total binder content, fly ash replacement percentage, water content, super plasticizer and age of specimens. The training and testing results in the fuzzy logic model have shown a strong potential for predicting the compressive strength of SCC containing fly ash in the considered range.

Keywords: self-compacting concrete, fly ash, strength prediction, fuzzy logic

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Authors: Reena Murali, David Peter S.

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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: Debjyoti Bhakat, Indranil Mondal, Asish K. Mukhopadayay, Nabendu S. Chatterjee

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Enterotoxigenic Escherichia coli is one of the leading causes of diarrhea in infants and travelers in developing countries. Colonization factors play an important role in pathogenesis and are one of the main targets for Enterotoxigenic Escherichia coli (ETEC) vaccine development. However, ETEC vaccines had poorly performed in the past, as the prevalence of colonization factors is region-dependent. There are more than 25 classical colonization factors presently known to be expressed by ETEC, although all are not expressed together. Further, there are other multiple non-classical virulence factors that are also identified. Here the presence and expression of common classical and non-classical virulence factors were studied. Further studies were done on the expression of prevalent colonization factors in different strains. For the prevalence determination, multiplex polymerase chain reaction (PCR) was employed, which was confirmed by simplex PCR. Quantitative RT-PCR was done to study the RNA expression of these virulence factors. Strains negative for colonization factors expression were confirmed by SDS-PAGE. Among the clinical isolates, the most prevalent toxin was est+elt, followed by est and elt, while the pattern was reversed in the control strains. There were 29% and 40% strains negative for any classical colonization factors (CF) or non-classical virulence factors (NCVF) among the clinical and control strains, respectively. Among CF positive ETEC strains, CS6 and CS21 were the prevalent ones in the clinical strains, whereas in control strains, CS6 was the predominant one. For NCVF genes, eatA was the most prevalent among the clinical isolates and etpA for control. CS6 was the most expressed CF, and eatA was the predominantly expressed NCVF for both clinical and controlled ETEC isolates. CS6 expression was more in strains having CS6 alone. Different strains express CS6 at different levels. Not all strains expressed their respective virulence factors. Understanding the prevalent colonization factor, CS6, and its nature of expression will contribute to designing an effective vaccine against ETEC in this region of the globe. The expression pattern of CS6 also will help in examining the relatedness between the ETEC subtypes.

Keywords: classical virulence factors, CS6, diarrhea, enterotoxigenic escherichia coli, expression, non-classical virulence factors

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Authors: Shayem Saleh ALresheedi, Lu Song Feng, Abdulaziz Abdulwahab M. Fatani

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Cloud computing is an emerging concept in the technological sphere. Its development enables many applications to avail information online and on demand. It is becoming an essential element for businesses due to its ability to diminish the costs of IT infrastructure and is being adopted in Saudi Arabia. However, there exist many factors that affect its adoption. Several researchers in the field have ignored the study of the TAM model for identifying the relevant factors and their impact for adopting of cloud computing. This study focuses on evaluating the acceptability of cloud computing and analyzing its impacting factors using Technology Acceptance Model (TAM) of technology adoption in Saudi Arabia. It suggests a model to examine the influential factors of the TAM model along with external factors of technical support in adapting the cloud computing. The proposed model has been tested through the use of multiple hypotheses based on calculation tools and collected data from customers through questionnaires. The findings of the study prove that the TAM model along with external factors can be applied in measuring the expected adoption of cloud computing. The study presents an investigation of influential factors and further recommendation in adopting cloud computing in Saudi Arabia.

Keywords: cloud computing, acceptability, adoption, determinants

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: Georgios Koronis, Arlindo Silva

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This study seeks to understand the mechanical behavior of a natural fiber reinforced composite (epoxy/flax) in more depth, utilizing both experimental and numerical methods. It is attempted to identify relationships between the design parameters and the product performance, understand the effect of noise factors and reduce process variations. Optimization of the mechanical performance of manufactured goods has recently been implemented by numerous studies for green composites. However, these studies are limited and have explored in principal mass production processes. It is expected here to discover knowledge about composite’s manufacturing that can be used to design artifacts that are of low batch and tailored to niche markets. The goal is to reach greater consistency in the performance and further understand which factors play significant roles in obtaining the best mechanical performance. A prediction of response function (in various operating conditions) of the process is modeled by the DoE. Normally, a full factorial designed experiment is required and consists of all possible combinations of levels for all factors. An analytical assessment is possible though with just a fraction of the full factorial experiment. The outline of the research approach will comprise of evaluating the influence that these variables have and how they affect the composite mechanical behavior. The coupons will be fabricated by the vacuum infusion process defined by three process parameters: flow rate, injection point position and fiber treatment. Each process parameter is studied at 2-levels along with their interactions. Moreover, the tensile and flexural properties will be obtained through mechanical testing to discover the key process parameters. In this setting, an experimental phase will be followed in which a number of fabricated coupons will be tested to allow for a validation of the design of the experiment’s setup. Finally, the results are validated by performing the optimum set of in a final set of experiments as indicated by the DoE. It is expected that after a good agreement between the predicted and the verification experimental values, the optimal processing parameter of the biocomposite lamina will be effectively determined.

Keywords: design of experiments, flax fabrics, mechanical performance, natural fiber reinforced composites

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Authors: Manirath Wongsim, Ekkachai Naenudorn, Nipotepat Muangkote

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Information Technology (IT), play an important role in business management strategies and can provide assistance in all phases of decision making. Thus, many organizations need to be seen as adopting IT, which is critical for a company to organize, manage and operate its processes. In order to implement IT successfully, it is important to understand the underlying factors that influence agricultural system's adoption success. Therefore, this research intends to study this perspective of factors that influence and impact successful IT adoption and related agricultural performance. Case study and survey methodology were adopted for this research. Case studies in two Thai- organizations were carried out. The results of the two main case studies suggested 21 factors that may have an impact on IT adoption in agriculture in Thailand, which led to the development of the preliminary framework. Next, a survey instrument was developed based on the findings from case studies. Survey questionnaires were gathered from 217 respondents from two large-scale surveys were sent to selected members of Thailand farmer, and Thailand computer to test the research framework. The results indicate that the top five critical factors for ensuring IT adoption in agricultural were: 1) network and communication facilities; 2) software; 3) hardware; 4) farmer’s IT knowledge, and; 5) training and education. Therefore, it is now clear which factors are influencing IT adoption and which of those factors are critical success factors for ensuring IT adoption in agricultural organization.

Keywords: agricultural systems adoption, factors influencing IT adoption, factors affecting in agricultural adoption

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Authors: Raziyeh Teimouri, Sadasivam Karuppannan, Alpana Sivam, Ning Gu

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Urban Green Space (UGS) is one of the most critical components of urban systems to create sustainable cities. UGS has valuable social benefits that closely correlate with people's life quality. Studying social sustainability factors that can be achieved by green spaces is required for optimal UGS planning to increase urban social sustainability. This paper aims to identify key factors that enhance urban social sustainability through UGS planning. To reach the goal of the study international experts’ survey has been conducted. According to the results of the survey analysis, factors of proper distribution, links to public transportation, walkable access, sense of place, social interactions, public education, safety and security, walkability and cyclability, physical activity and recreational facilities, suitability for all ages, disabled people, women, and children are among the key factors that should consider in UGS planning programs to promote urban social sustainability.

Keywords: UGS, planning, social sustainability, key factors

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Authors: Weerakarj Dokchan

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The purposes of this research are to find out the factors for developing trainers in the auto part manufacturing factories (AMF) in Amata Nakon Industrial Estate Cholburi. Population in this study included 148 operators to complete the questionnaires and 10 trainers to provide the information on the interview. The research statistics consisted of percentage, mean, standard deviation and step-wise multiple linear regression analysis.The analysis of the training model revealed that: The research result showed that the development factors of trainers in AMF consisted of 3 main factors and 8 sub-factors: 1) knowledge competency consisting of 4 sub-factors; arrangement of critical thinking, organizational loyalty, working experience of the trainers, analysis of behavior, and work and organization loyalty which could predict the success of the trainers at 55.60%. 2) Skill competency consisted of 4 sub-factors, arrangement of critical thinking, organizational loyalty and analysis of behavior and work and the development of emotional quotient. These 4 sub-factors could predict the success of the trainers in skill aspect 55.90%. 3) The attitude competency consisted of 4 sub-factors, arrangement of critical thinking, intention of trainee computer competency and teaching psychology. In conclusion, these 4 sub-factors could predict the success of the trainers in attitude aspect 58.50%.

Keywords: the development factors, trainers development, trainer competencies, auto part manufacturing factory (AMF), AmataNakon Industrial Estate Cholburi

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Authors: Y. Rathiranee, D. M. Semasinghe

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This study attempts to identify the factors influencing on women empowerment of rural area in Sri Lanka through micro finance services. Data were collected from one hundred (100) rural women involving self employment activities through a questionnaire using direct personal interviews. Judgment and Convenience Random sampling technique was used to select the sample size from three Divisional Secretariat divisions of Kandawalai, Poonakari and Karachchi in Kilinochchi District. The factor analysis was performed on fourteen (14) variables for screening and reducing the variables to identify the influencing factors on empowerment. Multiple regression analysis was used to identify the relationship between the three empowerment factors and the impact of micro-finance on overall empowerment of rural women. The result of this study summarized the variables into three factors namely decision making, freedom to mobility and family support and which are positively associated with empowerment. In addition to this the value of adjusted R2 is 0.248 indicates that all the variables extracted can be explained 24.8% of the variation in the women empowerment through microfinance. Independent variables of these three factors have a positive correlation with women empowerment as well as significant values at 5 percent level.

Keywords: influencing factors, micro finance, rural women, women empowerment

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Authors: Abdolreza Noroozi Chakoli

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This study aims to examine the impact of five effective factors on the success of the managers of distance knowledge sharing centers in Iran. To conduct it, 3 centers, including the National Library and Archives of Iran (NLAI), Scientific Information Database Center (SID), and Islamic World Science Citation Center (ISC), were selected to study the effect of five factors 'infrastructure of information technology', 'experienced staff', 'specialized staff', 'employee public relations' and 'the geographical location of the establishment' on the success of the centers. ANOVA test, Scheffe test, and Pearson's correlation test were used to analyze the data. The findings confirmed the effect of all 5 factors on the success of these centers. However, their effects are not the same on each factor. The results show each of these factors is not only individually but also together affect the success of centers for distance knowledge sharing. Moreover, it was demonstrated that there is a correlation between these factors. The results of this study show what factors determine the success of the centers and their efficiency in distance knowledge sharing in Iran.

Keywords: distance knowledge sharing centers, Iran’s knowledge centers, knowledge sharing centers, staff success

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Authors: Masoud Nasiri Sarvi, Yunhua Luo

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Low-trauma hip fracture, usually caused by fall from standing height, has become a main source of morbidity and mortality for the elderly. Factors affecting hip fracture include sex, race, age, body weight, height, body mass distribution, etc., and thus, hip fracture risk in fall differs widely from subject to subject. It is therefore necessary to develop a subject-specific biomechanical model to predict hip fracture risk. The objective of this study is to develop a two-level, image-based, subject-specific biomechanical model consisting of a whole-body dynamics model and a proximal-femur finite element (FE) model for more accurately assessing the risk of hip fracture in lateral falls. Required information for constructing the model is extracted from a whole-body and a hip DXA (Dual Energy X-ray Absorptiometry) image of the subject. The proposed model considers all parameters subject-specifically, which will provide a fast, accurate, and non-expensive method for predicting hip fracture risk.

Keywords: bone mineral density, hip fracture risk, impact force, sideways falls

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Authors: Mehrnoosh Askarizadeh

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The increasing variations of the business world correspond with a high diversity of theoretical perspectives used in project management research. This diversity is reflected by a variety of influencing factors, which have been the subject of empirical studies. This article aims to systemize the different streams of research on the basis of a literature review and at developing a research framework influencing factors. We will identify fundamental elements of a project management theory. The framework consists of three dimensions: design, context, and goal. Its purpose is to support the combination of different perspectives and the development of strategies for further research.

Keywords: project, goal, project management, influencing factors

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Authors: Ajay Kumar Rana

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Predicting human behaviour, discerning monozygotic twins or left over remnant tissues/fluids of a single human source remains a big challenge in forensic science. Recent advances in the field of DNA methylations which are broadly chemical hallmarks in response to environmental factors can certainly help to identify and discriminate various single-source DNA samples collected from the crime scenes. In this review, cytosine methylation of DNA has been methodologically discussed with its broad applications in many challenging forensic issues like body fluid identification, race/ethnicity identification, monozygotic twins dilemma, addiction or behavioural prediction, age prediction, or even authenticity of the human DNA. With the advent of next-generation sequencing techniques, blooming of DNA methylation datasets and together with standard molecular protocols, the prospect of investigating and solving the above issues and extracting the exact nature of the truth for reconstructing the crime scene events would be undoubtedly helpful in defending and solving the critical crime cases.

Keywords: DNA methylation, differentially methylated regions, human identification, forensics

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Authors: Thidathit Prachanukool, Chaiyaporn Yuksen, Welawat Tienpratarn, Sorravit Savatmongkorngul, Panvilai Tangkulpanich, Chetsadakon Jenpanitpong, Yuranan Phootothum, Malivan Phontabtim, Promphet Nuanprom

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Background: Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to developed the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods: This study was diagnostic, retrospectively cross-sectional and exploratory model at the Emergency Medicine Department in Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN)>75%, and the pain duration before presentation. The predictive model and prediction score for ruptured appendicitis was developed by multivariable logistic regression analysis. Result: During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score > 6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion: Using the Ramathibodi Welawat Ruptured Appendicitis Score. (RAMA WeRA Score) developed in this study, a score of > 6 was associated with ruptured appendicitis.

Keywords: predictive model, risk score, ruptured appendicitis, emergency room

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Authors: Ali Asghar Asad Sangabi, Afsaneh Aeen, Mohammad Behroozi

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Knowledge management is vital in today's world as one of the most valuable intangible assets regarded by companies. This study aimed to identify factors that affect the application of knowledge management in the Iranian Oil Terminals Company in 2022. In this study, 12 of the factors affecting the application of knowledge management have been studied, and implement practical solutions, and reuse has been studied. This study is descriptive data from the questionnaire factors affecting knowledge management application used by Cronbach's Coefficient Alpha equal to 0.85. The population of this study consisted of 1500 IOTC employees. The sample is determined by the Cochran formula sample; the results of this study showed that between the application of knowledge management and factors, there is a significant correlation. Among the factors that have been studied, valuable teamwork and organizational culture were the most effective, and the infrastructure of information systems had the least impact on Knowledge management.

Keywords: knowledge management, knowledge-based organization, Iranian Oil Terminals

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Authors: Fahimeh Khatami, Maryam Ziyaee, Elham Sanagar Darbani

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Variety of different factors could influence on the measure urban perception. Both physical and non-physical factors, at least, make the quality of perception through the urban spaces. The value of lighting is one of the important factors which could make the better quality of environmental perception for the user. The perception of urban space in most of the Iranian cities is offer by different factors during the night time which caused to the death of nightlife and social activities. Therefore, this research is an attempt to study on the different of user perception during day and night in the Koohsangi Street. As the case study area in Iran in order to bring out the main influential factors during perception process. To deal with this good we used chi-square test on a sample size made up of on hundred participants. The result shows that for improving the night quality of urban spaces the legibility, navigation, and role stimulation were in important perception factors. Therefore, by focusing on these factors it would be possible to find out more functional solution for improving the activity of night perception.

Keywords: perception, urban space, legibility, imageability, nightscape

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Authors: O. Kahraman, M. S. Alatas, O. B. Citil

Abstract:

In determination of values of feeds which, are used in ruminant nutrition, different methods are used like in vivo, in vitro, in situ or in sacco. Generally, the most reliable results are taken from the in vivo studies. But because of the disadvantages like being hard, laborious and expensive, time consuming, being hard to keep the experiment conditions under control and too much samples are needed, the in vitro techniques are more preferred. The most widely used in vitro techniques are two-staged digestion technique and gas production technique. In vitro gas production technique is based on the measurement of the CO2 which is released as a result of microbial fermentation of the feeds. In this review, the factors affecting the results obtained from in vitro gas production technique (Hohenheim Feed Test) were discussed. Some factors must be taken into consideration when interpreting the findings obtained in these studies and also comparing the findings reported by different researchers for the same feeds. These factors were discussed in 3 groups: factors related to animal, factors related to feeds and factors related with differences in the application of method. These factors and their effects on the results were explained. Also it can be concluded that the use of in vitro gas production technique in feed evaluation routinely can be contributed to the comprehensive feed evaluation, but standardization is needed in this technique to attain more reliable results.

Keywords: In vitro, gas production technique, Hohenheim feed test, standardization

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Authors: Fidelia A. Orji, Julita Vassileva

Abstract:

This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Rahele Hasani, Iraj Mehregan, Kambiz Larijani, Taher Nejadsattari, Romain Scalone

Abstract:

Essential oils of Heracleum persicum (Apiaceae) have been widely used from many years ago, but the difference of its properties among different populations have not been identified up to now. Hydrodistilation Clevenger type was used to obtaining the fruit essential oils of four populations of H. persicum from different localities in Iran, then they were characterized by GC-FID and GC-MS analyses. Some ecological factors were also measured. The oils of four populations were compared to determine the similarities and differences and the relationships between these factors and ecological factors. Based on the result, 18-32 different components were identified in four populations, while the percentage of the main components was higher in population with lower number of components. According to the statistical analyses of chemical components and ecological factors, it can be concluded that some ecological factors such as altitude, less humidity, high difference between day and night temperature and salty soil would lead to lower number of components in essential oil, whereas they consist the higher percentage.

Keywords: Chemotaxonomy, Persian hogweed, Ecological factors, Apiaceae.

Procedia PDF Downloads 430