Search results for: predicting factors

Authors: Olor Obi, Ishiekwen Bridget, Ekpeyong Edom

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Even though gout is becoming more common, the role of dietary risk factors in the development and management of this condition remains unclear. Therefore, this review work will aim at clarifying the role of dietary factors in the risk and management of gout. An extensive search of literature published between 1960 and 2018 will be performed on the databases of PubMed, CINAHL, Science Direct, Cochrane, BMJ, Ann Rheum Dis, and BioMed to identify relevant cohort, prospective, population-based, or cross-sectional studies that examined the effect of diet on gout. About 19 studies will be included in this review work. The methodological quality of these studies will be evaluated using the quality assessment tool for observational and cross-sectional studies developed by the National Heart, Lungs, and Blood Institute. This work intends to reveal that a positive association exists between the intake of sugary, sweetened beverages and the risk of gout. It will also reveal the relationship between the increase in coffee consumption and the risk of gout.

Keywords: gout, dietary factors, management of gout, gouty arthritis

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Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

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Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

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Authors: F.Sadoughi, A. Sarsarshahi, L, Eerfannia, S.M.A. Khatami

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Hospital information systems with lots of ability would lead to health care quality improvement. Evaluation of this system has done according different method and criteria. The main goal of present study is to prioritize the most important factors which are influence these systems evaluation. At the first step, according relevant literature, three main factor and 29 subfactors extracted. Then, study framework was designed. Based on analytical hierarchical process (AHP), 28 paired comparisons with Saaty range, in a questionnaire format obtained. Questionnaires were filled by 10 experts in health information management and medical informatics field. Human factors with weight of 0.55 were ranked as the most important. Organization (0.25) and technology (0.14) were in next place. It seems MADM methods such as AHP have enough potential to use in health research and provide positive opportunities for health domain decision makers.

Keywords: Analytical hierarchy process, Multiple criteria decision-making (MCDM), Hospital information system, Evaluation factors

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Authors: Marziyeh Khavari

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In recent decades, climate change, global warming, and the growing population worldwide face some challenges, such as increasing food consumption and shortage of resources. Assessing how climate change could disturb crops, especially hazelnut production, seems crucial for sustainable agriculture production. For hazelnut cultivation in the mid-warm condition, such as in Iran, here we present an investigation of climate parameters and how much they are effective on fertility and nut production of hazelnut trees. Therefore, the climate change of the northern zones in Iran has investigated (1960-2017) and was reached an uptrend in temperature. Furthermore, the descriptive analysis performed on six cultivars during seven years shows how this small-scale survey could demonstrate the effects of climate change on hazelnut production and stability. Results showed that some climate parameters are more significant on nut production, such as solar radiation, soil temperature, relative humidity, and precipitation. Moreover, some cultivars have produced more stable production, for instance, Negret and Segorbe, while the Mervill de Boliver recorded the most variation during the study. Another aspect that needs to be met is training and predicting an actual model to simulate nut production through a neural network and linear regression simulation. The study developed and estimated the ANN model's generalization capability with different criteria such as RMSE, SSE, and accuracy factors for dependent and independent variables (environmental and yield traits). The models were trained and tested while the accuracy of the model is proper to predict hazelnut production under fluctuations in weather parameters.

Keywords: climate change, neural network, hazelnut, global warming

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Ayoub Kafyulilo, Petra Fisser, Joke Voogt

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This study was conducted to assess the continuation of the use of technology in science and mathematics teaching of the pre-service and in-service teachers who attended the professional development programme. It also assessed professional development, personal, institutional, and technological factors contributing to the continuous use of technology in teaching. The study involved 42 teachers, thirteen pre-service teachers, and twenty-nine in-service teachers. A mixed-method research approach was used to collect data for this study. Findings showed that the continuous use of technology in teaching after the termination of the professional development arrangement was high among the pre-service teachers, and differed for the in-service teachers. The regression model showed that knowledge and skills, access to technology and ease of use were strong predictors (R2 = 55.3%) of the teachers’ continuous use of technology after the professional development arrangement. The professional development factor did not have a direct effect on the continuous use of technology, rather had an influence on personal factors (knowledge and skills). In turn, the personal factors had influence on the institutional factors (access to technology) and technological factors (ease of use), which together had an effect on the teachers’ continuous use of technology in teaching.

Keywords: technology, professional development, teachers, science and mathematics

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Authors: K. Allaw, J. Adjizian Gerard, M. Chehayeb, A. Raad, W. Fahs, A. Badran, A. Fakherdin, H. Madi, N. Badaro Saliba

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Population estimation using Geographic Information System (GIS) and remote sensing faces many obstacles such as the determination of permanent residents. A permanent resident is an individual who stays and works during all four seasons in his village. So, all those who move towards other cities or villages are excluded from this category. The aim of this study is to identify the factors affecting the percentage of permanent residents in a village and to determine the attributed weight to each factor. To do so, six factors have been chosen (slope, precipitation, temperature, number of services, time to Central Business District (CBD) and the proximity to conflict zones) and each one of those factors has been evaluated using one of the following data: the contour lines map of 50 m, the precipitation map, four temperature maps and data collected through surveys. The weighting procedure has been done using decision tree method. As a result of this procedure, temperature (50.8%) and percentage of precipitation (46.5%) are the most influencing factors.

Keywords: remote sensing, GIS, permanent residence, decision tree, Lebanon

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Lucia Mavudzi

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Socioeconomic and demographic factors influence male attendance of antenatal care (ANC) activities which are beneficial in improving maternal health and birth outcome. When a male, as the head of the family is expected to solely make decisions of how finances are managed, when and where health services are sought, it impacts on the woman’s health seeking behavior. Using the data from the Zimbabwe Demographic and Health Survey 2010-2011 this paper seeks to assess the prevalence of male ANC attendance in Zimbabwe and factors that influence male ANC attendance. We hypothesized that socioeconomic and demographic factors do not influence male ANC attendance. To achieve the objectives of this paper, descriptive analysis was used to describe the characteristics of men and the Binomial logistic modelling was used to assess the relationship between male ANC attendance and selected socioeconomic and demographic factors. Male ANC attendance was used as the dependent variable, and the independent variables are age, marital status, place of residence, wealth, education, religion and employment. A high percentage of males did not attend ANC with their pregnant partners. Religion, education, and place of residence were found to be significantly associated with male ANC attendance. There was no evidence to show that there was a difference in male ANC attendance by employment, marital status, and age. Findings from this paper are relevant to public health. They will be used to develop strategies and intervention programs to improve pregnant women’s attendance of ANC attendance by involving men in maternal health.

Keywords: antenatal care, male participation, maternal health, socio-economic and demographic factors

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Authors: Abedalbasit M. Abedalhafiz, Aman Kasawneh, Zyad Altahynah, Ahmad Okor

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The purpose of this study was to determine the construct validity of the morale sprit scale (MSS). Ninety faculty members from colleges of physical education at Jordanian universities were chosen to participate in this study. The design of this study was an ex-post facto. The MSS consists of (48) items that measure different dimensions of morale spirit among faculty members. Principle axis factoring with oblique rotation was utilized to uncover the underlying structure of the instrument. The findings revealed eight factor solution explaining (72.825%). Seven factors were accepted according to the conditions of accepting factors. The seven factors were named morale as reflection of faculty and department's administration, regulations and instructions, working environment and conditions, promotions and incentives and salaries, relations between the faculty member's, the trend toward the college and university, the trend toward self factors.

Keywords: Factorial validity, morale sprit, faculty members, Jordanian Universities

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Effective management of hypertension remains a critical public health challenge, particularly among racially and ethnically diverse populations. This study employs sophisticated statistical models to rigorously investigate the predictors of poor blood pressure (BP) control, with a specific focus on demographic, socioeconomic, and clinical risk factors. Leveraging a large sample of 19,253 adults drawn from the National Health and Nutrition Examination Survey (NHANES) across three distinct time periods (2013-2014, 2015-2016, and 2017-2020), we applied multivariable logistic regression and generalized estimating equations (GEE) to account for the clustered structure of the data and potential within-subject correlations. Our multivariable models identified significant associations between poor BP control and several key predictors, including race/ethnicity, age, gender, body mass index (BMI), prevalent diabetes, and chronic kidney disease (CKD). Non-Hispanic Black individuals consistently exhibited higher odds of poor BP control across all periods (OR = 1.99; 95% CI: 1.69, 2.36 for the overall sample; OR = 2.33; 95% CI: 1.79, 3.02 for 2017-2020). Younger age groups demonstrated substantially lower odds of poor BP control compared to individuals aged 75 and older (OR = 0.15; 95% CI: 0.11, 0.20 for ages 18-44). Men also had a higher likelihood of poor BP control relative to women (OR = 1.55; 95% CI: 1.31, 1.82), while BMI ≥35 kg/m² (OR = 1.76; 95% CI: 1.40, 2.20) and the presence of diabetes (OR = 2.20; 95% CI: 1.80, 2.68) were associated with increased odds of poor BP management. Further analysis using GEE models, accounting for temporal correlations and repeated measures, confirmed the robustness of these findings. Notably, individuals with chronic kidney disease displayed markedly elevated odds of poor BP control (OR = 3.72; 95% CI: 3.09, 4.48), with significant differences across the survey periods. Additionally, higher education levels and better self-reported diet quality were associated with improved BP control. College graduates exhibited a reduced likelihood of poor BP control (OR = 0.64; 95% CI: 0.46, 0.89), particularly in the 2015-2016 period (OR = 0.48; 95% CI: 0.28, 0.84). Similarly, excellent dietary habits were associated with significantly lower odds of poor BP control (OR = 0.64; 95% CI: 0.44, 0.94), underscoring the importance of lifestyle factors in hypertension management. In conclusion, our findings provide compelling evidence of the complex interplay between demographic, clinical, and socioeconomic factors in predicting poor BP control. The application of advanced statistical techniques such as GEE enhances the reliability of these results by addressing the correlated nature of repeated observations. This study highlights the need for targeted interventions that consider racial/ethnic disparities, clinical comorbidities, and lifestyle modifications in improving BP control outcomes.

Keywords: hypertension, blood pressure, NHANES, generalized estimating equations

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Authors: Valeda Dent

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This study explores reading and library factors related to secondary school student academic outcomes in rural areas in Uganda. This mixed methods study utilized quantitative data collected as part of a more extensive project to explore six student factors in relation to students’ school, library, and home environments. The Kitengesa Community Library in Uganda (www.kitengesalibrary.org) served as the site for this study. The factors explored for this study include reading frequency, library use frequency, library access, overall grade average (OGA), and presence and type of reading materials in the home. Results indicated that both reading frequency and certain types of reading materials read for recreational purposes are correlated with higher OGA. Reading frequency was positively correlated with student OGA for all students.

Keywords: rural village libraries, secondary school students, reading, academic achievement

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Authors: Jiajun Lu, Yun Ma

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As a member of the tertiary industry, tourism is an important driving factor for urban economic development. As a well-known tourist city in China, according to statistics, the added value of tourism and related industries in 2022 will reach 106.326 billion yuan, a year-on-year increase of 1.2%, accounting for 3.7% of the city's GDP. However, the overall tourism development level of Chongqing is seriously unbalanced, and the tourism strength of the main urban area is much higher than that of the southeast Chongqing, northeast Chongqing and the surrounding city tourism area, and the overall tourism strength of the other three regions is relatively balanced. Based on the estimation of tourism development level and the geographic detector method, this paper finds that the important factors affecting the tourism development level of non-main urban areas in Chongqing are A-level tourist attractions. Through GIS geospatial analysis technology and SPSS data correlation research method, the spatial distribution characteristics and influencing factors of A-level tourist attractions in Chongqing were quantitatively analyzed by using data such as geospatial data cloud, relevant documents of Chongqing Municipal Commission of Culture and Tourism Development, planning cloud, and relevant statistical yearbooks. The results show that: (1) The spatial distribution of tourist attractions in non-main urban areas of Chongqing is agglomeration and uneven. (2) The spatial distribution of A-level tourist attractions in non-main urban areas of Chongqing is affected by ecological factors, and the degree of influence is in the order of water factors> topographic factors > green space factors.

Keywords: tourist attractions, geographic detectors, quantitative research, ecological factors, GIS technology, SPSS analysis

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: Nasir Khalid

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In Malaysia, higher education is big business. There are many companies that are willing if not already to invest heavily in higher education for students that aspire to pursue their degree in diploma, undergraduate as well as graduate studies. These companies sometimes even have a joint venture twinning program with other already established universities in and across Europe, Australia, the United States, and Canada. Some of these investments have been successful whereas others find themselves limited by the obstacle of receiving new students. Recently, the Malaysian Ministry of Higher Education has stopped issuing licenses to set up private institutions of higher education. This paper will thus examine the factors affecting the success of private higher education businesses in Malaysia. The samples will consist of thirty private institutions [N=30]. Among the factors that will be mentioned in the literature are academic programs, student quality and achievement, student employability, alumni satisfaction, student enrolment, institutional environment, lecturer-quality and effectiveness of supporting staff. A questionnaire was developed and analyzed using statistical analysis. The result of this study found that the top three factors affecting the success of private higher education businesses in Malaysia are student enrolment, institutional environment and the academic programs offered.

Keywords: higher education business, successful business factors, private institutions, business in Malaysia

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Authors: Elahe Memari, Ahmad Aslizadeh, Ahmad Memari

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Today strategic planning is counted as the most important duties of senior directors in each organization. Strategic planning allows the organizations to implement compiled strategies and reach higher competitive benefits than their competitors. The present research work tries to prepare and rank the strategies form effective factors on strategic planning in fulfillment of the State Road Management and Transportation Organization in order to indicate the role of organizational factors in efficiency of the process to organization managers. Connection between six main factors in fulfillment of State Road Management and Transportation Organization were studied here, including Improvement of Strategic Thinking in senior managers, improvement of the organization business process, rationalization of resources allocation in different parts of the organization, coordination and conformity of strategic plan with organization needs, adjustment of organization activities with environmental changes, reinforcement of organizational culture. All said factors approved by implemented tests and then ranked using fuzzy multivariate decision-making technique.

Keywords: Fuzzy TOPSIS, improvement of organization business process, multivariate decision-making, strategic planning

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Authors: Saptiadi Nugroho, A. Nur Muhamad Afif

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Understanding factors that influence employee performance is very important. By finding the significant factors, organization could intervene to improve the employee performance that simultaneously will affect organization itself. In this research, four aspects consist of PCCD training, education level, corrective action, and work location were tested to identify their influence on employee performance. By using correlation analysis and T-Test, it was found that employee performance significantly influenced by PCCD training, work location, and corrective action. Meanwhile the education level did not influence employee performance.

Keywords: employee development, employee performance, performance management system, organization

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Authors: Oyesoji Aremu, Taiwo Williams

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This study examined some factors (academic self-efficacy, locus of control, motivation and gender) that could predict academic dishonesty among serially frustrated students in Ogun State, South West, Nigeria. Serial academically frustrated students are students who are unable to attain and meet academic expectations set by themselves or significant others. A sample of 250 undergraduate students selected from two faculties from a University in Ogun State,South West Nigeria took part in the study. Multiple regression analysis was employed to determine the joint and relative contributions of the independent variables to the prediction of the dependent variable. T-test was used to test the hypothesis determining the gender difference between the independent variables (academic self-efficacy, locus of control and motivation) and academic dishonesty of serial academically frustrated male and female students. The results of the study showed all the independent variables jointly contributed to predicting academic dishonesty, while only academic self-efficacy and motivation had relative contributions to the dependent measure. There was no significant difference in the academic self-efficacy and motivation among males and females on academic dishonesty of the serial academically frustrated students but locus of control showed a significant difference between male and female students on academic dishonesty. Implications for counseling of the findings are discussed in the study.

Keywords: academic dishonesty, serially frustrated students, academic self-efficacy, locus of control

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Authors: Yuhanis Ibrahim, Sung-Pil Lee

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This study attempts to address factors that contribute to outline public art factors assessment, memorial monument specifically. Memorial monuments hold significant and rich message whether the intention of the art is to mark and commemorate important event or to inform younger generation about the past. Public monument should relate to the public and raise awareness about the significant issue. Therefore, by investigating the impact of the existing public memorial art will hopefully shed some lights to the upcoming public art projects’ stakeholders to ensure the lucid memorial message is delivered to the public directly. Public is the main actor as public is the fundamental purpose that the art was created. Perception is framed as one of the reliable evaluation tools to assess the public art impact factors. The Malaysia National Monument was selected to be the case study for the investigation. The public’s perceptions were gathered using a questionnaire that involved (n-115) participants to attain keywords, and next Semantical Differential Methodology (SDM) was adopted to evaluate the perceptions about the memorial monument. These perceptions were then measured with Reliability Factor and then were factorised using Factor Analysis of Principal Component Analysis (PCA) method to acquire concise factors for the monument assessment. The result revealed that there are four factors that influence public’s perception on the monument which are aesthetic, audience, topology, and public reception. The study concludes by proposing the factors for public memorial art assessment for the next future public memorial projects especially in Malaysia.

Keywords: factor analysis, public art, public perception, semantical differential methodology

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Authors: Guilherme Gorgulho, Carlos Roberto Camello Lima

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Critical success factors (CSF) are used to design the practice of project management that can lead directly or indirectly to the success of the project. This management includes many elements that have to be synchronized in order to ensure the project on-time delivery, quality and the lowest possible cost. The objective of this work is to develop a proposal for evaluation of the FCS in lean manufacturing projects, and apply the evaluation in a pilot project. The results show that the use of continuous improvement programs in organizations brings benefits as the process cost reduction and improve productivity.

Keywords: continuous improvement, critical success factors (csf), lean thinking, project management

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Authors: Rafael Andres Rincon Barrera

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Safety in global aviation plays a preponderant role in organizations that seek to avoid accidents in an attempt to preserve their most precious assets (the people and the machines). Human factors-based programs have shown to be effective in managing human-generated risks. The importance of training on human factors in maintenance has not been indifferent to the Colombian Air Force (COLAF). This research, which has a mixed quantitative, qualitative and descriptive approach, deals with its absence of structuring an instruction program in Human Factors in Aeronautical Maintenance, which serves as a tool to improve Operational Safety in the military air units of the COLAF. Research shows the trends and evolution of human factors programs in aeronautical maintenance through the analysis of a data matrix with 33 sources taken from different databases that are about the incorporation of these types of programs in the aeronautical industry in the last 20 years; as well as the improvements in the operational safety process that are presented after the implementation of these ones. Likewise, it compiles different normative guides in force from world aeronautical authorities for training in these programs, establishing a matrix of methodologies that may be applicable to develop a training program in human factors in maintenance. Subsequently, it illustrates the design, validation, and development of a human factors knowledge measurement instrument for maintenance at the COLAF that includes topics on Human Factors (HF), Safety Management System (SMS), and aeronautical maintenance regulations at the COLAF. With the information obtained, it performs the statistical analysis showing the aspects of knowledge and strengthening the staff for the preparation of the instruction program. Performing data triangulation based on the applicable methods and the weakest aspects found in the maintenance people shows a variable crossing from color coding, thus indicating the contents according to a training program for human factors in aeronautical maintenance, which are adjusted according to the competencies that are expected to be developed with the staff in a curricular format established by the COLAF. Among the most important findings are the determination that different authors are dealing with human factors in maintenance agrees that there is no standard model for its instruction and implementation, but that it must be adapted to the needs of the organization, that the Safety Culture in the Companies which incorporated programs on human factors in maintenance increased, that from the data obtained with the instrument for knowledge measurement of human factors in maintenance, the level of knowledge is MEDIUM-LOW with a score of 61.79%. And finally that there is an opportunity to improve Operational Safety for the COLAF through the implementation of the training program of human factors in maintenance for the technicians working in this area.

Keywords: Colombian air force, human factors, safety culture, safety management system, triangulation

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Authors: Chin-Shan Lu, Yi-Pei Liu

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This study examines the factors influencing the adoption of MASS in Taiwan's shipping industry. Digital technology and unmanned vehicle advancements have enhanced efficiency and reduced environmental impact in the shipping industry. The IMO has set regulations to promote low-carbon emissions and autonomous ship technology. Using the TOE framework and DOI theory, a research model was constructed, and data from 132 Taiwanese shipping companies were collected via a questionnaire survey. A structural equation modeling (SEM) was conducted to examine the relationships between variables. Results show that technological and environmental factors significantly influence operators' attitudes toward MASS, while organizational factors impact their willingness to adopt. Enhancing technological support, internal resource allocation, top management support, and cost management are crucial for promoting adoption. This study identifies key factors and provides recommendations for adopting autonomous ships in Taiwan's shipping industry.

Keywords: MASS, technology-organization-environment, diffusion of innovations theory, shipping industry

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Authors: Kalayanee Senasu

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Research on happiness has been growing in recent decades. In the early stages, scholars were primarily concerned with establishing the validity of happiness measures and with exploring socio-economic correlates of happiness. More recent studies have focused on outcomes of happiness as well as the identification of happiness policies. This research investigates the happiness levels and influences of quality of life in terms of mental health satisfaction, family satisfaction, community satisfaction, and work satisfaction as determinants of happiness in Thailand during 2009-2014. The data collected by the National Statistic Office of Thailand in the project of Socio-economic Survey inclusion of Mental Health Survey in 2009, 2010, and 2012; and in the project of Labor Force Survey inclusion of Mental Health Survey in August 2014 were employed. There was a total of 59,430, 64,720, 54,736, and 9,997 respondents who were at least 15 years old in the survey during 2009-2014. Statistical analyses include both descriptive and inferential statistics. All research hypotheses were tested by means of hierarchical regression analysis. The analysis results reveal that happiness means during the studied period are quite at high levels (in the range of 7.42 to 7.60 from the scale 0-10). And the results indicate that all model variables (i.e., mental health satisfaction, family satisfaction, community satisfaction, and work satisfaction), have positive effects on happiness in Thailand. Additionally, the mental health satisfaction plays the most important role in predicting happiness. Further, our results indicate significant positive relationship between education, and income/expense and happiness, while other socio-economic variables reveal variety relationships during the studied period. Our results not only validate research findings in other countries but also verify the importance of quality of life (in terms of mental health satisfaction, family satisfaction, community satisfaction, and work satisfaction) as important factors of happiness for public policy makers. One conclusion stands firm in our study: happiness can be advanced in many ways. At the society level, greater happiness for people can be achieved by policies that aim to promote good health, an engaged family relationship, a high community as well as work qualities. A contented population is advantaged in many ways over one that is not. Government or policy makers should understand and realize that happiness is a valuable and tangible aspect of the population for which they are responsible. Therefore, they should include happiness issues in their political agenda.

Keywords: community satisfaction, family satisfaction, mental health satisfaction, work satisfaction, happiness, Thailand

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Authors: Marta Maria Aguilar Carceles

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This theoretical study argues how terrorism is rising around the world and which are the factors and situations that contribute to this process. Linked to aspects of human development, minors are one of the most vulnerable collectives to be engaged for this purpose. Its special weakness and lower possibility of self-defense makes them more likely to become victims as a result of a brainwashing process. Terrorism is an illicit way to achieve political and social changes and new technologies and available resources make it easier to spread. In this sense, throughout a theoretical revision of different recent and scientific articles, it is evaluated which risk factors can provoke its affiliation and later develop of antisocial and illicit behavior. An example of this group of factors could be the inter-generational continuity between parents and their children, as well as the sociodemographic aspects joined to cultural experiences (i.e. sense of dishonor, frustration, etc.). The assess of this kind of variables must be accompanied by the evaluation of protective factors, because the reasons through one person decides to join to terrorism are inherently idiosyncratic and we can only install mechanisms of prevention knowing those personal characteristics. To sum, both aspects underline the relevance of the internalizing and externalizing personal factors, each of them in one specific direction: a) to increase the possibility of being recruited or follow this type of criminal group by himself, and b) to be able of avoiding the effects and consequences of terrorism thanks to personal and resilient characteristics (resilience).

Keywords: criminality, minors, recruitment, resilience, terrorism

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Authors: Uyen Tran Tu

Abstract:

The decision on capital structure is one of the most important and sophisticated decisions in financial management in order to improve firm performance. This article would study the two-way impact between capital structure and firm performance. The study use EVIEWS 6.0 software to determine a two-way relationship between the capital structure and firm performance based on two-stage regression (2SLS - Two-Stage Least Squares). The findings are: capital structure has the opposite effect on the business efficiency and vice versa, factors that effect on business efficiency include Size and Opportunities. Factors effects on the capital structure are size; liquidity. These factors also affect the ratio of capital structure (total debt/ total asset) of companies. In particular, liquidity has the opposite effect; and the size of the business has the same impact. The results of the study are in line with the theory and empirical studies presented, and the results of the study are unchanged for all three years 2015-2017.

Keywords: capital structure, firm performance, factors, two-way relationship

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Authors: Kim Kennedy, Daren Gibson, Stephanie Flukes, Chandra Diwakarla, Lisa Spalding, Leanne Pilkington, Andrew Redfern

Abstract:

Introduction: Head and neck cancers (HNC) are often associated with the development of non-HNC primaries, as the risk factors that predispose patients to HNC are often risk factors for other cancers. Aim: We sought to evaluate whether there was an increased risk of smoking and alcohol-related cancers and also other cancers in HNC patients and to evaluate whether there is a difference between the rates of non-HNC primaries in Aboriginal compared with non-Aboriginal HNC patients. Methods: We performed a retrospective cohort analysis of 320 HNC patients from a single center in Western Australia, identifying 80 Aboriginal and 240 non-Aboriginal patients matched on a 1:3 ratio by sites, histology, rurality, and age. We collected data on the patient characteristics, tumour features, treatments, outcomes, and past and subsequent HNCs and non-HNC primaries. Results: In the overall study population, there were 86 patients (26.9%) with a metachronous or synchronous non-HNC primary. Non-HNC primaries were actually significantly more common in the non-Aboriginal population compared with the Aboriginal population (30% vs. 17.5%, p=0.02); however, half of these were patients with cutaneous squamous or basal cell carcinomas (cSCC/BCC) only. When cSCC/BCCs were excluded, non-Aboriginal patients had a similar rate as Aboriginal patients (16.7% vs. 15%, p=0.73). There were clearly more cSCC/BCCs in non-Aboriginal patients compared with Aboriginal patients (16.7% vs. 2.5%, p=0.001) and more patients with melanoma (2.5% vs. 0%, p value not significant (p=NS). Rates of most cancers were similar between non-Aboriginal and Aboriginal patients, including prostate (2.9% vs. 3.8%), colorectal (2.9% vs. 2.5%), kidney (1.2% vs. 1.2%), and these rates appeared comparable to Australian Age Standardised Incidence Rates (ASIR) in the general community. Oesophageal cancer occurred at double the rate in Aboriginal patients (3.8%) compared with non-Aboriginal patients (1.7%), which was far in excess of ASIRs which estimated a lifetime risk of 0.59% in the general population. Interestingly lung cancer rates did not appear to be significantly increased in our cohort, with 2.5% of Aboriginal patients and 3.3% of non-Aboriginal patients having lung cancer, which is in line with ASIRs which estimates a lifetime risk of 5% (by age 85yo). Interestingly the rate of Glioma in the non-Aboriginal population was higher than the ASIR, with 0.8% of non-Aboriginal patients developing Glioma, with Australian averages predicting a 0.6% lifetime risk in the general population. As these are small numbers, this finding may well be due to chance. Unsurprisingly, second HNCs occurred at an increased incidence in our cohort, in 12.5% of Aboriginal patients and 11.2% of non-Aboriginal patients, compared to an ASIR of 17 cases per 100,000 persons, estimating a lifetime risk of 1.70%. Conclusions: Overall, 26.9% of patients had a non-HNC primary. When cSCC/BCCs were excluded, Aboriginal and non-Aboriginal patients had similar rates of non-HNC primaries, although non-Aboriginal patients had a significantly higher rate of cSCC/BCCs. Aboriginal patients had double the rate of oesophageal primaries; however, this was not statistically significant, possibly due to small case numbers.

Keywords: head and neck cancer, synchronous and metachronous primaries, other primaries, Aboriginal

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

Procedia PDF Downloads 431