Search results for: predictive factors

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: Abdul Waris Jalili, Yanqing Wang, Som Suor

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This research paper explores the relationship between entrepreneurship education and students' entrepreneurial intentions. It aims to determine if entrepreneurship education reliably predicts students' intention to become entrepreneurs and how and when this relationship occurs. This study aims to investigate the predictive relationship between entrepreneurship education and student entrepreneurial intentions. The goal is to understand the factors that influence this relationship and to identify any mediating or moderating factors. A thorough and systematic search and review of empirical articles published between 2013 and 2023 were conducted. Three databases, Google Scholar, Science Direct, and PubMed, were explored to gather relevant studies. Criteria such as reporting empirical results, publication in English, and addressing the research questions were used to select 35 papers for analysis. The collective findings of the reviewed studies suggest a generally positive relationship between entrepreneurship education and student entrepreneurial intentions. However, recent findings indicate that this relationship may be more complex than previously thought. Mediators and moderators have been identified, highlighting instances where entrepreneurship education indirectly influences student entrepreneurial intentions. The review also emphasizes the need for more robust research designs to establish causality in this field. This research adds to the existing literature by providing a comprehensive review of the relationship between entrepreneurship education and student entrepreneurial intentions. It highlights the complexity of this relationship and the importance of considering mediators and moderators. The study also calls for future research to explore different facets of entrepreneurship education independently and examine complex relationships more comprehensively.

Keywords: entrepreneurship, entrepreneurship education, entrepreneurial intention, entrepreneurial self-efficacy

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Authors: Mani Venkatesh, Catarina Delgado, Purvishkumar Patel

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The sustainable supply chain is gaining popularity among practitioners because of increased environmental degradation and stakeholder awareness. On the other hand supply chain, risk management is very crucial for the practitioners as it potentially disrupts supply chain operations. Prediction and addressing the risk caused by social issues in the supply chain is paramount importance to the sustainable enterprise. More recently, the usage of Big data analytics for forecasting business trends has been gaining momentum among professionals. The aim of the research is to explore the application of big data, predictive analytics in successfully mitigating supply chain social risk and demonstrate how such mitigation can help in achieving sustainability (environmental, economic & social). The method involves the identification and validation of social issues in the supply chain by an expert panel and survey. Later, we used a case study to illustrate the application of big data in the successful identification and mitigation of social issues in the supply chain. Our result shows that the company can predict various social issues through big data, predictive analytics and mitigate the social risk. We also discuss the implication of this research to the body of knowledge and practice.

Keywords: big data, sustainability, supply chain social sustainability, social risk, case study

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Authors: Amil Suleimanov, Aigul Saduakassova, Denis Vinnikov

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Objective: To assess functional visceral fat (VAT) activity evaluated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) as a predictor of metastases in colorectal cancer (CRC). Materials and methods: We assessed 60 patients with histologically confirmed CRC who underwent 18F-FDG PET/CT after a surgical treatment and courses of chemotherapy. Age, histology, stage, and tumor grade were recorded. Functional VAT activity was measured by maximum standardized uptake value (SUVmax) using ¹⁸F-FDG PET/CT and tested as a predictor of later metastases in eight abdominal locations (RE – Epigastric Region, RLH – Left Hypochondriac Region, RRL – Right Lumbar Region, RU – Umbilical Region, RLL – Left Lumbar Region, RRI – Right Inguinal Region, RP – Hypogastric (Pubic) Region, RLI – Left Inguinal Region) and pelvic cavity (P) in the adjusted regression models. We also report the best areas under the curve (AUC) for SUVmax with the corresponding sensitivity (Se) and specificity (Sp). Results: In both adjusted for age regression models and ROC analysis, 18F-FDG accumulation in RLH (cutoff SUVmax 0.74; Se 75%; Sp 61%; AUC 0.668; p = 0.049), RU (cutoff SUVmax 0.78; Se 69%; Sp 61%; AUC 0.679; p = 0.035), RRL (cutoff SUVmax 1.05; Se 69%; Sp 77%; AUC 0.682; p = 0.032) and RRI (cutoff SUVmax 0.85; Se 63%; Sp 61%; AUC 0.672; p = 0.043) could predict later metastases in CRC patients, as opposed to age, sex, primary tumor location, tumor grade and histology. Conclusions: VAT SUVmax is significantly associated with later metastases in CRC patients and can be used as their predictor.

Keywords: ¹⁸F-FDG, PET/CT, colorectal cancer, predictive value

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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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: Muhammad Waqas, Shahan Waheed, Mohsin Qadeer, Ehsan Bari, Salman Ahmed, Iqra Patoli

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Objective: To determine the value of aPTT, platelets and INR as the predictor of unfavorable outcomes in patients with blunt isolated traumatic brain injury. Methods: This was an observational cohort study conducted in a tertiary care facility from 1st January 2008 to 31st December 2012. All the patients with isolated traumatic brain injury presenting within 24 hours of injury were included in the study. Coagulation parameters at presentation were recorded and Glasgow Outcome Scale calculated on last follow up. Outcomes were dichotomized into favorable and unfavorable outcomes. Relationship of coagulopathy with GOS and unfavorable outcomes was calculated using Spearman`s correlation and area under curve ROC analysis. Results: 121 patients were included in the study. The incidence of coagulopathy was found to be 6 %. aPTT was found to a significantly associated with unfavorable outcomes with an AUC = 0.702 (95%CI = 0.602-0.802). Predictive value of platelets and INR was not found to be significant. Conclusion: Incidence of coagulopathy was found to be low in current population compared to data from the West. aPTT was found to be a good predictor of unfavorable outcomes compared with other parameters of coagulation.

Keywords: aPTT, coagulopathy, unfavorable outcomes, parameters

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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: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

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Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: dynamic time warping, glottal area waveform, linear predictive coding, high-speed laryngeal images, Hilbert transform

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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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: A. F. Suleimanov, A. B. Saduakassova, V. S. Pokrovsky, D. V. Vinnikov

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Relevance: Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy, with relapse occurring in about 70% of advanced cases with poor prognoses. The aim of the study was to evaluate functional visceral fat activity (VAT) evaluated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) as a predictor of metastases in epithelial ovarian cancer (EOC). Materials and methods: We assessed 53 patients with histologically confirmed EOC who underwent ¹⁸F-FDG PET/CT after a surgical treatment and courses of chemotherapy. Age, histology, stage, and tumor grade were recorded. Functional VAT activity was measured by maximum standardized uptake value (SUVₘₐₓ) using ¹⁸F-FDG PET/CT and tested as a predictor of later metastases in eight abdominal locations (RE – Epigastric Region, RLH – Left Hypochondriac Region, RRL – Right Lumbar Region, RU – Umbilical Region, RLL – Left Lumbar Region, RRI – Right Inguinal Region, RP – Hypogastric (Pubic) Region, RLI – Left Inguinal Region) and pelvic cavity (P) in the adjusted regression models. We also identified the best areas under the curve (AUC) for SUVₘₐₓ with the corresponding sensitivity (Se) and specificity (Sp). Results: In both adjusted-for regression models and ROC analysis, ¹⁸F-FDG accumulation in RE (cut-off SUVₘₐₓ 1.18; Se 64%; Sp 64%; AUC 0.669; p = 0.035) could predict later metastases in EOC patients, as opposed to age, sex, primary tumor location, tumor grade, and histology. Conclusions: VAT SUVₘₐₓ is significantly associated with later metastases in EOC patients and can be used as their predictor.

Keywords: ¹⁸F-FDG, PET/CT, EOC, predictive value

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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

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

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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.

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Authors: Francis Edum-Fotwe, Tony Thorpe, Charles Afetornu

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Risk perception informs the outcome of how issues are responded to in either solving or overcoming a problem or improving a situation. Risk perception is established to be affected by some key factors reflecting in the varying ways in which work is done as well as the level of efficiency achieved. These factors potentially would influence risk perception to different extents. Such that if these factors are said to determine risk perception, how does a change in any affect risk perception. Since the ability to address risk is influenced by risk perception, establishing and developing awareness of that perception should enable construction professionals to make viable decisions. Any act to improve the construction industry cannot be overemphasised, considering its contribution to national development. A survey questionnaire was conducted in Ghana to elicit data that measures the risk perception and the essential factors as well as the necessary demographics of the respondents, who are construction professionals. This study finds out the sensitivity of the critical factors of risk perception. It uses the Relative Importance Index analysis tool to investigate the differential effect of these essential factors on risk perception, such that a slight change in a factor makes a significant change in risk perception, having established that it is influenced by essential factors. The findings can lead to policy formation for employers on the prioritisation factors to undertake to improve the risk perception of employees. Other areas in which this study can be useful in team formation for sensitive and complex projects where efficient risk management is critical.

Keywords: construction industry, risk, risk management, risk perception

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Authors: Laura Rodrigues Carmona, Maria José Chambel, Vânia Sofia Carvalho

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Background: In peacekeeping operations, military personnel are often exposed to the same traumatic stress factors found during conventional war and may also be subject to the physical risks and psychological stressors associated with posttraumatic stress disorder (PTSD). Objectives: To discuss the prevalence of PTSD among peacekeepers as well as the risks of and protective factors against this disorder and its comorbidities and/or consequences. Methods: A systematic literature search was performed with relevant keywords, and 53 articles were identified for this review. Results and conclusions: Military personnel deployed in peacekeeping operations have a higher prevalence of PTSD than nonmilitary personnel, a prevalence similar to that of military personnel deployed in war situations. Concerning the salient risk factors, the contextual factors are highlighted, and in regard to the protective factors, the individual factors are highlighted. This study thus demonstrates that there are factors in which the role of the military is essential, via both its selection and monitoring of peacekeepers during and after their deployment, to protect deployed personnel’s mental health.

Keywords: peacekeepers, peacekeeping, military, PTSD, post-traumatic stress disorder, posttraumatic stress disorder

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Authors: Ghanim A. Bekr

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Project success is what must be done for the project to be acceptable to the client, stakeholders and end-users who will be affected by the project. The study of project success and the critical success factors (CSFs) are the means adopted to improve the effectiveness of project. This research is conducted to make an attempt to identify which variables influence the success of project implementation. This study has selected, through an extensive literature review and interviews, (83) factors categorized in (7) groups that the questionnaire respondents were asked to score. The responses from 66 professionals with an average of 15 years of experience in different types of construction projects in Jordan were collected and analyzed using SPSS and most important factors for success for various success criteria are presented depending on the relative importance index to rank the categories. The research revealed the significant groups of factors are: Client related factors, Contractor’s related factors, Project Manager (PM) related factors, and Project management related factors. In addition the top ten sub factors are: Assertion of the client towards short time of the project, availability of skilled labor, Assertion of the client towards high level of the quality, capability of the client in taking risk, previous experience of the PM in similar projects, previous experience of the contractor in similar projects, decision making by the client/ the client’s representative at the right time, assertion of client towards low cost of project, experience in project management in previous projects, and flow of the information among parties. The results would be helpful to construction project professionals in taking proactive measures for successful completion of construction projects in Jordan.

Keywords: construction projects, critical success factors, Jordan, project success

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Authors: Manirath Wongsim

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AIS plays an important role in business management, strategic and can provide assistance in all phases of decision making. Thus, many organisations needs to be seen as well adopting AIS, which is critical to a company in order to organise, manage and operate process in all sections. In order to implement AIS successfully, it is important to understand the underlying factors that influence the AIS adoption. Therefore, this research intends to study this perspective of factors influence and impact on AIS adoption’s success. The model has been designed to illustrate factors influences in AIS adoption. It also attempts to identify the critical success factors that organisations should focus on, to ensure the adoption on accounting process. This framework will be developed from case studies by collecting qualitative and quantitative data. Case study and survey methodology were adopted for this research. Case studies in two Thai- organisations were carried out. The results of the two main case studies suggested 9 factors that may have impact on in AIS adoption. Survey instrument was developed based on the findings from case studies. Two large-scale surveys were sent to selected members of Thailand Accountant, and Thailand Computer Society to further develop and test the research framework. The top three critical factors for ensuring AIS adoption were: top management commitment, steering committees, and Technical capability of AIS personnel. That is, it is now clear which factors impact in AIS adoption, and which of those factors are critical success factors for ensuring AIS adoption successes

Keywords: accounting information system, accounting information systems adoption, and inflecting AIS adoption

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Authors: Mazharuddin Syed Ahmed

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This research presents a framework used to develop The Ara Polytechnic College of Architecture Studies building “Kahukura” which is Green Building certified. This framework integrates the development of a smart building digital twin by utilizing Building Information Modelling (BIM) and its BIM maturity levels, including Levels of Development (LOD), eight dimensions of BIM, Heritage-BIM (H-BIM) and Facility Management BIM (FM BIM). The research also outlines a structured approach to building performance analysis and integration with the circular economy, encapsulated within a five-level digital twin framework. Starting with Level 1, the Descriptive Twin provides a live, editable visual replica of the built asset, allowing for specific data inclusion and extraction. Advancing to Level 2, the Informative Twin integrates operational and sensory data, enhancing data verification and system integration. At Level 3, the Predictive Twin utilizes operational data to generate insights and proactive management suggestions. Progressing to Level 4, the Comprehensive Twin simulates future scenarios, enabling robust “what-if” analyses. Finally, Level 5, the Autonomous Twin, represents the pinnacle of digital twin evolution, capable of learning and autonomously acting on behalf of users.

Keywords: building information modelling, circular economy integration, digital twin, predictive analytics

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Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

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Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

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Authors: Anucha Taiwong, Nirobol Kanogsunthornrat

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This predictive research aimed to investigate the symptom clusters that influence the quality of life among patients with chronic kidney disease, as indicated in the Theory of Unpleasant Symptoms. The purposive sample consisted of 150 patients with stage 3-4 chronic kidney disease who received care at an outpatient chronic kidney disease clinic of a tertiary hospital in Roi-Et province. Data were collected from January to March 2016 by using a patient general information form, unpleasant symptom form, and quality of life (SF-36) and were analyzed by using descriptive statistics, factor analysis, and multiple regression analysis. Findings revealed six core symptom clusters including symptom cluster of the mental and emotional conditions, peripheral nerves abnormality, fatigue, gastro-intestinal tract, pain and, waste congestion. Significant predictors for quality of life were the two symptom clusters of pain (Beta = -.220; p < .05) and the mental and emotional conditions (Beta=-.204; p<.05) which had predictive value of 19.10% (R2=.191, p<.05). This study indicated that the symptom cluster of pain and the mental and emotional conditions would worsen the patients’ quality of life. Nurses should be attentive in managing the two symptom clusters to facilitate the quality of life among patients with chronic kidney disease.

Keywords: chronic kidney disease, symptom clusters, predictors of quality of life, pre-dialysis

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Authors: Omran Alharbi, Victor Lally

Abstract:

In recent decades, a number of educational institutions around the world have come to depend on technology such as the Blackboard system to improve their educational environment. On the other hand, there are many factors that delay the usage of this technology, especially in developing nations such as Saudi Arabia. The goal of this study was to investigate learner’s views of the use of Blackboard in one Saudi university in order to gain a comprehensive view of the factors that delay the implementation of technology in Saudi institutions. This study utilizes a qualitative approach, with data being collected through semi-structured interviews. Six participants from different disciplines took part in this study. The findings indicated that there are two levels of factors that affect students’ use of the Blackboard system. These are factors at the institutional level, such as lack of technical support and lack of training support, which lead to insufficient training related to the Blackboard system. The second level of factors is at the individual level, for example, a lack of teacher motivation and encouragement. In addition, students do not have sufficient levels of skills or knowledge related to how to use the Blackboard in their learning. Conclusion: learners confronted and faced two main types of factors (at the institution level and individual level) that delayed and impeded their learning. Institutions in KSA should take steps and implement strategies to remove or reduce these factors in order to allow students to benefit from the latest technology in their learning.

Keywords: blackboard, factors, KSA, learners

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Authors: Hassan Karimi, Fred Piercy, Ruoxi Chen, Ana L. Jaramillo-Sierra, Wei-Ning Chang, Manjushree Palit, Catherine Martosudarmo, Angelito Antonio

Abstract:

Background: The two psychotherapy camps, the randomized clinical trials (RCTs) and the common factors model, have competitively claimed specific explanations for therapy effectiveness. Recently, scholars called for empirical evidence to show the role of common factors in therapeutic outcome in marriage and family therapy. Purpose: This cross-national study aims to explore how clinicians, across different nations and theoretical orientations, attribute the contribution of common factors to therapy outcome. Method: A brief common factors questionnaire (CFQ-with a Cronbach’s Alpha, 0.77) was developed and administered in seven nations. A series of statistical analyses (paired-samples t-test, independent sample t-test, ANOVA) were conducted: to compare clinicians perceived contribution of total common factors versus model-specific factors, to compare each pair of common factors’ categories, and to compare clinicians from collectivistic nations versus clinicians from individualistic nation. Results: Clinicians across seven nations attributed 86% to common factors versus 14% to model-specific factors. Clinicians attributed 34% of therapeutic change to client’s factors, 26% to therapist’s factors, 26% to relationship factors, and 14% to model-specific techniques. The ANOVA test indicated each of the three categories of common factors (client 34%, therapist 26%, relationship 26%) showed higher contribution in therapeutic outcome than the category of model specific factors (techniques 14%). Clinicians with psychology degree attributed more contribution to model-specific factors than clinicians with MFT and counseling degrees who attributed more contribution to client factors. Clinicians from collectivistic nations attributed larger contributions to therapist’s factors (M=28.96, SD=12.75) than the US clinicians (M=23.22, SD=7.73). The US clinicians attributed a larger contribution to client’s factors (M=39.02, SD=1504) than clinicians from the collectivistic nations (M=28.71, SD=15.74). Conclusion: The findings indicate clinicians across the globe attributed more than two thirds of therapeutic change to CFs, which emphasize the training of the common factors model in the field. CFs, like model-specific factors, vary in their contribution to therapy outcome in relation to specific client, therapist, problem, treatment model, and sociocultural context. Sociocultural expectations and norms should be considered as a context in which both CFs and model-specific factors function toward therapeutic goals. Clinicians need to foster a cultural competency specifically regarding the divergent ways that CFs can be activated due to specific sociocultural values.

Keywords: common factors, model-specific factors, cross-national survey, therapist cultural competency, enhancing therapist efficacy

Procedia PDF Downloads 261

Authors: Julian Thrash, Douglas Folk, Michael Ciracy, Audrey C. Tseng, Kristen M. Stromsodt, Amber Younggren, Christopher Maciolek

Abstract:

The cardiopulmonary system is comprised of the heart, lungs, and many dynamic feedback mechanisms that control its function based on a multitude of variables. The next generation of cardiopulmonary medical devices will involve adaptive control and smart pacing techniques. However, testing these smart devices on living systems may be unethical and exceedingly expensive. As a solution, a comprehensive computational model of the cardiopulmonary system was implemented in Simulink. The model contains over 240 state variables and over 100 equations previously described in a series of published articles. Simulink was chosen because of its ease of introducing machine learning elements. Initial results indicate that physiologically correct waveforms of pressures and volumes were obtained in the simulation. With the development of a comprehensive computational model, we hope to pioneer the future of predictive medicine by applying our research towards the initial stages of smart devices. After validation, we will introduce and train reinforcement learning agents using the cardiopulmonary model to assist in adaptive control system design. With our cardiopulmonary model, we will accelerate the design and testing of smart and adaptive medical devices to better serve those with cardiovascular disease.

Keywords: adaptive control, cardiopulmonary, computational model, machine learning, predictive medicine

Procedia PDF Downloads 146

Authors: Zhuang He, Andrei Kotousov

Abstract:

All current experimental methods for determination of stress intensity factors are based on the assumption that the state of stress near the crack tip is plane stress. Therefore, these methods rely on strain and displacement measurements made outside the near crack tip region affected by the three-dimensional effects or by process zone. In this paper, we develop and validate an experimental procedure for the evaluation of stress intensity factors from the measurements of the out-of-plane displacements in the surface area controlled by 3D effects. The evaluation of stress intensity factors is possible when the process zone is sufficiently small, and the displacement field generated by the 3D effects is fully encapsulated by K-dominance region.

Keywords: digital image correlation, stress intensity factors, three-dimensional effects, transverse displacement

Procedia PDF Downloads 582