Search results for: predictive analytics methodology

Authors: Tendü H. Göktuğ, Gönül T. İçemer, Bülent Deniz

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National parks, which are rich in natural and cultural resources values are protected in the context of the idea to develop sustainability, are among the most important recreated areas demanding with each passing day. Increasing recreational use or unplanned use forms negatively affect the resource values and visitor satisfaction. The intent of national parks management is to protect the natural and cultural resource values and to provide the visitors with a quality of recreational experience, as well. In this context, the current studies to improve the appropriate tourism and recreation planning and visitor management, approach have focused on recreational carrying capacity analysis. The aim of this study is to analyze recreational carrying capacity of Çıralı Coast in the Bey Mountains Coastal National Park to compare the analyze results with the current usage format and to develop alternative management strategies. In the first phase of the study, the annual and daily visitations, geographic, bio-physical, and managerial characteristics of the park and the type of recreational usage and the recreational areas were analyzed. In addition to these, ecological observations were carried out in order to determine recreational-based pressures on the ecosystems. On-site questionnaires were administrated to a sample of 284 respondents in the August 2015 - 2016 to collect data concerning the demographics and visit characteristics. The second phase of the study, the coastal area separated into four different usage zones and the methodology proposed by Cifuentes (1992) was used for capacity analyses. This method supplies the calculation of physical, real and effective carrying capacities by using environmental, ecological, climatic and managerial parameters in a formulation. Expected numbers which estimated three levels of carrying capacities were compared to current numbers of national parks’ visitors. In the study, it was determined that the current recreational uses in the north of the beach were caused by ecological pressures, and the current numbers in the south of beach much more than estimated numbers of visitors. Based on these results management strategies were defined and the appropriate management tools were developed in accordance with these strategies. The authors are grateful for the financial support of this project by The Scientific and Technological Research Council of Turkey (No: 114O344)

Keywords: Çıralı Coast, national parks, recreational carrying capacity, visitor management

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Authors: Yurike, Yonariza, Rudi Febriamansyah, Syafruddin Karimi

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Forest is a natural resource that has an important function as a supporting element of human life. Forest degradation enormous impact on global warming is a reality we have experienced together, that disruption of ecosystems, extreme weather conditions, disruption of water management system watersheds and the threat of natural disasters as floods, landslides and droughts, even disruption food security. Dharmasraya is a district in the province of West Sumatra, which has an area of 92.150 ha of forest, which is largely a former production forest concessions (Forest Management Rights) which is supposed to be a secondary forest. This study answers about the impact of land use in the former concession area Dharmasraya on the environment. The methodology used is the household survey, key informants, and satellite data / GIS. From the results of the study, the former concession area in Dharmasraya experienced a reduction of forest cover over time significantly. Forest concessions should be secondary forests in Dharmasraya, now turned conversion to oil palm plantations. Population pressures and growing economic pressures, resulting in more intensive harvesting. As a result of these forest disturbances caused changes in forest functions. These changes put more emphasis towards economic function by ignoring social functions or ecological function. Society prefers to maximize their benefits today and pay less attention to the protection of natural resources. This causes global warming is increasing and this is not only felt by people around Dharmasraya but also the world. Land clearing by the community through a process in slash and burn. This fire was observed by NOAA satellites and recorded by the Forest Service of West Sumatra. This demonstrates the ability of trees felled trees to absorb carbon dioxide (CO2) to be lost, even with forest fires accounted for carbon dioxide emitted into the air, and this has an impact on global warming. In addition to the change of control of land into oil palm plantations water service has been poor, people began to trouble the water and oil palm plantations are located in the watershed caused the river dried up. Through the findings of this study is expected to contribute ideas to the policy makers to pay more attention to the former concession forest management as the prevention or reduction of global warming.

Keywords: climate change, community, concession forests, environment

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Authors: Florim Grajcevci, Flamur Grajcevci, Fatos Tahiri, Hamdi Kurteshi

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The territory of Kosova is actually included in one of the most seismic-prone regions in Europe. Therefore, the earthquakes are not so rare in Kosova; and when they occurred, the consequences have been rather destructive. The importance of assessing the seismic resistance of existing masonry structures has drawn strong and growing interest in the recent years. Engineering included those of Vulnerability, Loss of Buildings and Risk assessment, are also of a particular interest. This is due to the fact that this rapidly developing field is related to great impact of earthquakes on the socioeconomic life in seismic-prone areas, as Kosova and Prishtina are, too. Such work paper for Prishtina city may serve as a real basis for possible interventions in historic buildings as are museums, mosques, old residential buildings, in order to adequately strengthen and/or repair them, by reducing the seismic risk within acceptable limits. The procedures of the vulnerability assessment of building structures have concentrated on structural system, capacity, and the shape of layout and response parameters. These parameters will provide expected performance of the very important existing building structures on the vulnerability and the overall behavior during the earthquake excitations. The structural systems of existing historical buildings in Pristina, Kosovo, are dominantly unreinforced brick or stone masonry with very high risk potential from the expected earthquakes in the region. Therefore, statistical analysis based on the observed damage-deformation, cracks, deflections and critical building elements, would provide more reliable and accurate results for the regional assessments. The analytical technique was used to develop a preliminary evaluation methodology for assessing seismic vulnerability of the respective structures. One of the main objectives is also to identify the buildings that are highly vulnerable to damage caused from inadequate seismic performance-response. Hence, the damage scores obtained from the derived vulnerability functions will be used to categorize the evaluated buildings as “stabile”, “intermediate”, and “unstable”. The vulnerability functions are generated based on the basic damage inducing parameters, namely number of stories (S), lateral stiffness (LS), capacity curve of total building structure (CCBS), interstory drift (IS) and overhang ratio (OR).

Keywords: vulnerability, ductility, seismic microzone, ductility, energy efficiency

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Jaehyung An, Sungjoo Lee

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Patent documents constitute an up-to-date and reliable source of knowledge for reflecting technological advance, so patent analysis has been widely used for identification of technological trends and formulation of technology strategies. But, identifying technological information from patent data entails some limitations such as, high cost, complexity, and inconsistency because it rely on the expert’ knowledge. To overcome these limitations, researchers have applied to a quantitative analysis based on the keyword technique. By using this method, you can include a technological implication, particularly patent documents, or extract a keyword that indicates the important contents. However, it only uses the simple-counting method by keyword frequency, so it cannot take into account the sematic relationship with the keywords and sematic information such as, how the technologies are used in their technology area and how the technologies affect the other technologies. To automatically analyze unstructured technological information in patents to extract the semantic information, it should be transformed into an abstracted form that includes the technological key concepts. Specific sentence structure ‘SAO’ (subject, action, object) is newly emerged by representing ‘key concepts’ and can be extracted by NLP (Natural language processor). An SAO structure can be organized in a problem-solution format if the action-object (AO) states that the problem and subject (S) form the solution. In this paper, we propose the new methodology that can extract the SAO structure through technical elements extracting rules. Although sentence structures in the patents text have a unique format, prior studies have depended on general NLP (Natural language processor) applied to the common documents such as newspaper, research paper, and twitter mentions, so it cannot take into account the specific sentence structure types of the patent documents. To overcome this limitation, we identified a unique form of the patent sentences and defined the SAO structures in the patents text data. There are four types of technical elements that consist of technology adoption purpose, application area, tool for technology, and technical components. These four types of sentence structures from patents have their own specific word structure by location or sequence of the part of speech at each sentence. Finally, we developed algorithms for extracting SAOs and this result offer insight for the technology innovation process by providing different perspectives of technology.

Keywords: NLP, patent analysis, SAO, semantic-analysis

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Authors: James Boylan, Denny Meyer, Won Sun Chen

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Background: Globally, there is an increasing trend of road traffic deaths, reaching 1.35 million in 2016 in comparison to 1.3 million a decade ago, and overall, road traffic injuries are ranked as the eighth leading cause of death for all age groups. The reported death rate for younger drivers aged 16-19 years is almost twice the rate reported for older drivers aged 25 and above, with a rate of 3.5 road traffic fatalities per annum for every 10,000 licenses held. Telematics refers to a system with the ability to capture real-time data about vehicle usage. The data collected from telematics can be used to better assess a driver's risk. It is typically used to measure acceleration, turn, braking, and speed, as well as to provide locational information. With the Australian government creating the National Telematics Framework, there has been an increase in the government's focus on using telematics data to improve road safety outcomes. The purpose of this study is to test the hypothesis that improvements in telematics measured driving behaviour to relate to improvements in road safety attitudes measured by the Driving Behaviour Questionnaire (DBQ). Methodology: 28 participants were recruited and given a telematics device to insert into their vehicles for the duration of the study. The participant's driving behaviour over the course of the first month will be compared to their driving behaviour in the second month to determine whether feedback from telematics devices improves driving behaviour. Participants completed the DBQ, evaluated using a 6-point Likert scale (0 = never, 5 = nearly all the time) at the beginning, after the first month, and after the second month of the study. This is a well-established instrument used worldwide. Trends in the telematics data will be captured and correlated with the changes in the DBQ using regression models in SAS. Results: The DBQ has provided a reliable measure (alpha = .823) of driving behaviour based on a sample of 23 participants, with an average of 50.5 and a standard deviation of 11.36, and a range of 29 to 76, with higher scores, indicating worse driving behaviours. This initial sample is well stratified in terms of gender and age (range 19-27). It is expected that in the next six weeks, a larger sample of around 40 will have completed the DBQ after experiencing in-vehicle telematics for 30 days, allowing a comparison with baseline levels. The trends in the telematics data over the first 30 days will be compared with the changes observed in the DBQ. Conclusions: It is expected that there will be a significant relationship between the improvements in the DBQ and the trends in reduced telematics measured aggressive driving behaviours supporting the hypothesis.

Keywords: telematics, driving behavior, young drivers, driving behaviour questionnaire

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Authors: Alvaro Figueira, Bruno Cabral

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Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

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Authors: Corina E. Niculaescu, Ivan Sangiorgi, Adrian R. Bell

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The COVID-19 pandemic has been life-changing in many aspects of people’s daily and social lives, but has it also changed attitudes towards investments? This paper explores the effect of the COVID-19 pandemic on retail investors’ levels of investments in the U.S. during the first COVID-19 wave in summer 2020. This is an unprecedented health crisis, which could lead to changes in investment behavior, including irrational behavior in retail investors. As such, this study aims to inform policymakers of what happened to investment decisions during the COVID-19 pandemic so that they can protect retail investors during extreme events like a global health crisis. The study aims to answer two research questions. First, was the level of investments affected by the COVID-19 pandemic, and if so, why? Second, how were investments affected by retail investors’ personal experience with COVID-19? The research analysis is based on primary survey data collected on the Amazon Mechanical Turk platform from a representative sample of U.S. respondents. Responses were collected between the 15th of July and 28th of August 2020 from 1,148 U.S. retail investors who hold mutual fund investments and a savings account. The research explores whether being affected by COVID-19, change in the level of savings, and risk capacity can explain the change in the level of investments by using regression analysis. The dependent variable is changed in investments measured as decrease, no change, and increase. For this reason, the methodology used is ordered probit regression models. The results show that retail investors in the U.S. increased their investments during the first wave of COVID-19, which is unexpected as investors are usually more cautious in crisis times. Moreover, the study finds that those who were affected personally by COVID-19 (e.g., tested positive) were more likely to increase their investments, which is irrational behavior and contradicts expectations. An increase in the level of savings and risk capacity was also associated with increased investments. Overall, the findings show that having personal experience with a health crisis can have an impact on one’s investment decisions as well. Those findings are important for both retail investors and policymakers, especially now that online trading platforms have made trading easily accessible to everyone. There are risks and potential irrational behaviors associated with investment decisions during times of crisis, and it is important that retail investors are aware of them before making financial decisions.

Keywords: COVID-19, financial decision-making, health crisis retail investors, survey

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Authors: B. Vadiraj, S. N. Omkar, B. Kapil Bharadwaj, Yash Vardhan Gupta

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During manned exploration of space, missions will require astronaut crewmembers to perform Extra Vehicular Activities (EVAs) for a variety of tasks. These EVAs take place after long periods of operations in space, and in and around unique vehicles, space structures and systems. Considering the remoteness and time spans in which these vehicles will operate, EVA system operations should utilize common worksites, tools and procedures as much as possible to increase the efficiency of training and proficiency in operations. All of the preparations need to be carried out based on studies of astronaut motions. Until now, development and training activities associated with the planned EVAs in Russian and U.S. space programs have relied almost exclusively on physical simulators. These experimental tests are expensive and time consuming. During the past few years a strong increase has been observed in the use of computer simulations due to the fast developments in computer hardware and simulation software. Based on this idea, an effort to develop a computational simulation system to model human dynamic motion for EVA is initiated. This study focuses on the simulation of an astronaut moving the orbital replaceable units into the worksites or removing them from the worksites. Our physics-based methodology helps fill the gap in quantitative analysis of astronaut EVA by providing a multisegment human arm model. Simulation work described in the study improves on the realism of previous efforts, incorporating joint stops to account for the physiological limits of range of motion. To demonstrate the utility of this approach human arm model is simulated virtually using ADAMS/LifeMOD® software. Kinematic mechanism for the astronaut’s task is studied from joint angles and torques. Simulation results obtained is validated with numerical simulation based on the principles of Newton-Euler method. Torques determined using mathematical model are compared among the subjects to know the grace and consistency of the task performed. We conclude that due to uncertain nature of exploration-class EVA, a virtual model developed using multibody dynamics approach offers significant advantages over traditional human modeling approaches.

Keywords: extra vehicular activity, biomechanics, inverse kinematics, human body modeling

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Frezer Okubay Weldegebriel

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The tourism sector in Ethiopia plays a significant role in the national economy. The government is granting its pledge and readiness to develop this sector through various initiatives since to eradicate poverty and encourage economic development of the country is one of the Millennium Development plans. The tourism sector has been identified as one of the priority economic sectors by many countries, and the Government of Ethiopia has planned to make Ethiopia among the top five African destinations by 2020. Nevertheless, the international tourism demand for Ethiopia currently lags behind other African countries such as South Africa, Egypt, Morocco, Tanzania, and Kenya. Meanwhile, the number of international tourists’ arrival in Ethiopia is recently increasing even if it cannot be competitive with other African countries. Therefore, to offer demand-driven tourism products, the Ethiopian government, Tourism planners, Tour & Travel operators need to understand the important factors, which affect international tourists’ decision to visit Ethiopian destinations. This study was intended to analyze Italian Tourists Demand towards Ethiopian destination. The researcher aimed to identify the demand for Italian tourists’ preference to Ethiopian destinations comparing to the top East African countries. This study uses both qualitative and quantitative research methodology, and the data is manipulating through primary data collection method using questionnaires, interviews, and secondary data by reviewing books, journals, magazines, past researches, and websites. An active and potential Italian tourist cohort, five well-functioning tour operators based in Ethiopia for Italian tourists and professionals from Ethiopian Ministry of Tourism and Culture participated. Based on the analysis of the data collected through the questionnaire, interviews, and reviews of different materials, the study disclosed that the majority of Italian tourists have a high demand on Ethiopian Tourist destination. Historical and cultural interest, safety and security, the hospitality of the people and affordable accommodation coast are the main reason for them. However, some Italian tourists prefer to visit Kenya, Tanzania, and Uganda due to the fact that they are fascinated by adventure, safari and beaches, while Ethiopia cannot provide these attractions. Most Italian tourists have little information and practical experiences on Ethiopian tourism possibilities via a tour and travel companies. Moreover, the insufficient marketing campaign and promotion by Ethiopian Government and Ministry of Tourism could also contribute to the failure of Ethiopian tourism.

Keywords: The demand of Italian tourists, Ethiopia economy, Ethiopia tourism destination, promoting Ethiopia tourism

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Authors: Xu Feifei

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The prevalence of COVID-19 currently is the biggest challenge to improving people's quality of life. Its impact on the health-related quality of life (HRQoL) is highly uncertain and has not been summarized so far. The aim of the present systematic review was to assess and provide an up-to-date analysis of the impact of the COVID-19 pandemic on the HRQoL of participants who have been infected, have not been infected but isolated, frontline, with different diseases, and the general population. Therefore, an electronic search of the literature in PubMed databases was performed from 2019 to July 2022 (without date restriction). PRISMA guideline methodology was employed, and data regarding the HRQoL were extracted from eligible studies. Articles were included if they met the following inclusion criteria: (a) reports on the data collection of the health state utility values (HSUVs) related to COVID-19 from 2019 to 2021; (b) English language and peer-reviewed journals; and (c) original HSUV data; (d) using EQ-5D tool to quantify the HRQoL. To identify studies that reported the effects on COVID-19, data on the proportion of overall HSUVs of participants who had the outcome were collected and analyzed using a one-group meta-analysis. As a result, thirty-two studies fulfilled the inclusion criteria and, therefore, were included in the systematic review. A total of 45295 participants and provided 219 means of HSUVs during COVID-19 were included in this systematic review. The range of utility is from 0.224 to 1. The study included participants from Europe (n=16), North America (n=4), Asia (n=10), South America (n=1), and Africa (n=1). Twelve articles showed that the HRQoL of the participants who have been infected with COVID-19 (range of overall HSUVs from 0.6125 to 0.863). Two studies reported the population of frontline workers (the range of overall HSUVs from 0.82 to 0.93). Seven of the articles researched the participants who had not been infected with COVID-19 but suffered from morbidities during the pandemic (range of overall HSUVs from 0.5 to 0.96). Thirteen studies showed that the HRQoL of the respondents who have not been infected with COVID-19 and without any morbidities (range of overall HSUVs from 0.64 to 0.964). Moreover, eighteen articles reported the outcomes of overall HSUVs during the COVID-19 pandemic in different population groups. The estimate of overall HSUVs of direct COVID-19 experience population (n=1333) was 0.751 (95% CI 0.670 - 0.832, I2 = 98.64%); the estimate of frontline population (n=610) was 0.906 ((95% CI 0.854 – 0.957, I2 = 98.61%); participants with different disease (n=132) were 0.768 (95% CI 0.515 - 1.021, I2= 99.26%); general population without infection history (n=29,892) was 0.825 (95% CI 0.766 - 0.885, I2 =99.69%). Conclusively, taking into account these results, this systematic review might confirm that COVID-19 has a negative impact on the HRQoL of the infected population and illness population. It provides practical value for cost-effectiveness model analysis of health states related to COVID-19.

Keywords: COVID-19, health-related quality of life, meta-analysis, systematic review, utility value

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Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: A. Tamimi, S. Greatrex-White, A. Narayanasamy

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Background: Spirituality has been increasingly acknowledged in the nursing literature as an important element of holistic patient care. To date there have been numerous studies investigating the meaning of spirituality in Western cultures. Spirituality in Middle Eastern countries however remains under-researched. We will present a study which aimed to address this gap. Aim: The study aimed to explore how spirituality manifests in the lives of Jordanian End Stage Renal Failure (ESRF) patients. Methodology and Method: A hermeneutic phenomenological approach was adopted informed by the philosophy of Martin Heidegger. Participants (n=27) were recruited from four different dialysis units: in a public hospital, a private hospital, an educational hospital and a refugee’s hospital in Jordan. Data was collected through in-depth unstructured interviews. Data Analysis: Analysis was guided by the tenets of hermeneutic phenomenology namely: gaining immediate sense of what was said both during and after each interview, transcribing data verbatim, translating interviews into the English language, intensive reading and re-reading, seeking meaning units by line to line coding, developing situated structures (how spirituality was manifest in each text), developing a general structure from the individual situated structures (how the phenomenon ‘spirituality’ comes into being). Findings: Three major themes emerged from analysis: Religion, Relationships and Desperation. We will argue that a ‘secular’ concept of spirituality had no meaning for the participants in the study. Spirituality is fundamentally part of religion and vice versa. Discussion: The findings may have consequences for the use of spirituality in multi-cultural settings in Western countries. Additionally, findings highlighted an important emphasis on the practice of spirituality, often underestimated in previous literature for Arab-Muslim Jordanian patients. Conclusion: The study findings contribute to the existing gap in knowledge regarding how Arab-Muslim Jordanian ESRF patients experience spirituality during their illness. It provides valuable insights into the importance of spirituality for this patient group and suggests how nurses, educators and policy makers might help address ESRF patients’ spiritual needs and provide appropriate spiritual care. We suggest the findings may have relevance beyond the Jordanian context in educating nurses’ on the importance of appreciating the religious dimension of spirituality.

Keywords: spirituality, nursing, muslim, Jordan

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Authors: Wissal Ben Arfi, Jean-Michel Sahut

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The purpose of this paper is to provide a deeper understanding of the succession process from a knowledge management perspective. By doing that, succession process in family businesses, as an environment for creating and sharing knowledge, was explored. Design/Methodology/Approach: To support our reasoning, we collected qualitative data through 16 in-depth interviews conducted with all decision makers involved in the family businesses succession process in France. These open-ended responses were subsequently exposed to thematic discourse analysis. Findings: Central to this exhibit is the nature and magnitude of knowledge creation and sharing among the actors within the family succession context and how can tacit knowledge sharing facilitate the succession process. We also identified factors that inhibit down the knowledge creation and sharing processes. The sharing and creation of knowledge among members of a family business appear to be a complex process that must be part of a strategy for change. This implies that it requests trust and takes a certain amount of time because it requires organizational change and a clear and coherent strategic vision that is accepted and assimilated by all the members. Professional and leadership skills are of particular importance in knowledge sharing and creation processes. In most cases, tacit knowledge is crucial when it is shared and accumulated collectively. Our findings reveal that managers should find ways of implementing knowledge sharing and creation processes while acknowledging the succession process within family firms. This study highlights the importance of generating knowledge strategies in order to enhance the performance and the success of intergenerational succession. The empirical outcomes contribute to enrich the field of succession management process and enhance the role of knowledge in shaping family performance and longevity. To a large extent, the lessons learned from the study of succession processes in family-owned businesses are that when there is a deliberate effort to introduce a knowledge-based approach, this action becomes a seminal event in the life of the organization. Originality/Value: The paper contributes to the deep understanding of interactions among actors by examining the knowledge creation and sharing processes since current researches in family succession focused on aspects such as personal development of potential, intra-family succession intention, decision-making processes in family businesses. Besides, as succession is one of the key factors that determine the longevity and the performance of family businesses, it also contributes to literature by examining how tacit knowledge is transferred, shared and created in family businesses and how this can facilitate the intergenerational succession process.

Keywords: family-owned businesses, succession process, knowledge, performance

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Authors: I. Poot-Ocejo, H. Silva-Poot, J. C. Cruz, A. Yeladaqui-Tello

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Mexico has remarkable archaeological remains mainly in the Maya area, which are critical to the preservation of our cultural heritage, so the authorities have an interest in preserving and restoring these vestiges of the most original way, by employing techniques traditional, which has advantages such as compatibility, durability, strength, uniformity and chemical composition. Recent studies have confirmed the addition of natural resins extracted from the bark of trees, of which Brosium alicastrum (Ramon) has been the most evaluated, besides being one of the most abundant species in the vicinity of the archaeological sites, like that Manilkara Zapota (Chicozapote). Therefore, the objective is to determine if these resins are capable of being employed in archaeological restoration. This study shows the results of the chemical composition and physical-mechanical behavior of mortar mixtures eight made with commercial lime and off by hand, calcium sand, resins added with Brosium alicastrum (Ramon) and Manilkara zapota (Chicozapote), where determined and quantified properties and chemical composition of the resins by X-Ray Fluorescence (XRF), the pH of the material was determined, indicating that both resins are acidic (3.78 and 4.02), and the addition rate maximum was obtained from resins in water by means of ultrasonic baths pulses, being in the case of 10% Manilkara zapota, because it contains up to 40% rubber and for 40% alicastrum Brosium contain less rubber. Through quantitative methodology, the compressive strength 96 specimens of 5 cm x 5 cm x 5 cm of mortar binding, 72 with partial substitution of water mixed with natural resins in proportions 5 to 10% in the case was evaluated of Manilkara Zapota, for Brosium alicastrum 20 and 40%, and 12 artificial resin and 12 without additive (mortars witnesses). 24 specimens likewise glued brick with mortar, for testing shear adhesion was found where, then the microstructure more conducive additions was determined by SEM analysis were prepared sweep. The test results indicate that the addition Manilkara zapota resin in the proportion of 10% 1.5% increase in compressive strength and 1% with respect to adhesion, compared to the control without addition mortar; In the case of Brosium alicastrum results show that compressive strengths and adhesion were insignificant compared to those made with registered by Manilkara zapota mixtures. Mortars containing the natural resins have improvements in physical properties and increase the mechanical strength and adhesion, compared to those who do not, in addition to the components are chemically compatible, therefore have considered that can be employed in Archaeological restoration.

Keywords: lime, mortar, natural resins, Manilkara zapota mixtures, Brosium alicastrum

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Authors: Sandra K. Enriquez, Claudia Camargo, Jesús E. Olguín, Juan A. López, German Galindo

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Currently companies have increased the number of disorders of cumulative trauma (CTDs), these are increasing significantly due to the Highly Repetitive Movements (HRM) performed in workstations, which causes economic losses to businesses, due to temporary and permanent disabilities of workers. This analysis focuses on the prevention of disorders caused by: repeatability, duration and effort; And focuses on reducing cumulative trauma disorders such as occupational diseases using sensory thermography as a noninvasive method, the above is to evaluate the injuries could have workers to perform repetitive motions. Objectives: The aim is to define rest periods or job rotation before they generate a CTD, this sensory thermography by analyzing changes in temperature patterns on wrists and elbows when the worker is performing HRM over a period of time 2 hours and 30 minutes. Information on non-work variables such as wrist and elbow injuries, weight, gender, age, among others, and work variables such as temperature workspace, repetitiveness and duration also met. Methodology: The analysis to 4 industrial designers, 2 men and 2 women to be specific was conducted in a business in normal health for a period of 12 days, using the following time ranges: the first day for every 90 minutes continuous work were asked to rest 5 minutes, the second day for every 90 minutes of continuous work were asked to rest 10 minutes, the same to work 60 and 30 minutes straight. Each worker was tested with 6 different ranges at least twice. This analysis was performed in a controlled room temperature between 20 and 25 ° C, and a time to stabilize the temperature of the wrists and elbows than 20 minutes at the beginning and end of the analysis. Results: The range time of 90 minutes working continuous and a rest of 5 minutes of activity is where the maximum temperature (Tmax) was registered in the wrists and elbows in the office, we found the Tmax was 35.79 ° C with a difference of 2.79 ° C between the initial and final temperature of the left elbow presented at the individual 4 during the 86 minutes, in of range in 90 minutes continuously working and rested for 5 minutes of your activity. Conclusions: It is possible with this alternative technology is sensory thermography predict ranges of rotation or rest for the prevention of CTD to perform HRM work activities, obtaining with this reduce occupational disease, quotas by health agencies and increasing the quality of life of workers, taking this technology a cost-benefit acceptable in the future.

Keywords: sensory thermography, temperature, cumulative trauma disorder (CTD), highly repetitive movement (HRM)

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Authors: Huang Yajing

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Context: Computer-aided language learning (CALL) in China has undergone significant development over the past few decades, with distinct stages marking its evolution. This paper aims to provide a comprehensive review of the practice and research in this field in China, tracing its journey from the early stages of audio-visual education to the current multimedia network integration stage. Research Aim: The study aims to analyze the historical progression of CALL in China, identify key developments in the field, and provide recommendations for enhancing CALL practices in the future. Methodology: The research employs document analysis and literature review to synthesize existing knowledge on CALL in China, drawing on a range of sources to construct a detailed overview of the evolution of CALL practices and research in the country. Findings: The review highlights the significant advancements in CALL in China, showcasing the transition from traditional audio-visual educational approaches to the current integrated multimedia network stage. The study identifies key milestones, technological advancements, and theoretical influences that have shaped CALL practices in China. Theoretical Importance: The evolution of CALL in China reflects not only technological progress but also shifts in educational paradigms and theories. The study underscores the significance of cognitive psychology as a theoretical underpinning for CALL practices, emphasizing the learner's active role in the learning process. Data Collection and Analysis Procedures: Data collection involved extensive review and analysis of documents and literature related to CALL in China. The analysis was carried out systematically to identify trends, developments, and challenges in the field. Questions Addressed: The study addresses the historical development of CALL in China, the impact of technological advancements on teaching practices, the role of cognitive psychology in shaping CALL methodologies, and the future outlook for CALL in the country. Conclusion: The review provides a comprehensive overview of the evolution of CALL in China, highlighting key stages of development and emerging trends. The study concludes by offering recommendations to further enhance CALL practices in the Chinese context.

Keywords: English education, educational technology, computer-aided language teaching, applied linguistics

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Authors: H. C. Chen, S. W. C. Chan, K. Cheng, A. Vathsala, H. K. Sran, H. He

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Background: End-stage renal disease (ESRD) is the last stage of chronic kidney disease. The numbers of patients with ESRD have increased worldwide due to the growing number of aging, diabetes and hypertension populations. Patients with ESRD suffer from physical illness and psychological distress due to complex treatment regimens, which often affect the patients’ social and psychological functioning. As a result, the patients may fail to perform daily self-care and self-management, and consequently experience worsening conditions. Aims: The study aims to examine the effectiveness of a self-efficacy psychoeducational programme on primary outcome (self-efficacy) and secondary outcomes (psychological wellbeing, treatment adherence, and quality of life) in patients with ESRD and haemodialysis in Singapore. Methodology: A randomised controlled, two-group pretest and repeated posttests design will be carried out. A total of 154 participants (n=154) will be recruited. The participants in the control group will receive a routine treatment. The participants in the intervention group will receive a self-efficacy psychoeducational programme in addition to the routine treatment. The programme is a two-session of educational intervention in a week. A booklet, two consecutive sessions of face-to-face individual education, and an abdominal breathing exercise are adopted in the programme. Outcome measurements include Dialysis Specific Self-efficacy Scale, Kidney Disease Quality of Life- 36 Hospital Anxiety and Depression Scale, Renal Adherence Attitudes Questionnaire and Renal Adherence Behaviour Questionnaire. The questionnaires will be used to measure at baseline, 1- and 3- and 6-month follow-up periods. Process evaluation will be conducted with a semi-structured face to face interview. Quantitative data will be analysed using SPSS21.0 software. Qualitative data will be analysed by content analysis. Significance of the study: This study will identify a clinically useful and potentially effective approach to help patients with end-stage renal disease and haemodialysis by enhancing their self-efficacy in self-care behaviour, and therefore improving their psychological well-being, treatment adherence and quality of life. This study will provide information to develop clinical guidelines to improve patients’ disease self-management and to enhance health-related outcomes and it will help reducing disease burden.

Keywords: end-stage renal disease (ESRD), haemodialysis, psychoeducation, self-efficacy

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Authors: Mompi Sharma, Labananda Choudhury, Anjana M. Saikia

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Background: The period life table is entirely based on the assumption that the mortality patterns of the population existing in the given period will persist throughout their lives. However, it has been observed that the mortality rate continues to decline. As such, if the rates of change of probabilities of death are considered in a life table then we get a dynamic life table. Although, mortality has been declining in all parts of India, one may be interested to know whether these declines had appeared more in an urban area of underdeveloped regions like North-Eastern India. So, attempt has been made to know the mortality pattern and the life expectancy under dynamic scenario in Guwahati, the biggest city of North Eastern India. Further, if the probabilities of death changes then there is a possibility that its different constituent probabilities will also change. Since cardiovascular disease (CVD) is the leading cause of death in Guwahati. Therefore, an attempt has also been made to formulate dynamic cause specific death ratio and probabilities of death due to CVD. Objectives: To construct dynamic life table for Guwahati for the year 2011 based on the rates of change of probabilities of death over the previous 10 and 25 years (i.e.,2001 and 1986) and to compute corresponding dynamic cause specific death ratio and probabilities of death due to CVD. Methodology and Data: The study uses the method proposed by Denton and Spencer (2011) to construct dynamic life table for Guwahati. So, the data from the Office of the Birth and Death, Guwahati Municipal Corporation for the years 1986, 2001 and 2011 are taken. The population based data are taken from 2001 and 2011 census (India). However, the population data for 1986 has been estimated. Also, the cause of death ratio and probabilities of death due to CVD are computed for the aforementioned years and then extended to dynamic set up for the year 2011 by considering the rates of change of those probabilities over the previous 10 and 25 years. Findings: The dynamic life expectancy at birth (LEB) for Guwahati is found to be higher than the corresponding values in the period table by 3.28 (5.65) years for males and 8.30 (6.37) years for females during the period of 10 (25) years. The life expectancies under dynamic consideration in all the other age groups are also seen higher than the usual life expectancies, which may be possible due to gradual decline in probabilities of death since 1986-2011. Further, a continuous decline has also been observed in death ratio due to CVD along with cause specific probabilities of death for both sexes. As a consequence, dynamic cause of death probability due to CVD is found to be less in comparison to usual procedure. Conclusion: Since incorporation of changing mortality rates in period life table for Guwahati resulted in higher life expectancies and lower probabilities of death due to CVD, this would possibly bring out the real situation of deaths prevailing in the city.

Keywords: cause specific death ratio, cause specific probabilities of death, dynamic, life expectancy

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Authors: Emilio Galdeano-Gómez, Ángeles Godoy-Durán, Juan C. Pérez-Mesa, Laura Piedra-Muñoz

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Eco-efficiency is receiving ever-increasing interest as an indicator of sustainability, as it links environmental and economic performances in productive activities. In agriculture, these indicators and their determinants prove relevant due to the close relationships in this activity between the use of natural resources, which is generally limited, and the provision of basic goods to society. In this context, various analyses have focused on eco-efficiency by considering individual family farms as the basic production unit. However, not only must the measure of efficiency be taken into account, but also the existence of a series of factors which constitute socio-economic, political-institutional, and environmental determinants. Said factors have been studied to a lesser extent in the literature. The present work analyzes eco-efficiency at a micro level, focusing on small-scale family farms as the main decision-making units in horticulture in southeast Spain, a sector which represents about 30% of the fresh vegetables produced in the country and about 20% of those consumed in Europe. The objectives of this study are a) to obtain a series of eco-efficiency indicators by estimating several pressure ratios and economic value added in farming, b) to analyze the influence of specific social, economic and environmental variables on the aforementioned eco-efficiency indicators. The present work applies the method of Data Envelopment Analysis (DEA), which calculates different combinations of environmental pressures (water usage, phytosanitary contamination, waste management, etc.) and aggregate economic value. In a second stage, an analysis is conducted on the influence of the socio-economic and environmental characteristics of family farms on the eco-efficiency indicators, as endogeneous variables, through the use of truncated regression and bootstrapping techniques, following Simar-Wilson methodology. The results reveal considerable inefficiency in aspects such as waste management, while there is relatively little inefficiency in water usage and nitrogen balance. On the other hand, characteristics, such as product specialization, the adoption of quality certifications and belonging to a cooperative do have a positive impact on eco-efficiency. These results are deemed to be of interest to agri-food systems structured on small-scale producers, and they may prove useful to policy-makers as regards managing public environmental programs in agriculture.

Keywords: data envelopment analysis, eco-efficiency, family farms, horticulture, socioeconomic features

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Authors: Shujon Mohammed Alazzam, Sarah Saad Alamer, Omar Hassan Kasule, Lama Suliman Aleid, Mohammad Abdulaziz Alakeel, Boshra Mosleh Alanazi, Abdullah Abdulelah Altowairqi, Yahya Ali Al-Asiri

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Background: Interventional radiology (IR) is a specialized field within radiology that diagnose and treat several conditions through a minimally invasive surgical procedure that involves the use of various radiological techniques. In the last few years, the role of IR has expanded to include a variety of organ systems which have been led to an increase in demand for these Specialties. The level of knowledge regarding IR is relatively low in general. In this study, we aimed to investigate the perceptions of interventional radiology (IR) as a specialty among medical students and medical interns in Riyadh, Saudi Arabia. Methodology: This study was a cross section. The target population is medical students in January 2023 in Riyadh city, KSA. We used the questionnaire for face-to-face interviews with voluntary participants to assess their knowledge of Interventional radiology. Permission was taken from participants to use their information. Assuring them that the data in this study was used only for scientific purposes. Results: According to the inclusion criteria, a total of 314 students participated in the study. (49%) of the participants were in the preclinical years, and (51%) were in the clinical years. The findings indicate more than half of the students think that they had good information about IR (58%), while (42%) reported that they had poor information and knowledge about IR. Only (28%) of students were planning to take an elective and radiology rotation, (and 27%) said they would consider a career in IR. (73%) of the participants who would not consider a career in IR, the highest reasons in order were due to "I do not find it interesting" (45%), then "Radiation exposure" (14%). Around half (48%) thought that an IRs must complete a residency training program in both radiology and surgery, and just (36%) of the students believe that an IRs must finish training in radiology. Our data show the procedures performed by IRs that (66%) lower limb angioplasty and stenting (58%) Cardiac angioplasty or stenting. (68%) of the students were familiar with angioplasty. When asked about the source of exposure to angioplasty, the majority (46%) were from a cardiologist, (and 16%) were from the interventional radiologist. Regarding IR career prospects, (78%) of the students believe that IRs have good career prospects. In conclusion, our findings reveal that the perception and exposure to IR among medical students and interns are generally poor. This has a direct influence on the student's decision regarding IR as a career path. Recommendations to attract medical students and promote IR as a career should be increased knowledge among medical students and future physicians through early exposure to IR, and this will promote the specialty's growth; also, involvement of the Saudi Interventional Radiology Society and Radiological Society of Saudi Arabia is essential.

Keywords: knowledge, medical students, perceptions, radiology, interventional radiology, Saudi Arabia

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Authors: Yuanyuan Song

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Background: As we all know, culture plays a pivotal role in the realm of education, influencing study perceptions and outcomes. Nevertheless, there remains a need to delve into how culture specifically impacts the perception of courses. Therefore, the impact of culture on students' perceptions and academic performance is explored in this study. Drawing from cultural constructionism and conflict theories, it is posited that when students hailing from diverse cultures and backgrounds converge in the same classroom, their perceptions of course content may diverge significantly. This study seeks to unravel the tangible disparities and ascertain how cultural nuances shape students' perceptions of classroom content when encountering diverse cultural contexts within the same learning environment. Methodology: Given the diverse cultural backgrounds of students within the US, this study draws upon data collected from a course offered by a US college. In pursuit of answers to these inquiries, a qualitative approach was employed, involving semi-structured interviews conducted in a college-level science class in the US during 2023. The interviews encompassed approximately nine questions, spanning demographic particulars, cultural backgrounds, science learning experiences, academic outcomes, and more. Participants were exclusively drawn from science-related majors, with each student originating from a distinct cultural context. All participants were undergraduates, and most of them were from eighteen to twenty-five years old, totaling six students who attended the class and willingly participated in the interviews. The duration of each interview was approximately twenty minutes. Results: The findings gleaned from the interview data underscore the notable impact of varying cultural contexts on students' perceptions. This study argues that female science students, for instance, are influenced by gender dynamics due to the predominant male presence in science majors, creating an environment where female students feel reticent about expressing themselves in public. Students of East Asian origin exhibit a stronger belief in the efficacy of personal efforts when contrasted with their North American counterparts. Minority students indicated that they grapple with integration into the predominantly white mainstream society, influencing their eagerness to engage in classroom activities that are conducted by white professors. All of them emphasized the importance of learning science.

Keywords: multiculture education, educational sociology, educational equality, STEM education

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Authors: Najeh Lakhoua

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Introduction: Scientific developments and techniques for the systemic approach generate several names to the systemic approach: systems analysis, systems analysis, structural analysis. The main purpose of these reflections is to find a multi-disciplinary approach which organizes knowledge, creates universal language design and controls complex sets. In fact, system analysis is structured sequentially by steps: the observation of the system by various observers in various aspects, the analysis of interactions and regulatory chains, the modeling that takes into account the evolution of the system, the simulation and the real tests in order to obtain the consensus. Thus the system approach allows two types of analysis according to the structure and the function of the system. The purpose of this paper is to present an application of system analysis of Unmanned Aerial Vehicle (UAV) components in order to represent the architecture of this system. Method: There are various analysis methods which are proposed, in the literature, in to carry out actions of global analysis and different points of view as SADT method (Structured Analysis and Design Technique), Petri Network. The methodology adopted in order to contribute to the system analysis of an Unmanned Aerial Vehicle has been proposed in this paper and it is based on the use of SADT. In fact, we present a functional analysis based on the SADT method of UAV components Body, power supply and platform, computing, sensors, actuators, software, loop principles, flight controls and communications). Results: In this part, we present the application of SADT method for the functional analysis of the UAV components. This SADT model will be composed exclusively of actigrams. It starts with the main function ‘To analysis of the UAV components’. Then, this function is broken into sub-functions and this process is developed until the last decomposition level has been reached (levels A1, A2, A3 and A4). Recall that SADT techniques are semi-formal; however, for the same subject, different correct models can be built without having to know with certitude which model is the good or, at least, the best. In fact, this kind of model allows users a sufficient freedom in its construction and so the subjective factor introduces a supplementary dimension for its validation. That is why the validation step on the whole necessitates the confrontation of different points of views. Conclusion: In this paper, we presented an application of system analysis of Unmanned Aerial Vehicle components. In fact, this application of system analysis is based on SADT method (Structured Analysis Design Technique). This functional analysis proved the useful use of SADT method and its ability of describing complex dynamic systems.

Keywords: system analysis, unmanned aerial vehicle, functional analysis, architecture

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Authors: Nathalie Stephenne, Mathieu Veschkens, Stéphane Palm, Christophe Charlemagne, Jacques Defoux

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According to Europe’s first River basin Management Plans (RBMPs), 56% of European rivers failed to achieve the good status targets of the Water Framework Directive WFD. In Central European countries such as Belgium, even more than 80% of rivers failed to achieve the WFD quality targets. Although the RBMP’s should reduce the stressors and improve water body status, their potential to address multiple stress situations is limited due to insufficient knowledge on combined effects, multi-stress, prioritization of measures, impact on ecology and implementation effects. This paper describes a webGis prototype developed for the Walloon administration to improve the communication and the management of sediment dredging actions carried out in rivers and lakes in the frame of RBMPs. A large number of stakeholders are involved in the management of rivers and lakes in Wallonia. They are in charge of technical aspects (client and dredging operators, organizations involved in the treatment of waste…), management (managers involved in WFD implementation at communal, provincial or regional level) or policy making (people responsible for policy compliance or legislation revision). These different kinds of stakeholders need different information and data to cover their duties but have to interact closely at different levels. Moreover, information has to be shared between them to improve the management quality of dredging operations within the ecological system. In the Walloon legislation, leveling dredged sediments on banks requires an official authorization from the administration. This request refers to spatial information such as the official land use map, the cadastral map, the distance to potential pollution sources. The production of a collective geodatabase can facilitate the management of these authorizations from both sides. The proposed internet system integrates documents, data input, integration of data from disparate sources, map representation, database queries, analysis of monitoring data, presentation of results and cartographic visualization. A prototype of web application using the API geoviewer chosen by the Geomatic department of the SPW has been developed and discussed with some potential users to facilitate the communication, the management and the quality of the data. The structure of the paper states the why, what, who and how of this communication tool.

Keywords: sediments, web application, GIS, rivers management

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Authors: Victor Au, Khin Maung Than, Zaw Win Aung, Linawati Jumat

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Background & Objectives: Cardiac arrests can occur anywhere or anytime, and most of the cases will be brought to the emergency department except the cases that happened in at in-patient setting. Raja IsteriPangiran Anak Saleha (RIPAS) Hospital is the only tertiary government hospital which located in Brunei Muara district and received all referral from other Brunei districts. Data of cardiac arrests in Brunei Darussalam scattered between Emergency Medical Ambulance Services (EMAS), Emergency Department (ED), general inpatient wards, and Intensive Care Unit (ICU). In this audit, we only focused on cardiac arrest cases which had happened or presented to the emergency department RIPAS Hospital. Theobjectives of this audit were to look at demographic of cardiac arrest cases and the survival to discharge rate of In-Hospital Cardiac Arrest (IHCA) and Out-Hospital Cardiac Arrest (OHCA). Methodology: This audit retrospective study was conducted on all cardiac arrest cases that underwent Cardiopulmonary Resuscitation (CPR) in ED RIPAS Hospital, Brunei Muara, in the year 2019-2020. All cardiac arrest cases that happened or were brought in to emergency department were included. All the relevant data were retrieved from ED visit registry book and electronic medical record “Bru-HIMS” with keyword diagnosis of “cardiac arrest”. Data were analyzed and tabulated using Excel software. Result: 313 cardiac arrests were recorded in the emergency department in year 2019-2020. 92% cases were categorized as OHCA, and the remaining 8% as IHCA. Majority of the cases were male with age between 50-60 years old. In OHCA subgroup, only 12.4% received bystander CPR, and 0.4% received Automatic External Defibrillator (AED) before emergency medical personnel arrived. Initial shockable rhythm in IHCA group accounted for 12% compare to 4.9% in OHCA group. Outcome of ED resuscitation, 32% of IHCA group achieved return of spontaneous circulation (ROSC) with a survival to discharge rate was 16%. For OHCA group, 12.35% achieved ROSC, but unfortunately, none of them survive till discharge. Conclusion: Standardized registry for cardiac arrest in the emergency department is required to provide valid baseline data to measure the quality and outcome of cardiac arrest. Zero survival rate for out hospital cardiac arrest is very concerning, and it might represent the significant breach in cardiac arrest chains of survival. Systematic prospective data collection is needed to identify contributing factors and to improve resuscitation outcome.

Keywords: cardiac arrest, OHCA, IHCA, resuscitation, emergency department

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Authors: Zunaira Zaman, David Bohannon

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This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Yasser O. Abualnaja1, Alexandra Pavlidou2, Taha Boksmati3, Ahmad Alharbi3, Hammad Alsulmi3, Saleh Omar Maghrabi3, Hassan Mowalad3, Rayan Mutwalli3, James H. Churchill4, Afroditi Androni2, Dionysios Ballas2, Ioannis Hatzianestis2, Harilaos Kontoyiannis2, Angeliki Konstantinopoulou2, Georgios Krokkos1, 5, Georgios Pappas2, Vassilis P. Papadopoulos2, Konstantinos Parinos2, Elvira Plakidi2, Eleni Rousselaki2, Dimitris Velaoras2, Panagiota Zachioti2, Theodore Zoulias2, Ibrahim Hoteit5.

Abstract:

The coastal areas along the Kingdom of Saudi Arabia on both the Red Sea and Arabian Gulf have been witnessing in the past decades an unprecedented economic growth and a rapid increase in anthropogenic activities. Therefore, the Saudi Arabian government has decided to frame a strategy for sustainable development of the coastal and marine environments, which comes in the context of the Vision 2030, aimed at providing the first comprehensive ‘Status Quo Assessment’ of the Kingdom’s coastal and marine environments. This strategy will serve as a baseline assessment for future monitoring activities; this baseline is relied on scientific evidence of the drivers, pressures, and their impact on the environments of the Red Sea and Arabian Gulf. A key element of the assessment was the cumulative pressures of the hotspots analysis, which was developed following the principles of the Driver-Pressure-State-Impact-Response (DPSIR) framework and using the cumulative pressure and impact assessment methodology. Ten hotspot sites were identified, eight in the Red Sea and two in the Arabian Gulf. Thus, multidisciplinary research cruises were conducted throughout the Red Sea and the Arabian Gulf coastal and marine environments in June/July 2021 and September 2021, respectively, in order to understand the relative impact of hydrography and the various pressures on the quality of seawater and sediments. The main objective was to record the physical and biogeochemical parameters along the coastal waters of the Kingdom, tracing the dispersion of contaminants related to specific pressures. The assessment revealed the effect of hydrography on the trophic status of the southern marine coastal areas of the Red Sea. Jeddah Lagoon system seems to face significant eutrophication and pollution challenges, whereas sediments are enriched in some heavy metals in many areas of the Red Sea and the Arabian Gulf. This multidisciplinary research in the Red Sea and the Arabian Gulf coastal waters will pave the way for future detailed environmental monitoring strategies for the Saudi Arabian marine environment.

Keywords: arabian gulf, contaminants, hotspot, red sea

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Authors: Suleyman Ogul Ertugrul, Mustafa Turgut, Serkan Inandı, Mustafa Gorkem Coban, Mustafa Kıgılı, Ali Mert, Oguzhan Kesmez, Murat Ozancı, Caglar Uyulan

Abstract:

In high-performance automotive engineering, the realistic simulation of suspension systems is crucial for enhancing vehicle dynamics and handling. This study focuses on the double wishbone suspension system, prevalent in racing vehicles due to its superior control and stability characteristics. Utilizing MATLAB and Adams Car simulation software, we conduct a comprehensive analysis of displacement behaviors and damper sizing under various dynamic conditions. The initial phase involves using MATLAB to simulate the entire suspension system, allowing for the preliminary determination of damper size based on the system's response under simulated conditions. Following this, manual calculations of wheel loads are performed to assess the forces acting on the front and rear suspensions during scenarios such as braking, cornering, maximum vertical loads, and acceleration. Further dynamic force analysis is carried out using MATLAB Simulink, focusing on the interactions between suspension components during key movements such as bumps and rebounds. This simulation helps in formulating precise force equations and in calculating the stiffness of the suspension springs. To enhance the accuracy of our findings, we focus on a detailed kinematic and dynamic analysis. This includes the creation of kinematic loops, derivation of relevant equations, and computation of Jacobian matrices to accurately determine damper travel and compression metrics. The calculated spring stiffness is crucial in selecting appropriate springs to ensure optimal suspension performance. To validate and refine our results, we replicate the analyses using the Adams Car software, renowned for its detailed handling of vehicular dynamics. The goal is to achieve a robust, reliable suspension setup that maximizes performance under the extreme conditions encountered in racing scenarios. This study exemplifies the integration of theoretical mechanics with advanced simulation tools to achieve a high-performance suspension setup that can significantly improve race car performance, providing a methodology that can be adapted for different types of racing vehicles.

Keywords: FSAE, suspension system, Adams Car, kinematic

Procedia PDF Downloads 49