Search results for: success metrics

Authors: Mariem Saadi, Sabri Kanzari, Adel Zghibi

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In semi-arid areas like the Kairouan region, the constant irrigation with saline water and the overuse of groundwater resources, soils and aquifers salinization has become an increasing concern. In this study, a methodology has been developed to evaluate the groundwater contamination risk based on the unsaturated zone hydraulic properties. Two soil profiles with different ranges of salinity, one located in the north of the plain and another one in the south of plain (each 30 m deep) and both characterized by direct recharge of the aquifer were chosen. Simulations were conducted with Hydrus-1D code using measured precipitation data for the period 1998-2003 and calculated evapotranspiration for both chosen profiles. Four combinations of initial conditions of water content and salt concentration were used for the simulation process in order to find the best match between simulated and measured values. The success of the calibration of Hydrus-1D allowed the investigation of some scenarios in order to assess the contamination risk under different natural conditions. The aquifer risk contamination is related to the natural conditions where it increased while facing climate change and temperature increase and decreased in the presence of a clay layer in the unsaturated zone. Hydrus-1D was a useful tool to predict the groundwater level and quality in the case of a direct recharge and in the absence of any information related to the soil layers except for the texture.

Keywords: Hydrus-1D, Kairouan, salinization, semi-arid region, solute transport, unsaturated zone

Procedia PDF Downloads 184

Authors: Michaela Dušková, Kateřina Šimůnková, Martin Hill, Hana Hruškovičová, Hana Pospíšilová, Eva Králíková, Luboslav Stárka

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Smoking represents the most widespread substance dependence in the world. Several studies show the nicotine's ability to alter women hormonal homeostasis. Women smokers have higher testosterone and lower estradiol levels throughout life compared to non-smoker women. We monitored the effect of smoking discontinuation on steroid spectrum with 40 premenopausal and 60 postmenopausal women smokers. These women had been examined before they discontinued smoking and also after 6, 12, 24, and 48 weeks of abstinence. At each examination, blood was collected to determine steroid spectrum (measured by GC-MS), LH, FSH, and SHBG (measured by IRMA). Repeated measures ANOVA model was used for evaluation of the data. The study has been approved by the local Ethics Committee. Given the small number of premenopausal women who endured not to smoke, only the first 6 week period data could be analyzed. A slight increase in androgens after the smoking discontinuation occurred. In postmenopausal women, an increase in testosterone, dihydrotestosterone, dehydroepiandrosterone, and other androgens occurred, too. Nicotine replacement therapy, weight changes, and age does not play any role in the androgen level increase. The higher androgens levels correlated with failure in smoking cessation. Women smokers have higher androgen levels, which might play a role in smoking dependence development. Women successful in smoking cessation, compared to the non-successful ones, have lower androgen levels initially and also after smoking discontinuation. The question is what androgen levels women have before they start smoking.

Keywords: addiction, smoking, cessation, androgens

Procedia PDF Downloads 382

Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Rashida Suleiman, Asamoah Jnr. Boakye, Suleiman Ahmed Ibn Ahmed

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In recent years, there has been an enormous success in discoveries of scientific knowledge in medicine coupled with the advancement of technology. Despite all these successes, diagnoses and treatment of diseases have become complex. MYCIN is a groundbreaking illustration of a clinical decision support system (CDSS), which was developed to assist physicians in the diagnosis and treatment of bacterial infections by providing suggestions for antibiotic regimens. MYCIN was one of the earliest expert systems to demonstrate how CDSSs may assist human decision-making in complicated areas. Relevant databases were searched using google scholar, PubMed and general Google search, which were peculiar to clinical decision support systems. The articles were then screened for a comprehensive overview of the functionality, consultative style and statistical usage of MYCIN, a clinical decision support system. Inferences drawn from the articles showed some usage of MYCIN for problem-based learning among clinicians and students in some countries. Furthermore, the data demonstrated that MYCIN had completed clinical testing at Stanford University Hospital following years of research. The system (MYCIN) was shown to be extremely accurate and effective in diagnosing and treating bacterial infections, and it demonstrated how CDSSs might enhance clinical decision-making in difficult circumstances. Despite the challenges MYCIN presents, the benefits of its usage to clinicians, students and software developers are enormous.

Keywords: clinical decision support system, MYCIN, diagnosis, bacterial infections, support systems

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Authors: Gyoomi Kim, Jiyoung Bae

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In the field of language education research, there are not many tries to empirically examine learners’ acceptance behavior and related factors of mobile-assisted language learning (MALL). This study is one of the few attempts to propose an integrated theoretical framework that explains MALL users’ acceptance behavior and potential factors. Constructs from technology acceptance model (TAM) and MALL research are tested in the integrated framework. Based on previous studies, a hypothetical model was developed. Four external variables related to the MALL user’s acceptance behavior were selected: subjective norm, content reliability, interactivity, self-regulation. The model was also composed of four other constructs: two latent variables, perceived ease of use and perceived usefulness, were considered as cognitive constructs; attitude toward MALL as an affective construct; behavioral intention to use MALL as a behavioral construct. The participants were 438 undergraduate students who enrolled in an intensive English program at one university in Korea. This particular program was held in January 2018 using the vacation period. The students were given eight hours of English classes each day from Monday to Friday for four weeks and asked to complete MALL courses for practice outside the classroom. Therefore, all participants experienced blended MALL environment. The instrument was a self-response questionnaire, and each construct was measured by five questions. Once the questionnaire was developed, it was distributed to the participants at the final ceremony of the intensive program in order to collect the data from a large number of the participants at a time. The data showed significant evidence to support the hypothetical model. The results confirmed through structural equation modeling analysis are as follows: First, four external variables such as subjective norm, content reliability, interactivity, and self-regulation significantly affected perceived ease of use. Second, subjective norm, content reliability, self-regulation, perceived ease of use significantly affected perceived usefulness. Third, perceived usefulness and perceived ease of use significantly affected attitude toward MALL. Fourth, attitude toward MALL and perceived usefulness significantly affected behavioral intention to use MALL. These results implied that the integrated framework from TAM and MALL could be useful when adopting MALL environment to university students or adult English learners. Key constructs except interactivity showed significant relationships with one another and had direct and indirect impacts on MALL user’s acceptance behavior. Therefore, the constructs and validated metrics is valuable for language researchers and educators who are interested in MALL.

Keywords: blended MALL, learner factors/variables, mobile-assisted language learning, MALL, technology acceptance model, TAM, theoretical framework

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Authors: Alireza Nikravanshallmani, Jamshid Dehmeshki, Mojtaba Ahmadi

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Modeling of business processes, based on BPMN (Business Process Modeling Notation), helps analysts and managers to understand business processes, and, identify their shortages. These models provide a context to make rational decision of organizing business processes activities in an understandable manner. The purpose of this paper is to provide a framework for better understanding of business processes and their problems by reducing the cognitive load of displayed information for their audience at different managerial levels while keeping the essential information which are needed by them. For this reason, we integrate business process diagrams across the different managerial levels to develop a framework to improve the performance of business process management (BPM) projects. The proposed framework is entitled ‘Business process improvement framework based on managerial levels (BPIML)’. This framework, determine a certain type of business process diagrams (BPD) based on BPMN with respect to the objectives and tasks of the various managerial levels of organizations and their roles in BPM projects. This framework will make us able to provide the necessary support for making decisions about business processes. The framework is evaluated with a case study in a real business process improvement project, to demonstrate its superiority over the conventional method. A questionnaire consisted of 10 questions using Likert scale was designed and given to the participants (managers of Bank Refah Kargaran three managerial levels). By examining the results of the questionnaire, it can be said that the proposed framework provide support for correct and timely decisions by increasing the clarity and transparency of the business processes which led to success in BPM projects.

Keywords: business process management (BPM), business process modeling, business process reengineering (BPR), business process optimizing, BPMN

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Authors: Emilee M. Cruz

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Teachers take part in short-term teaching fellowships abroad, and their preparation before, during, and after the experience is critical to affecting teachers’ feelings of success in the international classroom. A program evaluation of the teacher preparation within TALMA: The Israel Program for Excellence in English (TALMA) full-year teaching fellowship was conducted. A questionnaire was developed that examined professional development, deliberate reflection, and cultural and language immersion offered before, during, and after the short-term experience. The evaluation also surveyed teachers’ feelings of preparedness for the Israeli classroom and any recommendations they had for future teacher preparation within the fellowship program. The review suggests the TALMA program includes integrated professional learning communities between fellows and Israeli co-teachers, more opportunities for immersive Hebrew language learning, a broader professional network with Israelis, and opportunities for guided discussion with the TALMA community continued participation in TALMA events and learning following the full-year fellowship. Similar short-term international programs should consider the findings in the design of their participation preparation programs. The review also offers direction for future program evaluation of short-term participant preparation, including the need for frequent response item updates to match current offerings and evaluation of participant feelings of preparedness before, during, and after the full-year fellowship.

Keywords: educational program evaluation, international teaching, short-term teaching, teacher beliefs, teaching fellowship, teacher preparation

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Authors: B. Tiseo, V. Quaranta, G. Bruno, G. Sisinni

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There is an increasing demand of nano-satellite development among universities, small companies, and emerging countries. Low-cost and fast-delivery are the main advantages of such class of satellites achieved by the extensive use of commercial-off-the-shelf components. On the other side, the loss of reliability and the poor success rate are limiting the use of nano-satellite to educational and technology demonstration and not to the commercial purpose. Standardization of nano-satellite environmental testing by tailoring the existing test standard for medium/large satellites is then a crucial step for their market growth. Thus, it is fundamental to find the right trade-off between the improvement of reliability and the need to keep their low-cost/fast-delivery advantages. This is particularly even more essential for satellites of CubeSat family. Such miniaturized and standardized satellites have 10 cm cubic form and mass no more than 1.33 kilograms per 1 unit (1U). For this class of nano-satellites, the qualification process is mandatory to reduce the risk of failure during a space mission. This paper reports the description and results of the space qualification test campaign performed on Endurosat’s CubeSat nano-satellite and modules. Mechanical and environmental tests have been carried out step by step: from the testing of the single subsystem up to the assembled CubeSat nano-satellite. Functional tests have been performed during all the test campaign to verify the functionalities of the systems. The test duration and levels have been selected by tailoring the European Space Agency standard ECSS-E-ST-10-03C and GEVS: GSFC-STD-7000A.

Keywords: CubeSat, nano-satellite, shock, testing, vibration

Procedia PDF Downloads 189

Authors: Maria Fontenele, Claude-Gilles Dussap, Vincent Dumouilla, Baptiste Boit

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Roquette Frères is a producer of plant-based ingredients that employs many processes to extract relevant molecules and often transforms them through chemical and physical processes to create desired ingredients with specific functionalities. In this context, Roquette encounters numerous multi-component complex systems in their processes, including fibers, proteins, and carbohydrates, in an aqueous environment. To develop, control, and optimize both new and old processes, Roquette aims to develop new in silico tools. Currently, Roquette uses process modelling tools which include specific thermodynamic models and is willing to develop computational methodologies such as molecular dynamics simulations to gain insights into the complex interactions in such complex media, and especially hydrogen bonding interactions. The issue at hand concerns aqueous mixtures of polyols with high dry matter content. The polyols mannitol and sorbitol molecules are diastereoisomers that have nearly identical chemical structures but very different physicochemical properties: for example, the solubility of sorbitol in water is 2.5 kg/kg of water, while mannitol has a solubility of 0.25 kg/kg of water at 25°C. Therefore, predicting liquid-solid equilibrium properties in this case requires sophisticated solution models that cannot be based solely on chemical group contributions, knowing that for mannitol and sorbitol, the chemical constitutive groups are the same. Recognizing the significance of solvation phenomena in polyols, the GePEB (Chemical Engineering, Applied Thermodynamics, and Biosystems) team at Institut Pascal has developed the COSMO-UCA model, which has the structural advantage of using quantum mechanics tools to predict formation and phase equilibrium properties. In this work, we use molecular dynamics simulations to elucidate the behavior of polyols in aqueous solution. Specifically, we employ simulations to compute essential metrics such as radial distribution functions and hydrogen bond autocorrelation functions. Our findings illuminate a fundamental contrast: sorbitol and mannitol exhibit disparate hydrogen bond lifetimes within aqueous environments. This observation serves as a cornerstone in elucidating the divergent physicochemical properties inherent to each compound, shedding light on the nuanced interplay between their molecular structures and water interactions. We also present a methodology to predict the physicochemical properties of complex solutions, taking as sole input the three-dimensional structure of the molecules in the medium. Finally, by developing knowledge models, we represent some physicochemical properties of aqueous solutions of sorbitol and mannitol.

Keywords: COSMO models, hydrogen bond, molecular dynamics, thermodynamics

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Authors: Sanusi Abubakar Sadiq

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Despite the significance of tourism as a key revenue earner and employment generator, it is still being disregarded in many areas. The prospects of existing resources could boost development in communities; region, etc. are less used. This study is carried out with the view of developing Pandi-Tekki in Tanglang in Billiri Local Government Area as a Tourism Destination. It was primarily aimed at identifying features of Pandi-Tekki that could be developed into tourism attraction and suggest ways of developing the prospective site into a tourism destination, as well as exploring its possible contribution to tourism sector in Gombe State. Literature was reviewed based on relevant published materials. Data was collected through the use of qualitative and quantitative methods which include personal observation and structured questionnaire. Data was analyzed using the statistical package for the social sciences (SPSS) software. Result based on the data collected shows that Pandi-Tekki has potentials that can be developed as an attraction. The result also shows that the local community perceives tourism as a good development that will open them up to the entire world and also generate revenue to stimulate their economy. Conclusions were drawn based on the findings with regard to the analysis carried out in this research. It was discovered that Pandi-Tekki can be developed as a tourism destination, and there will be a great success towards achieving the aim and objectives of the development. Therefore, recommendations were made on creating awareness on the need to develop Pandi-Tekki as a Tourism Destination and the need for government to provide tourism facilities at the destination since it is a public outfit.

Keywords: attraction, destination, developing, features

Procedia PDF Downloads 289

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 105

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Nahed Al-Hajeri

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Time and cost is an integral part of every construction plan and can affect each party’s contractual obligations. The performance of both time and cost are usually important to the client and contractor during the project. Almost all construction projects are experiencing time overrun. These time overruns always contributed as expensive to both client and contractor. Construction of any project inside the gathering centers involves complex management skills related to work force, materials, plant, machineries, new technologies etc. It also involves many agencies interdependent on each other like the vendors, structural and functional designers including various types of specialized engineers and it includes support of contractors and specialized contractors. This paper mainly highlights the types of construction delays due to which project suffer time and cost overrun. This paper also speaks about the delay causes and factors that contribute to the construction sequence delay for the oil and gas projects. Construction delay is supposed to be one of the repeated problems in the construction projects and it has an opposing effect on project success in terms of time, cost and quality. Some effective methods are identified to minimize delays in construction projects such as: 1. Site management and supervision, 2. Effective strategic planning, 3. Clear information and communication channel. Our research paper studies the types of delay with some real examples with statistic results and suggests solutions to overcome this problem.

Keywords: non-compensable delay, delays caused by force majeure, compensable delay, delays caused by the owner or the owner’s representative, non-excusable delay, delay caused by the contractor or the contractor’s representative, concurrent delay, delays resulting from two separate causes at the same time

Procedia PDF Downloads 242

Authors: Saira Hossain

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Well-being commonly refers to the concept that equates to a good life. Similarly, student well-being can be understood as a notion of a good life at school. What constitutes a good life at school for students? – is an emerging question that poses huge interest in this area of research. Student well-being is not only associated with a student’s socio-emotional and academic development at school but also success in life after school as an adult. Today, student well-being is a popular agenda for educators, policymakers, teachers, parents, and most importantly, for students. With the emergence of student well-being, student's voice in matters important to them at school is increasingly getting priority. However, the coin has another side too. Despite the growing importance of understanding student well-being, it is still an alien concept in countries like Bangladesh. The education system of Bangladesh is highly rigid, centralized, and exam-focused. Student's academic achievement has been given the utmost priority at school, whereas their voice, as well as their well-being, is grossly neglected in practice. In this regard, the study set out to explore students' conceptualization of well-being at school in Bangladesh. The study was qualitative. It employed a participatory research approach to elicit the views of 25 secondary school students of aged 14-16 in Bangladesh to explore the concept of well-being. Data analysis was conducted following the thematic analysis technique. The results suggested that student conceptualized well-being as a multidimensional concept with multiple domains, including having, being, relating, feeling, thinking, functioning, and striving. The future implication of the study findings is discussed. Additionally, the study also underscores the implication of the participatory approach as a research technique to explore students' opinion in Bangladesh, where there exists a culture of silence regarding the student's voice.

Keywords: Bangladesh, participatory research, secondary school, student well-being

Procedia PDF Downloads 138

Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Fariba Ebrahimi, Mehdi Ghorbani

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Comprehensive water management considers economic, environmental, technical and social sustainability of water resources for future generations. Integrated water management implies cooperative approach and involves all stakeholders and also introduces issues to managers and decision makers. Solving these issues needs integrated and system approach according to the recognition of actors or key persons in necessary to apply cooperative management of water resources. Therefore, social network analysis can be used to demonstrate the most effective actors for environmental base decisions. The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often poses one of the greatest challenges in adaptive water management. Bridging stakeholder can facilitate interactions among actors in management settings by lowering the transaction costs of collaboration. This research examines how network connections between group members affect in co- management. Cohesive network structures allow groups to more effectively achieve their goals and objectives Strong; centralized leadership is a better predictor of working group success in achieving goals and objectives. Finally, geometric position of each actor was illustrated in the network. The results of the research based on between centrality index have a key and bridging actor in recognition of cooperative management of water resources in Darbandsar village and also will help managers and planners of water in the case of recognition to organization and implementation of sustainable management of water resources and water security.

Keywords: co-management, water management, social network, bridging stakeholder, darbandsar village

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Authors: Benyapa Thitimapong

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Teenage pregnancy and adolescent mothers have become a matter of increasing concern in Thailand. Since adolescent mothers have been a big problem for two main consequences; health outcomes and socio-economic impacts. Adolescent mothers often endure poor living conditions; limited financial resources while also experience high stress, family instability, and limited educational opportunities. These disadvantages are negative and have long-term effects on adolescent mothers, their families, and the community. The majority of pregnant students and adolescent mothers dropped out of school after becoming pregnant, and some of them return to study again after they gave birth. This research aimed to explain the meaning of adolescent mothers who had undergone with childrearing and studying simultaneously after childbirth. A phenomenological qualitative approach was undertaken to investigate this study. The participants were 20 adolescent mothers each of whom became a mother and a student concurrently within less than 2 years after giving birth to a healthy baby and had also undergone the experience of childrearing and studying in non-formal education. In-depth interview was carried out for data collection, and the data were analyzed using content analysis method. ‘Learning to move forward’ was the meaning of adolescent mothers who experienced with childrearing and studying simultaneously. Their expressions were classified into two categories 1) having more responsibility, and 2) conceding and going on. The result of this study can be used as evidence for health care providers, especially nurses to facilitate and support pregnant adolescents and adolescent mothers to continue their education. Also, it can be used to guide policy to promote in all educational system to enable these groups to remain in school for their life-long success in the future.

Keywords: adolescent mothers, childrearing, studying, teenage pregnancy

Procedia PDF Downloads 135

Authors: Júlia Alarcon Pinto, Carlos João Schaffhausser, Gustavo Alarcon Pinto

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Introduction: The deaf people in Brazil communicate through the Brazilian Sign Language (LIBRAS), which is restricted to this minority and people that received training. However, there is a lack of prepared professionals in the health system to deal with these patients. Therefore, effective communication, health education, quality of support and assistance are compromised. It is of utmost importance to develop measures that ensure the inclusion of deaf parents and children since there are frequent doubts about sphincter training and an absence of tools to promote effective communication between doctors and their patients. Objective: Use of an efficient, rapid and cheap communication method to promote social inclusion and patient education of deaf parents and children during pediatrics appointments. Results; The application demonstrates how to express phrases and symptoms within seconds and this allows patients to fully understand the information provided during the appointment and are capable to evaluate the signs of readiness, learn the correct approaches with the child, what are the adequate instruments, possible obstacles and the importance to execute medical orientations in order to achieve success in the process. Consequently, patients feel more satisfied, secured and embraced by professionals in the health system care. Conclusion: It is of utmost importance to use efficient and cheap methods that support patient care and education in order to promote health and social inclusion.

Keywords: application, deaf patients, social inclusion, sphincter training

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Authors: Hao-Yuan Tseng, Chuan-Yang Chang, Ying-Hui Chen, Sheng-Che Chen, Chih-Han Chang

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Orthodontic tooth treatment has demonstrated a high success rate in clinical studies. It has been agreed upon that orthodontic tooth movement is based on the ability of surrounding bone and periodontal ligament (PDL) to react to a mechanical stimulus with remodeling processes. However, the mechanism of the tooth movement is still unclear. Recent studies focus on the simple principle compression-tension theory while rare studies directly measure tooth movement. Therefore, tracking tooth movement information during orthodontic treatment is very important in clinical practice. The aim of this study is to investigate the mechanism responses of the tooth movement during the orthodontic treatments. A stereovision system applied to track the tooth movement of the patient with the stamp brackets. The system was established by two cameras with their relative position calibrate. And the orthodontic force measured by 3D printing model with the six-axis load cell to determine the initial force application. The result shows that the stereovision system accuracy revealed the measurement presents a maximum error less than 2%. For the study on patient tracking, the incisor moved about 0.9 mm during 60 days tracking, and half of movement occurred in the first few hours. After removing the orthodontic force in 100 hours, the distance between before and after position incisor tooth decrease 0.5 mm consisted with the release of the phenomenon. Using the stereovision system can accurately locate the three-dimensional position of the teeth and superposition of 3D coordinate system for all the data to integrate the complex tooth movement.

Keywords: orthodontic treatment, tooth movement, stereovision system, long-term tracking

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Authors: Wi Sung Yoo, Seung Woo Lee, Youn Kyoung Hur, Sung Hwan Kim

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Large-scale construction projects are continuously increasing, and the need for tools to monitor and evaluate the project success is emphasized. At the construction industry level, there are limitations in deriving performance evaluation factors that reflect the diversity of construction sites and systems that can objectively evaluate and manage performance. Additionally, there are difficulties in integrating structured and unstructured data generated at construction sites and deriving improvements. In this study, we propose the Key Performance Indicators (KPIs) to enable performance evaluation that reflects the increased diversity of construction sites and the unstructured data generated, and present a model for measuring performance by the derived indicators. The comprehensive performance of a unit construction site is assessed based on 6 areas (Time, Cost, Quality, Safety, Environment, Productivity) and 26 indicators. We collect performance indicator information from 30 construction sites that meet legal standards and have been successfully performed. And We apply data augmentation and optimization techniques into establishing measurement standards for each indicator. In other words, the KPI for construction site performance evaluation presented in this study provides standards for evaluating performance in six areas using institutional requirement data and document data. This can be expanded to establish a performance evaluation system considering the scale and type of construction project. Also, they are expected to be used as a comprehensive indicator of the construction industry and used as basic data for tracking competitiveness at the national level and establishing policies.

Keywords: key performance indicator, performance measurement, structured and unstructured data, data augmentation

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Authors: Yohannes Haile, Dipo Onipede, Jr., Omar Ashour

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The main thrust of our research is to determine Industry 4.0 readiness of small and mid-size manufacturing companies in our region and assist them to implement Cyber Physical Production System (CPPS) capabilities. Adopting CPPS capabilities will help organizations realize improved quality, order delivery, throughput, new value creation, and reduced idle time of machines and work centers of their manufacturing operations. The key metrics for the assessment include the level of intelligence, internal and external connections, responsiveness to internal and external environmental changes, capabilities for customization of products with reference to cost, level of additive manufacturing, automation, and robotics integration, and capabilities to manufacture hybrid products in the near term, where near term is defined as 0 to 18 months. In our initial evaluation of several manufacturing firms which are profitable and successful in what they do, we found low level of Physical-Digital-Physical (PDP) loop in their manufacturing operations, whereas 100% of the firms included in this research have specialized manufacturing core competencies that have differentiated them from their competitors. The level of automation and robotics integration is low to medium range, where low is defined as less than 30%, and medium is defined as 30 to 70% of manufacturing operation to include automation and robotics. However, there is a significant drive to include these capabilities at the present time. As it pertains to intelligence and connection of manufacturing systems, it is observed to be low with significant variance in tying manufacturing operations management to Enterprise Resource Planning (ERP). Furthermore, it is observed that the integration of additive manufacturing in general, 3D printing, in particular, to be low, but with significant upside of integrating it in their manufacturing operations in the near future. To hasten the readiness of the local and regional manufacturing companies to Industry 4.0 and transitions towards CPPS capabilities, our working group (ADMAR Working Group) in partnership with our university have been engaged with the local and regional manufacturing companies. The goal is to increase awareness, share know-how and capabilities, initiate joint projects, and investigate the possibility of establishing the Center for Cyber Physical Production Systems Innovation (C2P2SI). The center is intended to support the local and regional university-industry research of implementing intelligent factories, enhance new value creation through disruptive innovations, the development of hybrid and data enhanced products, and the creation of digital manufacturing enterprises. All these efforts will enhance local and regional economic development and educate students that have well developed knowledge and applications of cyber physical manufacturing systems and Industry 4.0.

Keywords: automation, cyber-physical production system, digital manufacturing enterprises, disruptive innovation, new value creation, physical-digital-physical loop

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Authors: Siwei Chang, Heng Li, Haitao Wu, Xin Fang

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Construction robots are receiving more and more attention as a promising solution to the manpower shortage issue in the construction industry. The development of intelligent control techniques that assist in controlling the robots to avoid transparency and reflected building obstacles is crucial for guaranteeing the adaptability and flexibility of mobile construction robots in complex construction environments. With the boom of computer vision techniques, a number of studies have proposed vision-based methods for transparent obstacle avoidance to improve operation accuracy. However, vision-based methods are also associated with disadvantages such as high computational costs. To provide better perception and value evaluation, this study aims to analyze the performance of vision-based techniques for avoiding transparent building obstacles. To achieve this, commonly used sensors, including a lidar, an ultrasonic sensor, and a USB camera, are equipped on the robotic platform to detect obstacles. A Raspberry Pi 3 computer board is employed to compute data collecting and control algorithms. The turtlebot3 burger is employed to test the programs. On-site experiments are carried out to observe the performance in terms of success rate and detection distance. Control variables include obstacle shapes and environmental conditions. The findings contribute to demonstrating how effectively vision-based obstacle avoidance strategies for transparent building obstacle avoidance and provide insights and informed knowledge when introducing computer vision techniques in the aforementioned domain.

Keywords: construction robot, obstacle avoidance, computer vision, transparent obstacle

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Authors: Xing Wang, Lin Feng, Xuan Zou, Hongchen liu

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Immediate Implantation after tooth extraction is considered to be the ideal way to retain the alveolar bone, but some scholars believe the aesthetic effect in the Early Implantation case are more reliable. In this study, 89 patients were added to this retrospective study up to 5 years. Assessment indicators was including the survival of the implant (peri-implant infection, implant loosening, shedding, crowns and occlusal), aesthetics (color and fullness gums, papilla height, probing depth, X-ray alveolar crest height, the patient's own aesthetic satisfaction, doctors aesthetics score), repair defects around the implant (peri-implant bone changes in height and thickness, whether the use of autologous bone graft, whether to use absorption/repair manual nonabsorbable material), treatment time, cost and the use of antibiotics.The results demonstrated that there is no significant difference in long-term success rate of immediate implantation, early implantation and delayed implantation (p> 0.05). But the results indicated immediate implantation group could get get better aesthetic results after two years (p< 0.05), but may increase the risk of complications and failures (p< 0.05). High-risk indicators include gingival recession, labial bone wall damage, thin gingival biotypes, planting position and occlusal restoration bad and so on. No matter which type of implanting methods was selected, the extraction methods and bone defect amplification techniques are observed as a significant factors on aesthetic effect (p< 0.05).

Keywords: immediate implantation, long-term effects, aesthetics area, dental implants

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Authors: Mohammad Saraj Sharifzai, Keisuke Kitagawa, Ahmad Javid Habib Mohammad Kamil Halimee, Daishi Sakaguchi

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The main purpose of this research is to recognize indoor comfort in contemporary Afghan courtyard house with outdoor welfare in housing layout and neighborhood design where sustainability is a local consideration. This research focuses on three new neighborhoods (Gawoond) in three different provinces of Afghanistan. Since 2001, the capital Kabul and major cities including Kandahar, which will be compared with Peshawar city in Pakistan, have faced a fast, rough-and-tumble process of urban innovation. The effects of this innovation necessitate reconsideration of the formation of sustainable urban environments and in-house thermal comfort. The lack of sustainable urban life in many newly developed Afghan neighborhoods can pose a major challenge to the process of sustainable urban development. Several factors can affect the success or failure of new neighborhoods in the context of urban life. For thermal analysis, we divide our research into three different climatic zones. This study is an evaluation of the environmental impacts of the interior comfort of contemporary courtyard housing with the exterior welfare of neighborhood sustainable design strategy in dry and cold, semi-hot and arid, and semi-humid and hot climates in Afghan cities and Peshawar.

Keywords: Afghan contemporary courtyard house, neighbourhood, street pattern and housing layout, sustainability, welfare, comfort, climate zone, Afghanistan

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Authors: Marvin Chibuzo Offiah, Susanne Rosenthal, Markus Borschbach

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Certain research projects in Computer Science often involve research on existing signal processing algorithms and developing improvements on them. Research budgets are usually limited, hence there is limited time for implementing the algorithms from scratch. It is therefore common practice, to use implementations provided by other researchers as a template. These are most commonly provided in a rapid development, i.e. 4th generation, programming language, usually Matlab. Rapid development is a common method in Computer Science research for quickly implementing and testing new developed algorithms, which is also a common task within agile project organization. The growing relevance of mobile devices in the computer market also gives rise to the need to demonstrate the successful executability and performance measurement of these algorithms on a mobile device operating system and processor, particularly on a smartphone. Open mobile systems such as Android, are most suitable for this task, which is to be performed most efficiently. Furthermore, efficiently implementing an interaction between the algorithm and a graphical user interface (GUI) that runs exclusively on the mobile device is necessary in cases where the project’s goal statement also includes such a task. This paper examines different proposed solutions for porting computer algorithms obtained through rapid development into a GUI-based smartphone Android app and evaluates their feasibilities. Accordingly, the feasible methods are tested and a short success report is given for each tested method.

Keywords: SMARTNAVI, Smartphone, App, Programming languages, Rapid Development, MATLAB, Octave, C/C++, Java, Android, NDK, SDK, Linux, Ubuntu, Emulation, GUI

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Authors: Nathaniel Carpenter

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Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Susan Cardillo

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This research will consider ways to bridge the gap between Computer Science and Media Communications and while doing so create Social Entrepreneurship for student success. New Media, as it has been referred to, is considered content available on-demand through Internet, a digital device, usually containing some kind of interactivity and creative participation. It is the interplay between technology, images, media and communications. The next generation of the newspaper, radio, television, and film students need to have a working knowledge of the technologies that are available for the creation of their work and taught to use this knowledge to create a voice. The work is interdisciplinary; in communications, we understand the necessity of reporting and disseminating information. In documentary film we understand the instructional and historic aspects of media and technology and in the non-profit sector, we see the need for expanding outlets for good. So, the true necessity is to utilize ‘new media’ technologies to advance social causes while reporting information, teaching and creating art. Goals: The goal of this research is to give communications students a better understanding of the technology that is both, currently at their disposal, and on the horizon, so that they can use it in their media, communications and art endeavors to be a voice for their generation. There is no longer a need to be a computer scientist to have a working knowledge of communication technologies and how they will benefit our work. There are many free and easy to use applications available for the creation of interactive communications. Methodology: This is Qualitative-Case Study that puts these ideas into action. There is a survey at the end of the experiment that is qualitative in nature and allows for the participants to share ideas and feelings about the technology and approach.

Keywords: interactive storytelling, web documentary, mass communications, teaching

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Authors: Lingfei Sun

Abstract:

Based on the significant value of ecology, the strategic planning for ecological civilization construction was mentioned in the 17th and 18th National Congress of the Communist Party of China. How to generate economic value based on the environmental capacity is not only an economic decision but also a political decision to make. Boshan took the full use of “Ecology” and transformed it as an inexhaustible green resource to benefit people, reflecting the sustainable value of new agriculture development mode. The Strawberry Harvest Festival and Blueberry Harvest Festival hosted approximately 96,000 and 54,000 leisure tourists respectively in 2014. For the Kiwi Harvest Festival in August 2014, in average, it attracted about 4600 tourists per day, which generated daily kiwi sales of 50,000 lbs and 3 million RMB (About 476,000 USD) of daily revenue. The purpose of this study is to elaborate the modes of agritourism development, by analyzing the cases in rural area of Boshan, China. Interviews with the local government officers were applied to discover operation mode of agritourism operation. The financial data was used to demonstrate the strength of government policy and improvement of the income of rural people. The result indicated that there are mainly three types of modes: the Intensive Mode, the Model Mode and the Mixed Mode, supported by case study respectively. With the boom of tourism, the development of agritourism in Boshan relies on the agriculture encouraging policy of China and the effort of local government; meanwhile, large scale of cultivation and the product differentiation are the crucial elements for the success of rural agritourism projects.

Keywords: agriculture, agritourism, economy, rural area development

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Authors: Chevon Fernando, Banuka Athuraliya

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The purpose of this research is to provide a solution to the students’ time management, which usually becomes an issue because students must study and manage their personal commitments. "TimeTune," an AI-based study planner that provides an opportunity to maneuver study timeframes by incorporating modern machine learning algorithms with calendar applications, is unveiled as the ideal solution. The research is focused on the development of LSTM models that connect to the Google Calendar API in the process of developing learning paths that would be fit for a unique student's daily life experience and study history. A key finding of this research is the success in building the LSTM model to predict optimal study times, which, integrating with the real-time data of Google Calendar, will generate the timetables automatically in a personalized and customized manner. The methodology encompasses Agile development practices and Object-Oriented Analysis and Design (OOAD) principles, focusing on user-centric design and iterative development. By adopting this method, students can significantly reduce the tension associated with poor study habits and time management. In conclusion, "TimeTune" displays an advanced step in personalized education technology. The fact that its application of ML algorithms and calendar integration is quite innovative is slowly and steadily revolutionizing the lives of students. The excellence of maintaining a balanced academic and personal life is stress reduction, which the applications promise to provide for students when it comes to managing their studies.

Keywords: personalized learning, study planner, time management, calendar integration

Procedia PDF Downloads 49