Search results for: comprehensive CFD model

Authors: Arun C. O., Shrijith M. B., Thakur Rajesh Singh

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The study aims to analyze and understand the biomechanical stability of the atlantoaxial complex under different posterior fixation techniques using the finite element method in the Indian context. The conventional cadaveric studies performed show heterogeneity in biomechanical properties. The finite element method being a versatile numerical tool, is being wisely used for biomechanics analysis of atlantoaxial complex. However, the biomechanics of posterior fixation techniques for an Indian subject is missing in the literature. It is essential to study in this context as the bone density and geometry of vertebrae vary from region to region, thereby requiring different screw lengths and it can affect the range of motion(ROM), stresses generated. The current study uses CT images for developing a 3D finite element model with C1-C2 geometry without ligaments. Instrumentation is added to this geometry to develop four models for four fixation techniques, namely C1-C2 TA, C1LM-C2PS, C1LM-C2Pars, C1LM-C2TL. To simulate Flexion, extension, lateral bending, axial rotation, 1.5 Nm is applied to C1 while the bottom nodes of C2 are fixed. Then Range of Motion (ROM) is compared with the unstable model(without ligaments). All the fixation techniques showed more than 97 percent reduction in the Range of Motion. The von-mises stresses developed in the screw constructs are obtained. From the studies, it is observed that Transarticular technique is most stable in Lateral Bending, C1LM-C2 Translaminar is found most stable in Flexion/extension. The Von-Mises stresses developed minimum in Trasarticular technique in lateral bending and axial rotation, whereas stress developed in C2 pars construct minimum in Flexion/ Extension. On average, the TA technique is stable in all motions and also stresses in constructs are less in TA. Tarnsarticular technique is found to be the best fixation technique for Indian subjects among the 4 methods.

Keywords: biomechanics, cervical spine, finite element model, posterior fixation

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Authors: Elias Jemal Abdella

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The Blue Nile River is an important shared resource of Ethiopia, Sudan and also, because it is the major contributor of water to the main Nile River, Egypt. Despite the potential benefits of regional cooperation and integrated joint basin management, all three countries continue to pursue unilateral plans for development. Besides, there is great uncertainty about the likely impacts of climate change in water availability for existing as well as proposed irrigation and hydropower projects in the Blue Nile Basin. The main objective of this study is to quantitatively assess the impact of climate change on the hydrological regime of the upper Blue Nile basin, western Ethiopia. Three models were combined, a dynamic Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate model (RCM) that is used to determine climate projections for the Upper Blue Nile basin for Representative Concentration Pathways (RCPs) 4.5 and 8.5 greenhouse gas emissions scenarios for the period 2021-2050. The outputs generated from multimodel ensemble of four (4) CORDEX-RCMs (i.e., rainfall and temperature) were used as input to a Soil and Water Assessment Tool (SWAT) hydrological model which was setup, calibrated and validated with observed climate and hydrological data. The outputs from the SWAT model (i.e., projections in river flow) were used as input to a Water Evaluation and Planning (WEAP) water resources model which was used to determine the water resources implications of the changes in climate. The WEAP model was set-up to simulate three development scenarios. Current Development scenario was the existing water resource development situation, Medium-term Development scenario was planned water resource development that is expected to be commissioned (i.e. before 2025) and Long-term full Development scenario were all planned water resource development likely to be commissioned (i.e. before 2050). The projected change of mean annual temperature for period (2021 – 2050) in most of the basin are warmer than the baseline (1982 -2005) average in the range of 1 to 1.4oC, implying that an increase in evapotranspiration loss. Subbasins which already distressed from drought may endure to face even greater challenges in the future. Projected mean annual precipitation varies from subbasin to subbasin; in the Eastern, North Eastern and South western highland of the basin a likely increase of mean annual precipitation up to 7% whereas in the western lowland part of the basin mean annual precipitation projected to decrease by 3%. The water use simulation indicates that currently irrigation demand in the basin is 1.29 Bm3y-1 for 122,765 ha of irrigation area. By 2025, with new schemes being developed, irrigation demand is estimated to increase to 2.5 Bm3y-1 for 277,779 ha. By 2050, irrigation demand in the basin is estimated to increase to 3.4 Bm3y-1 for 372,779 ha. The hydropower generation simulation indicates that 98 % of hydroelectricity potential could be produced if all planned dams are constructed.

Keywords: Blue Nile River, climate change, hydropower, SWAT, WEAP

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Mauricio Gracia, Luis Leal, Bharat Verma

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Computational fluid dynamics (CFD) has become nowadays an important tool in the process of hydrodynamic design of modern ships. CFD is used to model any phenomena related to fluid flow in a control volume like a ship or any offshore structure in the sea. In the present study, the current force drag coefficients for a Colombian Navy Frigate in deep and shallow water are estimated through the application of CFD. The study shows the process of simulating the ship current drag coefficients using the CFD simulations method, which is conducted using STAR-CCM+ software package. The Almirante Padilla class Frigate ship scale model is investigated. The results show the ship current drag coefficient calculated considering a current speed of 1 knot with a 90° drift angle for the full-scale ship. Predicted results were compared against the current drag coefficients published in the Lloyds register OCIMF report. It is shown that the simulation results agree fairly well with the published results and that STAR-CCM+ code can predict current drag coefficients.

Keywords: CFD, current draft coefficient, STAR-CCM+, OCIMF, Bollard pull

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Authors: Elham Ahmadi, Roshaali Khaturia, Pardis Sahraei, Mohammad Niyayesh, Omid Fatahi Valilai

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Nowadays, Information Technology (IT) is changing the way traditional enterprise management concepts work. One of the most dominant IT achievements is the Blockchain Technology. This technology enables the distributed collaboration of stakeholders for their interactions while fulfilling the security and consensus rules among them. This paper has focused on the application of Blockchain technology to enhance one of traditional inventory management models. The Vendor Managed Inventory (VMI) has been considered one of the most efficient mechanisms for vendor inventory planning by the suppliers. While VMI has brought competitive advantages for many industries, however its centralized mechanism limits the collaboration of a pool of suppliers and vendors simultaneously. This paper has studied the recent research for VMI application in industries and also has investigated the applications of Blockchain technology for decentralized collaboration of stakeholders. Focusing on sustainability issue for total supply chain consisting suppliers and vendors, it has proposed a Blockchain based VMI conceptual model. The different capabilities of this model for enabling the collaboration of stakeholders while maintaining the competitive advantages and sustainability issues have been discussed.

Keywords: vendor managed inventory, VMI, blockchain technology, supply chain planning, sustainability

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Authors: L. Rashidian, M. Rajabali

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Climate changes have unpredictable and costly effects on water resources of various basins. The impact of atmospheric phenomena on human life and the environment is so significant that only knowledge of management can reduce its consequences. In this study, using LARS.WG model and down scaling of general circulation climate model HADCM-3 and according to the IPCC scenarios, including series A1b, A2 and B1, we simulated data from 2010 to 2040 in order to using them for long term forecasting of climate parameters of the Caspian Sea and its impact on sea level. Our research involves collecting data on monthly precipitation amounts, minimum and maximum temperature and daily sunshine hours, from meteorological organization for Caspian Sea coastal station such as Gorgan, Ramsar, Rasht, Anzali, Astara and Ghaemshahr since their establishment until 2010. Considering the fact that the fluctuation range of water level in the Caspian Sea has various ups and downs in different times, there is an increase in minimum and maximum temperature for all the mentioned scenarios, which will last until 2040. Overall, the amount of rainfall in cities bordering the Caspian Sea was studied based on the three scenarios, which shows an increase in the amount. However, there will be a decrease in water level of the Caspian Sea till 2040.

Keywords: IPCC, climate change, atmospheric circulation, Caspian Sea, HADCM3, sea level

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Linung Kresno Adikusumo, Ivan Ramos Sampe Immanuel, Liston Sitanggang

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An excellent, well work program in the oil and gas industry can have numerous positive business impacts, contributing to operational efficiency, increased production, enhanced safety, and improved financial performance. In summary, an excellent, well work program not only ensures the immediate success of specific projects but also has a broader positive impact on the overall business performance and reputation of the oil and gas company. It positions the company for long-term success in a competitive and dynamic industry. Nevertheless, a number of challenges were encountered when developing a good work program, such as the poor quality and lack of integration of well documentation, the incompleteness of the well history, and the low accessibility of well documentation. As a result, the well work program was delivered less accurately, plus well damage was managed slowly. Our solution implementing digital technology by developing a web-based database and application not only solves those issues but also provides an easy-to-access report and user-friendly display for management as well as engineers to analyze the report’s content. This application aims to revolutionize the documentation of well history in the field of oil and gas exploration and production. The current lack of a streamlined and comprehensive system for capturing, organizing, and accessing well-related data presents challenges in maintaining accurate and up-to-date records. Our innovative solution introduces a user-friendly and efficient platform designed to capture well history documentation seamlessly.

Keywords: digital, drilling, well work, application

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Authors: Robert McGrath, Zara Qureshi

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A innovative leadership philosophy that is proposed herein is distinctly more humane than most leadership approaches Concerned Leadership. The central idea to this approach is to consider the whole person that comes to work; their professional skills and talents, as well as any personal, emotional challenges that could be affecting productivity and effectiveness at work. This paper explores Concerned Leadership as an integration of the two conceptual models areas examined in this paper –(1) leaderful organizations and practices, as well as (2) organizational culture, and defines leadership in the context of Mental Health and Wellness in the workplace. Leaderful organizations calls for organizations to implement leaderful practice. Leaderful practice is when leadership responsibility and decision-making is shared across all team members and levels, versus only delegated to top management as commonly seen. A healthy culture thrives off key aspects such as acceptance, employee pride, equal opportunity, and strong company leadership. Concerned Leadership is characterized by five main components: Self-Concern, Leaderful Practice, Human Touch, Belonging, and Compassion. As scholars and practitioners conceptualize leadership in practice, the present model seeks to uphold the dignity of each organizational member, thereby having the potential to transform workplaces and support all members.

Keywords: leadership, mental health, reflective practice, organizational culture

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Authors: Zheng Zhihao

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Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph, graph neural networks, structural adaptive receptive fields, information aggregation

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Zafar Iqbal

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This study involved students of 6th semester enrolled in a Bachelor of Computer Science Program at university level. In this era of science and technology, e-learning can be helpful for grassroots in providing them access to education tenant in less developed areas. It is a potential substitute of face-to-face teaching being used in different countries. The purpose of the study was to explore the views of students about e-learning (Facebook) as an instructional tool. By using purposive sampling technique an intact class of 30 students included both male and female were selected where e-learning was used as an instructional tool. The views of students were explored through qualitative approach by using focus group interviews. The approach was helpful to develop comprehensive understanding of students’ views towards e- learning. In addition, probing questions were also asked and recorded. Data was transcribed, generated nodes and then coded text against these nodes. For this purpose and further analysis, NVivo 10 software was used. Themes were generated and tangibly presented through cluster analysis. Findings were interesting and provide sufficient evidence that face book is a subsequent e-learning source for students of higher education. Students acknowledged it as best source of learning and it was aligned with their academic and social behavior. It was not time specific and therefore, feasible for students who work day time and can get on line access to the material when they got free time. There were some distracters (time wasters) reported by the students but can be minimized by little effort. In short, e-learning is need of the day and potential learning source for every individual who have access to internet living at any part of the globe.

Keywords: e-learning, facebook, instructional tool, higher education

Procedia PDF Downloads 377

Authors: Khaled Ayfi

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In industrial oxygen systems at high temperature and high pressure, contamination by solid particles is one of the principal causes of ignition hazards. Indeed, gas can sweep away particles, generated by corrosion inside the pipes or during maintenance operations (welding residues, careless disassembly, etc.) and produce accumulations at places where the gas velocity decrease. Moreover, in such an environment rich in oxygen (oxidant), particles are highly reactive and can ignite system walls more actively and at higher temperatures. Oxidation based thermal effects are responsible for mechanical properties lost, leading to the destruction of the pressure equipment wall. To deal with this problem, a numerical analysis is done regarding a sample representative of a wall subjected to pressure and temperature. The validation and analysis are done comparing the numerical simulations results to experimental measurements. More precisely, in this work, we propose a numerical model that describes the thermomechanical behavior of thin metal disks under pressure and subjected to laser heating. This model takes into account the geometric and material nonlinearity and has been validated by the comparison of simulation results with experimental measurements.

Keywords: ignition, oxygen, numerical simulation, thermomechanical behavior

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Jia-Lien Hsu, Chiung-Wen Chang

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Research articles may follow a simple and succinct structure of organizational patterns, called move. For example, considering extended abstracts, we observe that an extended abstract usually consists of five moves, including Background, Aim, Method, Results, and Conclusion. As another example, when publishing articles in PubMed, authors are encouraged to provide a structured abstract, which is an abstract with distinct and labeled sections (e.g., Introduction, Methods, Results, Discussions) for rapid comprehension. This paper introduces a method for computational analysis of move structures (i.e., Background-Purpose-Method-Result-Conclusion) in abstracts and introductions of research documents, instead of manually time-consuming and labor-intensive analysis process. In our approach, sentences in a given abstract and introduction are automatically analyzed and labeled with a specific move (i.e., B-P-M-R-C in this paper) to reveal various rhetorical status. As a result, it is expected that the automatic analytical tool for move structures will facilitate non-native speakers or novice writers to be aware of appropriate move structures and internalize relevant knowledge to improve their writing. In this paper, we propose a Bayesian approach to determine move tags for research articles. The approach consists of two phases, training phase and testing phase. In the training phase, we build a Bayesian model based on a couple of given initial patterns and the corpus, a subset of CiteSeerX. In the beginning, the priori probability of Bayesian model solely relies on initial patterns. Subsequently, with respect to the corpus, we process each document one by one: extract features, determine tags, and update the Bayesian model iteratively. In the testing phase, we compare our results with tags which are manually assigned by the experts. In our experiments, the promising accuracy of the proposed approach reaches 56%.

Keywords: academic English writing, assisted writing, move tag analysis, Bayesian approach

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Authors: Khatesiree Sripoothon, Usanee Sengpanich, Rattana Sittioum

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The objective of this study is to examine the factors influencing consumer purchasing decisions about healthy food. This model consists of two latent variables: Consumer Perception relating to NCDs and Consumer Perceived Product Value. The study was conducted in the northern provinces of Thailand, which are popular with tourists and have received support from the government for health tourism. A survey was used as the data collection method, and the questionnaire was applied to 385 tourists. An accidental sampling method was used to identify the sample. The statistics of frequency, percentage, mean, and structural equation model were used to analyze the data obtained. Additionally, all factors had a significant positive influence on healthy food purchasing decisions (p<0.01) and were predictive of healthy food purchasing decisions at 46.20 (R2=0.462). Also, these findings seem to underline a supposition that consumer perceptions of NCDs and perceived product value are key variables that strengthens the competitive effects of a healthy-friendly business entrepreneur. Moreover, reduce the country's public health costs for treating patients with the disease of NCDs in Thailand.

Keywords: healthy food, perceived product value, perception of non-communicable diseases, purchasing decisions

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Authors: Jarrod Sio Jyh Lih

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This paper discusses the factors contributing to student achievement in literacy and numeracy in primary schools within Sibu division. The study involved 694 level 1 primary schoolteachers. Using descriptive statistics, the study observed high levels of practice for teacher efficacy, instructional leadership and professional learning communities (PLCs). The differences between gender, teaching experience and academic qualification were analyzed using the t-test and one-way analysis of variance (ANOVA). The study reported significant differences in respondent perceptions based on teaching experience vis-à-vis teacher efficacy. Here, the post hoc Tukey test revealed that efficaciousness grows with experience. A correlation test observed positive and significant correlations between all independent variables. Binary logistic regression was applied to predict the independent variables’ influence on student achievement. The findings revealed that a dimension of instructional leadership – ‘monitoring student progress’ - emerged as the best predictor of student achievement for literacy and numeracy. The result indicated the students were more than 4 times more likely to achieve the national key performance index for both literacy and numeracy when student progress was monitored. In conclusion, ‘monitoring student progress’ had a positive influence on students’ achievement for literacy and numeracy, hence making it a possible course of action for school heads. However, more comprehensive studies are needed to ascertain its consistency within the context of Malaysia.

Keywords: efficacy, instructional, literacy, numeracy

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Authors: Philip A. Szlosek, M. Susan Guyer, Mary G. Barnum, Elizabeth M. Mullin

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In the United States, athletic training is a healthcare profession that encompasses the prevention, examination, diagnosis, treatment, and rehabilitation of injuries and medical conditions. Athletic trainers work under the direction of or in collaboration with a physician and are recognized by the American Medical Association as allied healthcare professionals. Internationally, this profession is often known as athletic therapy. As healthcare professionals, athletic trainers must be prepared for autonomous practice immediately after graduation. However, new athletic trainers have been shown to have clinical areas of strength and weakness.To better assess professional readiness and improve the preparedness of new athletic trainers, the factors of athletic training professional readiness must be defined. Limited research exists defining the holistic aspects of professional readiness needed for athletic trainers. Confirming the factors of professional readiness in athletic training could enhance the professional preparation of athletic trainers and result in more highly prepared new professionals. The objective of this study was to further explore and confirm the factors of professional readiness in athletic training. Authors useda qualitative design based in grounded theory. Participants included athletic trainers with greater than 24 months of experience from a variety of work settings from each district of the National Athletic Trainer’s Association. Participants took the demographic questionnaire electronically using Qualtrics Survey Software (Provo UT). After completing the demographic questionnaire, 20 participants were selected to complete one-on-one interviews using GoToMeeting audiovisual web conferencing software. IBM Statistical Package for the Social Sciences (SPSS, v. 21.0) was used to calculate descriptive statistics for participant demographics. The first author transcribed all interviews verbatim and utilized a grounded theory approach during qualitative data analysis. Data were analyzed using a constant comparative analysis and open and axial coding. Trustworthiness was established using reflexivity, member checks, and peer reviews. Analysis revealed four overarching themes, including management, interpersonal relations, clinical decision-making, and confidence. Management was categorized as athletic training services not involving direct patient care and was divided into three subthemes, including administration skills, advocacy, and time management. Interpersonal Relations was categorized as the need and ability of the athletic trainer to properly interact with others. Interpersonal relations was divided into three subthemes, including personality traits, communication, and collaborative practice. Clinical decision-making was categorized as the skills and attributes required by the athletic trainer whenmaking clinical decisions related to patient care. Clinical decision-making was divided into three subthemes including clinical skills, continuing education, and reflective practice. The final theme was confidence. Participants discussed the importance of confidence regarding relationships building, clinical and administrative duties, and clinical decision-making. Overall, participants explained the value of a well-rounded athletic trainer and emphasized that athletic trainers need communication and organizational skills, the ability to collaborate, and must value self-reflection and continuing education in addition to having clinical expertise. Future research should finalize a comprehensive model of professional readiness for athletic training, develop a holistic assessment instrument for athletic training professional readiness, and explore the preparedness of new athletic trainers.

Keywords: autonomous practice, newly certified athletic trainer, preparedness for professional practice, transition to practice skills

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Authors: Nwachukwu M. A., Nwachukwu J. I., Anyanwu J., Emeka U., Okorondu J., Acholonu C.

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Actualization of the 17 sustainable development goals (SDGs) conceived by the United Nations in 2015 is a global challenge that may not be feasible in sub-Saharan Africa by the year 2030, except universities play a committed role. This is because; there is a need to educate the people about the concepts of sustainability and sustainable development in the region to make the desired change. Here is a sensitization paper with a model of intervention and curricular planning to allow advancement in understanding and knowledge of SDGs. This Model Center for Sustainability Studies (MCSS) will enable partnerships with institutions in Africa and in advanced nations, thereby creating a global network for sustainability studies not found in sub-Saharan Africa. MCSS will train and certify public servants, government agencies, policymakers, entrepreneurs and personnel from organizations, and students on aspects of the SDGs and sustainability science. There is a need to add sustainability knowledge into environmental education and make environmental education a compulsory course in higher institutions and a secondary school certificate exam subject in sub-Saharan Africa. MCSS has 11 training modules that can be replicated anywhere in the world.

Keywords: sustainability, higher institutions, training, SDGs, collaboration, sub-Saharan Africa

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Authors: Zafar Mahmood, Surayya Naz, Nazir Shah Khattak

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The degree of freedom relates to our capability to observe or model the energy paths within the system. Higher the number of energy paths being modeled leaves to us a higher degree of freedom, but increasing the time and modeling complexity rendering it useless for today’s world’s need for minimum time to market. Since the number of residuals that can be uniquely isolated are dependent on the number of independent outputs of the system, increasing the number of sensors required. The examples of discrete position sensors that may be used to form an array include limit switches, Hall effect sensors, optical sensors, magnetic sensors, etc. Their mechanical design can usually be tailored to fit in the transitional path of an STME in a variety of mechanical configurations. The case studies into multi-sensor system were carried out and actual data from sensors is used to test this generic framework. It is being investigated, how the proper modeling of limit switches as timing sensors, could lead to unified and neutral residual space while keeping the implementation cost reasonably low.

Keywords: low-cost limit sensors, fault diagnostics, Single Throw Mechanical Equipment (STME), parameter estimation, parity-space

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Authors: Nouha Ben Salah, Mohamed Ali Omri

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This paper treats the association between disclosure level and the cost of equity capital in Tunisian’slisted firms. This relation is tested by using two models. The first is used for testing this relation directly by regressing firm specific estimates of cost of equity capital on market beta, firm size and a measure of disclosure level. The second model is used for testing this relation by introducing information asymmetry as mediator variable. This model is suggested by Baron and Kenny (1986) to demonstrate the role of mediator variable in general. Based on a sample of 21 non-financial Tunisian’s listed firms over a period from 2000 to 2004, the results prove that greater disclosure is associated with a lower cost of equity capital. However, the results of indirect relationship indicate a significant positive association between the level of voluntary disclosure and information asymmetry and a significant negative association between information asymmetry and cost of equity capital in contradiction with our previsions. Perhaps this result is due to the biases of measure of information asymmetry.

Keywords: cost of equity capital, voluntary disclosure, information asymmetry, and Tunisian’s listed non-financial firms

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Authors: M. Kosandi, V. Susanti, N. I. Subono, E. Kartini

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This paper discusses the risk of glorification trap in combating human trafficking, as it is shown in the case of Indonesia. Based on a research on Indonesian combat against trafficking in 2017-2018, this paper shows the tendency of misinterpretation and misapplication of the Indonesian anti-trafficking law into misusing the law for glorification, to create an image of certain extent of achievement in combating human trafficking. The objective of this paper is to explain the persistent occurrence of human trafficking crimes despite the significant progress of anti-trafficking efforts of Indonesian government. The research was conducted in 2017-2018 by qualitative approach through observation, depth interviews, discourse analysis, and document study, applying the three-dimensional model for analyzing human trafficking in the source country. This paper argues that the drive for glorification of achievement in the combat against trafficking has trapped Indonesian government in the loop of misinterpretation, misapplication, and misuse of the anti-trafficking law. In return, the so-called crime against humanity remains high and tends to increase in Indonesia.

Keywords: human trafficking, anti-trafficking policy, transnational crime, source country, glorification trap

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Authors: M. Panidou, F. Kilipiris, E. Christou, K. Alexandris

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This study intends to investigate the travel preferences of Generation Z (born between 1996 and 2012), focusing on their inclination towards unique and unconventional travel experiences, prioritization of authentic cultural immersion and local experiences over traditional tourist attractions, and their value for flexibility and spontaneity in travel plans. By examining these aspects, the research aims to provide insights into the preferences and behaviors of Generation Z travelers, contributing to a better understanding of their travel choices and informing the tourism industry in catering to their needs and desires. Secondary data was gathered from academic literature and industry reports to offer a thorough study of the topic. A quantitative method was used, and primary data was collected through an online questionnaire. One hundred Greek people between the ages of eighteen and twenty-seven were the study's sample. SPSS software was used to assist in the analysis of the data. The findings of the research showed that Gen Z is attracted to unusual and distinctive travel experiences, prioritizing genuine cultural immersion over typical tourist attractions, and they highly value flexibility in their travel decision-making. This research contributes to a deeper understanding of how Gen Z travelers are reshaping the travel industry. Travel companies, marketers, and destination management organizations will find the findings useful in adjusting their products to suit this influential demographic's changing demands and preferences. Considering the limitations of the sample size, future studies could expand the sample size to include individuals from different cultural backgrounds for a more comprehensive understanding.

Keywords: cultural immersion, flexibility, generation Z, travel preferences, unique experiences

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Authors: Muna Al-Tarshi, Dobretsov Sergey, Wenresti Gallardo

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Marine litter pollution is a global concern that has wide-ranging ecological, societal, and economic implications, along with potential health risks for humans. In Oman, inadequate solid waste management has led to the accumulation of litter in mangrove ecosystems. However, there is a dearth of information on marine litter and microplastic pollution in Omani mangroves, impeding the formulation of effective mitigation strategies. To address this knowledge gap, we conducted a comprehensive assessment of marine litter and microplastics in mangrove sediments in the Sea of Oman. Our study measured the average abundance of marine litter, which ranged from 0.83±1.03 to 19.42±8.52 items/m2. Notably, plastics constituted the majority of litter, accounting for 73-96% of all items, with soft plastics being the most prevalent. Furthermore, we investigated microplastic concentrations in the sediments, finding levels ranging from 6 to 256 pieces /kg. Among the studied areas, afforested mangroves in Al-Sawadi exhibited the highest average abundance of microplastics (27.52±5.32 pieces/ kg), while the Marine Protected Area Al Qurum had the lowest average abundance (0.60±1.12 pieces /kg). These findings significantly contribute to our understanding of marine litter and microplastic pollution in Omani mangroves. They provide valuable baseline data for future monitoring initiatives and the development of targeted management strategies. Urgent action is needed to implement effective waste management practices and interventions to protect the ecological integrity of mangrove ecosystems in Oman and mitigate the risks associated with marine litter and microplastics.

Keywords: microplastics, anthropogenic marine litter, ftir, polymer, khawr, mangrove, sediment

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Authors: Shereif. H. Mahmoud

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Egypt has limited water resources, and it will be under water stress by the year 2030. Therefore, Egypt should consider natural and non-conventional water resources to overcome such a problem. Rain harvesting is one solution. This Paper presents a geographic information system (GIS) methodology - based on decision support system (DSS) that uses remote sensing data, filed survey, and GIS to identify potential RWH areas. The input into the DSS includes a map of rainfall surplus, slope, potential runoff coefficient (PRC), land cover/use, soil texture. In addition, the outputs are map showing potential sites for RWH. Identifying suitable RWH sites implemented in the ArcGIS model environment using the model builder of ArcGIS 10.1. Based on Analytical hierarchy process (AHP) analysis taking into account five layers, the spatial extents of RWH suitability areas identified using Multi-Criteria Evaluation (MCE). The suitability model generated a suitability map for RWH with four suitability classes, i.e. Excellent, Moderate, Poor, and unsuitable. The spatial distribution of the suitability map showed that the excellent suitable areas for RWH concentrated in the northern part of Egypt. According to their averages, 3.24% of the total area have excellent and good suitability for RWH, while 45.04 % and 51.48 % of the total area are moderate and unsuitable suitability, respectively. The majority of the areas with excellent suitability have slopes between 2 and 8% and with an intensively cultivated area. The major soil type in the excellent suitable area is loam and the rainfall range from 100 up to 200 mm. Validation of the used technique depends on comparing existing RWH structures locations with the generated suitability map using proximity analysis tool of ArcGIS 10.1. The result shows that most of exiting RWH structures categorized as successful.

Keywords: rainwater harvesting (RWH), geographic information system (GIS), analytical hierarchy process (AHP), multi-criteria evaluation (MCE), decision support system (DSS)

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Authors: Desiree Vandenberghe, Johan Albrecht

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Non-communicable diseases (NCDs) are responsible for the vast majority of deaths in the European Union (EU) and represent a large share of total health care spending. A future increase in this health and financial burden is likely to be driven by population ageing, lifestyle changes and technological advances in medicine. Without adequate prevention measures, this burden can severely threaten population health and economic development. To tackle this challenge, a correct assessment of the current burden of NCDs is required, as well as a projection of potential increases of this burden. The contribution of this paper is to offer perspective on the evolution of the NCD burden towards the future and to give an indication of the potential of prevention policy. A Non-Homogenous, Semi-Markov model for the EU was constructed, which allowed for a projection of the cost burden for the four main NCDs (cancer, cardiovascular disease, chronic respiratory disease and diabetes mellitus) towards 2030 and 2050. This simulation is done based on multiple baseline scenarios that vary in demand and supply factors such as health status, population structure, and technological advances. Finally, in order to assess the potential of preventive measures to curb the cost explosion of NCDs, a simulation is executed which includes increased efforts for preventive health care measures. According to the Markov model, by 2030 and 2050, total costs (direct and indirect costs) in the EU could increase by 30.1% and 44.1% respectively, compared to 2015 levels. An ambitious prevention policy framework for NCDs will be required if the EU wants to meet this challenge of rising costs. To conclude, significant cost increases due to Non-Communicable Diseases are likely to occur due to demographic and lifestyle changes. Nevertheless, an ambitious prevention program throughout the EU can aid in making this cost burden manageable for future generations.

Keywords: non-communicable diseases, preventive health care, health policy, Markov model, scenario analysis

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Saifullahi Umar, Baba Mamman Yarima, Mohammed Bello Usman, Hassan Mohammed

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This study was conducted along with the Hadejia valley project irrigation under the Hadejia-Jama’are River Basin Development Authority (HRBDA) in Jigawa State. The multi-stage sampling procedure was used to select 72 rice farmers operating along with the Hadejia Valley Irrigation Project. Data for the study were collected using a structured questionnaire. The analytical tools employed for the study were descriptive statistics and Farm budget technique. The result shows that 55% of the farmers were between 31-40 years of age, 66.01% were male, and the result also revealed that the total cost of cultivation of an acre of land for rice production during the dry season was N73,900 with input cost accounting for 63.59% of the total cost of production. The gross return was N332,500, with a net return of N258,600 per acre. The estimated benefit-cost ratio of 3.449 indicates the strong performance of the dry season rice production. The leading constraints to dry season rice production were low access to quality extension services, low access to finance, poor quality fertilizers, and poor prices. The study, therefore, concludes that dry season rice production is a profitable enterprise in the study area hence, to productivity the farmers should be linked to effective extension service delivery institutions, expanding their access to productive sources of finances, the government should strengthen fertilizer quality control measures and comprehensive market linkages for the farmers.

Keywords: Auyo, dry season, Hadejia Valley, rice

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Authors: Tingting Liu, Yufeng Wu, Xi Tian, Yu Gong, Tieyong Zuo

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The household solid waste generated by household in Beijing is increasing quickly due to rapid population growth and lifestyle changes. However, there are no rigorous data on the generation and collection of the recyclable household solid wastes. The Beijing city government needs this information to make appropriate policies and plans for waste management. To address this information need, we undertook the first comprehensive study of recyclable household solid waste for Beijing. We carried out a survey of 500 families across sixteen districts in Beijing. We also analyzed the quantities, spatial distribution and categories of collected waste handled by curbside recyclers and permanent recycling centers for 340 of the 9797 city-defined residential areas of Beijing. From our results, we estimate that the total quantity of recyclable household solid waste was 1.8 million tonnes generated by Beijing household in 2013 and 71.6% of that was collected. The main generation categories were waste paper (24.4%), waste glass bottle (23.7%) and waste furniture (14.3%). The recycling rate was varied among different kinds of municipal solid waste. Also based on our study, we estimate there were 22.8 thousand curbside recyclers and 5.7 thousand permanent recycling centers in Beijing. The problems of household solid waste collecting system were inadequacies of authorized collection centers, skewed ratios of curbside recyclers and authorized permanent recycling centers, weak recycling awareness of residents and lack of recycling resources statistics and appraisal system. According to the existing problems, we put forward the suggestions to improve household solid waste management.

Keywords: Municipal waste; Recyclable waste; Waste categories; Waste collection

Procedia PDF Downloads 300