Search results for: accuracy improvement

Authors: M. A. Barros, M. V. Barros

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The aim of this work is to analyze the discursive dynamics of preservice teachers during the implementation of a didactic sequence on topics of Quantum Mechanics for High School. Our research methodology was qualitative, case study type, in which we selected two prospective teachers on the Physics Teacher Training Course of the Sao Carlos Institute of Physics, at the University of Sao Paulo/Brazil. The set of modes of communication analyzed were the intentions and interventions of the teachers, the established communicative approach, the patterns and the contents of the interactions between teachers and students. Data were collected through video recording, interviews and questionnaires conducted before and after an 8 hour mini-course, which was offered to a group of 20 secondary students. As teaching strategy we used an active learning methodology, called: Peer Instruction. The episodes pointed out that both future teachers used interactive dialogic and authoritative communicative approaches to mediate the discussion between peers. In the interactive dialogic dimension the communication pattern was predominantly I-R-F (initiation-response-feedback), in which the future teachers assisted the students in the discussion by providing feedback to their initiations and contributing to the progress of the discussions between peers. Although the interactive dialogic dimension has been preferential during the use of the Peer Instruction method the authoritative communicative approach was also employed. In the authoritative dimension, future teachers used predominantly the type I-R-E (initiation-response-evaluation) communication pattern by asking the students several questions and leading them to the correct answer. Among the main implications the work contributes to the improvement of the practices of future teachers involved in applying active learning methodologies in classroom by identifying the types of communicative approaches and communication patterns used, as well as researches on curriculum innovation in physics in high school.

Keywords: curricular innovation, high school, physics teaching, discursive dynamics

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Authors: Ru-Yu Lien, Shih-Hsin Hung, Shu-Fen Lu, Shu-I Chin, Wen-Ju Yang, Wan Ming-Shang, Chien-Ying Wang

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Background: Upon recovery from critical condition, patients are normally transferred from the intensive care units (ICUs) to the general wards. However, transferring patients to a new environment causes stressful experiences for both patients and their families. Therefore, there is a necessity to communicate with the patients and their families to reduce psychological stress and unplanned return. Methods: This study was performed in the general ICUs from January 1, 2021, to December 31, 2021, in Taipei Veteran General Hospital. The patients who were evaluated by doctors and liaison nurses transferred to the general wards were selected as the research objects and ranked by the Critical Care Transition Program (CCTP). The plan was applied to 40 patients in a study group and usual care support for a control group of 40 patients. The psychological condition of patients was evaluated by a migration stress scale and a hospital anxiety and depression scale. In addition, the rate of return to ICU was also measured. Results: A total of 63 patients out of 80 (78.8%) experienced moderate to severe degrees of anxiety, and 42 patients (52.6%) experienced moderate to severe degrees of depression before being transferred. The difference between anxiety and depression changed more after the transfer; moreover, when a transition program was applied, it was lower than without a transition program. The return to ICU rate in the study group was lower than in the usual transition group, with an adjusted odds ratio of 0.21 (95% confidence interval: 0.05-0.888, P=0.034). Conclusion: Our study found that the transfer program could reduce the anxiety and depression of patients and the associated stress on their families during the transition from ICU. Before being transferred out of ICU, the healthcare providers need to assess the needs of patients to set the goals of the care plan and perform patient-centered decision-making with multidisciplinary support.

Keywords: ICU, critical care transition program, healthcare, transition program

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Authors: Qin Zhu

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Under the background of accelerating the construction of Beautiful and Livable Park City in Chengdu, the Tianfu greenway system, as an important support system for the construction of parks in the whole region, its accessibility is one of the key indicators to measure the effectiveness of the greenway construction. In recent years, cycling has become an important transportation mode for residents to go to the greenways because of its low-carbon, healthy and convenient characteristics, and the study of greenway accessibility under cycling mode can provide reference suggestions for the optimization and improvement of greenways. Taking Jincheng Greenway in Chengdu City as an example, the Baidu Map Application Programming Interface (API) and questionnaire survey was used to improve the two-step floating catchment area (2SFCA) method from the three dimensions of search threshold, supply side and demand side, to calculate the cycling accessibility of the greenway and to explore the spatial matching relationship with the population density, the number of entrances and the comprehensive attractiveness. The results show that: 1) the distribution of greenway accessibility in Jincheng shows a pattern of "high in the south and low in the north, high in the west and low in the east", 2) the spatial match between greenway accessibility and population density of the residential area is imbalanced, and there is a significant positive correlation between accessibility and the number of selectable greenway access points in residential areas, as well as the overall attractiveness of greenways, with a high degree of match. On this basis, it is proposed to give priority to the mismatch area to alleviate the contradiction between supply and demand, optimize the greenway access points to improve the traffic connection, enhance the comprehensive quality of the greenway and strengthen the service capacity, to further improve the cycling accessibility of the Jincheng Greenway and improve the spatial allocation of greenway resources.

Keywords: accessibility, Baidu maps API, cycling, greenway, 2SFCA

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Authors: K. S. Vedasingha, K. K. M. T. Perera, K. I. Hathurusinghe, H. W. I. Akalanka, Nelum Chathuranga Amarasena, Nalaka R. Dissanayake

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Railways provide the most convenient and economically beneficial mode of transportation, and it has been the most popular transportation method among all. According to the past analyzed data, it reveals a considerable number of accidents which occurred at railways and caused damages to not only precious lives but also to the economy of the countries. There are some major issues which need to be addressed in railways of South Asian countries since they fall under the developing category. The goal of this research is to minimize the influencing aspect of railway level crossing accidents by developing the “railway process automation system”, as there are high-risk areas that are prone to accidents, and safety at these places is of utmost significance. This paper describes the implementation methodology and the success of the study. The main purpose of the system is to ensure human safety by using the Internet of Things (IoT) and image processing techniques. The system can detect the current location of the train and close the railway gate automatically. And it is possible to do the above-mentioned process through a decision-making system by using past data. The specialty is both processes working parallel. As usual, if the system fails to close the railway gate due to technical or a network failure, the proposed system can identify the current location and close the railway gate through a decision-making system, which is a revolutionary feature. The proposed system introduces further two features to reduce the causes of railway accidents. Railway track crack detection and motion detection are those features which play a significant role in reducing the risk of railway accidents. Moreover, the system is capable of detecting rule violations at a level crossing by using sensors. The proposed system is implemented through a prototype, and it is tested with real-world scenarios to gain the above 90% of accuracy.

Keywords: crack detection, decision-making, image processing, Internet of Things, motion detection, prototype, sensors

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Ayse Pekrioglu Balkis, Sawash Mecid

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Due to continued growth and rapid development of road construction in worldwide, road sub-layers consist of soil layers, therefore, identification and recognition of type of soil and soil behavior in different condition help to us to select soil according to specification and engineering characteristic, also if necessary sometimes stabilize the soil and treat undesirable properties of soils by adding materials such as bitumen, lime, cement, etc. If the soil beneath the road is not done according to the standards and construction will need more construction time. In this case, a large part of soil should be removed, transported and sometimes deposited. Then purchased sand and gravel is transported to the site and full depth filled and compacted. Stabilization by cement or other treats gives an opportunity to use the existing soil as a base material instead of removing it and purchasing and transporting better fill materials. Classification of soil according to AASHTOO system and USCS help engineers to anticipate soil behavior and select best treatment method. In this study soil classification and the relation between soil classification and stabilization method is discussed, cement stabilization with different percentages have been selected for soil treatment based on NCHRP. There are different parameters to define the strength of soil. In this study, CBR will be used to define the strength of soil. Cement by percentages, 0%, 3%, 7% and 10% added to soil for evaluation effect of added cement to CBR of treated soil. Implementation of stabilization process by different cement content help engineers to select an economic cement amount for the stabilization process according to project specification and characteristics. Stabilization process in optimum moisture content (OMC) and mixing rate effect on the strength of soil in the laboratory and field construction operation have been performed to see the improvement rate in strength and plasticity. Cement stabilization is quicker than a universal method such as removing and changing field soils. Cement addition increases CBR values of different soil types by the range of 22-69%.

Keywords: California Bearing Ratio, cement stabilization, clayey soil, mechanical properties

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Irsa Ejaz, Siyang He, Wei Li, Naiyue Hu, Chaochen Tang, Songbo Li, Meng Li, Boubacar Diallo, Guanghui Xie, Kang Yu

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Background: Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. Previously reported NIR model calibrations using the whole grain spectra had moderate accuracy. Improved predictions are achievable by using the spectra of whole grains, when compared with the use of spectra collected from the flour samples. However, the feasibility for determining the critical biochemicals, related to the classifications for food, feed, and fuel products are not adequately investigated. Objectives: To evaluate the feasibility of using NIRS and the influence of four sample types (whole grains, flours, hulled grain flours, and hull-less grain flours) on the prediction of chemical components to improve the grain sorting efficiency for human food, animal feed, and biofuel. Methods: NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours. A total of 20 hybrids of sorghum grains were selected from the two locations in China. Followed by NIR spectral and wet-chemically measured biochemical data, partial least squares regression (PLSR) was used to construct the prediction models. Results: The results showed that sorghum grain morphology and sample format affected the prediction of biochemicals. Using NIR data of grain flours generally improved the prediction compared with the use of NIR data of whole grains. In addition, using the spectra of whole grains enabled comparable predictions, which are recommended when a non-destructive and rapid analysis is required. Compared with the hulled grain flours, hull-less grain flours allowed for improved predictions for tannin, cellulose, and hemicellulose using NIR data. Conclusion: The established PLSR models could enable food, feed, and fuel producers to efficiently evaluate a large number of samples by predicting the required biochemical components in sorghum grains without destruction.

Keywords: FT-NIR, sorghum grains, biochemical composition, food, feed, fuel, PLSR

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Authors: Ismaila Olabisi Ogundiji, Hakeem Mayowa Olujide, Qasim Usamot

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The main reasons for site-specific management for agricultural inputs are to increase the profitability of crop production, to protect the environment and to improve products’ quality. Information about the variability of different soil attributes within a field is highly essential for the decision-making process. Lack of fast and accurate acquisition of soil characteristics remains one of the biggest limitations of precision agriculture due to being expensive and time-consuming. Adaptive sampling has been proven as an accurate and affordable sampling technique for planning within a field for site-specific management of agricultural inputs. This study employed spatial uncertainty of soil apparent electrical conductivity (ECa) estimates to identify adaptive re-survey areas in the field. The original dataset was grouped into validation and calibration groups where the calibration group was sub-grouped into three sets of different measurements pass intervals. A conditional simulation was performed on the field ECa to evaluate the ECa spatial uncertainty estimates by the use of the geostatistical technique. The grouping of high-uncertainty areas for each set was done using image segmentation in MATLAB, then, high and low area value-separate was identified. Finally, an adaptive re-survey was carried out on those areas of high-uncertainty. Adding adaptive re-surveying significantly minimized the time required for resampling whole field and resulted in ECa with minimal error. For the most spacious transect, the root mean square error (RMSE) yielded from an initial crude sampling survey was minimized after an adaptive re-survey, which was close to that value of the ECa yielded with an all-field re-survey. The estimated sampling time for the adaptive re-survey was found to be 45% lesser than that of all-field re-survey. The results indicate that designing adaptive sampling through spatial uncertainty models significantly mitigates sampling cost, and there was still conformity in the accuracy of the observations.

Keywords: soil electrical conductivity, adaptive sampling, conditional simulation, spatial uncertainty, site-specific management

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Justin P. Pool, Haruyo Yoshida

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This research focuses on the effects of creating an English as a foreign language curriculum that combines online learning and classroom teaching in a complementary manner. Through pre- and post-test results, teacher observation, and learner reflection, it will be shown that learners can benefit from online programs focusing on receptive skills if combined with a communicative classroom environment that encourages learners to develop their productive skills. Much research has lamented the fact that many modern mobile assisted language learning apps do not take advantage of the affordances of modern technology by focusing only on receptive skills rather than inviting learners to interact with one another and develop communities of practice. This research takes into account the realities of the state of such apps and focuses on how to best create a curriculum that complements apps which focus on receptive skills. The research involved 15 adult learners working for a business in Japan simultaneously engaging in 1) a commercial online English language learning application that focused on reading, listening, grammar, and vocabulary and 2) a 15-week class focused on communicative language teaching, presentation skills, and mitigation of error aversion tendencies. Participants of the study experienced large gains on a standardized test, increased motivation and willingness to communicate, and asserted that they felt more confident regarding English communication. Moreover, learners continued to study independently at higher rates after the study than they had before the onset of the program. This paper will include the details of the program, reveal the improvement in test scores, share learner reflections, and critically view current evaluation models for mobile assisted language learning applications.

Keywords: adult learners, communicative language teaching, mobile assisted language learning, motivation

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Authors: Yichen Lin, Hsiao-Han Chen, Chi-Chen Jan

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Since 1990, the research area of Integrated Marketing Communications (IMC) has been presented from a different perspective. With advances in information technology and the rise of consumer consciousness, businesses are in a competitive environment. There is an urgent need to adopt more profitable and effective integrated marketing strategies to increase core competitiveness. The goal of the company's sustainable management is to increase consumers' willingness to purchase and to maximize profits. This research uses six aspects of IMC, which includes awareness integration, unified image, database integration, customer-based integration, stakeholders-based integration, and evaluation integration to examine the role of marketing strategies in the strengths and weaknesses of the six components of integrated marketing communications, their effectiveness, the most important components and the most important components that need improvement. At the same time, social media such as FaceBook, Instagram, Youtube, Line, or even TikTok have become marketing tools which firms adopt them more and more frequently in the marketing strategy. In the end of 2019, the outbreak of COVID-19 did really affect the global industries. Lockdown policies also accelerated closure of brick-mentor stores worldwide. Online purchases rose dramatically. Hence, the effectiveness of online marketing will be essential to maintain the business. This study uses multiple-case studies to extend the effects of social media and IMC. Moreover, the study would also explore the differences of social media and IMC during COVID-19. Through literature review and multiple-case studies, it is found that using social media combined with IMC did really help companies expand their business and make good connections with stakeholders. One of previous studies also used system theory to explore the interrelationship among Integrated Marketing Communication, collaborative marketing, and global brand building. Even during pandemic, firms could still maintain the operation and connect with their customers more tightly.

Keywords: integration marketing communications, multiple-case studies, social media, system theory

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Shilpa Balan

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Radio Frequency Identification, also popularly known as RFID is used to automatically identify and track tags attached to items. This study focuses on the application of RFID in healthcare. The adoption of RFID in healthcare is a crucial technology to patient safety and inventory management. Data from RFID tags are used to identify the locations of patients and inventory in real time. Medical errors are thought to be a prominent cause of loss of life and injury. The major advantage of RFID application in healthcare industry is the reduction of medical errors. The healthcare industry has generated huge amounts of data. By discovering patterns and trends within the data, big data analytics can help improve patient care and lower healthcare costs. The number of increasing research publications leading to innovations in RFID applications shows the importance of this technology. This study explores the current state of research of RFID in healthcare using a text mining approach. No study has been performed yet on examining the current state of RFID research in healthcare using a data mining approach. In this study, related articles were collected on RFID from healthcare journal and news articles. Articles collected were from the year 2000 to 2015. Significant keywords on the topic of focus are identified and analyzed using open source data analytics software such as Rapid Miner. These analytical tools help extract pertinent information from massive volumes of data. It is seen that the main benefits of adopting RFID technology in healthcare include tracking medicines and equipment, upholding patient safety, and security improvement. The real-time tracking features of RFID allows for enhanced supply chain management. By productively using big data, healthcare organizations can gain significant benefits. Big data analytics in healthcare enables improved decisions by extracting insights from large volumes of data.

Keywords: RFID, data mining, data analysis, healthcare

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Authors: Matthew D. Baffa

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Infrared thermography is a non-destructive test method used to estimate surface temperatures based on the amount of electromagnetic energy radiated by building envelope components. These surface temperatures are indicators of various qualitative building envelope deficiencies such as locations and extent of heat loss, thermal bridging, damaged or missing thermal insulation, air leakage, and moisture presence in roof, floor, and wall assemblies. Although infrared thermography is commonly used for qualitative deficiency detection in buildings, this study assesses its use as a quantitative method to estimate the overall thermal conductance value (U-value) of the exterior above-grade walls of a study home. The overall U-value of exterior above-grade walls in a home provides useful insight into the energy consumption and thermal comfort of a home. Three methodologies from the literature were employed to estimate the overall U-value by equating conductive heat loss through the exterior above-grade walls to the sum of convective and radiant heat losses of the walls. Outdoor infrared thermography field measurements of the exterior above-grade wall surface and reflective temperatures and emissivity values for various components of the exterior above-grade wall assemblies were carried out during winter months at the study home using a basic thermal imager device. The overall U-values estimated from each methodology from the literature using the recorded field measurements were compared to the nominal exterior above-grade wall overall U-value calculated from materials and dimensions detailed in architectural drawings of the study home. The nominal overall U-value was validated through calendarization and weather normalization of utility bills for the study home as well as various estimated heat loss quantities from a HOT2000 computer model of the study home and other methods. Under ideal environmental conditions, the estimated overall U-values deviated from the nominal overall U-value between ±2% to ±33%. This study suggests infrared thermography can estimate the overall U-value of exterior above-grade walls in low-rise residential homes with a fair amount of accuracy.

Keywords: emissivity, heat loss, infrared thermography, thermal conductance

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Authors: Md. Mahamud Hassan, Shantanu Biswas Linkon, Nur Mohammad Khan

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Over the last three decades, the primary education system in Bangladesh has experienced significant improvement, but it has failed to cope with different social and cultural aspects, which present many challenges for children, families, and the public school system. Neglecting our own contextual learning environment, it is a matter of sorrow that much attention has been paid to the more physical outcome-focused model, which is nothing but mere infrastructural development, and less subtle to the environment that suits the child's psychology and improves their social, emotional, physical, and moral competency. In South Asia, the symbol of education was never the little red house of colonial architecture but “A Guru sitting under a tree", whereas a responsive and inclusive design approach could help to create more innovative learning environments. Such an approach incorporates how the built, natural, and cultural environment shapes the learner; in turn, learners shape the learning. This research will be conducted to, i) identify the major issues and drawbacks of government policy for primary education development programs; ii) explore and evaluate the morphology of the conventional model of school, and iii) propose an alternative model in a collaborative design process with the stakeholders for maximizing the relationship between the physical learning environments and learners by treating “the built environment” as “the third teacher.” Based on observation, this research will try to find out to what extent built, and natural environments can be utilized as a teaching tool for a more optimal learning environment. It should also be evident that there is a significant gap in the state policy, predetermined educational specifications, and implementation process in response to stakeholders’ involvement. The outcome of this research will contribute to a people-place sensitive design approach through a more thoughtful and responsive architectural process.

Keywords: built environment, conventional planning, indigenous learning space, responsive design

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Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

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Authors: Satyananda Behera, Ritwik Sarkar

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In iron and steel industries, MgO–C refractories are widely used in basic oxygen furnaces, electric arc furnaces and steel ladles due to their excellent corrosion resistance, thermal shock resistance, and other excellent hot properties. Conventionally magnesia carbon refractories contain about 8-20 wt% of carbon but the use of carbon is also associate with disadvantages like oxidation, low fracture strength, high heat loss and higher carbon pick up in steel. So, MgO-C refractory having low carbon content without compromising the beneficial properties is the challenge. Nano carbon, having finer particles, can mix and distribute within the entire matrix uniformly and can result in improved mechanical, thermo-mechanical, corrosion and other refractory properties. Previous experiences with the use of nano carbon in low carbon MgO-C refractory have indicated an optimum range of use of nano carbon around 1 wt%. This optimum nano carbon content was used in MgO-C compositions with flaky graphite followed by aluminum and silicon metal powder as an anti-oxidant. These low carbon MgO-C refractory compositions were prepared by conventional manufacturing techniques. At the same time 16 wt. % flaky graphite containing conventional MgO-C refractory was also prepared parallel under similar conditions. The developed products were characterized for various refractory related properties. Nano carbon containing compositions showed better mechanical, thermo-mechanical properties, and oxidation resistance compared to that of conventional composition. Improvement in the properties is associated with the formation of in-situ ceramic phase-like aluminum carbide, silicon carbide, and magnesium aluminum spinel. Higher surface area and higher reactivity of N220 nano carbon black resulted in greater formation in-situ ceramic phases, even at a much lower amount. Nano carbon containing compositions were found to have improved properties in MgO-C refractories compared to that of the conventional ones at much lower total carbon content.

Keywords: N220nano carbon black, refractory properties, conventionally manufacturing techniques, conventional magnesia carbon refractories

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

Procedia PDF Downloads 215

Authors: Yuzhen Hu, Sirui Wang

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As a training base for intellectual talents, universities have a large number of students. Teaching is a primary activity in universities, and during the teaching process, a large number of people gather both inside and outside the teaching buildings, posing a strong risk of close contact. The class schedule is the fundamental basis for teaching activities in universities and plays a crucial role in the management of teaching order. Different class schedules can lead to varying degrees of indoor gatherings and trajectories of class attendees. In recent years, highly contagious diseases have frequently occurred worldwide, and how to reduce the risk of infection has always been a hot issue related to public safety. "Reducing gatherings" is one of the core measures in epidemic prevention and control, and it can be controlled through scientific scheduling in specific environments. Therefore, the scientific prevention and control goal can be achieved by considering the reduction of the risk of excessive gathering of people during the course schedule arrangement. Firstly, we address the issue of personnel gathering in various pathways on campus, with the goal of minimizing congestion and maximizing teaching effectiveness, establishing a nonlinear mathematical model. Next, we design an improved genetic algorithm, incorporating real-time evacuation operations based on tracking search and multidimensional positive gradient cross-mutation operations, considering the characteristics of outdoor crowd evacuation. Finally, we apply undergraduate course data from a university in Harbin to conduct a case study. It compares and analyzes the effects of algorithm improvement and optimization of gathering situations and explores the impact of path blocking on the degree of gathering of individuals on other pathways.

Keywords: the university timetabling problem, risk prevention, genetic algorithm, risk control

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Authors: Tenzing Lama, Ganga Ram Regmi, Thakur Silwal, Rinzin Punjok Lama

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Livestock depredation by a wolf and associated financial loss suffered by herders is perhaps the most important issue leading to human-wolf conflict. As a result, recolonized wolves remained one of the most persecuted large carnivores in Nepal Himalaya suffering high mortality due to retaliatory killings by herdsmen. Reducing such depredation are crucial in gaining herder’s support in conservation program to ensure the long-term survival of such carnivores. In February 2018, a study was conducted through questionnaire survey with 33 herders from different settlements in Neshyang valley of Manang district to assess the status of human-wolf conflict in terms of livestock loss and herder’s attitude. A total of 36 livestock were lost to the wolf with an average loss of 1.09 ± 0.48 (SE) livestock heads per herder between March 2017 to February 2018 which represents 1.5% of the total holdings. The estimated financial value of livestock loss was equivalent to US$ 25,428 with an average of US$ 770 per herder. Majority of the herders (80%) expressed a negative attitude towards the wolf, but only a few herders (6.06%) suggested removal of the wolf from the valley. The incidences of livestock loss differed significantly with highest in day time and seasonally highest in winter, when herders freely leaves their livestock (except goat/sheep) in the pastures. Wolf showed positive selectivity to the horse (EI=0.59), yak (EI=0.24) and cattle (EI=0.14) but strong avoidance to goat/sheep (EI=-1). This study suggests that livestock depredation by wolf could be minimized through improved livestock husbandry practices and implication of mitigation measures (e.g. coral improvement) and immediate relief to the victims. Conservation education and awareness programs to enhance herders knowledge about the ecological importance of wolf, provision of relief scheme and law enforcement.

Keywords: canis lupus canco, conservation education, human wildlife conflict, compensation schemes

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Authors: Benjamin K. Ghansah, Richard K. Appoh, Iliya Nababa, Eric K. Forkuo

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The Nasia River is an important source of water for domestic and agricultural purposes to the inhabitants of its catchment. Major farming activities takes place within the floodplain of the river and its network of tributaries. The actual inundation extent of the river system is; however, unknown. Reasons for this lack of information include financial constraints and inadequate human resources as flood modelling is becoming increasingly complex by the day. Knowledge of the inundation extent will help in the assessment of risk posed by the annual flooding of the river, and help in the planning of flood recession agricultural activities. This study used a simple terrain based algorithm, Height Above Nearest Drainage (HAND), to delineate the floodplain of the Nasia River and its tributaries. The HAND model is a drainage normalized digital elevation model, which has its height reference based on the local drainage systems rather than the average mean sea level (AMSL). The underlying principle guiding the development of the HAND model is that hillslope flow paths behave differently when the reference gradient is to the local drainage network as compared to the seaward gradient. The new terrain model of the catchment was created using the NASA’s SRTM Digital Elevation Model (DEM) 30m as the only data input. Contours (HAND Contour) were then generated from the normalized DEM. Based on field flood inundation survey, historical information of flooding of the area as well as satellite images, a HAND Contour of 2m was found to best correlates with the flood inundation extent of the river and its tributaries. A percentage accuracy of 75% was obtained when the surface area created by the 2m contour was compared with surface area of the floodplain computed from a satellite image captured during the peak flooding season in September 2016. It was estimated that the flooding of the Nasia River and its tributaries created a floodplain area of 1011 km².

Keywords: digital elevation model, floodplain, HAND contour, inundation extent, Nasia River

Procedia PDF Downloads 459

Authors: Z. Nourmohammadi, F. Farahani, M. Shaker

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Aloe is an important commercial crop available in a wide range of species and varieties in international markets. The applications of this plant have been recorded in the ancient cultures of India, Egypt, Greece, Rome and China. Aloe has been used for centuries and is currently being actively studied for medicinal purposes. Aloe is propagated through lateral buds, which is slow, very expensive and low income practice. Nowadays, it has been cultured by in vitro propagation for rapid multiplication of plants, genetic improvement of crops, obtaining disease-free clones and for progressive valuable germplasm. The present study focused on the influence of different phytohormones on rapid in vitro propagation of Aloe plants. We also investigated the effect of gamma radiation on biochemical characters as well as genetic changes. Shoot tip of 2-3 cm were collected from offshoot of Aloe barbadensis and Aloe littoralis, and were inoculated with MS medium containing various concentrations of BA (0.5, 1, 2 mg/l), IAA (0.5, 1 mg/l). The best treatment for a highest shoot number and bud proliferation was MS medium containing 2 mg/l BAP and 0.5 mg/l IAA in A. barbadensis and A. littoralis. Maximum percentage of proliferated shoot buds (90% and 95%) from a single explant were obtained in MS medium after 4-5 weeks of the second and the first subcultures, respectively. Different genome sizes were also indicated among treatments and subcultures. The mixoploids identified in flow cytometery histograms in different treatments. The effect of gamma radiation on A. littoralis showed that by increasing the dose of gamma radiation, amounts of chlorophyll A, B, carotenoids, total protein content and superoxide dismutase were significantly increased compared to control plants. Genetic variation analysis also revealed significant genetic differences between control and gamma radiation treated regenerated plants by AMOVA test. Higher genetic heterozygocity was observed in radiation treated plants. Our findings may provide useful method for improving of Aloe plant proliferation with increasing of useful material such as antioxidant enzymes.

Keywords: aloe, antioxidant enzyme, micropropagation, gamma radiation, genetic variation

Procedia PDF Downloads 433

Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

Procedia PDF Downloads 385

Authors: Adelson G. Guillarte, M. D., Noel J. Belonguel, Jarungchai Anton S. Vatanagul

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Patients with malignant middle cerebral infarction (MCA) (with massive brain swelling and herniation) were reported to have a mortality rate of 80% even with the appropriate conservative medical therapy. European Trials (DECIMAL, DESTINY I, and II, HAMLET) showed significant improvement in mortality and functional outcome with hemicraniectomy. No known published local studies in the region, thus a local study is vital. This is a single center, retrospective, descriptive, cross-sectional, chart review study which includes ≥18 year-old patients with malignant MCA infarction, who underwent hemicraniectomy, and those who were given conservative medical therapy alone, from January 2008 to December 2012 at Perpetual Succour Hospital. Excluded were patients whose charts are with insufficient data, prior MCA stroke, with concomitant intracerebral hemorrhage and with other serious medical conditions or terminal illnesses. Minimum of 32 populations were needed. Data were presented in mean, standard deviation, frequency and percentage distribution. Man n Whitney U test and Chi Square test were used. P-values lesser than 0.05 alpha were considered statistically significant. A total of 672 stroke patients were admitted. 34 patients pass the inclusion criteria. 9 underwent hemicraniectomy and 25 were treated by conservative medical therapy alone. Although not statistically significant (64% vs 33%, p=0.112) there were more patients noted improved in the conservative treatment group. Meanwhile, the Hemicraniectomy group have increased percentage of mortality (67%) (p=0.112). There was a decreasing trend in the average NIHSS score in both groups from admission to post-op 7 days (p=0.198, p=0.78). A bigger multicenter prospective study is recommended to control inherent biases and limitations of a retrospective and smaller study.

Keywords: cerebral infarct, hemicraniectomy, ischemic stroke, malignant middle cerebral artery (MCA) infarct

Procedia PDF Downloads 318

Authors: Shoumodip Roy, Ankit Singhania, K. R. K. Rao, Ravi Shankar, M. K. Agarwal, R. V. Ramna, Uttam Singh

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The productivity of a blast furnace and the quality of the hot metal produced are significantly dependent on the smoothness and stability of furnace operation. The permeability of the furnace bed, as well as the gas flow pattern, influences the steady control of process parameters. The softening – melting zone that is formed inside the furnace contributes largely in distribution of the gas flow and the bed permeability. A better shape of softening-melting zone enhances the performance of blast furnace, thereby reducing the fuel rates and improving furnace life. Therefore, predictive model of the softening- melting zone profile can be utilized to control and improve the furnace operation. The shape of softening-melting zone depends upon the physical and chemical properties of the agglomerates and iron ore charged in the furnace. The variations in the agglomerate proportion in the burden at G Blast furnace disturbed the furnace stability. During such circumstances, it was analyzed that a w-shape softening-melting zone profile was formed inside the furnace. The formation of w-shape zone resulted in poor bed permeability and non-uniform gas flow. There was a significant increase in the heat loss at the lower zone of the furnace. The fuel demand increased, and the huge production loss was incurred. Therefore, visibility of softening-melting zone profile was necessary in order to pro-actively optimize the process parameters and thereby to operate the furnace smoothly. Using stave temperatures, a model was developed that predicted the shape of the softening-melting zone inside the furnace. It was observed that furnace operated smoothly during inverse V-shape of the zone and vice-versa during w-shape. This model helped to control the heat loss, optimize the burden distribution and lower the fuel rate at G Blast Furnace, TSL Jamshedpur. As a result of furnace stabilization productivity increased by 10% and fuel rate reduced by 80 kg/thm. Details of the process have been discussed in this paper.

Keywords: agglomerate, blast furnace, permeability, softening-melting

Procedia PDF Downloads 255

Authors: Nithra Kitreerawutiwong, Sunsanee Mekrungrongwong, Artitaya Wongwonsin, Chakkraphan Phetphoom, Buaploy Phromjang

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Background: Thailand has implemented a district health system based on the concept of primary health care. Since 2014, Family Care Team (FCT) was launched to improve the quality of care through a multidisciplinary team include not only the health sector but also social sector work together. FCT classified into 3 levels: district, sub-district, and community. This system now consists of 66,353 teams, including 3,890 teams at district level, 12,237 teams at the sub-district level, and 50,326 teams at the community level. There is a report regarding assessment the situation and perception on FCT, however, relatively few examined the operationality of this policy. This study aimed to explore the perception of district manager on the process of the implementation of FCT policy and the factors associating to implement FCT in the district health system. Methods/Results: Forty in-depth interviews were performed: 5 of primary care manager at the provincial medical health office, 5 of community hospital director, 5 of district administrative health office, 10 of sub-district health promoting hospital, and 10 of local organization. Semi-structure interview guidelines were used in the discussions. The data was analyzed by thematic analysis. This policy was formulated based on the demographic change and epidemiology transition to serve a long term care for elderly. Facilitator factors are social capital in district health systems such as family health leader and multidisciplinary team. Barrier factors are communication to the frontline provider and local organization. The output of this policy in relation to the structure of FCT is well-defined. Unanticipated effects include training of FCT in community level. Conclusion: Early feedback from healthcare manager is valuable information for the improvement of FCT to function optimally. Moreover, in the long term, health outcome need to be evaluated.

Keywords: family care team, district health system, primary care, qualitative study

Procedia PDF Downloads 410

Authors: Xiping Guo, Fanglin Ge, Ping Guan

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Refractory high entropy alloys are alternative materials expected to be employed at high temperatures. The comprehensive changes of microstructure and properties of NbTiZrCrAl refractory high entropy alloys are systematically studied by adjusting Al content. Five kinds of button alloy ingots with different contents of Al in NbTiZrCrAlX (X=0, 0.2, 0.5, 0.75, 1.0) were prepared by vacuum non-consumable arc melting technology. The microstructure analysis results show that the five alloys are composed of BCC solid solution phase rich in Nb and Ti and Laves phase rich in Cr, Zr, and Al. The addition of Al changes the structure from hypoeutectic to hypereutectic, increases the proportion of Laves phase, and changes the structure from cubic C15 to hexagonal C14. The hardness and fracture toughness of the five alloys were tested at room temperature, and the compressive mechanical properties were tested at 1000℃. The results showed that the addition of Al increased the proportion of Laves phase and decreased the proportion of the BCC phase, thus increasing the hardness and decreasing the fracture toughness at room temperature. However, at 1000℃, the strength of 0.5Al and 0.75Al alloys whose composition is close to the eutectic point is the best, which indicates that the eutectic structure is of great significance for the improvement of high temperature strength of NbTiZrCrAl refractory high entropy alloys. The five alloys were oxidized for 1 h and 20 h in static air at 1000℃. The results show that only the oxide film of 0Al alloy falls off after oxidizing for 1 h at 1000℃. After 20h, the oxide film of all the alloys fell off, but the oxide film of alloys containing Al was more dense and complete. By producing protective oxide Al₂O₃, inhibiting the preferential oxidation of Zr, promoting the preferential oxidation of Ti, and combination of Cr₂O₃ and Nb₂O₅ to form CrNbO₄, Al significantly improves the high temperature oxidation resistance of NbTiZrCrAl refractory high entropy alloys.

Keywords: NbTiZrCrAl, refractory high entropy alloy, al content, microstructural evolution, room temperature mechanical properties, high temperature compressive strength, oxidation resistance

Procedia PDF Downloads 87

Authors: Ivan Gunass Govender, Andrea Giampiccoli

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A developmental state should use community engagement to facilitate socio-economic development for disadvantaged groups and individual members of society through empowerment, social justice, sustainability, and self-reliance. In this regard, community-based tourism (CBT) as a growing market should be an indigenous effort aided by external facilitation. Since this form of tourism presents its own preconditions, characteristics, and challenges, it could be guided by higher education institutions engagement. In particular, the facilitation should not only serve to assist the community members to reach their own goals; but rather also focus on learning through knowledge creation and sharing with the engagement of higher education institutions. While the increased relevance of CBT has produced various CBT manuals (or handbooks/guidelines) documents aimed to ‘teach’ and assist various entities in CBT development, this research aims to analyse the current monitoring & evaluation (M&E) manuals and thereafter, propose an M&E model for CBT. It is important to mention that all too often effective monitoring is seldom carried out thus risking the long-term sustainability and improvement of the CBT ventures. Therefore, the proposed model will also consider some inputs external to the tourism field, but in relation to local economic development (LED) matters from the previously proposed development monitoring and evaluation system framework. M&E should be seen as fundamental components of any CBT initiative, and the whole CBT intervention should be evaluated. In this context, M&E in CBT should go beyond strict ‘numerical’ economic matters and should be understood in a holistic development. In addition, M&E in CBT should not consider issues in various ‘compartments’ such as tourists, tourism attractions, CBT owners/participants, and stakeholder engagement but as interdependent components of a macro-ecosystem. Finally, the external facilitation process should be structured in a way to promote community self-reliance in both the intervention and the M&E process. The research will attempt to propose an M&E model for CBT so as to enhance the CBT possibilities of long-term growth and success through effective collaborations with key stakeholders.

Keywords: community-based tourism, community-engagement, monitoring and evaluation, stakeholders

Procedia PDF Downloads 307