Search results for: destination prediction

Authors: Prasanti Widyasih Sarli, Prayatni Soewondo

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Greater access to sanitation in developing countries is urgent. However even though sanitation is crucial, overall budget for sanitation is limited. With this budget limitation, it is important to (1) allocate resources strategically to maximize impact and (2) take into account communal agency to potentially be a source for sanitation improvements. The Jodipan and Kesatrian Project in Malang, Indonesia is an interesting alternative for solving the sanitation problem in which resources were allocated strategically and communal agency was also observed. Although the projects initial goal was only to improve visually the situation in the slums, it became a new tourist destination, and the economic benefit that came with it had an effect also on the change of behavior of the residents and the government towards sanitation. It also grew from only including the Kesatrian Village to expanding to the Jodipan Village in the course of less than a year. To investigate the success of this project, in this paper a descriptive model will be used and data will be drawn from intensive interviews with the initiators of the project, residents affected by the project and government officials. In this research it is argued that three points mark the success of the project: (1) the strategic initial impact due to choice of location, (2) the influx of tourists that triggered behavioral change among residents and, (3) the direct economic impact which ensured its sustainability and growth by gaining government officials support and attention for more public spending in the area for slum development and sanitation improvement.

Keywords: behaviour change, sanitation, slum, strategic thinking

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Authors: Sunil Mekale, Debasish Chowdhury, Sanchita Patnaik, Amitav Das, Ashok Agarwal

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Background: Odisha is a low HIV prevalence state in India (ANC-HIV positivity of 0.42% as per HIV sentinel surveillance 2010-2011); however, it is an important source migration state with 3.2% of male migrants reporting to be PLHIV. USAID Public Health Foundation of India -PIPPSE project is piloting a source-destination corridor programme between Odisha and Gujarat. In Odisha, the focus has been on developing a comprehensive strategy to reach out to the out migrants and their spouses in the place of their origin based on their availability. The project has made concerted attempts to identify vulnerable districts with high out migration and high positivity rate. Description: 48 out of 97 ICTCs were selected from nine top high out migration districts through multistage sampling. A retrospective descriptive analysis of HIV positive male migrants and their spouses for two years (April 2013-March 2015) was conducted. A total of 3,645 HIV positive records were analysed. Findings: Among 34.2% detected HIV positive in the ICTCs, 23.3% were male migrants and 11% were spouses of male migrants; almost 50% of total ICTC attendees. More than 70% of the PLHIV male migrants and their spouses were less than 45 years old. Conclusions: Couple HIV testing approach may be considered for male migrants and their spouses. ICTC data analysis could guide in identifying the locations with high HIV positivity among male migrants and their spouses.

Keywords: HIV testing, migrants, spouse of migrants, Integrated Counselling and Testing Centre (ICTC)

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Authors: Alina P. Colling, Robert G. Hekkenberg

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Recent years have seen extensive technological advancement in truck platooning, as reflected in the literature. Its main benefits are the improvement of traffic stability and the reduction of air drag, resulting in less fuel consumption, in comparison to using individual trucks. Platooning is now being adapted to the waterborne transport sector in the NOVIMAR project through the development of a Vessel Train (VT) concept. The main focus of VT’s, as opposed to the truck platoons, is the decrease in manning on board, ultimately working towards autonomous vessel operations. This crew reduction can prove to be an important selling point in achieving economic competitiveness of the waterborne approach when compared to alternative modes of transport. This paper discusses the expected benefits and drawbacks of the VT concept, in terms of the technical logistic performance and generalized costs. More specifically, VT’s can provide flexibility in destination choices for shippers but also add complexity when performing special manoeuvres in VT formation. In order to quantify the cost and performances, a model is developed and simulations are carried out for various case studies. These compare the application of VT’s in the short sea and inland water transport, with specific sailing regimes and technologies installed on board to allow different levels of autonomy. The results enable the identification of the most important boundary conditions for the successful operation of the waterborne platooning concept. These findings serve as a framework for future business applications of the VT.

Keywords: autonomous vessels, NOVIMAR, vessel trains, waterborne platooning

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Authors: Eun-Taek Sin, Hyun-Ju Jang, Chang Geun Song, Yong-Sik Han

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Due to the climate change, the coastal urban area repeatedly suffers from the loss of property and life by flooding. There are three main causes of inland submergence. First, when heavy rain with high intensity occurs, the water quantity in inland cannot be drained into rivers by increase in impervious surface of the land development and defect of the pump, storm sewer. Second, river inundation occurs then water surface level surpasses the top of levee. Finally, Coastal inundation occurs due to rising sea water. However, previous studies ignored the complex mechanism of flooding, and showed discrepancy and inadequacy due to linear summation of each analysis result. In this study, inland flooding and river inundation were analyzed together by HDM-2D model. Petrov-Galerkin stabilizing method and flux-blocking algorithm were applied to simulate the inland flooding. In addition, sink/source terms with exponentially growth rate attribute were added to the shallow water equations to include the inland flooding analysis module. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. To consider the coastal surge, another module was developed by adding seawater to the existing Inland Flooding-River Inundation binding module for comprehensive flooding analysis. Based on the combined modules, the Coastal Inundation – Inland & River Flow Interface was simulated by inputting the flow rate and depth data in artificial flume. Accordingly, it was able to analyze the flood patterns of coastal cities over time. This study is expected to help identify the complex causes of flooding in coastal areas where complex flooding occurs, and assist in analyzing damage to coastal cities. Acknowledgements—This research was supported by a grant ‘Development of the Evaluation Technology for Complex Causes of Inundation Vulnerability and the Response Plans in Coastal Urban Areas for Adaptation to Climate Change’ [MPSS-NH-2015-77] from the Natural Hazard Mitigation Research Group, Ministry of Public Safety and Security of Korea.

Keywords: flooding analysis, river inundation, inland flooding, 2D hydrodynamic model

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Authors: Md. Abud Darda

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Different options for natural gas production in wide geographic areas may be described through diffusion of innovation models. This type of modeling approach provides an indirect estimate of an ultimately recoverable resource, URR, capture the quantitative effects of observed strategic interventions, and allow ex-ante assessments of future scenarios over time. In order to ensure a sustainable energy policy, it is important to forecast the availability of this natural resource. Considering a finite life cycle, in this paper we try to investigate the natural gas production of Myanmar and Algeria, two important natural gas provider in the world energy market. A number of homogeneous and heterogeneous diffusion models, with convenient extensions, have been used. Models validation has also been performed in terms of prediction capability.

Keywords: diffusion models, energy forecast, natural gas, nonlinear production

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Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: metabolic network, gene knockout, flux balance analysis, microarray data, integration

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Authors: Ridha Zgolli, Ahmed Belhaj, Maroua Ennouri

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This study presents a new method to predict cavitation area that may be eroded. It is based on the post-treatment of URANS simulations in cavitant flows. The most RANS calculations with incompressible consideration are based on cavitation model using mixture fluid with density (ρm) calculated as a function of liquid density (ρliq), vapour or gas density (ρvap) and vapour or gas volume fraction α (ρm = αρvap + (1-α) ρliq). The calculations are performed on hydrofoil geometries and compared with experimental works concerning flows characteristics (size of pocket, pressure, velocity). We present here the used cavitation model and the approach followed to evaluate the value of α fixing the shape of pocket around wall before collapsing.

Keywords: flows, CFD, cavitation, erosion

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

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Authors: Fathi Abid, Bilel Kaffel

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

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

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Authors: Soheila Sadeghi

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

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

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: Thomas Yeboah

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In contemporary times, the incessant movement of northern children especially girls to southern Ghana at the detriment of their education is worrisome. Due to the misplaced mindset of the migrants concerning southern Ghana, majority of them move without an idea of where to stay and what to do exposing them to hash conditions of living. Majority find menial work in cocoa farms, illegal mining and head porterage business. This study was conducted in the Kumasi Metropolis to ascertain the major causes of child migration from the northern part of Ghana to the south and their living conditions. Both qualitative and quantitative tools of data collection and analysis were employed. The purposive sampling technique was used to select 90 migrants below 18 years. Specifically, interviews, focus group discussions and questionnaires were used to elicit responses from the units of analysis. The study revealed that the major cause of child migration from northern Ghana to the south is poverty. It was evident that respondents were vulnerable to the new environment in which they lived. They are exposed to harsh environmental conditions; sexual, verbal and physical assault; and harassment from arm robbers. The paper recommends that policy decisions should be able to create an enabling environment for the labour force in the north to ameliorate the compelling effects poverty has on child migration. Efforts should also be made to create a proper psychological climate in the minds of the children regarding their destination areas through sensitization and education.

Keywords: child migration, vulnerability, social exclusion, child labour, Ghana

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Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

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This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

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Authors: Mahesh Kothari, K. D. Gharde

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The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

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Authors: Yoshio Kurosawa, Takao Yamaguchi

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High frequency automotive interior noise above 500 Hz considerably affects automotive passenger comfort. To reduce this noise, sound insulation material is often laminated on body panels or interior trim panels. For a more effective noise reduction, the sound reduction properties of this laminated structure need to be estimated. We have developed a new calculate tool that can roughly calculate the sound absorption and insulation properties of laminate structure and handy for designers. In this report, the outline of this tool and an analysis example applied to floor mat are introduced.

Keywords: automobile, acoustics, porous material, transfer matrix method

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Authors: Petar Kurecic, Josipa Penic

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The paper represents a product of mentor-graduate student cooperation, developed at the graduate study of Business Economics, major Tourism. The analysis was made through the anonymous questionnaire filled by the respondents from Croatia. Following the latest threatening events and having in mind those yet to come, it can be concluded that no country can benefit from the tourism industry if at the same time does not develop its security system as an integral part of the standard tourist offer. Analyzing the trends in contemporary tourism, the safety and security issues became the decisive factors for the choice of a certain destination. Consequently, countries must not perceive security systems and measures as an unnecessary expense but as an essential element in organizing their tourist services. All hotels and respectable tourist agencies should have a crisis management, with detailed, thoroughly elaborated procedures for emergency situations. Tourists should be timely informed about the potential dangers and risks and the measures taken to prevent them, as well as on procedures for emergency situations. Additionally, it would be good to have mobile applications that would enable tourists to make direct emergency calls with instructions on behavior in crisis situations. It is also essential to implement and put into effect sophisticated security measures such as using surveillance cameras, controlling access to buildings, information exchange with colleagues and neighbors, reporting the suspicious occurrences to the security services, and training staff for crisis management. The security issue is definitely one of the crucial factors in the development of tourism in a certain country.

Keywords: security, security measures in tourism, tourism, tourist destinations

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Sanusi Abubakar Sadiq, Rebecca William Chiwar

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Wildlife Park stands a chance of contributing to tourism development in different ways, but available infrastructure, and facilities required by visitors when they arrive, access road to the destination, and resources to facilitate positive experience are lacking in certain areas. The study set out to find out the need to develop Sumu Wildlife Park Bauchi State, to an international standard. The study focused on identifying the existing facilities and infrastructure at the park and to further identify the available resources used by visitors. In attempt to find out the impact of developing Sumu Wildlife Park and ways of filling the gap of the actual standard data were obtained from fifteen administrative staff of Sumu Wildlife Park, ten staff of Bauchi state Tourism Board and twenty-five residents of the community in Kafin Madaki, Bauchi. Relevant literature were reviewed in the study; data collected were organized and analyzed using Statistical Package of Social Sciences (SPSS), software for analysis. Findings revealed that though Sumu Wildlife Park has attractions to keep visitors patronage but has insufficient facilities to maintain visitors and has not been developed to an expected standard. The problem faced by the management of Sumu wildlife Park is lack of adequate facilities, infrastructure and resources. The need to develop Sumu Wildlife Park has enormous benefits in increasing patronage. Provision of more funds would help improve standard as there would be more activities within and around the park. Regular maintenance of those facilities protects the life span of the park.

Keywords: attractions, facilities, infrastructure, resources

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Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

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The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: U. Chattaraj, K. Dhusiya, M. Raviteja

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Perception reaction time of drivers is an outcome of human thought process, which is vague and approximate in nature and also varies from driver to driver. So, in this study a fuzzy logic based model for prediction of the same has been presented, which seems suitable. The control factors, like, age, experience, intensity of driving of the driver, speed of the vehicle and distance of stimulus have been considered as premise variables in the model, in which the perception reaction time is the consequence variable. Results show that the model is able to explain the impacts of the control factors on perception reaction time properly.

Keywords: driver, fuzzy logic, perception reaction time, premise variable

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: An-Na Li, Ying-Yu Chen, Chang-Kuang Chiou

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Food tourism is one of the growing industries and areas of interest in the tourism industry today. The Destination Marketing Organizations (DMOs) are aware of the importance of gastronomy in order to stimulate local and regional economic development. From the heritage and cultural aspects, gastronomy is becoming a more important part of the cultural heritage of region and countries. Heritage destinations provide culinary heritage, which fits the current interest in traditional food, and cuisine is part of a general desire for authentic experiences. However, few studies have empirically examining food tourist’s behavior. This study examined the effects of cuisine experience, emotions and tourists’ revisit intentions. A total of 402 individuals responded to the on-site survey in the historic town of Lu-Kang in Taiwan. The results indicated that tourists’ cuisine experience include place flavor, media recommended local learning, life transfer and interpersonal share. In addition, cuisine experience had significant impacts on emotions, which in turn cuisine experience and emotions had significant effects on tourists’ revisit intentions. The findings suggested that the cuisine experience is a multi- dimensions construct. On the other hands, the good quality of cuisine experience could evoke tourists’ positive emotions and it plays a significant role in promote tourist revisit intentions and word of mouth. Implications for theory and practice are discussed.

Keywords: culinary tourism, cuisine experience, emotions, revisit intentions

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Authors: Satarupa Bhattacharjee

Abstract:

Inter-religious dialogue and understanding are possible without losing one’s own identity. We find a unique blend of tradition, reason and human values in contemporary Indian thought. On this point, we may take note of the similarity between views of M. K. Gandhi and the religious discourse of Swami Vivekananda, i.e., all religions as different paths to God realisation but their unity lies in their goal, which is attainment of God, who is One. This enrichment guided us towards a kind of religious pluralism of John Hicks, who gives a solution to the problems of co-existence of diverse religions without undermining any religion. The plurality percolates into different spheres of Indian society and regarded as a chord with discord in a wonderful music. Swami Vivekananda believes that to serve man is to serve God. Both M. K. Gandhi and Swami Vivekananda were non-dualist and believed in the essential unity of man. Gandhi believes in the many foldedness of reality. Swami Vivekananda’s attitude towards religion is in principles of co-existence and acceptance. These principles have been accumulated in such a way that gave us a different world-view. The concept of unity, tolerance, equality, etc. can be achieved only by a spiritual attitude. Dynamism of spirituality stands in between man’s empirical existence and his spiritual destination and manifests itself in the different aspects of life including religious understanding. It is a movement towards pluralism. It is the fusion of spirituality with plurality which characterizes the concept of religious pluralism. This re-visited religious pluralism will open a new horizon of love and tolerance in our society. M. K. Gandhi and Swami Vivekananda paved the path for new horizon for a resurgent world. So the Indian spiritualism re-vitalised the concept of pluralism and stimulated its progress towards a new world.

Keywords: M. K. Gandhi, religious pluralism, Swami Vivekananda, worldview

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Authors: Yvette Reisinger

Abstract:

Relatively little is known about the Arab travel market, especially the outbound travel market from Arab countries in the Middle East. The Kuwaiti travel market is the smallest yet fastest growing in the Gulf Cooperation Council (GCC) region. The Kuwaiti travel market represents a great potential for the international tourism industry. Kuwaiti nationals have a very high spending power due to the Kuwaiti dinar being the highest-valued currency unit in the world. Although Europe, North America, and Asia/Pacific try to attract the Arab tourist market the number of Kuwaiti travellers attracted to these destinations is very low. The success in attracting the Kuwaiti travel market to Western countries must be guided by an analysis of the factors that affect its travel decisions. The objective of the study is to identify major factors that influence Kuwaiti nationals’ intentions to travel to Western countries. A model is developed and empirically tested on a sample of 343 Kuwaiti nationals. A series of regression analyses are run to determine the effects of different factors on Kuwaiti’s travel decisions. A Herman’s single factor test and Durbin-Watson test are used to assess the validity of the regression model. Analysis is controlled for socio-demographics. The results show that the Muslim friendly amenities and destination cognitive image exert significant effects on Kuwaiti nationals’ intentions to travel to Western countries. The study provides a better understanding of the factors that attract Kuwaiti tourists to Western countries. By knowing what encourages Kuwaitis to travel to Western countries marketers can plan and promote these countries accordingly. The study provides a foundation of future empirical research into the Kuwaiti/Arab travel market.

Keywords: Kuwaiti travel market, travel decisions, Western countries

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Authors: Seung Cho Yang, Jae Sung Lee, Se Hee Park

Abstract:

This study suggests the analysis method to predict the damages of tunnel concrete caused by fires. The result obtained from the analyses of concrete temperatures at a fire in a tunnel using ABAQUS was compared with the test result. After the reliability of the analysis method was verified, the temperatures of a tunnel at a real fire and those of concrete during the fire were estimated to predict fire damages. The temperatures inside the tunnel were estimated by FDS, a CFD model. It was deduced that the fire performance of tunnel lining and the fire damages of the structure at an actual fire could be estimated by the analysis method.

Keywords: fire resistance, heat transfer, numerical analysis, tunnel fire

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

Abstract:

According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

Procedia PDF Downloads 450