Search results for: market prediction

Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

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A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

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Authors: Angga S. Fajar, Y. Takahashi, J. Kiyono, S. Sawada

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The structural system concept of RC pier accompanied with multi sliding friction damping mechanism was developed based on numerical analysis approach. However in the implementation, to make design for such kind of this structural system consumes a lot of effort in case high of complexity. During making design, the special behaviors of this structural system should be considered including flexible small deformation, sufficient elastic deformation capacity, sufficient lateral force resistance, and sufficient energy dissipation. The confinement distribution of friction devices has significant influence to its. Optimization and prediction with multi function regression of this structural system expected capable of providing easier and simpler design method. The confinement distribution of friction devices is optimized with SNOPT in Opensees, while some design variables of the structure are predicted using multi function regression of ANN. Based on the optimization and prediction this structural system is able to be designed easily and simply.

Keywords: RC Pier, multi sliding friction device, optimal design, flexible small deformation

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Authors: Evgeniya Ivanova

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Nowadays, in the literature, there is still no fixed definition of knowledge management that often remains only as an academic discipline. The current market situation is changing very quickly, the need of new technologies is high, and knowledge management is the area that ensures that the know-how has not been lost during market development and adoption. The study examines how knowledge management is being leveraged and practiced in the management consultancy companies and provides not only the tips and best practices of applied knowledge management approaches but also the validation matrix for its successful or unsuccessful implementation. Different knowledge management approaches are explored on the basis of their practical implementation, including related challenges, knowledge sharing process, and barriers that are typical for consulting firms mostly driven by the agile working culture. The relevance of proposed topic is confirmed by the finding that corporate working culture and the exponentially developing technologies have a direct impact on the success of practical implementation of knowledge management.

Keywords: knowledge management, knowledge management in practice, consulting firm, knowledge management success

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Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Jihye Jeon

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This paper analyzes the political and economic issues that people with disabilities face related to globalization; how people with disabilities have been adapting globalization and surviving under worldwide competition system. It explains that economic globalization exacerbates inequality and deprivation of people with disabilities. The rising tide of neo-liberal welfare policies emphasized efficiency, downsized social expenditure for people with disabilities, excluded people with disabilities against labor market, and shifted them from welfare system to nothing. However, there have been people with disabilities' political responses to globalization, which are characterized by a global network of people with disabilities as well as participation to global governance. Their resistance can be seen as an attempt to tackle the problems that economic globalization has produced. It is necessary paradigm shift of disability policy from dependency represented by disability benefits to independency represented by labor market policies for people with disabilities.

Keywords: economic globalization, people with disability, deprivation, welfare cut, disability right movement, resistance

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Authors: Reena Aggarwal

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Wall art is a manifestation of the human mind and an absorbing form of cultural expression. Solapur wall hanging making art reflects cultural values, regional sensibilities, beliefs, and identity and helps to preserve the many different communities. The tango of warp and weft in many ways than one tells the story of civilization itself. Solapur wall hanging is a poem in multicolor, written with the warp and weft having long, rich, and complex history with indigenous design vocabularies made by the Padmasali communities. The wall-hanging weaving of Solapur has remained unaltered for years, from being very basic and monochrome having landscapes and portraits catering only to the local market, thereby becoming a potential family income generation tool. The study focuses on the need for contemporization of the Solapur wall hanging and also deliberates on the fact that wherever the culture of native people has been aided by intervention, in nearly every case, the quality of their craft has began to be enhanced. The study also found the underlying reason for diminishing sales to a declining market, low sales, lack of innovation in design, and product development. Keeping in mind that the artisans of Solapur have heroically always hold on to their ancient beliefs and practices, which give them strength and identity, and a sense of pride, an intervention program was developed with an objective of widening the market and help artisans have a sustaining income which include urban consumers and create designs suitable for the urban market. The process of defining and measuring the advantages of design intervention was achieved by using qualitative research methods. An ethnographic research methodology was adopted, which includes six months of close interface with artisans from ten families engaged in making of wall hanging in Solapur. Design solutions were proposed in terms of product diversification and design extensions of the existing product line for increased variety. A collection of contemporary wall arts (wall decor) and room dividers were designed and developed.

Keywords: wall hanging, Solapur, contemporization, traditional, sustainable

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Authors: Ayesha Obaid, Abdur Rehman Cheema

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This study is about understanding the role of the gender division of work that has been assigned to men and women in different societies and cultures and its impact on labour force participation through economic development. Development in Gilgit Baltistan has been challenging due to its geographical conditions and the human development indicators are lower than the rest of the Pakistan. Various socioeconomic factors are identified that play an important role in determining the choices and roles men and women undertake for contributing towards the labour force. Our research highlights the areas lagging behind in gender equality in the labour market. The availability and access of gender over these socioeconomic resources determine gender mainstreaming in the labour market. It is a need of time that gender gaps should be addressed at the grass root level by the policy makers to enhance the growth and improve human development indicators.

Keywords: gender division of work, human development, indicators of socioeconomic factors, labour force

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Authors: Elham Delzendeh, Song Wu, Mustafa Al-Adhami, Rima Alaaeddine

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Over the last 15 years, prediction of energy consumption has become a common practice and necessity at different stages of the building’s lifecycle, particularly, at the design and post-occupancy stages for planning and maintenance purposes. This is due to the ever-growing response of governments to address sustainability and reduction of CO₂ emission in the building sector. However, there is a level of uncertainty in the estimation of energy consumption in buildings. The accuracy of energy consumption predictions is directly related to the precision of the initial inputs used in the energy assessment process. In this study, multiple cases of large non-residential buildings at design, construction, and post-occupancy stages are investigated. The energy consumption process and inputs, and the actual and predicted energy consumption of the cases are analysed. The findings of this study have pointed out and evidenced various parameters that cause uncertainty in the prediction of energy consumption in buildings such as modelling, location data, and occupant behaviour. In addition, unavailability and insufficiency of energy-consumption-related inputs at different stages of the building’s lifecycle are classified and categorized. Understanding the roots of uncertainty in building energy analysis will help energy modellers and energy simulation software developers reach more accurate energy consumption predictions in buildings.

Keywords: building lifecycle, efficiency, energy analysis, energy performance, uncertainty

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Authors: Siham G. Farag

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The main objective of this paper is to provide a new methodology for road safety assessment in Oman through the development of suitable accident prediction models. GLM technique with Poisson or NBR using SAS package was carried out to develop these models. The paper utilized the accidents data of 31 un-signalized T-intersections during three years. Five goodness-of-fit measures were used to assess the overall quality of the developed models. Two types of models were developed separately; the flow-based models including only traffic exposure functions, and the full models containing both exposure functions and other significant geometry and traffic variables. The results show that, traffic exposure functions produced much better fit to the accident data. The most effective geometric variables were major-road mean speed, minor-road 85th percentile speed, major-road lane width, distance to the nearest junction, and right-turn curb radius. The developed models can be used for intersection treatment or upgrading and specify the appropriate design parameters of T- intersections. Finally, the models presented in this thesis reflect the intersection conditions in Oman and could represent the typical conditions in several countries in the middle east area, especially gulf countries.

Keywords: accidents prediction models (APMs), generalized linear model (GLM), T-intersections, Oman

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Authors: Saurabh Kumar

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In the fiercely competitive landscape of e-commerce, understanding and mitigating customer churn has become paramount for sustainable business growth. This paper presents a thorough investigation into the application of machine learning techniques for churn prediction in e-commerce, aiming to provide actionable insights for businesses seeking to enhance customer retention strategies. We conduct a comparative study of various machine learning algorithms, including traditional statistical methods and ensemble techniques, leveraging a rich dataset sourced from Kaggle. Through rigorous evaluation, we assess the predictive performance, interpretability, and scalability of each method, elucidating their respective strengths and limitations in capturing the intricate dynamics of customer churn. We identified the XGBoost classifier to be the best performing. Our findings not only offer practical guidelines for selecting suitable modeling approaches but also contribute to the broader understanding of customer behavior in the e-commerce domain. Ultimately, this research equips businesses with the knowledge and tools necessary to proactively identify and address churn, thereby fostering long-term customer relationships and sustaining competitive advantage.

Keywords: customer churn, e-commerce, machine learning techniques, predictive performance, sustainable business growth

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Authors: Mojtaba Molaahmadi, Morteza Raei Dehaghi, Abdolrahim Arghavan

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The present study aims to explore the effect of computerization on marketing performance in Snowa Company. In other words, this study intends to respond to this question that whether or not there is a relationship between utilization of computerization in marketing activities and marketing performance. The statistical population included 60 marketing managers of Snowa Company. In order to test the research hypotheses, Pearson correlation coefficient was employed. The reliability was equal to 96.8%. In this study, computerization was the independent variable and marketing performance was the dependent variable with characteristics of market share, improving the competitive position, and sales volume. The results of testing the hypotheses revealed that there is a significant relationship between utilization of computerization and market share, sales volume and improving the competitive position

Keywords: computerization, e-marketing information, information technology, marketing performance

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Authors: Xuan Tran

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The purpose of this paper is to present a comprehensive dynamic model for pricing strategies in the hotel competition to find a win-win situation for the competitive set. By utilizing the Cobb-Douglas utility model, the study establishes room rates by analyzing the price elasticity of demand across a competitive set of four hotels, with a focus on occupancy rates. To further enhance the analysis, game theory is applied to identify the best response for each competitive party, which illustrates the optimal pricing strategy for each hotel in the competitive landscape. This approach offers valuable insights into how hotels can strategically adjust their room rates in response to market conditions and competitor actions. The primary contributions of this research include as follows: (1) advantages for both individual hotels and the broader competitive hotel market, (2) benefits for hotel management overseeing multiple brands, and (3) positive impacts on the local community.

Keywords: dynamic model, game theory, best response, Cobb-Douglas

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Authors: Bojan Najdenov, Danco Davcev

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Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control.

Keywords: traffic, congestion reduction, crowdsource, social networks, twitter, android

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Authors: Imam Uddin, Imtiaz Ahmed Memon

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This paper analyzes the ideological contextuality of market oriented strategy language used by Industry leaders to envision the future of Islamic financial Institutions (IFIs) in the light of Wittgenstein language-games and Foucault’s power-discourse framework. The analysis infers that the explicit market orientation of strategy language and modern knowledge of finance now defines various concepts related of Islamic finance, let alone Islamic finance theory itself. Theorizing and practicing Islamic finance therefore under the dominant influence of modern strategy discourse and modern knowledge of finance has significant implications for developing an ethical and spiritual orientation of Islamic banks. The concerned academia and scholarship therefore need to review such trends and work around the possible degradation to the public image of IFIs and resulting disappointments of religiously inspired customers.

Keywords: Islamic finance discourse, strategy discourse, language games, strategic intent, productive misunderstanding

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Ravi Soni, Irfan Pathan, Manish Pande

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The current product development process needs simultaneous consideration of different physics. The performance of the product needs to be considered under both structural and fluid loads. Examples include ducts and valves where structural behavior affects fluid motion and vice versa. Simulation of fluid-structure interaction involves modeling interaction between moving components and the fluid flow. In these scenarios, it is difficult to calculate the damping provided by fluid flow because of dynamic motions of components and the transient nature of the flow. Abaqus Explicit offers general capabilities for modeling fluid-structure interaction with the Coupled Eulerian-Lagrangian (CEL) method. The Coupled Eulerian-Lagrangian technique has been used to simulate fluid spillage through fuel valves during dynamic closure events. The technique to simulate pressure drops across Eulerian domains has been developed using stagnation pressure. Also, the fluid flow is calculated considering material flow through elements at the outlet section of the valves. The methodology has been verified on Eaton products and shows a good correlation with the test results.

Keywords: Coupled Eulerian-Lagrangian Technique, fluid structure interaction, spillage prediction, stagnation pressure

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Authors: Subarna Ferdous, Mitsuru Ikeda

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For the shrimp companies to remain relevant to its local and international consumers, they must offer new shrimp product and services. It must work actively not just to create value for the consumer, but to involve the consumer in co-creating value for shrimp product innovation in the market. In this theoretical work, we conceptualize the business concept of value co-creation in the context of shrimp products, and propose a framework of value co-creation for shrimp product innovation in shrimp industries. With guidance on value co-creation in in shrimp industry, and shrimp value chain actors mapped to the co-creation cycle, companies can use the framework to offer new shrimp product to consumer communities. Although customer co-creation is known approach in the world, it is not commonly used by the companies in Bangladesh. This paper makes an original contribution by conceptualizing co-creation and set the examples of best co-creation practices in food sector. The results of the study provide management with guidelines for successful co-creation projects with an innovation- and market-oriented approach. The framework also provides a basis for further research in this area.

Keywords: bangladesh, shrimp industry, value co-creation, shrimp product

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Authors: Aslam Faheem, Hunjra Ahmed Imran, Tayachi Tahar, Verhoeven Peter, Tariq Yasir

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We investigate the evidence of three risk-adjusted calendar anomalies in eight frontier markets. Our sample consists of the daily closing prices of their stock indices for the period of January 2006 to September 2019. We categorize the data with respect to day-of-the-week, Lunar calendar and Islamic calendar. Using Morgan Stanley Capital International (MSCI) eight Markets Index as our proxy of the market portfolio, most of the frontier markets tested exhibit calendar seasonality. We confirm that systematic risk varies with respect to day-of-the-week, Lunar months and Islamic months. After consideration of time-varying risk and applying Bonferroni correction, few frontier markets exhibit profitable investment opportunities from calendar return anomalies for active investment managers.

Keywords: asset pricing, frontier markets, market efficiency, Islamic calendar effects, Islamic stock markets

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Authors: Francesco Berald, Yunhui Zhao

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The COVID-19 pandemic further extended the multi-year housing boom in advanced economies and emerging markets alike against massive monetary easing during the pandemic. In this paper, we analyze the pricing-out phenomenon in the U.S. residential housing market due to higher house prices associated with monetary easing. We first set up a stylized general equilibrium model and show that although monetary easing decreases the mortgage payment burden, it would raise house prices and lower housing affordability for first-time homebuyers (through the initial housing wealth channel and the liquidity constraint channel that increases repeat buyers’ housing demand), and increase housing wealth inequality between first-time and repeat homebuyers. We then use the U.S. household-level data to quantify the effect of the house price change on housing affordability relative to that of the interest rate change. We find evidence of the pricing-out effect for all homebuyers; moreover, we find that the pricing-out effect is stronger for first-time homebuyers than for repeat homebuyers. The paper highlights the importance of accounting for general equilibrium effects and distributional implications of monetary policy while assessing housing affordability. It also calls for complementing monetary easing with well-targeted policy measures that can boost housing affordability, particularly for first-time and lower-income households. Such measures are also needed during aggressive monetary tightening, given that the fall in house prices may be insufficient or too slow to fully offset the immediate adverse impact of higher rates on housing affordability.

Keywords: pricing-out, U.S. housing market, housing affordability, distributional effects, monetary policy

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Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

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The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

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Authors: Omar S. Sharaf

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The mitochondrial 12s rRNA (mt-12s rDNA) gene for pig-specific was developed to detect material from pork species in different products collected from Jordanian market. The amplification PCR products of 359 bp and 531 bp were successfully amplified from the cyt b gene of pig the amplification product using mt-12S rDNA gene were successfully produced a single band with a molecular size of 456 bp. In the present work, the PCR amplification of mtDNA of cytochrome b has been shown as a suitable tool for rapid detection of pig DNA. 100 samples from different dairy, gelatin and chocolate based products and 50 samples from baby food formula were collected and tested to a presence of any pig derivatives. It was found that 10% of chocolate based products, 12% of gelatin and 56% from dairy products and 5.2% from baby food formula showed single band from mt-12S rDNA gene.

Keywords: halal food, baby infant formula, chocolate based products, PCR, Jordan

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Authors: I. L. Kim, J. Y. Lee, A. K. Tekile

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In consequence of the very high urbanization rate of Korea, the number of traffic noise damages in areas congested with population and facilities is steadily increasing. The current environmental noise levels data in major cities of the country show that the noise levels exceed the standards set for both day and night times. This research was about comparative analysis in search for optimal soundproof panel shape and design factor that can minimize sound reflection noise. In addition to the normal flat-type panel shape, the reflection noise reduction of swelling-type, combined swelling and curved-type, and screen-type were evaluated. The noise source model Nord 2000, which often provides abundant information compared to models for the similar purpose, was used in the study to determine the overall noise level. Based on vehicle categorization in Korea, the noise levels for varying frequency from different heights of the sound source (directivity heights of Harmonize model) have been calculated for simulation. Each simulation has been made using the ray-tracing method. The noise level has also been calculated using the noise prediction program called SoundPlan 7.2, for comparison. The noise level prediction was made at 15m (R1), 30 m (R2) and at middle of the road, 2m (R3) receiving the point. By designing the noise barriers by shape and running the prediction program by inserting the noise source on the 2nd lane to the noise barrier side, among the 6 lanes considered, the reflection noise slightly decreased or increased in all noise barriers. At R1, especially in the cases of the screen-type noise barriers, there was no reduction effect predicted in all conditions. However, the swelling-type showed a decrease of 0.7~1.2 dB at R1, performing the best reduction effect among the tested noise barriers. Compared to other forms of noise barriers, the swelling-type was thought to be the most suitable for reducing the reflection noise; however, since a slight increase was predicted at R2, further research based on a more sophisticated categorization of related design factors is necessary. Moreover, as swellings are difficult to produce and the size of the modules are smaller than other panels, it is challenging to install swelling-type noise barriers. If these problems are solved, its applicable region will not be limited to other types of noise barriers. Hence, when a swelling-type noise barrier is installed at a downtown region where the amount of traffic is increasing every day, it will both secure visibility through the transparent walls and diminish any noise pollution due to the reflection. Moreover, when decorated with shapes and design, noise barriers will achieve a visual attraction than a flat-type one and thus will alleviate any psychological hardships related to noise, other than the unique physical soundproofing functions of the soundproof panels.

Keywords: reflection noise, shaped noise barriers, sound proof panel, traffic noise

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Authors: Anne C. Richer-De-Forges, Dominique Arrouays, Songchao Chen, Mercedes Roman Dobarco

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Most of the digital soil mapping (DSM) products rely on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs. However, many other observations (often qualitative, nominal, or ordinal) could be used as proxies of lab measurements or as input data for ML of PTF predictions. DSM and ML are briefly described with some examples taken from the literature. Then, we explore the potential of an ordinal qualitative variable, i.e., the hand-feel soil texture (HFST) estimating the mineral particle distribution (PSD): % of clay (0-2µm), silt (2-50µm) and sand (50-2000µm) in 15 classes. The PSD can also be measured by lab measurements (LAST) to determine the exact proportion of these particle-sizes. However, due to cost constraints, HFST are much more numerous and spatially dense than LAST. Soil texture (ST) is a very important soil parameter to map as it is controlling many of the soil properties and functions. Therefore, comes an essential question: is it possible to use HFST as a proxy of LAST for calibration and/or validation of DSM predictions of ST? To answer this question, the first step is to compare HFST with LAST on a representative set where both information are available. This comparison was made on ca 17,400 samples representative of a French region (34,000 km2). The accuracy of HFST was assessed, and each HFST class was characterized by a probability distribution function (PDF) of its LAST values. This enables to randomly replace HFST observations by LAST values while respecting the PDF previously calculated and results in a very large increase of observations available for the calibration or validation of PTF and ML predictions. Some preliminary results are shown. First, the comparison between HFST classes and LAST analyses showed that accuracies could be considered very good when compared to other studies. The causes of some inconsistencies were explored and most of them were well explained by other soil characteristics. Then we show some examples applying these relationships and the increase of data to several issues related to DSM. The first issue is: do the PDF functions that were established enable to use HSFT class observations to improve the LAST soil texture prediction? For this objective, we replaced all HFST for topsoil by values from the PDF 100 time replicates). Results were promising for the PTF we tested (a PTF predicting soil water holding capacity). For the question related to the ML prediction of LAST soil texture on the region, we did the same kind of replacement, but we implemented a 10-fold cross-validation using points where we had LAST values. We obtained only preliminary results but they were rather promising. Then we show another example illustrating the potential of using HFST as validation data. As in numerous countries, the HFST observations are very numerous; these promising results pave the way to an important improvement of DSM products in all the countries of the world.

Keywords: digital soil mapping, improvement of digital soil mapping predictions, potential of using hand-feel soil texture, soil texture prediction

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Bengan Simbarashe Manwere

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The entire African continent, comprising 55 countries, holds a 2% share of the global carbon market. The World Bank attributes the continent’s insignificant share and participation in the carbon market to the limited access to electricity. Approximately 800 million people spread across 47 African countries generate as much power as Spain, with a population of 45million. Only South Africa and North Africa have carbon-reduction investment opportunities on the continent and dominate the 2% market share of the global carbon market. On the back of the 2015 Paris Agreement, South Africa signed into law the Carbon Tax Act 15 of 2019 and the Customs and Excise Amendment Act 13 of 2019 (Gazette No. 4280) on 1 June 2019. By these laws, South Africa was ushered into the league of active global carbon market players. By increasing the cost of production by the rate of R120/tCO2e, the tax intentionally compels the internalization of pollution as a cost of production and, relatedly, stimulate investment in clean technologies. The first phase covered the 1 June 2019 – 31 December 2022 period during which the tax was meant to escalate at CPI + 2% for Scope 1 emitters. However, in the second phase, which stretches from 2023 to 2030, the tax will escalate at the inflation rate only as measured by the consumer price index (CPI). The Carbon Tax Act provides for carbon allowances as mitigation strategies to limit agents’ carbon tax liability by up to 95% for fugitive and process emissions. Although the June 2019 Carbon Tax Act explicitly makes provision for a carbon trading scheme (CTS), the carbon trading regulations thereof were only finalised in December 2020. This points to a delay in the establishment of a carbon trading scheme (CTS). Relatedly, emitters in South Africa are not able to benefit from the 95% reduction in effective carbon tax rate from R120/tCO2e to R6/tCO2e as the Johannesburg Stock Exchange (JSE) has not yet finalized the establishment of the market for trading carbon credits. Whereas most carbon trading schemes have been designed and constructed from the beginning as new tailor-made systems in countries the likes of France, Australia, Romania which treat carbon as a financial product, South Africa intends, on the contrary, to leverage existing trading infrastructure of the Johannesburg Stock Exchange (JSE) and the Clearing and Settlement platforms of Strate, among others, in the interest of the Paris Agreement timelines. Therefore the carbon trading scheme will not be constructed from scratch. At the same time, carbon will be treated as a commodity in order to align with the existing institutional and infrastructural capacity. This explains why the Carbon Tax Act is silent about the involvement of the Financial Sector Conduct Authority (FSCA).For South Africa, there is need to establish they equilibrium stability of the CTS. This is important as South Africa is an innovator in carbon trading and the successful trading of carbon credits on the JSE will lead to imitation by early adopters first, followed by the middle majority thereafter.

Keywords: carbon trading scheme (CTS), Johannesburg stock exchange (JSE), carbon tax act 15 of 2019, South Africa

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Authors: Miang Hong Ngerng, Noor Diyana Jasme, May Jin Theong

Abstract:

Volatile market environment is inevitable. Fund managers are struggling to choose the right strategy to survive and overcome uncertainties and adverse market movement. Therefore, finding certainty in the mist of uncertainty future is one of the key performance objectives for fund managers. Current available theoretical results are not practical due to strong reliance on the investment assumption made. This paper is to identify the component that can be forecasted in Ex-ante setting which is the realistic situation facing a fund manager in the actual execution of asset allocation in portfolio management. Partial lease square method was used to generate an index with 10 years accounting data from 191 companies listed in KLSE. The result shows that the index reflects the inner nature of the business and up to 30% of the stock return can be explained by the index.

Keywords: active portfolio management, asset allocation ex-ante investment, asset class, partial lease square

Procedia PDF Downloads 270

Authors: Feyi Olorunshola

Abstract:

Over the last few years, the senior travel market has been noted for its potential in the wider tourism industry. The tourism sector includes the hotel and hospitality, travel, transportation, and several other subdivisions to make it economically viable. In particular, the hotel attracts a substantial part of the expenditure in tourism activities as when people plan to travel, suitable accommodation for relaxation, dining, entertainment and so on is paramount to their decision-making. The global retail value of the hotel as of 2018 was significant for tourism. But, despite indications of the hotel to the tourism industry at large, very few empirical studies are available to establish how this sector can leverage on the senior demographic to achieve competitive advantage. Predominantly, studies on the mature market have focused on destination tourism, with a limited investigation on the hotel which makes a significant contribution to tourism. Also, several scholarly studies have demonstrated the importance of the senior travel market to the hotel, yet there is very little empirical research in the field which has explored the driving factors that will become the accepted new normal for this niche segment post-pandemic. Giving that the hotel already operates in a highly saturated business environment, and on top of this pre-existing challenge, the ongoing global health outbreak has further put the sector in a vulnerable position. Therefore, the hotel especially the full-service luxury category must evolve rapidly for it to survive in the current business environment. The hotel can no longer rely on corporate travelers to generate higher revenue since the unprecedented wake of the pandemic in 2020 many organizations have invented a different approach of conducting their businesses online, therefore, the hotel needs to anticipate a significant drop in business travellers. However, the rooms and the rest of the facilities must be occupied to keep their business operating. The way forward for the hotel lies in the leisure sector, but the question now is to focus on the potential demographics of travelers, in this case, the seniors who have been repeatedly recognized as the lucrative market because of increase discretionary income, availability of time and the global population trends. To achieve the study objectives, a mixed-method approach will be utilized drawing on both qualitative (netnography) and quantitative (survey) methods, cognitive and decision-making theories (means-end chain) and competitive theories to identify the salient drivers explaining senior hotel choice and its influence on their decision-making. The target population are repeated seniors’ age 65 years and over who are UK resident, and from the top tourist market to the UK (USA, Germany, and France). Structural equation modelling will be employed to analyze the datasets. The theoretical implication is the development of new concepts using a robust research design, and as well as advancing existing framework to hotel study. Practically, it will provide the hotel management with the latest information to design a competitive marketing strategy and activities to target the mature market post-pandemic and over a long period.

Keywords: competitive advantage, covid-19, full-service hotel, five-star, luxury hotels

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Authors: Kasim Görenekli, Ali Gülbağ

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This study presents a comprehensive analysis of water consumption patterns in Kocaeli province, Turkey, utilizing various machine learning approaches. We analyzed data from 5,000 water subscribers across residential, commercial, and official categories over an 80-month period from January 2016 to August 2022, resulting in a total of 400,000 records. The dataset encompasses water consumption records, weather information, weekends and holidays, previous months' consumption, and the influence of the COVID-19 pandemic.We implemented and compared several machine learning models, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Particle Swarm Optimization (PSO) was applied to optimize hyperparameters for all models.Our results demonstrate varying performance across subscriber types and models. For official subscribers, Random Forest achieved the highest R² of 0.699 with PSO optimization. For commercial subscribers, Linear Regression performed best with an R² of 0.730 with PSO. Residential water usage proved more challenging to predict, with XGBoost achieving the highest R² of 0.572 with PSO.The study identified key factors influencing water consumption, with previous months' consumption, meter diameter, and weather conditions being among the most significant predictors. The impact of the COVID-19 pandemic on consumption patterns was also observed, particularly in residential usage.This research provides valuable insights for effective water resource management in Kocaeli and similar regions, considering Turkey's high water loss rate and below-average per capita water supply. The comparative analysis of different machine learning approaches offers a comprehensive framework for selecting appropriate models for water consumption prediction in urban settings.

Keywords: mMachine learning, water consumption prediction, particle swarm optimization, COVID-19, water resource management

Procedia PDF Downloads 15