Search results for: market prediction

Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

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Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

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Authors: Joanna Smoluk-Sikorska, Władysława Łuczka

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Organic farming is an important element of sustainable agriculture. It has been developing very dynamically in Poland, especially since Poland’s accession to the EU. Nevertheless, properly functioning organic market is a necessary condition justifying development of organic agriculture. Despite significant improvement, this market in Poland is still in the initial stage of growth. An important element of the market is distribution, especially retail, which offers specified product range to consumers. Therefore, there is a need to investigate retail outlets offering organic food in order to improve functioning of this part of the market. The inquiry research conducted in three types of outlets offering organic food, between 2011 and 2012 in the 8 largest Polish cities, shows that the majority of outlets offer cereals, processed fruit and vegetables as well as spices and the least shops – meat and sausages. The distributors mostly indicate unsatisfactory product range of suppliers as the reason for this situation. The main providers of the outlets are wholesalers, particularly in case of processed products, and in fresh products – organic farms. A very important distribution obstacle is dispersion of producers, which generates high transportation costs and what follows that, high price of organics. In the investigated shops, the most often used price calculation method is a cost method. The majority of the groceries and specialist shops apply margins between 21 and 40%. The margin in specialist outlets is the highest, in regard to the qualified service and advice. In turn, most retail networks declare the margin between 0 and 20%, which is consistent with low-price strategy applied in these shops. Some lacks in the product range of organics and in particular high prices cause that the demand volume is rather low. Therefore there is a need to support certain market actions, e.g. on-farm processing or promotion.

Keywords: organic food, retail, product range, supply sources

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Authors: Noemia Bessa Vilela, Jose Caramelo Gomes

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All law student are familiarized, in the first years of their bachelor of laws with the concepts of “public goods” and “ private goods”; often, such legal concept does not exactly match such economic concept, and there are consequences are some sort of confusion being created. The list of goods that follow under each category is not exhaustive, nor are students given proper mechanisms to acknowledge that some legal fields can, on its own, be considered as a “public good”; this is the case of Competition. Legal authors consider that “competition law is used to promote public interest” and, as such, it is a “public good”; in economics theory, Competition is the first public good in a market economy, as the enabler of allocation efficiency. Competition law is the legal tool to support the proper functioning of the market economy and democracy itself. It is fact that Competition Law only applies to economic activities, still, competition is object of private litigation as an integral part of Public Law. Still, regardless of the importance of Competition Law in the economic activity and market regulation, most student complete their studies in law, join the Bar Associations and engage in their professional activities never having been given sufficient tools to deal with the increasing demands of a globalized world. The lack of knowledge of economics, market functioning and the mechanisms at their reach in order to ensure proper realization of their duties as lawyers/ attorneys-at-law would be tackled if Competition Law would be included as part of the curricula of Law Schools. Proper teaching of Competition Law would combine the foundations of Competition Law, doctrine, case solving and Case Law study. Students should to understand and apply the analytical model. Special emphasis should be given to EU Competition Law, namely the TFEU Articles 101 to 106. Damages Directive should also be part of the curriculum. Students must in the first place acquire and master the economic rationale as competition and the world of competition law are the cornerstone of sound and efficient market. The teaching of Competition Law in undergraduate programs in Law would contribute to fulfill the potential of the students who will deal with matters related to consumer protection, economic and commercial law issues both in private practice and as in-house lawyers for companies.

Keywords: higher education, competition law, legal education, law, market economy, industrial economics

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Authors: R. G. Ariyawansa, M. A. N. R. M. Perera

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“House” is a powerful symbol of socio-economic background of individuals and families. In fact, housing provides all types of needs/wants from basic needs to self-actualization needs. This phenomenon can be realized only having analyzed hidden motives of buyers and sellers of the housing market. Hence, the aim of this study is to examine the socio-economic insight of the secondary housing market in Colombo suburbs. This broader aim was achieved via analyzing the general pattern of the secondary housing market, identifying socio-economic motives of sellers of the secondary housing market, and reviewing sellers’ experience of buyer behavior. A purposive sample of 50 sellers from popular residential areas in Colombo such as Maharagama, Kottawa, Piliyandala, Punnipitiya, and Nugegoda was used to collect primary data instead of relevant secondary data from published and unpublished reports. The sample was limited to selling price ranging from Rs15 million to Rs25 million, which apparently falls into middle and upper-middle income houses in the context. Participatory observation and semi-structured interviews were adopted as key data collection tools. Data were descriptively analyzed. This study found that the market is mainly handled by informal agents who are unqualified and unorganized. People such as taxi/tree-wheel drivers, boutique venders, security personals etc. are engaged in housing brokerage as a part time career. Few fulltime and formally organized agents were found but they were also not professionally qualified. As far as housing quality is concerned, it was observed that 90% of houses was poorly maintained and illegally modified. They are situated in poorly maintained neighborhoods as well. Among the observed houses, 2% was moderately maintained and 8% was well maintained and modified. Major socio-economic motives of sellers were “migrating foreign countries for education and employment” (80% and 10% respectively), “family problems” (4%), and “social status” (3%). Other motives were “health” and “environmental/neighborhood problems” (3%). This study further noted that the secondary middle income housing market in the area directly related with the migrants who motivated for education in foreign countries, mainly Australia, UK and USA. As per the literature, families motivated for education tend to migrate Colombo suburbs from remote areas of the country. They are seeking temporary accommodation in lower middle income housing. However, the secondary middle income housing market relates with the migration from Colombo to major global cities. Therefore, final transaction price of this market may depend on migration related dates such as university deadlines, visa and other agreements. Hence, it creates a buyers’ market lowering the selling price. Also it was revealed that the buyers tend to trust more on this market as far as the quality of construction of houses is concerned than brand new houses which are built for selling purpose.

Keywords: informal housing market, hidden motives of buyers and sellers, secondary housing market, socio-economic insight

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Authors: Nino Damenia

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The agricultural sector plays an important role in the development of Georgia's economy, it contributes to employment and food security. It faces various types of risks that may lead to heavy financial losses. Agricultural insurance is one of the means of combating agricultural risks. The paper discusses the agricultural insurance experience of those countries (European countries and the USA) that have successfully implemented the agricultural insurance program. Analysis of international cases shows that a well-designed and implemented agri-insurance system can bring significant benefits to farmers, insurance companies and the economy as a whole. In the background of all this, the Government of Georgia recognized the importance of agro-insurance and took important steps for its development. In 2014, in cooperation with insurance companies, an agro-insurance program was introduced, the purpose of which is to increase the availability of insurance for farmers and stimulate the agro-insurance market. Despite such a step forward, challenges remain such as awareness of farmers, insufficient infrastructure for data collection and risk assessment, involvement of insurance companies and other important factors. With the support of the government and stakeholders, it is possible to overcome the existing challenges and establish a strong and effective agro-insurance system. Objectives. The purpose of the research is to analyze the development trends of the agricultural insurance market, to identify the main factors affecting its growth, and to further develop recommendations for development prospects for Georgia. Methodologies. The research uses mixed methods, which combine qualitative and quantitative research techniques. The qualitative method includes the study of the literature of Georgian and foreign economists, which allows us to get acquainted with the challenges, opportunities, legislative and regulatory frameworks of agricultural insurance. Quantitative analysis involves collecting data from stakeholders and then analyzing it. The paper also uses the methods of synthesis, comparison and statistical analysis of the agricultural insurance market in Georgia, Europe and the USA. Conclusions. As the main results of the research, we can consider that the analysis of the insurance market has been made and its main functions have been identified; The essence, features and functions of agricultural insurance are analyzed; European and US agricultural insurance market is researched; The stages of formation and development of the agricultural insurance market of Georgia are studied, its importance for the agricultural sector of Georgia is determined; The role of the state for the development of agro-insurance is analyzed and development prospects are established based on the study of the current trends of the agro-insurance market of Georgia.

Keywords: agricultural insurance, agriculture, agricultural insurance program, risk

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Authors: Aneta Oblouková, Eva Vítková

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The article deals with the issue of water and sewerage charge rate prices in the Czech Republic. The research is specifically focused on the analysis of the development of the average prices of water and sewerage charge rate in the Czech Republic in the years 1994-2021 and on the validation of the chosen methodology relevant for the prediction of the development of the average prices of water and sewerage charge rate in the Czech Republic. The research is based on data collection. The data for this research was obtained from the Czech Statistical Office. The aim of the paper is to validate the relevance of the mathematical linear trend estimate technique for the calculation of the predicted average prices of water and sewerage charge rates. The real values of the average prices of water and sewerage charge rates in the Czech Republic in the years 1994-2018 were obtained from the Czech Statistical Office and were converted into a mathematical equation. The same type of real data was obtained from the Czech Statistical Office for the years 2019-2021. Prediction of the average prices of water and sewerage charge rates in the Czech Republic in the years 2019-2021 were also calculated using a chosen method -a linear trend estimation technique. The values obtained from the Czech Statistical Office and the values calculated using the chosen methodology were subsequently compared. The research result is a validation of the chosen mathematical technique to be a suitable technique for this research.

Keywords: Czech Republic, linear trend estimation, price prediction, water and sewerage charge rate

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Authors: Malo Pocheau-Lesteven, Olivier Le Maître

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Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.

Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program

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Authors: Norm Josephy, Lucy Kimball, Victoria Steblovskaya

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Hedging of a portfolio of European options under proportional transaction costs is considered. Our discrete time financial market model extends the binomial market model with transaction costs to the case where the underlying stock price ratios are distributed over a bounded interval rather than over a two-point set. An optimal hedging strategy is chosen from a set of admissible non-self-financing hedging strategies. Our approach to optimal hedging of a portfolio of options is based on theoretical foundation that includes determination of a no-arbitrage option price interval as well as on properties of the non-self-financing strategies and their residuals. A computational algorithm for optimizing an investor relevant criterion over the set of admissible non-self-financing hedging strategies is developed. Applicability of our approach is demonstrated using both simulated data and real market data.

Keywords: extended binomial model, non-self-financing hedging, optimization, proportional transaction costs

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Lavinia B. Dulla

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The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data.

Keywords: fuzzy regression approach, minimum fuzziness criterion, interval regression, prediction interval

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Authors: Nika GorgoShadze, Anri Shainidze, Bachuki Katamadze

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In parallel to the development of the digital economy in the world, electronic commerce is also widely developing. Internet and ICT (information and communication technology) have created new business models as well as promoted to market consolidation, sustainability of the business environment, creation of digital economy, facilitation of business and trade, business dynamism, higher competitiveness, etc. Electronic commerce involves internet technology which is sold via the internet. Nowadays electronic commerce is a field of business which is used by leading world brands very effectively. After the research of internet market in Georgia, it was found out that quality of internet is high in Tbilisi and is low in the regions. The internet market of Tbilisi can be evaluated as high-speed internet service, competitive and cost effective internet market. Development of electronic commerce in Georgia is connected with organizational and methodological as well as legal problems. First of all, a legal framework should be developed which will regulate responsibilities of organizations. The Ministry of Economy and Sustainable Development will play a crucial role in creating legal framework. Ministry of Justice will also be involved in this process as well as agency for data exchange. Measures should be taken in order to make electronic commerce in Georgia easier. Business companies may be offered some model to get low-cost and complex service. A service centre should be created which will provide all kinds of online-shopping. This will be a rather interesting innovation which will facilitate online-shopping in Georgia. Development of electronic business in Georgia requires modernized infrastructure of telecommunications (especially in the regions) as well as solution of institutional and socio-economic problems. Issues concerning internet availability and computer skills are also important.

Keywords: electronic commerce, internet market, electronic business, information technology, information society, electronic systems

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

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Authors: T. Niracharapa

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The research studied and examined the competitiveness of the animation industry in Thailand. Data were collected based on articles, related reports and websites, news, research, and interviews of key persons from both public and private sectors. The diamond model was used to analyze the study. The major factor driving the Thai animation industry forward includes a quality workforce, their creativity and strong associations. However, discontinuity in government support, infrastructure, marketing, IP creation and financial constraints were factors keeping the Thai animation industry less competitive in the global market.

Keywords: animation, competitiveness, government, Thailand, market

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Authors: Mohammad Azam

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This work is an endeavor to empirically investigate the Gross Domestic Product-Growth as mediating variable between various factors and portfolio returns using a broad sample of 522 financial and non-financial firms enlisted on Pakistan Stock Exchange between January-1993 and June-2022. The study employs the Structural Equation modeling and Ordinary Least Square regression to determine the findings before and during the Covid-19 epidemiological situation, which has not received due attention by researchers. The analysis reveals that market and investment factors are redundant, whereas size and value show significant results, whereas Gross Domestic Product-Growth performs significant mediating impact for the whole time frame. Using before Covid-19 period, the results reveal that market, value, and investment are redundant, but size, profitability, and Gross Domestic Product-Growth are significant. During the Covid-19, the statistics indicate that market and investment are redundant, though size and Gross Domestic Product-Growth are highly significant, but value and profitability are moderately significant. The Ordinary Least Square regression shows that market and investment are statistically insignificant, whereas size is highly significant but value and profitability are marginally significant. Using the Gross Domestic Product-Growth augmented model, a slight growth in R-square is observed. The size, value and profitability factors are recommended to the investors for Pakistan Stock Exchange. Conclusively, in the Pakistani market, the Gross Domestic Product-Growth indicates a feeble moderating effect between risk-premia and portfolio returns.

Keywords: asset pricing puzzle, mediating role of GDP-growth, structural equation modeling, COVID-19 pandemic, Pakistan stock exchange

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Authors: Adedayo Jeremiah Adeyekun, Samuel Oluwagbemiga Ishola

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This research paper focuses on the study of the master plan of rural Farrukhnagar, a town in Gurugram with an aim to proffer solutions to the problems associated with the planning of the town. The commercial zone has been selected for the case study. The findings from the case studies will reveal problems that will require a proposed design of a new ultra-modern market to position traders selling along the road in well-deserved stalls, waste disposal/incinerator system for proper management of waste and cleanliness within the market square, design of stormwater drainage to avoid flood during the rainy season and the design of car/auto – tricycle parks to create more space in the existing market cycle and thereby avoiding congestion. The research proposes urban and architectural solutions to improve the rural commercial service settings in Farrukhnagar which is a study area in Gurugram, Haryana, India.

Keywords: management, commercial, service, planning, congestion

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Aswathy Vijayan, Sharath Sunder Rajeev

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Trade has been fundamental in the footprint of human civilization since ancient time. In most of the historic cities, city development was along trading routes, where marketplaces are the major entrance to a city and hence a major element of the urban fabric. Marketplaces are where the commercial activities flourish, people, having a sense of belonging to the place, where they easily fit in. Acknowledging the built environment in and around the market in a way, creating a sense of place is an important factor in the success of public spaces. Local markets are developed in an organic manner, which adds on to the people experience and perception of urban space. With the city development, the commercial needs within the city increase, hence marketplaces flourish, irrespective of the functional segregation within. The work-live culture in the marketplaces diminishes as the commercial expansion washes away the residential patches within it. Real estate flourishes as the newer infills are without considering the carrying capacity of the place. Chalai market is a prominent business center serving the regional level of Thiruvananthapuram city. The transformation trend of marketplaces in city cores are understood from case study on Fatimid Cairo Marketplace. The parameters that led to transformation of marketplaces in a global context is considered for the analysis of the Chalai market. The structure of the marketplace over the years is analyzed in terms of transformation in location, transformation in the land- use, change in commodity, and transformation in movement and activity. The aim of the research is to emphasize the need to understand the transformation trend, in creating a suitable development pattern for the city. The unregulated transformation within the city core has led to tremendous transformation in the user group and user pattern and eventually to the commercial trend. With the change in lifestyle and need for new amenities have led to addition of new infills leading to the degradation of the native commerce. Hence addressing the transformation of marketplaces are crucial to maintaining the locational significance and cultural importance and heritage of the place.

Keywords: bazaar, market centers, marketplaces, traditional city, traditional market, urban fabric

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Authors: Siddhant Sahu, P. James Daniel Paul

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Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.

Keywords: technical analysis, expert system, law of demand, stocks, portfolio analysis, Indian automotive sector

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Authors: Anwar Hussain, Ahmed Imran, Farida Faisal, Fatima Sultana

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The present research evaluates a comparative assessment of dividend policy and share price across the South Asian countries including Pakistan, India and Sri-Lanka over the period of 2010 to 2014. Academic writers found that dividend policy and share price relationship is not same in south Asian market due to different reasons. Moreover, Panel Models used = for the evaluation of current study. In addition, Redundant fixed effect Likelihood and Hausman test used for determine of Common, Fixed and Random effect model. Therefore Indian market dividend policies play a fundamental role and significant impact on Market Share Prices. Although, present research found that different as compared to previous study that dividend policy have no impact on share price in Sri-Lanka and Pakistan.

Keywords: dividend policy, share price, South Asian countries, panel data analysis, theories and parameters of dividend

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Tamar Lazariashvili

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Introduction: The agro-insurance has great importance for agricultural development in the country. In the article, the insurance market of the Georgian agricultural sector has been studied, the level of interest of farmers with insurance products and the trend of demand for those products are revealed; also, the importance of insurance is substantiated. Methodology: The following research methods are applied in the presented paper: statistical (selection, grouping, observation, trend) and qualitative research (in-depth interview with farmers). They claim that the main reason for aggravation is the low level of trust, less awareness about the conditions of the insurance contract. In order to eradicate distrust towards agro-insurance, it is recommended to increase awareness of insured farmers in terms of an insurance agreement. In the case of disputable issues between insurance companies and the customers (farmers), it is advisable to enact the Mediation Service, which will be able to protect the rights of insured farmers. Main Findings: Insurance companies prefer to deal with large farmers, the number of them is very small in Georgia as the credit market. The government interference in this sector is also a very cautious topic. However, the government can strengthen the awareness of farmers about the characteristics and advantages of the insurance system in order to increase the number of insured and reduce insurance premiums for farmers. Conclusion: Enactment of agro-insurance will increase the interest and confidence of financial institutions in the farming sector, financial resources will be accessible to the farmers that will facilitate the stable development of the sector in the country. The size of the agro-insurance market in the country should be increased, and the new territories should be covered. The State must have an obligation to ensure the risk of farmers and subsidize insurance companies. Based on the analysis of the insurance market, the conclusions on agro-insurance issues and the relevant recommendations are proposed.

Keywords: Agro-insurance, agricultural product, Agro-market, farming

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Authors: Ida Madieha Abdul Ghani Azmi, Heng Gee Lim, Adlan Abdul Razak, Nasaruddin Abdul Rahman

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The objective of competition law is to maximise consumer welfare through the regulation of anti-competitive behaviour that results in the distortion of the market. Intellectual property law also seeks to enhance consumer welfare in the long run by encouraging the development of useful devices and processes. Nevertheless, in some circumstances, the IP owners behave in such a way that makes it difficult for rival companies to sell substitute products and technology in the market. Intellectual property owners may also reach a dominant position in the market such that they are able to dictate unfair terms and conditions on other market players. Among the two major categories of anti-competitive behavior is the use of horizontal and vertical agreement to constrain effective competition and abuse of dominant position. As a result, many countries have regulated the conduct of the IP owners that are considered as anti-competitive including the US, Canada, and Singapore. This paper visits the proposed IP Guidelines recently drafted by the Malaysian Competition Commission and investigates to what extent it resolves most of the anti-competitive behavior of the IP owners. The paper concludes by suggesting some of the rules that could be prescribed by the Competition Commission in order to maintain the relevancy of competition law as the main check against the abuse of rights by the intellectual property owners.

Keywords: abuse of dominant position, consumer welfare, intellectual property rights, vertical and horizontal agreements

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Authors: Pongchan Na-Lampang

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The objective of this study was to compare the results of transport of slaughter pigs to slaughterhouse by 2 methods, i.e. individual confined and group confined on the truck on meat quality. The pigs were transported for 1 h on a distance of 70 km. The stocking densities were 0.35 m2/pig and 0.48 m2 for group and individual crate treatment, respectively. It was found that meat quality of pigs transported by 2 different methods as measured in terms of pH level (at 45 min and 48 hr post mortem), color (brightness, redness and yellowness) and water holding capacity was not significantly different.

Keywords: market pig, transportation, meat quality, confinement

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Sultan Samah A Almjlad

Abstract:

The translation industry in Saudi Arabia is experiencing significant shifts driven by Vision 2030, which aims to diversify the economy and enhance international engagement. This change highlights the need for translators who are skilled in various languages and cultures, playing a crucial role in the nation's global integration efforts. However, there's a notable gap between the skills taught in academic institutions and what the job market demands. Many translation programs in Saudi universities don't align well with industry needs, resulting in graduates who may not meet employer expectations. To tackle this challenge, it's essential to thoroughly analyze the market to identify the key skills required, especially in sectors like legal, medical, technical, and audiovisual translation. At the same time, existing translation programs need to be evaluated to see if they cover necessary topics and provide practical training. Involving stakeholders such as translation agencies, professionals, and students is crucial to gather diverse perspectives. Identifying discrepancies between academic offerings and market demands will guide the development of targeted strategies. These strategies may include enriching curricula with industry-specific content, integrating emerging technologies like machine translation and CAT tools, and establishing partnerships with industry players to offer practical training opportunities and internships. Industry-led workshops and seminars can provide students with valuable insights, and certification programs can validate their skills. By aligning academic programs with industry needs, Saudi Arabia can build a skilled workforce of translators, supporting its economic diversification goals under Vision 2030. This alignment benefits both students and the industry, contributing to the growth of the translation sector and the overall development of the country.

Keywords: translation industry, briging gap, labor market, requirements

Procedia PDF Downloads 37

Authors: Danilo López, Johana Hernández, Edwin Rivas

Abstract:

The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

Procedia PDF Downloads 371

Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 257

Authors: Pascal François Faye, Baye Mor Sall, Bineta Dembele, Jeanne Ana Awa Faye

Abstract:

In agriculture, estimating crop yields is key to improving productivity and decision-making processes such as financial market forecasting and addressing food security issues. The main objective of this paper is to have tools to predict and improve the accuracy of crop yield forecasts using machine learning (ML) algorithms such as CART , KNN and SVM . We developed a mobile app and a web app that uses these algorithms for practical use by farmers. The tests show that our system (collection and deployment architecture, web application and mobile application) is operational and validates empirical knowledge on agro-climatic parameters in addition to proactive decision-making support. The experimental results obtained on the agricultural data, the performance of the ML algorithms are compared using cross-validation in order to identify the most effective ones following the agricultural data. The proposed applications demonstrate that the proposed approach is effective in predicting crop yields and provides timely and accurate responses to farmers for decision support.

Keywords: prediction, machine learning, artificial intelligence, digital agriculture

Procedia PDF Downloads 80