Dr. Omid M. Ardakani, Associate ProfessorParker College of Business, Georgia Southern University, USA
Speech Title: The Impact of Marketing Time on Housing Prices: A Control Function Approach
Abstract: Hedonic modeling can be used to examine the impacts of housing characteristics on selling prices. This paper estimates a hedonic price function for single-family houses in Savannah, GA, for the period 2007–2016. Digressing from conventional approaches of modeling a reduced-form hedonic price function, we estimate a structural function whereby the house sale price is directly affected by the usual house attributes and marketing time. Both the home sale price and time on the market, however, are endogenously determined. To account for endogeneity, we estimate the structural hedonic function using a control function approach. The control-function estimator utilizes conditional heteroskedasticity of structural errors in the triangular model. Using this approach, we identify the relationship between the house price and its time on the market solely based on nonlinearities in the control function without looking for excludable instrumental variables for the latter endogenous variable. Our findings suggest that housing prices increase with marketing time.
Keywords: Control function, endogeneity, hedonic pricing, marketing time
Dr. Laura Piedra-Muñoz, Associate ProfessorDepartment of Economics and Business, Faculty of Economics and Business Sciences, University of Almería, Spain
Speech Title: Sustainability Management of Agri-Food Smallholders with Mobile Applications
Abstract: Sustainable Assessment Tools (SATs) are generally designed for medium and large enterprises with structured and available information, while small producers are generally excluded from the evaluation process. The present work fills this gap by analysing how new technologies, such as SATs developed using mobile applications (apps), can promote the sustainability management of small agri-producers in Ecuador. In this regard, the SAFA (Sustainability Assessment of Food and Agriculture) App is the first SAT specifically designed to evaluate sustainability for small and micro-producers. To operationalise the process, it implements a one hundred-item questionnaire. To answer the questions, the interviewee does not need to review documents. Considering that interviews may be held in areas with no internet service, the answers are processed using an offline mobile application that registers the data immediately. The results show that the good governance is the dimension that achieves the best result and associations are key drivers for the development of sustainable practices. Additionally, this study highlights that SAFA App is useful in catching the specific features of small producers. However, this SAT should be improved in terms of its versatility and the depth of its analysis in order to be taken as a benchmark for sustainability policies.
Keywords: small producers, sustainable development, natural resource management systems, SAFA App, agriculture, rural.
Dr. Zhihan Lv, Associate ProfessorSchool of Data Science and Software Engineering, Qingdao University, China
Speech Title: An Optimized Byzantine Fault Tolerance Algorithm for Consortium Blockchain
Abstract: According to different application scenarios of blockchain system, it is generally divided into public chain, private chain and consortium chain. Consortium chain is a typical multi-center blockchain, because it has better landing, it is supported by more and more enterprises and governments. This paper analyzes the advantages and problems of Practical Byzantine Fault Tolerance (PBFT) algorithm for the application scenarios of the consortium chain. In order to be more suitable for consortium chains, this paper proposes a new optimized consensus algorithm based on PBFT. Aiming at the shortcomings of PBFT, such as the inability to dynamically join nodes, low multi-node consensus efficiency, and primary master node selection, our optimized algorithm has designed a hierarchical structure to increase scalability and improve consensus efficiency. The simulation results show that compared with PBFT and RAFT, our new consensus algorithm increases the data throughput while supporting more nodes, and effectively reducing the consensus delay and the number of communication times between nodes.
Keywords: Blockchain, PBFT, Consensus Algorithm, Consortium Blockchain
Dr. Prem Kumar Singh, Associate ProfessorDepartment of Computer Science and Engineering, Gandhi Institute of Technology and Management, Vishakhaptanm-Andhra Pradesh, India
Speech Title: Knowledge Processing from the given Unstructured Data Set with its Graphical Visualization
Abstract: Recently, data analysis and its application have given a chance for various researchers to utilize it for decision making process. In this process, most of the researchers addressed the issue of data analysis, its representation as well as graphical structure visualization. Most of the time spent on understanding and categorization of the data in form of static, dynamic, complete, incomplete or uncertain due to its large veracity. Some time it may happen that the given data set is unstructured or semi-structured. Due to that, a problem arises in precise representation of these data and finding some useful information for knowledge processing tasks. Another problem arises with time complexity evaluation of the given research problem as basic. This talk will be focused on handling large and static data set for knowledge processing tasks. The glimpses will be given on unstructured data representation, its pre-processing and its graphical visualization using one of the algorithms. The step by step demonstration will be shown with an illustrative example. The analysis derived from the given data set is also discussed for decision making process. The comparative study of the obtained results will be also discussed. This talk will be helpful for those scholars who work in data analysis, data visualization, and knowledge processing tasks, decision making or other areas. In addition some useful information will be given for further extension of the research activities.
Keywords: Data Visualization, Knowledge processing data, Static Data, Decision Making
Dr. Ashraf DewanSpatial Sciences Discipline, Curtin University, Australia
Speech Title: Mapping Cloud-to-ground Lightning with Big Data
Abstract: Bangladesh is one of the most lightning-prone countries of the world. A clear understanding of lightning incidence of the country will be useful given perceived increase of lightning-related deaths in recent years. In this work, spatiotemporal distribution of lightning activity over Bangladesh for a six-year period (2015-2020) was examined by utilizing Global Lightning Dataset (popularly called, GLD360). Annual, monthly and diurnal stroke frequency was analysed. More than fifty million (50,481,181) lightning flashes were recorded in the Bangladesh territory during 2015-2020. Analysis of this big data showed that number of lightning was highest (10,144,601) in 2016. Thunder events occur more in the hot summer months than in the monsoon season as around three-fifth of the total lightning activity occurred in summer season (March-May) as opposed to monsoon (June-September) when lightning activity was 35.6%. Lightning events are more common in the late night and early morning. The ~50 million data points were aggregated to 1,962 square grids, utilising a 10 × 10 km grid size, to depict spatial patterning. Results revealed that north-western Sylhet region experienced extreme lightning and southern Bangladesh had low lightning activity. Sylhet, Sunamganj, and Manikganj districts had stroke density of 60 or higher. In Bandarban, Rangamati, Cox's Bazaar, Meherpur, Chuadanga, Satkhira, and Bagerhat, the density was fewer than 30. It is believed that the findings of this study would help to raise awareness about lightning and avoid casualties.
Keywords: GLD360, lightning activity, stroke density, Bangladesh
Nuria Recuero VirtoAssistant Professor, Management & Marketing Department, Faculty of Commerce and Tourism, Universidad Complutense de Madrid
Speech Title: Education metamorphosis: Rewiring the use of learning analytics
Abstract: Lockdown measures due to the pandemic have caused unprecedented disruption to the worldwide education system. In view of COVID-19 spread, many educational institutions have been determining what procedures to adopt so as to fulfill sanitary protocol, which has meant on many occasions the limit of face-to-face contacts. The social media role has gained popularity during these breakouts, not just because of its significance as an entertainment tool but rather due to its socializing function. Twitter has been identified as the most popular microblog platform and a reliable source for examining and studying society's behavior. It is yet unknown how the use of User-Generated Content (UGC) will impact the design and performance of future educational programs. The main goal of this research is to explore the future of data-driven decision-making, based on UGC and the students’ performance presented in their learning analytics.