Use of a Blockchain-Based Marketplace to Incentivize Participation in Virtual Clinical Trials

There is an emerging need for advancements in how clinical trials are conducted in the current pandemic situation. Healthcare institutions are moving towards using digital technologies to avoid physical interactions between doctors and clinical trial participants. However, difficulties in recruiting and retaining participants are still prevalent. To overcome this issue, an incentive system that can be trusted by doctors as well as trial participants is required. The authors present a detailed report of Cashish, a blockchain-based incentivization system that rewards trial participants in the form of cryptocurrency tokens that they can utilize in an online marketplace that is also backed by the same blockchain. Usage of blockchain technology to provide research participation incentives eliminates the need for trust systems and ensures transparency between doctors and clinical trial participants, while ensuring participant anonymity.

An Action Research on a French Law Firm, Solutions for a law firm with IT post-adoption

This study qualitatively analyzes interactional coping strategies used to managed technostress in the post-adoption stage of information technology implementation at a French legal firm. The nine strategies are participatory, collaborative, conflict resolution, bureaucratic coping (adaptation), perceived contribution to exchange, loyalty, affect, professional respect, and mutual trust. A critical perspective was applied to a longitudinal study of the intervention process. First, a problem with task distribution at the individual level affected the efficacy of perceived contribution strategies to exchange and professional enhancement. Second, a lack of creative interaction between group level colleagues undermined strategies of collaboration, mutual aid and assistance, participatory adaptation, and mutual trust. Third, a lack of digitalization commitment at the organizational level negatively affected conflict resolution and bureaucratic adjustment. Finally, an absence of loyalty and affect strategies was evident.

Optimal Kernel Selection Based on GPR for Adaptive Learning of Mean Throughput Rates in LTE Networks

Machine learning models and algorithms have been employed in various applications, from prognostic scrutinizing, learning and revealing patterns in data, knowledge extracting, and knowledge deducing. One promising computationally efficient and adaptive machine learning method is the Gaussian Process Regression (GPR). An essential ingredient for tuning the GPR performance is the kernel (covariance) function. The GPR models have been widely employed in diverse regression and functional approximation purposes. However, knowing the right GPR training to examine the impacts of the kernel functions on performance during implementation remains. In order to address this problem, a stepwise approach for optimal kernel selection is presented for adaptive optimal prognostic regression learning of throughput data acquired over 4G LTE networks. The resultant learning accuracy was statistically quantified using four evaluation indexes. Results indicate that the GPR training with the mertern52 kernel function achieved the best user throughput data learning among the ten contending Kernel functions.

An Empirical Study of Artificial Participants

This study is an investigation of artificial participants operating in their appropriate environment. The artificial participants in this study are artificial chess players and their appropriate environment is a chess game. This study is an empirical investigation testing the hypothesis that the length of a chess game is inversely related to the difference in the chess skill levels of the artificial chess players. Five series of chess games of 18 games in each series were instituted between five pairings of web-based chess engines. The chess engine, chess.com Level 10, was a player in each series and won all 18 games in each series. The opposing players came from the Play Magnus app at five different levels of chess skill. This study provided an investigation of 90 chess games involving artificial chess players. The hypothesis for the study was confirmed. Game length was significantly inversely related to the disparity in chess proficiency between artificial chess players. This is one of the first scientific studies of artificial participants.

An Empirical Study of Artificial Participants - A Factor Affecting Game Length in Chess

This study is an investigation of artificial participants operating in their appropriate environment. The artificial participants in this study are artificial chess players and their appropriate environment is a chess game. This study is an empirical investigation testing the hypothesis that the length of a chess game is inversely related to the difference in the chess skill levels of the artificial chess players. Five series of chess games of 18 games in each series were instituted between five pairings of web-based chess engines. The chess engine, chess.com Level 10, was a player in each series and won all 18 games in each series. The opposing players came from the Play Magnus app at five different levels of chess skill. This study provided an investigation of 90 chess games involving artificial chess players. The hypothesis for the study was confirmed. Game length was significantly inversely related to the disparity in chess proficiency between artificial chess players. This is one of the first scientific studies of artificial participants.

The Value of Economic Information in the Digital Society

In the contemporary organizational context, the sharing and transfer of information and knowledge plays a significant role. What can be facilitated with the emerging interdisciplinary approach, which crosses the fieldsof InformationSciences, Business Sciences and other sciences. The problem is that, in addition to being a new construct and still little studied, conceptual divergences are fed by the amplitude of the possible dimensions of analysis. In this context, the objective of this study arises in identifying the conceptualization of the construct of the value of economic information proposed in the scientific literature to support its better understanding and prospect of future investigations. A theoretical research was carried out through a systematic literature review, followed by an analysis of the most relevant publications on the subject. The discussion on the relevant construct is considered in the context of contemporary organizations, however, it signals the importance of future studies of empirical and theoretical order, to foster discussions, on the subject today.

Dynamic Impact of Government Expenditure and Debt Policy Instruments on Agricultural Growth in Nigeria

This research examined the impact of government expenditure and debt policy instruments on agricultural growth in Nigeria for the period 1980-2018. Findings revealed that, for every billion rise in aggregate government expenditure, agricultural growth significantly increased by 1.66%. Additionally, per capital income and inflation were significant determinants of agricultural growth. However, the response of agricultural growth to increased debt was inelastic with a coefficient of -0.3152. Thus, macroeconomic policy instruments dynamics impacted agricultural growth. It recommended increased government expenditure to agricultural sector, education, investing in human capital development through budgetary allocations and intervention funds for increased growth while policy makers should desist from increasing the debt profile as it gave less than proportionate effect on agricultural growth with negative consequences on the Nigerian economy.

Spark Performance Optimization Analysis With Multi-Layer Parameter Using Shuffling and Scheduling With Data Serialization in Different Data Caching Options

As social networking services and e-commerce are growing rapidly, the number of online users also dynamically growing that facilitate contribution of huge contents to digital world. In such dynamic environment, meeting the demand of computing is very challenging special with existing computing model. Although Spark is recently introduced to alleviate the problems with concept of in-memory computing for big data analytic with many parameters configuration that allow to configure and improve its performance, still it has performance bottleneck which require to investigate performance improvement mechanism by focus on the combinations of Scheduling and Shuffle Manager with data serialization with intermediate data caching options. Standalone cluster computing model was selected as experimental methodology with submit command line for data submission. Three Spark application such as WorkCount, TeraSort and PageRank were selected and developed for experiment. As a result, 2.45% and 8.01% performance improvement are achieved in OFFHEAP and Memory Only Ser data caching option, respectively.

The Evolution of Women Entrepreneurs

Purpose. The chapter is in illusion of the utility of neuroeconomics in decision making and behavior. Scientific knowledge will be advanced in the need for the application of neuroeconomics focused on one of the services of the information and communication technologies (ICT) of companies that is e-commerce of exponential artisanal SMEs of women entrepreneurs, by developing a proposal for a business model to increase the possibility of growth of their companies at the level national and international level. Methodological approach. The methodology used was deductive, exploratory, descriptive, correlational and documentary. Findings. Neuroeconomics have the potential to explain the phenomena that are considered as a deviation from the prediction or behavioral bias of decision-making models in economic theory. Practical implications. The study up to this point is quantitative using primary and secondary sources for research. Keywords. Neuroeconomics, decision making, behavioral economics, e-commerce.

An Exploratory Study on the Effect of Artificial Intelligence-Enabled Technology on Customer Experiences in the Banking Sector

This study investigates how artificial intelligent technology in the banking sector has affected consumers’ overall experience. It focuses on how consumers’ personal digital transformation has affected digital banking development and how this further affects consumer’s expectations and experience. It assesses how banks use Artificial Intelligent Virtual Agents such as Chatbots to transform how consumers use their banking facilities. Lastly, this study investigates the scope of neobanks in the banking sector. The study found that digital transformations have led to an increase in consumers’ expectations from their banks. Whilst banks revolutionise their customer service offerings through virtual agents, customers are not engaging with these at an expected rate. Findings revealed that Neobanks are not operating at their expected traction due to consumer knowledge gaps, occasioned by a lack of advertised information to customers from their banks.