Developing Cost-Effective Blockchain-Powered Applications

2021 ◽  
Vol 30 (3) ◽  
pp. 1-38
Author(s):  
Abdullah A. Zarir ◽  
Gustavo A. Oliva ◽  
Zhen M. (Jack) Jiang ◽  
Ahmed E. Hassan
Keyword(s):  
Prediction Models ◽  
Free Market ◽  
Cost Effective ◽  
Percentage Error ◽  
Future Research ◽  
Smart Contract ◽  
Gas System ◽  
Transaction Fees ◽  
Application Developers ◽  
Gas Price

Ethereum is a blockchain platform that hosts and executes smart contracts. Executing a function of a smart contract burns a certain amount of gas units (a.k.a., gas usage). The total gas usage depends on how much computing power is necessary to carry out the execution of the function. Ethereum follows a free-market policy for deciding the transaction fee for executing a transaction. More specifically, transaction issuers choose how much they are willing to pay for each unit of gas (a.k.a., gas price). The final transaction fee corresponds to the gas price times the gas usage. Miners process transactions to gain mining rewards, which come directly from these transaction fees. The flexibility and the inherent complexity of the gas system pose challenges to the development of blockchain-powered applications. Developers of blockchain-powered applications need to translate requests received in the frontend of their application into one or more smart contract transactions. Yet, it is unclear how developers should set the gas parameters of these transactions given that (i) miners are free to prioritize transactions whichever way they wish and (ii) the gas usage of a contract transaction is only known after the transaction is processed and included in a new block. In this article, we analyze the gas usage of Ethereum transactions that were processed between Oct. 2017 and Feb. 2019 (the Byzantium era). We discover that (i) most miners prioritize transactions based on their gas price only, (ii) 25% of the functions that received at least 10 transactions have an unstable gas usage (coefficient of variation = 19%), and (iii) a simple prediction model that operates on the recent gas usage of a function achieves an R-Squared of 0.76 and a median absolute percentage error of 3.3%. We conclude that (i) blockchain-powered application developers should be aware that transaction prioritization in Ethereum is frequently done based solely on the gas price of transactions (e.g., a higher transaction fee does not necessarily imply a higher transaction priority) and act accordingly and (ii) blockchain-powered application developers can leverage gas usage prediction models similar to ours to make more informed decisions to set the gas price of their transactions. Lastly, based on our findings, we list and discuss promising avenues for future research.

Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention

2019 ◽  
pp. 191-218 ◽  
Author(s):  
Konstantia Zarkogianni ◽  
Konstantina S. Nikita
Keyword(s):  
Early Diagnosis ◽  
Health Systems ◽  
Prediction Models ◽  
Diabetes Management ◽  
Cost Effective ◽  
Future Research ◽  
Personal Health ◽  
Medical Decision Support ◽  
Risk Assessment Models ◽  
Medical Decision Support Systems

This chapter aims at the presentation and comparative assessment of tools and methodologies used for the development of Personal Health Systems (PHSs) for diabetes management, early diagnosis and prevention. Medical decision support systems such as glucose prediction models, risk assessment models for long-term diabetes complications, models for early diagnosis of diabetes and closed-loop glucose controllers along with integrated systems for diabetes management are described. The outcomes of a wide range of research studies demonstrate the feasibility of providing safe, reliable and cost-effective solutions towards improving patients' quality of life through the application of PHSs. Specific limitations that prevent these systems from being fully adopted in clinical practice are highlighted, while challenges and future research directions are summarized.

Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention

2016 ◽  
pp. 465-492
Author(s):  
Konstantia Zarkogianni ◽  
Konstantina S. Nikita
Keyword(s):  
Early Diagnosis ◽  
Health Systems ◽  
Prediction Models ◽  
Diabetes Management ◽  
Cost Effective ◽  
Future Research ◽  
Personal Health ◽  
Medical Decision Support ◽  
Risk Assessment Models ◽  
Medical Decision Support Systems

This chapter aims at the presentation and comparative assessment of tools and methodologies used for the development of Personal Health Systems (PHSs) for diabetes management, early diagnosis and prevention. Medical decision support systems such as glucose prediction models, risk assessment models for long-term diabetes complications, models for early diagnosis of diabetes and closed-loop glucose controllers along with integrated systems for diabetes management are described. The outcomes of a wide range of research studies demonstrate the feasibility of providing safe, reliable and cost-effective solutions towards improving patients' quality of life through the application of PHSs. Specific limitations that prevent these systems from being fully adopted in clinical practice are highlighted, while challenges and future research directions are summarized.

Physical and Psychological Benefits of Written Emotional Expression

2015 ◽  
Vol 20 (4) ◽  
pp. 242-251 ◽  
Author(s):  
Éva Kállay
Keyword(s):  
Mental Health ◽  
Expressive Writing ◽  
Mental Health Problems ◽  
Cost Effective ◽  
Health Problems ◽  
Future Research ◽  
Psychological Benefits ◽  
Cost Effective Treatment ◽  
Meta Analyses

Abstract. The last several decades have witnessed a substantial increase in the number of individuals suffering from both diagnosable and subsyndromal mental health problems. Consequently, the development of cost-effective treatment methods, accessible to large populations suffering from different forms of mental health problems, became imperative. A very promising intervention is the method of expressive writing (EW), which may be used in both clinically diagnosable cases and subthreshold symptomatology. This method, in which people express their feelings and thoughts related to stressful situations in writing, has been found to improve participants’ long-term psychological, physiological, behavioral, and social functioning. Based on a thorough analysis and synthesis of the published literature (also including most recent meta-analyses), the present paper presents the expressive writing method, its short- and long-term, intra-and interpersonal effects, different situations and conditions in which it has been proven to be effective, the most important mechanisms implied in the process of recovery, advantages, disadvantages, and possible pitfalls of the method, as well as variants of the original technique and future research directions.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

Motivating Compliance: Firm Response to Mandatory Disclosure Policies

2018 ◽  
Vol 32 (2) ◽  
pp. 103-119
Author(s):  
Colleen M. Boland ◽  
Chris E. Hogan ◽  
Marilyn F. Johnson
Keyword(s):  
Conflict Of Interest ◽  
Low Cost ◽  
Cost Effective ◽  
Data Availability ◽  
Policy Adoption ◽  
Future Research ◽  
Regulated Firms ◽  
Firm Response ◽  
Disclosure Rules ◽  
The Impact

SYNOPSIS Mandatory existence disclosure rules require an organization to disclose a policy's existence, but not its content. We examine policy adoption frequencies in the year immediately after the IRS required mandatory existence disclosure by nonprofits of various governance policies. We also examine adoption frequencies in the year of the subsequent change from mandatory existence disclosure to a disclose-and-explain regime that required supplemental disclosures about the content and implementation of conflict of interest policies. Our results suggest that in areas where there is unclear regulatory authority, mandatory existence disclosure is an effective and low cost regulatory device for encouraging the adoption of policies desired by regulators, provided those policies are cost-effective for regulated firms to implement. In addition, we find that disclose-and-explain regulatory regimes provide stronger incentives for policy adoption than do mandatory existence disclosure regimes and also discourage “check the box” behavior. Future research should examine the impact of mandatory existence disclosure rules in the year that the regulation is implemented. Data Availability: Data are available from sources cited in the text.

scholarly journals Removal of heavy metal ions from wastewater: a comprehensive and critical review

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Naef A. A. Qasem ◽  
Ramy H. Mohammed ◽  
Dahiru U. Lawal
Keyword(s):  
Heavy Metal ◽  
Metal Ions ◽  
Large Volume ◽  
Heavy Metal Ions ◽  
Membrane Separation ◽  
Cost Effective ◽  
Operating Conditions ◽  
Future Research ◽  
Sludge Formation ◽  
Metals Removal

AbstractRemoval of heavy metal ions from wastewater is of prime importance for a clean environment and human health. Different reported methods were devoted to heavy metal ions removal from various wastewater sources. These methods could be classified into adsorption-, membrane-, chemical-, electric-, and photocatalytic-based treatments. This paper comprehensively and critically reviews and discusses these methods in terms of used agents/adsorbents, removal efficiency, operating conditions, and the pros and cons of each method. Besides, the key findings of the previous studies reported in the literature are summarized. Generally, it is noticed that most of the recent studies have focused on adsorption techniques. The major obstacles of the adsorption methods are the ability to remove different ion types concurrently, high retention time, and cycling stability of adsorbents. Even though the chemical and membrane methods are practical, the large-volume sludge formation and post-treatment requirements are vital issues that need to be solved for chemical techniques. Fouling and scaling inhibition could lead to further improvement in membrane separation. However, pre-treatment and periodic cleaning of membranes incur additional costs. Electrical-based methods were also reported to be efficient; however, industrial-scale separation is needed in addition to tackling the issue of large-volume sludge formation. Electric- and photocatalytic-based methods are still less mature. More attention should be drawn to using real wastewaters rather than synthetic ones when investigating heavy metals removal. Future research studies should focus on eco-friendly, cost-effective, and sustainable materials and methods.

Keynote

2021 ◽  
Vol 48 (4) ◽  
pp. 3-3
Author(s):  
Ingo Weber
Keyword(s):  
Cost Estimation ◽  
Estimation Method ◽  
Main Topic ◽  
System Throughput ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Wide Range ◽  
The Cost ◽  
Application Developers

Blockchain is a novel distributed ledger technology. Through its features and smart contract capabilities, a wide range of application areas opened up for blockchain-based innovation [5]. In order to analyse how concrete blockchain systems as well as blockchain applications are used, data must be extracted from these systems. Due to various complexities inherent in blockchain, the question how to interpret such data is non-trivial. Such interpretation should often be shared among parties, e.g., if they collaborate via a blockchain. To this end, we devised an approach codify the interpretation of blockchain data, to extract data from blockchains accordingly, and to output it in suitable formats [1, 2]. This work will be the main topic of the keynote. In addition, application developers and users of blockchain applications may want to estimate the cost of using or operating a blockchain application. In the keynote, I will also discuss our cost estimation method [3, 4]. This method was designed for the Ethereum blockchain platform, where cost also relates to transaction complexity, and therefore also to system throughput.

Cultivating Resilience During the COVID-19 Pandemic: A Socioecological Perspective

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Ning Zhang ◽  
Shujuan Yang ◽  
Peng Jia
Keyword(s):  
Cost Effective ◽  
Future Research ◽  
Annual Review ◽  
Publication Date ◽  
Physical And Mental Health ◽  
Pandemic Preparedness ◽  
Predicting Factors ◽  
Effective Interventions ◽  
Interdisciplinary Framework ◽  
The Individual

The coronavirus disease 2019 (COVID-19) pandemic poses wide-ranging impacts on the physical and mental health of people around the world, increasing attention from both researchers and practitioners on the topic of resilience. In this article, we review previous research on resilience from the past several decades, focusing on how to cultivate resilience during emerging situations such as the COVID-19 pandemic at the individual, organizational, community, and national levels from a socioecological perspective. Although previous research has greatly enriched our understanding of the conceptualization, predicting factors, processes, and consequences of resilience from a variety of disciplines and levels, future research is needed to gain a deeper and comprehensive understanding of resilience, including developing an integrative and interdisciplinary framework for cultivating resilience, developing an understanding of resilience from a life span perspective, and developing scalable and cost-effective interventions for enhancing resilience and improving pandemic preparedness. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 633-643
Author(s):  
Niccolo Pescetelli
Keyword(s):  
Prediction Models ◽  
Collective Intelligence ◽  
Machine Intelligence ◽  
Future Research ◽  
Human Errors ◽  
Wide Range ◽  
Hybrid Intelligence ◽  
Forecasting System ◽  
Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

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