scholarly journals Optimizing the Abandonment of a Technological Innovation

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 27
Author(s):  
Albert Joseph Parvin ◽  
Mario G. Beruvides
Keyword(s):  
Technological Innovation ◽  
Exploratory Data Analysis ◽  
Diffusion Rate ◽  
Primary Objective ◽  
Decision Makers ◽  
Stochastic Simulations ◽  
Macro Level ◽  
Assessment Framework ◽  
Exploratory Data ◽  
The Impact

The primary objective of this study is to reveal macro-level knowledge to aid the optimization, evaluation, and strategic planning of technological innovation abandonment. This research uses an exploratory data analysis (EDA) approach to extract directional and associative patterns (macro-level knowledge) to assess technological innovation abandonment optimization. Deterministic and stochastic simulations are employed to reveal the impact of three factors on abandonment optimization, namely, a technological innovation’s diffusion rate, a technological innovation’s probability of achieving a given diffusion rate, and the point of abandonment. The patterns and insights revealed through the graphical examination of the simulation provide associative and directional knowledge to assess the abandonment optimization of technological innovation. These revealed patterns and insights enable decision-makers to develop an abandonment assessment framework for optimizing, evaluating, and proactively planning abandonment at the macro level.

scholarly journals Macro Patterns and Trends of U.S. Consumer Technological Innovation Diffusion Rates

Systems ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Albert Joseph Parvin ◽  
Mario G. Beruvides
Keyword(s):  
Innovation Diffusion ◽  
Technological Innovation ◽  
Research Effort ◽  
Decision Makers ◽  
Macro Level ◽  
Technological Innovations ◽  
Exploratory Data ◽  
Diffusion Rates ◽  
Technological Innovation Diffusion ◽  
Aid Decision

Macro-level trends and patterns are commonly used in business, science, finance, and engineering to provide insights and estimates to assist decision-makers. In this research effort, macro-level trends and patterns were explored on the diffusion rates of technological innovations, a component of a sorely under-studied question in technology assessment: When should a technological innovation be abandoned? A quantitative exploratory data analysis (EDA)-based approach was employed to examine diffusion market data of 42 U.S. consumer technological innovations from the early 1900s to the 2010s to extract general macro-level knowledge on technological innovation diffusion rates. A goal of this effort is to grow diffusion rate knowledge to enable the development of general macro-based forecasting tools. Such tools would aid decision-makers in making informed and proactive decisions on when to abandon a technological innovation. This research offers several significant contributions to the macro-level understanding of the boundaries and likelihood of achieving a range of technological innovation diffusion rates. These contributions include the determination that the frequency of diffusion rates are positively skewed when ordered from slowest to fastest, and the identification and ranking of probability density functions that best represent the rates of technological innovation diffusion.

Assessing impacts of implementing low-carbon tourism program for sustainable tourism in a world heritage city

2019 ◽  
Vol 74 (2) ◽  
pp. 216-234 ◽  
Author(s):  
Machima Thongdejsri ◽  
Vilas Nitivattananon
Keyword(s):  
Impact Assessment ◽  
Carbon Emission ◽  
Sustainable Tourism ◽  
World Heritage ◽  
Decision Makers ◽  
Assessment Process ◽  
Low Carbon ◽  
Assessment Framework ◽  
Content Type ◽  
The Impact

Purpose This study aims to illustrate the impact-assessment procedure of low-carbon tourism (LCT) program implemented in a world heritage city and to develop specific indicators toward sustainability. Design/methodology/approach The impact-assessment framework was indicator-based and designed for creating sustainable tourism (ST) in a case study. A set of indicators in various dimensions was developed and applied, referring to the UNWTO guideline. A mixed method of primary and secondary data collected from different sources included document review, site observation, key informant interview, questionnaire survey and focus-group discussions. Assessment of actual/observed impacts was proceeded based on the data collected from tourists and stakeholders, especially on tourist behaviors and resource consumptions. Findings The implementation of LCT program in a world heritage city provided impacts in different dimensions and characters. The observed activities were majorly tourism activities in accommodations and recreational places. The indicator initiation is the first development toward sustainability in a case of tourism study in a city destination. Indicators were developed with participation from key stakeholders and covered sustainability and carbon-emission dimensions. Impact-assessment results show a positive theme in less carbon emission, enhanced local income distribution and community capacity. However, the negative impacts include increased amounts of resource consumption and waste generation in visiting sites. The impact matrix works as the map for decision-makers to maximize benefits and manage the cons of the LCT program toward ST principles. Research limitations/implications Research methodology, procedure and results on impact assessment with holistic perspectives imply academic contribution and practical benefits for decision-makers regarding ST development. The number of samples and enterprises was limited because of the program implementation period. Originality/value The research illustrates the impact-assessment process for an implemented city-based LCT program toward ST, where stakeholder participation was also functioning. A list of indicators was specially designed and can be practically applied for other LCT programs in city destinations. Applying a sustainability impact-assessment framework to the program can provide a clear presentation on how to develop ST.

Use of Objective Safety Evidence to Deploy Automated Enforcement Resources

2019 ◽  
Vol 2673 (10) ◽  
pp. 726-735
Author(s):  
Shewkar Ibrahim ◽  
Tarek Sayed
Keyword(s):  
Traffic Safety ◽  
Strong Evidence ◽  
Decision Makers ◽  
Macro Level ◽  
Speed Limit ◽  
Deployment Strategy ◽  
Micro Level ◽  
A Site ◽  
The Impact ◽  
Road Segments

Automated enforcement programs have been an important tool for improving traffic safety. Previous work provides strong evidence supporting the impact that these programs have on increasing safety either on a micro-level (e.g., road segments), or at a macro-level (e.g., neighborhood, city). In both cases, there are many variables that can influence and affect the safety impacts of the enforcement program. Additionally, there is a lack of understanding of how specific deployment parameters (e.g., how often to visit an enforcement site) can influence the overall safety on a macro-level (e.g., traffic analysis zone). The objective of this study is to quantify the impact that automated enforcement has on collisions on a macro-level as well as to develop models that would provide enforcement authorities with an empirical tool to help plan their deployment strategy. The results show that an increase in the number of tickets issued for exceeding the speed limit resulted in a decrease in collisions, for all collision severities. Moreover, the results also showed that collision reductions were also associated with spending a longer time enforcing a site for each visit. Quantifying these safety impacts supports decision makers by providing them with an opportunity to analyze the safety benefits in relation to their deployment strategy to maximize the efficiency of their resources.

Effects of fiscal decentralization on carbon emissions in China

2020 ◽  
Vol 14 (1) ◽  
pp. 213-228 ◽  
Author(s):  
Yuanhua Yang ◽  
Dengli Tang ◽  
Peng Zhang
Keyword(s):  
Data Analysis ◽  
Carbon Emissions ◽  
Local Governments ◽  
Exploratory Data Analysis ◽  
Fiscal Decentralization ◽  
Free Riding ◽  
Emissions Control ◽  
Content Type ◽  
Exploratory Data ◽  
The Impact

Purpose Fiscal fund is the key support of carbon emissions control for local governments. This paper aims to analyze the impact of fiscal decentralization on carbon emissions by spatial Durbin model (SDM), and verify the existence of “free-riding” phenomenon to reveal the behavior of local governments in carbon emissions control. Design/methodology/approach Based on the provincial data of carbon emissions from 2005 to 2016 in China, this paper uses spatial exploratory data analysis technology to analyze the spatial correlation characteristics and constructs SDM to test the impact of fiscal decentralization on carbon emissions. Findings The results show that carbon emissions exhibits significant spatial autocorrelation in China, and the increasing of fiscal decentralization in the region will increase carbon emissions in surrounding areas and on the whole. Then, by comparing the impact of fiscal decentralization on carbon emissions and industrial solid waste, it is found that “free-riding” phenomenon of carbon emissions control exists in China. Practical implications Based on the spatial cluster characteristics of China’s provincial carbon emissions, carbon emissions control regions can be divided into regions and different carbon emission control policies can be formulated for different cluster regions. Carbon emissions indicators should be included in the government performance appraisal policy, and carbon emissions producer survey should be increased in environmental policies to avoid “free-riding” behaviors of local government in carbon emissions control in China. Originality/value This paper contributes to fill this gap and fully considers the spatial spillover characteristics of carbon emissions by introducing spatial exploratory data analysis technology, constructs SDM to test the impact of fiscal decentralization on carbon emissions in the perspective of space econometrics, and tests the existence of “free-riding” phenomenon in carbon emissions control for local governments in China.

scholarly journals Drug Disease Association Prediction Using NLP & Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1004-1012
Author(s):  
Suchith Reddy Vemula
Keyword(s):  
Machine Learning ◽  
Exploratory Data Analysis ◽  
Medication Administration ◽  
Disease Association ◽  
The Internet ◽  
Product Reviews ◽  
Pharmaceutical Firms ◽  
The Us ◽  
Exploratory Data ◽  
The Impact

Abstract: The relevance of analysing user-generated data on the internet has lately increased owing to the vast amount of information that can be obtained via proper study of such data. The majority of this information can be found on social media sites like Facebook, Twitter, and LinkedIn. Opinions and reviews on goods, movies, prescriptions, hotels, and other items are among the data accessible on such platforms. Companies are increasingly relying on data mining and analysis to get a better understanding of public opinion on a certain topic. There has been sufficient study on the use of sentiment analysis in many areas such as product reviews, movies, hotels, and so on. However, in the field of medicine, such techniques must be given more weight, since the US Food and Medication Administration has done multiple research on the consequences of adverse drug responses on patients. Pharmaceutical firms must study the impact of regularly used pharma products on patients in order to understand the good and bad impacts of medications on patients. The goal of this study is to use ML models to analyse the review of patient evaluations in order to evaluate if the opinions represented in the reviews are positive (or) negative. Keywords: Data Pre-Processing, Exploratory Data Analysis, Feature Extraction, Sentimental Classification, ML Algorithms, Prediction, Visualisation .

scholarly journals An Exploratory Data Analysis of Covid-19 Outbreak Situation in India

2021 ◽  
Vol 9 (VII) ◽  
pp. 360-364
Author(s):  
Arpit Verma
Keyword(s):  
Data Analysis ◽  
Statistical Model ◽  
Exploratory Data Analysis ◽  
Exploratory Data ◽  
The Impact ◽  
Outbreak Situation

The number of COVID-19 cases in India is increasing expeditiously. The National and native authorities are having a tough time to make a pattern, analyze and forecast the spread of COVID-19 in India. The main focus of this paper is to draw a statistical model for better understanding of COVID-19 spread in India by thoroughly studying the reported cases in the country till 14 March 2020. An Exploratory Data Analysis (EDA) technique is being implemented to review and analyze the reported COVID-19 cases in India. The results of the analysis divulge the impact of COVID-19 in India on daily and weekly manner, analysis of different states of India, analogize India with abutting countries also like the countries who are badly affected.

Severity Model Based Prediction of Early Trend and Pattern Recognition of the COVID-19 Infection in India: Exploratory Data Analysis and Machine Learning Study (Preprint)

2020 ◽  
Author(s):  
Afreen Khan ◽  
Swaleha Zubair
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Data Analysis ◽  
Inflection Point ◽  
Exploratory Data Analysis ◽  
Logistic Function ◽  
Acute Stage ◽  
The Future ◽  
Exploratory Data ◽  
The Impact

UNSTRUCTURED Objective: Recent Coronavirus Disease 2019 (COVID-19) pandemic has inflicted the whole world critically. Despite the fact that India has not been listed amongst the top ten highly affected countries, one cannot rule out COVID-19 associated complications in the near future. The accumulative testing facilities has resulted in exponential increase in COVID-19 infection cases. In figures, the number of positive cases have risen up to 33,614 as of 30 April, 2020. Keeping into consideration the serious consequences of pandemic, we aim to establish correlations between the numerous features which was acquired from the various Indian-based COVID datasets, and the impact of the containment of the pandemic on the current state of Indian population using machine learning approach. We aim to build the COVID-19 severity model employing logistic function which determines the inflection point and help in prediction of the future number of confirmed cases. Methods: An empirical study was performed on the COVID-19 patient status in India. We performed the study commencing from 30 January, 2020 to 30 April, 2020 for the analysis. We applied the machine learning (ML) approach to gain the insights about COVID-19 incidences in India. Several diverse exploratory data analysis ML tools and techniques were applied to establish a correlation amongst the various features. Also, the acute stage of the disease was mapped in order to build a robust model. Results: We collected five different datasets to execute the study. The data sets were integrated extract the essential details. We found that men were more prone to get infected of the coronavirus disease as compared to women. Also, the age group was the middle-young age of patients. On 92-days based analysis, we found a trending pattern of number of confirmed, recovered, deceased and active cases of COVID-19 in India. The as-developed growth model provided an inflection point of 85.0 days. It also predicted the number of confirmed cases as 48,958.0 in the future i.e. after 30th April. Growth rate of 13.06 percent was obtained. We achieved statistically significant correlations amongst growth rate and predicted COVID-19 confirmed cases. Conclusion: This study demonstrated the effective application of exploratory data analysis and machine learning in building a mathematical severity model for COVID-19 in India.

Sign in / Sign up

Export Citation Format

Share Document