Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.6.005
I N. Putra, Amarulla Octavian, A.K. Susilo, A. R. Prabowo
Emerging maritime cyber threats put Indonesia's marine technology-based systems at risk This study aims to determine the dimensions and analysis of risk assessment in maritime cyber security based on 3D models in the Indonesian sea area. A statistical descriptive qualitative method approach supported by the Analytical Hierarchy Process (AHP) and Technique for Order by Similarity to Ideal Solution (TOPSIS) methods were used in this study. Risk analysis in maritime cybersecurity has 3 (three) main criteria: Threat, Vulnerability, and consequence. Based on the results of 3D risk analysis, the six dimensions of MCS are identified as having a level of risk at Very Low and Low Risk. The highest risk value is obtained by the dimension of Cyber security-related company procedures (D2) (0.368) and the lowest risk value is Ship's systems readiness (D3) (0.048).
{"title":"A hybrid AHP-TOPSIS for risk analysis in maritime cybersecurity based on 3D models","authors":"I N. Putra, Amarulla Octavian, A.K. Susilo, A. R. Prabowo","doi":"10.5267/j.dsl.2023.6.005","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.6.005","url":null,"abstract":"Emerging maritime cyber threats put Indonesia's marine technology-based systems at risk This study aims to determine the dimensions and analysis of risk assessment in maritime cyber security based on 3D models in the Indonesian sea area. A statistical descriptive qualitative method approach supported by the Analytical Hierarchy Process (AHP) and Technique for Order by Similarity to Ideal Solution (TOPSIS) methods were used in this study. Risk analysis in maritime cybersecurity has 3 (three) main criteria: Threat, Vulnerability, and consequence. Based on the results of 3D risk analysis, the six dimensions of MCS are identified as having a level of risk at Very Low and Low Risk. The highest risk value is obtained by the dimension of Cyber security-related company procedures (D2) (0.368) and the lowest risk value is Ship's systems readiness (D3) (0.048).","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.1.006
I. Yulita, Firman Ardiansyah, Aulia Siska, I. Suryana
In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.
{"title":"Time series prediction of novel coronavirus COVID-19 data in west Java using Gaussian processes and least median squared linear regression","authors":"I. Yulita, Firman Ardiansyah, Aulia Siska, I. Suryana","doi":"10.5267/j.dsl.2023.1.006","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.1.006","url":null,"abstract":"In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"13 8","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72478695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2022.12.003
Rosita de los Ángeles Caisahuana Indigoyen, Sheylla Leydi Cuyutupac Osores, Stefany Andrea Curichimba Macedo, Ángel Narcizo Aquino Fernandez
It is not a secret that road accidents cause significant suffering. Those accidents can last from wounded to dead people, negatively impacting a country. A bunch of recent investigations tried to tie road accidents with the quality of roads. Therefore, in a country with a significant infrastructure gap, it is necessary to analyze the relationship between the different kinds of roads and the severity of car accidents. The current research examined such a relationship by employing the Multi logit regression. It was found that the significance of different car accidents will vary among the road types. Moreover, with the help of probability analysis, it was discovered that speeding, emergency services availability, and road security seemed to have a crucial impact on road accidents.
{"title":"The effect of road types on severe road accidents in Peru","authors":"Rosita de los Ángeles Caisahuana Indigoyen, Sheylla Leydi Cuyutupac Osores, Stefany Andrea Curichimba Macedo, Ángel Narcizo Aquino Fernandez","doi":"10.5267/j.dsl.2022.12.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.12.003","url":null,"abstract":"It is not a secret that road accidents cause significant suffering. Those accidents can last from wounded to dead people, negatively impacting a country. A bunch of recent investigations tried to tie road accidents with the quality of roads. Therefore, in a country with a significant infrastructure gap, it is necessary to analyze the relationship between the different kinds of roads and the severity of car accidents. The current research examined such a relationship by employing the Multi logit regression. It was found that the significance of different car accidents will vary among the road types. Moreover, with the help of probability analysis, it was discovered that speeding, emergency services availability, and road security seemed to have a crucial impact on road accidents.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"66 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86702756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.5.001
Arief Fadhillah, C. Sukmadilaga, I. Farida
This research conducted testing on the influence of business strategy, leadership style, and internal control system (IC) on implementation of Good Government Governance (GGG) and its implication on organizational performance of Social Security Administrator for Health (known as BPJS Kesehatan). Data analysis was performed using a descriptive method, assisted by a statistical tool Structural Equation Modeling (SEM)-Lisrel. The data was tabulated from distributed and returned questionnaires from 325 deputy offices, branch offices, and service offices. The results showed that business strategy, leadership style, and the effectiveness of IC influenced the implementation of Good Government Governance. The result also provides evidence that leadership style had a positive significant influence on performance. Conversely, the business strategy and effectiveness of IC did not have a positive significant influence on BPJS performance.
{"title":"The influence of business strategy, leadership style, and effectiveness of internal control system on implementation of good government governance and its implication on organizational performance","authors":"Arief Fadhillah, C. Sukmadilaga, I. Farida","doi":"10.5267/j.dsl.2023.5.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.5.001","url":null,"abstract":"This research conducted testing on the influence of business strategy, leadership style, and internal control system (IC) on implementation of Good Government Governance (GGG) and its implication on organizational performance of Social Security Administrator for Health (known as BPJS Kesehatan). Data analysis was performed using a descriptive method, assisted by a statistical tool Structural Equation Modeling (SEM)-Lisrel. The data was tabulated from distributed and returned questionnaires from 325 deputy offices, branch offices, and service offices. The results showed that business strategy, leadership style, and the effectiveness of IC influenced the implementation of Good Government Governance. The result also provides evidence that leadership style had a positive significant influence on performance. Conversely, the business strategy and effectiveness of IC did not have a positive significant influence on BPJS performance.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"104 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80678533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2022.12.005
I. Jaya, A. Chadidjah, Y. Andriyana, Gatot Riwi Setyanto, Enny Supartini, F. Kristiani
Traditionally, endemic diseases such as dengue, diarrhea, and tuberculosis are modeled separately, which leads to a limited understanding of current disease dynamics and an inaccurate evaluation of the parameters of the different models. In this study, we propose a joint spatiotemporal model to predict the risks of multiple endemic diseases and identify hotspots. The model includes spatial shared component random effects and a covariate for healthy behavior. The model was applied to the joint modeling of dengue, diarrhea, and tuberculosis in thirty districts in Bandung, Indonesia over a five-year period. Our findings show that the joint model was effective in understanding the characteristics of the diseases. One potential advantage of using shared component models is that they can identify diseases with spatial or temporal distribution patterns and consider shared risk factors that may be spatially correlated, such as climate. It is recommended to conduct exploratory analyses to determine the correlation between the risks of the diseases being studied and the reference disease before using this type of model.
{"title":"Multiple endemic disease risk modeling using a Bayesian spatiotemporal shared components model","authors":"I. Jaya, A. Chadidjah, Y. Andriyana, Gatot Riwi Setyanto, Enny Supartini, F. Kristiani","doi":"10.5267/j.dsl.2022.12.005","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.12.005","url":null,"abstract":"Traditionally, endemic diseases such as dengue, diarrhea, and tuberculosis are modeled separately, which leads to a limited understanding of current disease dynamics and an inaccurate evaluation of the parameters of the different models. In this study, we propose a joint spatiotemporal model to predict the risks of multiple endemic diseases and identify hotspots. The model includes spatial shared component random effects and a covariate for healthy behavior. The model was applied to the joint modeling of dengue, diarrhea, and tuberculosis in thirty districts in Bandung, Indonesia over a five-year period. Our findings show that the joint model was effective in understanding the characteristics of the diseases. One potential advantage of using shared component models is that they can identify diseases with spatial or temporal distribution patterns and consider shared risk factors that may be spatially correlated, such as climate. It is recommended to conduct exploratory analyses to determine the correlation between the risks of the diseases being studied and the reference disease before using this type of model.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"17 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81983563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.4.003
E. T. Tosida, R. Setiawan, Irma Anggraeni, Roni Jayawinangun, S. Sukono, Jumadil Saputra
The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model.
{"title":"Modeling of citizen science cluster in making decision for readiness towards bogor smart village: An application of fuzzy c-means algorithm","authors":"E. T. Tosida, R. Setiawan, Irma Anggraeni, Roni Jayawinangun, S. Sukono, Jumadil Saputra","doi":"10.5267/j.dsl.2023.4.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.4.003","url":null,"abstract":"The construction of smart villages has begun in many Indonesian villages, along with the advancement of technology and local economic growth. Villagers must participate in constructing the smart economy-smart village by becoming familiar with the characteristics of the village's inhabitants using the citizen science model. This study intends to categorize villagers so that researchers can assess and decide their level of readiness for a smart economy in an ecosystem based on a smart village. Clustering is required to find communities of residents who are ready based on their traits. Using fuzzy C-Means with a Davied Bouldin Index value of 0.129, the data were divided into 4 clusters. The most important variables were chosen using information from the test's 300 responders, and the Kaiser Mayer Olkin assumption of 0.975 was used to validate the results. Our paper provides new information on how smart village readiness is assessed by the citizen science cluster. It was decided to divide residents into four groups: those who are less prepared (24.33%), those who are somewhat prepared (29.33%), those who are ready ( 25.67%) %), those who are ready (level of participatory knowledge), and those who are very ready for the smart economy (20.67%) based on the cluster model.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82214884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.2.005
Yazen Oroud, Mohammad Almashaqbeh, Hamed Ahmad Almahadin, A. Hashem, Marwan Altarawneh
This study investigates how audit quality moderates the effect of financial performance indicators on the stock returns of Amman Stock Exchange-listed firms (ASE). The panel data analysis selected the data of 95 ASE-listed firms from 2013 through 2021. This analysis demonstrates a significant inverse relationship between a company's book value and its stock returns. A statistically negative relationship was observed between cash flow, dividends per share, and stock return. The empirical results of this study confirm the moderating influence of audit quality in the relationship between financial performance and stock return. Firstly, auditor's fees have a significant impact on the relationship between firm stock returns and EPS, BV, DPS, and cash flows (CFO). The size of the auditing firm moderates the relationship between company stock returns and EPS, DPS, and the CFO, but not with book value (BV). The auditor's opinion moderates the relationship between business stock returns and EPS, BV, and DPS but not the relationship between firm stock returns and cash flows (CFO). The study suggests that regulatory bodies like the Companies Control Department (CCD) and ASE should make sure that local audit firms in Jordan improve their audit quality to be on par with the Big 4 audit firms in order to improve their financial performance measures and stock returns.
{"title":"The effect of audit quality as a moderator on the relationship between financial performance indicators and the stock return","authors":"Yazen Oroud, Mohammad Almashaqbeh, Hamed Ahmad Almahadin, A. Hashem, Marwan Altarawneh","doi":"10.5267/j.dsl.2023.2.005","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.2.005","url":null,"abstract":"This study investigates how audit quality moderates the effect of financial performance indicators on the stock returns of Amman Stock Exchange-listed firms (ASE). The panel data analysis selected the data of 95 ASE-listed firms from 2013 through 2021. This analysis demonstrates a significant inverse relationship between a company's book value and its stock returns. A statistically negative relationship was observed between cash flow, dividends per share, and stock return. The empirical results of this study confirm the moderating influence of audit quality in the relationship between financial performance and stock return. Firstly, auditor's fees have a significant impact on the relationship between firm stock returns and EPS, BV, DPS, and cash flows (CFO). The size of the auditing firm moderates the relationship between company stock returns and EPS, DPS, and the CFO, but not with book value (BV). The auditor's opinion moderates the relationship between business stock returns and EPS, BV, and DPS but not the relationship between firm stock returns and cash flows (CFO). The study suggests that regulatory bodies like the Companies Control Department (CCD) and ASE should make sure that local audit firms in Jordan improve their audit quality to be on par with the Big 4 audit firms in order to improve their financial performance measures and stock returns.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"2006 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83024040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2022.10.004
A. Alkhateeb
Tracking abundant gene transcripts quantification over continuous cancer progression stages may reveal the mechanism of disease advancement. In this work, we profile the transcript quantification over the stages using a time-series approach, in which the stages/sub-stages of the disease are the time points, and the quantification measurements are the values. The values over time points are used to interpolate the growth of the progression using the cubic spline function. Then, the transcripts profiles are universally aligned and clustered using the time-series profile hierarchical clustering method based on the area between each pair of the aligned profiles; the method is named (HC-UAP). We compare the proposed method with a hierarchical clustering method based on Euclidean distance (HC-ED). Both methods were applied on two next-generation sequencing (NGS) prostate cancer datasets, the first from the Chinese and the second from the North American population. HC-ED clusters the dataset to find patterns while HC-UAP was able to single out outliers that trend differently in both datasets. While finding patterns in gene expression that trend over stages is the standard approach for analyzing time-series models, identifying outlier transcripts that grow differently than other transcripts can provide more details about the contribution of the mRNA transcriptional activity to the disease. They also can be a potential biomarker for the disease progression.
{"title":"HC-UAP: Outliers detection method based-on hierarchical clustering for universally aligned time-series RNA-Seq profiles","authors":"A. Alkhateeb","doi":"10.5267/j.dsl.2022.10.004","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.10.004","url":null,"abstract":"Tracking abundant gene transcripts quantification over continuous cancer progression stages may reveal the mechanism of disease advancement. In this work, we profile the transcript quantification over the stages using a time-series approach, in which the stages/sub-stages of the disease are the time points, and the quantification measurements are the values. The values over time points are used to interpolate the growth of the progression using the cubic spline function. Then, the transcripts profiles are universally aligned and clustered using the time-series profile hierarchical clustering method based on the area between each pair of the aligned profiles; the method is named (HC-UAP). We compare the proposed method with a hierarchical clustering method based on Euclidean distance (HC-ED). Both methods were applied on two next-generation sequencing (NGS) prostate cancer datasets, the first from the Chinese and the second from the North American population. HC-ED clusters the dataset to find patterns while HC-UAP was able to single out outliers that trend differently in both datasets. While finding patterns in gene expression that trend over stages is the standard approach for analyzing time-series models, identifying outlier transcripts that grow differently than other transcripts can provide more details about the contribution of the mRNA transcriptional activity to the disease. They also can be a potential biomarker for the disease progression.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"5 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81254453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.6.001
Wibawati Wibawati, Muhammad Ahsan, Hidayatul Khusna
The MEWMA chart is one of the traditional multivariate charts which are widely employed in inspecting the quality of manufacturing and services. This chart is created through monitoring the small shifts of mean vectors of variable quality characteristics. Often in practice, the measurement of a quality characteristic produces uncertain, incomplete values, so that ambiguous numbers are obtained. In this condition, a neutrosophic-based control chart can overcome the problem resulting from the ambiguous data. The paper’s objective is to construct a new multivariate monitoring scheme based on a neutrosophic chart, namely the neutrosophic Multivariate EWMA (NMEWMA). Furthermore, the performance of the new multivariate monitoring scheme is evaluated in detecting process shifts employing the Average Run Length (ARL) and Standard Deviation Run Length (SDRL). This control chart is an innovation in the quality monitoring of uncertain data. The research result obtained indicates that the NMEWMA chart performs better than the MEWMA in finding the small mean shifts as well as in the real case application.
{"title":"Neutrosophic multivariate EWMA control chart","authors":"Wibawati Wibawati, Muhammad Ahsan, Hidayatul Khusna","doi":"10.5267/j.dsl.2023.6.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.6.001","url":null,"abstract":"The MEWMA chart is one of the traditional multivariate charts which are widely employed in inspecting the quality of manufacturing and services. This chart is created through monitoring the small shifts of mean vectors of variable quality characteristics. Often in practice, the measurement of a quality characteristic produces uncertain, incomplete values, so that ambiguous numbers are obtained. In this condition, a neutrosophic-based control chart can overcome the problem resulting from the ambiguous data. The paper’s objective is to construct a new multivariate monitoring scheme based on a neutrosophic chart, namely the neutrosophic Multivariate EWMA (NMEWMA). Furthermore, the performance of the new multivariate monitoring scheme is evaluated in detecting process shifts employing the Average Run Length (ARL) and Standard Deviation Run Length (SDRL). This control chart is an innovation in the quality monitoring of uncertain data. The research result obtained indicates that the NMEWMA chart performs better than the MEWMA in finding the small mean shifts as well as in the real case application.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.7.003
Yan-Yan Dong, Dong-Qiang Wang
The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.
{"title":"China's artificial intelligence efficiency and its influencing factors: Based on DEA-Malmquist and Tobit regression model","authors":"Yan-Yan Dong, Dong-Qiang Wang","doi":"10.5267/j.dsl.2023.7.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.7.003","url":null,"abstract":"The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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