A novel CRITIC-driven framework for fine-scale urban sprawl typology classification: evidence from Colombo, Kandy, and Hong Kong

Light-gauge steel frame (LGSF) materials are inherently susceptible to stochastic imperfections arising from their design, manufacturing, and erection. These defects can compromise operational integrity and adversely impact structural stability, especially during the construction period. Consequently, a thorough investigation into the buckling characteristics of LGSF materials with such imperfections is imperative. Conventional stochastic probabilistic methods, such as Monte Carlo simulations, often fail to fully capture intrinsic material and complex structural properties, leading to discrepancies between computational predictions and actual behavior. To address these limitations, this study introduces an innovative model using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to assess LGSF materials under combined defects scenarios. The CRITIC method systematically evaluates various buckling modes in LGSFs under combined defects to identify the most detrimental modal combination, representing the most unfavorable scenario. Rigorous finite element analysis is then performed on the LGSF model based on this critical scenario. Compared to conventional approaches, the proposed CRITIC-based combined defects analysis model predicts a 0%~5% reduction in the critical load factor and a 1%~3% increase in ultimate displacement at control nodes. These findings indicate that the CRITIC-based method yields a more critical combination of buckling modes, thereby enhancing the reliability and safety of the simulation results. Furthermore, this research demonstrates that, for LGSF materials, the common assumption that the first-order buckling mode is inherently the most deleterious failure pattern is inaccurate.

This study aims to evaluate the members of the Black Sea Economic Cooperation Organization (BSEC) in terms of human capital performance using CRITIC (Criteria Importance Through Intercriteria Correlation) and COPRAS (Complex Proportional Assessment) methods. A hybrid method has been used in analysis. This integrated model consists of a combination of CRITIC and COPRAS methods. The CRITIC method was used to find the objective weights of the criteria. The COPRAS method was used to rank the countries according to their performance. The infant mortality rate (per 1,000 live births), unemployment rate (percentage of the total labour force), average life expectancy at birth, total (years), labour force participation rate (percentage of the total population aged 15-64), current health expenditure (percentage of GDP), internet users (percentage of the total population) and population aged 15-64 (percentage of the total population) are used as criteria for measuring the human capital of countries. 2000, 2005, 2010, 2015, and 2018 data of the countries were used in the study. According to the analysis result, Serbia, Greece, Romania in 2000, Greece, Romania, Bulgaria in 2005, Greece, Russia, Bulgaria, in 2010, Russia, Moldova, Bulgaria in 2015, Russia, Romania and Bulgaria in 2018 are the top three countries with the highest human capital performance. Countries with the lowest human capital performance are as follows: Azerbaijan, Albania, Armenia in 2000 and 2005; Azerbaijan, Armenia, Turkey, in 2010; Turkey, Azerbaijan, Albania in 2015 and 2018.

Purpose This study investigates the challenges of adopting Zero Trust (ZT) as a security strategy in contemporary organizations, where traditional security measures are insufficient. This paper aims to provide a robust and objective framework for managing ZT implementation through an integrated approach. Design/methodology/approach An expert panel of 29 professionals contributed to identifying and weighting key management criteria for ZT adoption. This study used fuzzy Delphi to achieve consensus and the CRITIC (Criteria Importance through Intercriteria Correlation) method to ensure objectivity in determining criteria importance. Findings The results reveal four critical dimensions – culture, operations and processes, compliance, and investments – along with 33 specific criteria essential for successful ZT implementation. This study highlights the potential for reversal of rank under dynamic decision-making conditions, emphasizing the necessity for continuous refinement and adaptation. Practical implications The insights derived from this research offer valuable guidance for security leaders, professionals and consultants in navigating the complexities of ZT adoption. By addressing managerial challenges and providing a structured approach, this study contributes to a smoother transition from traditional cybersecurity models to ZT frameworks. Originality/value This research offers a novel contribution to ZT management by applying an innovative, management-centered methodology to systematically identify and prioritize the critical challenges involved. It provides a structured framework that enables security managers to prioritize critical areas – cultural, financial, operational and compliance – essential for the successful implementation of ZT.

This study aims at using the CRITIC (Criteria Importance Through Intercriteria Correlation) and COPRAS (Complex Proportional Assessment) techniques to evaluate the members of Association of Southeast Asian Nations according to the performance of human capital. In the analysis, a hybrid technique was applied. The CRITIC and COPRAS methodologies are combined in this integrated model. The objective weights of the criteria were determined using the CRITIC approach. For the purpose of ranking the countries according to human capital performance, the COPRAS technique was utilized. Countries' human capital is measured using the infant mortality rate (per 1,000 live births), unemployment rate (percentage of the total labor force), average life expectancy at birth, total (years), labor force participation rate (percentage of the total population aged 15-64), current health expenditure (percentage of GDP), internet users (percentage of the total population), and population aged 15-64 (percentage of the total population). The data covers the years of 2016, 2017 and 2018. According to the results, Thailand has the best performance from the point of human capital in all examined period. Indonesia has the worst performance in 2016 and Lao has the worst performance in 2017 and 2018. Turkey has a performance in ninth rank in all periods.

In modern BIS system, a scientific approach is adopted for grading the raw jute fibers based on six apposite physical properties, namely fiber strength, defect, root content, fineness, color, and bulk density, of which the latter two parameters are still evaluated subjectively. Several researchers have attempted, over the years, many exponents of multi-criteria decision making (MCDM) approaches and got encouraging results. However, in all these approaches, subjective weighting methods have been used in the form of analytic hierarchy process (AHP). In this paper, a maiden integrated approach using criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) is presented for grading jute fibers on the basis of abovementioned six attributes. Ranking performance of the present approach closely resembles with other approaches hitherto attempted by several researchers. The efficacy and robustness of the new approach are validated using sensitivity analysis through altering the weightage levels of the criteria. The rank correlation coefficients between the ranking patterns obtained in each of the weightage levels are, in most of the cases, 1 or very close to 1, which signifies no occurrence of rank reversal or change in ranking. During sensitivity analysis in dynamic decision conditions, the approach is stable up to certain level and then rank reversal takes place for some of the alternatives including the top ranked one. The uniqueness of the present study lies in the fact that here criteria weights are determined objectively using CRITIC and hence more reliable and devoid of influence/preferences of decision-makers (DMs). Moreover, the new approach is very simple with a few simple mathematical equations, albeit a potent tool of decision making with strong background logic.

As a sustainable and cost-effective biofuel source for power generation and heating systems, wood pellets play a critical role in the renewable energy landscape. This leads to the discussion on their international trade on the edge of major global challenges such as climate change and energy security. In this paper, we focus on the wood pellets trade and analyze its growing markets by decision-making models. A hybrid multi-criteria decision-making model (MCDM) is proposed, supported by critical criteria of international trade, to trading countries’ executives to make informed decisions on target markets. The model includes both criteria (value imported, trade balance, unit value, annual growth in value, CO2 emission, logistic performance index, concentration of supplying countries) and alternatives (17 prior importing countries). It determines the weights of criteria by Criteria Importance Through Intercriteria Correlation (CRITIC) method and ranks alternatives by Additive Ratio Assessment (ARAS) technique. Based on CRITIC analysis, “concentration of supplying countries” is found as the most significant criterion. According to the results, within informed trading decisions, the top three markets for exporting countries are determined as the United Kingdom, Japan, and the Netherlands in terms of growing demand for wood pellets.

Urban sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, adversely affects the provision of ecosystem services. The quantification of US is thus crucial for effective urban planning and environmental management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive US triggered by the doubling of total population over the past three decades. However, the extent and level of US has not yet been quantified and a prediction for future extent of US is lacking. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10 km suburban buffer of Chennai. The level of US was then quantified using Renyi’s entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services.

To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. Secondly, the Criteria Importance Through Intercriteria Correlation (CRITIC) weight method is employed to determine the importance of these risk indicators, thereby enhancing the model's prediction ability and stability. Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. The experimental results show that the model achieves an F1 score of 87.26%, an accuracy of 87.95%, an Area under the Curve (AUC) of 91.20%, a precision of 89.29%, and a recall of 87.48%. They are respectively 6.45%, 4.45%, 5.09%, 4.81%, and 3.83% higher than the traditional RF model. In this study, an improved RF model based on FCM clustering is successfully constructed, and the accuracy of risk early warning models and their ability to handle complex data are significantly improved.

Parametric optimization and sensitivity analysis of the integrated Taguchi-CRITIC-EDAS method to enhance the surface quality and tensile test behavior of 3D printed PLA and ABS parts.

Measuring urban landscapes for urban function classification using spatial metrics

Optimizing shared bike systems for economic gain: Integrating land use and retail

This paper selects the order parameters of 11 regional economic subsystems that reflect economic structure, economic aggregate and economic benefits from the perspective of system analysis, and reflects development level and economic strength. And the order parameters of the 12 regional high-tech industrial cluster subsystems of innovation ability, and the method of measuring the order parameters of the system synergy degree is applied. Based on the Criteria Importance Through Intercriteria Correlation (CRITIC) fixed-weight model, a synergy evaluation system of regional economy and regional high-tech industrial clusters is constructed, and finally, an empirical analysis is carried out with Jilin Province, China. The results show that the overall change trend of the synergy degree of the composite system of the regional economy and regional high-tech industrial clusters in Jilin Province can be divided into four stages, and presents an M-shaped development trend of alternating increase and decrease. The development of enterprises in the high-tech zone is not obvious, but the feedback effect of enterprises in the high-tech zone on the regional economy is not obvious. This paper not only fills the research gap on the coordinated development of regional economy and regional high-tech industrial clusters in China's underdeveloped regions in terms of content, but also selects the CRITIC weighting method in terms of methods to accurately define the weights of each indicator.

BRICS countries’ recent investments in technology have attracted attention, and they have become a part of the nations that conduct research around the world. The European Innovation Scoreboard (EIS), accepted as an effective benchmarking tool for technology policies, provides a comparative analysis of the innovation performances of many countries, including BRICS. In the current research, the innovation performances of BRICS countries were compared through EIS data, one of the most adopted benchmarking tools in technology policy discussions. Thus, it was aimed to determine the importance levels of the criteria used in the EIS data and to analyze the innovation processes of the countries in question. In this study, an integrated framework using CRiteria Importance Through Intercriteria Correlation (CRITIC) and Grey Relational Analysis (GRA) methods is presented to compare the innovation performances of BRICS countries. In the first stage of the application, the importance levels of the criteria are obtained using the CRITIC method, while in the second stage, countries are ranked according to their innovation performance through GRA. Data are obtained by compiling statistics from the EIS database created by the Commission of the European Communities. The results obtained in the practical application of the model rank the criteria according to their weights as follows: higher education (0.249), international joint publications (0.176), medium and high technology exports (0.122), frequently cited publications (0.113), PCT patents (0.094), public-private joint publications (0.085), designs (0.083) and trademarks (0.078). In addition, the BRICS countries are ranked according to their innovation performance as China (0.76), Russia (0.6), South Africa (0.516), Brazil (0.426), and India (0.378).

Selection of the appropriate Roll-on Roll-off (Ro-Ro) port is one of the crucial tasks for the maritime industry. Because there are many factors affecting the selection process, this selection process is essentially a multi-criteria decision-making problem. This paper proposes a integrated approach consisting of the CRITIC (Criteria Importance Through Intercriteria Correlation) technique and the EDAS (Evaluation based on Distance from Average Solution) method to evaluate the Ro-Ro marine ports selection. The obtained results by using the proposed model have been verified carrying out a comprehensive sensitivity analysis. In accordance with this purpose, 10 different scenarios were established and five MCDM methods were applied to make a comparison. Results obtained using the suggested model were verified in dynamic conditions. The main purpose of this implementation is to determine whether any change in the obtained results for each determined scenario. Carried out sensitivity analysis shows that the suggested hybrid MCDM model consisting of CRITIC and EDAS techniques has validity and the obtained results are accurate and realistic. When the results of the sensitivity analysis are reviewed, it can be seen that the P1 option is the best alternative for all scenarios.

In allusion to the incomplete indexes system and not making decision on the subjectivity and objectivity of PV power station connecting system, based on the combination of improved Analytic Hierarchy Process (AHP), Criteria Importance Through Intercriteria Correlation (CRITIC) as well as grey correlation degree analysis (GCDA) is comprehensively proposed to select the appropriate system connecting scheme of PV power station. Firstly, indexes of PV power station connecting system are divided the recursion order hierarchy and calculated subjective weight by the improved AHP. Then, CRITIC is adopted to determine the objective weight of each index through the comparison intensity and conflict between indexes. The last the improved GCDA is applied to screen the optimal scheme, so as to, from the subjective and objective angle, select the connecting system. Comprehensive decision of Xinjiang PV power station is conducted and reasonable analysis results are attained. The research results might provide scientific basis for investment decision.