Order Preference Research Articles

Health Insurance Provider Selection Through Novel Correlation Measure of Neutrosophic Sets Using TOPSIS

Risk monitoring aims to recognize and control potential threats to our assets or health activities. Insurance is vital, as it compensates for any unexpected loss of property or life. Clients selecting the best health insurance provider must consider various factors and their relative significance to their circumstances. Multi-criteria decision-making (MCDM) helps people analyze and compare policy alternatives to find the best option. This research aims to assist potential health policy purchasers by addressing the selection of health insurance providers as an MCDM problem. The correlation coefficient is useful for identifying the importance of several conflicting criteria. The idea of correlation coefficients is extended in a neutrosophic context to capture the indeterminacy and incomplete information in the relationship among the criteria. The technique for order preference by similarity to an ideal solution (TOPSIS) approach is a useful and straightforward approach to solving MCDM problems. However, it often became ambiguous to researchers due to its involvement in the distance measure technique. The proposed neutrosophic correlation measure may also replace the ambiguity of using a suitable distance measure in the TOPSIS approach. This study extends the TOPSIS method by using the proposed neutrosophic correlation coefficient on single-valued neutrosophic sets (SVNSs). The criteria preferences are computed using a method based on the removal effects of the criteria (MEREC) approach. Some valuable concepts, like the weighted closeness measure of type I and type II and the weighted index parameter, are introduced with their properties to establish the proposed neutrosophic TOPSIS approach. An MCDM approach for health insurance providers has been constructed to illustrate the proposed approach numerically. The proposed method suggest that the health insurance provider ϒ2 is the most beneficial alternative, whereas ϒ1 is the least suitable. The client considers the terms and conditions for non-coverage and the facilities provided for pediatric and maternity care while buying health insurance. The comparative analysis of the suggested technique demonstrates the merit of the research in terms of consistency. The sensitivity analysis demonstrates the flexibility and robustness of the obtained results.

An expert system based decision-making framework for benchmarking industry in sustainable manufacturing

PurposeThe purpose of this study is to evaluate and enhance sustainable manufacturing practices across various industries, focusing on environmental, economic and social dimensions, to promote a comprehensive understanding and implementation of sustainability, thereby improving overall industry performance and fostering long-term ecological and economic health.Design/methodology/approachThe study uses multi-criteria decision-making-multivariate analysis technique to examine sustainable manufacturing practices (SMPs) in the Indian manufacturing sector. It identifies 11 SMP criteria through literature review and expert recommendations. Data are collected through questionnaires, expert committees and interviews. The study focuses on four key industries: automobile, steel, textile and plastic. Techniques like principal component analysis (PCA), technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) are used to rank and assess performance.FindingsThe Indian automobile industry has shown the most effective SMPs compared to steel, textile and plastic sectors. The automobile sector is the benchmark for sustainable measures, emphasizing the importance of green practices for environmental, social and economic performance. Recommendations extend beyond the automobile sector to cement, electronics and construction.Practical implicationsThe research emphasizes the importance of SMPs across various industries, focusing on economic, environmental and social considerations. It advocates for a holistic approach that enhances resource efficiency and minimizes ecological footprint.Originality/valueThe study uses ranking methods like PCA-integrated TOPSIS and COPRAS to evaluate performance in different industries, focusing on the benchmarked automobile sector. The research offers valuable insights and advocates for the widespread adoption of sustainable policies beyond the studied sectors.

Chemoresistive H2S sensor based on PANI/TiO2/CuCl2 nanocomposite impregnated conductive fabric using TOPSIS and Taguchi method

Abstract Conductive polymer/metal oxide nanocomposites have been widely used for chmoresistive gas sensors. PANI/TiO2/CuCl2 nanocomposite(PTCN) impregnated conductive fabric was prepared by in-situ synthesis method. To improve the performance of PTCN impregnated fabric for detecting H2S gas, Taguchi orthogonal array(TOA) is used to experimental design, and best values of factors affecting to the sensing performance are determined using Technique for order preference by similarity to ideal solution (TOPSIS) and Taguchi optimization method. PTCNs were charactierized by SEM, XRD, FTIR. The sensing performances of the proposed sensor such as linear detection range, sensitivity, repeatability and effect of humidity for hydrogen sulfide gas were evaluated.

Determining method of optimal process parameters using Taguchi method and technique for order preference by similarity to ideal solution in plastic injection molding

Many plastic injection molding (PIM) process optimization problems are multi-objective optimization problems that optimize multiple mechanical properties. This paper proposed a determining method of optimal process parameters and their effect ranks using Taguchi method and TOPSIS in the PIM. TOPSIS was used to convert multiple mechanical properties into single comprehensive response. It was selected as a reasonable multi-attribute decision-making (MADM) method from among some well-known MADM methods based on mean rank correlation coefficient and mean absolute rank deviation. Taguchi method was used to design experiment and find the optimal process parameters. The proposed method was applied to determine the optimal values of the process parameters: melt temperature (MT), packing pressure (PP), cooling time (CT), and injection pressure (IP) for improving tensile strength, elasticity module, flexural modulus, and impact strength in the PIM with Acrylonitrile-Butadiene-Styrene compound as plastic materials and AISI 1020 as mold materials. The optimal values of the process parameters were MT of 280°C, PP of 28 MPa, CT of 22 s, and IP of 50 MPa, and their effect ranking was IP (51.941%), PP (32.280%), CT (9.045%), and MT (6.734%). The method could be widely applied to not only the PIM process optimization but also various manufacturing process optimization problems.

The Optimal Placement of the Head in the Noun Phrase. The Case of Demonstrative, Numeral, Adjective and Noun

ABSTRACT The word order of a sentence is shaped by multiple principles. The principle of syntactic dependency distance minimization is in conflict with the principle of surprisal minimization (or predictability maximization) in single head syntactic dependency structures: while the former predicts that the head should be placed at the centre of the linear arrangement, the latter predicts that the head should be placed at one of the ends (either first or last). A critical question is when surprisal minimization (or predictability maximization) should surpass syntactic dependency distance minimization. In the context of single head structures, it has been predicted that this is more likely to happen when two conditions are met, i.e. (a) fewer words are involved and (b) words are shorter. Here, we test the prediction on the noun phrase when it is composed of demonstrative, numeral, adjective, and noun. We find that, across preferred orders in languages, the noun tends to be placed at one of the ends, confirming the theoretical prediction. We also show evidence of anti-locality effects: syntactic dependency distances in preferred orders are longer than expected by chance.

U.S. against the world: Authoritarianism among American public servants

AbstractWhat is the prevalence of authoritarianism among public servants in the United States, and what are its resulting consequences? Several underlying psychological traits are associated with both authoritarianism and a desire to work in the public sector, such as an aversion to ambiguity and a preference for order and security. Scholarship also suggests that those with high authoritarian attributes may see the world through an ‘us versus them’ prism; in this case, the ‘them’ may be represented by other countries. Therefore, it is hypothesised that those with higher authoritarian attributes are more likely to work in the public sector and that public servants with higher authoritarian attributes are more likely to be sensitive to potential threats to the country. Using six surveys that cover a 20‐year period from the American National Elections Study, both hypotheses are supported. Authoritarian attributes are associated with an increased likelihood of government employment. Additionally, authoritarian public servants tend to support government wiretapping and express greater concern about terrorism. The presence of authoritarianism among public sector employees has implications for a range of governance issues, which makes it important to understand where and to what degree it is prevalent.Points for practitioners More attention should be devoted to the division and prevalence of authoritarian attributes within public servants . Checks and oversight mechanisms should be implemented for public sector employees in sensitive areas to help counterbalance potential authoritarian impulses. Organisational cultures that value openness, evidence‐based policymaking, and tolerance for dissenting viewpoints should be fostered as a counterweight to authoritarian preferences for conformity.

Multi-objective optimization towards cooling performance of teardrop section Kagome trusses array jet impingement composite cooling structure with cross-flow

Teardrop Section Kagome trusses array can be a spoiler for a new generation of advanced combustion chambers. In this study, multi-objective optimizations are applied to optimize a jet impingement composite cooling structure with cross-flow. Four structural parameters with five levels and three performance parameters are calculated in design of experiment and fitted by response surface methodology. The database is expanded by non-dominated sorting genetic algorithm-II, and then technique for order preference by similarity to an ideal solution is used to calculate the optimal structures under different operating conditions by different weights of the performance parameter. The conclusions are as follows: The minimal Equality of variances and fitting accuracy are 35.4371 and 93.6%. Diameter of the cross-section has the largest positive main effect on average Nusselt number and pressure loss coefficient, while angle between the rod and the horizontal plane has the largest positive effect on temperature uniformity coefficient. The interaction of trailing edge angle with angle between the front rod and the back rod in the horizontal plane is large. Case15 is chosen, where the weight ratio is 1:5:5, improvement of the corresponding optimized performance parameters of the simulation results with respect to the Origin of three performance parameters are 26.72%, −2.93%, 91.59% respectively. It provides a feasible reference for future advanced combustion chambers and accelerates their development process.

Novel Multi-Criteria Decision Analysis Based on Performance Indicators for Urban Energy System Planning

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems.

Optimizing green hydrogen strategies in Tunisia: A combined SWOT-MCDM approach

Tunisia's rapid industrial expansion and population growth have created a pressing energy deficit, despite the country's significant, yet largely untapped, renewable energy potential. This study addressed this challenge by developing a comprehensive framework to identify and evaluate strategies for promoting green hydrogen production from renewable energy sources in Tunisia. A Strength, Weakness, Opportunity, and Threat (SWOT) analysis, incorporating social, economic, and environmental dimensions, was conducted to formulate potential solutions. The Step-wise Weight Assessment Ratio Analysis (SWARA) method facilitated the weighting of SWOT factors and sub-factors. Subsequently, a multi-criteria decision-making approach, employing the gray technique for order preference by similarity to ideal solution (TOPSIS-G) method (validated by gray additive ratio assessment (ARAS-G), gray complex proportional assessment (COPRAS-G), and gray multi-objective optimization by ratio analysis (MOORA-G), was used to rank the identified strategies.The SWOT analysis revealed "Strengths" as the most influential factor with a relative weight of 47.3 %, followed by "Weaknesses" (26.5 %), "Threats" (15.6 %), and "Opportunities" (10.6 %). Specifically, experts emphasized Tunisia's renewable energy potential (21.89 %) and robust power system (12.11 %) as primary strengths. Conversely, high investment costs (11.2 %) and political instability (7.77 %) posed substantial threat. Positive socio-economic impacts represented a key opportunity with a score of 5.2 %. As for the strategies prioritizing criteria, production cost ranked first with a score of 13.5 %, followed by environmental impact (12.8 %), renewable energy potential (12.0 %), and mitigation costs (11.3 %). The gray TOPSIS analysis identified two key strategies: leveraging Tunisia's wind and solar resources and fostering regional cooperation for project implementation. The robustness of these strategies is confirmed by the strong correlation between TOPSIS-G, ARAS-G, COPRAS-G, and MOORA-G results. Overall, the study provides a comprehensive roadmap and expert-informed decision-support tools, offering valuable insights for policymakers, investors, and stakeholders in Tunisia and other emerging economies facing similar energy challenges.

Driving forces and obstacles analysis of urban high-quality development in Chengdu.

High-quality development paths in important cities are blurry and lacking. In order to explore the important engine for Chengdu high-quality development, driving forces and obstacles recognition has emerged as a pivotal technological solution. Using the Chengdu in Sichuan province of China as a research area and quantitative data from 2010 to 2019, this study has used content mining to recognize urban high-quality development (UHQD) variables, and calculated variables' weights by entropy weight method, and explored driving forces and obstacles of UHQD by the technique for order preference by similarity to ideal solution (TOPSIS) method. The main findings are: (1) there are 36 UHQD variables; (2) Chengdu high-quality development overall level soars from 2017 to 2019, only with two negative growth rates in 2011, 2015; (3) There are 3 key driving force paths: (1) improving green development by volume of industrial wastewater discharged, comprehensively utilised ratio of industrial solid wastes, harmless treatment rate of domestic garbage; (2) stressing open development by total import and export/GDP, actual use of foreign capital, number of foreign tourists/total tourists; and (3) intensifying shared development by funds for urban residents under basic provision protection. (4) 3 clearing obstacles paths can also realize Chengdu high-quality development: (1) improving innovative development level by R&D internal outlay, patent authorisations, state high-level tech enterprises; (2) optimizing coordinated development level by the proportion of tertiary industry; (3) promoting shared development level by urban basic pension insurance. According to these findings, suggestions are put forward to promote Chengdu high-quality development from the perspective of policy implementation.

Similarity Measures of Probabilistic Interval Preference Ordering Sets and Their Applications in Decision-Making

The concept of probabilistic interval preference ordering sets (PIPOSs) provides a scientific and intuitive framework for solving real-life multi-criteria group decision-making problems. In some areas such as investment decision-making and supplier selection, PIPOSs have a wider application space, and the development of similarity and distance measures based on PIPOSs holds great significance. Similarity measure is a basic and prominent tool for dealing with imperfect and ambiguous information in fuzzy sets, but it can also be used to deal with uncertain information in preference ordering. These metrics play an important role in the actual decision-making process, as they effectively quantify the degree of similarity between two PIPOSs, and further allow for the prioritization of different scenarios. In this article, we sort out the definitions and arithmetic rules of PIPOSs, and creatively propose several new similarity measures based on PIPOSs. Then, we propose a group decision-making method based on similarity measures and conduct a comparative study with three existing similarity measures to illustrate its advantages over existing metrics. Finally, we confirm its validity through numerical illustrations in the case study, and also conduct a comparative assessment to verify the scientific validity and effectiveness of the newly introduced measure against the existing metrics.

A Novel Method for Assessing Pirate Attack Risks and Spatial Distribution

Pirate attacks pose one of the most severe challenges to the safety of maritime navigation. Effectively quantifying the risk of pirate attacks and understanding their spatial distribution through historical records is crucial for planning safe shipping routes. Given the diverse data types and multiple factors involved in assessing pirate attacks recorded in the Global Integrated Shipping Information System (GISIS) database, we propose a spatiotemporal influence factor analysis model based on the K-means clustering algorithm. Features are encoded using an Autoencoder, and the evaluation is conducted using the Entropy Weight Method- Technique for Order Preference by Similarity to Ideal Solution (EWM-TOPSIS). The model then simulates and predicts the geographical distribution of pirate risks. The results indicate that the model effectively captures the geographical distribution patterns of pirate attack incidents and successfully predicts the risk distribution across different sea areas. This approach aids in ship route planning and reduces the risk of pirate attacks.

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

Performance benchmarking of European postal incumbents with TOPSIS and BMW-TOPSIS

Given the uncertain position and specific role of postal incumbents in Europe, creating a framework for mutual comparison is essential. In this regard, this study aims to develop a methodology that will compare their relevant performance. On the other hand, management and host countries can be promptly alerted if something hinders postal incumbents from providing optimal performance. Two methods have been applied: the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and its variation, Best-Middle-Worst (BMW)-TOPSIS, on a sample of 16 incumbents from the EU and candidate countries. The results indicate that a finer adjustment is achieved in terms of ranking by the BMW-TOPSIS method. An open question regarding the selection of postal incumbents is whether it is necessary to strictly adhere to the rule that the number of their attributes is less than or equal to half of the attributes of the Middle point. The example of Latvia shows that if an alternative contains values from which the Best point is composed, an exception can be made in the selection. The development of the Middle point provides an opportunity for a more in-depth analysis, especially in cases where the values of the criteria differ significantly. Unlike classical TOPSIS, it offers a clearer visualization that supports the understanding of postal incumbents and their performance.

Protective effects of probiotic supplemented vegetable jams in E. coli induced diarrhoeal mice model

This study aimed to investigate the effect of Lacticaseibacillus rhamnosus GG (LGG) fermented milk supplemented to different vegetable jams, on physiological and immunomodulatory activities in E. coli induced diarrhoeagenic mouse model. A 12.5% mortality with two days reduction in diarrhoeal duration was observed in LGG fermented milk supplemented tomato jam group as compared to 50% mortality in diarrhoeal control group. A reduction in daily feed intake and body weight was observed in the diarrhoeal control group. An increase in phagocytosis (35%) and reduction in splenocyte stimulation index was observed in the mice fed with LGG fermented milk supplemented tomato jam (p < 0.05). A significantly higher level of anti-inflammatory markers and immunoglobulins were observed in all the treatment groups in comparison to the diarrhoeal group. On the contrary, a higher level of proinflammatory markers, IL-6, IFN γ, CRP, TNF-α were observed in diarrhoeal conditions. A reduction in total E. coli counts in faeces without any translocation to other organs was observed in all the treatment groups. The molecular docking analysis revealed the role of phenolic acids towards higher levels of anti-inflammatory markers and carotenoids contributing towards reduced levels of proinflammatory markers. Furthermore, the Multidimensional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis ranked the tomato jam supplemented with LGG fermented milk may have potential in management of diarrhoeal conditions.

Optimal Capacity Allocation for Life Cycle Multiobjective Integrated Energy Systems Considering Capacity Tariffs and Eco-Indicator 99

Traditional energy systems pose a significant threat to human social development due to fossil fuel depletion and environmental pollution. Integrated energy systems (IESs) are widely studied and applied due to their clean and low-carbon characteristics to achieve sustainable development. However, as integrated energy systems expand, their impact on ecosystems becomes more pronounced. This paper introduces the concept of the ecological damage index (EDI) to promote the sustainable development of integrated energy systems. Moreover, the introduction of a capacity tariff mechanism will impact the energy structure, making it essential to consider its effects on capacity allocation within integrated energy systems. This paper proposes a multiobjective optimization framework for constructing a capacity planning model for integrated energy systems, focusing on achieving a multidimensional balance between the economy, environment, and ecosystem using the life cycle assessment (LCA) method. Finally, the nondominated sorting genetic algorithm-II (NSGA-II) is employed to optimize the three objectives and obtain the Pareto frontier solution set. The optimal solution is selected from the solution set by combining the technique for order preference by similarity to ideal solution (TOPSIS) and Shannon entropy method. In comparison to scenarios with incomplete considerations, the multiobjective capacity optimization model proposed in this study exhibits significant improvements across the three metrics of cost, carbon emissions, and the ecological damage index, with a 19.05% reduction in costs, a 26.24% decrease in carbon emissions, and an 8.85% decrease in the ecological damage index. The study demonstrates that the model abandons traditional single-objective research methods by incorporating a multidimensional balance of the economy, environment, and ecosystems. This approach forms a foundational basis for selecting the optimal energy mix and achieving sustainable development in integrated energy systems. The life cycle assessment methodology evaluates impacts across all stages of integrated energy systems, providing a comprehensive basis for assessing and planning the sustainable development of the systems. The study offers guidance for the rational allocation of the integrated energy system capacity and advances the sustainable development of such systems.

New Fermatean Fuzzy Distance Metric and Its Utilization in the Assessment of Security Crises Using the MCDM Technique

The problem of insecurity is a global phenomenon that has several forms like terrorism, banditry, kidnappings, etc. Insecurity has taken hold in the Sub-Saharan Region of West Africa, especially in Nigeria, for over two decades. Nigeria’s security crisis is more pronounced in the Northern Region, with a new wave in the North-Central Region of Nigeria. It is herculean to assess insecurity in the North-Central Region of Nigeria because of the region’s fuzzy or imprecise nature of insecurity. This constitutes the rationale for deploying the Fermatean fuzzy technique to assess insecurity due to the capacity of the Fermatean fuzzy scheme to handle imprecision. To this end, a new Fermatean fuzzy distance metric is presented to evaluate insecurity in the North-Central Region of Nigeria using a multi-criteria decision-making technique. To express the logic for creating the new Fermatean fuzzy distance metric, some existing Fermatean fuzzy distance metrics are discussed, along with their drawbacks. The mathematical properties of the new technique are discussed, and the new method is applied computationally to assess insecurity in the North-Central Region of Nigeria. The data for the security assessment are collected via Fermatean fuzzy linguistic variables using the opinions of security experts and analyzed using the technique for order of preference by similarity to ideal solution, which is a commonly used multi-criteria decision-making method. Finally, the numerical validity of the new technique is expressed with comparative results, and the finding shows the benefit of the new distance approach over the existing methodologies. The outcome of the work will provide reliable traveling advisories for safe voyages within the region.

Application of a GIS-Based Multi-Criteria Decision-Making Approach to the Siting of Ocean Thermal Energy Conversion Power Plants: A Case Study of the Xisha Sea Area, China

In order to achieve the goals of carbon neutrality and reduced carbon emissions, China is increasingly focusing on the development and utilization of renewable energy sources. Among these, ocean thermal energy conversion (OTEC) has the advantages of small periodic fluctuations and large potential reserves, making it an important research field. With the development of the “Maritime Silk Road”, the Xisha Islands in the South China Sea will see a growing demand for electricity, providing the potential for OTEC development in this region. Optimal site selection of OTEC power plants is a prerequisite for developing thermal energy provision, affecting both the construction costs and future benefits of the power plants. This study establishes a scientific evaluation model based on the decision-making frameworks of geographic information systems (GISs) and multi-criteria decision-making (MCDM) methods, specifically the analytic hierarchy process (AHP) for assigning weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to reclassify the factors, and weighted linear combination (WLC) to compute the suitability index. In addition to commonly considered factors such as temperature difference and marine usage status, this study innovatively incorporates geological conditions and maximum offshore distances of cold seawater based on cost control. The final evaluation identifies three suitable areas for OTEC development near the Xuande Atoll and the Yongle Atoll in the Xisha Sea Area, providing valuable insights for energy developers and policymakers.

Land cover and drought risk assessment in Türkiye's mountain regions using neutrosophic decision support system.

Earth observation (EO) provides dynamic scientific methods for tracking and defining ecological parameters in mountainous regions. Open-source platforms are frequently utilized in this context to efficiently collect and evaluate spatial data. In this study, we used Collect Earth (CE), an open-source land monitoring platform, to reveal and assess land cover, land cover change, and relevant ecological parameters such as drought risk. Mountain ecosystems were subject to an evaluation for the first time by combining remote sensing with a hybridization of Decision-Making Trial and Evaluation Laboratory (DEMATEL), analytic hierarchy process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for neutrosophic sets in risk assessment problems of several connected criteria. The high and dispersed high alpine environment of Türkiye accommodates land with relatively less human influence, making it suitable to observe climate change impacts. In the framework of the study, we evaluated more than two decades (2000-2022) of land use and land cover (LULC) changes in the mountain regions of the country. Using nine identified ecological parameters, we also evaluated drought risk. The parameters included were the LULC classes and their change, elevation, slope, aspect, precipitation, temperature, normalized difference vegetation index (NDVI), water deficit, and evapotranspiration (ET). The risk map we produced revealed a high to very high drought risk for almost throughout the Türkiye's mountainous areas. We concluded that integrating geospatial techniques with hybridization is promising for mapping drought risk, helping policymakers prepare effective drought mitigation measures to reasonably adapt to climate change impacts.

Illness-related communication between siblings and parents of children with chronic illness and life-limiting conditions: A qualitative analysis.

Having a brother or sister who has a chronic illness (lasting >6 months and requiring long-term care) or life-limiting condition (LLC; where cure is highly unlikely and the child is expected to die) has major impacts on siblings. Parent-sibling illness-related communication may contribute to siblings' capacity to cope. In this study, we aimed to explore parent-sibling illness-related communication, from the perspectives of parents and siblings. We also aimed to qualitatively compare participants' responses according to illness group (chronic illness vs. LLCs). We collected qualitative data from siblings (32 with a brother/sister with a chronic illness, 37 with a brother/sister with an LLC) and parents of a child with a chronic illness (n = 86) or LLC (n = 38) using purpose-designed, open-ended survey questions regarding illness-related communication. We used an inductive qualitative content analysis and matrix coding to explore themes and compare across illness groups. Two-thirds of siblings expressed satisfaction with their family's illness-related communication. Siblings typically reported satisfaction with communication when it was open and age-appropriate, and reported dissatisfaction when information was withheld or they felt overwhelmed with more information than they could manage. Parents generally favored an open communication style with the siblings, though this was more common among parents of children with an LLC than chronic illness. Our findings show that while many siblings shared that they felt satisfied with familial illness-related communication, parents should enquire with the siblings about their communication preferences in order to tailor illness-related information to the child's maturity level, distress, and age.