Individual Criteria Research Articles

A Geospatial Approach to Identify and Evaluate Ecological Restoration Sites in Post-Fire Landscapes

Wildfires are a pervasive natural phenomenon in Mediterranean forest ecosystems, causing significant ecological imbalances that demand immediate restoration efforts. The intricacy of reinstating the ecological balance necessitates a proactive approach to identifying and assessing suitable restoration sites. The assessment and investigation of the most suitable restoration sites is of particular importance both for the relevant authorities and for planning and decision making by the state. This study proposes the development of a user-friendly model for evaluating and identifying the most suitable restoration sites immediately after a fire, using geoinformation technologies. For the purposes of demonstrating the method’s applicability, the 2016 fire of “Prinos”, Thasos, Greece, an area that has been repeatedly affected by forest fires, was chosen as a case study. The methodology evaluation was carried out by applying the weighted multicriteria decision analysis method (MCDAM) and was based on a number of variables. The analysis, processing and extraction of the results were performed using primarily remote sensing datasets in a geographical information system (GIS) environment. The methodology proposed herein includes the classification of the individual criteria and their synthesis based on different weighting factors. In the final results, the restoration suitability maps are presented in five suitability zones based on two different scenarios. Based on this study, the integration of geospatial and remote sensing data offers a valuable and cost-effective means for promptly assessing post-fire landscapes, with the aim of identifying suitable restoration sites.

More Than a Match: “Fit” as a Tool in Hiring Decisions

The concept of “fit” has become important for understanding hiring decisions and labor market outcomes. While social scientists have explored how fit functions as a legitimized evaluative criterion to match candidates to jobs in the hiring process, less is known about how fit functions as a hiring tool to aid in decision-making when hiring decisions cannot—or should not—be justified. Drawing on in-depth interviews with 53 hiring professionals, we develop a theoretical argument that hiring professionals can use fit as a tool to circumvent legitimized hiring criteria and justify their hiring goals. Specifically, we show how hiring professionals use fit as a tool to explain their hiring decisions when these decisions cannot or should not be justified and we outline two mechanisms through which this process occurs: (1) fit as a tool for circumventing human capital concerns, and (2) fit as a tool to circumvent hiring policies based upon social characteristics. We argue that fit is more than an evaluative criterion for matching individuals to jobs. Hiring professionals deploy fit as a tool to justify their decisions amid uncertainty and constraint. Fit, then, becomes a placeholder when these hiring decisions are not able to be justified through legitimized means. Our findings reveal some of the potential negative consequences of using fit during the hiring process and contribute important theoretical insights about the role of fit in scholarship on inequality and labor markets.

Methods for REM Sleep Density Analysis: A Scoping Review.

Rapid eye movements (REM) sleep density is the parameter proposed to explain the variability in the amount of eye movements during REM sleep. Alterations in REM sleep density have been proposed as a screening criterion for individuals with depression and other mental health conditions, but its accuracy has not been properly evaluated. The lack of consensus and the variability of the methods used to score it reduces the external validity of the results, hindering an adequate analysis of its diagnostic accuracy and clinical applicability. This scoping review aimed to identify and quantify the methods used to score REM sleep density, describing their main characteristics. A literature search was conducted in PubMed, Scopus, PsycInfo, and Web of Science. Only studies with objective measures for REM sleep density analysis in individuals with depression were considered eligible. The final sample comprised 57 articles, covering 64 analyses of REM sleep density. The relative frequency methods were the predominant measurement parameter for analyzing REM sleep density across studies. The most frequently adopted REM estimation unit was the number of REM events followed by mini-epochs containing REM. The most common unit of measurement were frequency/time measures. The results demonstrate that there is no consistency in the methods used to calculate REM sleep density in the literature, and a high percentage of studies do not describe their methods in sufficient detail. The most used method was the number of REM episodes per minute of REM sleep, but its use is neither unanimous nor consensual. The methodological inconsistencies and omissions among studies limit the replicability, comparability, and clinical applicability of REM sleep density. Future guidelines should discuss and include a specific methodology for the scoring of REM sleep density, so it can be consensually implemented in clinical services and research.

Exploring the Relationship of Cultural Intelligence, Employee Performance, and Cross-cultural Adjustment in the Context of Transnational Operations

The phenomenon of globalization and the advent of a borderless economy have given rise to urgent requirements and criteria for individuals' cultural proficiency in adapting to multicultural situations. Among the measurement indicators related to cultural competence, cultural intelligence has been intensively investigated and emphasized. Meanwhile, in the field of firm management, the most valued aspect by scholars and entrepreneurs is employee performance, which is therefore related to the profitability and development of enterprises. Based on this, this article adopts a systematic literature review methodology to gather papers related to cultural intelligence, employee performance with cross-cultural adjustment serving as a mediator in transnational management both domestically and internationally in the past 10 years. This will help to clarify these three notions-- cultural intelligence, employee performance, and cultural adaptability and accordingly help multinational companies better respond to the era and improve their operational ability and cultural adaptability. Finally, it explores the relevance and limitations of the present research, while providing fresh ideas and pathways for future research.

Evaluation of the Transport Environmental Effects of an Urban Road Network in a Medium-Sized City in a Developing Country

A decision support model (DSM) involving a combination of five different prediction models for the environmental effects of transport and the powerful HMADM approach was introduced for the first time to assess the multiple criteria environmental effects of transport in an urban road network of the Khon Kaen Metropolitan Municipality (KKMM) in Khon Kaen City, Thailand. Five mathematical models were adopted to quantify the CO2 emissions (CO2Es), PM2.5 concentration (PM2.5C), CO concentrations (COCs), noise levels (NOLs), and pedestrian accident risk (PAR) values of all road segments in the study area. The FAHP, FSM, and TOPSIS were integrated into the HMADM to estimate the composite transport environmental effect scores (CTEESs) of each road segment. The FAHP was applied to determine the relative weights of each environmental criterion for three land use types, and the FSM was utilized to transform linguistic (fuzzy) scores into numerical (crisp) scores. Both the FAHP and FSM are principally used to deal with uncertain, incomplete, and ambiguous (fuzzy) information that appears during decision-making processes. Finally, TOPSIS was used to estimate the CTEESs of each road segment. An integrated DSM was applied to comprehend and evaluate each individual environmental criterion and the combined environmental criteria for each road segment in the study area. The DSM was employed to rank the problematic locations of all road segments. For instance, the ranking of the top 12 road segments with the greatest CTEESs was 75, 80, 48, 89, 76, 5, 64, 59, 60, 16, 65, and 62. In addition, this DSM can also be used to identify the possible causes of such locations and allocate limited government budgets for the implementation of appropriate remedial measures for resolving such environmental problems due to transport in an urban road network in the study area.

Time-dependent association between STOPP and START criteria and gastrointestinal bleeding in older patients using routinely collected primary care data

Purpose Only few studies have assessed the preventive effect of the STOPP/START criteria on adverse events. We aim to quantify 1) the association between nonadherence to STOPP/START criteria and gastrointestinal bleedings, and 2) the association between exposure to the potentially harmful START-medications and gastrointestinal bleedings. Design A retrospective cohort study using routinely collected data of patients aged ≥ 65 years from the electronic health records (EHR) of 49 general practitioners (GPs) in 6 GP practices, from 2007 to 2014. The database is maintained in the academic research network database (AHA) of Amsterdam UMC, the Netherlands. Methods Gastrointestinal bleedings were identified using ICPC codes and free text inspections. Three STOPP and six START criteria pertaining to gastrointestinal bleedings were selected. Cox proportional hazards regression with time-dependent covariate analysis was performed to assess the independent association between nonadherence to the STOPP/START criteria and gastrointestinal bleedings. The analysis was performed with all criteria as a composite outcome, as well as separately for the individual criteria. Results Out of 26,576 participants, we identified 19,070 Potential Inappropriate Medications (PIM)/Potential Prescribing Omission (PPO) instances for 3,193 participants and 146 gastrointestinal bleedings in 143 participants. The hazard ratio for gastrointestinal bleedings of STOPP/STARTs, taken as composite outcome, was 5.45 (95% CI 3.62–8.21). When analysed separately, two out of nine STOPP/STARTs showed significant associations. Conclusion This study demonstrates a significant positive association between nonadherence to the STOPP/START criteria and gastrointestinal bleeding. We emphasize the importance of adherence to the relevant criteria for gastrointestinal bleeding, which may be endorsed by decision support systems.

A decision support system for supplier quality evaluation based on MCDM-aggregation and machine learning

Evaluating suppliers’ quality performance is one of critical tasks of quality management since it is directly related to quality assurance, improvement and development, especially for manufacturing companies. However, the supplier quality evaluation remains challenging due to the involvement of a large amount of qualitative and quantitative data, multi-dimensional attributes, as well as a large number of items and suppliers, etc. In addition, while various multiple criteria decision making methods (MCDMs) exist and can be used for supplier quality evaluation, individual choice and application of the methods often result in inconsistent or conflicting evaluation results. To overcome the challenges, in this study, we develop a decision support system for supplier quality evaluation to deal with the practical complexity and to take advantage of the availability of the big industrial data. The system is presented as a conceptual framework including three modules: the decision matrix and criteria initialization module, the MCDM method selection and implementation module, and the aggregation module. The sequential conduction of each module formulates and solves the multiple supplier and item quality evaluation problem. The implementation of the system can be applied to dynamically monitor and evaluate supplier and item quality performance. Moreover, as the core intelligent part of the decision support system, in the aggregation module, we propose an aggregated MCDM model based on the machine learning concepts. It aims at providing more reliable evaluations across multiple suppliers and items. The aggregation model also contributes to the MCDM literature as an independent method which is not restricted to the purpose of supplier quality evaluation. The methods shed lights on how to construct and solve MCDM problems in the big-data era. Finally, the application of the decision support system is illustrated by a case of a large automotive company. The case shows that our decision support system provide more robust and reliable evaluation results than the traditional individual multiple criteria decision making method.

A Review and Comparative Analysis of Methods for Determining Criteria Weights in MCDM Tasks

To select optimal solutions in multicriteria decision-making (MCDM) problems, many practical approaches have been developed. In almost all of these approaches, it is necessary to assess the importance of individual criteria for decision makers. Subjective assessments of importance are transformed into numerical assessments of decision weights by applying appropriate computational procedures. A large number of methods for determining the weights of the criteria have been proposed. These methods differ in their operating principles and in the calculation procedures underlying each method. The paper presents the most well-known methods and provides a brief comparative analysis.

Context-dependent preferences for a decision support system's level of automation

Many organizations use decision support systems (DSS) to support DSS users in their daily work demands (e.g., high workload, insufficient information, ambiguous situations). A key question regarding their interaction is how the decision-control is divided between the DSS and the user, represented by the system's level of automation (LoA). To investigate the need for an adaptable DSS where users can manually adjust the LoA across situations, we used a vignette design to examine whether users prefer different LoA in different situations (i.e., six situational criteria, each manipulated by two specifications; e.g., low vs. high workload). In the twelve vignettes, the 116 participants should imagine working in an emergency control-center—a setting they were familiar with from previous experiments. Our results showed significant differences between the two corresponding vignettes, indicating that users prefer different LoA across situations. However, after controlling for the participants' overall preference for a situation-independent baseline LoA, the significant differences between all paired vignettes disappear. Our results have implications for whether situational or individual criteria are more important regarding LoA preferences, adaptable DSS, and for human-centered design based on user profiles. We discuss our findings in relation to the broader literature on trust and acceptance of DSS.

Peran Social Media Marketing Dalam Meningkatkan Purchase Intention Pada Frozen Food

This study aims to determine the effect of the role of Social Media Marketing in increasing Purchase Intention on Frozen Food. Samples were taken with individual criteria, namely people who had purchased Frozen Food products online at Social Media Marketing in the past year. This study uses primary data obtained through questionnaires distributed to 100 respondents. Based on the time period, the data collected in this study is cross-sectional in nature. The analytical method used is the Structural Equation Model (SEM). The results of this study indicate that Social Media Marketing has a positive impact on Brand Awareness; Social Media Marketing has a positive impact on Brand Engagement; Social Media Marketing does not have a positive impact on Purchase Intention; Brand Awareness does not have a positive impact on Purchase Intention; Brand Awareness does not mediate relationship between Social Media Marketing and Purchase Intention; Brand Engagement does not have a positive effect on Purchase Intention; Brand Engagement mediates the relationship between Social Media Marketing and Purchase Intention

Quality of health care and interdisciplinary collaboration from the perspective of employees and patients

Abstract Introduction. Cooperation in an interdisciplinary team is an important aspect of ensuring the quality and safety of patient care, although the interaction of individuals from different disciplines in the clinical environment can also be hampered by inadequate communication. Aim. This study attempts to fill the research gap and identify professionals’ perceptions of interdisciplinary collaboration and quality measures, the impact of interdisciplinary collaboration on the quality of care in a general hospital and clarify the perspective of patients and healthcare professionals. Material and methods. The case study was conducted using a survey of healthcare professionals (N = 150) and a qualitative approach by interviewing healthcare professionals (N = 47) and patients (N = 20). Results and conclusions. The results of the survey show that the nurses rated all the criteria of interdisciplinary cooperation and individual quality criteria the highest, and the quality of care in general the worst, as they were not satisfied with the implementation due to the lack of staff. The exchange of information did not affect the quality of care because organizational and national authorities did not sufficiently consider the views of staff. In in-depth interviews, patients rate teamwork as poor because they do not get enough explanation and do not feel treated as equal members of the medical team. In in-depth interviews, patients and healthcare professionals are aware of the lack of teamwork among themselves, as well as of the quality of care due to staff shortages in general and especially during the pandemic.

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varies between individuals. When passing a fragile object, risk-averse individuals may adopt a larger margin by following the longer path than risk-prone people do. However, it is not known whether this variation is associated with a personalized cost function used for the individual optimal control policies and how it is represented in our brain activity. This study investigates whether such individual variations in evaluation criteria during reaching results from differentiated weighting given to energy minimization versus comfort, and monitors brain error-related potentials (ErrPs) evoked when subjects observe a robot moving dangerously close to a fragile object. Seventeen healthy participants monitored a robot performing safe, daring and unsafe trajectories around a wine glass. Each participant displayed distinct evaluation criteria on the energy efficiency and comfort of robot trajectories. The ErrP-BCI outputs successfully inferred such individual variation. This study suggests that ErrPs could be used in conjunction with an optimal control approach to identify the personalized cost used by CNS. It further opens new avenues for the use of brain-evoked potential to train assistive robotic devices through the use of neuroprosthetic interfaces.

Optimization methods for multi-criteria decisions in pharmacy

Optimization methods for multi-criteria decisions in pharmacy
 In pharmaceutical technological research, the determination of the quantitative composition of granules is considered as a task of multi-criteria selection. Today, to solve this problem, the regression analysis and multi-criteria optimization methods are widely used; they are based on mathematical models obtained for the object under study.
 Aim. To identify a decision-making method in a multi-criteria space that is effective for use in pharmaceutical technology research with quantitative factors.
 Materials and methods. The study uses tools of the popular computer mathematics system Mathcad (MathSoft Ins., USA) to automate the solution of mathematical problems. To automatically search for the type and coefficients of regression equations, the MS Excel application was used, namely: the data analysis package (regression analysis). The MS Word processor was used to edit the code.
 Results. A variety of approaches to the formalization of the multi-criteria optimization task have been studied. The optimal quantitative content of excipients when developing the granule technology has been found using two different optimization criteria, which are formed according to different methodical approaches. The method proposed does not provide for the mandatory introduction of gradation of individual criteria or their weighting factors.
 Conclusions. As a result of the comparison of multi-criteria optimization methods, the effectiveness of the decision-making method in the multi-criteria space has been shown; it synthesizes a mathematical procedure related to the vector of criteria and is based on determining the ideal point and introducing the concept of a norm into the space of functionals; it has not been mathematically proven, but it is practically useful decision-making algorithm compared to the mathematical method of convolution of criteria. The optimization method proposed has advantages that are manifested in the possibility of using a relatively simple mathematical apparatus and simplified logic of obtaining a solution.

Prioritizing conservation practice locations for effective water quality improvement using the Agricultural Conservation Planning Framework (ACPF) and the Soil and Water Assessment Tool (SWAT)

Adopting the right agricultural conservation practices (CPs) at the right place is critical to maximizing water quality benefits. The Agricultural Conservation Planning Framework (ACPF) tool identifies all potential CPs and their locations within a target watershed based on the landscape characteristics. The ACPF tool suggests hundreds of CP locations in a watershed, making it challenging to prioritize the CP implementation. We develop and demonstrate an efficient approach using a multicriteria ranking technique for prioritizing the CPs suggested by ACPF, considering pollution hotspots and CP cost and effectiveness to support decision-makers. The pollution hotspots are estimated with simulations from an ecohydrological model, the Soil and Water Assessment Tool (SWAT). The CP cost and effectiveness were estimated from CP installation costs and pollutant reduction efficiencies from the literature. The methodology was demonstrated in the Conewago and Mahantango watersheds in Pennsylvania, US, for grassed waterways (GWWs) and water and sediment control basins (WASCOBs) for sediment load reduction. Multiple CP prioritization scenarios were evaluated with individual and combined criteria for reductions in total sediment load, yield (loading per area), and cost. In single criterion-based prioritization, the cost-based and load-based prioritization indicated cost efficiency and fewer CPs, respectively. The yield-based approach correctly prioritized CPs in sediment loss hotspots in the case study watersheds; however, it needed more CPs to meet the target reductions. The multicriteria approach efficiently prioritized CPs in sediment hotspots to meet target reductions. Although this approach was demonstrated in two case study watersheds and for sediment loss reduction, it is applicable for any location or pollutant for which similar input variables can be provided, thereby providing a means for prioritizing the results of ACPF for implementation in the Mid-Atlantic region of the US.

Performance Evaluation of Priority-based Scheduling in Hybrid VANETs for Different Criteria Weights Using AHP-AHP and AHP-TOPSIS

Multi-Criteria Decision Making (MCDM) approaches have been developed to support a wider range of application areas. For example, channel allocation, among the vehicles in infrastructure-based/hybrid Vehicular Ad hoc NETworks (VANETs), is done by exploiting MCDMs to minimize the waiting time and maximize the request service rate by the Road Side Unit (RSU). The Analytic Hierarchy Process (AHP) developed by Saaty, however, has gained enormous popularity and been used all over the world since its invention in 1977. AHP is capable of prior weight calculation of individual criteria using Criteria Preference Matrix (CPM) and rank computation of alternatives as well. Another MCDM technique, termed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was created by Hwang and Yoon in 1981. It is a ranking technique that requires inputs of the weights of the criteria. This paper aims to analyze the impact on ranks of various alternatives for different CPMs using the AHP-AHP method and the AHP-TOPSIS method. For both cases weights of criteria have been calculated using AHP before rank computation. In addition to that, this paper deals with the performance evaluation of infrastructure-based VANETs for priority-based channel allocation using the AHP-AHP and the AHP-TOPSIS method in terms of Request Service Rate and Average Waiting Time for various CPMs. It is observed that AHP-TOPSIS performs better in terms of Request Service Rate with the increasing alternatives. Furthermore, the AHP-AHP method shows frequent rank change while increasing the number of alternatives for various CPMs.

Evaluation of disease progression in patients with ATTR amyloidosis with cardiomyopathy following treatment with patisiran: post-hoc analysis of the APOLLO-B study

Abstract Introduction Transthyretin-mediated (ATTR) amyloidosis is a progressive, fatal disease in which cardiac deposition of transthyretin (TTR) amyloid commonly manifests as cardiomyopathy. Patisiran, an RNA interference therapeutic that reduces TTR synthesis, is approved for the treatment of hereditary ATTR amyloidosis with polyneuropathy. The Phase 3 APOLLO-B study (NCT03997383) evaluated the clinical efficacy and safety of patisiran in patients with ATTR amyloidosis with cardiomyopathy (CM). Purpose This post-hoc analysis evaluated disease progression in APOLLO-B patients following treatment with patisiran vs placebo, based on the European Society of Cardiology (ESC) expert consensus on monitoring patients with ATTR amyloidosis with CM every 6–12 months using three domains: Clinical/Functional, Laboratory Biomarker, and Imaging/Electrocardiography (ECG). Methods Patients with ATTR amyloidosis with CM were randomised (1:1) to intravenous patisiran 0.3 mg/kg or placebo every 3 weeks for 12 months. Disease progression was defined at Month 12 (M12) relative to baseline using Clinical/Functional criteria (≥1 heart failure-related hospitalisation, increased New York Heart Association class ≥1, decline in Kansas City Cardiomyopathy Questionnaire ≥5, or decline in 6-minute walk test ≥30 metres), Laboratory Biomarker criteria (increased N-terminal prohormone B-type natriuretic peptide ≥30% and absolute change ≥300 ng/L, troponin I ≥30%, or ATTR disease stage ≥1), and Imaging/ECG criteria (conduction disturbances or change in left ventricular wall thickness ≥2 mm, change in diastolic dysfunction grade ≥1, or change in systolic function measurements). One marker from each domain provides the minimum criteria for disease progression. Results At M12, the patisiran group (N = 180) had improved odds of no disease progression compared with the placebo group (N = 178; odds ratio [OR] 1.4; 95% confidence interval [CI] 0.77, 2.55) (Figure). The patisiran group had benefits in Clinical and Functional markers vs placebo at M12 (OR 1.58; 95% CI 1.03, 2.42). Patisiran also showed favourable changes in Laboratory Biomarkers vs placebo at M12 (OR 2.14; 95% CI 1.33, 3.43) and a favourable trend in Imaging assessments/ECG vs placebo at M12 (OR 1.31; 95% CI 0.80, 2.15). Conclusion In this post-hoc analysis of APOLLO-B, fewer patisiran-treated patients had disease progression with respect to clinical, functional, and biomarker parameters vs placebo. The risk of disease progression was lower by Clinical/Functional and Laboratory Biomarker composite criteria, and trended lower by individual criterion from the ESC consensus statement among patisiran-treated patients at 12 months vs placebo patients. Long-term follow-up will further assess the impact of patisiran in patients with ATTR amyloidosis with CM.Figure 1

Abstract 14598: Predictive Value of the High Bleeding Risk Criteria of the Academic Research Consortium in Black and White Individuals

Introduction: The High Bleeding Risk (HBR) criteria of the Academic Research Consortium (ARC) are a valuable tool for bleeding risk stratification in White and Asian patients undergoing percutaneous coronary intervention (PCI). Hypothesis: The predictive value of the ARC-HBR criteria may vary in Black patients. Methods: Patients who underwent PCI at a large tertiary-care center from 2012 to 2019 who self-reported as non-Hispanic White or Black were included and stratified in HBR and no-HBR based on the ARC-HBR definition. The primary outcome was major bleeding (according to a study specific definition) at 1 year after PCI; secondary outcomes included post-discharge major bleeding, all-cause death and myocardial infarction (MI). Results: Among 9462 patients, 1881 (19.9%) identified as Black and 7581 (80.1%) as White. In these two groups, the HBR criteria were met by 50.8% and 45.6% of patients, respectively, and the frequency of each individual ARC-HBR criterion differed (Figure 1). HBR was associated with a consistent higher risk of major bleeding in Black (7.8% vs. 3.5%, adj. HR 2.02, 95% CI 1.29-3.17) and White patients (7.5% vs. 2.7%, adj. HR 2.49, 95% CI 1.95-3.18, interaction p-value 0.451) (Figure 1). The HBR-related risk of post-discharge bleeding tended to be higher in Black patients (adj. HR 1.67, 95% CI 0.84-3.33) and was significantly increased in White patients (adj HR 3.09, 95% 2.06-4.64, interaction p-value 0.341). All-cause death and MI were increased in HBR patients consistently in the Black and the White group. The predictive value of the ARC-HBR criteria for major bleeding and post-discharge bleeding was good in both Black (0.72 and 0.74, respectively) and White (0.70 and 0.68, respectively). Conclusions: In Black and White patients undergoing PCI, the ARC-HBR criteria were differently distributed and had a good predictive value for major bleeding in both groups.

Applying Utility Criteria to Select the Design Variant of the Transport System in Underground Mine Workings

This article presents a new, in-house developed method of selecting a variant of the transport system in the underground of a mine, using multi-variant decision support, taking into account the specificity of an underground mining plant. The implementation of the method should facilitate the selection of the most optimal transport system, ensuring continuity and the lowest operating costs. Seven functional criteria are proposed herein, which may be of a stimulant or destimulant nature. Each criterion was assigned a specific scoring weight reflecting the level of significance, with the sum of the weights being 100. The highest scores for the variants in the individual criteria go to those characterized by the following traits: the shortest transport time, the highest compatibility with the transport system already existing in the mine, transport routes with the greatest coverage communication, allow workers to be transported to the front of the excavation as quickly as possible, are most compatible with the existing transport systems in terms of the reinforcement and removal of longwalls, have a drive with the lowest operational hazard, have the least negative impact on the atmosphere of workings (exhaust gas emissions), and those that will ensure the best functioning of transport in emergency situations involving risk or uncertainty. For each criterion, a scoring formula based on specific parameters is provided. The method was used to select the optimal variant of the transport system in one of the mines, where four long walls were cut and four long galleries were drilled. Out of ten variants, the variant that should ensure the highest degree of reliable transport operation and continuity of operation has been determined using seven usability criteria.

A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Selecting the best out of a pool of potential suppliers is one of the most significant challenges in supply chain management. Although numerous works have studied supplier selection (SS) as a function of individual criterion (e.g., sustainability or resiliency), few studies have explored it as an all-in-one. To address this gap, this study proposes a novel fuzzy multi-criteria decision-making (MCDM) framework for a real-world color and glaze case study. First, resilient-sustainable criteria are identified for evaluating the performance of the suppliers. Expert opinions and literature review are then utilized to establish a set of criteria in two different categories: sustainability and resiliency. This two-pronged approach ensured that important criteria are considered in the evaluation process. Next, a new fuzzy simultaneous evaluation of criteria and alternatives (FSECA) model is developed to highlight the most crucial criteria and the best supplier. The proposed model utilizes a predetermined set of criteria reference weights as benchmarks to calculate the final weights that closely align with these reference points. Moreover, the consideration of fuzzy standard deviation and correlation coefficients of fuzzy numbers are incorporated to take into account the inherent variability and uncertainty in the data, resulting in a more accurate and robust analysis of the data. To validate the proposed method, the findings are compared to the state-of-the-art fuzzy approaches for SECA method, and other MCDM techniques such as fuzzy weighted aggregated sum product assessment (FWASPAS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (FMOORA), fuzzy simple additive weighting (FSAW), and fuzzy complex proportional assessment (FCOPRAS). Moreover, by applying the geometrical analysis for interactive aid (GAIA), a visualization approach, the results demonstrate that the performance of the proposed framework is satisfactory and highlight the most crucial criteria, such as the supplier responsiveness in resiliency and delivery in economic impact of sustainability categories. Since choosing supplier is a strategic decision, the proposed work can help managers to choose the right suppliers.

Association Between Slow-Wave Sleep Loss and Incident Dementia

Slow-wave sleep (SWS) supports the aging brain in many ways, including facilitating the glymphatic clearance of proteins that aggregate in Alzheimer disease. However, the role of SWS in the development of dementia remains equivocal. To determine whether SWS loss with aging is associated with the risk of incident dementia and examine whether Alzheimer disease genetic risk or hippocampal volumes suggestive of early neurodegeneration were associated with SWS loss. This prospective cohort study included participants in the Framingham Heart Study who completed 2 overnight polysomnography (PSG) studies in the time periods 1995 to 1998 and 2001 to 2003. Additional criteria for individuals in this study sample were an age of 60 years or older and no dementia at the time of the second overnight PSG. Data analysis was performed from January 2020 to August 2023. Changes in SWS percentage measured across repeated overnight sleep studies over a mean of 5.2 years apart (range, 4.8-7.1 years). Risk of incident all-cause dementia adjudicated over 17 years of follow-up from the second PSG. From the 868 Framingham Heart Study participants who returned for a second PSG, this cohort included 346 participants with a mean age of 69 years (range, 60-87 years); 179 (52%) were female. Aging was associated with SWS loss across repeated overnight sleep studies (mean [SD] change, -0.6 [1.5%] per year; P < .001). Over the next 17 years of follow-up, there were 52 cases of incident dementia. In Cox regression models adjusted for age, sex, cohort, positivity for at least 1 APOE ε4 allele, smoking status, sleeping medication use, antidepressant use, and anxiolytic use, each percentage decrease in SWS per year was associated with a 27% increase in the risk of dementia (hazard ratio, 1.27; 95% CI, 1.06-1.54; P = .01). SWS loss with aging was accelerated in the presence of Alzheimer disease genetic risk (ie, APOE ε4 allele) but not hippocampal volumes measured proximal to the first PSG. This cohort study found that slow-wave sleep percentage declined with aging and Alzheimer disease genetic risk, with greater reductions associated with the risk of incident dementia. These findings suggest that SWS loss may be a modifiable dementia risk factor.