Search Results (277)

Respectful maternity care involves privacy, dignity, and informed choice within the process of delivery as stipulated by the World Health Organization (WHO). Informed consent is a cornerstone of patient-centered care, representing not just a formal document, but an ongoing ethical and clinical process through which women are offered objective, understandable information to support autonomous, informed decision-making. This narrative review critically examines the literature on informed consent in maternity care, with particular attention to both obstetric-led and midwifery-led models of care. In addition to identifying institutional, cultural, and systemic obstacles to its successful implementation, the review examines the definition and application of informed consent in perinatal settings and evaluates its effects on women’s autonomy and satisfaction with care. Important conclusions emphasize that improving women’s experiences and minimizing needless interventions require active decision-making participation, a positive provider–patient relationship, and ongoing support from medical professionals. However, significant gaps persist between legal mandates and actual practice due to provider attitudes, systemic constraints, and sociocultural influences. Women’s experiences of consent can be more effectively understood through the use of instruments such as the Mothers’ Respect (MOR) Index and the Mothers’ Autonomy in Decision Making (MADM) Scale. To promote genuinely informed and considerate maternity care, this review emphasizes the necessity of legislative reform and improved provider education in order to close the gap between policy and practice. Full article

Failure Mode and Effects Analysis (FMEA) is a proactive management technique widely used to improve the reliability of products and processes across various business sectors. Due to rapid changes stemming from uncertain environments, numerous studies have proposed different approaches to enhance the effectiveness of the FMEA method. However, there is a lack of systematic literature reviews and classification of research on this topic. The purpose of this paper is to systematically review the literature on the integration of FMEA with Multi-Attribute Decision-Making (MADM) theories and various mathematical models. This study analyses a total of 68 papers published between 2015 and 2024, selected from 51 peer-reviewed journals indexed in Scopus and/or Web of Science. Furthermore, a bibliometric analysis was conducted based on the frequency of different mathematical theories used to model existing uncertainties, methods for determining the weighting vectors of risk factors (RFs), the use of MADM theories extended with uncertain numbers for weighting RFs and prioritizing identified failure modes, publication years, journals, and application domains. This research aims to support both researchers and practitioners in effectively adopting uncertain MADM methods to address the limitations of traditional FMEA and provide insight into the current state of the art in this field. Full article

The selection of an appropriate cloud platform represents a highly important strategic decision for any IT company. In pursuit of business optimization, cost reduction, improved reliability, and enhanced market competitiveness, selecting the most suitable cloud platform has become a major practical challenge. This paper proposes a novel two-stage multi-attribute decision-making (MADM) model, enhanced through the use of interval type-2 fuzzy sets (IT2FMADM). This was demonstrated through a case study in an IT company based in Serbia. In the first stage, three experts from the company were surveyed to assess the relative importance of the attributes, and their evaluations were aggregated using the fuzzy harmonic mean operator. As a result, unified fuzzy weight vectors were obtained. In the second stage, two MADM methods extended with interval type-2 fuzzy sets, namely COmplex PRoportional Assessment (IT2FCOPRAS) and Evaluation based on Distance from Average Solution (IT2FEDAS), were applied to support the selection of the most suitable cloud platform. Each platform was evaluated by decision-makers (DMs), who reached a consensus in their assessments, supported by data from company records. A comparative analysis of the results revealed that different methods may produce varying rankings of alternatives, particularly when the alternatives are objectively similar in their characteristics. Nevertheless, the proposed model can serve as a highly useful decision-support tool for company management. Full article

Natural disasters and emergencies continue to increase in frequency and severity worldwide, necessitating robust emergency management (EM) systems and evaluation methodologies. This study addresses critical gaps in current emergency response capacity (ERC) evaluation frameworks by developing a comprehensive quantitative decision-making model to assess ERC more effectively. This research constructs a systematic ERC assessment framework based on the four phases of the disaster management cycle (DMC): prevention, preparedness, response, and recovery. The methodology employs multi-criteria decision analysis to evaluate ERC using three distinct information representation environments: intuitionistic fuzzy (IF) sets, linguistic variables (LV), and a novel mixed IF-LV environment. For each environment, we derive appropriate aggregation operators, weight determination methods, and information fusion mechanisms. The proposed model was empirically validated through a case application to emergency plan selection in Shenzhen, China. A statistical analysis of results demonstrates high consistency across all three decision environments (IF, LV, and mixed IF-LV), confirming the model’s robustness and reliability. A sensitivity analysis of key parameters further validates the model’s stability. Results indicate that the proposed decision-making approach provides significant value for EM by enabling more objective, comprehensive, and flexible ERC assessment. The indicator system and evaluation methodology offer decision-makers (DMs) tools to quantitatively analyze ERC using various information expressions, accommodating both subjective judgments and objective metrics. This framework represents an important advancement in emergency preparedness assessment, supporting more informed decision-making in emergency planning and response capabilities. Full article

In response to the threat assessment challenge posed by unmanned aerial vehicles (UAVs) in air defense operations, this paper proposes a dynamic assessment model grounded in fuzzy multi-attribute decision making. First, a three-dimensional evaluation index system is established, encompassing capability, opportunity, and intention. Quantification functions for assessing the threat level of each attribute are then designed. To account for the temporal dynamics of the battlefield, an innovative fusion approach is developed, integrating inverse Poisson distribution time weights with subjective–objective comprehensive weighting, thereby establishing a dynamic variable weight fusion mechanism. Among these, the subjective weights are determined by integrating the intention probability matrix, effectively incorporating the intentions into the threat assessment process to reflect their dynamic changes and enhancing the overall evaluation accuracy. Leveraging the improved technique for order preference by similarity to ideal solution (TOPSIS), the model achieves threat prioritization. Experimental results demonstrate that this method significantly enhances the reliability of threat assessments in uncertain and dynamic battlefield environments, offering valuable support for air defense command and control systems. Full article

The natural environment plays a vital role in children’s health, influencing their physical, emotional, social, psychological, and spiritual well-being. Maintaining a continuous relationship with nature is essential for children and is a key consideration for professionals, such as architects, urban and interior designers, and landscape architects. School design should balance students’ abilities and environmental challenges and offer opportunities to alleviate mental fatigue, supporting sustained learning. The well-known architectural approach, biophilic, fostering a stronger connection between nature and humans, can significantly enhance students’ learning experiences and mental health in school settings. However, implementing this style in Iranian primary schools has largely been overlooked despite its potential to develop a more peaceful and dynamic environment. This research ranked several schools in northern Iran based on biophilic criteria to help authorities identify which schools require improvements. To this end, biophilic design elements in schools were identified through a literature review and provided to research experts. The most important criteria for evaluating and prioritizing options (schools) were selected based on their opinions. Subsequently, each criterion’s importance (weight) was determined using pairwise comparisons, and, finally, the schools were prioritized using the TOPSIS method. Full article

Against the backdrop of global economic changes and rapid technological innovation, the sharing economy model is gradually transforming the operational mechanisms of traditional industries. However, some industries have experienced stagnation and recession during this transition, leading to market development constraints. The necessity of this study lies in filling the gap in the existing literature by conducting an in-depth analysis of the critical factors contributing to industrial stagnation and recession in the sharing economy. This study aims to provide concrete countermeasures for businesses and policymakers. The novelty of this research study lies in integrating multiple key variables affecting industrial development, including green production concepts, the circular economy, large-scale production, high-quality product demand driven by industrial automation, the sharing economy, and smart production. By employing multi-criterion decision-making methods, we quantitatively assess the impact of these factors more accurately. This study employs the Multi-Attribute Decision-Making (MADM) model, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) to form D&ANP for analytical research. Highly automated industries are selected as the research subjects. The DEMATEL technique is used to construct the Influential Network Relationship Map (INRM), while the ANP concept is incorporated to develop the D&ANP model. Through the D&ANP method, influential weights are calculated and combined with industry-specific assessments of the suitability of potential causes (or attributes) contributing to economic stagnation and recession to determine the average performance values for each industry. These values are further compared with benchmark suitability performance values to distinguish ideal and non-ideal conditions across industries facing economic stagnation and recession. The analysis results indicate that different industries are influenced by varying factors, requiring strategic adjustments based on their unique development environments. Accordingly, this study provides industry-specific recommendations to optimize business models and resource allocation, mitigate the risks of economic stagnation and recession, and promote sustainable industrial development and economic recovery. The findings of this study not only contribute to empirical research on the impact of the sharing economy on industrial development but also serve as a decision-making reference for businesses. By offering strategic insights, enterprises can better respond to market dynamics, enhance competitiveness, and ensure long-term stable growth. Full article

As people’s environment awareness increases and the “double carbon” policy is implemented, the new energy vehicle (NEV) becomes a popular form of transformation and more and more car manufacturers start to produce NEVs. Thus, how to choose an appropriate type of NEVs from many brands is an interesting topic for customers, which can be regarded as a multiple-attribute decision-making (MADM) problem because customers often concern several different factors such as the price, endurance mileage, appearance and so on. This paper proposes a possible degree-based D–S evidence theory method for helping customers select a proper type of NEVs in the probabilistic linguistic environment. In order to derive decision information reflecting customer demands, online customer reviews (OCRs) are crawled from multiple websites and converted into five-granularity probabilistic linguistic term sets (PLTSs). Afterwards, by maximizing deviation and minimizing the information uncertainty, a bi-objective programming model is built to determine attribute weights. Furthermore, a possible degree-based D–S evidence theory method in the PLTS environment is proposed to rank alternatives in each website. For fusing these ranking results, a 0–1 programming model is set up by maximizing the consensus between the comprehensive ranking and individual ones in each website. At length, a case study of selecting a type of NEVs is provided to show the application and validity of the proposed method. Full article

It was recently proposed to extend the spherical fuzzy set to spherical fuzzy credibility sets (SFCSs). In this paper, we define the concept of SFCSs. We then define new operational laws for SFCSs using Dombi operational laws. Various spherical fuzzy credibility aggregation operators such as spherical fuzzy credibility Dombi weighted averaging (SFCDWA), spherical fuzzy credibility Dombi ordered weighted averaging (SFCDOWA), spherical fuzzy credibility Dombi weighted geometric (SFCDWG), and spherical fuzzy credibility Dombi ordered weighted geometric (SFCDOWG) are defined. We also show the boundedness, monotonicity, and idempotency aspects of the suggested operators. We proposed the spherical fuzzy credibility entropy to find the unknown weight information of the attributes. Symmetry analysis is a useful and important tool in artificial intelligence that may be used in a variety of fields. To calculate the significant factor, we determine the multi-attribute decision-making (MADM) method using the suggested operators for SFCSs to increase the value of the assessed operators. To demonstrate the effectiveness and superiority of the suggested approach, we compare our findings to those of many other approaches. Full article

Beginning with a symmetrical multiple-choice individual as the foundation, I develop a sociophysics model of decision-making. By simplifying the range of choices, the framework incorporates the complex Polytopic fuzzy model to capture nuanced dynamics. This approach enables a deeper analysis of decision-making processes within social systems. Decision-making problems commonly involve uncertainty and complexity, posing considerable challenges for organizations and individuals. Due to their structure and variable parameters, the Einstein t-norm (ETN) and t-conorm (ETCN) offer more elasticity than the algebraic t-norm (ATN) and t-conorm (ATCN). This flexibility makes them commonly effective and valuable in fuzzy multi-attribute decision-making (MADM) problems, where nuanced valuations are critical. Their application enhances the ability to model and analyze vagueness and uncertain information, eventually leading to more informed decision outcomes. The complex Polytopic fuzzy set (CPFS) improves the Polytopic fuzzy set (PFS) and complex fuzzy set (CPFS), allowing for a more precise valuation of attributes in complex (MADM) problems. This study aims to propose a MADM scheme using the ETN and ETCN within the framework of a complex Polytopic fuzzy environment. It begins by presenting the Einstein product and sum operations for complex Polytopic fuzzy numbers (CPFNs) and explores their necessary properties. This method enhances the accuracy and applicability of DM processes in ambiguous environments. Subsequently, three complex Polytopic fuzzy operators with known weighted vectors are developed: the complex Polytopic fuzzy Einstein weighted averaging (CPFEWA) operator, complex Polytopic fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Polytopic fuzzy Einstein hybrid averaging (CPFEHA) operator. Moreover, some substantial properties of the operators are studied. Finally, a method based on novel operators is planned, and a numerical example is provided to prove the practicality and effectiveness of the new proposed methods. Full article

This investigation reports on the efficacy of a photoelectrocatalysis (PEC) system enhanced by a nitrogen-doped TiO₂ nanocrystal-modified TiO₂ nanotube array (N-TiO₂ NCs/TNTAs) anode paired with a graphene oxide/activated carbon (GO/AC) photocathode for diclofenac removal from effluent. The FE-SEM and EDX analyses validated the elemental composition of the anode—27.56% C, 30.81% N, 6.03% O, and 26.49% Ti. The XRD results confirmed the anatase phase and nitrogen integration, essential for photocatalytic activity enhancement. Quantum chemical simulations provided a comprehensive understanding of the red-shifted absorption bands in N-TiO₂, and UV-vis DRS demonstrated a red-shift in absorption to the visible spectrum, indicating improved light utilization. The PEC configuration achieved a photocurrent density of 9.8 mA/dm², significantly higher than the unmodified and solely nitrogen-doped counterparts at 4.8 mA/dm² and 6.1 mA/dm², respectively. Notably, this system reduced diclofenac concentrations by 58% within 75 min, outperforming standard photocatalytic setups. These findings underscore the potential of N-TiO₂ NCs/TNTAs-AC-GO/PTFE composite material for advanced environmental photoelectrocatalytic applications. Full article

The paper presents an innovative method for tackling multi-attribute decision-making (MADM) problems within a hesitant fuzzy (HF) framework. Initially, the paper generalizes the Chi-square distance measure to the hesitant fuzzy context, defining the HF generalized Chi-square distance. Following this, the paper introduces the power average (P-A) operator and the power geometric (P-G) operator to refine the weights derived from Shannon entropy, taking into account the inter-attribute support. Leveraging the strengths of Aczel–Alsina operations and the power operation, the paper proposes the hesitant fuzzy Aczel–Alsina power weighted average (HFAAPWA) operator and the hesitant fuzzy Aczel–Alsina power weighted geometric (HFAAPWG) operator. Consequently, a hesitant fuzzy Aczel–Alsina power model is constructed. The applicability of this model is demonstrated through a case study examining the urban impacts of cyclonic storm Amphan, and the model’s superiority is highlighted through comparative analysis. Full article

In multi-attribute decision making (MADM), complex situations often arise where decision attributes are interval-valued hesitant fuzzy numbers (IVHFNs) and the attributes are interrelated. Traditional decision-making methods may be ineffective in handling such cases, highlighting the practical importance of seeking more effective approaches. Therefore, finding a more effective decision-making approach has important practical significance. By combining the theories of Archimedean S-norms and T-norms, we innovatively propose a multi-attribute decision-making method based on the generalized interval-valued hesitant fuzzy weighted Heronian mean (GIVHFWHM) operator to address the aforementioned issues. Initially, based on the operational laws of IVHFNs and the Heronian mean (HM) operator, we introduce the generalized interval-valued hesitant fuzzy Heronian mean (GIVHFHM) operator and the GIVHFWHM operator. We then examine properties of the GIVHFHM operator, including permutation invariance, idempotency, monotonicity, boundedness, and parameter symmetry. A multi-attribute decision-making model is constructed based on the GIVHFWHM operator. Finally, we validate the proposed model through numerical experiments in MADM. The results demonstrate that the new decision-making method, based on the GIVHFWHM operator, is feasible and effective in handling multi-attribute decision problems involving IVHFNs with interdependent attributes. This approach provides a novel perspective and method for solving MADM problems under interval-valued hesitant fuzzy conditions with interdependent attributes. It enriches the theoretical framework of multi-attribute hesitant decision models and expands the application of the Heronian mean operator within interval-valued hesitant fuzzy environments. This methodology assists decision makers in making more accurate decisions within complex decision-making contexts, enhancing both the scientific rigor and reliability of decision-making processes. Full article

Personnel appraisal and promotion are fundamental processes in Human Resource Management (HRM), requiring advanced methodologies that adeptly combine objective data with subjective assessments. This paper introduces ES-MADM II, an enhanced iteration of the Entropy Synergy Multi-Attribute Decision-Making model, designed to strengthen decision-making robustness and stability. The model incorporates key entropy-based indices such as Normalized Mutual Information (NMI), Criteria Effectiveness Score (CES), Conditional Stability Factor (CSF), and the newly introduced Alternatives Distinction Index (ADI). Together, these indices offer a comprehensive framework for assessing not only decision accuracy but also the overall resilience and clarity of the evaluation process. The effectiveness of ES-MADM II is showcased through military HRM case studies, illustrating how the model enhances personnel performance appraisals and promotion decisions by harmonizing subjective judgments with objective metrics. A detailed sensitivity analysis further demonstrates the model’s adaptability to variations in input data while preserving decision integrity. ES-MADM II ultimately fosters a more transparent, balanced, and equitable decision-making process, making it an indispensable tool for HR decision makers in complex organizational settings. This refined approach underscores the model’s capacity to navigate the complexities of HR evaluations with rigor and fairness. Full article

Secondary intention healing offers an alternative when surgical options are infeasible. This study analyzed the effect of micronized acellular dermal matrices (mADMs; CGderm Matrix^®, CG Bio, Seoul, Republic of Korea) on secondary intention healing in patients with complex soft-tissue defects and assessed mADMs’ efficacy in promoting secondary healing and improving clinical outcomes in these challenging cases. This retrospective study included 26 patients treated with sheet-type reprocessed mADMs between August 2022 and December 2022 at Soonchunhyang University Cheonan Hospital. Patients with full-thickness skin defects classified as complex wounds were included. Data on demographics, wound characteristics, and treatment outcomes were collected and analyzed. Wound area was measured using ImageJ software, and statistical analyses were conducted using SPSS. The application of mADMs resulted in a median wound area reduction of 81.35%, demonstrating its significant efficacy in wound healing. Most patients presented with compromised vascular supply, significant tissue loss, or infections that precluded conventional surgical interventions. No significant correlations were observed between patient variables and wound-healing outcomes, indicating the complex nature of wound healing. mADMs effectively promote secondary intention healing by providing a supportive extracellular matrix scaffold that enhances epithelialization and angiogenesis. Their rapid absorption, ease of handling, and ability to improve wound tensile strength make them particularly suitable for complex wounds. Full article