The prevalent economic principle of weak disposability has been the foundation for studies in environmental assessment using Data Envelopment Analysis (DEA). Recently, a shift from classical free disposability to weak disposability has been observed as an emerging trend for treating undesirable factors in research. Weak disposability is perceived to have significant analytical power in measuring the efficiency of Decision-Making Units (DMUs). Addressing the increment of undesirable inputs, a non-radial model grounded on a non-uniform augment factor is presented. The application of this proposed model anticipates a suitable quantity for the increment of undesirable inputs. Concurrently, the model ensures a corresponding reduction in desirable inputs. Numerical instances illuminate the practicality and robustness of the proposed model and demonstrate its superior performance over its original counterpart.

The prevalence of decision-making methodologies catering to quantitative attributes considerably overshadows those designed for qualitative attributes. This study seeks to address this gap by extending the traditional Ideal Solutions with Constraint on Values (ISOCOV) method to a fuzzy environment, thereby enhancing its capability to handle optimal decision-making based on qualitative attributes. In this improved method, $\alpha$-cut representations are employed for managing linguistic value constraints in performance-oriented data. The proposed approach is then utilized for the selection of nanomaterials, evaluated based on five essential criteria. By subjecting the performance-based decision matrix to the modified fuzzy method, a ranking of alternatives is derived. Compared to its traditional counterpart, this fuzzy-enhanced ISOCOV method demonstrates enhanced efficiency in processing qualitative data, promising its potential compatibility and utility for decision makers dealing with performance-oriented decision-making.

This study introduces a novel approach to decision-making problems, especially in the context of hard disk selection, using the concept of the fuzzy parameterized single-valued neutrosophic soft set (FP-SVNSS). Primarily, the focus is on assigning different levels of importance to each parameter within the set, which enables a more nuanced and flexible evaluation process. This is underpinned by the development of several related concepts and the definition of basic operations such as complement, subset, union, and intersection. In the quest for clarity, the nuances of these operations and the overall framework of the FP-SVNSS method are illustrated via numerous examples. The superiority of the FP-SVNSS method over other decision-making methods is affirmed through a comprehensive comparison. The unique strength of the proposed approach lies in its ability to handle imperfect, ambiguous, and inconsistent data. Consequently, it offers greater accuracy and practicality than existing models. In the latter part of the study, the theory is put to the test by tackling a real-world decision-making problem. The selected case involves the optimal selection of hard disks, a common issue in information technology procurement. The successful application of the FP-SVNSS method to this issue provides a compelling demonstration of its potential value in practical settings. Through the exploration of this innovative decision-making methodology, this research contributes to the broader field of soft computing and decision-making theory. The findings suggest a myriad of future applications of the FP-SVNSS method in dealing with various complex and fuzzy problems in both academic and industrial contexts.

Neutrosophic sets, expanded from the constructs of fuzzy and intuitionistic fuzzy sets, can accommodate degrees of truth, indeterminacy, and falsity for each element. This attribute equips them with an aptitude for a more refined interpretation of ambiguous or uncertain data. This study presents an innovative application of Neutrosophic Data Envelopment Analysis (Neu-DEA), incorporating pentagonal neutrosophic numbers in both input and output data. This novel methodology involves the transformation of traditional DEA models into a Pentagonal neutrosophic DEA model, subsequently converting it into a Crisp Linear Programming (CrLP) model. A unique ranking function is integral to this process. Performance evaluation of decision-making units (DMUs) is accomplished through the resolution of the CrLP model, with subsequent ranking of the DMUs based on their relative efficiency scores. The utility and effectiveness of this novel technique is validated through a numerical example.

This study emphasizes the limitations observed in the prevailing neutrosophic AHP group decision-making model. To address these limitations, an augmented neutrosophic AHP group decision-making model has been established, leveraging the potential of neutrosophic trapezoidal numbers. A comprehensive exploration of a key property of the neutrosophic trapezoidal pairwise comparison matrix is performed in this research, revealing that the current model inadequately maintains the reciprocal property of the neutrosophic trapezoidal pairwise comparison matrix. A real-world decision-making problem is resolved utilizing the introduced model, and a comparative analysis is furnished between the pre-existing neutrosophic AHP group decision-making model and the revised version. The results unequivocally demonstrate the superiority of the enhanced model.

The present study scrutinizes the decision-making strategies and enhancement techniques aimed at minimizing progressive collapse in steel moment frame structures. Comparative analyses of both three-story and five-story frames were carried out, focusing on the reinforcement of external frames through the introduction of bracing. Employing ABAQUS, a sophisticated finite element software, simulations of these frames resulted in the exploration of 16 unique steel frame configurations. In an assessment of column loss impact, the middle column of the lowest story in the supporting frame was deliberately removed. Findings reveal that the axial force of the beams adjacent to the removal site in the three-story frame escalates approximately 2.15 times in relation to the values connected with corner beam extraction. Conversely, the increase in axial force of the beams adjacent to the column removal in the five-story frame varied between 5% and 49% of the respective values for beam removal conditions. Furthermore, a reduction in maximum displacement was found to correlate with an increase in the number of stories. Maximum displacements in five-story frames were observed to be roughly 7% to 22% of the corresponding values in three-story frames, with variability depending on the location of the removed column. These results indicate that the effectiveness of bracing-based reinforcement to prevent progressive collapse in steel moment frame structures intensifies with the increase in the number of stories. This performance enhancement against progressive collapse becomes particularly significant for structures comprising a higher number of stories.