The enhancement of electrode materials' properties for improving mercantile supercapacitors' performances is a remarkable research area. Throughout recent years, a significant amount of research has been devoted to improving the electrochemical performance of supercapacitors via the improvement of novel electrode materials. The nanocomposite structure provides a greater specific surface area (SSA) and lower ion/electron diffusion tracks, consequently enhancing supercapacitors' energy density and specific capacitance. These significant properties offer a wide range of potential for the electrode materials to be applied in diverse applications. For instance, their applications are in portable electronic systems such as all-solid-state supercapacitors, flexible/transparent supercapacitors and hybrid supercapacitors. The authors of this paper introduced a multi-criteria model to assess the priority of nanostructured electrode materials (NEMs) for high-performance supercapacitors (HPSCs). This work combines Analytic Hierarchy Process (AHP) with the Evaluation Based on Distance from Average Solution (EDAS) and Grey Relational Analysis (GRA) methods. Herein, the rough concept addresses the uncertainties resulting from the group decision-making process and the vague values of the properties of the NEMs. The modified R-AHP method was employed to find the criteria weights based on the multi-experts' opinions. The results reveal that specific capacitance (SC) and energy density (ED) are the most important criteria. R-AHP was integrated with R-EDAS and R-GRA models to evaluate the fourteen NEMs. The results of the R-EDAS method were compared with those provided by the R-GRA method. The results of the proposed integrated approach confirmed that it results in reliable and reputable ranks that will provide a framework for further applications and help physicists find optimal materials by evaluating various alternatives.

Yemen has suffered from a civil war since 2015, which caused the largest famine in the world at this time. People came in need of urgent humanitarian relief in all sectors. In this situation, the donor countries are offering funds to non-profit humanitarian organizations to help Yemen in critical sectors, such as food, health, water, education and other sectors. We propose a new bi-level optimization distribution model for large-scale emergency logistics in Yemen. The upper-level model aims to minimize the unmet demand. The lower-level model seeks to maximize the funds sent to affected areas that fulfill the needs of the affected people by appealing to the donor countries to increase the funds. This model ensures a satisfying rate of equity and efficiency distribution among aid recipients of all governorates of Yemen based on their needs. We consider in this work the top ten donor countries, the nine sectors of the sustainable development goals, the five top humanitarian organizations and twenty-two disastrous regions. The model is applied and validated using actual data collected from Yemen in 2021. The results indicate the necessity of redistributing funds to all governorates of Yemen according to their needs and the priority of the supporting sectors. This proposed model is essential to humanitarian relief decision-makers in general and workers in Yemen in particular as it ensures the continuous flow of aid from donors to beneficiaries and is equitable and effectively distributed. It also gives a glimpse of the importance of continuing to appeal for fundraising from the donors to increase funds and their importance to cover the most significant percentage of those affected.

Sperm Motility Algorithm (SMA), inspired by the human fertilization process, was proposed by Abdul-Raof and Hezam ^[1] to solve global optimization problems. Sperm flow obeys the Stokes equation or the Schrۤinger equation as its derived equivalent. This paper combines a classical SMA with quantum computation features to propose two novel Quantum-Inspired Evolutionary Algorithms: The first is called the Quantum Sperm Motility Algorithm (QSMA), and the second is called the Improved Quantum Sperm Motility Algorithm (IQSMA). The IQSMA is based on the characteristics of QSMA and uses an interpolation operator to generate a new solution vector in the search space. The two proposed algorithms are global convergence guaranteed population-based optimization algorithms, which outperform the original SMA in terms of their search-ability and have fewer parameters to control. The two proposed algorithms are tested using thirty-three standard dissimilarities benchmark functions. Performance and optimization results of the QSMA and IQSMA are compared with corresponding results obtained using the original SMA and those obtained from three state-of-the-art metaheuristics algorithms. The algorithms were tested on a series of numerical optimization problems. The results indicate that the two proposed algorithms significantly outperform the other presented algorithms.

This study aims to introduce a decision-making framework for prioritizing gerontechnologies (GTs) for aging persons and people with disability under an intuitionistic fuzzy set (IFS) context. First, the intuitionistic fuzzy (IF)-divergence measure and its properties are developed to obtain the criteria weight. Second, a new exponential function-based score function and its properties for the IFS are introduced to order the different IFSs. Third, an IF-relative closeness coefficient (RCC)-based method is proposed to determine the criteria weights. Fourth, the double normalization (DN) procedure-based weighted integrated sum product (WISP) approach is introduced under the IFSs. To demonstrate the applicability and usefulness of the proposed IF-RCC-DN-WISP model, a case study that involves ranking the different GTs for aging persons and people with disability is conducted from an IF perspective. The results of the developed model show that mobility is the most appropriate gerontechnology for aging persons and people with disability. A comparison with different models is also performed to prove the superiority of the obtained results. The comparative study shows how the developed model outperforms the other extant models, as it can offer more sensible outcomes. Therefore, it is more suitable and efficient for expressing uncertain information when treating practical decision-making problems.

Sustainable supplier selection (SSS) is recognized as a prime aim in supply chain because of its impression on profitability, adorability, and agility of the organization. This work introduces a multi-phase intuitionistic fuzzy preference-based model with which decision experts are authorized to choose the suitable supplier using the sustainability "triple bottom line (TBL)" attributes. To solve this issue, an intuitionistic fuzzy gained and lost dominance score (IF-GLDS) approach is proposed using the developed IF-entropy. To make better use of experts' knowledge and fully represent the uncertain information, the evaluations of SSS are characterized in the form of intuitionistic fuzzy set (IFS). To better distinguish fuzziness of IFSs, new entropy for assessing criteria weights is proposed with the help of an improved score function. By considering the developed entropy and improved score function, a weight-determining process for considered criterion is presented. A case study concerning the iron and steel industry in India for assessing and ranking the SSS is taken to demonstrate the practicability of the developed model. The efficacy of the developed model is certified with the comparison by diverse extant models.