Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.

The fuzzy soft set (FSS) that combines soft set theory with fuzzy set theory has been introduced to deal with uncertainty in many practical decision-making problems. However, there exist some less important and superfluous information in the decision-making process which should be removed. In order to settle these problems, different methods have been developed for parameter reduction of FSS which are based on different decision criteria. In this paper, the problem of parameter reduction of FSS is studied based on choice value criteria. Initially, some previous reduction approaches such as; normal parameter reduction (NPR), proximate normal parameter reduction (PNPR) and distance-based parameter reduction (DBPR) of FSS are analyzed and their inherent difficulties are discussed. Then, a new method for parameter reduction of FSS is proposed and its heuristic algorithm is given. The proposed algorithm is compared with NPR, PNPR and DBPR algorithms from the aspects of success rate of finding reduction, reduction ratio and decision ability, respectively. The comparison results show that the proposed algorithm has much higher applicability and efficiency as compared to rest of the three algorithms. Finally, an application of our proposed algorithm is discussed in a real life decision-making problem.

Natural disasters have a widespread impact globally, often posing significant challenges to sustainable development in affected regions. Effective disaster recovery requires coordinated efforts from various stakeholders; however, this process is frequently hindered by inadequate resources and policies. Consequently, a thorough exploration of resource- and policy-related barriers is essential for sustainable recovery. This study aims to identify and prioritize resource and policy barriers (RPBs) that hamper the ongoing post-flood recovery process in Pakistan. Twenty-eight barriers were selected through an extensive literature review and further classified into five categories: human, financial, infrastructure, technological, and policy and governance. The fermatean fuzzy analytical hierarchical process (FF-AHP) was used to determine the weights of the selected barriers and prioritize them according to their local and global weights. The results reveal that financial resource barriers are the most important category, while low income of households, lack of appropriate disaster management policies, and unemployment are the top three barriers that significantly affect the post-disaster recovery process. Additionally, the study provides a comparative analysis to highlight the effectiveness of the proposed model over the spherical fuzzy analytical hierarchy process (SF-AHP). The study’s findings can provide valuable insights for decision-makers, assisting them in identifying critical challenges and implementing effective recovery strategies for sustainable development.

The intuitionistic fuzzy soft set (IFSS) is one of the useful mathematical tools for uncertainty description and has many applications in real-world decision-making problems. However, the computations become more complex when these decision-making problems involve less important or redundant parameters. To solve this problem, in this paper, we study the problem of parameter reduction of IFSS based on evaluation score criteria. Initially, we developed a new approach to IFSS-based decision-making. Then using the new decision criteria, we propose three different algorithms for parameter reduction of IFSSs satisfying the different needs of decision-makers. We compare the proposed algorithms with Ghosh et al.’s algorithms in terms of different aspects. It is evident from the comparison results that the proposed algorithms are much better than Ghosh et al.’s algorithms in terms of efficiency and applicability. We also provide a comparative study among the new algorithms to decide their feasibilities in different situations. Finally, we take a university recruitment problem to verify the effectiveness of the proposed algorithms in real-life decision-making problems.