Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. To predict a global minimum (GM) structure of a given sequence is a difficult task. A genetic algorithm (GA) is an efficient approach to find lowest-energy conformation for HP lattice model. We have introduced some new operators (symmetric and cornerchange operators) to speed up the searching process and give the result more biology significance. The result shows these new operators improved the success of prediction, compared with standard GA for benchmark HP sequence up to 50 residues

Z-number that proposed by Zadeh is an effective tool to describe the information with uncertainty in decision-making problems. However, most of the researches on Z-numbers employed linguistic cardinalities with uniformly distributed scales. In fact, unbalance situation is much common in terms of the psychology of experts. In this paper, we propose a new computational method based on Probabilistic Linguistic Z-number with Unbalanced semantics(UPLZ), which can represent the linguistic evaluations of experts precisely combined with individual risk appetite. A new score function of UPLZs is provided based on hesitant degree and linguistic scale function to reduce the computational complexity. Afterward, a linear programming is constructed to determine weights of criteria by considering cross entropy maximization. The robust decision result can be obtained by applying MULTIMOORA method since it is specific with peculiarities of three subordinate models. Finally, a case study concerning medicine selection for the patients with mild symptoms of the COVID-19 is provided to illustrate the feasibility and effectiveness of the proposed method. The advantages of it are highlighted by sensitivity analysis and comparative analysis with two outstanding multi-criteria decision-making methods.

With the rapid advancement of intelligent technologies and network environments, the efficient and accurate handling of uncertain decision-making information has become an urgent challenge. Traditional methods often struggle to process complex and incomplete information, especially in cyber defense. To address this, we introduce discrete multi-dimensional Z-numbers (MZs) as a mathematical tool for modeling uncertainty and reliability in network defense decisions. This paper proposes a synthesis method for MZs, enabling the integration of multi-source information while considering both uncertainty and reliability. By leveraging a hidden probability model, we extend MZs into multi-dimensional Z⁺-numbers, enhancing their expressiveness in handling uncertainty. Furthermore, we define utility functions based on MZs and develop a multi-attribute group decision-making framework tailored for network defense. This approach offers a novel perspective for designing strategies against highly adaptive and covert cyberattacks. The proposed method is validated through a case study on the network security assessment of an intelligent logistics company. Results demonstrate significant improvements in the accuracy and efficiency of decision-making, highlighting the method’s advantages and broad potential in cyber defense. Beyond logistics, this integrated MZ-based decision framework provides an adaptable and intelligent tool for strengthening network security defenses.