The increasing complexity of constrained engineering design problems has intensified the demand for metaheuristic optimization algorithms that are both computationally efficient and robust against premature convergence and search stagnation. This paper presents the Signal Lock Optimization Algorithm (SLOA), a novel population-based metaheuristic founded on the dual mechanisms of confidence reinforcement and noise suppression. The core principle of SLOA lies in identifying high-confidence solution components-referred to as signal locks-and reinforcing them during the search process while dynamically filtering stochastic perturbations that may mislead exploration in multimodal and highly constrained landscapes. The proposed algorithm incorporates adaptive parameter updating and an effective constraint-handling strategy to maintain a balanced exploration–exploitation trade-off. SLOA is extensively evaluated on a suite of widely adopted engineering benchmark problems, including Welded Beam Design, Pressure Vessel Design, Tension/Compression Spring Design, and Car Side-Impact Design. Comparative experimental results and statistical analyses demonstrate that SLOA consistently achieves superior or highly competitive solution quality, faster convergence rates, and high feasibility compared to several state-of-the-art metaheuristic algorithms. The findings confirm that the signal lock mechanism provides a reliable and scalable optimization framework for solving complex real-world engineering design problems.

In today's digitally driven world, computer science has transcended its role as a mere tool and has become the driving force behind innovation in commerce. This article takes a comprehensive look at the multifaceted applications of computer science in commerce. It underscores how computer science is enhancing customer experiences, optimizing supply chains, facilitating data-driven decision-making, and paving the way for a more dynamic and customer-centric business landscape.

In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

A flip-flop is a basic storage element used to store information. It is used to build RAM, latches, shift registers, counters, and other digital circuits. This paper proposes a new innovative design for D flip-flops. This design consumes much less power than previous flip-flop designs. The first proposed design introduces two gate-leakage transistors at the output gate using the GALEOR method. The second proposed design uses ONOFIC technology. Therefore, compared to the previous design, the power consumption of Design-I and Design-II is reduced by 35.61% and 34.36%, respectively. The proposed design is simulated using a Cadence tool using 90nm CMOS technology operating at 500MHz.