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Background: Perceptions of exercise benefits and barriers in medical and non- medical students needs to be explored as it is expected that medical students have more awareness regarding physical activities. Objective: To compare the perception of exercise benefits and barriers among medical and non-medical students. Methodology: This comparative cross-sectional study was conducted in 6 months from November 2020 to April 2020, on sample of n=289 regular male and female students in the age range of 19-27 years. The data of final year medical students was collected from Azra Naheed Medical College and nonmedical students from Superior University, Lahore Through on probability convenient sampling technique. The perception of exercise benefits and barriers was assessed using the exercise benefit/barrier scale (EBBS). The data was carried out by an online Google form due to the pandemic situation. The independent sample t-test was used to estimate of difference among both groups. Results: The mean age of the participants was 23.75±2.11 years. A total of n=164(56.74%) were males and n=125 (43.25%) were females. The result of independent t-test showed no significant difference between medical and non-medical students regarding exercise benefit (91.2±11.66 ver. 91.09±12.83, p=0.93) and barriers (34.97±7.22 ver. 35.27±7.62, p=0.72) as well in total score of EBBS (126.23±14.450 ver. 125.81±16.390, p=0.810).Conclusion: It is concluded that medical and non-medical final year students are equally aware about exercise benefits and barriers.

The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence. [ABSTRACT FROM AUTHOR]

Direct current (DC) servo motors are central to many complex systems, such as electrical, electro-mechanical, and electro-hydraulic frameworks. In practice, these systems can have nonlinear characteristics and parameter variations. Accurate model representation and position tracking of DC motors are the main issues in many real systems, such as twin rotors, aircraft, airships, and robot manipulators. The precise position tracking of these systems has already been achieved using conventional H-infinity (H ∞ ) controllers. However, the order and structure become more intricate when employing complex weights to shape the closed-loop system, which limits the current proposals. To overcome the above-mentioned limitations, in this article, we provide a precise angular position tracking of a DC servo motor utilizing an intelligent, robust linear controller based on a fixed-structure linear fractional transformation. The conventional H ∞ controllers are based on the minimization of an unstructured linear fractional transformation objective function that leads to a complex design of these controllers. The main advantage of the proposed intelligent H ∞ synthesis is the fixed and simple structure that increases its practical implementation. The methodology is formulated in the MATLAB software for the robust design of the proposed synthesis based on an intelligent fixed-structure H ∞ optimization. Simulation results are compared with conventional H ∞ and proportional-integral-derivative controllers. The results are also validated experimentally. [ABSTRACT FROM AUTHOR]