یوں مہرباں ہوا ہے مہینہ رسولؐ کا
دل بن گیا ہے پھر سے مدینہ رسولؐ کا
خاکِ مدینہ پائی تو خوشبو خجل ہوئی
شامل ہے اِس میں پاک پسینہ رسولؐ کا
فدیہ ہو علم کا کہ مواخات کا عمل
تاریخ دیکھتی تھی قرینہ رسولؐ کا
قربان ہم تو دونوں کی آب و ہوا پہ ہیں
مکّہ خدا کا اور مدینہ رسولؐ کا
طوفان میں گھرا ہوں پہ مدحت زباں پہ ہے
یہ نعت بھی ہے ایک سفینہ رسولؐ کا
Background: Sleep is an essential function of our body. Many surveys have reported the prevalence of poor sleep in university students, especially in medical students. Objectives: The objective of the study was to evaluate the effect of physical activity and stress on sleep quality among medical students in Pakistan. Materials & Methods: An observational cross-sectional study was conducted on medical students of private medical colleges in Lahore. A convenient sampling technique was used and 210 students were selected. The Pittsburgh Sleep Quality Index (PSQI), Godin Shephard Leisure Time Physical Activity Questionnaire (GSLTPAQ), and Perceived Stress Scale (PSS 10) were used for data collection. We used SPSS version 20 to analyze data and applied statistical tests: Chi-square test and Logistic Regression. p-value < 0.05 was taken to establish significance. Results: Among the study participants 91(43.3%) were males and 119 (57.7%) were females. There was a significant effect of stress level on sleep quality (P=0.000*). The frequency of good sleepers was seen to increase by almost three times with increasing physical activity, however, this difference remained non-significant (p=0.07). The logistic regression test showed a significant relationship between poor sleep and stress (p=0. 008**) while no significant relationship was seen between sleep quality and physical activity. Conclusion: There was a significant association between poor sleep and high-stress levels and an increase in physical activity showed an increase in the frequency of good sleep, however, this difference was non-significant. It can be inferred that this positive effect of increasing physical activity on the quality of sleep could be indirectly due to its relieving effect on stress.
As the global stock of natural resources depletes the need of electricity efficient processes emerges. Laser cutting, an advance non-contact processing technique, outweighs the old methods such as hotwire and milling due to the requirement of retightening and replacement of cutting tools with time. Orthogonal array and Factorial design are selected as a design of experiment for modelling and optimization of Laser cutting process. The range adjustment of laser machine requires knowledge of experimental design, laser cutting process and material properties, otherwise missing values generate due to unsuccessful cutting. For this reason, many universities are unable to utilize these machines effectively. It is essential to formulate a technique which allows modelling the data with some missing values, consequently, it enhance the utilization of laser machines for research and other purposes. Initially, the qualities of output characteristic were modelled by Statistical and Neural network without missing values and then by supervised and novel Semi-supervised learning algorithms with missing values. The Statistical modelling results using one and two way analysis of variance with replication were better than other data mining techniques like linear and nonlinear regression, however, it is difficult to use these methods with missing values. Therefore, supervised neural network modelling is carried out and the effects of its parametric change are observed along the datasets size to model the orthogonal array. The neural network modelling results in edge quality and kerf width signal to noise ratio, it is acceptable, the edge quality indicates that modelling improves by pre-normalization, further improvement was made by increasing training data size to factorial design. It is observed that for the artificial neural network, supervised learning is not sufficient associated to orthogonal array, only due to edge quality mean modelling, average error were higher than the acceptable limit. The average error with factorial design was under 10%. The vast modelling experience of supervised learning engenders the development of novel Semi-Supervised learning algorithm. Consequently, the average error was reduced by utilizing the systematic randomize techniques to initialize the neural network weights and increase the number of initialization by using orthogonal array design of experiment, with up to 22% missing values. This algorithm reduces modelling time and cost thus reduces electricity consumption. The average error in Perspex sheet did not exceed 8.0% and 11.5% for edge quality and kerf width respectively. The overall quality was calculated by aggregation technique of data mining and a more generous and better aggregation is carried out by the novel combination of Fuzzy logic which provides overall quality for the customer while saving cost, time and Electricity.