اقبال سہیلؔ
افسوس ہے کہ گزشتہ مہینہ اقبال سہیلؔ بھی چل بسے ۔وہ مسلم یونیورسٹی علی گڑھ کے باقیاتِ صالحات اور اُس عہد کی دیرینہ روایات کے حاملین میں سے تھے۔غیر معمولی ذہین وذکی تھے ۔فارسی اور اردو دونوں زبانوں کے بلند پایہ شاعر تھے۔ وہ اگر وکیل نہ ہوتے یامزاج لااُبالی نہ ہوتا توعلم وادب کے میدان میں ان کی شہ سواری کا مقابلہ بہت کم لوگ کرسکتے تھے ۔طبیعت حددرجہ دقیقہ رس اور دماغ بڑا نکتہ آفریں پایاتھا۔ نغز گوئی کے ساتھ اشعار میں روانی غضَب کی ہوتی تھی۔ غزلوں اور نظموں کے علاوہ انھوں نے جو نعتیہ نظمیں لکھی ہیں وہ بھی بڑے معرکہ کی ہیں، نثر بھی بہت اچھی لکھتے تھے ۔اگر کوئی صاحب اُن کے مضامین ِ نثر و نظم کو مرتب کرکے یک جا شائع کردیں تویہ اردو ادب کی مفید اورلائقِ قدر خدمت ہوگی۔ورنہ ان ادبی جواہر پاروں کے ضائع ہوجانے کا اندیشہ ہے ۔حق تعالیٰ مغفرت وبخشش کے فضل ِ خاص سے نوازے ۔ [دسمبر۱۹۵۵ء]
Background and Aim: Sacroiliac joint pain is localized in the region of sacroiliac joint which can be increased by stress and provocation tests of the joint. Aim of this study was to compare two interventions for reduction of sacroiliac joint pain.
Methodology: Study design was randomized clinical trial. Study was conducted in bajwah hospital and children polyclinic Lahore. Duration of study was six months. The total sample size was 64 patients. Females of 20-50 years old with diagnosed sacroiliac joint pain were included in this study. Compression and distraction objective tests were performed for further confirmation of sacroiliac joint pain. Purposive sampling technique was used. Numeric pain rating scale (NPRS) and Oswestry low back disability questionnaire (ODI) were used to collect the data. Exclusion criteria was females with fractures and other abnormalities of spine.
Results: Results showed that both groups were equal when assessed on baseline by normality test colmogorov-smirnova. Independent t test was applied to compare the mean value of NPRS. Pretreatment mean of NPRS scale for both the regional treatment and standard treatment groups was 7.After 4 weeks NPRS of regional treatment group was 4 and of standard treatment group was 7. The mean value of pretreatment ODI for regional treatment group was 33 and for standard treatment group was 34.After 4 weeks ODI of regional treatment group was 24 and mean of standard treatment group was 27.
Conclusion: It is concluded that after giving equal sessions to both groups when results were assessed regional treatment is more effective than standard treatment.
The existence of self-similar or fractal nature of network traffic has been proven by recent studies over a wide range of time scale. These properties are very different from the traditional Poisson processes/models. If one analyzes the network traffic by these traditional Poisson models then this leads toward wrong or incorrect decisions. The issues with Poisson models are overestimation of the performance of computer network and false allocation of data processing and network resources. Hence complete understanding of self-similar nature is required for fast and appropriate performance of the network. This thesis describes a number of problems arising from the experimental study of telecommunication networks. The work can be split into two main areas: full understanding of self-similarity within a network traffic and secondly focused over the comparison of two different networks i.e. searching similarity between two or more self- similar network traffic by using wavelet based time warping technique. Estimation techniques of self-similarity measure i.e. Hurst Index H is considered first the critical analysis of these methods is studied. There is a lack of GUI based tool for the estimation of Hurst Parameter we developed a new Statistical tool that can estimate the Hurst Parameter H by different methods along with their statistical analysis. Next we focused over the generation of self-similar traffic using simulation methods. A comparison of these methods was also studied. Main focus was on the Wavelet based estimation and generation methods for computer network traffic. Secondly concentration is made over filtering of temporal data to get the smooth form of the signal that may be used for further analysis. The main goal of second part is to explore new trends for filtering and smoothing of temporal data. A recently developed technique of wavelets for smoothing of temporal data is explored. New approach was developed using different features of wavelet transformation for dimensionality reduction and tested them for various decision making processes in business and computer science. The developed smoothing techniques are applied for forecasting, query processing of vsimilar sequences in huge databases and their clustering on the basis of membership value. The developed forecasting technique is based on Wavelet families and Seasonal Autoregressive integrated moving average model. In this thesis, two new approaches for smoothing of large temporal databases using the wavelet filtering theory for approximations of query procedures for decision support systems (DSS) are also proposed. For these smooth signals, a novel query processing algorithms was developed while selecting wavelet based features and time warping distance metric, called wavelet based features time warping. For first proposed algorithm we utilized the features extracted on the basis of minima, maxima and averages of wavelet based compressed signals and for second proposed algorithm local features of wavelet transformation using average of approximation coefficients at the coarsest scale and maxima of maxima and minima of minima of detail coefficients at all scales were used. Our both models support index based time warping distance. It is proved by carrying out extensive experiments with synthesized databases using different wavelet families that the proposed methods are very effective and ensure the nonoccurrence of false dismissals and minimal false alarms with least compromise over accuracy. The developed method gives extremely fast response times as our approximate query executes its maximum processing over synoptic set of wavelet features. A new measure of degree of similarity is introduced for clustering using time warping distance method which gives better control over the number of clusters.