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Physiotherapy Services Available to Disabled Population in Karachi.

Thesis Info

Author

Ambar Maqssod

Department

Department of Special Education

Institute

University of Karachi

City

Karachi

Province

Sindh

Country

Pakistan

Thesis Completing Year

2002

Subject

Special Education

Language

English

Added

2021-02-17 19:49:13

Modified

2023-01-08 00:31:10

ARI ID

1676728333095

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تعلیمی انقلاب تقاضائے وقت

آدمی ہیں بے شمار مگرانسان کوئی نہیں
بسکہ دشوار ہے ہر کام کا آساں ہونا
آدمی کو بھی میسر نہیں انساں ہونا
اس کائنات میں رنگینیاں ہی رنگینیاں ہیں، کہیں صحراء ہیں کہیں دریا ہیں، کہیں شجر ہیں تو کہیں حجر ہیں ،کہیں ندی نالے ہیں جو موتیوں کی طرح چمکنے والے پانی کو کھیتوں کھلیانوں میں پہچانے کے لیے رواں دواں ہیں، کہیں فلک بوس پہاڑ ہیں جو سیاحوں کی نظر اپنی جانب مبذول کروارہے ہیں۔ فلک پرکواکب اپنی سج دھج دینے سے موجود ہیں ، ماہتاب و آفتاب مفوضہ فریضہ سرانجام دینے کے لیے پرعزم ہیں۔
جملہ مخلوقات اپنی اپنی جگہ پر انتہائی اہمیت کی حامل ہے لیکن’’ احسن تقویم‘‘ اور اشرف المخلوقات کا تاج اللہ تعالیٰ نے انسان کے سر سجایا ہے ، شرف انسانی کی خلعت فاخرہ انسان ہی نے زیب تن کی ہے۔ میدان شرف و بزرگی کا شاہسوار انسان ہی کو بنایا ہے، آسمانِ رفعت کا آفتاب و ماہتاب انسان ہی ہے۔ انسان جب انسانیت کی خصوصیات سے مزیّن ومرصعّ ہوتا ہے تو فرشتوں کو اس پر رشک آتا ہے۔ بقول شاعر
فرشتے سے بہتر ہے انسان بننا!
مگر اس میں پڑتی ہے محنت زیادہ
انسان کا مادہ انس ہے، جس کا مفہوم محبت پیار اور خلوص نکلتا ہے، اگر کوئی بظاہر انسان دکھائی دے رہا ہے، اس کے اعضائے جسمانی اس کے انسان ہونے پر دلالت کر رہے ہیں، دیکھنے والی دو آنکھیں ، سننے والے دوکان اور بولنے والی زبان یہ سب اعضاء اس کے انسان ہونے کا پتہ بتارہے ہیں کہ یہ انسان ہے ،لیکن اہل لُب کے نزدیک وہ انسان انسان نہیں جس کی شکل و صورت انسانوں والی ہو بلکہ وہ انسان انسان ہے جس کے کام انسانوں والے ہوں اور جو انسانیت کی معراج پر فائز ہو، صاف ستھرا لباس ہے، قد...

علامہ اسید الحق قادری بدایونی کی کتاب ”قرآن کی سائنسی تفسیر“: ایک مطالعہ

Allama Usaid-ul-Haq Badayuni (1975-2014) was a great Islamic thinker, researcher and religious scholar. He wrote 14 Islamic books were on academic and research works. 17 books were arranged and prefaced by him. 12 books were translated and reviewed by him. 22 books were completed under his supervision. The book “Quran ki Saainsi Tafseer” (Scientific exegesis of Quran) authored by Allama Usaid-ul-Haq Qadri Badayuni is an educational, scholarly and critically acclaimed masterpiece. A comprehensive explanation and meaning of scientific exegesis are given at the beginning of this book. After that, the opinions of the modem and contemporary scholars regarding the justification and non-justification of the scientific interpretation are presented lucidly. The differences between the Quran and Science and several misinterpretations of the scientific exegesis have also been recorded. The conditions set by Islamic scholars and researchers regarding the justification of scientific exegesis have been explained in the book. This book of Allama Badayuni is a wonderful addition to the chapter of scientific exegesis in terms of research and critics. And this book will be remembered as an academic reference in the history of Indo-Pak. KEYWORDS

Recent Trends in Time Series Modeling and Prediction of Wind Data: Statistical and Fuzzy Reasoning Approach

We developed stochastic time series models such as ARMA( p,q), non- seasonal ARIMA, seasonal ARIMA (SARIMA) and MTM models to simulate and forecast hourly averaged wind speed sequences on twenty year data ,.i.e, 1985-2004 of Quetta, Pakistan. Stochastic Time Series Models take into account several basic features of wind speed including autocorrelation, non-Gaussian distribution and non-stationarity. The positive correlation between consecutive wind speed observations is taken into account by fitting ARMA process to wind speed data. The data are normalized to make their distributions approximately Gaussian and standardized to remove scattering of transformed data (stationary,.i.e., without chaos).Diurnal variations has been taken into account to observe forecasts and its dependence on lead times. We find the ARMA (p,q) model suitable for prediction interval and probability forecasts. But the MTM model is relatively better as a simulator compared to ARMA modeling. The suitability of ARMA (p,q) model for both long range (1-6 hours) and short range (1-2 hours) indicates that forecast values are the deciding components for an appropriate wind energy conversion systems, WECS. ARMA processes work with non-stationary (chaotic) data. Non-seasonal ARIMA models and the prediction equations for each month and indeed for each season of a twenty year wind data are presented. The seasonal ARIMA (SARIMA) and its prediction equations for each month of a twenty year data are also studied. With non- stationarity or chaos in data, stochastic simulator in the ARIMA processes does not effectively work although its prediction equations are good enough to forecast relatively short range reliable values. Various statistical techniques are used on twenty five years, .i.e., 1980-2004 data of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) are developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We followed the coefficient of determination,.i.e., a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) are also developed from deciphering the interdependence of weather parameters. We used Spearman’s rank correlation and Goldfeld-Quandt tests to check the uniformity or non uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity (uniformity of variances in the distribution of data). We also employed Bartlett’s test for homogeneity of variances on a twenty five years data of rainfall and humidity, respectively which showed that the variances in rainfall data are not homogenous while in case of humidity, are homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. We performed design free fuzzy logic (FL) time series prediction modeling on a twenty year wind data, .i.e., 1985-2004 for Quetta, Pakistan. We followed design free fuzzy logic and obtained prediction of hourly wind data for spring (February, March and April). Non-stationarity or random walk in wind data exists but it does not influence prediction. Mackey Glass (MG) simulation of wind data indicated chaos or non periodicity. Moreover, stable attractors are observed in MG-time series, the origin of which is yet unknown. The attractors seen in MG simulation do not influence FL time series prediction. We studied singleton and non-singleton type-1 back propagation (BP) designed sixteen rule fuzzy logic system (FLS) on hourly averaged wind data of twenty years ,.i.e., 1985-2004. We found that the BP designed 16 rule non-singleton-type-1 FLS is relatively a better forecaster than singleton-type-1.We find hidden or unraveled uncertainties such as non-stationarity and stable attractors. These uncertainties make the data chaotic. The criterion of selecting root mean square error (RMSE) for establishing comparison is not suitable for chaotic data. Non-stationarity in the data can be properly handled with non- singleton type-1 FLS, therefore, there appears no reason to use a type-2 FLS. The stable attractors and non-stationarity in our data do not affect the predicted values as confirmed by Mackey Glass simulation. The chaos can be effectively resolved through parallel structure fuzzy system (PSFS) which exploits time-delays.. A variety of Artificial Neural Network models for prediction of hourly wind speed (which a few hours in advance is required to ensure efficient utilization of wind energy systems) at Quetta, Pakistan is studied and the results are compared. Satisfactory results are obtained with Feed Forward Back Propagation Neural Networks (FFBPNN). An empirical relationship is developed which shows the Gaussian profile for the number of neurons which varies with lag inputs, .i.e., nn = k exp(-il2) where nn shows the number of neurons, il the lag inputs, and k the sloping ratio. Feed Forward Neural Networks (FFNNs) can be corrected with optimization of empirical relationship for simulators followed by back propagation technique. The disadvantages of FFNNs comprise of heavy computational requirements, and non-existence of Artificial Neural Network(ANN) design methodologies for deciding the value of the learning rate and momentum. Neural Network (NN) modeling is not suitable for chaotic data characterized by randomness and non-stationarity.