شمس العلماء عبدالرحمان شاطرؔ مرحوم
دکن ٹائمز مدراس میں یہ پڑھ کر بہت افسوس ہوا کہ مدراس کے مشہور و ممتاز فلسفی شاعر مولانا شاطرؔ کا وسط اپریل ۱۹۴۳ء میں انتقال ہوگیا۔
ارکاٹ احاطۂ مدراس میں اسلامی علم و تمدن کی فراموش شدہ تاریخ کا ایک ورق ہے، نواب ارکاٹ کا محل ارکاٹ کے جنگی خاتمہ کے بعد خود شہر مدراس ہے، شمس العلماء عبدالرحمان شاطر اسی برج فلکی کے آفتاب تھے، عمر ستر کے قریب ہوگی، اردو، عربی، فارسی اور انگریزی زبانوں سے واقف تھے، نواب صاحب ارکاٹ کے سکریٹری بھی تھے اور مدراس ہائیکورٹ میں مترجم بھی رہے تھے، گو وطن مدراس تھا، مگر ۱۹۰۱ء اور ۱۹۰۳ء میں حیدرآباد دکن کے بزم علمی میں اس کے شریک تھے، جب مولانا شررؔ اور داغؔ اور گرامیؔ حیدرآباد کی زینت تھے، وہ ان کی مجلسوں میں شریک ہوتے تھے اور ان سے اپنے ذوقِ ادب کی پرورش کرتے تھے اور اسی زمانہ سے وہ مولانا کے قریب رہنے والوں اور قریب سے جاننے والوں میں تھے اور ان کی وہی محبت تھی جو حضرت الاستاذ کی وراثت میں مجھے ملی تھی۔
عبدالرحمان مرحوم شاعر تھے، شاطرؔ تخلص کرتے تھے، اشعار حکیمانہ اور فلسفیانہ کہتے تھے، قطعات، رباعیات اور قصائد موزوں کرتے تھے، جدید سائنس اور فلسفہ کے مسائل کو اسلامی الٓہیات سے تطبیق دیتے تھے، زبان سخت تھی اور مشکل الفاظ کے استعمال سے ان کو پرہیز نہ تھا، ان کی سب سے مشہور فلسفیانہ نظم ’’اعجازِ عشق‘‘ ہے، جو ایک طویل رائیہ قصیدہ ہے جس میں جدید و قدیم فلسفیانہ مسائل و آراء سے الہیات اسلامیہ کی تفسیر و تشریح کی ہے یہ نظم ۱۹۰۴ء میں لکھی گئی تھی اور اس زمانہ کے تمام اکابر و مشاہیر مولانا حالی،مولاناشبلی، مولانا نذیر احمد، مولوی ذکاء اﷲ خاں، نواب عمادالملک، مولوی سیداکبر حسین، پروفیسر عبدالغفور شہباز، امجد...
A thorough critical analysis of human history highlights that the clash of civilizations centered on themes of conflict, war, and struggle. Historical experts use the word encounter to interpret or explain these relations among civilizations. Trade played a vital role in flourishing these ties, however, conflict and encounter have also been a part of almost all the phases or eras of human history. Developing nations have always looked up to the western world as a role model of economic and military progression, but this philosophy has also resulted in the escalation of tensions among these nations. World peace faces daunting challenges and is one of the most talked-about human concerns in the present times. Islam is currently being challenged by the western world in terms of the right interpretation of religious teachings and the true message of the Holy Quran and Sunnah. Islam is still struggling to make its mark in the present world order, particularly after the 9/11 incident which painted Muslims as terrorists and fundamentalists. It is quite evident that the world is divided into factions or groups, where one group is the torchbearer of Islam and religious teachings, while the second group endorses western ideals or secularism. According to Samuel Huntington, it won’t be wrong to say that the next ideological challenge that the world faces after the ultimate demise of the Soviet Union is Islam and the prospective clash between the East and the West in the 21st century will be the most discussed topic. The present-day relationship between the East and the West is based upon rivalry. This conflict is not a new area of research and is centuries old. The only solution to this problem is to promote global peace and harmony and facilitate dialogue among nations. This would help in creating a social system that can center on values, harmony, peace, and love. Also, Iqbal’s philosophy can act as a guiding stone and can help in resolving this crisis. Iqbal’s universal social reconstruction theory highlights how different civilizations can live together and can facilitate constructive dialogue to improve civilizational ties. This article aims to incorporate the teachings of Iqbal, especially the universal social reconstruction theory to propose solutions for inter-civilizational clashes. This article aims to use the teachings of Iqbal as a beacon of light to promote constructive dialogue and peaceful coexistence among the two dominant sides of the world, resultantly leading in much prosperous and peaceful world order.
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.