نہالؔ سیوہاروی
جناب نہال ؔ سیوہاروی کی شہرت کاآغاز ’’برہان کے شاعر خاص‘‘ کی حیثیت سے ہوا جس میں تقسیم ہند سے قبل وہ بالا لتزام غزلیں اورنظمیں لکھتے رہے۔ مرحوم کاذوقِ شعر وسخن اور ملکۂ شعر گوئی فطری اوروہبی تھاجس کوانھوں نے خلاف طبع معمولی سی سرکاری ملازمت کے باوجود مسلسل مطالعہ اور مشق و مزاولت کے ذریعہ جلا دے کراتنا اجاگرکرلیا تھاکہ ان کاشمار پختہ کار اورصاحب فن اساتذہ کے زمرہ میں ہونے لگاتھا۔ ان کے کلام میں دردواثر، سوزو گداز، عمقِ خیال،نزاکتِ احساس اورلطافت وشستگیِ بیان، جوحسنِ شعر کی جان ہیں یہ سب اوصاف پائے جاتے تھے۔ علاوہ سینکڑوں منتشر غزلوں اورنظموں کے آزادی پران کی رباعیات کاایک مجموعہ مکتبۂ برہان سے اور نظموں اورغزلوں کا ایک مجموعہ ’’شباب و انقلاب‘‘ کے نام سے دلی کے ہی کسی ایک مکتبہ کی طرف سے شائع ہوچکے ہیں۔
حق مغفرت کرے عجب آزاد مرد تھا
[جنوری ۱۹۵۲ء]
Marriage is a preamble of human life. The human society builds its façade on the characteristics of this unit. Humanity has been granted with divine rules for a successful life. The latest version of divine rules are conveyed through Islam and presented by the Holy Prophet Muhammad of benefits everlasting and grand the declared Islam of history Early. (صلى الله عليه وسلم) these rules in all nooks and corners of life; as Qur’ān and Ḥadīth prominences the trend of in-time marriages to ensure the tangible merits of life. Qur’ān declares marriage as ‘God given relationship’, ‘order from God’, ‘A lawful manner’, ‘Firm covenant’, ‘Love and compassion’, ‘Chastity of life’, ‘Right of women and Sign of safety’ and along with it, as marriage emphasizes (صلى الله عليه وسلم) Muhammad Prophet the of traditions the depicting it a ‘Half of religion’, ‘Protection of eyes’, ‘Prevention of character from corruption’, ‘source of Devil’s desperation’ etc. Owing to this stress of in-time marriages, a Muslim society is not supposed to delay or ignore in time marriages. But in present era, the trend of delaying in making marriages or ignoring the importance of in-time marriages is observing everywhere. This delay in making in-time marriages of young generation shows the weaknesses of faith and confidence in life. It is also the cause of many physical diseases and mental disorders. It destroys the best capabilities and abilities of young force. This paper highlights the importance of timely marriages, strength of married-couple life and damages of late marriage so that the requisite of Islam to form the society on the desired shape may be fulfilled. Lastly, this article emphasizes the need to review/change the social behavior regarding late marriages it also emphasizes the need to review/change the social behavior regarding late marriages and a herald to initiate some sorts of laws for making in-time marriages in society so that prevailing damages due to late marriages can be curtailed.
In modern complex and highly interconnected power systems, load forecasting is the first and most critical step in operational planning. The ability to predict load from few hours ahead to several days in the future can help utility operators to efficiently schedule and utilize power generation. The main focus of this research is to have an accurate and robust solution to the Short-term Load Forecasting (STLF) problem using Artificial Intelligence based techniques. Amongst several techniques reported in the literature, Artificial Neural Network (ANN) has been proposed as one of the promising solution for STLF. The ANN is more advantageous than statistical models, because it is able to model a multivariate problem without making complex dependency assumptions among input variables. By learning from training data, the ANN extracts the implicit nonlinear relationship among input variables. However, ANN-based STLF models use Backward Propagation (BP) algorithm for training, which does not ensure convergence and hangs in local minima more often. BP requires much longer time for training, which makes it difficult for real- time application. To overcome this problem, we use Particle Swarm Optimization (PSO) algorithm to evolve directly ANN by considering it as an optimization problem. With PSO responsible for training, we can modify ANN in any way to suit the problem or class of problems. Secondly, load series is complex and exhibit several level of seasonality due to which sometimes ANN is unable to capture the trend. To overcome this shortcoming, we have used modularized approach. We used smaller ANN models of STLF based on hourly load data and train them through the use of PSO algorithm. A variety of Swarm based ANN hourly load models have been trained and tested over real time data spread over a period of 10 years. Keeping in view the various seasonal effects and cyclical behavior, we divided the load data in different scenarios and results were analyzed and compared. The forecast results in majority of the cases are fairly accurate and prove the promise of proposed methodology. This approach gives better-trained models capable of performing well over time varying window and results in fairly accurate forecasts.