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عمل سے زندگی بنتی ہے جنت بھی جہنم بھی

عمل سے زندگی بنتی ہے جنت بھی جہنم بھی
نحمدہ ونصلی علی رسولہ الکریم امّا بعد فاعوذ بااللہ من الشیطن الرجیم
بسم اللہ الرحمن الرحیم
والعصر ان الانسان لفی خسر . الا الذین امنو وعملو الصلحت ط
صدر ِذی وقار اور میرے ہم مکتب ساتھیو!
آج مجھے جس موضوع پر لب کشائی کا موقع ملا ہے وہ ہے ڈاکٹر علا مہ محمد اقبال رحمۃ اللہ علیہ کے شعر کا مصرع:’’عمل سے زندگی بنتی ہے جنت بھی جہنم بھی‘‘
جنابِ صدر!
اگرچہ پڑھنے میں قاری کو ایک مصرع نظر آتا ہے۔ لیکن اپنے اندر مفا ہیم اور مطالب کا ایک جہان آباد کے ہوئے ہے۔ علامہ اقبال رحمۃ اللہ علیہ قوم کو خواب غفلت سے بیدار کرتے ہوئے فرماتے ہیں کہ عمل کے بغیر تصور زیست ممکن ہی نہیں، زندگی حرکت وعمل کا دوسرا نام ہے۔ اوربے عملی یا جمود کا دوسرا نام موت ہے، عمل سے ہی زندگی کا بگاڑ ہے، اورعمل سے ہی زندگی کا نکھار ہے۔ جام زندگی کے دوام کا راز گردش پیہم میں پوشیدہ ہے۔ بے عملی نہ صرف انسان کو کاہل ، سست اور کمزور بناتی ہے بلکہ بے یقین اور بزدل بھی بناتی ہے، اس کے برعکس عمل انسان کومستعد ،معتمد اور معزز بناتا ہے۔ اقبال کے الفاظ ہیں :
چلنے والے نکل گئے ہیں
جو ٹھہرے ذرا کچل گئے ہیں
کلام پاک میں یہ بات قسم اُٹھا کر بتائی جارہی ہے کہ انسان نقصان میں ہے لیکن جو لوگ ایمان لائے اور نیک کام کرتے ہیں وہ نقصان میں نہیں ۔معلوم ہوا کہ انسان کا انسانیت کی معراج پر فائز ہونابغیر عمل کے ممکن نہیں۔ بقول شاعر:۔
خود عمل تیرا ہے صورت گر تری تقدیر کا
شکوہ کرنا ہو تو اپنا کر مقدر کا نہ کر
جو انسان صاحب عمل ہوتا ہے وہ اپنے کسی کام...

Negative Wash-Back of Formative Assessment to Learning in Saudi Higher Education Context

This article reports the wash-back of formative assessment on what students learn, how they learn and the depth of their learning in Saudi higher education context. Previous research indicates that assessment methods affect different aspects of learning either positively or negatively depending on the nature of assessment tasks. Observations indicate a clear association between Saudi students’ learning and how their learning is assessed; so this research was needed to determine how exactly the correlation looked like—positive or negative. The data in this study were collected from Saudi undergraduates by employing a student survey and semi-structured interviews. The survey included Likert scale items of agreement regarding research assignments, quizzes and midterm examinations administered to 250 English-major students. To validate the survey results, sixteen students from different levels with GPA 3 and above were interviewed. The results showed that formative assessment narrowed down the scope of learning materials. The students mostly adopted surface level learning strategies to prepare for formative assessment tasks. Higher order thinking skills were not tested in any of the formative assessment methods. Therefore, it is suggested that assessments tasks should be subjected to thorough validation and moderation. Sound assessment practices should be put in place and practiced judiciously. To achieve these objectives, sustained institutional and departmental professional backing is a prerequisite.

Nonlinear Stochastic Analysis of Machinery Health Dynamics

Condition based maintenance of machinery is being much talked about in the engineering sector of defense and commercial industry. A lot of expenditure is generally incurred on condition monitoring of machinery to avoid unexpected downtimes and failures vis-à-vis optimizing machinery operation. The concept is ever evolving due to technological advancements as well as with the emergence of unique nature of defects in complex systems. The features of machinery health extracted through modern condition monitoring technologies helps in diagnostics of current health; however, utilizing the current data for prediction of future machinery state i.e Prognostics is a challenging task. Prognostic is one of the key elements of modern maintenance philosophies. Effective prognostic, from the machinery data, leads towards operational reliability, reduced machinery downtime, cost savings, secondary/catastrophic failures etc. Machinery health prognosis follows a sequential methodology inclusive of various processes ranging from data acquisition till remaining useful life estimation. Every step depicts distinct statistical features, which are helpful in estimating present and future health state of a machine. Various methodologies have been adopted by the researchers in an effort to precisely forecast/predict machinery health. Research in this area, where stochastic models have been applied, revealed encouraging results. In this thesis, we have presented three nonlinear stochastic models with their application on bearing health prognosis. These include Markov Switching Auto Regressive Model with Time Varying Regime Probabilities, Threshold Auto Regressive Model and Structural Break Point Classifier Model. The results showed that the applied models can be effectively utilized for data driven machinery health prognosis.
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