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آمد اور معجزات

اُجلی زمین رنگِ فلک جگمگا اُٹھا
قوسِ خدا کو ایک نیا رنگ مل گیا
بادِ شمیم سے وہ معطر ہوئی فضا
مبعوث کر رہا تھا نبیؐ کو وہ کبریا
کھلنے لگی طبعیتِ ادوار دیکھیے
پوری ہوئی ضرورتِ ادوار دیکھیے

چہرہ زمیں کا سارے کا سارا چمک اٹھا
جب آمنہ کی گود میں آئے ہیں مصطفیٰؐ
اور مُطّلب کی آنکھ نے دیکھا کہ جا بجا
کعبہ میں ایک نور ہے جیسے اُتر رہا
احساں ہے کائنات پر ربِ غفور کا
الطافِ بے بہا ہوا صدقہ حضورؐ کا

شاہی محل کے جتنے تھے سارے کلس گرے
دریا و بحر خشک ہُوئے، در زمیں چھپے
آتش کدہ بجھا ہے، لبِ کافراں سِلے
حق یوں ادا ہُوا کہ سبھی حق ادا ہُوئے
اہلِ نظر نے دیکھے ہیں یہ معجزے ضرور
آتے ہی فارقِ حق و باطل ہوئے حضورؐ

تاریخ نے یہ بات لکھی بات ٹھیک ہے
اک اور یہ مثال ملی، بات ٹھیک ہے
محرابِ کعبہ دیکھ جُھکی ، بات ٹھیک ہے
یعنی کہ احترامِ نبیؐ بات ٹھیک ہے
خالق کو سب سے بڑھ کے محمدؐ عزیز ہیں
جو مانتے نہیں ہیں بڑے بے تمیز ہیں

بے آب و بے گیاہ زمیں گلستاں ہُوئی
سُنبل کھلا کہیں کلی ریحان کی کھِلی
نسرین و نسترن کو نئی زندگی مِلی
ہر شاخِ بے ثمر میں رگِ جاں پھڑک اُٹھی
اللہ کے نبی کی دعا کارگر ہُوئی
آئے حضورؐ اور زمیں زندہ ہو گئی

بدلی فضا کہ آئے یہاں شاہِؐ مُرسلیں
ذاتِ نبیؐ پہ ہو گیا اکمل خدا کا دیں
نازِ فلک بھی آپؐ ہوئے ارضِ نازنیں
ایمان بھی اُنھی سے ہے اقرار بالیقیں
اُن کے طفیل کوہ و دمن لہلہا اُٹھے
پژمردۂ حیات سبھی مسکرا اُٹھے

اُنؐ کا ظہور، جیسے اُجالے کی منتہا
اُنؐ سے...

Academic Stress Among Pre-University Students of The Social Science Stream: A Study in Poonch Azad Jammu And Kashmir

Academic-related demands that exceed students’ adaptive capabilities are collectively known as academic stress. High levels of academic stress are associated with an increased likelihood of depression, insomnia, substance addiction, self-harm, suicidal ideation, and subsequently, quitting education. Globally, academic stress is now a common phenomenon due to COVID-19-induced changes in the education system. Knowledge of the magnitude of academic stress and its factors can enable early recognition, intervention, and alleviation of the problem. The objective of this study was to assess the magnitude of perceived academic stress and identify the main stressors through a cross-sectional survey using the Manipal Inventory of Academic Stress scale. The study participants involved 2152 Grade 11 and 12 Commerce students enrolled in 34 pre-university colleges in Poonch AJK. A stratified cluster sampling method was used in the study. Statistical methods, namely descriptive statistics, multiple linear regression analysis, two-sample independent t-test, and one-way ANOVA tests, were used in the study. The study observed that one in every four pre-university students experienced high levels of perceived academic stress.Parent expectations, academic queries from neighbors and relatives, and lack of time for revision were identified as the top three stressors. Gender, grade, and mother’s education were associated with academic stress. Interventions at the individual, family, institutional, and community levels are the need of the hour to safeguard adolescents from negative experiences that might deprive them of wellness in their future.

Semantic Annotation and Retrieval in E-Recruitment

E-recruitment processes prioritize matching between job descriptions and user queries to identify relevant candidates. Existing e-recruitment systems face chal lenges in extracting job descriptions due to unstructured nature of content and text nomenclature differences for defining the same content. The systems are par ticularly unable to extract effectively contextual entities, such as job requirements and job responsibilities from job descriptions. They also lack in producing effec tively desired search results due to semantic differences in job descriptions and users English natural language queries. This thesis proposes a framework to cater for challenges in the existing e-recruitment systems. The proposed Semantic Extraction, Enrichment and Transformation (SExEnT) framework extracts entities from job descriptions using a domain specific dictio nary. The extraction process first performs linguistic analysis and then extracts entities and compound words. After the extraction of entities and compound words, it builds job context using a job description domain ontology. The ontol ogy provides an underlying schema for defining how concepts are related to each other. Besides building a contextual relationship among entities, the entities are also enriched using Linked Open Data (LOD) that improves search capability in finding suitable jobs. In the proposed framework, Web Ontology Language (OWL) is used to represent information for machine-understanding. The framework ap propriately matches users queries and job descriptions. The evaluation data set has been collected from various jobs portals, such as Indeed, Personforce, DBWorld. A total of 860 jobs were collected that belong to multiple categories, such as technology, medical, management and others. The data set was vetted and verified by HR experts. The evaluation has been performed using precision, recall, F-1 measure, accuracy and error rate. The proposed frame work achieved an overall F-1 measure of 87.83% and accuracy of 94% for entities extraction. The application has a precision of 99.9% in representing and retriev ing job descriptions from its knowledge base. The job description ontology has an overall concept coverage of 96%. The evaluation results show that the pro posed framework performs well in extracting, modelling, enriching, and retrieving job description against queries. At current, the proposed framework is neither able to automatically generate pattern/action rules, nor provide a complex ranked retrieval of job descriptions against a user profile nor automatically extend dictio nary to increase extraction precision. In future, the framework can be extended to resolve these limitations.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

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