النتائج
1۔ أولاً:
تعرفنا علی بدایۃ الشعر الحر وما ھي حقیقتہُ وکیف کان التجدید في الشعر ولا یقصد بذلک التنکر لقوانینہِ إنما یکون الابتکار في المعاني۔
2۔ ثانیاً:
من ھم أھم شعراء وشاعرات العصر الجديد ؟ وما ھي مکانۃ نازک الملائکۃ بین شاعرات عصرھا ؟
3۔ ثالثاً:
إتفاق الشعراء حول نازک الملائکۃ بأنھا شاعرة ممتازۃ لھا ممیزات أدبیۃ وشعریۃ رائعۃ وأنھا تستجیب لشعورھا وإحساسھا قبل کل شيء وتعتبر ھي رائدۃ الشعر العربي الحر و تعرفنا على بعض شعراء و شاعرات عصرها .
4۔ رابعاً:
تحدثنا عن الشعر الجدید في شبہ القارۃ الھندیۃ وأن حالي أعطی لغزل ( اللغة الأردية) الأسلوب الجدید واستوعب النقاد من التجارب علی أن ذلک شعر علی شکل نثر کما نقولُہ في العربیۃ الشعر الحُر۔
5۔ خامساً:
الأدب النسائي في الباكستان وما ھي دور المرأۃ بین الرجال من البدایۃ وحتی العھد الحاضر ، و التعرف على بعض الشعراء والشاعرات العصر الجديد .
6۔ سادساً:
مکانۃ بروین شاکر في الساحۃ الأدبیۃ وخاصۃً في الغزل وھي من بعض ألمع نجوم السماء علی الأرض في الغزل الأردو والشعر الحر في اللغة الأردية۔
Objectives: 1. To determine the impact of duration of exposure to industrial chemical fumes on Peak Expiratory Flow Rate (PEFR) and blood pressure of the industrial workers. 2. To find out the association between changes in blood pressure and PEFR due to exposure to industrial chemical fumes in these subjects. Methods: This cross-sectional study was performed at Aziz Fatimah Medical and Dental College, Faisalabad. The study participants were 151 males working in the chemical industries. The study was approved by institutional ethical committee and informed consent was taken from the participants. Free camp was arranged for three days in September 2020 in the industrial area of Faisalabad. Thorough history of exposure to chemicals was taken using structured proforma. PEFR values were recorded using Wrights handheld peak flow meter. Blood pressure was taken by auscultatory method using mercury sphygmomanometer. Data was analyzed using SPSS version 22. Results: Systolic and diastolic blood pressures were significantly raised with increase in duration of exposure. PEFR levels were significantly declined with increase in duration of exposure to chemicals. Significant negative association was noted between diastolic blood pressure and PEFR (p value = 0.003). Negative correlation was observed between PEFR and systolic Blood pressure, however it was not statistically significant (p value = 0.92). Conclusions: PEFR decreased while Systolic and diastolic blood pressures increased significantly with increase in the duration of exposure to chemicals. There was a significant negative correlation between PEFR and diastolic blood pressure while there was no association between PEFR and systolic blood pressure.
The role of e-learning systems has become imperative in effectively educating masses of knowledge communities while maximizing the learner’s productivity. Barring this important role, e-learning systems face major challenges such as having context-aware and reusable learning contents. Furthermore, aspects oflearner profiling and categorization for deliverance of relevant learning contents, personalization and adaptive content recommendation to learners need to be focused. Currently, learning contents are static and not machine processable.Learner profiling may not fully comprehend the implicit as well as explicit characteristics of learners with subjective consideration of academic aspects at abstract level of granularity. Learner categorization techniques lack in dynamically considering the cognitive and inclinatory attributes of learners at finer level of granularity across the learning cycle. The learning contents offered may not accord with learning capacity of learners (lack personalization) with minimal support for content adaptivity.In proposed research, Ontology based Adaptive Semantic E-learning Framework (OASEF) is presented that exploits comprehensive set of learner attributes identified for effectively profiling the learners based on discriminative ones. Machine learning based dynamic and adaptive technique named Learner Categorization based on Hybrid Artificial Intelligence Techniques (LCHAIT) has been proposed for learner categorization. A supervised mode of learning was employed on a labeled data set modeled through a LearnerOntology. It has diverse learner’s profiles with implicit and explicit attributes pertinent to learner’s perspectives of demographics, academics, inclinations and behaviors. A comparative analysis of LCHAIT with three other machine learning techniques (Fuzzy Logic, Case Based Reasoning, and Artificial Neural Networks) is also presented. The learning contents maintained in the ontologies (CourseOntology, AssessmentOntoloy and DomainOntology) were recommended by considering the learner’s category to ensure personalization by a dynamic content recommender named Knowledge based Adaptive Semantic e-Learning Recommender (KASER). The efficacy of all categorization techniques was empirically measured while categorizing the learners based on their profiles through metrics of accuracy, precision, recall, f-measure and associated costs. These empirical quantifications assert LCHAIT as a better option than contemporary techniques as exhibited by greater accuracy of performance metrics. The performance of KASER was measured through degree of correctness in recommending the relevant learning contents compared with domain experts. Overall performance of OASEF was measured while recording the learner’s results spanning three years. The comparative analysis of proposed framework exhibits visibly improved results compared to prevalent approaches. These improvements are signified to the comprehensive attribute selection, learner profiling, dynamic techniques for learner categorization and effective content recommendation while ensuring personalization and adaptivity.