اے خالقِ حقیقی ہویدا ہے تیرا نام
وردِ زباں ہے تو کہ جلیسا ہے تیرا نام
زخموں کا اندمال ہے ایسا ہے تیرا نام
یعنی مداوا رنج و الم کا ہے تیرا نام
واحد ہے، لاشریک ہے، یکتا ہے صرف تو
تیرے سوا نہیں کوئی مولا ہے صرف تو
یکتا صفات و ذات میں تو اور صرف تو
پختہ ہر ایک بات میں تو اور صرف تو
رہتا ہے دن میں، رات میں تو اور صرف تو
بچتا ہے کیا ثبات میں؟ تو اور صرف تو
بس تو ہی لم یزل بھی ہے اور لا یزال بھی
تیری مثال کیا ہو کہ ہے بے مثال بھی
بخشا ہے تو نے ہی مجھے ایقانِ حسنِ ذوق
تیری ولا سے پایا ہے عرفانِ حسنِ ذوق
تیری عطا نے بخشا ہے ایمانِ حسنِ ذوق
تیرا ہے یعنی سب سروسامانِ حسنِ ذوق
تو نے کیا ہے ذہن کو بیدار اے خدا
اس پہ کرم کہ کر دیا سرشار اے خدا
The study examines English as Foreign Language (EFL) students’ attitude towards developing their speaking abilities at KRI University in order to better understand the disparities in speaking competency among undergraduates. The study utilized a quantitative approach and employed a 4-item interview survey to gather data for the study. The survey interview questionnaire was adopted from Wang, Kim, Bong, and Ahan (2013) and administered to 100 students in the departments of English of six universities in Iraq's Kurdistan Region. A semi-structured interview was developed for EFL students. The questionnaire was online and an open-ended one. The data from the participants was analyzed using thematic analysis with (SPSS) software. The finding revealed a perceived failure in EFL students' English-speaking skills and this was reported along with causes of the perceived difficulty. The finding also revealed a poor level of speaking ability among EFL undergraduates as well as little education in the skill at the university level. Apart from these, the study discovered some major challenges for EFL students such as lack of confidence, lack of planning, a demotivating atmosphere, incorrect word choice, poor gestures, and incorrect style which made the students not to be successful in their speaking abilities. The study suggested that EFL learners' competency should be securitized to strengthen their speaking abilities in the light of the results of the study. Speaking is a crucial ability in language acquisition and EFL teachers should help their students acquire it. As a means of improving students' communicative ability, task-based instruction should be utilized in educational institutions and universities. The implication of this paper is that speaking difficulties among EFL students in the Iraqi Kurdistan Region institution can be solved by putting greater focus on this ability. There are several issues to consider, including teachers, instructional methodologies, the curriculum, extracurricular activities, and assessment rules.
Vehicular Ad hoc Networks (VANETs) are attaining vital focus from research community as well as from manufacturers. All major vehicle manufacturers have proclaimed to embrace VANETs ability in their products. Fine tuning the parameters manually by the network operator or administrator is quite time consuming. So this claims the need for having a good optimal technique that could solve the issue and improve the quality of service in a better way. Primarily, the main purpose of these networks is to provide safety related services. Then, now-a-days investigators are working to provide streaming services in VANETs e.g. video call, online gaming, video conference etc. Importance of these applications requires not only just bandwidth but focuses on less delay, such strict Quality of Service (QoS) requirements are to be addressed in order to adapt the users need. Researchers are addressing this dispute, as it will become a vast problem when most of the vehicles will have VANETs capabilities. A novel technique for optimization of QoS in Vehicular Ad hoc Networks, which will cater for the stringent requirements of the future vehicular networks is proposed in this research. The proposed Refined Regression Statistical Classifier Model (RRSCM) aims to resolve the above speculated issue and aids in improving the QoS. The optimized results compared with the existing built in classifier models has revealed the overpowering performance of proposed RRSCM. Artificial Neural Networks classification seems to be quite time consuming and the accuracy percentage rate of RRSCM outperforms ANN. The proposed multivariate analysis scheme optimizes by parting parameters and predicts the traffic behavior throughput. It aids to improve the QoS and envisage the traffic over VANETs. Earlier, the parameters were as such used as predictor variables in multiple linear regression (MLR) models. The other classification models used for prediction are evaluated with independent traffic samples. The root-mean-square percent relative error (RMS%RE) will measure the merit and characterize the performance of various other classification models over the proposed RRSCM. This RRSCM technique, when adopted by the cellular network operators and ISPs, shall be benefited and will be able to provide better services to the vehicles on the road.