Membrane science is a state-of-the-art environmentally green technology that ascertains superior advantages over traditional counterparts for CO2 capture and separation. In this research, mixed matrix membranes (MMMs) comprising cellulose acetate (CA) with various loadings of bentonite (Bt) clay were fabricated by adopting the phase-inversion technique for CO2/CH4 and CO2/N2 separation. The developed pristine and MMMs were characterized for morphological, thermal, structural, and mechanical analyses. Several techniques such as scanning electron microscopy, thermogravimetric analysis, Fourier transformed infrared spectroscopy, and nano-indentation investigations revealed the promising effect of Bt clay in MMMs as compared to pristine CA membrane. Nano-indentation test identified that elastic modulus and hardness of the MMM with 1 wt. loading was increased by 64% and 200%, respectively, compared to the pristine membrane. The permeability decreased with the incorporation of Bt clay due to uniform dispersion of filler attributed to enhanced tortuosity for the gas molecules. Nevertheless, an increase in gas separation performance was observed with Bt addition up to 1 wt. loading. The opposite trend prevailed with increasing Bt concentration on the separation performance owing to filler agglomeration and voids creation. The maximum value of ideal selectivity (CO2/CH4) was achieved at 2 bar pressure with 1 wt. % Bt loading, which is 79% higher than the pristine CA membrane. For CO2/N2, the ideal selectivity was 123% higher compared to the pristine membrane with 1 wt. % Bt loading at 4 bar pressure.
Background: COVID-19 was announced as a pandemic issue globally on 11th March, 2020. In response to this situation, all educational activities including medical and clinical education in various colleges across the country were suspended on the 15th of March. So, online education emerged as an alternative method of teaching & learning to maintain continuity of education Aim: To evaluate the use of online learning modalities and to find their feasibility and usability in medical education. Methods: A cross-sectional study was performed across the government and private medical colleges of Lahore. Eligible participants were undergraduate medical students from 10 medical colleges of Lahore. A questionnaire linked to a Google form was distributed to the medical students across 10 government and private medical colleges through different social platforms. Results: A total of 439 valid questionnaires were collected. 31.7% of students disagreed that interaction between students and teachers was possible through online teaching. Only 7.7% of students agreed that online learning can be used for clinical teaching of medical sciences, as compared to 35.8% who disagreed with this answer and 12.8% who were neutral. 23% of the students agreed that online learning was more convenient and flexible than traditional learning, while 24% disagreed and 21.4% were neutral in this regard. Only 19.8% of students had problems with poor internet services. Conclusion: As Pakistan has faced four waves of the COVID-19 which is not over yet due to the emergence of new strains. Due to vaccination of medical students medical education is back to conventional physical learning but online learning has gained importance as an effective alternate to continue learning processes in exceptional situations like COVID-19 pandemic. Keywords: Covid-19 pandemic, online education
In the last decade, object detection is one of the interesting topics that played an important role in revolutionizing the presentera. Especially when it comes to computervision, object detection is a challenging and most fundamental problem. Researchersin the last decade enhanced object detection and made many advance discoveries using thetechnological advancements. When wetalk about object detection, we also must talk about deep learning and its advancements over the time. This research work describes theadvancements in object detection over last10 years (2010-2020). Different papers published in last 10 years related to objectdetection and its types are discussed with respect to their role in advancement of object detection. This research work also describesdifferent types of object detection, which include text detection, face detection etc. It clearly describes the changes inobject detection techniques over the period of the last 10 years. The Objectdetection is divided into two groups. General detectionand Task based detection. General detection is discussed chronologically and with its different variants while task based detectionincludes many state of the art algorithms and techniques according to tasks. Wealso described the basic comparison of how somealgorithms and techniques have been updated and played a major role in advancements of different fields related to object detection.We conclude that the most important advancements happened in the last decade and the future is promising much more advancement inobject detection on the basis of work done in this decade.In the last decade, object detection is one of the interesting topics that played an important role in revolutionizing the presentera. Especially when it comes to computervision, object detection is the challenging and most fundamental problem. Researchersinlast decade enhanced object detection and made many advance discoveries using thetechnological advancements. When wetalk about object detection, we also must talk about deep learning and its advancements over the time. This research work describes theadvancements in object detection over last10 years (2010-2020). Different papers published in last 10 years related to objectdetection and its types are discussed with respect to their role in advancement of object detection. This research work also describesdifferent types of object detection, which include text detection, face detection etc. It clearly describes the changes inobject detection techniques over the period of last 10 years. The Objectdetection is divided into two groups. General detectionand Task based detection. General detection is discussed chronologically and with its different variants while task based detectionincludes many state of the art algorithms and techniques according to tasks. Wealso described the basic comparison of how somealgorithms and techniques have been updated and played a major role in advancements of different fields related to object detection.We conclude that the most important advancements happened in last decade and future is promising much more advancement inobject detection on the basis of work done in this decade.
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