Teaching Challenges Using the Zoom Application That Focuses on Student Concentration in Online Classrooms

The fast advancement in information technology stimulates educational creativity as well. Learning and training practices are often not only carried sout in the traditional manner, but also with the usage of a multitude of learning technologies options. The zoom program is one of the learning resources used in online courses. Zoom was an application developed during the Covid-19 timeframe to address the limitations between educators and students, especially in terms of space and time. With E-learning, educators and students are not limited to one dimension of time and space, and learning will run and neglect both. Learning by simulated children's attention, on the other hand, causes disruption in the learning phase. During the learning phase, students must maintain a high level of concentration. The level of concentration of students has a significant impact on the learning process' outcomes. Since attitude and focus have a beneficial association, high focus may often affect a person's attitude in a learning phase in order to produce optimal performance. Many factors may affect a person's attention, one of which is the learning environment. A peaceful atmosphere can undoubtedly improve a person's concentration level, while a silent / noisy environment will exacerbate one's focus during the learning phase.

Modeling the Potential Impacts of Climate Change and Adaptation Options for Rainfed Wheat

The environment of our globe is changing due to climate change (CC) which is influencing agricultural and agronomic practices. Being open to vagaries of nature, agriculture sector is highly vulnerable to climate change phenomena. Any change in climate ultimately influenced food security. Keeping in view the climate change phenomenon the current study aided with simulation modeling was carried out during wheat growing season of 2013-14 and 2014-15 at rainfed Pothwar. Experiments arranged in Randomized Complete Block Design were conducted having five wheat genotypes (Dharabi, Chakwal-50, NARC-2009, Pak-13 and AUR- 809), four sowing dates (21-30 Oct, 10-20 Nov, 01-10 Dec and 20-30 Dec of 2013- 14 and 2014- 15), and three climatically varying locations i.e. Islamabad, URFKoont and Talagang characterized by high, medium a nd low rainfall and low, medium and high temperature areas respectively. Climate data required for the study was collected from the met observatory located at NARC and for URF-Koont and Talagang it was generated by downscaling. Increase in temperature at Islamabad (1oC), URF-Koont (4.6 oC) and Talagang (4.8oC) was observed during study period than the baseline temperature. Wheat crop days to maturity and LAI reduced by 15% and 51% respectively due to higher temperature (4.8 oC). Biological yield ranged 9.7 t ha-1 to 4.72 t ha-1 from low temperature zone (Islamabad) to higher temperature zone (Talagang). Therefore, due to 1 oC increase in temperature, 7.6% reduction in biological yield was observed. Maximum grain yield observed at Islamabad (2.39 t ha-1) while minimum grain yield recorded at Talagang (2.19 t ha-1). Similarly, among genotypes, the performance of Pak-13 and AUR-809 was better than other genotypes therefore, their genetic blood should be used in future for breeding programs. Grain growth curve was also developed in relation to temperature. Grain growth curve depicted 8.2 % reduction in individual grain weight due to 1oC increased temperature. Maximum water use efficiency was recorded at URF-Koont (7.43 kg mm-1) than Talagang (7.14 kg mm-1) and Islamabad (6.23 kg mm-1) which showed higher water productivity due to less evapotranspiration. Agricultural Production Systems Simulator (APSIM), Cropping System Model (CropSyst) and Decision Support System for Agrotechnology Transfer (DSSAT) models were parameterized. Validation skill scores like d-Index, RMSE, and coefficient of determination (R2) were used to determine the performance of models which confirmed the field results with good accuracy (R2 for grain yield was 0.97, 0.96, 0.92 for APSIM, CropSyst and DSSAT respectively). Different global circulation models (GCMs) were used under varying RCPs (Representative concentration pathways). Future projections showed that under current management practices at Islamabad, URF-Koont and Talagang wheat yield will be reduced. Under RCP 4.5, GCMs showed that during early scenarios i.e. 2030, SD2 (5-10 November sowing) will be beneficial, while under later years 15-October sowing will be beneficial.Under RCP 8.5 (higher temperature and low rainfall), GCMs showed even severe results showing reduction in wheat productivity. Adaptation based upon results should include SD2 (01-10 November sowing) for early scenarios i.e. 2030, while for later scenarios i.e. 2050 and 2070, SD1 i.e. 15-25 October sowing will be beneficial in order to avoid terminal heat stress during grain filling stages of wheat. Genotypes Pak-13 and AUR-809 showed great potential to produce sustainable crop yield under higher temperature therefore, these two genotypes should be considered for temperature extremes. Crop models should be used as decision support tools to reduce agricultural environment vulnerability to climate variability.