MATERIALS AND METHODS: In this nonrandomized trial on interrupted time series study, flipped class was conducted on group of 112 students of bachelor of pharmacy semester V. The topic selected was popular herbal remedies of the complementary medicine module. Flipped class was conducted with audio and video presentation in the form of a quiz using ten one-best-answer type of multiple-choice questions covering the learning objectives. Audience response was captured using web-based interaction with Poll Everywhere. Feedback was obtained from participants at the end of FC activity and debriefing was done.
RESULTS: Randomly selected 112 complete responses were included in the final analysis. There were 47 (42%) male and 65 (58%) female respondents. The overall Cronbach's alpha of feedback questionnaire was 0.912. The central tendencies and dispersions of items in the questionnaire indicated the effectiveness of FC. The low or middle achievers of quiz session (pretest) during the FC activity were three times (95% confidence interval [CI] = 1.1-8.9) at the risk of providing neutral or negative feedback than high achievers (P = 0.040). Those who gave neutral or negative feedback on FC activity were 3.9 times (95% CI = 1.3-11.8) at the risk of becoming low or middle achievers during the end of semester examination (P = 0.013). The multivariate analysis of "Agree" or "Disagree" and "Agree" or "Strongly Agree" was statistically significant.
CONCLUSION: This study provides insight on how the pharmacy students learn and develop their cognitive functions. The results revealed that the FC activity with Poll Everywhere is an effective teaching-learning method.
RESULTS: We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).
CONCLUSIONS: Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
METHODS: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance's ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.
RESULTS: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.
CONCLUSIONS: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.