Let Students Engage in Real Learning: An Evaluation of Protocol-guided Learning

Both a student-centered instruction approach and a classroom management technique based on the learning protocol are known as protocol-guided learning. This paper describes the protocol-guided learning model’s implications for classroom practice and its impacts on classroom reconstruction with the aim of ensuring that learning actually occurs on students. Its definition, advantages, and practical roles are described

QUALITY ANALYSIS IN FLEXIBLE MANUFACTURING SYSTEMS WITH BATCH PRODUCTIONS

To improve product quality and reduce cost, batch production is often implemented in many exible manufacturing systems. However, the current literature does not provide any method to analyze the quality performance in a flexible manufacturing system with batch production. In this research, we present an analytical method with closed-form formula to evaluate the quality performance in such systems. Based on the model, we discover and investigate monotonic and non-monotonic properties in quality to provide practical guidance for operation management. To improve product quality, we introduce the notions of quality improvability with respect to product sequencing. In addition, we develop the indicators for quality improvability based on the data available on the factory floor rather than complicated calculations. We define the bottleneck sequence and bottleneck transition as the ones that impede quality in the strongest manner, investigate the sensitivity of quality performance with respect to sequences and transitions, and propose quality bottleneck sequence and transition indicators based on the measured data. Finally, we provide a case study at an automotive paint shop to show how this method is applied to improve paint quality. Moreover, we explore a potential application to reduce energy consumption and atmospheric emissions at automotive paint shops. By selecting appropriate batch and sequence policies, the paint quality can be improved and repaints can be reduced so that less material and energy will be consumed, and less atmospheric emissions will be generated. It is shown that such scheduling and control method can lead to significant energy savings and emission reduction with no extra investment nor changes to existing painting processes. The successful development of such method would open up a new area in manufacturing systems research and contribute to establish a solid foundation for an integrated study on productivity, quality and exibility. In addition, it will provide production engineers and operation managers a quantitative tool for continuous improvement on product quality in flexible manufacturing environmen

The Role of Information Technology-assisted Instruction and its Implementation Strategies

As a result of the rapid advancement of information technology (IT), the integration of IT into classroom learning has become a major topic of discussion in the education community. It is widely acknowledged as an effective method for optimizing teaching effectiveness and student learning efficiency. This paper’s objective is to assess the current situation of IT-assisted classroom instruction in China and to offer practical suggestions

Generalizations of Markov model to characterize biological sequences

BACKGROUND: The currently used k(th )order Markov models estimate the probability of generating a single nucleotide conditional upon the immediately preceding (gap = 0) k units. However, this neither takes into account the joint dependency of multiple neighboring nucleotides, nor does it consider the long range dependency with gap>0. RESULT: We describe a configurable tool to explore generalizations of the standard Markov model. We evaluated whether the sequence classification accuracy can be improved by using an alternative set of model parameters. The evaluation was done on four classes of biological sequences – CpG-poor promoters, all promoters, exons and nucleosome positioning sequences. Using di- and tri-nucleotide as the model unit significantly improved the sequence classification accuracy relative to the standard single nucleotide model. In the case of nucleosome positioning sequences, optimal accuracy was achieved at a gap length of 4. Furthermore in the plot of classification accuracy versus the gap, a periodicity of 10–11 bps was observed which might indicate structural preferences in the nucleosome positioning sequence. The tool is implemented in Java and is available for download at . CONCLUSION: Markov modeling is an important component of many sequence analysis tools. We have extended the standard Markov model to incorporate joint and long range dependencies between the sequence elements. The proposed generalizations of the Markov model are likely to improve the overall accuracy of sequence analysis tools

Cascade Learning Localises Discriminant Features in Visual Scene Classification

Lack of interpretability of deep convolutional neural networks (DCNN) is a well-known problem particularly in the medical domain as clinicians want trustworthy automated decisions. One way to improve trust is to demonstrate the localisation of feature representations with respect to expert labeled regions of interest. In this work, we investigate the localisation of features learned via two varied learning paradigms and demonstrate the superiority of one learning approach with respect to localisation. Our analysis on medical and natural datasets show that the traditional end-to-end (E2E) learning strategy has a limited ability to localise discriminative features across multiple network layers. We show that a layer-wise learning strategy, namely cascade learning (CL), results in more localised features. Considering localisation accuracy, we not only show that CL outperforms E2E but that it is a promising method of predicting regions. On the YOLO object detection framework, our best result shows that CL outperforms the E2E scheme by $2\%$ in mAP

Basonuclin Regulates a Subset of Ribosomal RNA Genes in HaCaT Cells

Basonuclin (Bnc1), a cell-type-specific ribosomal RNA (rRNA) gene regulator, is expressed mainly in keratinocytes of stratified epithelium and gametogenic cells of testis and ovary. Previously, basonuclin was shown in vitro to interact with rRNA gene (rDNA) promoter at three highly conserved sites. Basonuclin's high affinity binding site overlaps with the binding site of a dedicated and ubiquitous Pol I transcription regulator, UBF, suggesting that their binding might interfere with each other if they bind to the same promoter. Knocking-down basonuclin in mouse oocytes eliminated approximately one quarter of RNA polymerase I (Pol I) transcription foci, without affecting the BrU incorporation of the remaining ones, suggesting that basonuclin might regulate a subset of rDNA. Here we show, via chromatin immunoprecipitation (ChIP), that basonuclin is associated with rDNA promoters in HaCaT cells, a spontaneously established human keratinocyte line. Immunoprecipitation data suggest that basonuclin is in a complex that also contains the subunits of Pol I (RPA194, RPA116), but not UBF. Knocking-down basonuclin in HaCaT cells partially impairs the association of RPA194 to rDNA promoter, but not that of UBF. Basonuclin-deficiency also reduces the amount of 47S pre-rRNA, but this effect can be seen only after cell-proliferation related rRNA synthesis has subsided at a higher cell density. DNA sequence of basonuclin-bound rDNA promoters shows single nucleotide polymorphisms (SNPs) that differ from those associated with UBF-bound promoters, suggesting that basonuclin and UBF interact with different subsets of promoters. In conclusion, our results demonstrate basonuclin's functional association with rDNA promoters and its interaction with Pol I in vivo. Our data also suggest that basonuclin-Pol I complex transcribes a subset of rDNA

Position and distance specificity are important determinants of cis-regulatory motifs in addition to evolutionary conservation

Computational discovery of cis-regulatory elements remains challenging. To cope with the high false positives, evolutionary conservation is routinely used. However, conservation is only one of the attributes of cis-regulatory elements and is neither necessary nor sufficient. Here, we assess two additional attributes—positional and inter-motif distance specificity—that are critical for interactions between transcription factors. We first show that for a greater than expected fraction of known motifs, the genes that contain the motifs in their promoters in a position-specific or distance-specific manner are related, both in function and/or in expression pattern. We then use the position and distance specificity to discover novel motifs. Our work highlights the importance of distance and position specificity, in addition to the evolutionary conservation, in discovering cis-regulatory motifs

Focusing on what to decode and what to train: Efficient Training with HOI Split Decoders and Specific Target Guided DeNoising

Recent one-stage transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOI) task by leveraging the detection of DETR. However, the current methods redirect the detection target of the object decoder, and the box target is not explicitly separated from the query embeddings, which leads to long and hard training. Furthermore, matching the predicted HOI instances with the ground-truth is more challenging than object detection, simply adapting training strategies from the object detection makes the training more difficult. To clear the ambiguity between human and object detection and share the prediction burden, we propose a novel one-stage framework (SOV), which consists of a subject decoder, an object decoder, and a verb decoder. Moreover, we propose a novel Specific Target Guided (STG) DeNoising training strategy, which leverages learnable object and verb label embeddings to guide the training and accelerate the training convergence. In addition, for the inference part, the label-specific information is directly fed into the decoders by initializing the query embeddings from the learnable label embeddings. Without additional features or prior language knowledge, our method (SOV-STG) achieves higher accuracy than the state-of-the-art method in one-third of training epochs. The code is available at this https://github.com/cjw2021/SOV-STG

Bayesian detection of embryonic gene expression onset in C. elegans

To study how a zygote develops into an embryo with different tissues, large-scale 4D confocal movies of C. elegans embryos have been produced recently by experimental biologists. However, the lack of principled statistical methods for the highly noisy data has hindered the comprehensive analysis of these data sets. We introduced a probabilistic change point model on the cell lineage tree to estimate the embryonic gene expression onset time. A Bayesian approach is used to fit the 4D confocal movies data to the model. Subsequent classification methods are used to decide a model selection threshold and further refine the expression onset time from the branch level to the specific cell time level. Extensive simulations have shown the high accuracy of our method. Its application on real data yields both previously known results and new findings.Comment: Published at http://dx.doi.org/10.1214/15-AOAS820 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org