The Effect of List-Group-Label Strategy on Students’ Reading Interpretative Comprehension

Reading at the level of interpretive understanding is a student activity to respond, get information and meaning by making inferences and reading between the lines of the reading text. This study reveals the problems, namely: students are not able to identify content words such as verbs, nouns, adjectives, and adverbs from the text, students lack vocabulary, students are not able to answer comprehension questions from the text by applying the List-Group-Label Strategy in the teaching and learning process. reading narrative text. The purpose of this study was to determine whether or not there was a significant effect of List-Group-Label Strategy on Students' Reading Comprehension in Narrative Text. This research is a quasi-experimental design. The population in this study were junior high school students and the sample was class VIII students which consisted of two classes as the experimental class and the control class. Each class consists of 30 students. Researchers selected samples using saturated sampling technique. The instrument used in data collection is a written test. Next, the researcher gave a pretest and posttest to the experimental group and the control group to determine the normality of the data and the homogeneity of the sample. After conducting the research, the researcher analyzed the data and produced hypothesis testing, the t-count was 5.067 and the t-table was 2.002. While t-count (5.067) > t-table (2.002), it can be concluded that Ho is rejected and Ha is accepted. In conclusion, there is a significant effect of the List-Group-Label Strategy on students' reading comprehension in narrative texts

Multi-DOA estimation based on the KR image tensor and improved estimation network

Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around $$10\%$$ 10 % . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of $$3^\circ $$ 3 ∘ . Moreover, the proposed estimation network has root mean square estimation error lower than $$1^\circ $$ 1 ∘ when signal noise ratio equals $$-\,10\,{\mathrm {dB}}$$ - 10 dB and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments.

Multi-DOA Estimation Based on the KR Image Tensor and Improved CNN

In this paper, we propose an improved convolutional neural network (CNN) to solve the multi-DOA estimation problem. We use Khatri-Rao (KR) product to obtain the KR image tensor of covariance matrix and use the proposed estimation CNN to process the tensor. In order to increase the generalization of the proposed CNN and adapt the multi-label classification problem, we use the curriculum learning scheme (CLS) and partial label strategy (PLS) to develop an efficient training procedure. We implement several experiments to demonstrate the satisfying performance of the proposed estimation method. The simulation results show that our proposed method can finish the high resolution multi-DOA estimation use only a few sensors. Furthermore, the proposed method can obtain high estimation accuracy under low SNR situations and use fewer snapshots.

Multi-Label Learning based Semi-Global Matching Forest

Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently.

Radiological Pharmacology Drugs: Therapeutic Potential of Melatonin

The purpose of the article is to draw attention to Melatonin as a means of radiological pharmacology within the framework of drugs’ reprofiling [13] and the “off-label” strategy (application for medical purposes does not correspond to the instructions for the basic medical use of the drug). Melatonin has, to varying degrees, a dose-dependent antistressor, sedative, hypnogenic, neuroprotective, geroprotective (a general consistent pattern for all geroprotectors – earlier initiation of drug use provides a greater effect), antidepressant, antioxidant, antitumor, antiapoptotic (in normal cells), proapoptotic (in cancer cells), oncostatic, antimetastatic, immunomodulatory, radioprotective, radiosensitizing, anti-infectious, analgesic, hepatoprotective geroprotective, antihypertensive, anti-inflammatory, moderate contraceptive (for women) action. Melatonin regulates neuroendocrine functions, respiratory rate, reproductive function, osteogenic differentiation of mesenchymal stem cells, formation and protection of bones; modulates the activity of bone-forming osteoblasts and bone-resorbing osteoclasts; reduces pain sensitivity; affects the intracellular calcium content. The antioxidant properties of Melatonin are closely related to its antitumor effect. Studies have demonstrated that melatonin has a self-sufficient oncostatic effect in cancer of the breast, ovaries, endometrium, pancreas, prostate, lungs; melanoma, hepatocellular carcinoma, colorectal cancer, glioblastoma, and leiomyosarcoma. Key words: pineal gland, melatonin, radioprotector, radiological pharmacology.

Eco-Labels

Eco-label products are very appealing. To increase sales most of the companies adopt eco-label strategy. On the other hand, the eco-labels often assure more than the products can in reality deliver. In particular, eco-labels may lead consumers to mechanically infer that the products are friendly to the environmentally friendly. The rising significance of corporate social responsibility provides strong motivation for companies to market unsustainable conventional products as environmentally friendly. Eco-labels are designed to inform consumers that the labeled product is more environmentally friendly than the competitors. Eco-labels are increasingly facilitating manufacturers, wholesalers, retailers, and consumers in their purchasing decisions. The chapter explains in detail the objectives of eco-labels, benefits of eco-labels, consequences of eco-labels, and different types of eco-labels. It also mentions the adoption process of eco-labels by the consumers. The chapter ends with examples of best practices.