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This study investigates the potential benefits for incidental vocabulary acquisition of implementing a particular sequence of input-output-input activities. More specifically, learners of English as a foreign language (EFL; n = 32) were asked to watch a TED Talk video, orally sum up its content in English, and then watch the video once more. A comparison group (n = 32) also watched the TED Talk video twice but were not required to sum it up in between. Immediate and delayed posttests showed significantly better word-meaning recall in the former condition. An analysis of the oral summaries showed that it was especially words that learners attempted to use that stood a good chance of being recalled later. These findings are interpreted with reference to Swain's (1995) output hypothesis, Laufer and Hulstijn's (2001) involvement load hypothesis, and Nation and Webb's (2011) technique feature analysis. What makes the text-based output task in this experiment fundamentally different from many previous studies that have investigated the merits of text-based output activities is that it was at no point stipulated for the participants that they should use particular words from the input text. The study also illustrates the potential of TED Talks as a source of authentic audiovisual input in EFL classrooms.

The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta.

Over the last twenty years, there has been swift growth in industrialization and technological advancements, driving economic progress. Nevertheless, it is inevitable that these sectors will bring about environmental shifts. Thus far, endeavors have been undertaken to assess the influence of industrialization and technological advancements on environmental deterioration. Additionally, the extensive discussion surrounding the impact of financial development, trade openness, and technological innovation on the environment has not yielded conclusive empirical findings. Studies often operate under the assumption of symmetric relationships, potentially leading to biased results. Adding to the discussion on the drivers of carbon neutrality, the time-dependent effects of critical aspects such as financial development and technological innovation should inform meaningful policies for environmental management. This article explores the time-varying causal association between trade openness, industrialization, financial development, technological innovation, and CO2 emissions in Thailand using novel time-varying Granger causality tests. The time-varying causality outcomes demonstrate that the associations change significantly over time, in contrast to the results of Toda-Yamamoto causality. Overall, there exists a bidirectional relationship between industrialization, financial development, trade openness, technological innovation, and CO2 emissions over different time sequences. These outcomes have implications for both policy and research.

[This corrects the article DOI: 10.1371/journal.pone.0304830.].

The overspread of the novel coronavirus—SARS-CoV-2—over the globe has caused significant damage to manufacturing and service businesses, regardless of whether they are commercial, public, or not-for-profit sectors. While both the short-term and long-term impacts of most companies can be approximately measured or estimated, it is challenging to address the enduring effects of COVID-19 on teaching and learning activities. The target of this research is to investigate students’ manners of studying at home during the school suspension time as a result of COVID-19. Through analyzing original survey data from 420 K6–12 students in Hanoi, Vietnam, this work demonstrates the different learning habits of students with different socioeconomic statuses and occupational aspirations during the disease’s outbreak. In particular, we featured the differences in students’ learning behaviors between private schools and public schools, as well as between students who plan to follow STEM-related careers and those who intend to engage in social science-related careers. The empirical evidence of this study can be used for the consideration of the local government to increase the sustainability of coming policies and regulations to boost students’ self-efficacy, as it will affect 1.4 million students in Hanoi, as well as the larger population of nearly 10 million Vietnamese students. These results can also be the foundation for future investigations on how to elevate students’ learning habits toward Sustainable Development Goal 4 (SDG4)—Quality Education—especially in fanciful situations in which the regular school operation has been disrupted, counting with limited observation and support from teachers and parents.

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

Stress response pathways are critical for cellular homeostasis, promoting survival through adaptive changes in gene expression and metabolism. They play key roles in numerous diseases and are implicated in cancer progression and chemoresistance. However, the underlying mechanisms are only poorly understood. We have employed a multi-omics approach to monitor changes to gene expression after induction of a stress response pathway, the unfolded protein response (UPR), probing in parallel the transcriptome, the proteome, and changes to translation. Stringent filtering reveals the induction of 267 genes, many of which have not previously been implicated in stress response pathways. We experimentally demonstrate that UPR‐mediated translational control induces the expression of enzymes involved in a pathway that diverts intermediate metabolites from glycolysis to fuel mitochondrial one‐carbon metabolism. Concomitantly, the cells become resistant to the folate-based antimetabolites Methotrexate and Pemetrexed, establishing a direct link between UPR‐driven changes to gene expression and resistance to pharmacological treatment. The unfolded protein response (UPR) is a stress response pathway implicated in numerous diseases and chemotherapy resistance. Here, the authors define the UPR regulon with a multi-omics strategy, uncovering changes to mitochondrial one-carbon metabolism and concomitant resistance to folate-based therapeutics.

Targeted therapy with tyrosine kinase inhibitors (TKI) provides survival benefits to a majority of patients with non-small cell lung cancer (NSCLC). However, resistance to TKI almost always develops after treatment. Although genetic and epigenetic alterations have each been shown to drive resistance to TKI in cell line models, clinical evidence for their contribution in the acquisition of resistance remains limited. Here, we employed liquid biopsy for simultaneous analysis of genetic and epigenetic changes in 122 Vietnamese NSCLC patients undergoing TKI therapy and displaying acquired resistance. We detected multiple profiles of resistance mutations in 51 patients (41.8%). Of those, genetic alterations in EGFR , particularly EGFR amplification (n = 6), showed pronounced genome instability and genome-wide hypomethylation. Interestingly, the level of hypomethylation was associated with the duration of response to TKI treatment. We also detected hypermethylation in regulatory regions of Homeobox genes which are known to be involved in tumor differentiation. In contrast, such changes were not observed in cases with MET (n = 4) and HER2 (n = 4) amplification. Thus, our study showed that liquid biopsy could provide important insights into the heterogeneity of TKI resistance mechanisms in NSCLC patients, providing essential information for prediction of resistance and selection of subsequent treatment.

Drought is a major abiotic stress that negatively impacts plant growth and crop production. Among various techniques used to alleviate drought stress in plants, nanoparticle application is considered to be effective and promising. In this study, the responses of plants treated with iron, copper, cobalt, and zinc oxide nanoparticles (NPs) were analyzed in soybean under drought-induced conditions. The obtained results indicated that these metal-based NPs supported the drought tolerance of NP-treated plants. The desired physiological traits, viz., relative water content, drought tolerance index, and biomass reduction rate, were significantly improved, especially in iron NP-treated plants. At the molecular level, quantitative PCR analysis of several drought-responsive genes revealed a gene-, tissue-, and NP-dependent upregulation of gene expression. Iron NP treatment promoted the expression of all tested genes in roots; additionally, the expression of three drought-responsive genes increased in leaves of all NP-treated plants, while the expression of GmERD1 (Early Responsive to Dehydration 1) was induced in both roots and shoots under the four NP treatments tested. Our findings suggest that NP application can improve drought tolerance of soybean plants by triggering drought-associated gene expression.

Global climate change and water pollution effects have caused many problems to the farmers in fish/shrimp raising, for example, the shrimps/fishes had early died before harvest. How to monitor and manage quality of the water to help the farmers tackling this problem is very necessary. Water quality monitoring is important when developing IoT systems, especially for aquaculture and fisheries. By monitoring the real-time sensor data indicators (such as indicators of salinity, temperature, pH, and dissolved oxygen - DO) and forecasting them to get early warning, we can manage the quality of the water, thus collecting both quality and quantity in shrimp/fish raising. In this work, we introduce an architecture with a forecasting model for the IoT systems to monitor water quality in aquaculture and fisheries. Since these indicators are collected every day, they becomes sequential/time series data, we propose to use deep learning with Long-Short Term Memory (LSTM) algorithm for forecasting these indicators. Experimental results on several data sets show that the proposed approach works well and can be applied for the real systems.