Navigating toward an uncertain future:how students regulated goals during the emergency remote learning

Abstract. This study aims to examine the goal-setting regulation of higher students in the context of emergency remote learning. Using Emergency Remote Learning (ERL), Self-Regulated Learning (SRL) and Achievement Goal Theory (AGT) as the main theoretical frameworks, this study sought to examine which type of goals students set during the pandemic, how the emergency remote learning influenced students’ goal setting and regulating, which challenges students confronted and strategies to successfully overcome them. The thesis targets international higher students who started their studies in 2020, using qualitative methods and designed individual in-depth interviews to gain insights about students of 2020’s goals. Besides, a small set of quantitative data is also collected and analyzed to investigate the situated awareness of students about emergency remote learning. The findings reveal several notable points. First, studying under the circumstance of emergency remote learning is not only in an emergency manner but also has a persistency characteristic, thus long time of online teaching might cause a relatively high extent of stress in students. Second, students in their first year (during the emergency remote learning) tended to pay more attention to their mastery ambitions. After the emergency remote learning, there was a tendency to shift goals to performance-orientation. Third, not all goal changes were related to the influence of emergency remote learning, since goal change is a natural phenomenon in life. Forth, among challenges confronted during the emergency remote learning, instructional challenges and emotional and motivational challenges are most repeated and notable. Fifth, students shared numerous useful tips and strategies to overcome the hard situation, noteworthily, some of them are avoidance-oriented. The implications of this study include the potential for instructors to design their teaching to better facilitate students in emergency remote learning, especially to compensate for the shortcomings of support systems and ill-designed instructions. Besides, insights from the finding also contribute to furthering research on the consequences of emergency remote learning not only during but after the pandemic, focusing on goal-setting, one of the key elements of self-regulated learning

Energy cost optimization in microgrids using model predictive control and mixed integer linear programming

© 2019 IEEE. This paper presents a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) to develop an optimal power management strategy (PMS) for minimizing the electricity bill of commercial buildings in a domestic on-grid system. The optimal PMS is first formulated as a MILP-MPC with time-varying constraints. The constraints are then linearized at each sampling time so that a receding horizon principle can be used to determine the control input applied to the plant and update the model. The time-varying efficiency of power electronic converters is evaluated for each time interval and assumed to be persistent for the prediction time horizon. The numerical results show that the proposed MILP-MPC strategy with variable efficiency is effective in utilizing photovoltaic power generation to save the cost on electricity for buildings

Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations from multiple annotators with varying levels of expertise. We then aim to improve the efficiency of predictive models in abnormal detection tasks by estimating hidden labels from multiple annotations and using a re-weighted loss function to improve detection performance. Our method is evaluated on a real-world medical imaging dataset and outperforms relevant baselines that do not consider disagreements among annotators.Comment: This is a short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US

Stakeholder Delphi-perception analysis on impacts and responses of acid rain on agricultural ecosystems in the Vietnamese upland

Vietnam is one of most vulnerable countries to acid rain in Asia. In the Vietnamese Northern Mountains, acid rainwater affects negatively to local agricultural ecosystems. This paper analyzes how major agricultural stakeholders living in the mountains assess the impacts of acid rain and their responses on agricultural ecosystems. A two-round Stakeholder Delphi combined with the pressure-state-response (PSR) model allows ranking effects, mitigation and adaptation measures. Eight themes, 14 sub-themes, and 35 indicators for acid rain are structured in the PSR model. The results show that deforestation and rainfall variability relate to changes in the concentrations of acid ions in rainwater. Energy consumption in the industry and transportation, chemical fertilizer use in agriculture, and air pollution from neighboring areas contribute significantly to acid rain. Acid rain affects agriculture and decreases crop yields, causes arable land loss, reduces nutrients and organic matter, and accumulates heavy metals. Panel members perceive that applying local knowledge in agricultural practices, rational energy use, promotion of integrated agricultural policies, and changing farmer behaviors are measures to mitigate acid rain and its adverse effects. The results contribute to a vision on local adaptation actions and policy to foster the capacity and the resilience of major local group

The Performance of ECMWF sub-seasonal forecasts to predict the Rainy Season Onset Dates in Vietnam

The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Sub-seasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic sub-regions of Vietnam. The results show that the ECMWF model reproduces well the observed inter-annual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all sub-regions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all sub-regions indicate that the model outperforms the observed climatology in predicting the RSOD at sub-seasonal lead times (~28–35 days). However, the model is overall more skilful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analysing the highest skill score for each sub-region at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers

Pharmacist-Led Intervention to Enhance Medication Adherence in Patients With Acute Coronary Syndrome in Vietnam:A Randomized Controlled Trial

Background: Patient adherence to cardioprotective medications improves outcomes of acute coronary syndrome (ACS), but few adherence-enhancing interventions have been tested in low-income and middle-income countries. Objectives: We aimed to assess whether a pharmacist-led intervention enhances medication adherence in patients with ACS and reduces mortality and hospital readmission. Methods: We conducted a randomized controlled trial in Vietnam. Patients with ACS were recruited, randomized to the intervention or usual care prior to discharge, and followed 3 months after discharge. Intervention patients received educational and behavioral interventions by a pharmacist. Primary outcome was the proportion of adherent patients 1 month after discharge. Adherence was a combined measure of self-reported adherence (the 8-item Morisky Medication Adherence Scale) and obtaining repeat prescriptions on time. Secondary outcomes were (1) the proportion of patients adherent to medication; (2) rates of mortality and hospital readmission; and (3) change in quality of life from baseline assessed with the European Quality of Life Questionnaire - 5 Dimensions - 3 Levels at 3 months after discharge. Logistic regression was used to analyze data. Registration: ClinicalTrials.gov (NCT02787941). Results: Overall, 166 patients (87 control, 79 intervention) were included (mean age 61.2 years, 73% male). In the analysis excluding patients from the intervention group who did not receive the intervention and excluding all patients who withdrew, were lost to follow-up, died or were readmitted to hospital, a greater proportion of patients were adherent in the intervention compared with the control at 1 month (90.0% vs. 76.5%; adjusted OR = 2.77; 95% CI, 1.01-7.62) and at 3 months after discharge (90.2% vs. 77.0%; adjusted OR = 3.68; 95% CI, 1.14-11.88). There was no significant difference in median change of EQ-5D-3L index values between intervention and control [0.000 (0.000; 0.275) vs. 0.234 (0.000; 0.379); p = 0.081]. Rates of mortality, readmission, or both were 0.8, 10.3, or 11.1%, respectively; with no significant differences between the 2 groups. Conclusion: Pharmacist-led interventions increased patient adherence to medication regimens by over 13% in the first 3 months after ACS hospital discharge, but not quality of life, mortality and readmission. These results are promising but should be tested in other settings prior to broader dissemination