Predicting sustainable arsenic mitigation using machine learning techniques.

This study evaluates state-of-the-art machine learning models in predicting the most sustainable arsenic mitigation preference. A Gaussian distribution-based Naïve Bayes (NB) classifier scored the highest Area Under the Curve (AUC) of the Receiver Operating Characteristic curve (0.82), followed by Nu Support Vector Classification (0.80), and K-Neighbors (0.79). Ensemble classifiers scored higher than 70% AUC, with Random Forest being the top performer (0.77), and Decision Tree model ranked fourth with an AUC of 0.77. The multilayer perceptron model also achieved high performance (AUC=0.75). Most linear classifiers underperformed, with the Ridge classifier at the top (AUC=0.73) and perceptron at the bottom (AUC=0.57). A Bernoulli distribution-based Naïve Bayes classifier was the poorest model (AUC=0.50). The Gaussian NB was also the most robust ML model with the slightest variation of Kappa score on training (0.58) and test data (0.64). The results suggest that nonlinear or ensemble classifiers could more accurately understand the complex relationships of socio-environmental data and help develop accurate and robust prediction models of sustainable arsenic mitigation. Furthermore, Gaussian NB is the best option when data is scarce

Stakeholder Perceptions towards the Quality of Coursera MOOCs Blended Learning in Vietnam: A Qualitative Study

Coursera MOOCs blended learning (CMBL) has been implemented at a Vietnamese Higher Education Institute (HEI) since the Fall 2019 semester. Our case study, which shows how Coursera MOOCs and the traditional classroom may work together, is unique in the context of Vietnamese higher education. In this case, students must complete the courses and earn certifications through Coursera MOOCs to qualify for the HEI’s offline final examinations. Meanwhile, students also engage in offline mentoring sessions with their classmates and lecturers (mentors). By employing the Service Quality (SERVQUAL) and 3P models, the research was conducted to explore how key factors might influence the quality of CMBL. This research conducted semi-structured interviews and employed thematic analysis with thirty interview participants, including ten administrators, eleven lecturers, three curriculum developers, and six students across four campuses of the HEI. We found that assessment, learning outcomes, learning content, Coursera staff’s responsiveness, offline mentors’ responsiveness and assurance, interaction, and student satisfaction might have considerably significant relationships with the quality of CMBL. On the other hand, Coursera instructors and offline mentors’ reliability have insignificant relationships with the quality of CMBL. This study has both theoretical and practical implications for universities and academics. Regarding the theoretical implications, this qualitative study provides critical criteria to measure the quality of the CMBL. Regarding the practical implications, it provides implications for curriculum development, teaching and learning, and assessment to improve the quality of CMBL. However, the authors could not travel across Vietnam to conduct face-to-face interviews in 2021 due to the COVID-19 pandemic. Therefore, twenty-eight online interviews were conducted via Microsoft Teams and two email interviews. A downside of an online interview is that personal qualities that are critical to a study may be amended during the interview, forcing the researcher to rely on the participant’s words. Additionally, unlike a face-to-face interview, an email interview lacks simultaneous communication between the interviewer and the interviewee. Keywords: Higher Education Institution (HEI), blended MOOCs, Coursera MOOCs Blended Learning (CMBL), Coursera MOOCs, offline mentoring, sustainable developmen

Women's perspectives on termination service delivery in Vietnam: a cross-sectional survey in three provinces.

OBJECTIVE: To explore the perspectives of abortion service users regarding termination methods and abortion service delivery in Vietnam. MATERIALS AND METHODS: Structured exit interviews were conducted between August and November 2011 with women who underwent termination of pregnancy at 62 public health facilities in Hanoi, Khanh Hoa, and Ho Chi Minh City in Vietnam. All women presenting for termination during the study period were recruited to participate in the study. Following their abortion, women were asked about their perspectives on abortion service delivery and attributes of medical abortion (MA) versus manual vacuum aspiration (MVA). Multiple logistic regression was used to assess the association between current method uptake and each attribute. RESULTS: A total of 1,233 women were included in the survey: 541 (43.9%) from Hanoi, 163 (13.2%) from Khanh Hoa, and 529 (42.9%) from Ho Chi Minh: 23.1% underwent MA; 78.9% reported that women should be given a choice between MA and MVA; and 77.6% thought that abortion services were accessible. Among the 48% who responded, 30.1% thought that MA should be made available at primary/secondary health care facilities. Among women who had previously undergone both methods, women who reported that MA "feels more natural" (like a menstrual regulation/period) were more likely to choose MA for their current abortion (odds ratio 2.15, 95% confidence interval 1.26-3.69). CONCLUSION: MA uptake is significantly lower than MVA uptake. Further insights to women's perceptions of MA in Vietnam could help improve abortion service delivery in the country

Attributes and perspectives of public providers related to provision of medical abortion at public health facilities in Vietnam: a cross-sectional study in three provinces.

BACKGROUND: The purpose of this study was to investigate attributes of public service providers associated with the provision of medical abortion in Vietnam. METHODS: We conducted a cross-sectional study via interviewer-administered questionnaire among abortion providers from public health facilities in Hanoi, Khanh Hoa, and Ho Chi Minh City in Vietnam between August 2011 and January 2012. We recruited abortion providers at all levels of Vietnam's public health service delivery system. Participants were questioned about their medical abortion provision practices and perspectives regarding abortion methods. RESULTS: A total of 905 providers from 62 health facilities were included, comprising 525 (58.0%) from Hanoi, 122 (13.5%) from Khanh Hoa, and 258 (28.5%) from Ho Chi Minh City. The majority of providers were female (96.7%), aged ≥25 years (94%), married (84.4%), and had at least one child (89%); 68.9% of providers offered only manual vacuum aspiration and 31.1% performed both medical abortion and manual vacuum aspiration. Those performing both methods included physicians (74.5%), midwives (21.7%), and nurses (3.9%). Unadjusted analyses showed that female providers (odds ratio 0.1; 95% confidence interval 0.01-0.30) and providers in rural settings (odds ratio 0.3; 95% confidence interval 0.08-0.79) were less likely to provide medical abortion than their counterparts. Obstetricians and gynecologists were more likely to provide medical abortion than providers with nursing/midwifery training (odds ratio 22.2; 95% confidence interval 3.81-129.41). The most frequently cited advantages of medical abortion for providers were that no surgical skills are required (61.7%) and client satisfaction is better (61.0%). CONCLUSION: Provision of medical abortion in Vietnam is lower than provision of manual vacuum aspiration. While the majority of abortion providers are female midwives in Vietnam, medical abortion provision is concentrated in urban settings among physicians. Individuals providing medical abortion found that the method yields high client satisfaction

NeuSub: A Neural Submodular Approach for Citation Recommendation

Citation recommendation is a task that aims to automatically select suitable references for a working manuscript. This task has become increasingly urgent as the typical pools of candidates continue to grow, in the order of tens or hundreds of thousands or more. While several approaches to citation recommendation have been proposed in the literature, they generally seem to lack principled mechanisms to ensure diversity and other global properties among the recommended citations. For this reason, in this paper we propose a novel citation recommendation approach that leverages a submodular scoring function and a deep document representation to achieve an effective trade-off between relevance to the query and diversity of the references. To optimally train the scoring function and the deep representation, we propose a novel training objective based on a structural/multiclass hinge loss and incremental recommendations. The experimental results over three popular citation datasets have showed that the proposed approach has led to remarkable accuracy improvements, with an increase of up to 1.91 pp of MRR and 3.29 pp of F1@100 score with respect to a state-of-the-art citation recommendation system

Learning Neural Textual Representations for Citation Recommendation

With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several approaches for automated citation recommendation have been proposed in the recent years, effective document representations for citation recommendation are still elusive to a large extent. For this reason, in this paper we propose a novel approach to citation recommendation which leverages a deep sequential representation of the documents (Sentence-BERT) cascaded with Siamese and triplet networks in a submodular scoring function. To the best of our knowledge, this is the first approach to combine deep representations and submodular selection for a task of citation recommendation. Experiments have been carried out using a popular benchmark dataset - the ACL Anthology Network corpus - and evaluated against baselines and a state-of-the-art approach using metrics such as the MRR and F1-at-k score. The results show that the proposed approach has been able to outperform all the compared approaches in every measured metric

A hybrid computational intelligence approach to groundwater spring potential mapping

© 2019 by the authors. This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely "AB-ADTree", for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including singleADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB-ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources

Using Field-Based Monitoring to Enhance the Performance of Rainfall Thresholds for Landslide Warning

Landslides are natural disasters which can create major setbacks to the socioeconomic of a region. Destructive landslides may happen in a quick time, resulting in severe loss of lives and properties. Landslide Early Warning Systems (LEWS) can reduce the risk associated with landslides by providing enough time for the authorities and the public to take necessary decisions and actions. LEWS are usually based on statistical rainfall thresholds, but this approach is often associated to high false alarms rates. This manuscript discusses the development of an integrated approach, considering both rainfall thresholds and field monitoring data. The method was implemented in Kalimpong, a town in the Darjeeling Himalayas, India. In this work, a decisional algorithm is proposed using rainfall and real-time field monitoring data as inputs. The tilting angles measured using MicroElectroMechanical Systems (MEMS) tilt sensors were used to reduce the false alarms issued by the empirical rainfall thresholds. When critical conditions are exceeded for both components of the systems (rainfall thresholds and tiltmeters), authorities can issue an alert to the public regarding a possible slope failure. This approach was found effective in improving the performance of the conventional rainfall thresholds. We improved the efficiency of the model from 84% (model based solely on rainfall thresholds) to 92% (model with the integration of field monitoring data). This conceptual improvement in the rainfall thresholds enhances the performance of the system significantly and makes it a potential tool that can be used in LEWS for the study area.</jats:p

Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)