IMPROVEMENT OF CO2 PURIFYING SYSTEM BY PHOTOCATALYST FOR APPLICATION IN MICROALGAE CULTURE TECHNOLOGY

By reactive grinding method Vanadium-doped rutile TiO2 nanoparticle material was obtained with an average particle size of 20‐40nm, the Brunauer–Emmet–Teller (BET) specific surface area about 20 m2g−1 and it absorbed strongly in the UV region and increased at the visible wavelength of 430 – 570 nm. This study focused on the improvement of exhaust gas treatment from coal-fired flue gas of the traditional adsorption-catalysis system (Modular System for Treating Flue Gas - MSTFG) by using the V2O5/TiO2 Rutile as photocatalyst. The results showed that integrating both catalytic systems mentioned above increased the gas treatment efficiency: CO from 77 % to over 98 %, NOx from 50 % to 93 %, SO2 was absent as opposed to the input gas component. Also it showed that V2O5/TiO2 Rutile integrated with MSTFG has got high efficiency of CO treatment, also secured the high obtained CO2 concentration as a valuable carbon source for microagal mass culture as well as saving energy and simplifying devices

A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Assessment of aflatoxin B1 contamination in rice and maize

Introduction: Aflatoxins B1 are among the most common poisonous mycotoxins produced by certain fungi that harm animals and crops. Mycotoxins can cause a variety of adverse health effects and pose a serious health threat to humans. The Maximum Residue Limits of aflatoxin B1 in processed cereals and ingredients are 2 parts per billion (ppb) and 5 ppb, respectively. Objectives: To evaluate the status of aflatoxin B 1 contamination in rice, corn and staple food produced in Ha Giang province compared with the maximum permitted levels. Methods: A total of 210 rice and maize samples were analyzed to quantify the level of aflatoxin B1. Analysis of mycotoxins was conducted by High Performance Liquid Chromatography using a fluorescence detector. Results: It was found that rice, rice products, maize, and maize products had a mean aflatoxin B1 content of 1.79 ppb, 2.55 ppb, 2.19 ppb, and 6.35 ppb, respectively. The results also showed that 71.9% of samples were contaminated with mycotoxins, and 14.28% of samples exceeded the maximum allowable limit. Conclusion: The concentration of aflatoxin B1 in 14.28% of the samples are over permissible limits by nationwide regulations

Combined Application of Rhizosphere Bacteria with Endophytic Bacteria Suppresses Root Diseases and Increases Productivity of Black Pepper (Piper nigrum L.)

Black pepper (Piper nigrum L.) is one of the most important crops and global demand continues to increase, giving it a high export value. However, black pepper cultivation has been seriously affected by a number of pathogenic diseases. Among them, “quick wilt” caused by Phytophthora sp., “slow decline” caused by Fusarium sp., and root-knot nematode Meloidogyne sp. have a serious negative effect on black pepper growth and productivity. There have been different chemical and biological methods applied to control these diseases, but their effectiveness has been limited. The aim of this research was to evaluate different combinations of rhizosphere bacteria and endophytic bacteria isolated from black pepper farms in the Central Highland of Vietnam for their ability to suppress pathogens and promote black pepper growth and yield. Formula 6, containing the strains Bacillus velezensis KN12, Bacillus amyloliquefaciens DL1, Bacillus velezensis DS29, Bacillus subtilis BH15, Bacillus subtilis V1.21 and Bacillus cereus CS30 exhibited the largest effect against Phytophthora and Fusarium in the soil and in the roots of black pepper. These bio-products also increased chlorophyll a and b contents, which led to a 1.5-fold increase of the photosynthetic intensity than the control formula and a 4.5% increase in the peppercorn yield (3.45 vs. 3.30 tons per hectare for the control). Our results suggest that the application of rhizosphere and endophytic bacteria is a promising method for disease control and growth-promotion of black pepper

Strategy in Dispatching Trucks and Shovels with Different Capacity to Increase the Operating Efficiency in Cao Son Surface Coal Mine, Vietnam

In surface mining operations, the operating costs of truck-shovel system constitutes 50-60% of the total. Only a little save in the operation costs in this system will bring large profit for the mines. Due to many investment periods, the capacity of both trucks and shovels in Cao Son surface coal mine is different. This leads to the low efficiency and the difficulty in dispatching strategy for the mine. This paper presents the current situation and selection of advanced dispatching strategy for increasing the efficiency trucks and shovels at this surface coal mine. The results show the detailed match factor reflects the state of each team of loader and trucks and should be use as the indicator for dispatching decision for the heterogeneous truck and shovel fleet at Cao Son surface coal mine

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Designing and developing a mobile app (BeBo) in a randomized controlled trial study to promote breastfeeding among Vietnamese mothers

Abstract Background Breastfeeding should begin as soon as possible after birth and continue exclusively to 6 months of age. In Vietnam, as in many other countries, breastfeeding is decreasing because of modern lifestyles and the promotion of infant formula. It is important to provide mothers, family members, and the community with the knowledge and strategies to improve breastfeeding rates. Smartphones are almost ubiquitous in Vietnam and of the potential to provide information about breastfeeding. This study aimed to document the process of designing and developing a mobile app to increase breastfeeding rates in Vietnamese women. Methods We used a four-step mixed methods approach with a literature review, formative research (22 in-depth interviews and 49 self-administered online questionnaires), and testing of prototype apps (3 focus groups discussion and external experts). Formative research and focus group discussion involved 99 participants. Finally, the revisions of the app were tested. All of the formative research was undertaken in Hanoi in 2019–2020. Target behaviors followed by key determinants, to improve breastfeeding self-efficacy were studied and this information was then applied in developing the messages and library content. Barriers and facilitators to breastfeeding were identified from literature reviews and qualitative research. The messages were targeted at not only mothers but also included fathers, mothers-in-law, or families. Results Mothers were mostly concerned about the initiation of breastfeeding, preventing and reducing difficulties encountered during breastfeeding, and nutrition for breastfeeding mothers. Mental health and well-being in the postnatal period are also concerns. Three key features to be included in the app were identified from the formative research: (1) notifications; (2) an information library; and (3) a searching function. The research found that the app should be installed during pregnancy rather than after delivery (81% vs 17%, respectively). Notifications that convey breastfeeding messages should be sent 2–3 times per week. Conclusion The development of the app followed a best practice approach, including the involvement of stakeholders and grounding in behavior change theory. The next step is to evaluate the effectiveness of the BeBo mobile app in a well-conducted randomized controlled trial. Trial registration ACTRN12619000531112

Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538

The authors wish to make the following corrections to this paper [...