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[This corrects the article DOI: 10.1371/journal.pone.0262402.].

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.

The vanadium dioxide (VO 2 ) microstructure arrays were integrated with In x GaN 1–x light-emitting diodes (LEDs) to develop a long-wavelength infrared sensor array. By utilizing GaN-based LEDs as a readout unit, the VO 2 /LED heterostructure directly converts temperature-induced resistance changes into visible light emission. The VO 2 layer exhibits uniform chemical configuration and atomic bonding, leading to consistent metal-to-insulator transition behavior across all pixels. The low-voltage operation was available at 2.5 V, which was readily above the turn-on voltage of the LED. The brightness of the LED and the resistivity changes of the VO 2 layer were inversely proportional, where the temperature variations can be gauged optically. Moreover, the VO 2 /LED heterostructure pixels are individually addressable, which can detect directions and degrees of external heat.

We fabricated GaN/BN double heterostructure light-emitting diodes (LEDs) where the BN layer exhibited an amorphous-like short-range order and facilitated the in-situ epitaxial lateral overgrowth (ELOG) of GaN films. Using an identical metal-organic chemical vapor deposition, the BN layer was reliably formed on the GaN film and then served as a growth mask during the high-temperature growth of the GaN overlayer. The BN layers were well dispersed over the entire surface with a partial coverage of 40–60% and a thickness of a few nm. The laterally overgrown GaN was epitaxially related to the initial GaN film exhibiting single crystallinity with flat and smooth surface morphology. Meanwhile, the in-situ-formed BN layer effectively blocked the threading dislocations where its density reductions were comparable to those of typical ex-situ ELOG processes. Furthermore, the BN-assisted ELOG reduced the mosaic of the practical single crystalline GaN grains and drastically improved crystallographic alignment and internal quantum efficiency. More importantly, the BN-assisted ELOG yielded high device performance of the GaN LEDs demonstrating that the benefits of ELOG were fully achieved with the fast and instant fabrication process. The in-situ growth of epitaxial GaN with a short-range ordered (SRO) BN interlayer is proposed to demonstrate a high manufacturing scalability of the epitaxial lateral overgrowth (ELOG) process. During the GaN growth, the mask formation of the SRO BN occurred in the on-site chamber within a few minutes. The BN interlayer efficiently reduced microstructural defects, such as screw-type and edge-type threading dislocations, to achieve high structural and optical characteristics of the GaN overlayer, whose results are comparable to those of the previously reported ex-situ ELOG approaches. These improvements were also demonstrated in the device performances of the GaN light-emitting diodes.

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.

Collaborative filtering (CF) in the recommendation system using user habits, behaviors, and item rating to recommend the products which suit customer’s needs. Therefore, analyzing user rating data is one of the factors that improve the efficiency of the recommendation system. This paper proposes a new approach to analyze rating item and input the implicit effect of items rating to the recommendation system based on the TrustSVD model and matrix factorization (MF) techniques. The experimental results showed that our proposed solution achieves 18% better than the matrix factorization method and 15% the Multi-Relational Matrix Factorization method, respectively.

The vanadium dioxide (VO ) microstructure arrays were integrated with In GaN light-emitting diodes (LEDs) to develop a long-wavelength infrared sensor array. By utilizing GaN-based LEDs as a readout unit, the VO /LED heterostructure directly converts temperature-induced resistance changes into visible light emission. The VO layer exhibits uniform chemical configuration and atomic bonding, leading to consistent metal-to-insulator transition behavior across all pixels. The low-voltage operation was available at 2.5 V, which was readily above the turn-on voltage of the LED. The brightness of the LED and the resistivity changes of the VO layer were inversely proportional, where the temperature variations can be gauged optically. Moreover, the VO /LED heterostructure pixels are individually addressable, which can detect directions and degrees of external heat.

Severinia buxifolia (Rutaceae) is a promising source of bioactive compounds since it has been traditionally used for the treatment of various diseases. The present study aimed at evaluating the impact of different solvents on extraction yields, phytochemical constituents and antioxidants, and in vitro anti-inflammatory activities of S. buxifolia. The results showed that the used solvents took an important role in the yield of extraction, the content of chemical components, and the tested biological activities. Methanol was identified as the most effective solvent for the extraction, resulting in the highest extraction yield (33.2%) as well as the highest content of phenolic (13.36 mg GAE/g DW), flavonoid (1.92 mg QE/g DW), alkaloid (1.40 mg AE/g DW), and terpenoids (1.25%, w/w). The extract obtained from methanol exhibited high capacity of antioxidant (IC50 value of 16.99 μg/mL) and in vitro anti-inflammatory activity (i.e., albumin denaturation: IC50 = 28.86 μg/mL; antiproteinase activity: IC50 = 414.29 μg/mL; and membrane stabilization: IC50 = 319 μg/mL). The antioxidant activity of the S. buxifolia extract was found to be 3-fold higher than ascorbic acid, and the anti-inflammatory activity of S. buxifolia extract was comparable to aspirin. Therefore, methanol is recommended as the optimal solvent to obtain high content of phytochemical constituents as well as high antioxidants and in vitro anti-inflammatory constituents from the branches of S. buxifolia for utilization in pharmacognosy.

This study investigates distribution, pollution indices, and potential risk assessment for human health and ecology of eight heavy metals in twenty-five street dust samples collected from metropolitan area—Ho Chi Minh City, Vietnam. Results showed that Zn was of the highest concentration (466.4 ± 236.5 mg/kg), followed by Mn (393.9 ± 93.2 mg/kg), Cu (153.7 ± 64.7 mg/kg), Cr (102.4 ± 50.5 mg/kg), Pb (49.6 ± 21.4 mg/kg), Ni (36.2 ± 15.4 mg/kg), Co (7.9 ± 1.9 mg/kg), and Cd (0.5 ± 0.5 mg/kg). The principal component analysis revealed that three sources of heavy metals measured in street dust include vehicular activities (32.38%), mixed source of vehicular and residential activities (26.72%), and mixture of industrial and natural sources (20.23%). The geo-accumulation index values showed levels of non-pollution to moderately pollution for Mn and Co; moderately pollution for Ni; moderately to strongly pollution for Cd, Cr, and Pb; and strongly pollution for Cu and Zn. The potential ecological risk values of all sampling sites were close to the high-risk category. Zn (28.9%), Cu (25.4%), and Mn (24.4%) dominantly contributed to the ecological risk. For non-carcinogenic risk, the hazard quotient values for both children and adults were within a safety level. For carcinogenic risk, the TCR Children was about 3 times higher than TCR Adults , but still within a tolerable limit (1 × 10 −6 to 1 × 10 −4 ) of cancer risk. Cr was a major contribution to potential risks in humans. Such studies on heavy metal in street dust are crucial but are still limited in Vietnam/or metropolitan area in Southeast Asia. Therefore, this study can fill the information gap about heavy metal contaminated street dust in a metropolitan area of Vietnam.