Flash flood is a typical natural hazard that occurs within a short time with high flow velocities and is difficult to predict. In this study, we propose and validate a new soft computing approach ...that is an integration of an Extreme Learning Machine (ELM) and a Particle Swarm Optimization (PSO), named as PSO-ELM, for the spatial prediction of flash floods. The ELM is used to generate the initial flood model, whereas the PSO was employed to optimize the model. A high frequency tropical typhoon area at Northwest of Vietnam was selected as a case study. In this regard, a geospatial database for the study area was constructed with 654 flash flood locations and 12 influencing factors (elevation, slope, aspect, curvature, toposhade, topographic wetness index, stream power index, stream density, NDVI, soil type, lithology, and rainfall). The model performance was validated using several evaluators such as kappa statistics, root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and area under the ROC curve (AUC-ROC) and compared to three state-of-the-art machine learning techniques, including multilayer perceptron neural networks, support vector machine, and C4.5 decision tree. The results revealed that the PSO-ELM model has high prediction performance (kappa statistics = 0.801, RMSE = 0.281; MAE = 0.079, R2 = 0.829, AUC-ROC = 0.954) and successfully outperformed the three machine learning models. We conclude that the proposed model is a new tool for the prediction of flash flood susceptibility at high frequency tropical typhoon areas.
•PSO-ELM is proposed and verified for flash flood susceptibility modeling.•PSO-ELM has high prediction performance.•PSO-ELM performs better than ANN, SVM, and the C4.5 decision tree.
Clostridium acetobutylicum has received renewed interest worldwide as a promising producer of biofuels and bulk chemicals such as n-butanol, 1,3-propanediol, 1,3-butanediol, isopropanol, and ...butyrate. To develop commercial processes for the production of bulk chemicals via a metabolic engineering approach it is necessary to better characterize both the primary metabolism and metabolic regulation of C. acetobutylicum. Here, we review the history of the development of omics studies of C. acetobutylicum, summarize the recent application of quantitative/integrated omics approaches to the physiological analysis and metabolic engineering of this bacterium, and provide directions for future studies to address current challenges.
Systems biology tools for C. acetobutylicum have developed rapidly in the last few years with an emphasis on quantitative analysis.It is now possible to determine the number of mRNA molecules per cell as well as the number of protein molecules per cell for most of the genes encoding enzymes involved in the central metabolism.Quantitative omics tools have been used to characterize the central metabolism of C. acetobutylicum and the regulation of this metabolism under different physiological conditions.Several metabolic mutants have been characterized using quantitative omics tools, providing an understanding of the metabolic flexibility of C. acetobutylicum at the molecular level.Construction of industrial C. acetobutylicum mutant strains for the production of bulk chemicals benefits from the quantitative omics tools that are now available
Severe fever with thrombocytopenia syndrome (SFTS), a tickborne viral disease, has been identified in China, South Korea, and Japan since 2009. We found retrospective evidence of SFTS virus (SFTSV) ...infection in Vietnam, which suggests that SFTSV infections also occur in Vietnam, where the virus has not been known to be endemic.
Developing a commercial process for the biological production of n-butanol is challenging as it needs to combine high titer, yield, and productivities. Here we engineer Clostridium acetobutylicum to ...stably and continuously produce n-butanol on a mineral media with glucose as sole carbon source. We further design a continuous process for fermentation of high concentration glucose syrup using in situ extraction of alcohols by distillation under low pressure and high cell density cultures to increase the titer, yield, and productivity of n-butanol production to the level of 550 g/L, 0.35 g/g, and 14 g/L/hr, respectively. This process provides a mean to produce n-butanol at performance levels comparable to that of corn wet milling ethanol plants using yeast as a biocatalyst. It may hold the potential to be scaled-up at pilot and industrial levels for the commercial production of n-butanol.
Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. ...This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg⁻Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.
The prefrontal cortex (PFC) plays a critical role in curbing impulsive behavior, but the underlying circuit mechanism remains incompletely understood. Here we show that a subset of dorsomedial PFC ...(dmPFC) layer 5 pyramidal neurons, which project to the subthalamic nucleus (STN) of the basal ganglia, play a key role in inhibiting impulsive responses in a go/no-go task. Projection-specific labeling and calcium imaging showed that the great majority of STN-projecting neurons were preferentially active in no-go trials when the mouse successfully withheld licking responses, but lateral hypothalamus (LH)-projecting neurons were more active in go trials with licking; visual cortex (V1)-projecting neurons showed only weak task-related activity. Optogenetic activation and inactivation of STN-projecting neurons reduced and increased inappropriate licking, respectively, partly through their direct innervation of the STN, but manipulating LH-projecting neurons had the opposite effects. These results identify a projection-defined subtype of PFC pyramidal neurons as key mediators of impulse control.
PurposeA carbon tax has been widely discussed and implemented in developed countries to mitigate carbon emissions, but this measure is still quite new in developing countries. Recently, the ambition ...of Vietnam's government in mitigating emissions has been mentioned in international commitments. To achieve these targets, the government is making efforts to seek and implement mitigation measures in the country. While carbon pricing was introduced in Vietnam, there is no study simulating the effects of a carbon tax in the country. This study simulates the environmental and economic effects of a carbon tax and then proposes appropriate policies in Vietnam.Design/methodology/approachThis study investigates the impact on the Vietnamese economy within the static computable general equilibrium (CGE) framework. Compared with previous models, the proposed model in this paper is a fairly standard CGE approach that tries to picture the economic system of Vietnam. In addition, a carbon tax on output will be modeled in this framework. This carbon tax mechanism is more flexible and direct when a carbon tax is based on direct emissions by industry level and the industry's carbon intensity. The paper decomposes the Vietnamese economy into 18 different production sectors, based on the different emission levels of CO2. The CGE model makes possible to examine the impact of a carbon tax on the whole economy through all possible channels and to differentiate a separate carbon tax among different production sectors. The impact of a differentiated carbon tax is explored not only at the macroeconomic level but also at each different industrial level. Another feature of this paper is to investigate the impact of reallocation revenue from the carbon tax.FindingsThis paper has found that by designing carbon tax scenarios at different carbon prices ($1/tCO2, $5/tCO2, $10/tCO2) with different targeted industries, this study shows that higher carbon prices cause greater damage to GDP and welfare, but also better reductions in emissions. In addition, a carbon tax on the energy sectors results in milder economic and welfare damage but less emission reduction than when levying on all sectors. At the sectoral level, a carbon tax might cause sectoral restruction. Interestingly, the electricity sector is the most affected and also is the main contributor to reducing emissions in Vietnam. Finally, the study also shows that reallocation policies of new revenue from the carbon tax would reduce the economic damage caused by carbon taxes, and in many cases promote GDP and welfare. However, these policies reduce the environmentally positive impact of the carbon tax and even induce an increase in emissions in some cases.Originality/valueThis paper studies the pure impacts of a carbon tax, it also simulates the impact of several recycling policies where the increased tax revenue is incorporated. Thereby, this research supports to design and implement carbon tax policies in Vietnam. This paper also would contribute to the literature an example of the adoption of the carbon tax in a developing country, and it could be a lesson for others with similar conditions. Compared with previous models, the proposed model in this paper is a fairly standard CGE approach that tries to picture the economic system of Vietnam. In addition, a more flexible carbon tax mechanism is proposed to improve adequate coverage of emission resources.
Data corroborated in this study highlights laundry wastewater as a primary source of microfibers (MFs) in the aquatic environment. MFs can negatively impact the aquatic ecosystem via five possible ...pathways, namely, acting as carriers of other contaminats, physical damage to digestive systems of aquatic organisms, blocking the digestive tract, releasing toxic chemicals, and harbouring invasive and noxious plankton and bacteria. This review shows that small devices to capture MFs during household laundry activities are simple to use and affordable at household level in developed countries. However, these low cost and small devices are unrealiable and can only achieve up to 40 % MF removal efficiency. In line filtration devices can achieve higher removal efficiency under well maintained condition but their performance is still limited compared to over 98 % MF removal by large scale centralized wastewater treatment. These results infer that effort to increase sanitation coverage to ensure adequate wastewater treatment prior to environmental discharge is likely to be more cost effective than those small devices for capturing MFs. This review also shows that natural fabrics would entail significantly less environmental consequences than synthetic materials. Contribution from the fashion industry to increase the share of natural frabics in the current textile market can also reduce the loading of plastic MFs in the environment.
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•Laundry and textile industry are the main source of plastic microfiber pollution.•Washing conditions and fabrics material significantly influence microfibers release.•Microfibers pollution can direct and indirectly harm the aquatic environment.•Fashion industry should reverse to natural fabrics to phase out synthetic materials.•Microfiber removal by centralized treatment is preferred over passive devices.
In agricultural production, abiotic stresses are known as the main disturbance leading to
negative impacts on crop performance. Research on elucidating plant defense mechanisms against the
stresses ...at molecular level has been addressed for years in order to identify the major contributors in
boosting the plant tolerance ability. From literature, numerous genes from different species, and from
both functional and regulatory gene categories, have been suggested to be on the list of potential candidates
for genetic engineering. Noticeably, enhancement of plant stress tolerance by manipulating
expression of Transcription Factors (TFs) encoding genes has emerged as a popular approach since
most of them are early stress-responsive genes and control the expression of a set of downstream target
genes. Consequently, there is a higher chance to generate novel cultivars with better tolerance to
either single or multiple stresses. Perhaps, the difficult task when deploying this approach is selecting
appropriate gene(s) for manipulation. In this review, on the basis of the current findings from molecular
and post-genomic studies, our interest is to highlight the current understanding of the roles of TFs
in signal transduction and mediating plant responses towards abiotic stressors. Furthermore, interactions
among TFs within the stress-responsive network will be discussed. The last section will be reserved
for discussing the potential applications of TFs for stress tolerance improvement in plants.
•PSO-MARS is proposed for flash-flood modeling and prediction.•PSO-MARS has high performance on the training and validation datasets.•PSO-MARS outperforms benchmark models i.e. BPANN, SVM, and ...decision tree.
The main objective of this research work was to propose and verify a new soft computing approach based on Multivariate Adaptive Regression Splines (MARS) and Particle Swarm Optimization (PSO) for spatial prediction of flash flood susceptible areas. A high frequency tropical typhoon area located on Northwest of Vietnam was selected as a case study. For this purpose, a GIS database for the study areas was prepared, including 654 flash-flood inundations and 12 influencing variables (elevation, slope, curvature, toposhade, aspect, topographic wetness index, stream power index, stream density, Normalized Difference Vegetation Index, soil type, lithology, and rainfall), which were compiled from various sources. The database was used to build and verify the prediction model. We assessed the model’s performance through various indices including Classification Accuracy Rate, Area under the Curve (AUC), Precision, and Recall. We also compared the model’s usability with five state-of-the-art machine learning techniques including the Backpropagation Neural Network, Support Vector Machine, and Classification Tree. The results revealed that the hybrid PSO-MARS model outperformed other benchmark models in all the employed statistical measures. We conclude that the proposed model can be particularly suited for flash flood forecasting problems at high frequency tropical typhoon area.
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