To determine the effectivity of bacteriophages in controlling the mass mortality of striped catfish (Pangasianodon hypophthalmus) due to infections caused by Aeromonas spp. in Vietnamese fish farms, bacteriophages against pathogenic Aeromonas hydrophila were isolated. A. hydrophila-phage 2 and A. hydrophila-phage 5 were successfully isolated from water samples from the Saigon River of Ho Chi Minh City, Vietnam. These phages, belonging to the Myoviridae family, were found to have broad activity spectra, even against the tested multiple-antibiotic-resistant Aeromonas isolates. The latent periods and burst size of phage 2 were 10 min and 213 PFU per infected host cell, respectively. The bacteriophages proved to be effective in inhibiting the growth of the Aeromonas spp. under laboratory conditions. Phage treatments applied to the pathogenic strains during infestation of catfish resulted in a significant improvement in the survival rates of the tested fishes, with up to 100% survival with MOI 100, compared to 18.3% survival observed in control experiments. These findings illustrate the potential for using phages as an effective bio-treatment method to control Motile Aeromonas Septicemia (MAS) in fish farms. This study provides further evidence towards the use of bacteriophages to effectively control disease in aquaculture operations.

This paper investigated specific surface treatments, aimed to improve the adhesion between PLA and rice and Einkorn wheat husks. In the first part, husks were treated by alkaline solutions at three concentrations (2, 5 and 10%) at room temperature for different times between 6 and 48 h. Results revealed that the alkaline treatment dissolved a fraction of waxes, lignin and hemicellulose, so that the polarity of their surface was increased and therefore alkaline treated husks were more sensitive to moisture. Moreover the alkaline treatments have more influence on wheat than on rice husks, as shown by chemical composition determination, FTIR measurements and ESEM observations. In the second part, husks were treated by two kinds of organosilanes (␥-aminopropyltriethoxysilane (APS) and ␥-glycidoxypropyltrimethoxysilane GPS)). These silane treatments were applied either alone or on previously alkaline treated husks (NaOH 5% for 24 h). The results showed that silane treatments reduced the moisture sensitivity and raise the energy surface of the husks. In the last part, treated husks were incorporated into polylactic acid (PLA) to produce biocomposites. Biocomposites reinforced by husks treated by alkaline solution and silane have higher bending moduli and stresses than those reinforced by untreated husks and husks treated by silane alone. It can be supposed that the PLA/husks adhesion was enhanced. No difference was observed according to the silane nature.

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

In this paper, we develop an integrated model for slacks-based measure (SBM) simultaneously of both the eciency and the super-eciency for decision-making units (DMUs) in data envelopment analysis (DEA). Unlike the traditional solution approaches in which we need to identify the ecient DMUs by the SBM model of Tone [20] before applying the super SBM model of Tone [21] for the DMUs to achieve their super-eciency scores, our integration can obtain the eciency scores of the inecient DMUs and the super-eciency scores of the ecient DMUs by solving simultaneously these two models by an onestage approach. Therefore, it may save computational time for large-scale practical applications. Due to the non-linearity in the objective function of this integrated model, we develop a linearisation technique to deal with the non-linear model. The numerical experiments, carried out on several examples in the literature and a case study, have demonstrated the accuracy and the computational time eectiveness of our proposed model as compared with the traditional solution approaches.

We have developed an efficient heuristic algorithm for location of alternative-fuel stations. The algorithm is constructed based on solving the sequence of subproblems restricted on a set of promising station candidates, and fixing a number of the best promising station locations. The set of candidates is initially determined by solving a relaxation model, and then modified by exchanging some stations between the promising candidate set and the remaining station set. A number of the best station candidates in the promising candidate set can be fixed to improve computation time. In addition, a parallel computing strategy is integrated into solving simultaneously the set of subproblems to speed up computation time. Experimental results carried out on the benchmark instances show that our algorithm outperforms genetic algorithm and greedy algorithm. As compared with CPLEX solver, our algorithm can obtain all the optimal solutions on the tested instances with less computation time.

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Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.