Hanoi University of Science and Technology

Recently, there has been a rapid growth in research and innovation in the natural fibre composite (NFC) area. Interest is warranted due to the advantages of these materials compared to others, such as synthetic fibre composites, including low environmental impact and low cost and support their potential across a wide range of applications. Much effort has gone into increasing their mechanical performance to extend the capabilities and applications of this group of materials. This review aims to provide an overview of the factors that affect the mechanical performance of NFCs and details achievements made with them.

In recent years the outbreak of re-emerging and emerging infectious diseases has been a significant burden on global economies and public health. The growth of population and urbanization along with poor water supply and environmental hygiene are the main reasons for the increase in outbreak of infectious pathogens. Transmission of infectious pathogens to the community has caused outbreaks of diseases such as influenza (A/H5N1), diarrhea (Escherichia coli), cholera (Vibrio cholera), etc throughout the world. The comprehensive treatments of environments containing infectious pathogens using advanced disinfectant nanomaterials have been proposed for prevention of the outbreaks. Among these nanomaterials, silver nanoparticles (Ag-NPs) with unique properties of high antimicrobial activity have attracted much interest from scientists and technologists to develop nanosilver-based disinfectant products. This article aims to review the synthesis routes and antimicrobial effects of Ag-NPs against various pathogens including bacteria, fungi and virus. Toxicology considerations of Ag-NPs to humans and ecology are discussed in detail. Some current applications of Ag-NPs in water-, air- and surface- disinfection are described. Finally, future prospects of Ag-NPs for treatment and prevention of currently emerging infections are discussed.

Abstract Risk management has reduced vulnerability to floods and droughts globally 1,2 , yet their impacts are still increasing 3 . An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data 4,5 . On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change 3 .

MOTIVATION: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. MAIN TYPES OF VARIABLES INCLUDED: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. SPATIAL LOCATION AND GRAIN: ). TIME PERIOD AND GRAIN: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. MAJOR TAXA AND LEVEL OF MEASUREMENT: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. SOFTWARE FORMAT: .csv and .SQL.

We report here on a new series of CO2-reducing molecular catalysts based on Earth-abundant elements that are very selective for the production of formic acid in dimethylformamide (DMF)/water mixtures (Faradaic efficiency of 90 ± 10%) at moderate overpotentials (500–700 mV in DMF measured at the middle of the catalytic wave). The [CpCo(PR2NR′2)I]+ compounds contain diphosphine ligands, PR2NR′2, with two pendant amine residues that act as proton relays during CO2-reduction catalysis and tune their activity. Four different PR2NR′2 ligands with cyclohexyl or phenyl substituents on phosphorus and benzyl or phenyl substituents on nitrogen were employed, and the compound with the most electron-donating phosphine ligand and the most basic amine functions performs best among the series, with turnover frequency >1000 s–1. State-of-the-art benchmarking of catalytic performances ranks this new class of cobalt-based complexes among the most promising CO2-to-formic acid reducing catalysts developed to date; addressing the stability issues would allow further improvement. Mechanistic studies and density functional theory simulations confirmed the role of amine groups for stabilizing key intermediates through hydrogen bonding with water molecules during hydride transfer from the Co center to the CO2 molecule.

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker's well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.

Abstract Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

Identifying polyps is challenging for automatic analysis of endoscopic images in computer-aided clinical support systems. Models based on convolutional networks (CNN), transformers, and their combinations have been proposed to segment polyps with promising results. However, those approaches have limitations either in modeling the local appearance of the polyps only or lack of multi-level feature representation for spatial dependency in the decoding process. This paper proposes a novel network, namely ColonFormer, to address these limitations. ColonFormer is an encoder-decoder architecture capable of modeling long-range semantic information at both encoder and decoder branches. The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales. The decoder is a hierarchical network structure designed for learning multi-level features to enrich feature representation. Besides, a refinement module is added with a new skip connection technique to refine the boundary of polyp objects in the global map for accurate segmentation. Extensive experiments have been conducted on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that our ColonFormer outperforms other state-of-the-art methods on all benchmark datasets.

A novel optimization method is proposed to minimize a convex function subject to bilinear matrix inequality (BMI) constraints. The key idea is to decompose the bilinear mapping as a difference between two positive semidefinite convex mappings. At each iteration of the algorithm the concave part is linearized, leading to a convex subproblem. Applications to various output feedback controller synthesis problems are presented. In these applications, the subproblem in each iteration step can be turned into a convex optimization problem with linear matrix inequality (LMI) constraints. The performance of the algorithm has been benchmarked on the data from the <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm COMPl}_{\rm e}{\rm ib}$</tex></formula> library.

Supported V2O5/Ce1–xTixO2 (3, 5, and 7 wt % V; x = 0, 0.1, 0.3, 0.5, 1) and bare supports have been tested in the selective catalytic reduction (SCR) of NO by NH3 at different gas hourly space velocities (GHSVs) and were comprehensively characterized using XRD, pseudo in situ XPS, and UV–vis DRS as well as EPR and DRIFTS in in situ and operando mode. The best V/Ce1–xTixO2 (x = 0.3, 0.5) catalysts showed almost 100% NO conversion and N2 selectivity already at 190 °C with a GHSV value of 70000 h–1, which belongs to the best performances observed so far in low-temperature NH3-SCR of NO. The corresponding bare supports still converted around 80% to N2 under the same conditions. On bare supports, SCR proceeds via a Langmuir–Hinshelwood mechanism comprising the reaction of adsorbed surface nitrates with adsorbed NH3. On V/Ce1–xTixO2, nitrate formation is not possible, and an Eley–Rideal mechanism is working in which gaseous NO reacts with adsorbed NH3 and NH4+. Lewis and Brønsted acid sites, though adsorption of NH3, do not scale with the catalytic activity, which is governed rather by the redox ability of the materials. This is boosted in the supports by replacing Ce with the more redox active Ti and in catalysts by tight connection of vanadyl species via O bridges to the support surface forming −Ce–O–V(═O)–O–Ti– units in which the equilibrium valence state of V under reaction conditions is close to +5.

Abstract This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E–146.5° E, 14.8° S–27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976–2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December–January–February (DJF) and June–July–August (JJA) over future periods of the early (2011–2040), mid (2041–2070) and late twenty-first century (2071–2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10–20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10–30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections.

Highly luminescent carbon dots (C-dots) were synthesized by the one-pot simple hydrothermal method directly from lemon juice using different temperatures, time, aging of precursors, and diluted solvents to control the luminescence of C‐dots. The obtained C-dots were characterized by high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, Fourier transform infrared spectrophotometry, dynamic light scattering, ultraviolet-visible spectrophotometry, and photoluminescent spectrophotometry. The results show that C‐dots had strong green light emission with quantum yield in the range of 14.86 to 24.89% as a function of hydrothermal temperatures. Furthermore, light emission that is dependent on hydrothermal time, aging of precursor, and diluted solvent was observed. These results suggest that the C‐dots have potential application in optoelectronics and bioimaging.

Bedaquiline, a new antituberculosis drug, has already been used in >50 countries. The emergence of bedaquiline resistance is alarming, as it may result in the rapid loss of this new drug. This article aims to review currently identified mechanisms of resistance and the emergence of bedaquiline resistance, and discuss strategies to delay the resistance acquisition. In vitro and clinical studies as well as reports from compassionate use have identified the threat of bedaquiline resistance and cross-resistance with clofazimine, emphasizing the crucial need for the systematic surveillance of resistance. Currently known mechanisms of resistance include mutations within the atpE, Rv0678, and pepQ genes. The development of standardized drug susceptibility testing (DST) for bedaquiline is urgently needed. Understanding any target and non-target-based mechanisms is essential to minimize resistance development and treatment failure and help to develop appropriate DST for bedaquiline and genetic-based resistance screening.

This paper introduces a new no-equilibrium chaotic system that is constructed by adding a tiny perturbation to a simple chaotic flow having a line equilibrium. The dynamics of the proposed system are investigated through Lyapunov exponents, bifurcation diagram, Poincaré map and period-doubling route to chaos. A circuit realization is also represented. Moreover, two other new chaotic systems without equilibria are also proposed by applying the presented methodology.

A bstract The angular distribution and differential branching fraction of the decay B 0 → K ∗0 μ + μ − are studied using a data sample, collected by the LHCb experiment in pp collisions at $ \sqrt{s}=7 $ TeV, corresponding to an integrated luminosity of 1 . 0 fb −1 . Several angular observables are measured in bins of the dimuon invariant mass squared, q 2 . A first measurement of the zero-crossing point of the forward-backward asymmetry of the dimuon system is also presented. The zero-crossing point is measured to be $ q_0^2={{{4.9\pm 0.9\;\mathrm{Ge}{{\mathrm{V}}^2}}} \left/ {{{c^4}}} \right.} $ , where the uncertainty is the sum of statistical and systematic uncertainties. The results are consistent with the Standard Model predictions.

Abstract Flash floods have long been common in Asian cities, with recent increases in urbanization and extreme rainfall driving increasingly severe and frequent events. Floods in urban areas cause significant damage to infrastructure, communities and the environment. Numerical modelling of flood inundation offers detailed information necessary for managing flood risk in such contexts. This study presents a calibrated flood inundation model using referenced photos, an assessment of the influence of four extreme rainfall events on water depth and inundation area in the Hanoi central area. Four types of historical and extreme rainfall were input into the inundation model. The modeled results for a 2008 flood event with 9 referenced stations resulted in an R 2 of 0.6 compared to observations. The water depth at the different locations was simulated under the four extreme rainfall types. The flood inundation under the Probable Maximum Precipitation presents the highest risk in terms of water depth and inundation area. These results provide insights into managing flood risk, designing flood prevention measures, and appropriately locating pump stations.

Lake Chad, in the Sahelian zone of west-central Africa, provides food and water to ~50 million people and supports unique ecosystems and biodiversity. In the past decades, it became a symbol of current climate change, held up by its dramatic shrinkage in the 1980s. Despites a partial recovery in response to increased Sahelian precipitation in the 1990s, Lake Chad is still facing major threats and its contemporary variability under climate change remains highly uncertain. Here, using a new multi-satellite approach, we show that Lake Chad extent has remained stable during the last two decades, despite a slight decrease of its northern pool. Moreover, since the 2000s, groundwater, which contributes to ~70% of Lake Chad's annual water storage change, is increasing due to water supply provided by its two main tributaries. Our results indicate that in tandem with groundwater and tropical origin of water supply, over the last two decades, Lake Chad is not shrinking and recovers seasonally its surface water extent and volume. This study provides a robust regional understanding of current hydrology and changes in the Lake Chad region, giving a basis for developing future climate adaptation strategies.