Ton Duc Thang University

Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.

Abstract The 2018 edition of the United Nations World Water Development Report stated that nearly 6 billion peoples will suffer from clean water scarcity by 2050. This is the result of increasing demand for water, reduction of water resources, and increasing pollution of water, driven by dramatic population and economic growth. It is suggested that this number may be an underestimation, and scarcity of clean water by 2050 may be worse as the effects of the three drivers of water scarcity, as well as of unequal growth, accessibility and needs, are underrated. While the report promotes the spontaneous adoption of nature-based-solutions within an unconstrained population and economic expansion, there is an urgent need to regulate demography and economy, while enforcing clear rules to limit pollution, preserve aquifers and save water, equally applying everywhere. The aim of this paper is to highlight the inter-linkage in between population and economic growth and water demand, resources and pollution, that ultimately drive water scarcity, and the relevance of these aspects in local, rather than global, perspective, with a view to stimulating debate.

The proton exchange membrane (PEM) water electrolysis is one of the most promising hydrogen production techniques. The oxygen evolution reaction (OER) occurring at the anode dominates the overall efficiency. Developing active and robust electrocatalysts for OER in acid is a longstanding challenge for PEM water electrolyzers. Most catalysts show unsatisfied stability under strong acidic and oxidative conditions. Such a stability challenge also leads to difficulties for a better understanding of mechanisms. This review aims to provide the current progress on understanding of OER mechanisms in acid, analyze the promising strategies to enhance both activity and stability, and summarize the state-of-the-art catalysts for OER in acid. First, the prevailing OER mechanisms are reviewed to establish the physicochemical structure-activity relationships for guiding the design of highly efficient OER electrocatalysts in acid with stable performance. The reported approaches to improve the activity, from macroview to microview, are then discussed. To analyze the problem of instability, the key factors affecting catalyst stability are summarized and the surface reconstruction is discussed. Various noble-metal-based OER catalysts and the current progress of non-noble-metal-based catalysts are reviewed. Finally, the challenges and perspectives for the development of active and robust OER catalysts in acid are discussed.

The purpose of this paper is to propose that the Contrastive Analysis Hypothesis (CAH) should be revised to incorporate a notion of degree of difficulty. This notion corresponds to typological markedness which can be determined independently of any particular language and independently of the facts concerning second language acquisition. Moreover, it is argued that if typological markedness is incorporated into the CAH, it is possible to predict not only the areas of difficulty for a second language learner, but also the relative degree of difficulty. Finally, it is argued that given certain assumptions about language and human learning, typological markedness is a natural and highly plausible notion of difficulty.

BACKGROUND: The massive abundance of studies relating to tropical medicine and health has increased strikingly over the last few decades. In the field of tropical medicine and health, a well-conducted systematic review and meta-analysis (SR/MA) is considered a feasible solution for keeping clinicians abreast of current evidence-based medicine. Understanding of SR/MA steps is of paramount importance for its conduction. It is not easy to be done as there are obstacles that could face the researcher. To solve those hindrances, this methodology study aimed to provide a step-by-step approach mainly for beginners and junior researchers, in the field of tropical medicine and other health care fields, on how to properly conduct a SR/MA, in which all the steps here depicts our experience and expertise combined with the already well-known and accepted international guidance.We suggest that all steps of SR/MA should be done independently by 2-3 reviewers' discussion, to ensure data quality and accuracy. CONCLUSION: SR/MA steps include the development of research question, forming criteria, search strategy, searching databases, protocol registration, title, abstract, full-text screening, manual searching, extracting data, quality assessment, data checking, statistical analysis, double data checking, and manuscript writing.

Natural product compounds have recently attracted significant attention from the scientific community for their potent effects against inflammation-driven diseases, including cancer. A significant amount of research, including preclinical, clinical, and epidemiological studies, has indicated that dietary consumption of polyphenols, found at high levels in cereals, pulses, vegetables, and fruits, may prevent the evolution of an array of diseases, including cancer. Cancer development is a carefully orchestrated progression where normal cells acquires mutations in their genetic makeup, which cause the cells to continuously grow, colonize, and metastasize to other organs such as the liver, lungs, colon, and brain. Compounds that modulate these oncogenic processes can be considered as potential anti-cancer agents that may ultimately make it to clinical application. Resveratrol, a natural stilbene and a non-flavonoid polyphenol, is a phytoestrogen that possesses anti-oxidant, anti-inflammatory, cardioprotective, and anti-cancer properties. It has been reported that resveratrol can reverse multidrug resistance in cancer cells, and, when used in combination with clinically used drugs, it can sensitize cancer cells to standard chemotherapeutic agents. Several novel analogs of resveratrol have been developed with improved anti-cancer activity, bioavailability, and pharmacokinetic profile. The current focus of this review is resveratrol's in vivo and in vitro effects in a variety of cancers, and intracellular molecular targets modulated by this polyphenol. This is also accompanied by a comprehensive update of the various clinical trials that have demonstrated it to be a promising therapeutic and chemopreventive agent.

Summary In this paper, we develop a dual‐horizon peridynamics (DH‐PD) formulation that naturally includes varying horizon sizes and completely solves the ‘ghost force’ issue. Therefore, the concept of dual horizon is introduced to consider the unbalanced interactions between the particles with different horizon sizes. The present formulation fulfills both the balances of linear momentum and angular momentum exactly. Neither the ‘partial stress tensor’ nor the ‘slice’ technique is needed to ameliorate the ghost force issue. We will show that the traditional peridynamics can be derived as a special case of the present DH‐PD. All three peridynamic formulations, namely, bond‐based, ordinary state‐based, and non‐ordinary state‐based peridynamics, can be implemented within the DH‐PD framework. Our DH‐PD formulation allows for h ‐adaptivity and can be implemented in any existing peridynamics code with minimal changes. A simple adaptive refinement procedure is proposed, reducing the computational cost. Both two‐dimensional and three‐dimensional examples including the Kalthoff–Winkler experiment and plate with branching cracks are tested to demonstrate the capability of the method. Copyright © 2016 John Wiley & Sons, Ltd.

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer’s disease (AD), Parkinson’s disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Poly(ethylene glycol) (PEG) is widely used as a gold standard in bioconjugation and nanomedicine to prolong blood circulation time and improve drug efficacy. The conjugation of PEG to proteins, peptides, oligonucleotides (DNA, small interfering RNA (siRNA), microRNA (miRNA)) and nanoparticles is a well-established technique known as PEGylation, with PEGylated products have been using in clinics for the last few decades. However, it is increasingly recognized that treating patients with PEGylated drugs can lead to the formation of antibodies that specifically recognize and bind to PEG (i.e., anti-PEG antibodies). Anti-PEG antibodies are also found in patients who have never been treated with PEGylated drugs but have consumed products containing PEG. Consequently, treating patients who have acquired anti-PEG antibodies with PEGylated drugs results in accelerated blood clearance, low drug efficacy, hypersensitivity, and, in some cases, life-threatening side effects. In this succinct review, we collate recent literature to draw the attention of polymer chemists to the issue of PEG immunogenicity in drug delivery and bioconjugation, thereby highlighting the importance of developing alternative polymers to replace PEG. Several promising yet imperfect alternatives to PEG are also discussed. To achieve asatisfactory alternative, further joint efforts of polymer chemists and scientists in related fields are urgently needed to design, synthesize and evaluate new alternatives to PEG.

Extending the Technology Acceptance Model (TAM) for studying the e-learning acceptance is not a new research topic, and it has been tackled by many scholars. However, the development of a comprehensive TAM that could be able to examine the e-learning acceptance under any circumstances is regarded to be an essential research direction. To identify the most widely used external factors of the TAM concerning the e-learning acceptance, a literature review comprising of 120 significant published studies from the last twelve years was conducted. The review analysis indicated that computer self-efficacy, subjective/social norm, perceived enjoyment, system quality, information quality, content quality, accessibility, and computer playfulness were the most common external factors of TAM. Accordingly, the TAM has been extended by the aforementioned factors to examine the students' acceptance of e-learning in five different universities in the United Arab of Emirates (UAE). A total of 435 students participated in the study. The results indicated that system quality, computer self-efficacy, and computer playfulness have a significant impact on perceived ease of use of e-learning system. Furthermore, information quality, perceived enjoyment, and accessibility were found to have a positive influence on perceived ease of use and perceived usefulness of e-learning system.

Fragrance is an integral part of cosmetic products and is often regarded as an overriding factor in the selection of cosmetics among consumers. Fragrances also play a considerable role in masking undesirable smells arising from fatty acids, oils and surfactants that are commonly used in cosmetic formulations. Essential oils are vital assets in the cosmetic industry, as along with imparting pleasant aromas in different products, they are able to act as preservatives and active agents and, simultaneously, offer various benefits to the skin. Moreover, the stimulating demand for natural ingredients has contributed massively to a renewed interest in cosmetic and wellness industries in plant derivatives, especially essential oils. This has led popular cosmetic companies to endorse natural fragrances and opt for minimally processed natural ingredients, given the potentially adverse health risks associated with artificial fragrance chemicals, which are major elements of cosmetics. Among the high-valued essential oils used as fragrances are citrus, lavender, eucalyptus, tea tree and other floral oils, among others, while linalool, geraniol, limonene, citronellol, and citral are much-appreciated fragrance components used in different cosmetics. Thus, this review aimed to highlight the enormous versatility of essential oils as significant sources of natural fragrances in cosmetics and cosmeceuticals. Moreover, a special focus will be laid on the different aspects related to essential oils such as their sources, market demand, chemistry, fragrance classification, aroma profile, authenticity and safety.

Diabetic Retinopathy (DR) is an ophthalmic disease that damages retinal blood vessels. DR causes impaired vision and may even lead to blindness if it is not diagnosed in early stages. DR has five stages or classes, namely normal, mild, moderate, severe and PDR (Proliferative Diabetic Retinopathy). Normally, highly trained experts examine the colored fundus images to diagnose this fatal disease. This manual diagnosis of this condition (by clinicians) is tedious and error-prone. Therefore, various computer vision-based techniques have been proposed to automatically detect DR and its different stages from retina images. However, these methods are unable to encode the underlying complicated features and can only classify DR's different stages with very low accuracy particularly, for the early stages. In this research, we used the publicly available Kaggle dataset of retina images to train an ensemble of five deep Convolution Neural Network (CNN) models (Resnet50, Inceptionv3, Xception, Dense121, Dense169) to encode the rich features and improve the classification for different stages of DR. The experimental results show that the proposed model detects all the stages of DR unlike the current methods and performs better compared to state-of-the-art methods on the same Kaggle dataset.

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 (IC 50 value of 16.99 μ g/mL) and in vitro anti-inflammatory activity (i.e., albumin denaturation: IC 50 = 28.86 μ g/mL; antiproteinase activity: IC 50 = 414.29 μ g/mL; and membrane stabilization: IC 50 = 319 μ g/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 work was designed to evaluate whether external application of nitric oxide (NO) in the form of its donor S-nitroso-N-acetylpenicillamine (SNAP) could mitigate the deleterious effects of NaCl stress on chickpea (Cicer arietinum L.) plants. SNAP (50 μM) was applied to chickpea plants grown under non-saline and saline conditions (50 and 100 mM NaCl). Salt stress inhibited growth and biomass yield, leaf relative water content (LRWC) and chlorophyll content of chickpea plants. High salinity increased electrolyte leakage, carotenoid content and the levels of osmolytes (proline, glycine betaine, soluble proteins and soluble sugars), hydrogen peroxide (H2O2) and malondialdehyde (MDA), as well as the activities of antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and glutathione reductase in chickpea plants. Expression of the representative SOD, CAT and APX genes examined was also up-regulated in chickpea plants by salt stress. On the other hand, exogenous application of NO to salinized plants enhanced the growth parameters, LRWC, photosynthetic pigment production and levels of osmolytes, as well as the activities of examined antioxidant enzymes which is correlated with up-regulation of the examined SOD, CAT and APX genes, in comparison with plants treated with NaCl only. Furthermore, electrolyte leakage, H2O2 and MDA contents showed decline in salt-stressed plants supplemented with NO as compared with those in NaCl-treated plants alone. Thus, the exogenous application of NO protected chickpea plants against salt stress-induced oxidative damage by enhancing the biosyntheses of antioxidant enzymes, thereby improving plant growth under saline stress. Taken together, our results demonstrate that NO has capability to mitigate the adverse effects of high salinity on chickpea plants by improving LRWC, photosynthetic pigment biosyntheses, osmolyte accumulation and antioxidative defense system.

Drug delivery technology has a wide spectrum, which is continuously being upgraded at a stupendous speed. Different fabricated nanoparticles and drugs possessing low solubility and poor pharmacokinetic profiles are the two major substances extensively delivered to target sites. Among the colloidal carriers, nanolipid dispersions (liposomes, deformable liposomes, virosomes, ethosomes, and solid lipid nanoparticles) are ideal delivery systems with the advantages of biodegradation and nontoxicity. Among them, nano-structured lipid carriers and solid lipid nanoparticles (SLNs) are dominant, which can be modified to exhibit various advantages, compared to liposomes and polymeric nanoparticles. Nano-structured lipid carriers and SLNs are non-biotoxic since they are biodegradable. Besides, they are highly stable. Their (nano-structured lipid carriers and SLNs) morphology, structural characteristics, ingredients used for preparation, techniques for their production, and characterization using various methods are discussed in this review. Also, although nano-structured lipid carriers and SLNs are based on lipids and surfactants, the effect of these two matrixes to build excipients is also discussed together with their pharmacological significance with novel theranostic approaches, stability and storage.

Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

COVID-19 vaccines have been developed with unprecedented speed which would not have been possible without decades of fundamental research on delivery nanotechnology. Lipid-based nanoparticles have played a pivotal role in the successes of COVID-19 vaccines and many other nanomedicines, such as Doxil® and Onpattro®, and have therefore been considered as the frontrunner in nanoscale drug delivery systems. In this review, we aim to highlight the progress in the development of these lipid nanoparticles for various applications, ranging from cancer nanomedicines to COVID-19 vaccines. The lipid-based nanoparticles discussed in this review are liposomes, niosomes, transfersomes, solid lipid nanoparticles, and nanostructured lipid carriers. We particularly focus on the innovations that have obtained regulatory approval or that are in clinical trials. We also discuss the physicochemical properties required for specific applications, highlight the differences in requirements for the delivery of different cargos, and introduce current challenges that need further development. This review serves as a useful guideline for designing new lipid nanoparticles for both preventative and therapeutic vaccines including immunotherapies.

Emerging technologies such as computer vision and Artificial Intelligence (AI) are estimated to leverage the accessibility of big data for active training and yielding operational real time smart machines and predictable models. This phenomenon of applying vision and learning methods for the improvement of food industry is termed as computer vision and AI driven food industry. This review contributes to provide an insight into state-of-the-art AI and computer vision technologies that can assist farmers in agriculture and food processing. This paper investigates various scenarios and use cases of machine learning, machine vision and deep learning in global perspective with the lens of sustainability. It explains the increasing demand towards the AgTech industry using computer vision and AI which might be a path towards sustainable food production to feed the future. Also, this review tosses some implications regarding challenges and recommendations in inclusion of technologies in real time farming, substantial global policies and investments. Finally, the paper discusses the possibility of using Fourth Industrial Revolution [4.0 IR] technologies such as deep learning and computer vision robotics as a key for sustainable food production.

Since it was first discovered, thousands of years ago, silkworm silk has been known to be an abundant biopolymer with a vast range of attractive properties. The utilization of silk fibroin (SF), the main protein of silkworm silk, has not been limited to the textile industry but has been further extended to various high-tech application areas, including biomaterials for drug delivery systems and tissue engineering. The outstanding mechanical properties of SF, including its facile processability, superior biocompatibility, controllable biodegradation, and versatile functionalization have allowed its use for innovative applications. In this review, we describe the structure, composition, general properties, and structure-properties relationship of SF. In addition, the methods used for the fabrication and modification of various materials are briefly addressed. Lastly, recent applications of SF-based materials for small molecule drug delivery, biological drug delivery, gene therapy, wound healing, and bone regeneration are reviewed and our perspectives on future development of these favorable materials are also shared.