Ho Chi Minh City University of Science

Abstract The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy , which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.

varied extensively with the synthesis parameters. This study presents an efficient green synthesis route for ZnO NPs with a wide range of potential applications, especially in the biomedical field.

Visual tracking in unconstrained environments is very challenging due to the existence of several sources of varieties such as changes in appearance, varying lighting conditions, cluttered background, and frame-cuts. A major factor causing tracking failure is the emergence of regions having similar appearance as the target. It is even more challenging when the target leaves the field of view (FoV) leading the tracker to follow another similar object, and not reacquire the right target when it reappears. This paper presents a method to address this problem by exploiting the context on-the-fly in two terms: Distracters and Supporters. Both of them are automatically explored using a sequential randomized forest, an online template-based appearance model, and local features. Distracters are regions which have similar appearance as the target and consistently co-occur with high confidence score. The tracker must keep tracking these distracters to avoid drifting. Supporters, on the other hand, are local key-points around the target with consistent co-occurrence and motion correlation in a short time span. They play an important role in verifying the genuine target. Extensive experiments on challenging real-world video sequences show the tracking improvement when using this context information. Comparisons with several state-of-the-art approaches are also provided.

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 .

The binding pose and affinity between a ligand and enzyme are very important pieces of information for computer-aided drug design. In the initial stage of a drug discovery project, this information is often obtained by using molecular docking methods. Autodock4 and Autodock Vina are two commonly used open-source and free software tools to perform this task, and each has been cited more than 6000 times in the last ten years. It is of great interest to compare the success rate of the two docking software programs for a large and diverse set of protein–ligand complexes. In this study, we selected 800 protein–ligand complexes for which both PDB structures and experimental binding affinity are available. Docking calculations were performed for these complexes using both Autodock4 and Autodock Vina with different docking options related to computing resource consumption and accuracy. Our calculation results are in good agreement with a previous study that the Vina approach converges much faster than AD4 one. However, interestingly, AD4 shows a better performance than Vina over 21 considered targets, whereas the Vina protocol is better than the AD4 package for 10 other targets. There are 16 complexes for which both the AD4 and Vina protocols fail to produce a reasonable correlation with respected experiments so both are not suitable to use to estimate binding free energies for these cases. In addition, the best docking option for performing the AD4 approach is the long option. However, the short option is the best solution for carrying out Vina docking. The obtained results probably will be useful for future docking studies in deciding which program to use.

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation. Many authors have introduced different approaches and algorithms. In this survey, we examine methods that have been proposed to segment 3D point clouds. The advantages, disadvantages, and design mechanisms of these methods are analyzed and discussed. Finally, we outline the promising future research directions.

A crystalline material with a two-dimensional structure, termed metal-organic framework-901 (MOF-901), was prepared using a strategy that combines the chemistry of MOFs and covalent-organic frameworks (COFs). This strategy involves in situ generation of an amine-functionalized titanium oxo cluster, Ti6O6(OCH3)6(AB)6 (AB = 4-aminobenzoate), which was linked with benzene-1,4-dialdehyde using imine condensation reactions, typical of COFs. The crystal structure of MOF-901 is composed of hexagonal porous layers that are likely stacked in staggered conformation (hxl topology). This MOF represents the first example of combining metal cluster chemistry with dynamic organic covalent bond formation to give a new crystalline, extended framework of titanium metal, which is rarely used in MOFs. The incorporation of Ti(IV) units made MOF-901 useful in the photocatalyzed polymerization of methyl methacrylate (MMA). The resulting polyMMA product was obtained with a high-number-average molar mass (26 850 g mol(-1)) and low polydispersity index (1.6), which in many respects are better than those achieved by the commercially available photocatalyst (P-25 TiO2). Additionally, the catalyst can be isolated, reused, and recycled with no loss in performance.

Abstract The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes ( ASTN2 , DPP4 and MAST4 ) and one is found 200 kb upstream of SHH . A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer’s disease ( r g =−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.

SUMMARY This paper presents a novel numerical procedure based on the framework of isogeometric analysis for static, free vibration, and buckling analysis of laminated composite plates using the first‐order shear deformation theory. The isogeometric approach utilizes non‐uniform rational B‐splines to implement for the quadratic, cubic, and quartic elements. Shear locking problem still exists in the stiffness formulation, and hence, it can be significantly alleviated by a stabilization technique. Several numerical examples are presented to show the performance of the method, and the results obtained are compared with other available ones. Copyright © 2012 John Wiley & Sons, Ltd.

Streptococcus suis, a bacterium that affects pigs, is a neglected pathogen that causes systemic disease in humans. We conducted a systematic review and meta-analysis to summarize global estimates of the epidemiology, clinical characteristics, and outcomes of this zoonosis. We searched main literature databases for all studies through December 2012 using the search term "streptococcus suis." The prevalence of S. suis infection is highest in Asia; the primary risk factors are occupational exposure and eating of contaminated food. The pooled proportions of case-patients with pig-related occupations and history of eating high-risk food were 38.1% and 37.3%, respectively. The main clinical syndrome was meningitis (pooled rate 68.0%), followed by sepsis, arthritis, endocarditis, and endophthalmitis. The pooled case-fatality rate was 12.8%. Sequelae included hearing loss (39.1%) and vestibular dysfunction (22.7%). Our analysis identified gaps in the literature, particularly in assessing risk factors and sequelae of this infection.

The effects of relative humidity (RH) and high ambient temperature (T) on physiological responses and animal performance were studied using 12 groups (10 gilts per group) in pens inside respiration chambers. The microclimate in the chamber was programmed so that T remained constant within a day. Each day, the T was increased by 2 degrees C from low (16 degrees C) to high (32 degrees C). Relative humidity was kept constant at 50, 65, or 80%. The pigs' average initial BW was 61.7 kg (58.0 to 65.5 kg), and their average ending BW was 70.2 kg (65.9 to 74.7 kg). Respiration rate (RR), evaporative water (EW), rectal temperature (RT), skin temperature (ST), voluntary feed intake (VFI), water-to-feed ratio (rW:F), heat production (HP), and ADG were analyzed. The animals had free access to feed and water. We determined the T above which certain animal variables started to change: the so-called inflection point temperature (IPt) or "upper critical temperature." The first indicator of reaction, RR, was in the range from 21.3 to 23.4 degrees C. Rectal temperature was a delayed indicator of heat stress tolerance, with IPt values ranging from 24.6 to 27.1 degrees C. For both these indicators the IPt was least at 80% RH (P < 0.05). Heat production and VFI were decreased above IPt of 22.9 and 25.5 degrees C, respectively (P < 0.001). For each degree Celsius above IPt, the VFI was decreased by 81, 99, and 106 g/(pig.d) in treatments 50, 65, and 80% RH, respectively. The ADG was greatest at 50% RH (P < 0.05). Ambient temperature strongly affects the pigs' physiological changes and performance, whereas RH has a relatively minor effect on heat stress in growing pigs; however, the combination of high T and high RH lowered the ADG in pigs. The upper critical temperature can be considered to be the IPt above which VFI decreased and RT then increased. Temperatures of the magnitude of both these IPt are regularly measured in commercial pig houses. We conclude that the upper critical temperatures for 60-kg, group-housed pigs fed ad libitum are between 21.3 and 22.4 degrees C for RR, between 22.9 and 25.5 degrees C for HP and VFI, and between 24.6 and 27.1 degrees C for RT. It is clear that different physiological and productive measurements of group-housed, growing-finishing pigs have different critical temperatures.

Silica powder at nanoscale was obtained by heat treatment of Vietnamese rice husk following the sol-gel method. The rice husk ash (RHA) is synthesized using rice husk which was thermally treated at optimal condition at 600°C for 4 h. The silica from RHA was extracted using sodium hydroxide solution to produce a sodium silicate solution and then precipitated by adding H2SO4 at pH = 4 in the mixture of water/butanol with cationic presence. In order to identify the optimal condition for producing the homogenous silica nanoparticles, the effects of surfactant surface coverage, aging temperature, and aging time were investigated. By analysis of X-ray diffraction, scanning electron microscopy, and transmission electron microscopy, the silica product obtained was amorphous and the uniformity of the nanosized sample was observed at an average size of 3 nm, and the BET result showed that the highest specific surface of the sample was about 340 m2/g. The results obtained in the mentioned method prove that the rice husk from agricultural wastes can be used for the production of silica nanoparticles.

UV light, especially UVB, is known as a trigger of allergic reaction, leading to mast cell degranulation and histamine release. In this study, phlorotannin Fucofuroeckol-A (F-A) derived from brown algal Ecklonia stolonifera Okamura was evaluated for its protective capability against UVB-induced allergic reaction in RBL-2H3 mast cells. It was revealed that F-A significantly suppress mast cell degranulation via decreasing histamine release as well as intracellular Ca2+ elevation at the concentration of 50 μM. Moreover, the inhibitory effect of F-A on IL-1β and TNF-α productions was also evidenced. Notably, the protective activity of F-A against mast cell degranulation was found due to scavenging ROS production. Accordingly, F-A from brown algal E. stolonifera was suggested to be promising candidate for its protective capability against UVB-induced allergic reaction.

Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear frequently together in transactions made by customers. Although itemset mining was designed for market basket analysis, it can be viewed more generally as the task of discovering groups of attribute values frequently cooccurring in databases. Because of its numerous applications in domains such as bioinformatics, text mining, product recommendation, e‐learning, and web click stream analysis, itemset mining has become a popular research area. This study provides an up‐to‐date survey that can serve both as an introduction and as a guide to recent advances and opportunities in the field. The problem of frequent itemset mining and its applications are described. Moreover, main approaches and strategies to solve itemset mining problems are presented, as well as their characteristics are provided. Limitations of traditional frequent itemset mining approaches are also highlighted, and extensions of the task of itemset mining are presented such as high‐utility itemset mining, rare itemset mining, fuzzy itemset mining, and uncertain itemset mining. This study also discusses research opportunities and the relationship to other popular pattern mining problems, such as sequential pattern mining, episode mining, subgraph mining, and association rule mining. Main open‐source libraries of itemset mining implementations are also briefly presented. WIREs Data Mining Knowl Discov 2017, 7:e1207. doi: 10.1002/widm.1207 This article is categorized under: Algorithmic Development &gt; Association Rules Technologies &gt; Association Rules

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.

calculations, we are able to demonstrate that the primary adsorption mechanisms of the uptake of MB onto pomelo fruit peel are electrostatic attraction and hydrogen-bond formations, whereas the adsorption mechanisms for Cr(iii) are electrostatic attraction and n-d interactions.

Designing an efficient hybrid structure photocatalyst for photocatalytic decomposition and hydrogen (H2) evolution has been considered a great choice to develop renewable technologies for clean energy production and environmental remediation. Enhanced charge transfer (CT) based on the interaction between a noble metal and a semiconductor is a crucial factor influencing the movement of photogenerated electron–hole pairs. Herein, we focus on the recent advances related to plasmon-enhanced noble metals and the semiconductor nature to drive the photocatalytic H2 production and photodegradation of the organic dye rhodamine B (RhB) under UV and visible light irradiation. Specifically, the combination of concerted catalysis and green nanoengineering strategies to design ZnO-based composite photocatalysts and their decoration with metallic Ag have been realized by the radio frequency (RF) sputtering technique at room temperature. This simultaneity enhances the interface coupling between Ag and ZnO and reduces the energy threshold. The creation of charge transfer in the heterojunction and Schottky barrier changes the photoelectronic properties of the as-synthesized Al-doped ZnO (AZO); afterward, these effects promote the migration, transportation, and separation of photoinduced charge carriers and enhance the light-harvesting efficiency. As a result, the as-synthesized AZO-20 hybrid nanostructure exhibits a photocurrent density of 2.5 mA/cm2 vs Ag/AgCl, which is improved by almost 12 times compared with that of bare ZnO (0.2 mA/cm2). The hydrogen evolution rates of AZO-20 were ∼38 and ∼24 μmol/h under UV and visible light exposure, which are almost five- and tenfold higher than those of pristine ZnO, respectively. Additionally, the RhB degradation efficacies of the obtained AZO-20 were greater than almost 97 and 82% under UV and visible light illumination, respectively. The achieved apparent rate constant for the photocatalytic RhB decomposition was 0.014 min–1, indicating that it is 14-fold than that in pristine ZnO (0.001 min–1). Heterostructure AZO photocatalysts possess excellent practical stability in the water-splitting reaction and photocatalytic RhB decomposition, posing as promising candidates in practical works for pollution and energy challenges.

Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. Firstly, we separate the image into many sub-images based on the features of images. Each sub-image is classified into the responsive class by an ANN. Finally, SVM has been compiled all the classify result of ANN. Our proposal classification model has brought together many ANN and one SVM. Let it denote ANN_SVM. ANN_SVM has been applied for Roman numerals recognition application and the precision rate is 86%. The experimental results show the feasibility of our proposal model.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

A study was conducted to examine the levels of Salmonella spp. contamination in raw food samples, including chicken, beef, pork, and shellfish, from Vietnam and to determine their antibiotic resistance characteristics. A total of 180 samples were collected and examined for the presence of Salmonella spp., yielding 91 Salmonella isolates. Sixty-one percent of meat and 18% of shellfish samples were contaminated with Salmonella spp. Susceptibility of all isolates to a variety of antimicrobial agents was tested, and resistance to tetracycline, ampicillin/amoxicillin, nalidixic acid, sulfafurazole, and streptomycin was found in 40.7%, 22.0%, 18.7%, 16.5%, and 14.3% of the isolates, respectively. Resistance to enrofloxacin, trimethoprim, chloramphenicol, kanamycin, and gentamicin was also detected (8.8 to 2.2%). About half (50.5%) of the isolates were resistant to at least one antibiotic, and multiresistant Salmonella isolates, resistant to at least three different classes of antibiotics, were isolated from all food types. One isolate from chicken (serovar Albany) contained a variant of the Salmonella genomic island 1 antibiotic resistance gene cluster. The results show that antibiotic resistance in Salmonella spp. in raw food samples from Vietnam is significant.