•Structural test is conducted on 14 high strength Concrete Encased Steel (CES) composite short columns.•The axial compression capacity of high strength CES columns is evaluated and compared with ...various design codes.•A concrete strength reduction factor is proposed to account for the brittleness of high strength concrete.•Parametric studies are carried out to determine the critical parameters affecting strength and ductility of CES columns.•An empirical equation is proposed to assess the ductility of high strength CES columns.
This paper presents an experimental program that studies the structural behaviour of high strength Concrete Encased Steel (CES) composite columns. The structural performance under compression, including the damage pattern, load-carrying capacity, post-peak ductility, and load-displacement response is experimentally investigated. A total of 14 specimens were tested under concentric compression. The parameters studied in this program include concrete compressive strength, steel yield strength, stirrup spacing, incorporation of steel fiber, as well as the shape of the structural steel section. To evaluate the material compatibility between high strength concrete and high strength steel, two concrete grades (C90, C130) and two steel grades (S500, S690) were used to prepare the test specimens. In addition, 0.5% volume fraction of steel fiber was added in concrete mix to minimize the inherent brittleness of high strength concrete. The comparison between test results and analytical predictions reveals the inability of existing design codes to estimate high strength CES columns, unless steel fiber and dense reinforcement are used in combination. The effect of material strength, steel fibers, volumetric ratios of hoop reinforcement, and shape of steel section on both strength and ductility of CES columns was assessed through a comprehensive parametric study. The analysis of test results demonstrates that steel contribution ratio plays a dominant role in the ductility, whereas increasing hoop reinforcement ratio and adding steel fiber has negligible effect. Finally, a simplified formula is proposed to evaluate ductility of high strength CES columns.
Concrete-filled steel tube (CFST) columns are widely used in the construction industry. Prediction of the ultimate bearing capacity of CFST columns is complicated because it is influenced nonlinearly ...by many factors such as steel tube length, steel tube thickness, ratio length and column diameter, and concrete compressive strength. This study proposes an artificial intelligence (AI) model to predict the ultimate bearing capacity of CFST columns. The AI model was developed based on support vector regression (SVR) and grey wolf optimization (GWO). The GWO optimized the SVR configuration that produces highly accurate prediction results. A large experimental dataset with normal, high, and ultimate strength concretes was used to validate the model’s effectiveness through the learning and test phases. A
k
-fold cross-validation method was adopted to ensure the generalizability. The column diameter (
D
), thickness of steel tube (
t
), yield stress of steel, compressive strength of concrete, column length,
D
/
t
ratio were used as inputs for the model. Results show that the proposed SVR-GWO model was more effective than the compared models and empirical methods in the bearing capacity prediction of CFST columns. The SVR-GWO yielded the outstanding performance in which the accuracy improvements by the proposed model were ranged from 10.3 to 87.9% in the mean absolute percentage error and from 15.4 to 74.2% in the mean absolute error compared to baseline models and empirical methods. As contributions, the study suggested an AI-based tool for estimating the ultimate bearing capacity of CFST columns in structural design.
Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen in nature-centered ...scenes such as gardens or parks. To promote machine learning methods for nature-oriented applications, such as agriculture and gardening, we propose the multimodal synthetic dataset for Enclosed garDEN scenes (EDEN). The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. Experimental results on the state-of-the-art methods for semantic segmentation and monocular depth prediction, two important tasks in computer vision, show positive impact of pre-training deep networks on our dataset for unstructured natural scenes. The dataset and related materials will be available at https://lhoangan.github.io/eden.
Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential ...information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques.
The self-heating effect (SHE) in top-gate In-Ga-Zn-O (IGZO) thin-film transistors (TFTs) was examined systematically using short electrical pulse measurement methods. The temperature dependence of ...the pulse measurements of IGZO TFTs revealed a significant increase in temperature during the measurements, suggesting that conventional measurements can overestimate the device performance significantly. The effective temperature was introduced and extracted for IGZO TFTs at various heating powers and ambient temperatures. The short sampling time was determined to be a key in characterizing the intrinsic device properties that are not influenced by the SHE. The cooling behavior after self-heating was also examined using multipulse measurements. Because heating and cooling are significant even in a very short time, it is essential to consider the operation condition of the devices when characterizing TFTs to estimate the precise performance and reliability in a real operation.
This letter reports the low-temperature solution-based fabrication of indium oxide (In 2 O 3 ) thin-film transistors (TFTs) using a visible laser-assisted urea combustion process. An In 2 O 3 ...precursor solution containing a small amount of urea absorbed the photon energy from a blue laser and started the combustion of urea to form a crystallized In 2 O 3 phase. Atomic force microscopy and X-ray diffraction showed that both laser radiation and urea combustion together are necessary to convert a dried precursor solution layer to a crystallized In 2 O 3 phase. A TFT fabricated from the 0.2-mol% urea-added solution and laser annealed with a 250-J/cm 2 energy fluence exhibited superior transfer characteristics compared with the TFTs fabricated either without urea addition or with small energy fluence radiation. Based on these results and considering the price of blue laser diodes, this technique can be an economical solution for the fabrication of oxide semiconductor TFTs on flexible substrates with a low melting point.
A novel Gram-reaction positive-, catalase and oxidase negative-, rod-shaped, facultatively anaerobic bacterial strain, DCY120
T
,
was isolated from the gut of honeybee (
Apis cerana
) in Gyeonggi-do, ...South Korea. Strain DCY120
T
belongs to the genus
Bombilactobacillus
and is moderately related to
Bombilactobacillus mellis
Hon2
T
(94.1% similarity),
Bombilactobacillus bombi
BTLCH M1/2
T
(93.8%), and
Bombilactobacillus mellifer
Bin4N
T
(93.5%) based on 16S rRNA gene sequence analysis. The genome of strain DCY120
T
was sequenced and the average nucleotide identity (ANI) between strain DCY120
T
and the related
Bombilactobacillus
type strains were below the threshold value (95–96%) for species delineation. The major fatty acids were C
16:0
, C
18:1
ω
9
c,
Summed C
19:1
ω
6
c
/C
19:0
cyclo
ω
10
c
/C
19:0
ω
6 and Summed C
18:1
ω
7
c
/C
18:1
ω
6
c
. The major polar lipids were diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), one glycolipid (GL), and one unidentified aminophospholipid (APL). The amino acids in peptidoglycan of strain DCY120
T
were lysine, alanine, glutamic acid, and aspartic acid. In conclusion, the description of phenotypic and genotypic properties support strain DCY120
T
as a novel species within the genus
Bombilactobacillus
, for which the name
Bombilactobacillus apium
sp. nov. is proposed. The type strain is DCY120
T
(= KCTC 43194
T
= JCM 34006
T
).
The increase in the number of infectious diseases has exerted many negative consequences on human health. In this study, the antibacterial cotton fabric was fabricated for application in ...antibacterial products, contributing to the prevention of infectious diseases for humans. Particularly, ex‐situ and in‐situ dip‐coating techniques were compared and utilized for the fabrication of the antibacterial cotton fabric. Besides, the effects of the concentrations of precursors, including graphene oxide (GO), silver nanoparticles (AgNPs), silver/graphene oxide nanocomposite (Ag/GO), and dip‐coating times were evaluated to determine the most appropriate preparation conditions. Therewithal, the resulting cotton fabric was modified with stearic acid (SA) to enhance the hydrophobicity, in which the concentration of the SA was also assessed. Additionally, the antibacterial performance of the prepared material was investigated against Gram‐negative Pseudomonas aeruginosa and Gram‐positive Staphylococcus aureus. Different analytical techniques such as scanning electron microscope, contact angle measurement, and color stability were also utilized for the comparison between different cotton fabrics. According to the obtained results, the dip‐coated in‐situ Ag/GO (in‐situ‐Ag/GO/cotton) fabrics showed better antibacterial performance than that of dip‐coated ex‐situ Ag/GO (ex‐situ‐Ag/GO/cotton) ones, which can be attributed to the even distribution of Ag/GO nanocomposite on the fabric prepared by the in‐situ methods. According to the aforementioned results, the resulting antibacterial cotton fabric can be considered a promising material for the production of antibacterial face masks and protective clothing, which can be utilized in hospitals, textile industries, or the manufacture of sport gears.
The preparation scheme of cotton fabric.
White turmeric (Curcuma aromatica Salisb.) contains notable secondary metabolites with significant health benefits. However, the efficiency of traditional cultivation of white turmeric is limited by ...many environmental factors. Therefore, it is of great significance to study the large-scale production of superior-quality microrhizomes for cultivation and secondary metabolite extraction, without relying on natural rhizomes. In this study, the effects of sucrose, plant growth regulators (6-benzyl amino purine – BAP, kinetin – KIN,
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