REASERCH ON DETERMINATION FOR THE LIMITED COMPRESSIVE STRENGTH AND BENDING MOMENT OF THE STEEL COLUMN BASE USING EFFECTIVE AREA PANDEMIC

In the design of steel column base, parameters of limited compressive strength NRd and limited bending moment MRd are the control conditions that need to be paid due attention, otherwise it will damage the concrete foundation block at the foot of the column. The objective of the article is to use the effective area method in Eurocode 3 standard to determine NRd and MRd of the steel column base, then perform numerical calculations to check the calculation results. The results show that the NRd and MRd of the steel column base tends to increase when increasing the thickness of the base plate and the grade of the concrete foundation block. Then, corresponding to each thickness of the base plate (from t=18 mm to t=36 mm) and each concrete grade (from C16/20 to C50/60) will give corresponding results for each NRd and MRd. However, it is important to note that the compressive capacity and bending moment of the column base will be limited, can not increase forever, for example, can not overcome the compressive strength Npl;Rd = 1596 kN and the limit bending  strength Mpl;Rd=131.3 kN.m of the concrete foundation block at the base of the column. The article recommends using the results of this study to choose the thickness of the base plate and the grade of the concrete foundation block in the most effective, accurate and economical way

Fluidization of graphene nanoplatelets for atomic layer deposition

Graphene is an ideal catalysis support: it has a high surface area, is chemically and thermodynamically inert, and has high carrier mobility. A special type of graphene nanoparticles are graphene nanoplatelets. They consist of small stacks of graphene giving them a thickness of 1 – 15 nm while their diameters can range up to a few micrometres. However, for catalysis these nanoplatelets have to be provided with catalyst materials such as platinum or titania. One very promising technique for such a modification is Atomic Layer Deposition of nanoparticles on the graphene, which can provide a fast, highly controlled and scalable process. However, to separate the carbon nanoplatelets and achieve free large accessible surfaces in the reactor nanoplatelets have to be fluidized. While fluidization of carbon nanotubes is already well established, fluidization of nanoplatelets is a completely new research topic and was, to the best of our knowledge, not investigated so far. Based on the size of the carbon nanoplatelets they are treated as very cohesive (Geldart group C) powders which are hard to fluidize. Nevertheless, homogenous fluidization could be achieved by using assistance methods such as mechanical vibration. Here we present a detailed analysis of the fluidization behaviour of carbon nanoplatelets for atomic layer deposition. We analysed the bed expansion behaviour of the nanoplatelets depending on the gas velocities. Since the Atomic Layer Deposition process can be run at different temperatures, depending on the used precursors, we further analysed the influence of the temperature on the fluidization behaviour. Finally, we investigated the reproducibility of our results by an statistical analysis of our results. Please click Additional Files below to see the full abstract

Growth and properties of subnanometer thin titanium nitride films

This research brings new insights into the relation between properties of ultra-thin conductive metal nitrides made by atomic layer deposition (ALD) and their possible industrial applications. The advantage of conductive nitrides over pure metals is (i) better established ALD processes allowing depositing high-quality films and (ii) the presence of nitrogen as an extra tool to manipulate the electron transport properties. In this work, we study titanium nitride (TiN) films with the aim to investigate the growth mechanism in combination with physical and electrical properties as a function of the layer thickness. In microelectronic devices, thin continuous TiN films are commonly used as diffusion barriers and metal gate material. Scaling electronic devices to nanometer dimensions requires a close look at electrical material properties as ultra-thin conductive materials encounter an insulating regime due to the depletion of carriers

Changes in the Building Stock of Da Nang between 2015 and 2017

This descriptor introduces a novel dataset, which contains the number and types of buildings in the city of Da Nang in Central Vietnam. The buildings were classified into nine distinct types and initially extracted from a satellite image of the year 2015. Secondly, changes were identified based on a visual interpretation of an image of the year 2017, so that new buildings, demolished buildings and building upgrades can be quantitatively analyzed. The data was aggregated by administrative wards and a hexagonal grid with a diameter of 250 m to protect personal rights and to avoid the misuse of a single building’s information. The dataset shows an increase of 19,391 buildings between October 2015 and August 2017, with a variety of interesting spatial patterns. The center of the city is mostly dominated by building changes and upgrades, while most of the new buildings were constructed within a distance of five to six kilometers from the city center

Advances in scalable gas-phase manufacturing and processing of nanostructured solids: A review

Although the gas-phase production of nanostructured solids has already been carried out in industry for decades, only in recent years has research interest in this topic begun to increase. Nevertheless, despite the remarkable scientific progress made recently, many long-established processes are still used in industry. Scientific advancements can potentially lead to the improvement of existing industrial processes, but also to the development of completely new routes. This paper aims to review state-of-the-art synthesis and processing technologies, as well as the recent developments in academic research. Flame reactors that produce inorganic nanoparticles on industrial- and lab-scales are described, alongside a detailed overview of the different systems used for the production of carbon nanotubes and graphene. We discuss the problems of agglomeration and mixing of nanoparticles, which are strongly related to synthesis and processing. Finally, we focus on two promising processing techniques, namely nanoparticle fluidization and atomic layer deposition

Learning Navigational Visual Representations with Semantic Map Supervision

Being able to perceive the semantics and the spatial structure of the environment is essential for visual navigation of a household robot. However, most existing works only employ visual backbones pre-trained either with independent images for classification or with self-supervised learning methods to adapt to the indoor navigation domain, neglecting the spatial relationships that are essential to the learning of navigation. Inspired by the behavior that humans naturally build semantically and spatially meaningful cognitive maps in their brains during navigation, in this paper, we propose a novel navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps (Ego$^2$-Map). We apply the visual transformer as the backbone encoder and train the model with data collected from the large-scale Habitat-Matterport3D environments. Ego$^2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation. Experiments show that agents using our learned representations on object-goal navigation outperform recent visual pre-training methods. Moreover, our representations significantly improve vision-and-language navigation in continuous environments for both high-level and low-level action spaces, achieving new state-of-the-art results of 47% SR and 41% SPL on the test server

Central Neurocytoma: A Review of Clinical Management and Histopathologic Features.

Central neurocytoma (CN) is a rare, benign brain tumor often located in the lateral ventricles. CN may cause obstructive hydrocephalus and manifest as signs of increased intracranial pressure. The goal of treatment for CN is a gross total resection (GTR), which often yields excellent prognosis with a very high rate of tumor control and survival. Adjuvant radiosurgery and radiotherapy may be considered to improve tumor control when GTR cannot be achieved. Chemotherapy is also not considered a primary treatment, but has been used as a salvage therapy. The radiological features of CN are indistinguishable from those of other brain tumors; therefore, many histological markers, such as synaptophysin, can be very useful for diagnosing CNs. Furthermore, the MIB-1 Labeling Index seems to be correlated with the prognosis of CN. We also discuss oncogenes associated with these elusive tumors. Further studies may improve our ability to accurately diagnose CNs and to design the optimal treatment regimens for patients with CNs

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

Multi-Scale Characterisation for Micro-Architectures

Synthetic and natural micro-architectures (e.g. foams, bone, etc.) are becoming increasingly popular for applications requiring tailored material properties. The method developed by the authors enables the bulk response of large inhomogeneous domains with two distinct length-scales to be obtained through characterisation. The characteristics obtained from the process can also be visualised to highlight the variation of properties. By considering independent sub-volumes the characterisation technique provides a high degree of parallelism and considerably reduced memory requirements