Adoption of Virtual Reality in Construction Projects

In recent years, virtual reality has emerged in various fields such as manufacturing sectors, construction sectors etc. Virtual reality technology has a wide variety of applications in various fields. Several applications of VR in construction sector are design review and support, construction support, operations and management, safety training, stakeholder management. Recent researches had shown that VR can also be used widely in decision making process. Despite the various applications of VR in construction sectors, the adoption level of the VR technology is comparatively very low. This study aims to identify the factors that are preventing the adoption of virtual reality in construction projects. The factors are identified through a wide review of literature survey and analysed by means of questionnaire survey with statistical methods. The factors are ranked through the analysis and the most affecting factors are found out. The findings of this study show that requirements of specialized high processing equipment, high investment in VR, lack of awareness about the technology, lack of client’s interest, limited finance of the organizations, existence of champions using VR are the most preventing factors for the adoption of VR in construction projects. To mitigate these issues, several possible mitigation measures has been suggested in this study.

Camera-based OCR scene text detection issues: A review

Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images (signboards), documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the traditional scanner whose usability is limited as they are not portable in size and cannot be used on images captured by cameras. However, characters captured by traditional scanners pose fewer computational difficulties as compared to camera captured images that are associated with divers’ challenges with consequences of high computational complexity and recognition difficulties. This paper, therefore, reviews the various factors that increase the computational difficulties of Camera-Based OCR, and made some recommendations as per the best practices for Camera-Based OCR systems.

Searching for memory-lighter architectures for OCR-augmented image captioning

Current State-of-the-Art image captioning systems that can read and integrate read text into the generated descriptions need high processing power and memory usage, which limits the sustainability and usability of the models (as they require expensive and very specialized hardware). The present work introduces two alternative versions (L-M4C and L-CNMT) of top architectures (on the TextCaps challenge), which were mainly adapted to achieve near-State-of-The-Art performance while being memory-lighter when compared to the original architectures, this is mainly achieved by using distilled or smaller pre-trained models on the text-and-OCR embedding modules. On the one hand, a distilled version of BERT was used in order to reduce the size of the text-embedding module (the distilled model has 59% fewer parameters), on the other hand, the OCR context processor on both architectures was replaced by Global Vectors (GloVe), instead of using FastText pre-trained vectors, this can reduce the memory used by the OCR-embedding module up to a 94% . Two of the three models presented in this work surpassed the baseline (M4C-Captioner) of the challenge on the evaluation and test sets, also, our best lighter architecture reached a CIDEr score of 88.24 on the test set, which is 7.25 points above the baseline model.

Waste Treatment Innovation For Infusion Bottle Using Soil Solution

Disposal of medical or clinical waste into the environment can cause nosocomial and other environmental pollution. Treatment of medical or clinical waste requires modern technology and very high processing costs. Medical waste treatment requires innovation in processing medical waste to be easy to apply in health care facilities. For this reason, it is necessary to innovate antibacterial methods to clean bacteria. This study used an experimental design using a completely randomized design. The treatment was carried out by rinsing with sterile distilled water seven times, using 45% soil with a contact time of 2 minutes. Experiments in the study were carried out twice. The effect of treatment on decreasing the number of bacteria used a mathematical model of Multiple Linear Regression. The results show that the disinfection of infusion bottles is contaminated with bacteria. When using water only requires rinsing up to 6 times, it is sufficient to rinse only once if using a soil solution. Disinfection of infusion bottles contaminated with bacteria using soil solution was able to reduce the number of bacteria by 98%.

Implementation three-step algorithm based on signed digit number system by using neural network

The need for a simple and effective system that works with high efficiency features such as high processing speed, the ability to solve problems by learning method and accomplish the largest amount of data processing accurately and in little time produces that system, which attracted the efforts of the researcher to employ neural networks in computing away from the complexities that burden traditional computers. We presented a model for the design of the arithmetic circuit for the process of addition the sign digit numbers in a new way to deal with the arithmetic operations, which employment of the use of neural networks, this model includes a theoretical and practical simulation of them. The model relied on the implementation of the addition process based on a three-step algorithm adopted by the signed systems. Which is characterized by the possibility of execution in a parallel way, and therefore it provides the advantage of completion of arithmetic operation regardless of the length of their operands, or in other words, whatever the number of bits in the operands. The simulation of the model is done by entering operands for 6 addition operations (each one has operands are 15-bit length) to be executed simultaneously.

Towards Effective Detection of Recent DDoS Attacks: A Deep Learning Approach

Distributed Denial of Service (DDoS) is a predominant threat to the availability of online services due to their size and frequency. However, developing an effective security mechanism to protect a network from this threat is a big challenge because DDoS uses various attack approaches coupled with several possible combinations. Furthermore, most of the existing deep learning- (DL-) based models pose a high processing overhead or may not perform well to detect the recently reported DDoS attacks as these models use outdated datasets for training and evaluation. To address the issues mentioned earlier, we propose CyDDoS, an integrated intrusion detection system (IDS) framework, which combines an ensemble of feature engineering algorithms with the deep neural network. The ensemble feature selection is based on five machine learning classifiers used to identify and extract the most relevant features used by the predictive model. This approach improves the model performance by processing only a subset of relevant features while reducing the computation requirement. We evaluate the model performance based on CICDDoS2019, a modern and realistic dataset consisting of normal and DDoS attack traffic. The evaluation considers different validation metrics such as accuracy, precision, F1-Score, and recall to argue the effectiveness of the proposed framework against state-of-the-art IDSs.

Preparation of Luminescent Glass Aggregates from Soda-Lime Waste Glass

This research studied the preparation of luminescent glass aggregates prepared from soda-lime waste glass and strontium aluminate-based phosphors. The properties of the samples were determined by means of X-ray diffraction (XRD) technique, scanning electron microscopy (SEM), Archimedes’ method, and photoluminescence (PL) spectroscopy. It was found that the pore characteristics, density, and formation of crystallite phases in the glassy matrix depended on the phosphor content. The addition of fine phosphor powder tended to inhibit the glass crystallization and to reduce the apparent porosity of the aggregates. In general, the disadvantage of phosphors is their luminescent degradation under thermal attacks, which limits their use in applications involving high-temperature annealing. The phosphors, however, still had good luminescent properties and long-term stability with the sintering temperature as high as 750°C. The results indicated that the phosphors could be composited with glasses at high processing temperatures, enabling their widespread application.

Research and Application of Flutter Suppression Technology for Aeroengine Case Machining

The magazine is an important part of the aero-engine, its main role is to bear the load and mass inertia force generated by the engine in the work. The design and manufacturing level of the magazine is of great significance to improve the aero-engine’s quality. Engine magazine belongs to a thin-wall cylinder part with large size, complex shape and high processing precision. Due to its thin wall rigidity difference, the processing quality is easy to reduce caused by the vibration of machine tools and other processing system, and cannot meet the requirements. According to the absorption principle of TMD (Tuning Mass Damper), an auxiliary fixture is designed to transfer the magazine processing vibration energy to the auxiliary fixture to reduce the vibration of the processing system and to improve the processing quality. Through ANSYS analysis, the maximum vibration amplitude of the magazine was reduced by 60% by using this fixture. Therefore, the fixture can significantly reduce the vibration in the processing and have good effect on improving the quality of the processing. The auxiliary fixture has the advantages of simple installation, simple disassembly, and strong scalability.

Thermal Performance Investigation of Slotted Fin Minichannel Heat Sink for Microprocessor Cooling

Due to high heat flux generation inside microprocessors, water-cooled heat sinks have gained special attention. For the durability of the microprocessor, this generated flux should be effectively removed. The effective thermal management of high-processing devices is now becoming popular due to high heat flux generation. Heat removal plays a significant role in the longer operation and better performance of heat sinks. In this work, to tackle the heat generation issues, a slotted fin minichannel heat sink (SFMCHS) was investigated by modifying a conventional straight integral fin minichannel heat sink (SIFMCHS). SFMCHSs with fin spacings of 0.5 mm, 1 mm, and 1.5 mm were numerically studied. The numerical results were then compared with SIFMCHSs present in the literature. The base temperatures recorded for two slots per fin minichannel heat sink (SPFMCHS), with 0.5 mm, 1 mm, and 1.5 mm fin spacings, were 42.81 °C, 46.36 °C, and 48.86 °C, respectively, at 1 LPM. The reductions in base temperature achieved with two SPFMCHSs were 9.20 %, 8.74 %, and 7.39% for 0.5 mm, 1 mm, and 1.5 mm fin spacings, respectively, as compared to SIFMCHSs reported in the literature. The reductions in base temperature noted for three SPFMCHSs were 8.53%, 9.05%, and 5.95% for 0.5 mm, 1 mm, and 1.5 mm fin spacings, respectively, at 1 LPM, as compared to SIFMCHSs reported in the literature. In terms of heat transfer performance, the base temperature and thermal resistance of the 0.5 mm-spaced SPFMCHS is better compared to 1 mm and 1.5 mm fin spacings. The uniform temperature distribution at the base of the heat sink was observed in all cases solved in current work.

ViralFlow: an automated workflow for SARS-CoV-2 genome assembly, lineage assignment, mutations and intrahost variants detection

The COVID-19 pandemic, a disease caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology to monitor and study this emerging virus. The current deluge of SARS-CoV-2 genomic data being generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows for data analysis. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all the steps involved in SARS-CoV-2 genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The workflow presented here (https://github.com/dezordi/ViralFlow) is available through Docker or Singularity images, allowing implementation in laptops for small scale analyses or in high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores makes it a versatile tool for SARS-CoV-2 genomic analysis.