Enhancing the Mechanical Properties of Concrete and Self-Healing Phenomena by adding Bacteria, Silica fume and Fibres

Concrete which is the most useable material in the world after the water has flaws, it is susceptible to cracking over time. These cracks occur in the form of shear cracks, flexural cracks, tension cracks, shrinkage cracks etc. With these cracks, some hair-like cracks also occur in concrete which are not visible during the visual inspection. The propagation of these cracks in concrete allows the water and many other chemicals to seep inside the concrete and leads to a decrease in its properties. Such properties include decreasing durability, erosion of rebars, and progressive failure in the concrete strength. Therefore, the repair of hair-like cracks is also essential for the long-term safety of structures. In the present study the Silica fume, and Polypropylene fibres are added to a rich concrete along with the bacteria named Bacillus Subtilis and Calcium Lactate for enhancement of its mechanical properties and self-healing phenomena. The effect of bacteria in the healing phenomenon and other properties is compared to normal concrete by casting the cylinders and beams. The slump, compressive strength, tensile strength, and self-healing phenomena are tested and found the increase in mechanical properties of concrete. The self-healing phenomena of cracks is observed by the Scanning Electron Microscope (SEM)

Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.Comment: 8 pages, 5 figures, 1 table. Accepted to IEEE Internet of Things Magazin

Toward On-Device AI and Blockchain for 6G-Enabled Agricultural Supply Chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements

Time to endoscopy for acute upper gastrointestinal bleeding: results from a prospective multicentre trainee-led audit

Background: Endoscopy within 24 hours of admission (early endoscopy) is a quality standard in acute upper gastrointestinal bleeding (AUGIB). We aimed to audit time to endoscopy outcomes and identify factors affecting delayed endoscopy (>24h of admission).Methods: This prospective multicentre audit enrolled patients admitted with AUGIB who underwent inpatient endoscopy between Nov-Dec 2017. Analyses were performed to identify factorsassociated with delayed endoscopy, and to compare patient outcomes, including length of stay and mortality rates, between early and delayed endoscopy groups.Results: Across 348 patients from 20 centres, the median time to endoscopy was 21.2h (IQR 12.0- 35.7), comprising median admission to referral and referral to endoscopy times of 8.1h (IQR 3.7- 18.1) and 6.7h (IQR 3.0-23.1) respectively. Early endoscopy was achieved in 58.9%, although this varied by centre (range: 31.0% - 87.5%, p=0.002). On multivariable analysis, lower Glasgow-Blatchford score, delayed referral, admissions between 7am-7pm or via the Emergency Department were independent predictors of delayed endoscopy. Early endoscopy was associated with reduced length of stay (median difference 1d; p= 0.004), but not 30-day mortality (p=0.344).Conclusions: The majority of centres did not meet national standards for time to endoscopy. Strategic initiatives involving acute care services may be necessary to improve this outcome

Making tradable white certificates trustworthy, anonymous, and efficient using blockchains

Fossil fuel pollution has contributed to dramatic changes in the Earth’s climate, and this trend will continue as fossil fuels are burned at an ever-increasing rate. Many countries around the world are currently making efforts to reduce greenhouse gas emissions, and one of the methods is the Tradable White Certificate (TWC) mechanism. The mechanism allows organizations to reduce their energy consumption to generate energy savings certificates, and those that achieve greater energy savings can sell their certificates to those that fall short. However, there are some challenges to implementing this mechanism, such as the centralized and costly verification and control of energy savings. Moreover, the verification process is not transparent, which could lead to fraud or manipulation of the system. Therefore, in this paper, we propose a blockchain-based TWC mechanism to automatically create, verify, and audit the TWC certificates. In addition, we propose a smart-contract-based TWC trading mechanism that enables traders to trade their TWCs without exposing their private information in an untrusted environment. Evaluations show that the proposed TWC framework is scalable for 1000 TWC traders simultaneously, and optimization problem can be solved in less than 120ms. Moreover, it has been shown that Polygon Matic incurs least gas cost compared to other blockchain-based solutions

On Minimal Fuzzy Ideals of Semigroups

The present paper contains the sufficient condition of a fuzzy semigroup to be a fuzzy group using fuzzy points. The existence of a fuzzy kernel in semigroup is explored. It has been shown that every fuzzy ideal of a semigroup contains every minimal fuzzy left and every minimal fuzzy right ideal of semigroup. The fuzzy kernel is the class sum of minimal fuzzy left (right) ideals of a semigroup. Every fuzzy left ideal of a fuzzy kernel is also a fuzzy left ideal of a semigroup. It has been shown that the product of minimal fuzzy left ideal and minimal fuzzy right ideal of a semigroup forms a group. The representation of minimal fuzzy left (right) ideals and also the representation of intersection of minimal fuzzy left ideal and minimal fuzzy right ideal are shown. The fuzzy kernel of a semigroup is basically the class sum of all the minimal fuzzy left (right) ideals of a semigroup. Finally the sufficient condition of fuzzy kernel to be completely simple semigroup has been proved

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids

Evaluation of nuclear reaction cross section data of proton and deuteron induced reactions on 75 As, with particular emphasis on the production of 73 Se

75Se (T1/2 = 120 d), 73gSe (T1/2 = 7.1 h) and 72Se (T1/2 = 8.4 d) are important radioisotopes of selenium, being used in tracer studies, PET investigations and as a generator parent, respectively. Cross section data for the formation of those radionuclides in proton and deuteron induced reactions on 75As were critically analyzed up to about 70 MeV. A well-developed evaluation methodology was applied to generate the statistically fitted cross sections, based on the critically analyzed literature experimental data and the theoretical cross section values of three nuclear model codes ALICE-IPPE, TAYLS 1.9, and EMPIRE 3.2. Using the fitted cross sections the integral yield of each radionuclide was calculated. For the estimation of impurities, the integral yield of each radionuclide was compared with the yields of the other two radionuclides over a given energy region, and therefrom the energy range was suggested for the high purity production of each of the radionuclides 75Se, 73Se and 72Se. For production of the very important non-standard positron emitter 73Se via the 75As(p,3n)73Se reaction, the optimum energy range was deduced to be Ep = 4030 MeV, with a thick target yield of 1441 MBq/μAh and the 72, 75Se impurity level of <0.1%