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The present research used detailed experimental data to examine the structural behavior of concrete beams incorporating recycled fine aggregates, both treated and untreated.This paper also reveals that microstructural analysis of both treated and untreated recycled fine aggregates. The hydrochloric acid was used for treatment of recycled fine aggregates. Two groups of beam specimens prepared were controlling specimnes and testing specimens. Totally five beam specimens were prepared out of which one control beam with natural fine aggregates, four testing beams with untreated recycled fine aggregates and three beams with treated recycled fine aggregates. The varying molarity of HCL acted as the parameter for examining the structural behavior of the RCFA beams under flexure. The flexural strength of untreated RCFA beams was marginally impacted by the RCFA content, whereas the crack pattern of treated RCFA beams was comparable to that of NCFA beams. Untreated RCFA beams had greater fracture width. Nonetheless, the RCFA concentration had no discernible effect on the beam's ductility. The flexural strength of the RCFA concrete beams was conservatively projected by the present provisions, according to a comparison of the testing data and the calculations from the IS codes, ACI 318, and EC2 regulations for the flexural.

The most important challenges in the construction field is to do the experimentation of the designing at real time. It leads to the wastage of the materials and time consuming process. In this paper, an artificial neural network based model for the verification of sigma section characteristics like shear centre and deflection are designed and verified. The physical properties like weight, depth, flange, lip, outer web, thickness, and area to bring shear centre are used in the model. Similarly, weight, purlin centres with allowable loading of different values used in the model for deflection verification. The overall average error rate as 1.278 percent to the shear centre and 2.967 percent to the deflection are achieved by the model successfully. The proposed model will act as supportive tool to the steel roof constructors, engineers, and designers who are involved in construction as well as in the section fabricators industry.

Rubberized concrete is made up of scrap tyre rubbers where the fine aggregate is partially replaced by it, as the waste rubber is being a threat to the environment. It is estimated that only 4% of the waste tyre is used in the application of civil engineering and also there is shortage of fine aggregates. The primary objective of this study is to investigate the preliminary concrete properties of M25 and M30 concretes. The fine aggregate is replaced by pre-treated crumb rubber with 10, 15 and 20 % of total weight. Various tests are conducted on the rubberized concrete specimens such as compressive strength, split tensile strength, flexural strength and slump test. The investigation is carried out to determine the impact load behavior of hybrid rubberized composite slabs. In addition 0%, 1%, 1.5%, and 2% of replacement of rubber fibers for total weight of coarse aggregate is also made. The specimen of size 300 mm x 300 mm x 50 mm thickness is subjected to drop hammer test to find its performance against the impact loads. The number of blows for the first crack and complete failure of slab was found and the characteristics were studied.

Environmental pollution is one of the most pressing challenges in today’s world. The main cause of this pollution is fuel emissions from automobiles and other sources. As industrialization progresses, we will be unable to compromise on the use of energy to power heavy machines and will be forced to seek out the best options. As a consequence, utilizing green fuel, such as biodiesel derived from natural sources, is a realistic option. Jatropha curcas L. (Euphorbiaceae) is recognized as the greatest feedstock for biodiesel production throughout the world, and it has gained a huge market value in the recent years. Conventional cultivation alone will not be sufficient to meet the global need for the plant’s biomass for the production of biodiesel. Adoption of plant tissue culture techniques that improve the biomass availability is an immediate need. The present review provides detailed information regarding in-vitro plant propagation (direct and indirect organogenesis), somatic embryogenesis, and acclimatization protocols of plantlets for stabilized production of biomass. The review also focuses on biotechnological approaches such as gene transformation studies, production of haploids, and double haploids for developing elite germplasm for high biomass and improved traits for the production of biodiesel.

this study investigates the fresh and mechanical performance of concrete incorporating sintered fly ash lightweight aggregates (SFLWA) both with and without steel fibers. Comparative assessments of natural aggregates with sintered fly ash aggregates were evaluated. Mix design was obtained by the IS method for M30 grade concrete, and within the natural aggregates were replaced with 20%, 40%, and 60% amounts of SFLWA. The addition of SFLWA shows an increase in the workability of the concrete. Replacement with SFLWA increases with an increase in slump value, and decreases in strength parameters. Compressive strength of 42.6 MPa was achieved with a 40% replacement of SFLWA with steel fibers. The mechanical properties such as compressive strength, split tensile strength, flexural strength, elastic modulus, and structural efficiency of SFLWAC were examined, both with and without fibers. The incorporation of fibers drastically improved the mechanical properties of the mix.

Thermal resistance of cold-weather clothing is achieved by layering and filling. In this study, kapok/polypropylene blended (70/30, 60/40, and 50/50) needle-punched nonwovens were manufactured with 100 and 200 g/m2 areal density. Chicken feather fibers were used as fillers for developing quilted composite structures (QCS). To produce a QCS, 100 and 200 g/m2 nonwovens were individually used as the top and bottom layers, and the middle layer was filled with chicken feather fibers in 100, 200, 350, 550, 600, and 850 g by weights. The structures were characterized for thermal properties, and it was noted that 70/30 kapok/polypropylene nonwovens in 100 g/m2 set had the highest thermal resistance between 0.5901–0.6461 °C m2/W. Thermal resistance is greatly influenced by kapok, chicken feather fiber quantity in the assembly along with the areal density (1000 g/m2) of the QCS. Results show that the developed QCS has higher thermal insulation and can be used for cold-weather apparel and sleeping bag. [ABSTRACT FROM AUTHOR]

Medical imaging is essential to modern life support. Different approaches and standards exist for detecting life-threatening disorders. However, manually detecting anomalies takes time and is prone to error. Additionally, it demands great expertise and competence. Deep learning in medical imaging improves accuracy and provides several benefits. Privacy concerns limit access to medical data, especially images. This problem can be solved with data augmentation. Image quality determines prediction accuracy. Preprocessing images improves quality. This article addresses data augmentation and image enhancement with a multiscale self-attention generator. Foundational network is Inception. Elastic_net used for selection and regularisation of features. The anisotropic diffusion filter used for reducing speckle noise in the model. The ZONODO foetal planes dataset is used as input. The proposed model produces better results for classification as accuracy is 98.7, precision is 97.5, recall is 97.8 and F1-Score is 97.5 when compared with existing methods. [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc networks (VANETs) are increasingly essential for communication architecture utilized in daily human life. The presence of obstacles in the terrain area, as well as the vehicular nodes present in the network, is considered an essential constraint since it reduces the efficacy of the mobile ad hoc network (MANET). To attain the VANET requirements, a novel routing scheme is designed to identify the obstacle. The main aspect of the proposed work is to detect the obstacle and determine the path availability. Initially, the characteristics of vehicle nodes are gathered, which include the packet loss, received signal strength indicator (RSSI), connectivity, energy level, and so on. In the process of data transmission, the proposed model is intended to detect the obstacles and determine the available path by using an optimized hybrid model (OHL) combining radial basis function (RBF) and deep belief network (DBN). The parameter optimization in the hybrid classifier model is done with the help of the adaptive chimp optimization algorithm (ACOA). Once it checks the path availability, then the path optimization is accomplished by using the same ACOA. Finally, the objective function for the proposed work is constructed by considering various constraints like distance, energy, link availability, life span, link duration, and mobility. The performance of the implemented system is analyzed, and its results are validated through different measures. The outcome of the suggested model demonstrates that the implemented system tends to exploit effective routing with obstacle detection when compared to various other existing approaches. [ABSTRACT FROM AUTHOR]