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A Novel Material for the Removal of Zinc from Wastewater Using Sterculia Foetida
No full textIn the present work, optimization using central composite design (CCD) was studied to determine the ideal conditions for the adsorption of zinc using Sterculia foetida. The effect of four process parameters, such as time (30–240 min), initial concentration (10–100 mg/L), adsorbent dosage (0.1–1 g), and pH (3–10), was studied to obtain the best results for zinc adsorption on to Sterculia foetida using CCD. With these ranges, CCD has given 30 possible experimental runs. From the desirability values, the optimum values of initial concentration (20 mg/L), adsorbent dosage (0.1 g), and pH (10) were obtained with % removal value of 89.5. From the results obtained, it was clear that pH and initial concentration play a major role in the adsorption of zinc on to Sterculia foetida with p value equal to 0.0413 and 0.0005, respectively. The model F value obtained was 2.84, which implies that the quadratic model is significant
Effect of Plasma Sericin Glutaraldehyde Treatments on the Low stress Mechanical Properties of Micro denier Polyester/Cotton Blended Fabric
Get PDFThis study aimed to see how different treatments affect the low-stress mechanical properties of micro-denier polyester/cotton (MDP/C—65/35) fabrics. This blend was chosen for the study because it is the most popular blend used in polyester/cotton blended material. The results of fabric properties treated with sericin revealed that fabrics treated with sericin and glutaraldehyde as a cross-linking agent had higher bending rigidity, regardless of how it was tested. Concerning the blend fabrics, it was noticed that there was deterioration in tensile resilience following sericin treatment. Shear rigidity, accompanied by shear hysteresis, showed an increase in sericin-treated fabrics. Compression properties were affected by the treatment, and in general, the fabric suffered deterioration in those the samples were hard. Surface properties such as coefficient of friction, mean deviation of friction and mean deviation of surface contour were found to be higher than those of the control and sericin-treated fabrics in a few cases
Design and Performance Evaluation of Mechanical Weeder
Get PDFSoil tillage, crop production, and animal husbandry are all part of agriculture. Weed management with herbicides and tractors is only possible if the plants are seeded in straight and parallel rows, as weeds grow between them. It is critical to prepare the field before planting in order to achieve optimal outcomes. Manual labour necessitates a large workforce and accounts for roughly 25% of overall labour demand, which is typically 900-1200 M hours per hectare. By breaking the surface crust, aerating the soil, encouraging the microflora of the soil, minimizing soil moisture evaporation, and promoting infiltration of rainwater, mechanical control of the grass successfully prevents weeds and promotes cultivation. To cultivate using a cultivator mechanically. Following these issues, a multi-stage weeder was designed and constructed in Creo 2.0 software for multi-stage weed treatment. The performance of this newly created equipment was tested both in the lab and in the field. The field capacity was 0.30 ha/h, and the field efficiency was 83.06 percent at a speed of 1.36 km/h. At a wheel slippage of 4.62 percent, the multi-stage weeding efficiency was 80.47 percent for single pass and 68.26 percent for double pass
Vitrimers trigger covalent bonded bio-silica fused composite materials for recycling, reshaping and self-healing applications
Get PDFAbstract
In this work, a recycling, reshaping, and self-healing strategy was followed for polybenzoxazine through S–S bond cleavage reformation in vitrimers, and the supramolecular interactions are described. The E-ap benzoxazine monomer was synthesized through the Mannich condensation reaction using a renewable eugenol, 3-amino-1-propanol and paraformaldehyde. Furthermore, the E-3ap monomer was reinforced with various weight percentages (5, 10, and 15 wt%) of the thiol-ene group. Various weight percentages of functionalized bio-silica (BS) were also copolymerized with E-3ap (10%-SH) to increase the thermal stability. The structure of the monomers was confirmed by NMR and FT-IR analysis and the thermal properties of the cured materials were analyzed by DSC and TGA. Tensile test was used to study the mechanical property of the poly(E-3ap-co-SH)/BS material. The film was characterized by SEM and optical microscopy to investigate the self-healing properties of the poly(E-3ap-co-thiol-ene)/BS. Moreover, photos and video clips show the self-healing ability of a test specimen. The vitrimer-based renewable polybenzoxazine material exhibits a good recycling, reshaping, and self-healing abilities, and thus is a prime candidate for several industrial and engineering applications
Modeling of tool vibration and its effect on roundness and surface roughness of hole in helical milling of Inconel 718
No full textRelative vibration between the cutter and workpiece has an influence on the surface generation and dimensional accuracy.
In the present work, prediction models were developed for roundness and surface roughness of hole in terms of amplitude
and frequency of tool vibration. The proposed methodology carried out a theoretical investigation on the effect of tool
vibration components in X- and Y-directions on the mill cutter end point to estimate hole roundness and surface roughness.
Series of helical milling experiments were conducted at different levels of spindle rotational speed, cutter orbital speed, and
axial depth of cuts using 10- mm and 8- mm diameter mill cutters on Inconel 718. Predicted values of the roundness and
surface roughness of hole were compared with predicted values and verified accuracy of the proposed prediction models.
The experimental results indicate a good agreement with the predicted value. At spindle rotational speed of 2000 r/min and
cutter orbital speed of 50 r/min, the measured and predicted values of the roundness were found to be almost same as
the required roundness. The surface roughness was found to be very less at 50 r/min of cutter orbital speed
Dielectric relaxation in layer-structured SrBi2− xGdxNb2O9 (x= 0.0, 0.4, 0.6, and 0.8) lead-free ceramics
No full textThe Gadolinium (Gd 3+ ) doped SrBi 2 Nb 2 O 9 (SBN) ceramics with the chemical formula SrBi 2-x Gd x Nb 2 O 9 (x = 0.0, 0.4, 0.6 and 0.8) have been prepared through traditional solid-state sintering method. X-ray diffraction reveals that single-phase-layered perovskite structure for all compositions with shrinkage of the unit cell of SBN. The plate like morphology revealed from SEM is symbolic of characteristic Aurivillius phase of ceramics. Shifting of Raman phonon modes indicates the reduced
rattling space of NbO 6 octahedral with an increase in Gd concentration. The dielectric properties of all compositions are studied as a function of temperature (RT –500 ˚C) over the frequency range (50 Hz - 1 MHz). Softening lowest frequency mode with increasing x in SBGN shows the transition from ferroelectric to para electric at room temperature. The flattening of dielectric permittivity and low dielectric loss are
observed in SBN and gadolinium modified SBN (SBGN) ceramic samples at room temperature, which are desirable features to suit for Non-volatile Fe RAM applications. The phase transition becomes diffused and transition temperature gets shifted from
430 ˚C – 330 ˚C with an increase in gadolinium concentration at higher frequencies. The increase in broadness with increase in frequency suggests that the present materials are of ferroelectric relaxor type. The degree of relaxor behaviour (γ)
increases from 1.05 for x = 0.0 to 1.57 for x = 0.8. Hence, the studied relaxor ferroelectrics with diffuse phase transitions (1≤γ≤2) find energy storage applications in different devices such as piezoelectric actuators, multilayer capacitors, medical
imaging devices, non-volatile memories, pyroelectric detectors and microwave tunable applications
Privacy preserving framework using Gaussian mutation based firebug optimization in cloud computing
Get PDFIn recent years, the data exchange among the service providers and users has been increased tremendously. Various organizations like banking sectors, health as well as government associations collect and process the data regarding an individual for their benefcial purpose. However, data confdentiality and data privacy are still considered as signifcant challenges while sharing sensitive data. The cloud stor-
age servers based on unencrypted data are susceptible to both external and internal attacks established by strangers or untrustworthy cloud service providers. Since the medical data are sensitive, the risk based on privacy enhances at the moment of subcontracting entity medical records to the cloud. The signifcant intention of the proposed approach involves securing and preserving sensitive healthcare data. Here, data hiding and data restoration operations are considered as two signifcant opera- tions of the proposed framework. Initially, an optimal key is generated in the data hiding operation. This paper proposes a Gaussian mutation-based frebug optimiza-tion (GM-FBO) algorithm for the generation of an optimal key. The experiments are conducted using three diferent healthcare datasets, namely HPD, Medical MIMIC- III, and MHEALTH. The efciency of the proposed model is compared with difer-ent state-of-the-art techniques to determine the efciency of the system
Deep Learning Image Classification for Fashion Design
Get PDFFashion has always been an essential feature in our daily routine. It also plays a significant role in everyone’s lives. In this research, convolutional neural networks (CNN) were used to train images of different fashion styles, which were attempted to be predicted with a high success rate. Deep learning has been widely applied in a variety of fields recently. A CNN is a deep neural network that delivers the most accurate answers when tackling real-world situations. Apparel manufacturers have employed CNN to tackle various difficulties on their e-commerce sites, including clothing recognition, search, and suggestion. A set of photos from the Fashion-MNIST dataset is used to train a series of CNN-based deep learning architectures to distinguish between photographs. CNN design, batch normalization, and residual skip connections reduce the time it takes to learn. The CNN model’s findings are evaluated using the Fashion-MNIST datasets. In this paper, classification is done with a convolutional layer, filter size, and ultimately connected layers. Experiments are run with different activation functions, optimizers, learning rates, dropout rates, and batch sizes. The results showed that the choice of activation function, optimizer, and dropout rate impacts the correctness of the results
Twitter based sentimental analysis of Covid-19 observations
Get PDFThe emergence of social media has provided people with the opportunity to express their feelings and thoughts about everything and everything in their lives. There is a massive amount of textual stuff avail-able, and approaches are required to make meaningful use of the information provided by isolating and evaluating the different types of text. Sentimental Analysis is a method of obtaining a human being’s point of view through mining his or her emotions. The entire world is sharing their thoughts on social media on the Corona Pandemic that is now underway. This research presents an analysis of attitudes
in order to determine whether or not people are optimistic in the face of a difficult circumstance. The technique of polarity is employed by the paper in order to determine if an opinion is positive, negative, or nonpartisan [1]. In order to determine the polarity, the following three major keywords are used:
‘‘COVID”, ‘‘Corona virus,” and ‘‘COVID-19.
Methodological Analysis of Blood Pressure Learning using Boro Receptors Model
Get PDFThis paper addresses blood pressure learning with blood
transfer and determination of high or low blood pressure (BP) as
per the boro receptors model. Norepinephrine (NE) is commonly
practiced for septic shock because it raises blood pressure. Blood pressure is critical for ensuring that BP ratio is fluctuating for septic patients which are appropriately controlled. Thus, this
paper analyzes an arterial blood pressure framework for patients based on the receiving NE infusion in real-time physiology. We considered data learning methodologies frequently to treat the physiological consequences of septic shock patients. We considered different physiological parameters to predict the NE infusion rate. Our experiments got the root mean square error for mean arterial blood pressure prediction