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721 research outputs found
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Influential study of oxygenated additives in waste cooking biodiesel blends on diesel engine performance
Full text linkThe rapid increase in energy demand brought on by the global population boom has led to a surge in the popularity of recycling waste materials into usable energy sources. That is to say, turning trash into usable energy will soon be a hot topic. More and more people are becoming aware of the benefits of biodiesel as a way to lessen their environmental impact by replacing their regular diesel fuel with a cleaner alternative. Usually, biodiesel is produced by mixing vegetable oils with animal-based oils. Because of its low production cost and low environmental impact, waste cooking oil (WCO) is suitable for biodiesel research. Using oxygenated fuels appears to be a practical strategy for lowering engine pollution. The purpose of this study is to examine the effects of varying amounts of diethyl ether (DEE) additives and waste cooking oil biodiesel on the efficiency and emissions of a diesel engine. Emission parameters were evaluated in comparison to those of pure diesel, and results were optimized for various load conditions and mixtures of oxygenated additives. The engine efficiency of WCO and DEE blends of 20% was found to be around 5.2% greater than that of standard diesel, while fuel consumption was reduced by 15%. Additionally, additives lowered CO emissions by 7-9% and HC emissions by 9%. The data were subsequently evaluated using the design of experiment tool, "Full Factorial Design" to establish the most optimal running condition with fuel consumption of 0.2720kg/kWh, hydrocarbon of 50ppm and carbon monoxide 0.277% at maximum load circumstances by the 20% fuel mixes
Discovery of Potential KRAS-SOS1 Inhibitors from South African Natural Compounds: An In silico Approach
Full text linkFor decades the direct targeting of KRAS as a driver of non small cell lung cancer, colorectal and pancreatic cancers as well as the inhibition of the RAF-MEK-ERK pathway has shown little success due to feedback networks that keep the pathway in control. Inhibiting SOS1, a KRAS activator, has therefore become a promising escape route to treating KRAS-driven cancers. The search for SOS1 inhibitors has since gained momentum although no drug has been approved yet. Owing to the time-consuming and difficult processes that characterize the discovery and approval of drugs, natural products have become useful in addressing the unmet medical needs. In this study we employed computational techniques to screen South African natural products for inhibitors with the potential to inhibit SOS1-KRAS activation. In this study, eight natural compounds, from plants and marine life, possessing antineoplastic activities with good docking and ADMET properties were identified. These compounds, viz., SANCDB00219, SANCDB0290, SANCDB00369, SANCDB00416, SANCDB00421, SANCDB00749, SANCDB00957 and SANCDB001124 exhibited
favorable total free binding energies in complex with SOS1-KRAScharacterized by conventional and carbon hydrogen bonds, van der Waals, pi-alkyl and pi-sigma interactions with the binding site residues. It was further revealed that these compounds induced conformational stability on the structural architecture of SOS1-KRAS, and decreased the structural flexibility of its individual C-α atoms as a mechanism of inhibition. Upon experimental validation, these compounds from a natural origin could serve as lead identification of small molecules to address SOS1-KRAS associated diseases
Green synthesis of naphthyl derivative as an optical sensor for the detection of l-carnitine in food samples
No full textAbstract
An economical and green approach to the synthesis of naphthyl derivative for detec�tion of L-carnitine (3-hydroxy-4-N-trimethyl-aminobutyrate) is practically important.
We developed a naphthyl derivative as a probe showing ‘turn-on’ response towards L-carnitine selectively at pH 7.2 through ICT mechanism with a good limit of detec�tion (LOD) of 0.126 μM. Using Job's plot for determining the binding stoichiometry,
it was found that probe could form a more stable complex (1:1) with carnitine. The binding constant (K) between probe and carnitine was calculated as 8 � 107 M�1 using the Benesi–Hildebrand plot. The binding interaction of the probe with
L-carnitine was confirmed by nuclear magnetic resonance titrations, Fourier�transform infrared spectroscopy, photo physical studies and density functional theory calculations. Meanwhile, the probe can be used to quantitatively detect carnitine in food samples
Inclusion of IoT technology in additive manufacturing: Machine learning-based adaptive bead modeling and path planning for sustainable wire arc additive manufacturing and process optimization
No full textIndustrial civilization transforms current cutting edge technologies and the evolution of Industry 5.0 is more aggressive with the use of IoT-enabled smart machines and robots in the manufacturing sector today. IoT technology deals with digital data as in additive manufacturing (AM). The potential and progressive aspects of AM embarks for functional part development instead of initial prototyping. AM is one of large-scale production with less buy-to-fly (BTF) ratio. In the
present work, a novel framework has been proposed and utilized to attain adaptive bead modeling and an appropriate
path plan for enhanced deposition and surface quality of weld beads. Further, the influence of input process parameters
toward sustainable wire arc additive manufacturing (WAAM) is also focused. Machine learning-based hybrid-TLBO (hTLBO) and support vector machine (SVM) is deployed for the optimization process. With the aid of graph theory, weights are estimated for h-TLBO. The overall process parameters and entire data module is handled with IoT technology and can be accessed for processing. The simulated post-processing results are validated experimental test results and found to be in good concurrenc
Development of thermally reduced corn stover biochar and its satin weaved sisal-reinforced vinyl ester composites
No full textThe aim of this research is to develop thermally reduced corn stover biochar and combined it with the satin weaved sisalreinforced vinyl ester composites. This investigation focus on mechanical, wear, DMA, fatigue, and hydrophobic properties of this composite, characterized according to ASTM standards. Composites are made after silane surface treatment on
reinforcements and fabricated by hand layup process. Results show that the composite with the 5.0 vol.% biochar had the
highest values of mechanical properties, which were 134 MPa, 4372 MPa, and 4.74 J for tensile strength, fexural strength,
and Izod impact, respectively. But further increased in biochar up to 5.0 vol.% shows the reduction in mechanical properties.
On the other hand, the composite designation VS3 was discovered to have the maximum storage modulus and lowest loss factor with inclusion of 5.0 vol.% of biochar particles as well as it shows the better wear characteristics of about 0.28 coeffcient of friction and 0.009mm3 /Nm sp. wear rate. However, maximum fatigue life counts of about 28,813 were observed
by addition of 3.0 vol.% of biochar. The composite material designated VS3 exhibits the highest recorded contact angle,
which is around 71°, indicating that it is hydrophobic in nature. SEM fractography demonstrates better fber-to-matrix adhesion as a result of surface treatment on reinforcing materials. Furthermore, such composites could be used in industrial and
domestic application
Machine learning-based bead modeling of wire arc additive manufacturing (WAAM) using an industrial robot
No full textThe complex and intricate metallic parts can be made by wire arc additive manufacturing (WAAM) with advantages such as minimum time, and more accuracy than other deposition techniques. The influence of process parameters on the built wall geometry of multi-bead overlapped wire arc additive manufacturing with the assistance of the welding robot was focused on and studied in this work. The process parameters
namely weld travel speed (WTS), wire feed speed (WFS), tool waiting time (TW), input voltage (V), and shielding gas mixture (SGM) exposure were considered to be more predominant parameters in building the wall geometry. Hence, these parameters were considered to be optimized in the current paper and also discussed their effect on the mechanical properties, dimensional accuracy, and surface quality.
Further, with the optimized parameters, an ANN model was constructed and simulated with the specified boundary conditions. The simulation results were found to be a good agreement with the experimental inferences
Sign language comprehension and vocalization: A Game-Changer for Auditory-challenged and Mute Communication
Full text linkSign language plays a crucial role in facilitating communication between individuals who are unable to speak and those who can. Mute individuals face significant challenges in conveying
their message to the hearing population, particularly in times of emergency when swift communication is essential. Since most individuals are not trained in sign language, it becomes particularly difficult to comprehend their messages. To overcome this problem, sign language can be converted into spoken language to enable effective communication. There
are two primary methods for detecting hand gestures, namely vision-based and non-visionbased techniques, which are used to convert the detected information into voice. While the
former employs a camera to detect gestures, the latter is used in this project. It is worth noting that many mute individuals are also deaf, making it necessary to convert spoken language
into sign language for effective communicatio
Computer Tomography Image Based Interconnected Antecedence Clustering Model Using Deep Convolution Neural Network for Prediction of COVID-19
Full text linkThe sudden appearance of the COVID-19 pandemic as a major health threat is a serious concern for global health professionals. The world's most pressing problem has now been revealed to be a deadly virus. Because of the limited supply of test kits and the need to screen and diagnose patients quickly, a self-operating detection strategy is required for the detection of COVID-19 infections and disorders. SARS-CoV-2 can be adequately screened to lessen the impact on healthcare systems. Models that incorporate a multitude of factors can predict the likelihood of infection. Deep convolutional neural networks (DCNN) use a fullresolution Convolutional network to partition the effected region for easier identification and classification. Use of an existing patient dataset with trained and tested samples for
recognition, segmentation and classification is used to evaluate the proposed model. For clinicians worldwide, especially those in countries with little resources in the healthcare sector, new technologies are being developed. Computer Tomography (CT) testing results can be improved by using larger datasets from outside the field. There is a considerable possibility that CT scan interpretation could benefit from knowledge gained from out-ofthe-field training. In order to accurately classify and predict COVID-19 from CT scans, an effective Interconnected Antecedence Clustering Model employing DCNN (IACM-DCNN)
is proposed in this research. There are a number of datasets taken into account by the proposed model, including https://andrewmvd.kaggle.com/datasets and https://mosmed.ai/datasets and https://github.com/UCSDAI4H/COVID-CT/tree/master.
When compared to current models, the proposed model's detection accuracy is better
Empirical Wavelet Transform Method for Enhancement of Medical Image Fusion
Full text linkThe process of creating an image's emulsion is selecting the crucial details from numerous images and combining them into smaller images, often one bone. In the areas of satellite imaging, remote seeing, target shadowing, medical imaging, and many other areas, image emulsion is quite useful. This design tries to illustrate how Empirical Wavelet transfigures work when used with the Simple Average Emulsion Rule to emulsify multi-focus images. The suggested approach has been tested using common datasets for merging images with various focal points. Empirical Wavelet Transform is primarily a method that uses an adaptive approach to produce a Multi-Resolution Analysis of the signal. The effectiveness of the suggested approach is calculated in a variety of ways. Visual perception and the evaluation of common quality metrics, such as Root Mean Squared Error, Entropy, and Peak Signal to Noise ratio, are used to compare the performance of the proposed system. The proposed fashion based on the Empirical Wavelet Transform (EWT) outperforms the existing methods, according to the study of the experimental results. According to the suggested criteria, the fused image's entropy should be higher than the component images' because the emulsion's efficiency decreases as entropy increases. This technique takes MRI and CT scans into account
A New Approach for Detecting Network Intrusion Based on Anomalies Using a Deep Clustering Variational Auto-Encoder
Full text linkSemi-supervised network intrusion detection systems are becoming more vital in today's fast-evolving digital ecosystem. While increasing interest in commercial and academic contexts is rising, specific accuracy difficulties still need to be resolved. Two significant challenges contributing to this fear are accurately learning the probability distribution of standard network data and identifying the boundary between normal and abnormal data locations in the latent space. Several methods have been proposed for semisupervised learning of the latent representation of standard data, including clustering-based Autoencoders (CAEs) and hybridized approaches combining Principal Component Analysis (PCA) and CAEs. Inadequate handling of highdimensional data and excessive dependence on feature engineering remain limitations of current methods. To combat these problems and boost the efficiency of network intrusion detection, we introduce a novel deep learning model called Cluster Variational Autoencoder (CVAE). This approach
allows for a more condensed and dominant representation of
the latent space. Thanks mainly to the VAE's ability to
comprehend the fundamental probability distribution of
specific network data, we have broken through these barriers.
The proposed model is tested on eight different network
intrusion benchmark datasets. These datasets include NSLKDD, UNSW-NB15, and CICIDS2017. Experimental findings demonstrate that our method outperforms state-ofthe-art semi-supervised methods