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Nano-silica from rice husk for environmental remediation: Synthesis, characterization, and application in heavy metal removal
No full textRice husk is a byproduct that will tend to amass following harvesting, yet the agricultural waste possesses viable prospects as a nano- silica sustainability source. In this experiment we treated rice husk with acid treatment and alkaline extraction and then treated it with microwave-assisted sol-gel to extract amorphous nano-silica. We analyzed the resulting material using FTIR, XRD, BET, and SEM, and found that it consisted of non- crystalline silica nanoparticles, 0.5 0.5 0.5 m in size with a surface area of 185 m 2/g. We considered its adsorption capacity towards Pb(II) and Cd(II) ions to determine its performance. We changed pH (3 to 7), contact time (until three hours), and metal ion concentrations (10- 100mg/L). The Langmuir isotherm model was best fitting the data. Our silica impregnated 102.4 mg/kg of lead and 87.5 mg/kg of cadmium. It retained more than 85 percent of the original efficiency even after four adsorptiondesorption cycles. The overall yield was about 70%. Traditional methods presuppose more energy and chemicals than the process itself. In total, rice husk silica is a cheap and friendly to the environment adsorbent that is suitable in the removal of heavy metals in water
Insight into the Anti-Corrosion Performance of
No full textGreen corrosion inhibitors play a significant role in lowering the rate at which metal corrodes as well as the environmental effect of hazardous substances. Using electrochemical and gravimetric methods, Spinacia Oleracea extract was employed in this work as an environmentally friendly green inhibitor to minimize Cu dissolution in 1 M HNO3. Activated parameters were identified and addressed, with varying inhibitor doses. The extract exhibits a corrosion inhibition efficacy of 98.9% at 500 ppm. Furthermore, its corrosion inhibition performance continuously stays above 90% even at higher temperatures. According to the Langmuir adsorption isotherms, the experimental results show that the adsorption process protects Cu from corrosion. Scanning electron microscopy was used to analyze the surface and show that a protective extract coating had precipitated on the Cu surface. Fourier transform infrared spectroscopy was used to examine the extract's interaction with copper. The application not only broadens the resource utilization of plant extracts but also brings an effective and innovative corrosion inhibitor to the field of environmentally friendly chemistry
Impact of machining control variables on the surface roughness and kerf taper angle in the AWJC of A356 - 10 wt.% Al₂O₃ functionally graded composite
No full textThis study delves into the topic of Abrasive Water Jet Cutting (AWJC) of Functionally Graded Composites (FGCs) made of Al6061-10 wt.% Alumina (Al₂O₃) through stir casting and horizontal centrifugal casting. Specifically, it examines how various cutting parameters affect surface roughness and kerf taper angle. Three separate zones were identified by microstructural analysis and Vickers microhardness testing in the manufactured FGC: an intermediate zone, a zone lacking in Al₂O₃, and an enriched zone. It is possible for the particle-dense area to reach a maximum hardness of 108 HV. Using a Taguchi L9 orthogonal array, machining trials were carried out on the Al₂Oₜ-rich zone, with feed, stand-off length, and abrasive supply rate being adjusted. By analyzing the Signal-to-Noise Ratio (SNR) and Analysis of Variance (ANOVA), it was shown that surface roughness was mainly affected by feed (47.11%), whereas kerf taper angle was mainly dictated by abrasive supply rate (78.12%). In order to minimize both responses, the optimal feed rate, stand-off length, and abrasive supply rates were found to be 10 mm/min, 1 mm, and 500 g/min, respectively. The creation of thicker chips, intensified particle collisions, and wider top kerfs was caused by increasing the feed and abrasive supply rates. As a result, surface roughness and kerf taper were enhanced. Machined surfaces analyzed with High-Resolution Scanning Electron Microscopy (HRSEM) showed plough marks, pits, ridges, and imbedded abrasives. These features became more pronounced as the abrasive supply rates were increased
Development of Malware Detection Web Plugin Using One-Dimensional Convolutional Neural Networks Model
No full textThe internet has become increasingly essential in daily life, yet threats to computer systems, networks, and personal privacy continue to rise. Many users turn to unverified or illegal websites to bypass access restrictions, exposing themselves to embedded malware or malicious downloadable files. This study addresses this challenge by developing a web plugin capable of detecting malware on websites and files in real time. The plugin integrates a One-Dimensional Convolutional Neural Network (1D-CNN) model to analyze sequential patterns extracted from webpage structures and file headers. The 1D-CNN model demonstrated strong performance across both datasets, achieving 0.88 accuracy, 0.88 precision, 0.89 recall, and 0.88 F1-score for web-based threat detection, and 0.98 accuracy, 0.99 precision, 0.98 recall, and 0.98 F1-score for file-based malware detection. These results highlight the model's reliability for realtime website and file scanning, empowering users to make safer and more informed browsing decisions
Impact of structural modifications on progressive collapse vulnerability of seismically designed buildings
No full textThis study investigates the progressive collapse resistance of a mid-rise reinforced concrete structure imperilled to critical column loss scenarios. A seven-story RC frame structure, with plan dimensions of 36 meters by 36 meters and uniform 6-meter bays in both orthogonal directions, was modelled using ETABS. The structural model incorporated both material nonlinearity and geometric nonlinearity to capture realistic behaviour under sudden load redistribution accurately. A total of six simulation cases were analysed. The initial three examples consisted of the immediate elimination of a vital column, to be precise, an interior column, a corner column and an edge column that did not undergo any additional structural support. The other three cases were the same column removal situations but with the introduction of X-type steel bracing systems at the outer rim of the first story. These bracing systems were modelled with Fe250 grade I-section steel members, intended to enhance the frame’s lateral stiffness and provide alternative load transfer paths in case the core structural element is lost. A nonlinear time-history analysis was used to model the triggering events of a progressive collapse in the real world. Axial stiffness of the target column was rapidly reduced to near zero at 0.1 seconds, thus commencing the collapse mechanism. Parameters of structural response, including joint displacements, rotational deformations, the development of plastic hinges, and Demand-Capacity Ratios (DCRs), were seriously considered to determine collapse behaviour. The analysis results indicate that the provision of steel bracing will considerably enhance the resistance of the structure against progressive collapse. Bracing presence significantly minimised the lateral displacements and minimised the spread of plastic hinges throughout the frame. It was also observed that the DCR values were significantly smaller in braced models, which implies that it have a better stress distribution and increased load carrying capacity
A Preliminary Simulation of RF Power Evolution with Parametric Decay Processes during Lower Hybrid Wave Injection into Plasmas
No full textNonlinear interactions between radio frequency (RF) waves and plasmas have been researched for ages. We have developed a ray tracing program coupled with the nonlinear mechanism parametric decay instabilities (PDI) called Parametric Instabilities embedded Propagation and Evolution of RF Spectrum (PIPERS). The program can calculate the propagation and absorption in the scrape-off layer (SOL) plasma of lower hybrid waves (LHW) on the basis of the nonlocal convective amplification model of a quasi-mode PDI. To analyse the energy transfer and spectral evolution of LHW self-consistently and quantitatively, the characteristics of the numerical solution of the energy conservation equations need to be studied in terms of the simulation model
Fokker-Planck simulations of fast ion ICRF and electron EC heating in a mirror plasma using CQL3D-m
No full textThe CQL3D-m continuum bounce-average Fokker-Planck code is adapted for magnetic mirror plasmas [1] and is now routinely used in no-free-parameter classical integrated modeling of mirror devices [2, 3]. In the present effort, we report on two RF methods of plasma heating in mirror machine. The fast ions (FI) are heated by Fast waves at 2nd-4th harmonic, where FIs originate from neutral beam injection at 45 degrees to the magnetic field. The scenario shows an efficient ion heating near the FI bouncing point. The electrons are heated by X-mode launched from the high magnetic field side towards the resonance. Different from the tokamak applications, CQL3D-m provides an evolving self-consistent ambipolar parallel electric field, which determines the shape of the loss cone and hence an accurate confinement time of both ions and electrons. Also, it includes a description of ion and electron sources and sinks (related to charge exchange and impact ionization) which are updated at every time step. CQL3D-m utilizes a fully nonlinear Coulomb collision operator that is important for the significantly non-Maxwellian ion distributions typically established in mirror plasmas
Retraction Notice: Enhanced machine learning models for predicting breast cancer: healthcare system
No full textWe take a zero tolerance to any situation where fraudulent research is published in our journals. As a result, this article has been retracted by the Publisher because it is suspected to be a nonsensical computer-generated publication with a number of tortured phrases and irrelevant references.
Additional measures have been implemented to prevent these issues from reoccurring.
EDP Sciences is extremely grateful to anonymous whistleblowers and the Problematic Paper Screene
Nanobubbles of oxygen enhance water quality and increase the survival rate of
No full textNanobubbles (NBs) are gas bubbles that range in size from 1 to 100 nanometers. NBs can induce gases, such as oxygen, specifically oxygen Nanobubbles (NBsC2). NBs have been applied in various contexts involving both plants and animals, including their use in fisheries. Dissolved oxygen (DO) is crucial for the survival of aquatic life, and oxygen is also essential for all living organisms, whether on land or in water. One fish species that is commonly cultivated is the Nile Tilapia (Oreochromis niloticus) due to its high economic value, ease of breeding, high survival rate, and rich nutritional content, including protein and omega-3 fatty acids. The objective of this study was to observe the water quality and survival rates of Tilapia fish subjected to NBsO2 treatment. The method was to compare NBsO2 and without NBsO2 in Tilapia biofloc for three weeks. The results of the study showed that biofloc water treated with NBsO2 was clearer, had a higher DO, and showed a higher survival rate compared to water not treated with NBsO2. NBsO2 can improve water quality and fish survival
Feature Extraction Facial Expression Recognition using Convolution Neural Network
No full textEmotion classification from facial expressions is a significant research area in pattern recognition and artificial intelligence, with wide-ranging applications such as human-computer interaction and behavioral analysis. This study aims to develop a reliable emotion classification system using static images from the FER2013 dataset. A Convolutional Neural Network (CNN) model is implemented as the primary method, initially without preprocessing, followed by the integration of preprocessing techniques to enhance model performance. These techniques include face detection and illumination adjustment, which contribute to generating more representative feature inputs. Feature extraction is performed to optimally identify prominent facial regions such as the jaw, mouth, eyes, nose, and eyebrows. The experimental procedure involves training the CNN model for 33 epochs and evaluating its performance using standard metrics such as accuracy, precision, recall, and F1-score. The results show that the proposed method achieves an average accuracy of 0.9688, a precision of 0.9687, a recall of 0.9688, and an overall Fl-score of 0.9687. Based on these findings, this study recommends the incorporation of preprocessing steps to improve system robustness, particularly for real-time or unconstrained environment applications