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Antidotes and their Mechanism of Action: A Systematic Review
Get PDFAn antidote is a therapeutic agent that counteracts the hazardous effects of a medicine or toxin, according to the International Programmed of Chemical Safety. Antidotes have been defined as agents that alter the poisonous substance's kinetics or interfere with its impact at receptor sites. This could be due to the poison being prevented from being absorbed, bound, and neutralized immediately, antagonizing its end-organ impact, or inhibiting conversion to more hazardous metabolites. The kind of toxin eaten, the anticipated amount taken by the individual, the route of exposure, clinical toxicity characteristics, half-life, and pharmacokinetics, as well as the risk versus benefit of administering the antidote, all influence the length of antidotal therapy. An infusion may be necessary if the antidote has a short half-life, especially if poisoning symptoms return. To treat the negative effects of toxins, it is required. Occasionally, that intervention is necessary. may entail the use of pharmacological antagonists, also referred to as an antidote The most common poisons, according to the American Association of Poison Control Centers, include Acetylcysteine, naloxone, atropine, and deferoxamine are some of the most widely used antidotes
Adsorption of Safranin O on halloysite nanotubes: a mechanistic case
No full textAbstract
Industrial efuents, laden with various organic dyes, are extremely hazardous to the environment and all living organisms. Among these, Safranin O, a reddish brown, water-soluble, synthetic, azine-based cationic dye is extensively used in various industries and biological laboratories. As a possible dye removal strategy, clay materials have emerged as a potential earth�abundant low-cost adsorbent owing to their conducive surface properties for favorable dye adsorption. However, system�atic mechanistic investigation with relatively unexplored clay materials is required to fnd the “best” adsorbate–adsorbent combination. Herein, halloysite nanotube, a unique nanoaluminosilicate, is thoroughly characterized and evaluated as an adsorbent for removal of Safranin O using batch adsorption studies and thermodynamic and kinetic analyses. Under the optimized condition, 98% removal efciency was achieved within 6 h at room temperature (100 g halloysite in 10 mL of dye solution with conc. of 100 mg mL−1). The corresponding adsorption efciency reaches 37.518 mg g−1, and the process follows Langmuir isotherm and pseudo-second-order kinetics. The experimental results coupled with several advanced characterization techniques indicate that adsorption proceeds via various difusion processes, followed by a combination of coulombic, hydrogen bonding, and hydrophobic interactions between adsorbent and adsorbate, by replacing the surface�bound solvents. Subsequent optimization by response surface methodology validates the feasibility of the process, and the material can be reused for at least three cycles. The present fndings ofer generalized protocols to elucidate the mechanism of adsorption for similar adsorbate–adsorbent combinations and help to develop scalable inexpensive sustainable wastewater remediation technologie
Experimental Investigations on Mechanical Properties of AZ31/Eggshell Particle-Based Magnesium Composites
Get PDFMagnesium (AZ31) is an excellent choice for a bionic implant. To enhance biocompatibility, the hardest graphene nanoparticles
were reinforced with biocompatible materials. In this paper, biocompatibility composite material is produced by stir-casting
nanoshell particles reinforced with various weight percentages (0, 1, 2, 3, and 4 wt. percent) of AZ31 magnesium alloy. To
understand the mechanical properties of the composite material, results of which are compared to the base alloy (AZ31) are used.*e study mentioned how AZ31 magnesium alloy, reinforced with reinforcing particles, may be used to create implant-related human bone materials. Magnesium alloy reinforced with reinforcing particles is described in the stud
A comparative study on cutting forces and power consumption in plain and ultrasonic vibration helical milling of AISI 1020 steel
No full textThe present study aimed to implement a sustainable machining method to improve energy efficiency in helical milling
(HM) of AISI 1020. Therefore, ultrasonic vibration is integrated with conventional helical milling to reduce cutting forces.
A model was developed for power consumption in terms of cutting forces in x, y and z directions, tangential and axial
feed speeds. Series of plain and ultrasonic vibration helical milling (UVHM) experiments are conducted using 10 and
8 mm diameter mill cutters at different working conditions and experimental results for cutting forces are collected.
Formation of chip and its geometry are investigated using the cutting trajectories of the bottom cutting edges of the cutter. Cutting forces and power consumption are estimated related to chip geometry in plain and UVHM processes and compared. In UVHM, the axial force is reduced by around 47% as the ultrasonic vibration is applied in the axial direction and the power consumption is reduced by 34%. The results showed that ultrasonic vibration has a significant effect on chip morphology, cutting force and power consumption, indicating that ultrasonic vibration assisted machining has a wide application in manufacturing. The process parameters are optimised as 2000 rpm of cutter rotational speed, 156 rpm of cutter orbital speed and 0.3 mm of axial depth of cut using 8 mm diameter cutter and the chip thickness, chip depth and power consumption are found to be 0.3969 mm, 0.2665 mm and 835.6W respectively at optimal working condition
Sustainability Improvement of Ethanol Blended Gasoline Fuelled Spark Ignition Engine by Nanoparticles
Get PDFThe sophisticated technology being used in automotive technology, as well as the increased use of vehicles, enables the engine to operate on a variety of alternative fuels. Natural or synthetic carbon-based connections are responsible for the formation of ethanol. They may be produced from a variety of sources, including agricultural feedstock, local crops, and even agricultural trash and waste products. Because they are in the form of a renewable resource, they may be employed in a variety of applications, including IC engines, where they can be used as fuel or as an addition, depending on their composition. It is possible to dramatically improve the performance of gasoline engines using a novel mix of nanoadditives, ethanol, and gasoline while simultaneously reducing the negative environmental impact. An ethanol-gasoline combination was used to power the engine in this work, which examined the effects of the alumina nanoaddition. Results reveal that thermal efficiency can be improved by up to 17% while fuel consumption can be reduced by up to 16% on a volume basis, indicating a considerable improvement over the basic engine. Also validated was a decrease in dangerous carbon monoxide emissions of as much as 14%, a reduction in unburned hydrocarbon emissions of 18.5%, and a significant reduction in oxygen of as much as 18
A Design of Disease Diagnosis based Smart Healthcare Model using Deep Learning Technique
No full textA Smart Healthcare System (SHS) is developed from
traditional healthcare by integrating the Internet of Things (IoT) with
Artificial Intelligence (AI). The data are captured by millions of
devices and sensors, where it is exchanged continuously with medical
staff to monitor the health of patients. An important message can then
be analyzed using various machine learning (ML) / deep learning
(DL) algorithms to predict the severity of diseases and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. The main aim of the research work is to develop a disease detection model based on SHS for diabetic disease using DL classifiers. The method considered both collected and public datasets stored in the cloud for building SHS to allow accurate time monitoring of patient health conditions. IoT devices as sensors enable smooth data gathering, while AI algorithms use the data to diagnose diseases. For disease diagnosis, Restricted Boltzmann Machine based generative adversarial network (RBM -GAN) model has only three stages for the full prediction process. The experiments are carried out in two datasets, where the performance of the proposed RBM-GAN model is compared with existing DL classifiers. The simulation results show that the proposed model increased the accuracy by 5% on both two datasets than current DL classifiers. The proposed RBM -GAN model is used as a suitable illness analysis tool for SHS from these results
Investigation of Machining Characteristics of nickel-based alloy with Copper Coating and Application of Magnet by using DS-EDM Process
No full textNickel based super alloy material are extensively used in various industrial applications such as aerospace, automobile etc., due to their unique mechanical and chemical properties. However, machining of such hard materials is most difficult by using traditional machining process and also increases the manufacturing time and cost as well as to reduce machine tool durability. Nontraditional machining techniques are playing a major role to resolve the issues present in traditional machining. This paper presents study and investigation of machining characteristics of a non-contacting or non-traditional machining such as die-sinking electrical discharge machining (DS-EDM) process on nickel-based alloy Inconel 718 material with copper coating and application of magnet. Further, the investigation is performed based on Taguchi L-16 orthogonal array to analyze the DS-EDM process dependent parameters like material removal rate (MRR) and tool wear rate (TWR) and corresponding independent parameters are current, pulse-ON, duty cycle and voltage. Furthermore, statistical significance analysis tools via. parametric analysis, ANOVA and 3D response is carried out to evaluate the effect of input variables on output variables of DS-EDM. The result shows that the optimal parameter setting is obtained (i.e., current at 4 amps, pulse-on at 40 µs, duty cycle at 10 µs and voltage at 40 v) by using MOORA optimization technique and achieved quality products with higher production throughput
COVID ‐19 diagnosis system by deep learning approaches
Get PDFThe novel coronavirus disease 2019 (COVID‐19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID‐19 patients, which are not effective. The above complex circumstances need to detect suspected COVID‐19 patients based on routine techniques like chest X‐Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID‐19 or Non‐COVID‐19 patients with a full‐resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID‐19 patient dataset. The evaluation results are generated using a fourfold cross‐validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1‐score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset
Unification of Multiple Bank Cards and Smart Card with Formula Based Authentication in Big Data
Get PDFBig data provides a greater space for various organizations to obtain knowledge-oriented decisions. Big data analytics would lead to an increased rate of success. Capturing, storing, searching, sharing, transferring, visualizing, querying, updating are the challenges in big data. In our proposal, the most recent developments in information management include the convergence of big data and RFID technology. We are proposing an advanced Banking, Hospital, and Passport & Ration application for our implementation. For all these four applications, RFID is used as a user identification number. User behavior is monitored through Hidden Markov Model (HMM). Formula-based authentication is used for verifying the withdrawal of money above the limit. This card can also be used as a smart card in Ration shops. Users can use this multicard in hospitals to get their information. Multi-card can also be used in airports to register travel. All the information is kept in several Cloud Servers
Weak nonlinear analysis of nanofluid convection with g-jitter using the Ginzburg--Landau model
Get PDFAbstract
Nanofluid has emerged as a remarkable heat and mass transfer fluid due to its thermal characteristics. Despite this, continuing research is required to address problems in real applications and offer a solution for controlling transfer analysis. Therefore, in this study, the authors intend to model (Ginzburg–Landau equation) and analyze the two-dimensional nanofluid convection with gravity modulation. The perturbed analysis is adapted to convert the leading equations into Ginzburg–Landau equation. Lower amplitude ( δ \delta values from 0 to 0.5) values are taken since they influence transfer analysis. The values of Pr are considered as 0 to 2 to retain the local acceleration term in the system of equations. A lower amount of frequency of modulation ( Ω \Omega values from 0 to 70) is sufficient to enhance the heat and mass transfer rates. It is found that g-jitter and concentration Rayleigh numbers control the stability of the system. The Prandtl number and the amplitude of modulation enhance nano-heat and nano-mass transfer. This shows a destabilizing effect of modulation on nano-convection. Also the nano-Rayleigh number Rn has a dual nature on the kinetic energy transfer for positive and negative signs. A comparison is made between modulated and unmodulated systems, and it is found that the modulated systems influences the stability problem than the unmodulated systems. Finally, it is found that g-jitter influences effectively to regulate the transport process in the layer