Novel nanocomposites of reduced graphene oxide (rGO)–Fe3O4, denoted as ‘rGO:IO, and nitrogen doped rGO–ϵ-Fe3N, denoted as ‘NrGO:IN’, were prepared by a modified polyol method, wherein both the ...reduction of graphene oxide and oxidation of Fe2+/Fe3+ ions occurred simultaneously, followed by ammonia nitridation. The electron microscopy analysis of the rGO:IO and NrGO:IN nanocomposites revealed unique morphologies. In rGO:IO, the Fe3O4 nanoparticles having a mean diameter of 38 nm were found to be uniformly anchored to the rGO sheet surface, whereas in NrGO:IN, the ϵ-Fe3N nanoparticles (∼150 nm) were shielded by the NrGO sheets. Superparamagnetic and weak ferromagnetic characteristics with saturation magnetization values of 39.5 and 46 emu g−1 were observed in the rGO:IO and NrGO:IN nanocomposites respectively, which can be attributed to the nature of the constituent magnetic nanoparticles, Fe3O4 and ϵ-Fe3N. In addition, the graphene derivatives such as rGO and NrGO contributed to the enhanced electrical properties of the nanocomposite. The electrochemical impedance spectroscopy analysis showed that, compared to pure Fe3O4 and ϵ-Fe3N nanoparticles, the total electrical resistance of rGO:IO and NrGO:IN was reduced by 33 344.8 and 1569.87 Ω cm−2, respectively, when combined with the rGO and NrGO sheets. Further, the electromagnetic shielding performance of the NrGO:IN nanocomposite was investigated for the first time and was compared with the other samples. Of the two prepared nanocomposites, NrGO:IN exhibited electromagnetic shielding effectiveness of 35.33 dB at 11.4 GHz, which is considerably larger than that of rGO:IO (14.4 dB at 8 GHz). This enhanced shielding effectiveness is not only due to the high inherent magnetic and electrical properties of ϵ-Fe3N nanoparticles, but also due to the ‘particle shielded by sheet’ morphology of the NrGO:IN, which enhances the charge accumulation at the heterogeneous interfaces of NrGO sheets/ϵ-Fe3N nanoparticles.
Summary
The sensor nodes localization is very advantageous in wireless sensor network (WSN). This allows effective data transfer between the sensor node networks. Therefore, it saves energy and ...prolongs network life. Here, a hybrid FWCELM‐CPO method is proposed for solving the sensor node localization problem in WSN. The proposed hybrid method is executed in MATLAB, and the performance is analyzed with different existing algorithms like centroid, fuzzy centroid, and ELM. The simulation results show that the proposed FWCELM‐CPO method attains the higher detection rate of 14.117%, 5.435%, and 11.494%, higher segmentation accuracy of 9.556%, 26.41%, and 16%, lower execution time of 66.667%, 75%, and 70.37%, higher segmented region of 65.957%, 20%, and 44.444%, and higher precision of 34.72%, 18.29%, and 8.78% compared to the existing algorithms. The simulation results demonstrate that the proposed method can be able to find the optimal global solutions efficiently with accurately.
The ANFIS establishes an optimal value by implementing a learning algorithm, and the sensor node (SN) is measured using centroid localization (CL) technique. Hybrid fuzzy weighted centroid‐exact localization estimation (FWC‐ELM) promises incredibly higher learning rates with precision adjustment capability. The force vector is used to track the movement of the estimate of the switching point and the efficiency of the solution is improved by a local search operator obtained by combining the two optimization algorithms, namely, PSO and CSA.
Optimization algorithms have come a long way in the last several decades, with the goal of reducing energy consumption and minimizing interference with primary users during data transmission over ...shorter distances. The adaptive ant colony distributed intelligent based clustering algorithm (AACDIC) is a key component of the cognitive radio (CR) system because of its superior performance in spectrum sensing among a group of multi-users in terms of reduced sensing errors, power conservation, and faster convergence times. This study presents the AACDIC method, which improves energy efficiency by determining the ideal cluster count using connectedness and distributed cluster-based sensing. In this study, we take into account the reality of a system with an unpredictable number of both primary users and secondary users. As a result, the proposed AACDIC method outperforms pre-existing optimization algorithms by increasing the rate at which solutions converge via the utilisation of multi-user clustered communication. Experiments show that compared to other algorithms, the AACDIC method significantly reduces node power usage by 9.646 percent. The average power of Secondary Users nodes is reduced by 24.23 percent compared to earlier versions. The AACDIC algorithm is particularly strong at reducing the Signal-to-Noise Ratio to levels as low as 2 dB, which significantly increases the likelihood of detection. When comparing AACDIC to other primary detection optimization strategies, it is clear that it has the lowest false positive rate. The proposed AACDIC algorithm optimizes network capacity performance, as shown by the results of simulations, due to its ability to solve multimodal optimization challenges. Our analysis reveals that variations in SNR significantly affect the probability of successful detection, shedding light on the intricate interplay between signal strength, noise levels, and the overall reliability of sensor data. This insight contributes to a more comprehensive understanding of the proposed scheme's performance in realistic deployment scenarios, where environmental conditions may vary dynamically. The experimental results demonstrate the effectiveness of the proposed algorithm in mitigating the identified drawback and highlight the importance of SNR considerations in optimizing detection reliability in energy-constrained WSNs.
Over the past decade, automatic speech emotion detection has been a great challenge in the human–computer interaction area. Generally, individuals express their feelings explicitly or implicitly ...through words, facial expressions, gestures, or writing. Different datasets such as speech, text, and visuals are used to explore emotions. Here, seven emotions such as neutrality, happiness, sadness, fear, surprise, disgust, and anger are detected using speech signals. To perform speech emotion recognition, several datasets are available. SAVEE and TESS datasets are used here. In most of the earlier works, separate databases were used to identify emotions. But here, SAVEE and TESS databases are merged to create a new database and identified their emotions. Our main objective is to use this robust dataset to characterize their emotions. For this purpose, we have proposed a new machine learning algorithm. Initially, Mel-frequency cepstral coefficients are utilized to extract the features from the voice signal datasets. Finally, a hybrid of gray wolf optimizer and naïve Bayes machine learning algorithm was proposed for classification. From the results, our proposed classification algorithm provides better performance compared to current machine learning.
Summary
The study aimed to determine the prevalence of occult hepatitis B virus infection among HIV‐infected persons and to evaluate the use of a pooling strategy to detect occult HBV infection in ...the setting of HIV infection. Five hundred and two HIV‐positive individuals were tested for HBV, occult HBV and hepatitis C and D with serologic and nucleic acid testing (NAT). We also evaluated a pooled NAT strategy for screening occult HBV infection among the HIV‐positive individuals. The prevalence of HBV infection among HIV‐positive individuals was 32 (6.4%), and occult HBV prevalence was 10%. The pooling HBV NAT had a sensitivity of 66.7% and specificity of 100%, compared to HBV DNA NAT of individual samples. In conclusion, this study found a high prevalence of occult HBV infection among our HIV‐infected population. We also demonstrated that pooled HBV NAT is highly specific, moderately sensitive and cost‐effective. As conventional HBV viral load assays are expensive in resource‐limited settings such as India, pooled HBV DNA NAT might be a good way for detecting occult HBV infection and will reduce HBV‐associated complications.
The present research was investigated to eliminate the cationic dye (malachite green (MG)) from the water environment using coal-associated soil. The adsorbent material was characterized using ...scanning electron microscopy (SEM) and Fourier Transform Infrared Spectrophotometer (FTIR) analyses. Batch experiments were performed to investigate the different factors which affect the adsorption study. The maximum percentage removal of MG dye was attained as follows: adsorbent dose of 1.0 g/L (0.2 to 1.6 g/L), solution pH of 6.0 (2.0 to 9.0), temperature of 30°C (30 to 60°C), time contact of 60min (10 to 90 min), and dye’s concentration of 25 mg/L (25 to 150 mg/L). The adsorption isotherm was studied with four different isotherm models and results showed that the Freundlich isotherm model gave the best fit than the other nonlinear models to designate the isotherm behaviours with R2 value of 0.9568, and the maximum adsorption capacity of coal-associated soil for MG dye adsorption is 89.97 mg/g. The evaluation of kinetic studies was performed by using three different kinetic models, where it exposed that pseudofirst order providing the best fit with R2 value of 0.96 (25 to 150 mg/L). The thermodynamic parameters Gibbs free energy (ΔG°), entropy (ΔS°), and enthalpy (ΔH°) were endorsing that the present adsorption system was exothermic. Thus, the experimental results state that coal-associated soil could be an alternative material for the exclusion of dyes from water.
The detection of faulty bearings is an essential step in guaranteeing the safe and efficient operation of rotating machinery. Bearings, which also transmit the loads and pressures generated by the ...machinery, support the rotating shafts. A common method for bearing fault diagnostics is using signal processing techniques. In terms of accuracy, dependability, and sensitivity to various fault types and severity levels, these techniques do, however, have significant limits. To address these limitations, practitioners often integrate signal processing with advanced techniques like machine learning and data analytics to enhance diagnostic accuracy, reliability, and overall effectiveness in bearing fault detection and predictive maintenance. Exploration of various models has demonstrated enhanced results in managing nonlinear data to a certain degree; however, these models face challenges when dealing with intricately complex patterns. Moreover, CNNs can automatically learn relevant features from raw sensor data, capturing intricate patterns and relationships in the data without the need for manual feature engineering. CNNs can be optimized for scalability and real-time processing, essential for applications requiring quick decisions and computationally less expensive for large datasets compared to other models. An optimized one-dimensional Convolutional Neural Network (1D CNN) using different kernel sizes is proposed for predicting and finding bearing problems to overcome these constraints. This method creates a feature vector by applying many filters of various sizes to the input signal. Using the created feature vector, the input signal can be divided into many categories, such as healthy or unhealthy. In comparison to other methods, the proposed technology performs better and offers a high accuracy of 99.52% in bearing fault identification. The 1D CNN model with multiple kernel sizes excels in preserving data structure during dimensionality reduction, as confirmed by comparing t-Distributed Stochastic Neighbor Embedding plots. Particularly, the optimized 1D CNN with multiple kernel sizes accurately classifies faults with minimal errors, showcasing its fault classification proficiency compared with the other state-of-the-art methods. The visualization underscores the methodology's efficacy in discerning intricate fault patterns within the data.
The improvement of electric vehicles and their parts is currently the subject of several recent innovations, with an emphasis on advancements in batteries, energy management systems, autonomous ...features, and charging infrastructure. The current logistics and transportation (L&T) systems comprise heterogeneous fleets made up of both conventional internal combustion engine cars and other vehicle types that use environmentally friendly technology, such as electric and plug-in hybrid vehicles. This helps significantly to the development of the upcoming generations of electric vehicles and promotes a more sustainable and effective ecosystem. This article offers insights into the most recent advancements in the field of electric cars (EVs) as new and innovative EV technologies based on data and statistics from science that may prove to be technically possible by 2030. Potential design and modeling techniques, including digital twins with linked IoT, are discussed in this paper. Additionally, all EV-related topics are covered, including hard-core battery material sciences, power electronics, and powertrain engineering, as well as market and environmental considerations and prospective technological obstacles and research gaps. This investigation is interesting since it offers thorough information on every facet of EVs. In conclusion, we offer to the readers several open issues in the field of EV as well as specific research directions to wrap up our work.
Oxytetracycline (OTC), an antibacterial agent, is extensively used in aquaculture practices all over the world. Despite its use, the toxicity of OTC to freshwater fish has been scarcely investigated. ...In this study,
Labeo rohita
were exposed to different concentrations (20, 40, 60, 80, 100, and 120 mg L
−1
) of OTC. Based on the survival-to-mortality ratio, an 80 mg L
−1
concentration was selected for sublethal toxicity analysis. Fish were exposed to the above-mentioned concentration for a period of 25 days, during which fish were killed at the end of every 5 days to analyse certain hematological and enzymological parameters. During the exposure period, a mixed trend was observed in hemoglobin (Hb), hematocrit, mean cell volume, mean cellular Hb, and mean cellular Hb concentration, whereas decreased red blood cell count and increased white blood cell was noted. A biphasic trend was observed in the enzymatic levels of aspartate aminotransferase, alanine aminotransferase, and lactate dehydrogenase in the vital organs (gill, liver and muscle) of fish. The alterations of these parameters lead to the conclusion that these parameters may be used as biomarkers in monitoring OTC toxicity in aquaculture and fisheries farms.
The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the ...infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising
-squared and
value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper.
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