An Approach for the optimization of thermal conductivity and viscosity of hybrid (Graphene Nanoplatelets, GNPs : Cellulose Nanocrystal, CNC) nanofluids using Response Surface Methodology (RSM)

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions

SDN-based VANET routing: A comprehensive survey on architectures, protocols, analysis, and future challenges

As the automotive and telecommunication industries advance, more vehicles are becoming connected, leading to the realization of intelligent transportation systems (ITS). Vehicular ad-hoc network (VANET) supports various ITS services, including safety, convenience, and infotainment services for drivers and passengers. Generally, such services are realized through data sharing among vehicles and nearby infrastructures or vehicles over multi-hop data routing mechanisms. Vehicular data routing faces many challenges caused by vehicle dynamicity, intermittent connectivity, and diverse application requirements. Consequently, the software-defined networking (SDN) paradigm offers unique features such as programmability and flexibility to enhance vehicular network performance and management and meet the quality of services (QoS) requirements of various VANET services. Recently, VANET routing protocols have been improved using the multilevel knowledge and an up-to-date global view of traffic conditions offered by SDN technology. The primary objective of this study is to furnish comprehensive information regarding the current SDN-based VANET routing protocols, encompassing intricate details of their underlying mechanisms, forwarding algorithms, and architectural considerations. Each protocol will be thoroughly examined individually, elucidating its strengths, weaknesses, and proposed enhancements. Also, the software-defined vehicular network (SDVN) architectures are presented according to their operation modes and controlling degree. Then, the potential of SDN-based VANET is explored from the aspect of routing and the design requirements of routing protocols in SDVNs. SDVN routing algorithms are uniquely classified according to various criteria. In addition, a complete comparative analysis will be achieved to analyze the protocols regarding performance, optimization, and simulation results. Finally, the challenges and upcoming research directions for developing such protocols are widely stated here. By presenting such insights, this paper provides a comprehensive overview and inspires researchers to enhance existing protocols and explore novel solutions, thereby paving the way for innovation in this field

A survey of federated learning from data perspective in the healthcare domain : Challenges, methods, and future directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and January 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies

Vibration signal for bearing fault detection using random forest

Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame's actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults

Construction novel highly active photocatalytic H2 evolution over noble-metal-free trifunctional Cu3P/CdS nanosphere decorated g-C3N4 nanosheet

Hydrogen energy possesses immense potential in developing a green renewable energy system. However, a significant problem still exists in improving the photocatalytic H2 production activity of metal-free graphitic carbon nitride (g-C3N4) based photocatalysts. Here is a novel Cu3P/CdS/g-C3N4 ternary nanocomposite for increasing photocatalytic H2 evolution activity. In this study, systematic characterizations have been carried out using techniques like X-ray diffraction (XRD), scanning electron microscopy (SEM), high resolution transmission electron microscopy (HR-TEM), Raman spectra, UV–Vis diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), surface area analysis (BET), electrochemical impedance (EIS), and transient photocurrent response measurements. Surprisingly, the improved 3CP/Cd-6.25CN photocatalyst displays a high H2 evolution rate of 125721 μmol h−1 g−1. The value obtained exceeds pristine g-C3N4 and Cu3P/CdS by 339.8 and 7.6 times, respectively. This could be the maximum rate of hydrogen generation for a g–C3N4–based ternary nanocomposite ever seen when exposed to whole solar spectrum and visible light (λ > 420 nm). This research provides fresh perspectives on the rational manufacture of metal-free g-C3N4 based photocatalysts that will increase the conversion of solar energy. By reusing the used 3CP/Cd/g-C3N4 photocatalyst in five consecutive runs, the stability of the catalyst was investigated, and their individual activity in the H2 production activity was assessed. To comprehend the reaction mechanisms and emphasise the value of synergy between the three components, several comparison systems are built

Harnessing nature’s ingenuity: A comprehensive exploration of nanocellulose from production to cutting-edge applications in engineering and sciences

Primary material supply is the heart of engineering and sciences. The depletion of natural resources and an increase in the human population by a billion in 13 to 15 years pose a critical concern regarding the sustainability of these materials; therefore, functionalizing renewable materials, such as nanocellulose, by possibly exploiting their properties for various practical applications, has been undertaken worldwide. Nanocellulose has emerged as a dominant green natural material with attractive and tailorable physicochemical properties, is renewable and sustainable, and shows biocompatibility and tunable surface properties. Nanocellulose is derived from cellulose, the most abundant polymer in nature with the remarkable properties of nanomaterials. This article provides a comprehensive overview of the methods used for nanocellulose preparation, structure–property and structure–property correlations, and the application of nanocellulose and its nanocomposite materials. This article differentiates the classification of nanocellulose, provides a brief account of the production methods that have been developed for isolating nanocellulose, highlights a range of unique properties of nanocellulose that have been extracted from different kinds of experiments and studies, and elaborates on nanocellulose potential applications in various areas. The present review is anticipated to provide the readers with the progress and knowledge related to nanocellulose. Pushing the boundaries of nanocellulose further into cutting-edge applications will be of particular interest in the future, especially as cost-effective commercial sources of nanocellulose continue to emerge

Heat transfer enhancement by hybrid nano additives—Graphene nanoplatelets/cellulose nanocrystal for the automobile cooling system (Radiator)

A radiator is used to remove a portion of the heat generated by a vehicle engine. It is challenging to efficiently maintain the heat transfer in an automotive cooling system even though both internal and external systems need enough time to keep pace with catching up with evolving engine technology advancements. The effectiveness of a unique hybrid’s heat transfer nanofluid was investigated in this study. The hybrid nanofluid was mainly composed of graphene nanoplatelets (GnP), and cellulose nanocrystals (CNC) nanoparticles suspended in a 40:60 ratio of distilled water and ethylene glycol. A counterflow radiator equipped with a test rig setup was used to evaluate the hybrid nano fluid’s thermal performance. According to the findings, the proposed GNP/CNC hybrid nanofluid performs better in relation to improving the efficiency of heat transfer of a vehicle radiator. The suggested hybrid nanofluid enhanced convective heat transfer coefficient by 51.91%, overall heat transfer coefficient by 46.72%, and pressure drop by 34.06% with respect to distilled water base fluid. Additionally, the radiator could reach a better CHTC with 0.01% hybrid nanofluid in the optimized radiator tube by the size reduction assessment using computational fluid analysis. In addition to downsizing the radiator tube and increasing cooling capacity over typical coolants, the radiator takes up less space and helps to lower the weight of a vehicle engine. As a result, the suggested unique hybrid graphene nanoplatelets/cellulose nanocrystal-based nanofluids perform better in heat transfer enhancement in automobiles

Enhancing Heat Transfer in Compact Automotive Engines using Hybrid Nano Coolants

This research aimed to compare the performance of a reduced-scale automotive radiator using single nano coolant (CNC and CuO) and its hybrid nano coolant (CNC and CuO nanoparticles) to enhance heat transmission. Three ratios of 70:30, 80:20, and 90:10 of hybrid nano coolants was tested. UV Vis stability characterization of the nanofluids showed that all samples were highly stable for up to 30 days. A modest concentration (0.01 vol per cent) of the hybrid nano coolant was shown to efficiently increase the heat transfer rate of a reduced-size automobile radiator, demonstrating that the heat transfer behaviour of the nano coolant was reliant on the particle volume percentage. The results show the potential use of hybrid nano coolants in increasing heat transfer efficiency, decreasing cooling system size by up to 71 percent, and thus lowering fuel consumption; these benefits have significant implications for developing more efficient cooling systems in various industrial applications. The experimental findings showed that 80:20 exhibited a significant amount of improvement in thermal properties. The consistency of the low volume concentration of hybrid nano coolants throughout the experiment is further evidence of their promise as a practical substitute for conventional cooling media in the compact size of an automotive engine cooling system

Exploring the Potentials of Copper Oxide and CNC Nanocoolants

The characteristics, stability, kinematic viscosity, viscosity index, thermal conductivity, and specific heat changes of Copper Oxide (CuO) and Cellulose Nanocrystal (CNC) hybrid nanocoolants at low concentrations are investigated in this work. The hybrid nanocoolants were created using different ratios of CNC and CuO nanoparticles and compared to single nanoparticle coolants. The existence of Cu-O and other similar formations was verified using Fourier Transform Infrared Spectroscopy (FTIR). Visual examination and UV Spectrophotometry stability study revealed that the nanocoolants were stable for up to 8 weeks, with little precipitation seen for single nanoparticle coolants after 12 weeks. When tested against temperature, kinematic viscosity decreased with increasing temperature, with very minor differences amongst coolants. The results of the Viscosity Index (VI) indicated that the hybrid nanocoolant performed similarly to the basic fluid, Ethylene Glycol (EG), even at high temperatures. Thermal conductivity rose as temperature increased, with a single CuO nanocoolant and a CNC:CuO (80:20) hybrid having the maximum conductivity. Specific heat capacity measurements revealed a declining trend as temperature rose. Overall, the CNC:CuO (80:20) hybrid nanocoolant and the CuO single nanocoolant displayed improved characteristics and stability, suggesting their potential for increased heat transfer applications

Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model

Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications