In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA.
In this paper, an integrated thermoelectric (TE) and photovoltaic (PV) hybrid energy harvesting system (HEHS) is proposed for self-powered internet of thing (IoT)-enabled wireless sensor networks (WSNs). The proposed system can run at a minimum of 0.8 V input voltage under indoor light illumination of at least 50 lux and a minimum temperature difference, ∆T = 5 °C. At the lowest illumination and temperature difference, the device can deliver 0.14 W of power. At the highest illumination of 200 lux and ∆T = 13 °C, the device can deliver 2.13 W. The developed HEHS can charge a 0.47 F, 5.5 V supercapacitor (SC) up to 4.12 V at the combined input voltage of 3.2 V within 17 s. In the absence of any energy sources, the designed device can back up the complete system for 92 s. The sensors can successfully send 39 data string to the webserver within this time at a two-second data transmission interval. A message queuing telemetry transport (MQTT) based IoT framework with a customised smartphone application 'MQTT dashboard' is developed and integrated with an ESP32 Wi-Fi module to transmit, store, and monitor the sensors data over time. This research, therefore, opens up new prospects for self-powered autonomous IoT sensor systems under fluctuating environments and energy harvesting regimes, however, utilising available atmospheric light and thermal energy.
An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents' positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.
Head-mounted displays (HMDs) have the potential to greatly impact the surgical field by maintaining sterile conditions in healthcare environments. Google Glass (GG) and Microsoft HoloLens (MH) are examples of optical HMDs. In this comparative survey related to wearable augmented reality (AR) technology in the medical field, we examine the current developments in wearable AR technology, as well as the medical aspects, with a specific emphasis on smart glasses and HoloLens. The authors searched recent articles (between 2017 and 2022) in the PubMed, Web of Science, Scopus, and ScienceDirect databases and a total of 37 relevant studies were considered for this analysis. The selected studies were divided into two main groups; 15 of the studies (around 41%) focused on smart glasses (e.g., Google Glass) and 22 (59%) focused on Microsoft HoloLens. Google Glass was used in various surgical specialities and preoperative settings, namely dermatology visits and nursing skill training. Moreover, Microsoft HoloLens was used in telepresence applications and holographic navigation of shoulder and gait impairment rehabilitation, among others. However, some limitations were associated with their use, such as low battery life, limited memory size, and possible ocular pain. Promising results were obtained by different studies regarding the feasibility, usability, and acceptability of using both Google Glass and Microsoft HoloLens in patient-centric settings as well as medical education and training. Further work and development of rigorous research designs are required to evaluate the efficacy and cost-effectiveness of wearable AR devices in the future.
This study investigates the effects of stirring duration on the synthesis of graphene oxide (GO) using an improved Hummers' method. Various samples are examined under different stirring durations (20, 40, 60, 72, and 80 h). The synthesized GO samples are evaluated through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDX), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy. The GO sample with 72 h stirring duration (GO72) has the highest d-spacing in the XRD results, highest atomic percentage of oxygen in EDX (49.57%), highest intensity of oxygen functional group in FTIR spectra, and highest intensity ratio in Raman analysis (ID/IG = 0.756). Results show that GO72 with continuous stirring has the highest degree of oxidation among other samples. Electrochemical impedance spectroscopy analysis shows that GO72-titanium dioxide (TiO2) exhibits smaller charge transfer resistance and higher electron lifetime compared with the TiO2-based photoanode. The GO72 sample incorporating TiO2 nanocomposites achieves 6.25% photoconversion efficiency, indicating an increase of more than twice than that of the mesoporous TiO2 sample. This condition is fully attributed to the efficient absorption rate of nanocomposites and the reduction of the recombination rate of TiO2 by GO in dye-sensitized solar cells.
Binary metal phosphate electrodes have been widely studied for energy storage applications due to the synergistic effects of two different transition elements that able to provide better conductivity and stability. Herein, the battery-type binder-free nickel-manganese phosphate (NiMn-phosphate) electrodes were fabricated with different Ni:Mn precursor ratios via microwave-assisted hydrothermal technique for 5 min at 90 °C. Overall, NiMn3P electrode (Ni:Mn = 1:3) showed an outstanding electrochemical performance, displaying the highest specific (areal) capacity at 3 A/g of 1262.4 C/g (0.44 C/cm2), and the smallest charge transfer resistance of 108.8 Ω. The enhanced performance of NiMn3P electrode can be ascribed to the fully grown amorphous nature and small-sized flake and flower structures of NiMn3P electrode material on the nickel foam (NF) surface. This configuration offered a higher number of active sites and a larger exposed area, facilitating efficient electrochemical reactions with the electrolyte. Consequently, the NiMn3P//AC electrode combination was chosen to further investigate its performance in supercapattery. The NiMn3P//AC supercapattery exhibited remarkable energy density of 105.4 Wh/kg and excellent cyclic stability with 84.7% retention after 3000 cycles. These findings underscored the superior electrochemical performance of the battery-type binder-free NiMn3P electrode, and highlight its potential for enhancing the overall performance of supercapattery.
In this present work, we report the deposition of cadmium selenide (CdSe) particles on titanium dioxide (TiO2) nanotube thin films, using the chemical bath deposition (CBD) method at low deposition temperatures ranging from 20 to 60 °C. The deposition temperature had an influence on the overall CdSe-TiO2 nanotube thin film morphologies, chemical composition, phase transition, and optical properties, which, in turn, influenced the photoelectrochemical performance of the samples that were investigated. All samples showed the presence of CdSe particles in the TiO2 nanotube thin film lattice structures with the cubic phase CdSe compound. The amount of CdSe loading on the TiO2 nanotube thin films were increased and tended to form agglomerates as a function of deposition temperature. Interestingly, a significant enhancement in photocurrent density was observed for the CdSe-TiO2 nanotube thin films deposited at 20 °C with a photocurrent density of 1.70 mA cm-2, which was 17% higher than the bare TiO2 nanotube thin films. This sample showed a clear surface morphology without any clogged nanotubes, leading to better ion diffusion, and, thus, an enhanced photocurrent density. Despite having the least CdSe loading on the TiO2 nanotube thin films, the CdSe-TiO2 nanotube thin films deposited at 20 °C showed the highest photocurrent density, which confirmed that a small amount of CdSe is enough to enhance the photoelectrochemical performance of the sample.
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.
This review provides the recent advances in triglyceride catalytic pyrolysis using heterogeneous dolomite catalysts for upgrading biofuel quality. The production of high-quality renewable biofuels through catalytic cracking pyrolysis has gained significant attention due to their high hydrocarbon and volatile matter content. Unlike conventional applications that require high operational costs, long process times, hazardous material pollution, and enormous energy demand, catalytic cracking pyrolysis has overcome these challenges. The use of CaO, MgO, and activated dolomite catalysts has greatly improved the yield and quality of biofuel, reducing the acid value of bio-oil. Modifications of the activated dolomite surface through bifunctional acid-base properties also positively influenced bio-oil production and quality. Dolomite catalysts have been found to be effective in catalyzing the pyrolysis of triglycerides, which are a major component of vegetable oils and animal fats, to produce biofuels. Recent advances in the field include the use of modified dolomite catalysts to improve the activity and selectivity of the catalytic pyrolysis process. Moreover, there is also research enhancement of the synthesis and modification of dolomite catalysts in improving the performance of biofuel yield conversion. Interestingly, this synergy contribution has significantly improved the physicochemical properties of the catalysts such as the structure, surface area, porosity, stability, and bifunctional acid-base properties, which contribute to the catalytic reaction's performance.
Renewable solar energy is the key target to reduce fossil fuel consumption, minimize global warming issues, and indirectly minimizes erratic weather patterns. Herein, the authors synthesized an ultrathin reduced graphene oxide (rGO) nanosheet with ~47 nm via an improved Hummer's method. The TiO2 was deposited by RF sputtering onto an rGO nanosheet with a variation of temperature to enhance the photogenerated electron or charge carrier mobility transport for the photoanode component. The morphology, topologies, element composition, crystallinity as well as dye-sensitized solar cells' (DSSCs) performance were determined accordingly. Based on the results, FTIR spectra revealed presence of Ti-O-C bonds in every rGO-TiO2 nanocomposite samples at 800 cm-1. Besides, XRD revealed that a broad peak of anatase TiO2 was detected at ~25.4° after incorporation with the rGO. Furthermore, it was discovered that sputtering temperature of 120 °C created a desired power conversion energy (PCE) of 7.27% based on the J-V plot. Further increase of the sputtering temperature to 160 °C and 200 °C led to excessive TiO2 growth on the rGO nanosheet, thus resulting in undesirable charge recombination formed at the photoanode in the DSSC device.
Graphene and its derivative materials present high potential towards medical and biological applications, including drug delivery and bioimaging, due to their exceptional properties such as thermal conductivity and high specific surface area. The main focus of this work is to review the current development of graphene materials and the derivatives for biocompatible, bioimaging and drug delivery applications. Also, the synthesis methods with variation of graphene nanocomposites and the functionalisation will be further explained. For the graphene approaches, chemical vapour deposition (CVD) is the best-known technique to make high-quality graphene sheet by growth route with mass production. By considering the organic graphene nanocomposites, the biocompatibility and cytotoxic effects against graphene nanocomposites were evaluated for biomedical employments such as high quality bioimaging and effective drug delivery for cancer treatments. For example, graphene oxide incorporated with PEG and loaded with SN 38 for camptothecin analolgue as anticancer drug and revealed high cytotoxicity has an effect of 1000 times better effect than CPT in HCT-116 cells. Their drug delivery ability for both in-vivo and in-vitro applications compared to the controlled drugs such as doxorubicin (DOX) will be discussed accordingly. The graphene and its deriavatives possess some intriguing properties, which will lead to drug delivery due to strong biocompatibility and cyctotoxic effect towards biomedicine applications.
Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.
Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.