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Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.

Balance disorders affect approximately 30% of the population throughout their lives and result in debilitating symptoms, such as spontaneous vertigo, nystagmus, and oscillopsia. The main cause of balance disorders is peripheral vestibular dysfunction, which may occur as a result of hair cell loss, neural dysfunction, or mechanical (and morphological) abnormality. The most common cause of vestibular dysfunction is arguably vestibular hair cell damage, which can result from an array of factors, such as ototoxicity, trauma, genetics, and ageing. One promising therapy is the vestibular prosthesis, which leverages the success of the cochlear implant, and endeavours to electrically integrate the primary vestibular afferents with the vestibular scene. Other translational approaches of interest include stem cell regeneration and gene therapies, which aim to restore or modify inner ear receptor function. However, both of these techniques are in their infancy and are currently undergoing further characterization and development in the laboratory, using animal models. Another promising translational avenue to treating vestibular hair cell dysfunction is the potential development of artificial biocompatible hair cell sensors, aiming to replicate functional hair cells and generate synthetic 'receptor potentials' for sensory coding of vestibular stimuli to the brain. Recently, artificial hair cell sensors have demonstrated significant promise, with improvements in their output, such as sensitivity and frequency selectivity. This article reviews the history and current state of bioelectronic devices to interface with the labyrinth, spanning the vestibular implant and artificial hair cell sensors.

Accurate nuclear and cell segmentations plays an important role in improving the accuracy of target recognition in microscopic cell images. As the traditional SLIC (Simple Linear Iterative Clustering) algorithm cannot segment microscopic cell images well, an improved SLIC superpixel segmentation algorithm based on gray scale enhancement and regional equalization is proposed. According to the characteristics of microscopic cell images, selecting different transformation parameters with the conditional iterative algorithm, the best classification multi-threshold method based on maximum entropy criterion is used to nonlinearly enhance the gray scale of the original images, while enhancing the contrast of the image, it also greatly improves the balance of each classification region. Then the gray distance and spatial distance are calculated respectively in the circle neighborhood of the cluster center to realize the superpixel segmentation of the image. Finally, the improved SLIC algorithm and the comparison algorithm are tested and evaluated. The experimental results show that our improved SLIC algorithm model has higher segmentation accuracy and is more suitable for cell segmentation in microscopic cell images than original SLIC algorithm.

The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.

Coronavirus Disease 2019 (COVID-19) has affected global economies due to lockdowns, business closures, and travel and other restrictions. To control the spread of the virus, several countries, including Australia, imposed strict border restrictions and lockdown measures. Accordingly, international borders have been closed, and all incoming international passengers are mandated to a 14-day hotel quarantine. Residents’ movements and businesses have been limited to essential services only. Employees have been directed to work from home while businesses moved to a remote working model. Due to such stringent measures, small and medium businesses such as cafes, restaurants, hotels, childcare centers, and tourism-based institutions incurred heavy losses, pushing a considerable portion of such small businesses to close. The airlines, education, tourism, and hospitality sector were the worst impacted among all. Due to such closures and associated effects of COVID-19, the unemployment rates are assumed to be significantly increased in countries like Australia. However, a study investigating this unemployment and reporting its status does not exist for Australia. Therefore, in this study, we investigated the effects of COVID-19 control measures such as travel restriction and lockdown on Australia’s employment status and labor markets. The data for the local transport network, unemployment rates and impacts on the tourism industry in Australia were extracted from the public data sources to assess the unemployment rates at both national and state-wide levels. Further, we also looked into the rehabilitation measures by the Australian government, such as the Job Keeper and Job Seeker programs in March 2020, that aim to provide support to people who are unable to run their businesses or have lost their jobs due to the pandemic. Overall, we observed that despite the global crisis, the Australian unemployment rate has reduced in the last year.

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing re-gion. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimension

Coronavirus disease 2019 (COVID-19) is an infectious disease characterised by symptoms that are like the common cold. The current pandemic situation in anticipation of a vaccine has posed serious threats to the health and economic sectors of countries worldwide. To overcome the quick transmission of the virus, the government of Australia has also taken drastic measures to prevent its spread. These policies include an international and interstate travel ban, social distancing rules, lockdown, shutdown of educational institutes and work-from-home policies. Such rules have affected people on both behavioural and psychological levels. This study aims to analyse the effect of COVID-19 on Australian citizens, and therefore, the changed behaviour of citizens concerning their mobility patterns, transport preferences and shopping methods under the pandemic have been studied. A detailed literature search was adopted for gathering data related to the study theme, along with real-time evidence of changes in the behaviour of people following the pandemic. The socioeconomic impact of the pandemic on social inequality and thereby the effect on the vulnerable people of the population are also studied. Authentic surveys and statistical data are consulted to figure out how the new lifestyle choices of people will linger in the post-pandemic era. It was found that people in Australia have adopted the work-from-home regime, and new habits suiting the nationwide restrictions have become routine for many people.

This paper presents a simple and comprehensive model of a dual-gate graphene field effect transistor (FET). The quantum capacitance and surface potential dependence on the top-gate-to-source voltage were studied for monolayer and bilayer graphene channel by using equivalent circuit modeling. Additionally, the closed-form analytical equations for the drain current and drain-to-source voltage dependence on the drain current were investigated. The distribution of drain current with voltages in three regions (triode, unipolar saturation, and ambipolar) was plotted. The modeling results exhibited better output characteristics, transfer function, and transconductance behavior for GFET compared to FETs. The transconductance estimation as a function of gate voltage for different drain-to-source voltages depicted a proportional relationship; however, with the increase of gate voltage this value tended to decline. In the case of transit frequency response, a decrease in channel length resulted in an increase in transit frequency. The threshold voltage dependence on back-gate-source voltage for different dielectrics demonstrated an inverse relationship between the two. The analytical expressions and their implementation through graphical representation for a bilayer graphene channel will be extended to a multilayer channel in the future to improve the device performance.

In this article, the parameters of the proportional-integral (PI) controller of the wind turbine (WT) emulator, i.e., proportional and integral gain of the PI controller, are optimized using a black widow optimization algorithm (BWOA). The proposed system is developed and analyzed using MATLAB/Simulink environment. The performance of the BWOA optimized PI controller is compared with a BAT algorithm, particle swarm optimization, and genetic algorithm optimized PI controller to measure the effectiveness of the proposed control system. The developed system is tested for different operating conditions such as static wind speed settings, static pitch angle conditions, step-change in wind speed settings, and step-change in pitch angle settings. Finally, the proposed system is realized in real-time by hardware experimentations. The results of the experimentation are compared with simulation results as well. The presented simulation and hardware result shows good agreement, which confirms the effectiveness of the proposed method. Thereby, the proposed optimization-based PI-controlled wind emulator can be recommended for emulating the characteristics of any type of WT with a low-cost system.

Power converter with high frequency transformers (HFTs) are being widely used in the field of electric vehicle, switching mode power supply, renewable energy sectors including oceanic wave energy converter and so on. This paper proposes an improved compact design of a 4.75 kW, 100 kHz superconducting HFT that would offer size minimization facilities of power converters. Yttrium barium copper oxide (YBCO) material-based superconductor is used for maximum power transfer analysis. The proposed superconducting winding is compact which would minimize the thermal limitation and proximity effect. The proposed HFT is characterized for using the conventional and advanced magnetic materials with gapped and ungapped structures. Gapped toroidal core structure of HFT offers improved capability of withstanding de bias magnetizing current which occurs in many cases including isolation. Simulation results show the comparison of magnetic flux density, core loss, and power including air gap effect. YBCO windings based HFT contributes to transfer approximately 11.76% more power compared to that of using the copper winding.