The Influence of Special Environments on SiC MOSFETs.

In this work, the influences of special environments (hydrogen gas and high temperature, high humidity environments) on the performance of three types of SiC MOSFETs are investigated. The results reveal several noteworthy observations. Firstly, after 500 h in a hydrogen gas environment, all the SiC MOSFETs exhibited a negative drift in threshold voltage, accompanied by an increase in maximum transconductance and drain current (@ VGS/VDS = 13 V/3 V). This phenomenon can be attributed to that the hydrogen atoms can increase the positive fixed charges in the oxide and increase the electron mobility in the channel. In addition, high temperature did not intensify the impact of hydrogen on the devices and electron mobility. Instead, prolonged exposure to high temperatures may induce stress on the SiO2/SiC interface, leading to a decrease in electron mobility, subsequently reducing the transconductance and drain current (@ VGS/VDS = 13 V/3 V). The high temperature, high humidity environment can cause a certain negative drift in the devices' threshold voltage. With the increasing duration of the experiment, the maximum transconductance and drain current (@ VGS/VDS = 18V (20 V)/3 V) gradually decreased. This may be because the presence of moisture can lead to corrosion of the devices' metal contacts and interconnects, which can increase the devices' resistance and lead to a decrease in the devices' maximum transconductance and drain current.

Effect of Thermal Aging on the Interfacial Reaction Behavior and Failure Mechanism of Ni-xCu/Sn Soldering Joints under Shear Loading.

Ni-xCu/Sn soldering joints were aged at 200 °C, and the microstructure evolution and mechanical properties during the solid-state reaction were studied under shear loading. Results showed that the intermetallic compounds (IMCs) exhibited a Cu content-dependent transformation from the (Ni,Cu)3Sn4 phase to the (Cu,Ni)6Sn5 phase at the Ni-xCu/Sn interface. Furthermore, a Cu3Sn layer was observed exclusively at the Cu/Sn interface. The shear strength of the soldering joints after thermal aging exhibited an initial decrease followed by an increase, except for a significant enhancement at the Cu content of 60 wt.%. In addition, the evolution law of mechanical properties and failure mechanism of the thermal aging joints were elucidated based on the fracture microstructure and the fracture curve of the joints.

A case report of rare complication of brucellosis infection: myocarditis and pneumonitis.

The involvement of cardiovascular or respiratory complications in cases of brucellosis are extremely rare. Herein, a case of myocarditis and pneumonia with pericardial effusion, pleural effusion and biliteral pleural thickening with pleural adhesion in a 35-year-old female patient, is described. Using next-generation sequencing, the patient was differentially diagnosed with Brucella-related myocarditis and pneumonitis, and treatment with oral doxycycline, rifampicin, and trimethoprim/sulfamethoxazole, along with intravenous gentamycin, was commenced. Following treatment, the patient was clinically improved. When a patient with brucellosis presents with chest pain, clinicians should be aware of this clinical manifestation. Next-generation sequencing may be used to identify pathogens and provide insights into the disease when appropriate cultures are negative.

A novel mathematical modeling with solution for movement of fluid through ciliary caused metachronal waves in a channel.

In the present research, a novel mathematical model for the motion of cilia using non-linear rheological fluid in a symmetric channel is developed. The strength of analytical perturbation technique is employed for the solution of proposed physical process using mectachoronal rhythm based on Cilia induced flow for pseudo plastic nano fluid model by considering the low Reynolds number and long wave length approximation phenomena. The role of ciliary motion for the fluid transport in various animals is explained. Analytical expressions are gathered for stream function, concentration, temperature profiles, axial velocity, and pressure gradient. Whereas, transverse velocity, pressure rise per wave length, and frictional force on the wall of the tubule are investigated with aid of numerical computations and their outcomes are demonstrated graphically. A comprehensive analysis for comparison of Perturb and numerical solution is done. This analysis validates the analytical solution.

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing.

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

Entropy Generation Analysis and Radiated Heat Transfer in MHD (Al2O3-Cu/Water) Hybrid Nanofluid Flow.

This research concerns the heat transfer and entropy generation analysis in the MHD axisymmetric flow of Al2O3-Cu/H2O hybrid nanofluid. The magnetic induction effect is considered for large magnetic Reynolds number. The influences of thermal radiations, viscous dissipation and convective temperature conditions over flow are studied. The problem is modeled using boundary layer theory, Maxwell's equations and Fourier's conduction law along with defined physical factors. Similarity transformations are utilized for model simplification which is analytically solved with the homotopy analysis method. The h-curves up to 20th order for solutions establishes the stability and convergence of the adopted computational method. Rheological impacts of involved parameters on flow variables and entropy generation number are demonstrated via graphs and tables. The study reveals that entropy in system of hybrid nanofluid affected by magnetic induction declines for ß while it enhances for Bi, R and λ. Moreover, heat transfer rate elevates for large Bi with convective conditions at surface.

Design of Nonlinear Autoregressive Exogenous Model Based Intelligence Computing for Efficient State Estimation of Underwater Passive Target.

In this study, an intelligent computing paradigm built on a nonlinear autoregressive exogenous (NARX) feedback neural network model with the strength of deep learning is presented for accurate state estimation of an underwater passive target. In underwater scenarios, real-time motion parameters of passive objects are usually extracted with nonlinear filtering techniques. In filtering algorithms, nonlinear passive measurements are associated with linear kinetics of the target, governing by state space methodology. To improve tracking accuracy, effective feature estimation and minimizing position error of dynamic passive objects, the strength of NARX based supervised learning is exploited. Dynamic artificial neural networks, which contain tapped delay lines, are suitable for predicting the future state of the underwater passive object. Neural networks-based intelligence computing is effectively applied for estimating the real-time actual state of a passive moving object, which follows a semi-curved path. Performance analysis of NARX based neural networks is evaluated for six different scenarios of standard deviation of white Gaussian measurement noise by following bearings only tracking phenomena. Root mean square error between estimated and real position of the passive target in rectangular coordinates is computed for evaluating the worth of the proposed NARX feedback neural network scheme. The Monte Carlo simulations are conducted and the results certify the capability of the intelligence computing over conventional nonlinear filtering algorithms such as spherical radial cubature Kalman filter and unscented Kalman filter for given state estimation model.

3D Multiple-Antenna Channel Modeling and Propagation Characteristics Analysis for Mobile Internet of Things.

The demand for optimization design and performance evaluation of wireless communication links in a mobile Internet of Things (IoT) motivates the exploitation of realistic and tractable channel models. In this paper, we develop a novel three-dimensional (3D) multiple-antenna channel model to adequately characterize the scattering environment for mobile IoT scenarios. Specifically, taking into consideration both accuracy and mathematical tractability, a 3D double-spheres model and ellipsoid model are introduced to describe the distribution region of the local scatterers and remote scatterers, respectively. Based on the explicit geometry relationships between transmitter, receiver, and scatterers, we derive the complex channel gains by adopting the radio-wave propagation model. Subsequently, the correlation-based approach for theoretical analysis is performed, and the detailed impacts with respect to the antenna deployment, scatterer distribution, and scatterer density on the vital statistical properties are investigated. Numerical simulation results have shown that the statistical channel characteristics in the developed simulation model nicely match those of the corresponding theoretical results, which demonstrates the utility of our model.

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning.

Optimal Reactive Power Dispatch (ORPD) is the vital concern of network operators in the planning and management of electrical systems to reduce the real and reactive losses of the transmission and distribution system in order to augment the overall efficiency of the electrical network. The principle objective of the ORPD problem is to explore the best setting of decision variables such as rating of the shunt capacitors, output voltage of the generators and tap setting of the transformers in order to diminish the line loss, and improve the voltage profile index (VPI) and operating cost minimization of standard electrical systems while keeping the variables within the allowable limits. This research study demonstrates a compelling transformative approach for resolving ORPD problems faced by the operators through exploiting the strength of the meta-heuristic optimization model based on a new fractional swarming strategy, namely fractional order (FO)-particle swarm optimization (PSO), with consideration of the entropy metric in the velocity update mechanism. To perceive ORPD for standard 30 and 57-bus networks, the complex nonlinear objective functions, including minimization of the system, VPI improvement and operating cost minimization, are constructed with emphasis on efficacy enhancement of the overall electrical system. Assessment of the results show that the proposed FO-PSO with entropy metric performs better than the other state of the art algorithms by means of improvement in VPI, operating cost and line loss minimization. The statistical outcomes in terms of quantile-quantile illustrations, probability plots, cumulative distribution function, box plots, histograms and minimum fitness evaluation in a set of autonomous trials validate the capability of the proposed optimization scheme and exhibit sufficiency and also vigor in resolving ORPD problems.

Protective Effects of Nucleosides-Rich Extract from Cordyceps cicadae against Cisplatin Induced Testicular Damage.

Cisplatin (CISP) is an efficacious anticancer agent used in chemotherapy, however, the constraint to its clinical utility is the stray organ toxicity including testicular damage linked to oxidative and inflammatory cascades. This study aimed to explore the protective effect of nucleosides-rich extract from Cordyceps cicadae (NRCE) against CISP-induced testicular damage in rats. Rats were subjected to prophylactic oral administration of NRCE (50, 100 and 400 mg/kg body weight/day) for 7 days prior to testicular toxicity induced by CISP (10 mg/kg, ip) and were sacrificed after 72 h post-CISP injection. Cisplatin caused significant deficits in sperm count, viability and motility, testosterone and follicle stimulating hormone (FSH) compared to normal control. It depressed testicular activities of glutathione peroxidase (GSH-Px), superoxide dismutase (SOD), catalase (CAT), total antioxidant content (TAC), whereas malondialdehyde (MDA) increased remarkably. CISP considerably increased tumor necrosis factor-alpha (TNF-α) and interleukin-one beta (IL-1ß) with alterations in testis histology compared to normal control. Interestingly, NRCE pretreatment inhibited the CISP-induced alterations in reproductive indices, restored the antioxidant activities in testes as well as inflammatory mediators and histology comparable to control. Our findings demonstrate that NRCE could prevent CISP testicular damage via inhibition of oxidative stress and pro-inflammation in rats.

Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model.

The idea of Ubiquitous Power Internet of Things (UPIoTs) accelerates the development of intelligent monitoring and diagnostic technologies. In this paper, a diagnostic method suitable for power equipment in an interference environment was proposed based on the deep Convolutional Neural Network (CNN): MobileNet-V2 and Digital Image Processing (DIP) methods to conduct fault identification process: including fault type classification and fault localization. A data visualization theory was put forward in this paper, which was applied in frequency response (FR) curves of transformer to obtain dataset. After the image augmentation process, the dataset was input into the deep CNN: MobileNet-V2 for training procedures. Then a spatial-probabilistic mapping relationship was established based on traditional Frequency Response Analysis (FRA) fault diagnostic method. Each image in the dataset was compared with the fingerprint values to get traditional diagnosing results. Next, the anti-interference abilities of the proposed CNN-DIP method were compared with that of the traditional one while the magnitude of the interference gradually increased. Finally, the fault tolerance of the proposed method was verified by further analyzing the deviations between the wrong diagnosing results with the corresponding actual labels. Experimental results showed that the proposed deep visual identification (CNN-DIP) method has a higher diagnosing accuracy, a stronger anti-interference ability and a better fault tolerance.

A new fault diagnosis approach for analog circuits based on spectrum image and feature weighted kernel Fisher discriminant analysis.

Analog circuits are one of the most commonly used components in industrial equipment and, therefore, circuit failure may lead to significant causalities and even huge financial losses. To address this problem, this work presents a fault diagnosis method based on spectrum images for analog circuits. Unlike traditional analysis methods in a one-dimensional space, this study employs a computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed approach mainly involves the following steps. First, the sampling signals are converted into spectrum images by utilizing cross-wavelet transform, which can be further processed by the following image-based feature extraction method. Then, Krawtchouk moment is applied to extract both the global and local features of the spectrum images and finally form the feature vector. Feature weighted kernel Fisher discriminant analysis is then introduced for locating faults. Two typical analog circuits, video amplifier circuit and opamp high-pass filter circuit, are chosen to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed approach based on spectrum images achieves a high accuracy, thus providing a highly effective means to fault diagnosis for analog circuits.

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE.

In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

Novel Concrete Temperature Monitoring Method Based on an Embedded Passive RFID Sensor Tag.

This paper firstly introduces the importance of temperature control in concrete measurement, then a passive radio frequency identification (RFID) sensor tag embedded for concrete temperature monitoring is presented. In order to reduce the influences of concrete electromagnetic parameters during the drying process, a T-type antenna is proposed to measure the concrete temperature at the required depth. The proposed RFID sensor tag is based on the EPC generation-2 ultra-high frequency (UHF) communication protocol and operates in passive mode. The temperature sensor can convert the sensor signals to corresponding digital signals without an external reference clock due to the adoption of phase-locked loop (PLL)-based architecture. Laboratory experimentation and on-site testing demonstrate that our sensor tag embedded in concrete can provide reliable communication performance in passive mode. The maximum communicating distance between reader and tag is 7 m at the operating frequency of 915 MHz and the tested results show high consistency with the results tested by a thermocouple.

Distributed Finite-Time Cooperative Control of Multiple High-Order Nonholonomic Mobile Robots.

The consensus problem of multiple nonholonomic mobile robots in the form of high-order chained structure is considered in this paper. Based on the model features and the finite-time control technique, a finite-time cooperative controller is explicitly constructed which guarantees that the states consensus is achieved in a finite time. As an application of the proposed results, finite-time formation control of multiple wheeled mobile robots is studied and a finite-time formation control algorithm is proposed. To show effectiveness of the proposed approach, a simulation example is given.

Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.

Design of an Embedded CMOS Temperature Sensor for Passive RFID Tag Chips.

This paper presents an ultra-low embedded power temperature sensor for passive RFID tags. The temperature sensor converts the temperature variation to a PTAT current, which is then transformed into a temperature-controlled frequency. A phase locked loop (PLL)-based sensor interface is employed to directly convert this temperature-controlled frequency into a corresponding digital output without an external reference clock. The fabricated sensor occupies an area of 0.021 mm2 using the TSMC 0.18 1P6M mixed-signal CMOS process. Measurement results of the embedded sensor within the tag system shows a 92 nW power dissipation under 1.0 V supply voltage at room temperature, with a sensing resolution of 0.15 °C/LSB and a sensing accuracy of -0.7/0.6 °C from -30 °C to 70 °C after 1-point calibration at 30 °C.

A CMOS pressure sensor tag chip for passive wireless applications.

This paper presents a novel monolithic pressure sensor tag for passive wireless applications. The proposed pressure sensor tag is based on an ultra-high frequency RFID system. The pressure sensor element is implemented in the 0.18 µm CMOS process and the membrane gap is formed by sacrificial layer release, resulting in a sensitivity of 1.2 fF/kPa within the range from 0 to 600 kPa. A three-stage rectifier adopts a chain of auxiliary floating rectifier cells to boost the gate voltage of the switching transistors, resulting in a power conversion efficiency of 53% at the low input power of -20 dBm. The capacitive sensor interface, using phase-locked loop archietcture, employs fully-digital blocks, which results in a 7.4 bits resolution and 0.8 µW power dissipation at 0.8 V supply voltage. The proposed passive wireless pressure sensor tag costs a total 3.2 µW power dissipation.

Synthesis and properties of the strained alkene perfluorobicyclo[2.2.0]hex-1(4)-ene.

The title fluoroalkene has been generated by dehalogenation of dibromide and diiodide precursors and trapped in situ. retro-Diels-Alder reaction of its adduct with N-benzylpyrrole has made the alkene available in high yield and purity. In sharp contrast to its extremely labile hydrocarbon counterpart, the fluoroalkene is very stable yet highly reactive. Its characterization includes its electron affinity, photoelectron spectrum, and the previously reported structure determination by electron diffraction.

A CMOS humidity sensor for passive RFID sensing applications.

This paper presents a low-cost low-power CMOS humidity sensor for passive RFID sensing applications. The humidity sensing element is implemented in standard CMOS technology without any further post-processing, which results in low fabrication costs. The interface of this humidity sensor employs a PLL-based architecture transferring sensor signal processing from the voltage domain to the frequency domain. Therefore this architecture allows the use of a fully digital circuit, which can operate on ultra-low supply voltage and thus achieves low-power consumption. The proposed humidity sensor has been fabricated in the TSMC 0.18 µm CMOS process. The measurements show this humidity sensor exhibits excellent linearity and stability within the relative humidity range. The sensor interface circuit consumes only 1.05 µW at 0.5 V supply voltage and reduces it at least by an order of magnitude compared to previous designs.