Narrow Results

For the purposes of optimizing traffic flow composed of different types of vehicles, it is important to understand the interactions between them. This paper proposes a cellular automata model to investigate a bidirectional two-lane traffic flow under the periodic boundary condition. The vehicle flux and the phase diagrams of the system in the (${\rho }_1,{\rho }_2$) space are constructed by applying two different overtaking models (symmetric, asymmetric). The inter-lane correlation and the overtaking frequency are also studied. The simulation results show that the variation of the density of one lane has an apparent influence on the traffic of the adjacent lane. Furthermore, it is found that the phase diagram on both models is classified into several regions. Thus, for the symmetric model, as the overtaking probability increases, the traffic on the system becomes better. Likewise, the results also indicate that the asymmetric model can effectively enhance the traffic capacity and alleviate the congested state.

The objective of this study was to investigate the impact of connected and autonomous vehicles (CAVs) on traffic flow under various parameters. For this purpose, we propose a mixed CAV and conventional vehicle (CV) model to investigate a bidirectional two-lane traffic flow under the periodic boundary condition. The traffic flux and the phase diagrams of the system in the (${\rho }_1,{\rho }_2$) area are constructed in both cases: with and without CAVs. The overtaking frequency is also calculated. The simulation findings show that the traffic capacity is greatly enhanced with the increase in the CAV penetration ratio. Owing to the cooperative driving strategy, with the increase in penetration ratio of the CAV, the portion of smooth overtaking is boosted. Furthermore, it is found that the traffic throughput is positively correlated to the speed limit of the fast vehicle where the flux increases as $V_{\mathrm{\text{max}}-F}$ increases. Also, even if there is a low rate of slow moving vehicles in the system, it will have an appreciable and a significant negative influence.

This paper proposes two quantum privacy query protocols. In the single vector sub-privacy query of protocol 1, it is proposed for the first time to distinguish different measurement results by publishing 2 bits of classic information. In the protocol, the database Bob selects the Z base and the Bell base to measure the particles sent by Alice according to the key value of the encrypted database and publishes 2 bits of classic information based on the measurement results. The user Alice infers the key value of the encrypted database through these two pieces of classic information. In the two-way quantum privacy query of protocol 2, the database Bob measures the particles sent back by Alice, converts the measurement result into a key value of length 3, and prepares a particle that is the same as the measurement result and sends it back to Alice. Alice returns the measurement of particles can infer a set of data encryption keys, and the length of each key is 3 bits. Users in the two protocols proposed in the article can obtain the query key with a probability of 1/4. As the key length increases, the number of query bits in protocol 2 decreases, but the probability of execution failure increases. But protocol 2 has a higher quantum efficiency. Both protocols can guarantee the privacy of users and databases.

This study describes a novel bidirectional current shunt monitor (CSM) circuit operating at both positive and negative common-mode (CM) voltages. The proposed CSM circuit mainly consists of two comparators, three error amplifiers, several current-mirror transistors and a few resistors. One comparator is used to detect current flowing direction, and the other one is utilized to ensure good operation of CSM circuit with both positive and negative CM voltages. The proposed CSM circuit has been implemented in SMIC 0.18$\mu$m standard CMOS process and its performances have been verified by simulations. The simulated results show that the proposed CSM circuit, at a supply voltage of 5V and with an input CM voltage range from $-4$ to 4V, can sense a voltage difference of 4–40mV and keep a constant scaled gain of 100V/V. The gain error is less than 0.65% and the common-mode rejection ratio (CMRR) is higher than 130dB at 1kHz. Simulation results show that the output voltage of CSM circuit varies linearly with the CSM input sense voltage.

In this paper, a high gain nonisolated three-port bidirectional DC–DC converter is proposed to interface solar photovoltaic and battery energy storage system to DC bus with a reduced number of components. Four modes of operation based on the power flow and load demand are identified. The operating principle of the proposed converter and its operational waveform for all four modes of operation is described in this paper. The steady-state analysis of the proposed converter is performed to determine the voltage gain of the converter in all four modes of operation. The steady-state analysis is done for both continuous conduction mode and discontinuous conduction mode; and the boundary condition. The reduced number of components to achieve high-voltage gain ensures the lowering of the cost and weight of the converter. The analytical results are validated utilizing the simulation results from PSCAD and hardware results. Also, the results indicate that the ripple in the current and voltage is reduced significantly.

Sepsis is a common and deadly condition that must be treated eloquently within 19 hours. Numerous deep learning techniques, including Recurrent Neural Networks, Convolution Neural Networks, Long Short-Term Memory, and Gated Recurrent Units, have been suggested for diagnosing long-term sepsis. Regardless, a sizable portion of them are computationally risky and have precision problems. The primary issue described is that output will degrade, and resource utilization will expand proportionately as the volume of dependencies grows. To overcome these issues, we propose a G-Sep technique utilizing Bidirectional Gated Recurrent Unit Algorithm, which consumes much less resource to detect the disease and in a short time with better accuracy than the existing methods to diagnose the sepsis. AI models could assist with distinguishing potential clinical factors and give better than existing conventional low-execution models. The proposed model is implemented utilizing Conda and Tensorflow Framework using the California Inpatient Severe Sepsis (CISS) Patient Dataset. The comparative simulation of the various existing models and the proposed G-Sep model is done using Conda and Tensor frameworks. The simulation results revealed that the proposed model had outperformed other frameworks in terms of mean average precision (mAP), receiver operating characteristic curve (ROC), and Area under the ROC Curve (AUROC) metrics linearly.