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• The stochastic lane-changing trajectory is modeled with the logistic diffusion method, which is represented by a stochastic differential equation. The analytical solution is derived. • The analytical distribution of the lateral movement is solved by the Fokker Planck equation. • A procedure of inferring lane changing initiation moment (LCIM), cross-lane-mark moment (CLMM), and lane changing duration (LCD) is established. • The proposed method is tested against real-world data. The results are encouraging. Inferences of the lane changing behaviors (including lane changing initiation moment LCIM, cross-lane-mark moment CLMM, and lane changing duration LCD) of the surrounding vehicles and taking actions to avoid collision are essential for the safe operation of connected and autonomous vehicles (CAVs). The majority of current models rely on data-driven methods that need to be trained before deployment. Besides, an analytical and stochastic formulation of the lateral model which can generate CLMM and LCD distribution and adjust the parameters online is still lacking. To the best of our knowledge, currently, no method can provide an analytically stochastic lateral movement that explicitly considers the system noise and simultaneously outputs the information of lane changing initiation moment, cross-lane-mark moment, and lane changing duration in a real-time manner. To fill such a gap, a stochastic lateral trajectory framework with parsimonious parameters is established and an online simultaneous inference of LCIM, CLMM, and LCD is developed. The proposed method is tested with the highD and NGSIM datasets. The results show that 1) computational efficiency-wise, the algorithm takes milliseconds to run, which suggests promising prospects for field deployment; 2) the false negative and false positive errors of the LCIM inference are as low as 1.5%, indicating that the method is robust to the stochastic noise; 3) LCIM inference is insensitive to the lane width, which means the model is error tolerant when such information is lacking; and 4) the error of CLMM inference is within 2 sec , while most of the error of LCD locates within 4 sec , suggesting satisfactory performance. [ABSTRACT FROM AUTHOR]

Cubic boron nitride (c-BN) is a synthetic ultra-wide bandgap semiconductor with attractive application prospects in electronics and optoelectronics. At present, the size of c-BN single crystals obtained by hot pressing method is generally less than 1 mm extremely unfavorable to extracting its carrier concentration through Hall effect measurement. For c-BN crystals, the longitudinal optical phonon-plasma coupling (LOPC) mode which is produced by the coupling of longitudinal optical phonons and free carriers can be observed in mid-infrared reflectance spectrum. Thus, in this work, the mid-infrared reflectance spectrum of the black c-BN crystal grown by hot pressing method is measured. Based on classical dielectric function theory, the carrier concentration of that c-BN sample is finally extracted as 7.14 × 1017 cm−3. Combining electrical measurements, its mobility is also obtained as 11.91 cm2·V-1·s-1. • The highlights of our work is as follows. • This is the first time to extract carrier concentration by fitting LOPC + mode in the mid-infrared reflectance spectrum of the black c-BN crystal, which provides a practical method to obtain the electrical properties without any contact for tiny samples. • The reliability of carrier concentration extracted from mid-infrared reflectance spectrum is confirmed through theoretical simulation. [ABSTRACT FROM AUTHOR]

In order to describe capacity of intersections, and take the factors such as intersection geometric feature, traffic flow characteristic, channelized section and regulation measures into account, this research expresses the basic phase set based on graph theory through the analysis of conflict movements. Then the common expression of capacity is given by equations. The four-leg intersection is analyzed as an example, whose necessary and sufficient condition is derived. At last, the optimization model of intersection capacity is established. The results show that the signal parameters greatly influence the intersection capacity. Equivalent saturation degree assignment is actually intersection capacity maximal control. For common four-leg intersection, the sufficient and necessary condition for capacity is that the sum of flow ratio equals 2. [ABSTRACT FROM AUTHOR]

In this paper, a set of data is assumed to be obtained from an experiment that satisfies a Boolean dynamic process. For instance, the dataset can be obtained from the diagnosis of describing the diffusion process of cancer cells. With the observed datasets, several methods to construct the dynamic models for such Boolean networks are proposed. Instead of building the logical dynamics of a Boolean network directly, its algebraic form is constructed first and then is converted back to the logical form. Firstly, a general construction technique is proposed. To reduce the size of required data, the model with the known network graph is considered. Motivated by this, the least in-degree model is constructed that can reduce the size of required data set tremendously. Next, the uniform network is investigated. The number of required data points for identification of such networks is independent of the size of the network. Finally, some principles are proposed for dealing with data with errors. [ABSTRACT FROM AUTHOR]

• A quasi-real time estimator of turning movement spillover (TMS) using partial connect vehicle data is proposed. The estimator can identify the TMS triggering movements and estimate the TMS durations. • A nonlinear minimization problem was formulated to minimize the deviation of the theoretical trajectory from the observed trajectory. • The estimation algorithm is tested against dynamic demand and common signal settings. The results prove the accuracy and the computational efficiency. Turning movement spillover (TMS) is the result of a turning bay section (TBS) not being able to accommodate all arriving vehicles, so that the turning-vehicle queue spills back and blocks other vehicles turning in different directions. We are not aware of any TMS estimation method that can remedy this situation or support relevant applications in real time. This research proposes a quasi-real time algorithm for estimating TMS, which includes triggering movement as well as duration estimation. The proposed method is based on data for connected vehicles (CVs), including their trajectories and their desired turning directions. In addition, a model that uses partial trajectory data is proposed. For each assumed TMS, a "simplified trajectory" is developed by the construction of a piece-wise linear curve. To minimize any deviation of the simplified trajectory from observation, a TMS estimation can be made. This proposed method is effective and computationally efficient when tested against dynamic demand in two mainstream signal phase settings, with varied sample sizes. Even though data for a higher number of vehicle samples is generally favorable, the proposed model still makes a good estimate when only one trajectory is available. [ABSTRACT FROM AUTHOR]

In this paper, we study extended linear regression approaches for quantum state tomography based on regularization techniques. For unknown quantum states represented by density matrices, performing measurements under certain basis yields random outcomes, from which a classical linear regression model can be established. First of all, for complete or over-complete measurement bases, we show that the empirical data can be utilized for the construction of a weighted least squares estimate (LSE) for quantum tomography. Taking into consideration the trace-one condition, a constrained weighted LSE can be explicitly computed, being the optimal unbiased estimation among all linear estimators. Next, for general measurement bases, we show that ℓ 2 -regularization with proper regularization gain provides even a lower mean-square error under a cost in bias. The optimal regularization parameter is defined in terms of a risk characterization for any finite sample size and a resulting implementable estimator is proposed. Finally, a concise and unified formula is established for the regularization parameter with complete measurement basis under an equivalent regression model, which proves that the proposed implementable tuning estimator is asymptotically optimal as the number of copies grows to infinity. Additionally, several numerical examples are provided to validate the established results. [ABSTRACT FROM AUTHOR]

In this paper, we study dynamical quantum networks which evolve according to Schrödinger equations but subject to sequential local or global quantum measurements. A network of qubits forms a composite quantum system whose state undergoes unitary evolution in between periodic measurements, leading to hybrid quantum dynamics with random jumps at discrete time instances along a continuous orbit. The measurements either act on the entire network of qubits, or only a subset of qubits. First of all, we reveal that this type of hybrid quantum dynamics induces probabilistic Boolean recursions representing the measurement outcomes. With global measurements, it is shown that such resulting Boolean recursions define Markov chains whose state-transitions are fully determined by the network Hamiltonian and the measurement observables. Particularly, we establish an explicit and algebraic representation of the underlying recursive random mapping driving such induced Markov chains. Next, with local measurements, the resulting probabilistic Boolean dynamics is shown to be no longer Markovian. The state transition probability at any given time becomes dependent on the entire history of the sample path, for which we establish a recursive way of computing such non-Markovian probability transitions. Finally, we adopt the classical bilinear control model for the continuous Schrödinger evolution, and show how the measurements affect the controllability of the quantum networks. [ABSTRACT FROM AUTHOR]

The safety performance of an electric self-balancing scooter (ESS) has recently become a main concern in preventing its further wide application as a major candidate for green transportation. Scooter riders may suffer severe brain injuries in possible vehicle crash accidents not only from contact with a windshield or bonnet but also from secondary contact with the ground. In this paper, virtual vehicle–ESS crash scenarios combined with finite element (FE) car models and multi-body scooter/human models are set up. Post-impact kinematic gestures of scooter riders under various contact conditions, such as different vehicle impact speeds, ESS moving speeds, impact angles or positions, and different human sizes, are classified and analyzed. Furthermore, head–ground impact processes are reconstructed using validated FE head models, and important parameters of contusion and laceration (e.g., coup or contrecoup pressures and Von Mises stress and the maximum shear stress) are extracted and analyzed to assess the severity of regional contusion from head–ground contact. Results show that the brain injury risk increases with vehicle speeds and ESS moving speeds and may provide fundamental knowledge to popularize the use of a helmet and the vehicle-fitted safety systems, and lay a strong foundation for the reconstruction of ESS-involved accidents. There is scope to improve safety for the use of ESS in public roads according to the analysis and conclusions. [ABSTRACT FROM AUTHOR]

This paper investigates the remote state estimation for a cyber–physical system (CPS) where a group of (primary) sensors transmit sensing data packets to the remote estimators for state estimation via their individual channels. In view of the complexity arising from the scale of such system, it is desirable for the primary sensors to share their channels with the newly-added (potential) ones, especially when the sensing data of primary ones contains less valuable information. However, the channel sharing inevitably leads to signal interference among sensors using the same channels, and it may further degenerate the remote estimation performance. Thus, the system designer should allocate the transmission power for sensors to maximize the global estimation accuracy. We emphasize the non-cooperative nature among sensors, and solve the problem in an exchange market framework with a platform acting on behalf of the system designer, and prove that the optimal power allocation is a spontaneous outcome of the market under well-designed prices. More specifically, under (subsidized) prices announced by the platform, the primary sensors are willing to open up their channels for sharing, in which a distributed optimal power allocation is derived explicitly. To alleviate transmission interference, the platform will charge potential sensors for the use of channels, among which the mutual interaction is formulated as a non-cooperative game and the existence of a pure Nash equilibrium is proved. We also devise an algorithm for the platform to design subsidized/toll prices, which is given in an explicit recursive form with simple iterations, and therefore suited for the platform with limited computation capability. [ABSTRACT FROM AUTHOR]

This paper aims to provide a systemic analysis to social opinion dynamics subject to individual biases. As a generalization of the classical DeGroot social interactions, defined by linearly coupled dynamics of peer opinions that evolve over time, biases add to state-dependent edge weights and therefore lead to highly nonlinear network dynamics. Previous studies have dealt with convergence and stability analysis of such systems for a few specific initial node opinions and network structures, and here we focus on how individual biases affect social equilibria and their stabilities. Two categories of equilibria, namely the boundary and interior equilibria, are defined. For a few fundamental network structures, some important interior network equilibria are presented explicitly for a wide range of system parameters, which are shown to be locally unstable in general. Particularly, the interval centroid is proven to be unstable regardless of the bias level and the network topologies. Next, we prove that when the initial network opinions are polarized towards one side of the state space, node biases will drive the opinion evolution to the corresponding interval boundaries. Such polarization attraction effect continues to hold under even directed and switching network structures. Finally, a number of numerical examples are provided to validate our study and advance the understanding of the nonlinearity inherited within the biased opinion evolution. [ABSTRACT FROM AUTHOR]

With the pressing demand of environmentally friendly personal transportation vehicles, mobility scooters become more and more popular for the short-distance transportation. Similar to pedestrians and bicyclists, scooter riders are vulnerable road users and are expected to receive severe injuries during traffic accidents. In this research, a MADYMO model of vehicle–scooter crash scenarios is numerically set up. The model of the vehicle with the scenario is validated in pedestrian–vehicle accident investigation with previous literatures in terms of throwing distance and HIC 15 value. HIC 15 values gained at systematic parametric studies. Injury information from various vehicle crashing speeds (i.e. from 10 m/s to 24 m/s), angles (i.e. from 0 to 360°), scooter's speeds (i.e. from 0 m/s to 4 m/s), contact positions (i.e. left, middle and right bumper positions) are extracted, analyzed and then compared with those from widely studied pedestrian–vehicle and bicycle–vehicle accidents. Results show that the ESS provides better impact protection for the riders. Riding ESS would not increase the risk higher than walking at the same impact conditions in terms of head injury. The responsible reasons should be the smaller friction coefficient between the wheel-road than the heel-road interactions, different body gestures leading to different contact positions, forces and timing. Results may shed lights upon the future research of mobility scooter safety analysis and also the safety design guidance for the scooters. [ABSTRACT FROM AUTHOR]

Raman and infrared reflectance spectroscopy were used to study GaN single crystals with different doping types and carrier concentrations. In different samples, the characteristic spectra related to LO phonon-plasmon coupled (LOPC) mode are observed as changing regularly with carrier concentration. Furthermore, to obtain the carrier concentration and mobility of GaN crystals, Raman and infrared reflectance spectra with LOPC modes were fitted. The carrier concentration obtained agrees well with that obtained from the Hall measurement, which confirms that these non-contact and non-destructive spectroscopy methods with high spatial resolution are reliable to probe the carrier concentration of doped GaN single crystals. • Getting the carrier concentrations and mobility of GaN with different concentration (1016-1018) by Raman and infrared reflectance spectroscopy. • Verifying the reliability of spectroscopy methods in testing carrier concentration of bulk GaN with different doping types. • Discussing possible reasons for the different mobility obtained by Hall, Raman and infrared reflectance measurements. [ABSTRACT FROM AUTHOR]

As a representative of the third-generation semiconductors, ZnO has great development potential in the fields of optics and electricity, so the measurement on its electrical properties is quite important. Here, we try to extract the carrier concentration and mobility of ZnO by mid-infrared reflectance spectroscopy. Considering that the longitudinal optical phonon-plasmon coupling (LOPC) mode formed by the coupling of ZnO optical phonons and free carriers depends on carrier concentration and mobility, we conduct a test on the MIR spectra (400-1600 cm−1) of ZnO crystals with different carrier concentration (1018-1015 cm−3). By fitting the strong reflection peak shape corresponding to phonon-polaritons formed by the coupling of its LOPC and infrared light through m-CDF model, the carrier concentration and mobility of these samples are obtained, which are consistent with those got from the Hall-effect measurement. • Extracted carrier concentration by fitting LOPC+ mode of the MIR spectrum of ZnO with different doping concentrations. • Determined the relationship between the LO and TO phonon damping of semiconductor with the change of doping concentration. • Proposed that combining LO phonon frequency fluctuation and considering crystal anisotropy can further fit the LOPC peak. [ABSTRACT FROM AUTHOR]

The metasurfaces through the reasonable design and arrangement of subwavelength nanostructures to control the spatial light field are expected to replace the traditional lens elements. However, the low light use efficiency (LUE) and difficulty in preparation caused by the etching process restrict the development of its application. Here, an idea of "doped metasurfaces" based on a spatial and regular doping of semiconductor thin films is proposed for the first time. Since the metasurfaces has no etched micro-nano structure, other optical functional films are allowed to be added, which greatly improves and enriches its optical performance. The effectiveness of the design is verified by simulating a suitable metasurface lens. The simulation results show that this designed MIR metalens possesses wide operating range, high transmittance, and high LUE. The method proposed here provides a new idea or perspective for constructing metasurfaces devices compatible with traditional optical thin films., Competing Interests: The authors declare no competing financial interests., (© 2021 The Author(s).)

This paper provides a comprehensive framework for the state-space approach to Boolean networks. First, it surveys the authors' recent work on the topic: Using semitensor product of matrices and the matrix expression of logic, the logical dynamic equations of Boolean (control) networks can be converted into standard discrete-time dynamics. To use the state-space approach, the state space and its subspaces of a Boolean network have been carefully defined. The basis of a subspace has been constructed. Particularly, the regular subspace, Y-friendly subspace, and invariant subspace are precisely defined, and the verifying algorithms are presented. As an application, the indistinct rolling gear structure of a Boolean network is revealed.