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In this paper, we compute the expected number of vehicles with at least one two-hop path to a fixed roadside unit (RSU) in a multi-hop, one-dimensional vehicular ad hoc network (VANET) where other cars can act as relays. The pairwise channels experience Rayleigh fading in the random connection model, and so exist, with a probability given by a function of the mutual distance between the cars, or between the cars and the RSU. We derive exact expressions for the expected number of cars with a two-hop connection to the RSU when the car density $\rho$ tends to zero and infinity, and determine its behaviour using an infinite oscillating power series in $\rho$, which is accurate for all regimes of traffic density. We also corroborate those findings with a realistic scenario, using snapshots of actual traffic data. Finally, a normal approximation is discussed for the probability mass function of the number of cars with a two-hop connection to the RSU., Comment: 5 pages, 5 figures

Technological advancements are rapidly changing traffic management in cities. Massive adoption of mobile devices and cloud-based applications have created new mechanisms for urban traffic control and management. Specifically, navigation applications have impacted cities in multiple ways by rerouting traffic on their streets. As different routing strategies distribute traffic differently across the city network, understanding these differences across multiple dimensions is highly relevant for policymakers. In this paper, we develop a holistic framework of indicators, called Socially-Aware Evaluation Framework for Transportation (SAEF), that will assist in understanding how traffic routing and the resultant traffic dynamics impact city metrics, with the intent of avoiding unintended consequences and adhering to city objectives. SAEF is a holistic decision framework formed as an assembled set of city performance indicators grounded in the literature. The selected indicators can be evaluated for cities of various sizes and at the urban scale. The SAEF framework is presented for four Bay Area cities, for which we compare three different routing strategies. Our intent with this work is to provide an evaluation framework that enables reflection on the consequence of policies, traffic management strategies and network changes. With an ability to model out proposed traffic management strategies, the policymaker can consider the trade-offs and potential unintended consequences.

State-of-the-art noisy intermediate-scale quantum computers require low-complexity techniques for the mitigation of computational errors inflicted by quantum decoherence. Symmetry verification constitutes a class of quantum error mitigation (QEM) techniques, which distinguishes erroneous computational results from the correct ones by exploiting the intrinsic symmetry of the computational tasks themselves. Inspired by the benefits of quantum switch in the quantum communication theory, we propose beneficial techniques for circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the spatio-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain stabilizer formalism to circuit-oriented stabilizers. The applicability and implementational strategies of the proposed techniques are demonstrated by using practical quantum algorithms, including the quantum Fourier transform (QFT) and the quantum approximate optimization algorithm (QAOA)., 17 pages, 26 figures, submitted to IEEE Transactions on Signal Processing

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

For two unknown mixed quantum states $\rho$ and $\sigma$ in an $N$-dimensional Hilbert space, computing their fidelity $F(\rho,\sigma)$ is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in $\operatorname{poly}(\log (N), r, 1/\varepsilon)$ time, where $r$ is the lower rank of $\rho$ and $\sigma$, and $\varepsilon$ is the desired precision, provided that the purifications of $\rho$ and $\sigma$ are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in $N$., Comment: Final version with an improvement over the previous version. 19 pages, 2 tables, 1 algorithm

The major aspect of the paper is to understand the recurring traffic congestion and suggest a precautionary measure to the society. This implication of widening the existing roads or building new one by acquiring the space just results in increase of additional heavy traffic. Government also restricts the new building of roads or other transportation needs because already the total available space in cities is used for transportation needs. In this paper a new design implementation of automatic movable traffic road dividers is used for the congestion release strategy in traffic prone areas instead of thinking about the old mechanism of widening roads to reduce this congestion. Hence the paper aims to build a road divider which is automated road divider having the mechanism of moving or shifting the lane directing the rush of the traffic. It has the flexibility to add one or more lane based on the traffic in particular directions. This type of mechanisms in traffic systems not only save time and fuel but also create green benefits reducing pollution.

The inherent noise and complexity of quantum communication networks leads to challenges in designing quantum network protocols using classical methods. To address this issue, we develop a variational quantum optimization framework that simulates quantum networks on quantum hardware and optimizes the network using differential programming techniques. We use our hybrid framework to optimize nonlocality in noisy quantum networks. On the noisy IBM quantum computers, we demonstrate our framework's ability to maximize quantum nonlocality. On a classical simulator with a static noise model, we investigate the noise robustness of quantum nonlocality with respect to unital and nonunital channels. In both cases, we find that our optimization methods can reproduce known results, while uncovering interesting phenomena. When unital noise is present we find numerical evidence suggesting that maximally entangled state preparations yield maximal nonlocality. When nonunital noise is present we find that nonmaximally entangled states can yield maximal nonlocality. Thus, we show that variational quantum optimization is a practical design tool for quantum networks in the near-term. In the long-term, our variational quantum optimization techniques show promise of scaling beyond classical approaches and can be deployed on quantum network hardware to optimize quantum communication protocols against their inherent noise., 28 pages, 16 figures, 1 table

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts. As such, QGLMs are receiving growing attention from the quantum physics and computer science communities, where various QGLMs that can be efficiently implemented on near-term quantum machines with potential computational advantages are proposed. In this paper, we review the current progress of QGLMs from the perspective of machine learning. Particularly, we interpret these QGLMs, covering quantum circuit born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models. In this context, we explore their intrinsic relation and their fundamental differences. We further summarize the potential applications of QGLMs in both conventional machine learning tasks and quantum physics. Last, we discuss the challenges and further research directions for QGLMs., The first two authors contributed equally to this work

Based on the connection between the categorical derivation of classical programs from specifications and the category-theoretic approach to quantum physics, this paper contributes to extending the laws of classical program algebra to quantum programming. This aims at building correct-by-construction quantum circuits to be deployed on quantum devices such as those available at the IBM Q Experience. Quantum circuit reversibility is ensured by minimal complements, extended recursively. Measurements are postponed to the end of such recursive computations, termed "quantamorphisms", thus maximising the quantum effect. Quantamorphisms are classical catamorphisms which, extended to ensure quantum reversibility, implement quantum cycles (vulg. for-loops) and quantum folds on lists. By Kleisli correspondence, quantamorphisms can be written as monadic functional programs with quantum parameters. This enables the use of Haskell, a monadic functional programming language, to perform the experimental work. Such calculated quantum programs prepared in Haskell are pushed through Quipper to the Qiskit interface to IBM Q quantum devices. The generated quantum circuits - often quite large - exhibit the predicted behaviour. However, running them on real quantum devices incurs into a significant amount of errors. As quantum devices are constantly evolving, an increase in reliability is likely in the near future, allowing for our programs to run more accurately., 18 pages

Road region and non-road region separation in the unstructured road intends to be an important task for safe navigation and collision avoidance for autonomous driving vehicles. The road that connects rural areas and cities to the national highways are considered as unstructured roads. Absence of clear lane marking on these unstructured roads makes them more prone to accidents in comparison to highways, which have clear lane marking for indication of road and non-road regions. However, the unstructured roads have different color information from its background that paves an easy way for design and development of an efficient road detection system for recognition and classification of road and non-road regions. Hence, in this paper, we propose an efficient road detection system for the classification of unstructured roads into road and non-road regions using multiple nearest neighbors (NN) classifier and soft voting aggregation approach. The proposed system utilized the chromatic information (i.e. *a,*b, and Hue) to train the NN classifiers and aggregated their output using soft voting (SV) approach for final output response. The output results of multiple classifiers were aggregated using SV approach based on the confidence score obtained by each individual classifier. The performance of the proposed system is evaluated in terms of precision, recall, accuracy, intersection over union (IOU), true positive rate (TPR), and processing time and compared with current state of art methods reported in the literature. The proposed system achieved precision, recall, accuracy, IOU, and TPR of 96.79%, 96.92%, 97.8%, 96.08% and 96%, respectively with the processing time three times smaller than those of the existing state of art methods. The experimental results demonstrate that the proposed system can provide an effective guidance to the autonomous vehicles through recognition and classification of road and non-road regions in rural, urban, and city areas, wherein single unstructured roads connect the national highways.

With the enhancement of location-acquisition technologies, GPS trajectories play an essential role in data-driven intelligent transportation applications, which requires an accurate approach to match raw GPS trajectories to road segments on a digital map. However, for complex urban roads containing elevated roads and surface roads, map matching for low-frequency GPS data is still challenging. This paper aims to address the biases and instability problem in existing approaches. To this end, we combine the spatial-temporal characteristics of GPS data in complex roads with driving behaviours and present a novel global map matching method including truncated density clustering algorithm, statistic features based spatial-temporal analysis, and driving-behaviour-based track modification. Additionally, a weighted-matrix based interactive voting algorithm is proposed to select the best results from a global perspective. The experiments are conducted with two real GPS trajectory datasets under three road conditions. The result shows that our approach outperforms state-of-art approaches for urban complex road networks in both accuracy and efficiency.

Quantum algorithms can be described as quantum circuits and are supposed to be carried out on an ideal quantum device that is far from current ones. The current quantum devices have a significant limitation on the connectivity between quantum bits. In other words, a quantum bit is only allowed to interact with its nearest neighbors. In reality, quantum bits have to be placed on a grid, where the connectivity between quantum bits is predefined. The predefined connectivity of a grid further determines the possible range of architectures of a quantum device after the placement of quantum bits. In this paper, we propose to place quantum bits based on a 2-D hexagonal architecture rather than a 2-D Cartesian architecture. To validate the effectiveness, we leverage a workflow for mapping nearest neighbor compliant quantum circuits onto targeting grids, where the workflow consists of a global reordering strategy and a local reordering strategy. With the advantages of the hexagonal grid, the overhead of making quantum circuits nearest neighbor compliant is reduced significantly compared with the Cartesian grid. We also provide a comprehensive set of ablation analyses to gain a better understanding of the contributions of the components within our workflow. According to the experimental results, when changing the grid type from Cartesian to hexagonal, the global reordering strategy is crucial for small quantum circuits. In contrast, the local reordering strategy is more important than the global reordering strategy for large quantum circuits.

Routing technologies have long been available in many automobiles and smart phones, but the nearly random nature of traffic on road networks has always encouraged further efforts to improve the reliability of navigation systems. Given the networks' uncertainty, an adaptive dynamic route selection model based on reinforcement learning is proposed. In the proposed method, the Markov decision process (MDP) is used to train simulated agents in a network so that they are able to make independent decisions under random conditions and, accordingly, determine the set of routes with the shortest travel time. The aim of the research was to integrate the MDP with a multi-nomial logit model (a widely used stochastic discrete-choice model) to improve finding the stochastic shortest path by computing the probability of selecting an arc from several interconnected arcs based on observations made at the arc location. The proposed model, tested with real data from part of the road network in Isfahan, Iran, and the results obtained demonstrated its good performance under 100 randomly applied stochastic scenarios.

In this article I raise a new problem for quantum mechanics, which I call the control problem. Like the measurement problem, the control problem places a fundamental constraint on quantum t...

The number of vehicles is increasing every day and the existing roads are not sufficient. Road users are given prior information about the traffic and some strict rules are to be implemented for the usage of vehicles. There are many case studies regarding this and some of them done using OCR. Building new roads and renovating the existing roads will improve the traffic conditions. In this we can count the number of vehicles using IR sensor, adjusting the time delay and informing the drivers about traffic through display boards placed at traffic junctions.

High temporal-spatial resolution measurement of the air temperature is important for human health and research on the urban heat island. Since conventional methods can hardly meet the demands, a novel measurement of the air temperature has been proposed by using the battery temperature sensor of the smartphone, which can provide a refined data in a crowdsourcing way. Firstly, a heat transfer retrieval model among the air temperature, battery temperature, and the pocket of the clothing or the desk is established. Then coefficients of the retrieval model are obtained by the dataset measured through the smartphone and the thermometer in the linear fitting method. Finally, we retrieve the air temperature in a specific site with time resolution of one minute based on the retrieval model by three independent smartphones. Retrieval air temperature of four models strongly correlates to the true value, indicating that the retrieval model can sense the variation of the air temperature. Meanwhile, root mean square errors of the 1st and 4th model are 0.35 and 0.45∘, implying the high retrieval accuracy. The results suggest that the retrieval model can provide a short timescale (minute) and robust estimation of the air temperature by smartphone temperature sensors.

Road traffic state estimation is an essential component of intelligent transportation systems (ITSs). However, road traffic state data collected by traffic detectors are often incomplete, which can cause problems across a variety of transportation applications, such as traffic state prediction and pattern recognition. We present GA-GAN (Graph Aggregate Generative Adversarial Network), consisting of graph sample and aggregate (GraphSAGE) and a generative adversarial network (GAN), to impute missing road traffic state data. Instead of using the original road network structure, which presents the spatial information to process a graph operation, we reconstruct the road network according to the correlation coefficients of road historical data. We utilize GraphSAGE to aggregate the temporal-spatial information from the neighbors of each road in the reconstructed road network. GAN is used to generate complete traffic state data from the extracted temporal-spatial information to achieve traffic state data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle, Washington, showing that the proposed model outperforms state-of-the-art methods.

The traffic speed information of an urban road network is generally estimated using the widely available taxi GPS data. However, taxi usages are preponderantly restricted to areas with high population density, which results in limited spatial coverage of collected taxi GPS data. Moreover, the traffic speeds of taxies are not guaranteed to well represent the traffic speeds of other types of vehicles. In this study, we address these issues by introducing an infinite Gaussian mixture model to estimate traffic speed distribution. The variational inference method is employed to deal with the complicated parameter estimation problem. The proposed mixture model simultaneously combines taxi GPS data, bus GPS data, and mobile phone GPS data, which not only generates the mixed traffic-speed distribution of different types of vehicles but also improves the spatial coverage and the quality of traffic speed estimation. Surprisingly, we find that the incorporation of mobile phone GPS data can considerably improve the model's ability to sense anomalous traffic conditions. Finally, the mixed traffic-speed distribution is validated using the license plate recognition data.

The use of regenerative braking and recent electrohydraulic or electromechanical brake-by-wire systems with enhanced dynamics enables improved antilock braking control performance in electric vehicles. However, the possible benefit is often limited by suboptimal control architectures with separate distributed controllers for electric motor (EM) and friction brake torque requests. Furthermore, oversimplifications in synthesis models for brake control design constrain the achievable performance. Instead, to date, threshold-based algorithms are widely used in modern production vehicles despite having been designed for slower hydraulic brake systems originally. To exploit the full potential, this work proposes a continuous nonlinear control design using input-output linearization for robust wheel speed tracking control. The design uses a unified controller for the redundant actuators. In addition, it explicitly considers drivetrain oscillations induced by regenerative braking using onboard EMs in the synthesis model. The stability for the resulting zero dynamics is shown through the Lyapunov analysis. The nonlinear controller is augmented by a subsequent control allocation for splitting the control effort on the redundant actuators. The allocation design ensures consistent control performance for both regenerative braking and hybrid braking while simultaneously aiming for high-energy recovery. The overall antilock braking control design is implemented in an electric test vehicle. Its tracking performance, disturbance attenuation, and robustness are experimentally validated through various emergency braking maneuvers on different road surfaces.

An adaptive traffic-control system (ATCS) is an advanced traffic-signal control system that updates traffic signal timing in an automated way to stabilise and smooth traffic. It is important to examine updated cost and benefits data for the entire life cycle of ATCSs to develop benefit/cost analysis frameworks. The application of a high-level benefit/cost analysis framework is presented in this paper. The framework was first tested on ramp metering systems in a previous study by the authors and its application in ATCSs produced promising results. The framework is composed of four main components: life-cycle cost analysis, benefits achieved through travel time savings, benefits achieved through reductions in energy consumption and benefits achieved through reductions in the number of accidents. A theoretical case study was used to demonstrate the application of the framework. A sensitivity analysis was conducted to determine the effects of higher traffic demand on the benefits achieved. The results showed that ATCSs can be feasible in relieving congestion at junctions. It is anticipated that the proposed framework can improve the higher-level decision-making procedures of transportation agencies concerning the deployment of ATCSs.

An efficient transportation system can substantially benefit our society, but road intersections have always been among the major traffic bottlenecks leading to traffic congestion. Appropriate traffic signal timing adapted to real-time traffic may help mitigate such traffic congestion. However, most existing traffic signal control methods require a huge amount of road information, such as vehicle positions. In this paper, we focus on a particular road intersection and aim to minimize the average waiting time. We propose a traffic signal control (TSC) system based on an end-to-end off-policy deep reinforcement learning (deep RL) agent with background removal residual networks. The agent takes real-time images at the road intersection as input. Upon sufficient training, the agent can perform (near-) optimal traffic signaling based on real-time traffic conditions. We conduct experiments on different intersection scenarios and compare various TSC methods. The experimental results show that our end-to-end deep RL approach can adapt to the dynamic traffic based on the traffic images and outperforms other TSC methods.

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.

The purpose of this study is to find the right place for a parking lot that does not pair the mobility of central Nagano． This is to control the location of the parking lot ithout impairing the movement behavior of car users， eliminate the congestion of cars on the roads in the city， and promote the movement behavior of pedestrians． The purpose of this study is to collect the questionnaire surveys conducted for parking lot users in the city area and use it as a material for creating guidelines for the maintenance of parking lots and streets in the central city area of Nagano City．

Online car-hailing services have become an integral part of people’s daily travel in China. Considering that young people are the major consumer group in car purchases, it is worth investigating how the experience of online car-hailing services affects their intention to purchase a car. Based on the extended theory of Planned Behavior, this study found that the factors that negatively impact the car purchase intention of the youth are firstly the public transportation service quality, followed by the risks of private cars, and finally the online car-hailing services quality. Elevating the convenience and comfort of public transportation is conducive to reducing car purchasing intention. The indirect effect of online car-hailing services on car purchase intention is greater than its direct effect, and the most important factor is attitude. The car purchase intention is significantly heterogeneous across age and annual household income groups. Improving the convenience of public transportation will reduce the car purchase intention of people in the early youth. For middle and later youth, providing demand-responsive transit for important individuals to meet their diverse needs can reduce car purchase intention. As for online car-hailing services, youth care most about their convenience and comfort and worry most about their safety. Providing better online car-hailing services can reduce the car-purchasing intention of youth.

Buses are the mainstay of public transport systems in many cities but are typically subject to significant delays due to traffic congestion. We examine the welfare effects of providing dedicated bus lanes in the city of Rome, Italy. We demonstrate that a dedicated bus lane reduces bus travel time by about 18 percent. Our welfare analysis focuses on the situation where mixed road lanes are turned into dedicated bus lanes. We find that bus lanes are undersupplied, despite the additional time costs due to reducing road capacity available to cars.

The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter. Our results might have implications for developing quantum-inspired algorithms and designing practical quantum kernels., 31 pages, 4 figures, 8 supplementary figures, 1 supplementary table

This research makes a case for the importance of children-specific non-fiction media content in a digital age. Drawing from my professional experience making children’s television, I piloted a media education and video production curriculum with a grade eight class at an independent, all-girls school in Toronto, Canada. This paper contextualizes my research by outlining the informing framework of participatory culture and several related concepts which intersect media studies, children’s culture, and pedagogy; it also presents my reflections on creating and testing the curriculum. To both complement my creative research-as-practice and participatory action research approach and chronicle the research project, I created a website (http://betweenproductions.wixsite.com/comcult). It features curriculum materials and research documents, including samples of the students’ work and their reflections; I also produced a short video that incorporates a mix of student and researcher video footage to illustrate one group’s experience creating their final project.

Quantum process tomography is a critical capability for building quantum computers, enabling quantum networks, and understanding quantum sensors. Like quantum state tomography, the process tomography of an arbitrary quantum channel requires a number of measurements that scale exponentially in the number of quantum bits affected. However, the recent field of shadow tomography, applied to quantum states, has demonstrated the ability to extract key information about a state with only polynomially many measurements. In this work, we apply the concepts of shadow state tomography to the challenge of characterizing quantum processes. We make use of the Choi isomorphism to directly apply rigorous bounds from shadow state tomography to shadow process tomography, and we find additional bounds on the number of measurements that are unique to process tomography. Our results, which include algorithms for implementing shadow process tomography enable new techniques including evaluation of channel concatenation and the application of channels to shadows of quantum states. This provides a dramatic improvement for understanding large-scale quantum systems., Comment: 12 pages, 5 figures; Added citation to similar work; Errors corrected. Previous statements of main result first missed and then miscalculated an exponential cost in system size; Version accepted for publication

While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question. Here, we demonstrate a Bayesian algorithm for constructing quantum kernels for support vector machines that adapts quantum gate sequences to data. The algorithm increases the complexity of quantum circuits incrementally by appending quantum gates selected with Bayesian information criterion as circuit selection metric and Bayesian optimization of the parameters of the locally optimal quantum circuits identified. The goal is to build quantum kernels for SVM that can solve classification problems with as little training data as possible. The performance of the resulting quantum models for the classification problems considered here significantly exceeds that of optimized classical models with conventional kernels.

The precision of optical atomic clocks is approaching a regime where they resolve gravitational time dilation on smaller scales than their own extensions. Hence, an accurate description of quantum clocks has to take their spatial extension into account. In this article, as a first step towards a fully relativistic description of extended quantum clocks, we investigate a quantized version of Einstein's light clock fixed at a constant distance from a large massive object like the Earth. The model consists of a quantum light field in a one-dimensional cavity in Schwarzschild spacetime, where the distance between the mirrors is fixed by a rigid rod. By comparing a vertical and a horizontal clock, we propose an operational way to define the clock time when the clock resolves gravitational time dilation on scales smaller than its extension. In particular, we show that the time measured by the vertical light clock is equivalent to the proper time defined at its center. We also derive fundamental bounds on the precision of these clocks for measurements of proper time and the Schwarzschild radius., Comment: updated references

Machine learning can be substantially powered by a quantum computer owing to its huge Hilbert space and inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully exploit limited quantum resources. Here, we report the first experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available., 6 pages, 4 figures and supplement

With the development of VR and Internet of things and the characteristics of isomorphic block decoration, based on the two technologies, this paper comes to the realization of isomorphic block decoration in fashion design. Firstly, by analyzing the algorithm combining VR and the Internet of things, a platform based on the combination of both technologies is established. Then, the platform is applied to the same shape block decoration in different cities in the process of fashion design, and the differences in the process of fashion design are analyzed. The results of fashion design show that the isomorphic block decoration based on the combination of VR and Internet of things should be designed according to the local characteristics to promote the development of fashion design industry according to local conditions. In general, this paper provides some theoretical and practical experience for the application of isomorphic block decoration in fashion design.

Quantum data communications and networking involve classical hardware and software. Quantum storage is sensitive to environmental disturbances that may have malicious origins. Teleportation and entanglement swapping, two building blocks for the future quantum Internet, rely on secure classical bit communications. When lack of authenticity, integrity, and replay protection may have a high impact, quantum data communications are at risk and need to be protected. Building upon quantum cryptography and random generation of quantum operators, we propose a solution to protect the authenticity, integrity, and replay of quantum data communications. Our solution includes a classical data interface to quantum data cryptography. We describe how classical keying material can be mapped to quantum operators. This enables classical key management techniques for secure quantum data communications.

With the economic development in recent years, smart cities have also risen. Smart city is based on the next generation of innovation in the knowledge society, and the use of new generation information technology in all areas of the city is conducive to the improvement of the quality of urbanization and the quality of citizens’ lives. This article aims to study the significance of the reduction algorithm in the energy consumption analysis of smart cities. This article puts forward the problem of how to solve the problem of huge energy consumption in smart cities. In smart cities, the related objects that generate energy consumption mainly include industry, agriculture, and transportation construction. People’s lives are ubiquitous in the generation of energy consumption. This paper analyzes the energy consumption of smart cities through the reduction algorithm and puts forward a plan on how to reduce the energy consumption of smart cities, which is meaningful for the rational use of resources and reducing the cost of smart cities.

This paper explores employee attitudes on pay communication and pay transparency and compares perceptions from private and public sector employees. Primary research was conducted among 353 employees working in the public and private sector in Croatia. Research results indicate that employees from the public sector, when compared to employees from the private sector, showed a more positive attitude towards pay transparency, higher preference for pay transparency and willingness to communicate one’s pay. Employees from the public sector perceive greater beneficial effect of pay transparency than employees from the private sector. The pay communication policy and level of pay transparency can act as additional factors that attract employees to the public or private sector and thus cause employee sorting between the two sectors.

Continuous monitoring of road surface quality is necessary to maintain high functionality of the entire road networks and allow users to perceive road roughness. Although several fundamental methods have been used to evaluate road roughness by employing smart probe cars, the accuracy of these methods is affected by factors, such as vehicle speed, sensor positions, and vehicle suspension systems. Also, the long-term application of these methods to the entire road network is also limited due to the high cost. Further, the widely used roughness indices do not reflect road users' perception about road roughness as these indices are also affected by the accuracy of the roughness evaluation methods. Therefore, we propose a crowdsourcing based road roughness evaluation model which uses power spectral density accompanied with blind source separation technique to eliminate the vehicle effects. We also propose a road surface ranking based on majority voting algorithm for comparing roads based on their surface quality. Finally, the road surface roughness index is derived to widen the range of quality scale and capture roughness at fine granularity. The models are tested by real world experiments on different roads in Taiwan. The results show that the proposed model can accurately measure the roughness of roads, rank these roads and index them accordingly in a way that shows tiny differences in the road surface quality.

The enormous utilization of Internet of Things (IoT) is allowing Smart City projects and initiatives all overthe world. The IoT is a modular approach to merge various sensors with all the solutions. Due to the advancements in technologies, smart cities have been considered as a versatile parameter in order to control, monitor and operate in different fields (like electrical fault detection, corporation duties etc.) in an efficient manner without costing much of time and manpower thereby making the city smarter than before. Cities canbe interfaced with Internet of Things(IoT) which finds many applications in the conversion of a city to a Smart City. In this paper, a sincere attempt has been taken to list out the essentials of Smart Cities and to overcome the issues faced by normal cities. Also, the main purpose of this paper is that of providing a detailed review on the concepts of smart cities and their applications in different fields. In particular, this concept describes the IoT technologies for smart cities and the main features of a smart city. Keywords: Internet of Things(IoT), smart city, environment, sensors, electrical system.

When a high-speed loom is suddenly stopped or is driven again in a faulty state, it is easy to cause defects such as brake marks and driving marks on the fabric. This is because the brake system of the loom has insufficient control of the accuracy of the parking angle. To improve the control accuracy of the braking system, this paper analyzes the dynamic process of the braking system when braking, builds a mathematical model of the electromagnetic clutch of the braking system, and proposes to regulate the excitation current of the system by fuzzy PID algorithm. The rule table of this PID controller is established by expert experience, and the center of gravity method is used for defuzzification. In addition, this article combines internet communication and storage technology to build a cloud platform for the brake system, and realizes the monitoring, storage and diagnosis of the brake system data. Finally, hardware testing and on-site braking experiments (including parking angle data acquisition experiment and spindle speed change experiment during braking) verify that the design can achieve high-precision parking angle positioning at different speeds, and has a fast dynamic response and a smooth braking process.

The "smart city" concept is based on the wide use of information and communication technologies. The Internet of Things is the key to the new opportunities to collect and analyze the data needed to create a more pleasant environment. It will connect all the smart-city elements and turn them into a coherent mechanism. Under many definitions, a smart city is a city with good functionality and a perspective on the economy, management, mobility, and environment. At the same time, the city is "sustainable" if it is composed of activities of independent and conscious citizens who can make their own decisions. For the further development of the current smart cities and also the contenders for that title, constant state support for new projects is necessary. After discussing the general criteria for smart cities, the authors considered smart cities in Serbia. Also, they discussed the application of the geographic information system, activities of communal services, eGovernment, and some solutions that are already in use. The authors gave a critical review of shortcomings in practice and inconsistencies in the smart-cities creation in Serbia. They pointed out that the authorities are taking steps toward the digitalization of cities, trying to involve citizens in the decision-making process, realizing that the development of smart cities has a positive impact on the national economy. But also, it must be done more decisively and efficiently, primarily in the field of legislation and project financing.

Much of the smart cities literature urges greater citizen participation in smart city innovation. However, there is often little consideration given to how citizens might be more meaningfully involved in the processes of governance around smart cities, what enables their involvement, or what might need to change in order to facilitate their participation. Taking an institutional perspective, this paper seeks to move this aspect of the smart city debate forward. Using Mexico City as an exemplar, it examines the broader institutions of urban governance within which citizen-oriented smart city activities operate, identifying those which help and hinder citizen participation. It then considers the extent to which unhelpful institutions are embedded, and to what extent they are amenable to change to allow successful smart city participation initiatives to flourish. Our argument is that when considering citizen participation in smart city activities we need to attend more closely to the institutions which represent their context and the extent to which those institutions can be changed, where necessary, to create a more conducive environment. Many institutions will be beyond the reach of local actors to change or to deinstitutionalise; thus involving citizens in the smart city is ‘easier said than done’.

In the recent decades, smartphone addiction has been addressed its importance in daily life. With smartphones, people can use the Internet even more easily. People can also use smartphones at any time in any place. Nevertheless, the psychological causal variables for smartphone addiction are still not clear. Furthermore, people with low self-esteem are shown to reveal more personal information online for social compensation. It is also unclear what causes the social compensation. This study is therefore motivated to clarify users’ smartphone addiction based on the technology acceptance model (TAM). Survey method was adopted. Data of one hundred and seventy-one valid respondents who used smartphones were gathered online. Factor analysis and regression analysis were adopted for data analysis using SPSS 18.0. The regression path analysis results indicate that perceived escapism, increased by perceived enjoyment, significantly associates with users’ smartphone addiction. The results also show users’ different smartphone addiction patterns under different perceived usefulness of smartphones. The results facilitate users’ self-management of smartphone use via increasing perceived usefulness and the cure of smartphone addiction.

In this paper, a better layout of the computer room air conditioning (CRAC) units in the data center (DC) is obtained by comparatively analyzing the indices about velocity and temperature in the room. First, Computational Fluid Dynamics (CFD) is utilized to model the computer room of DC, and two layout schemes of air conditioners are presented. Then, to determine the better layout, the distribution of average air velocity and temperature field of the two layout schemes are compared. Besides, a uniformity index is proposed to conduct the quantitative analysis of the temperature uniformity at the inlet of racks under two layout modes. Considering the influences of seasonal factors on the cooling efficiency, the temperature distribution analysis with different outdoor temperature under two layouts is carried out contrastively.

Many countries and international organizations have put a significant amount of effort into improving road traffic safety by setting their own road traffic safety goals and strategies. This study aims to provide a conceptual framework for road traffic safety through an international comparison of traffic safety goals and strategies. Such a comparison can help improve mutual understanding and communication among stakeholders in different countries and international organizations. Based on a review of previous traffic safety literature and comparing traffic safety strategies of several different countries and international organizations, this study proposes a conceptual framework for road traffic safety consisting of a common vision, traffic safety indicators, a safe system including the 3Es and some additional Es, and a traffic safety culture, which is the basis of the framework.

A unidirectional microelectromechanical system (MEMS) microphone single module for handsfree and voice recognition systems in automobiles is presented. Because in-cabin noise and temperature variation affect the reduction in the handsfree and voice recognition system performance, noise suppression and increasing the signal-to-noise ratio (SNR) of the microphone are required. In this study, a capacitive MEMS microphone module with a high SNR and a unidirectional characteristic is achieved by designing the structure, package, and module of the MEMS microphone. To improve the SNR, the microphone is developed using a slit-edged membrane. The slit structure is designed to release the residual stress of the membrane to achieve improved sensitivity and SNR. The unidirectional characteristic of the microphone enables suppression of noise signals from undesired directions. The directional characteristic of the microphone is realized by attaching a porous SU-8 filter to delay the time to one of the two acoustic ports on the package. Tests on the proposed unidirectional MEMS microphone package and module show that an SNR of 62.4 dB and a front-back ratio of 27.1 dB are achieved.

Resolving the challenge of flight delays caused by air traffic congestion renders it necessary to explore the mode of congestion propagation. By applying complex network theory, this article establishes a complex network structure where airspace sectors act as nodes, and the edges represent traffic flow relationships between sectors. In addition, a cascading failure model is proposed to analyze the airspace sector network’s invulnerability. The critical threshold and the sector abnormality rate are determined according to the cascading failure measurement indexes. Based on the remaining capacity of sector nodes, two optimization strategies are proposed: adjacent load redistribution strategy and local load redistribution strategy. The simulation results demonstrate that the local load redistribution strategy greatly improves the airspace sector network’s invulnerability to cascading failure.

Adequate air ventilation systems and maintaining environmental air quality are essential things that need to be considered to create an excellent industrial environment in a company. Operating or maintaining ecological air quality is a challenge for companies to optimize the existing work environment. Especially at the economic and business level in the company are facing the main problems. In this case, monitoring and predicting future air quality are needed to maintain air quality conditions. PM10 concentration, humidity, and temperature were used to predict annual and seasonal indoor air quality using a recurrent neural network (RNN) and long short-term memory (LSTM). In this paper, we propose the IAQP method for air quality management systems that combines indoor air quality forecasting based on real-time data. To measure indoor air quality, we predict the outcome from the IIoT sensor and LoRa sensor. The result of prediction is that the multilevel RNN model outperformed the LSTM, as the model demonstrated excellent results and feasibility.

This article aims to explore the impact of digital communication tools application by citizens and the perceived usefulness of social media on the relationship between citizens and local authorities. The data were gathered in April–September 2020 through a survey (CAWI) among citizens of Poznan, Poland (n=502), and Kutaisi, Georgia (n=504), and were analyzed with structural equation modeling. The findings show that the intensity of digital communication tools usage for participation in the city branding and the perceived usefulness of social media contribute to the lasting relationships between citizens and local authorities in both countries. The novelty of this research concerns comparing two countries with different levels of development. Georgia is a developing economy in Europe and is in the process of modernizing the local governance across the cities. Poland, however, is a mature economy with a post-transformation heritage, where its cities benefit from considerable experience in building and developing citizen participation policies. Furthermore, the research was conducted amid the COVID-19 pandemic and evidenced the growing popularity of digital tools adoption by citizens in city matters. This study contributes to understanding the impact of digital tools on the relationship between citizens and local authorities in terms of city brand management. Citizens' participation in the city branding process via various digital communication tools increased citizen commitment towards long-lasting collaboration with local authorities. Moreover, citizens' perception of social media usefulness positively influences their desire to engage in the city branding process online, supporting the trust-building and collaboration between citizens and local authorities. Points for practitioners The intensiveness of digital tools usage – governments should identify the tools already trusted and popular among their audience and employ those tools to a greater extent to maximize the chances of feedback, high citizen participation, and commitment. Citizens’ perception of social media – such characteristics as ease of use, transparency, ease of communication with the municipality, and safety encourage citizens to get involved in the city brand management process. Consequently, local authorities should consider the features mentioned above and develop the online tools quality.

With the continuous development of economy, the traditional urban management system cannot meet the needs of the city. In order to solve many inconvenient urban problems brought by the traditional urban management system and provide convenience for urban development more quickly, the concept of “smart city” has been put forward and gradually applied to urban construction. Based on cloud computing and Internet of things, the service system and construction of smart city were studied in detail; on this basis, the demand of construction of smart city in coastal city A and the construction and practical application of smart city of city A in our country were analyzed so as to provide some reference value for the construction of smart city in our country. Through an in-depth test of the data mining algorithm, this paper verifies the calculation effect of the data mining algorithm and the optimization effect of a smart city public management system.

Quantum computing is widely considered a frontier of interdisciplinary research and involves fields ranging from computer science to physics and from chemistry to engineering. On the one hand, the stochastic essence of quantum physics results in the random nature of quantum computing; thus, there is an important role for statistics to play in the development of quantum computing. On the other hand, quantum computing has great potential to revolutionize computational statistics and data science. This article provides an overview of the statistical aspect of quantum computing. We review the basic concepts of quantum computing and introduce quantum research topics such as quantum annealing and quantum machine learning, which require statistics to be understood.

With the intelligent development of road traffic control and management, higher requirements for the accuracy and effectiveness of traffic data have been put forward. The issue of how to collect and integrate data for traffic scenes has sought importance in this field as various treatment technologies have emerged. A lot of research work have been carried out from the theoretical aspect to engineering application.