ObjectiveTo better understand the origins, manifestations and current policy responses to patient–physician mistrust in China.DesignQualitative study using in-depth interviews focused on personal experiences of patient–physician mistrust and trust.SettingGuangdong Province, China.ParticipantsOne hundred and sixty patients, patient family members, physicians, nurses and hospital administrators at seven hospitals varying in type, geography and stages of achieving goals of health reform. These interviews included purposive selection of individuals who had experienced both trustful and mistrustful patient–physician relationships.ResultsOne of the most prominent forces driving patient–physician mistrust was a patient perception of injustice within the medical sphere, related to profit mongering, knowledge imbalances and physician conflicts of interest. Individual physicians, departments and hospitals were explicitly incentivised to generate revenue without evaluation of caregiving. Physicians did not receive training in negotiating medical disputes or humanistic principles that underpin caregiving. Patient–physician mistrust precipitated medical disputes leading to the following outcomes: non-resolution with patient resentment towards physicians; violent resolution such as physical and verbal attacks against physicians; and non-violent resolution such as hospital-mediated dispute resolution. Policy responses to violence included increased hospital security forces, which inadvertently fuelled mistrust. Instead of encouraging communication that facilitated resolution, medical disputes sometimes ignited a vicious cycle leading to mob violence. However, patient–physician interactions at one hospital that has implemented a primary care model embodying health reform goals showed improved patient–physician trust.ConclusionsThe blind pursuit of financial profits at a systems level has eroded patient–physician trust in China. Restructuring incentives, reforming medical education and promoting caregiving are pathways towards restoring trust. Assessing and valuing the quality of caregiving is essential for transitioning away from entrenched profit-focused models. Moral, in addition to regulatory and legal, responses are urgently needed to restore trust.

This paper is based on findings resulting from ASHRAE Research Project RP-1271.Optimal control of enclosed environment requires detailed information of air distribution that could be obtained by numerically solving Navier-Stokes equations with a suitable turbulence model. This investigation evaluated the performance of eight turbulence models for transient airflow in an enclosed environment using experimental data obtained in a room. The study used the room to create three cases with gradually added flow features, which were jet, separations, and thermal plumes. The flow regimes were transitional. The study found that some Reynolds Averaged Navier-Stokes (RANS) models were good for simple but not complicated flows. The large-eddy-simulation (LES) model was the most accurate and stable. The detached-eddy-simulation model (DES) model underpredicted turbulence kinetic energy near the walls. If the DES model include the subgrid-scale turbulence kinetic energy, the results can be significantly improved. This study shows the advanced features of LES and DES models for solving airflow in enclosed environment.

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Abstract-We present a real-time algorithm which enables an autonomous car to comfortably follow other cars at various speeds while keeping a safe distance. We focus on highway scenarios.A velocity and distance regulation approach is presented that depends on the position as well as the velocity of the followed car. Radar sensors provide reliable information on straight lanes, but fail in curves due to their restricted field of view. On the other hand, Lidar sensors are able to cover the regions of interest in almost all situations, but do not provide precise speed information. We combine the advantages of both sensors with a sensor fusion approach in order to provide permanent and precise spatial and dynamical data.Our results in highway experiments with real traffic will be described in detail.

Software-Defined Networking (SDN) is an emerging architecture for the next-generation Internet, providing unprecedented network programmability to handle the explosive growth of big data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance characteristics of SDN networks, several research efforts from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale networks. However, for analytical tractability and simplification, existing analytical models are derived based on the unrealistic assumptions that the network traffic follows the Poisson process, which is suitable to model nonbursty text data, and the data plane of SDN is modelled by one simplified Single-Server Single-Queue (SSSQ) system. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of capturing such features of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this article presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by the Markov Modulated Poisson Process (MMPP). The Quality-of-Service performance metrics in terms of the average latency and average network throughput of the SDN networks are derived based on the developed analytical model. To consider a realistic multiqueue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model the SDN data plane. To address the challenging problem of obtaining the key performance metrics, for example, queue-length distribution of a PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approximation (EBA) method is proposed to facilitate the decomposition of such a PQ system to two SSSQ systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of the network configuration and resource allocation in SDN networks.

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

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