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Infrastructure supporting vehicular network (V2X) capability is the key factor to the success of smart city because it enables many smart transportation services. In order to reduce the traffic congestion and improve the public transport efficiency, many intelligent transportation systems (ITS) need to be developed. In this paper, a smart traffic signal control (STSC) system is designed and implemented, it supports several smart city transportation applications including emergency vehicle signal preemption (EVSP), public transport signal priority (TSP), adaptive traffic signal control (ATSC), eco-driving supporting, and message broadcasting. The roadside unit (RSU) controller is the core of the proposed STSC system, where the system architecture, middleware, control algorithms, and peripheral modules are detailed discussed in this paper. It is compatible with existed traffic signal controller so that it can be fast and cost−effectively deployed. A new traffic signal scheme is specially designed for the EVSP scenario, it can inform all the drivers near the intersection regarding which direction the emergency vehicle (EV) is approaching, smoothing the traffic flow, and enhancing the safety. EVSP scenario and the related control algorithms are implemented in this work; integration test and field test are performed to demonstrate the STSC system.

To solve the problem of jitter and low network throughput caused by the impact of background flows on IQ traffic in mobile fronthaul network, this paper proposed a new scheduling model for background flows, named hierarchical crossover traffic scheduling mechanism based on time-aware shaper (HC-TAS) by improving the traditional counterpart. Then, in this new model, we designed an inbound scheduling algorithm based on frame length matching and an outbound scheduling algorithm based on queue status, making sure that smaller data frames will not be blocked by large data frames. This greatly improves the utilization of timeslots in the scheduling process and reduces the jitter impact of background flows. To verify its performance, we conducted experiments in a simulated fronthaul network conforming to IEEE 802.1CM. The experimental results show that, under the condition that the jitter is guaranteed to be zero, compared with two mainstream scheduling schemes, Comb-FITting and TAS + Preemption, our proposed scheme can achieve lower maximum end-to-end delay and higher link utilization. The proposed HC-TAS meets the requirements of low jitter and high bandwidth utilization in 5G fronthaul network, and the research results provide a technical basis for the application and development of general time-sensitive networks as well.

The fog-assisted cloud computing gives better quality of service (QoS) to Internet of things (IoT) applications. However, the large quantity of data transmitted by the IoT devices results in the overhead of bandwidth and increased delay. Moreover, large amounts of data transmission generate resource management issues and decrease the system’s throughput. This paper proposes the o ptimized task s c heduling a nd p r eemption (OSCAR) model to overcome the limitations and improve the QoS. The dataset used for the study is a real-time crowd-based dataset which provides task information. The processes involved in this paper are as follows: (i) Initially, the tasks from the IoT devices are clustered based on the priority and deadline by implementing expectation–maximization (EM) clustering to decrease the computational complexity and bandwidth overhead. (ii) The clustered tasks are then scheduled by implementing a modified heap-based optimizer based on the QoS and service level agreement (SLA) constraints. (iii) Distributed resource management is performed by allocating resources to the tasks based on multiple constraints. The categorical deep Q network is the deep reinforcement learning model is implemented for this purpose. The dynamic nature of tasks from the IoT devices is addressed by performing preemption of tasks using the ranking method, where the tasks with higher priority, with a short deadline replaces less priority task by moving it into the waiting queue. The proposed model is experimented with in the iFogsim simulation tool and evaluated in terms of average response time, loss ratio, resource utilization, average makespan time, queuing waiting time, percentage of tasks satisfying the deadline and throughput. The proposed OSCAR model outperforms the existing model in achieving the QoS and SLA with maximal throughput and reduced response time.

Modern societies are characterized by a division of epistemic labor between laypeople and epistemic authorities. Authorities are often far more competent than laypeople and can thus, ideally, inform their beliefs. But how should laypeople rationally respond to an authority’s beliefs if they already have beliefs and reasons of their own concerning some subject matter? According to the standard view, the beliefs of epistemic authorities are just further, albeit weighty, pieces of evidence. In contrast, the Preemption View claims that, when one discovers what an authority believes, it is not permissible to rely on any of one’s own reasons concerning the subject matter. The original version of this view, as proposed by Linda Zagzebski, has recently been severely criticized for recommending blind trust and for abandoning even minimal standards for critical thinking. In our paper, we defend a new version of the Preemption View—Defeatist Preemptionism—in a way that differs radically from Zagzebski’s. We argue that our view can be derived from certain widely accepted general epistemic principles. In particular, we claim that preemption can be identified as a special case of source sensitive defeat. Moreover, we argue that Defeatist Preemptionism does not lead to the undesirable consequences that critics ascribe to the Preemption View. The paper thus articulates the foundations and refinements of the Preemption View, such that it adequately captures the phenomenon of epistemic authority and the rational requirements related to it.

For the last two decades, the United States has been the dominant enforcer of anti-bribery norms worldwide. Using the broad extraterritorial jurisdiction granted by the Foreign Corrupt Practices Act (FCPA), the Department of Justice (DOJ) and the Securities and Exchange Commission (SEC) have prosecuted domestic and foreign corporations for bribing foreign government officials. This transnational enforcement system has been described as a negative comity regime: foreign governments defer to American prosecutions even if the case involves their nationals. This system has created a robust enforcement environment but often faces foreign resistance to the perceived American dominance of the regime. This Article analyzes the recent rise of a new enforcement model: the global FCPA settlement, where multiple governments enter into parallel deferred prosecution agreements or other non-trial resolutions with corporate entities. This enforcement model is now the principal form for concluding blockbuster FCPA cases and has significant implications for the evolution of the transnational anti-bribery law regime. This Article argues that the rise of global settlements results from (1) a greater demand by foreign governments to be involved in foreign bribery resolutions when their national firms are on trial and (2) American prosecutors willingness to accommodate this demand when it results in more effective prosecutions (i.e., new cases, more claims, or stronger evidence). This Article contends that the rise of global FCPA settlements marks a significant shift in the international anti-bribery enforcement regime, transitioning from a negative comity to a coordinated comity regime. The global resolution model addresses some of the concerns of the negative comity regime by permitting multiple governments to have a voice in negotiating the level of the penalties, the distribution of the penalties, and any structural reforms that the firm will be required to adopt. It also can potentially increase the effectiveness of the enforcement regime by expanding the geographic scope of the investigations and quality of evidence. This Article additionally discusses the importance of these developments to the Trump Administration s executive order on FCPA enforcement and the June 2025 DOJ enforcement guidelines.

The past decade has witnessed the emergence and rapid spread of a new and aggressive form of state preemption of local government action across a wide range of subjects, including among others firearms, workplace conditions, sanctuary cities, antidiscrimination laws, and environmental and public health regulation. Particularly striking are punitive measures that do not just preempt local measures but also hit local officials or governments with criminal or civil fines, state aid cutoffs, or liability for damages, as well as broad preemption proposals that would virtually end local initiative over a wide range of subjects. The rise of the new preemption is closely linked to the partisan and ideological polarization between red states and their blue cities. This Essay examines the spread of the new preemption and explores the legal doctrines available to local governments for challenging it. It argues that the more extreme preemption measures threaten the capacity for local self-government and are at odds with the values of local autonomy, the cornerstone role local governments play in our governmental structure, and the widespread state constitutional commitment to home rule. It also considers whether arguments about localism, like arguments about federalism, are really just about means to specific policy ends. It concludes that particularly in the current era of polarization, our system ought to protect some local space for self-determination for problems that arise at the local level.