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Clozapine, whilst associated commonly with a transient and benign increase in liver enzymes, has also been associated with varying presentations of hepatitis in existing case reports. This report describes what we believe to be the first documented case of acute liver injury and pleural effusion associated with clozapine, resolving after cessation of the agent. The case supports existing literature in advocating a high index of suspicion, particularly in the 4-5 weeks following clozapine initiation, when considering nonspecific clinical symptoms and signs.

Persons with intellectual disability and/or low-functioning autism spectrum didorder are with high support need (ID/ASD-HSN) are among the people who are most vulnerable to the COVID-19 pandemic. The specific vulnerability and the protective factors for persons with ID/LF-ASD attending residential and rehabilitative facilities have however received little attention. This paper aims to describe how two facilities located in the Italian COVID-19 red zone faced the risks associated with the spread of the pandemic and the results they have achieved so far.

Interventions to contrast the spread of the pandemic and preserve clients’ health conditions have been systematically monitored and recorded since the very beginning of the pandemic.

26/138 clients had to undergo clinical screening and laboratory tests for COVID-like symptomatology, but only one resulted affected by COVID-19 and survived. Considering that Lombardy had 89,595 cases and 16,262 deaths (January–May 2020), one COVID-19 case/138 clients is a good result. Temporarily limiting physical contacts with friends/family in favor of reducing the burden of risk and adopting a system of prevention/safety strategies directed for persons with ID/LF-ASD attending and their caregivers have been useful measures.

Structured or semi-structured interviews (using professional caregivers as informant) to confirm behavioral and emotional changes in the clients could not be carried out because of lack of time and resources (which were captured by the management of the pandemic) and could be the next goal for our residential facilities to implement the management of epidemic acute phases in a research-oriented view.

This study is a service evaluation report about facing COVID-19 pandemic. Only few such studies are present in medical literature about ID/ASD.

This paper evaluates an alternative queuing concept for marine container terminals that utilize a truck appointment system (TAS). Instead of having all lanes providing service to trucks with appointments, this study considers the case where walk-in lanes are provided to serve those trucks with no appointments or trucks with appointments but arrived late due to traffic congestion.

To enable the analysis of the proposed alternative queuing strategy, the queuing system is shown mathematically to be stationary. Due to the complexity of the model, a discrete event simulation (DES) model is used to obtain the average waiting number of trucks per lane for both types of service lanes: TAS-lanes and walk-in lanes.

The numerical experiment results indicated that the considered queuing strategy is most beneficial when the utilization of the TAS lanes is expected to be much higher than that of the walk-in lanes.

The novelty of this study is that it examines the scenario where trucks with appointments switch to the walk-in lanes upon arrival if the TAS-lane server is occupied and the walk-in lane server is not occupied. This queuing strategy/policy could reduce the average waiting time of trucks at marine container terminals. Approximation equations are provided to assist practitioners calculate the average truck queue length and the average truck queuing time for this type of queuing system.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

This study aims to propose a centralized optimal control model for automated left-turn platoon at contraflow left-turn lane (CLL) intersections.

The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness. The proposed model is cast into a mixed-integer linear programming problem and then solved by the branch-and-bound technique.

The proposed model has a promising control effect under different geometric controlled conditions. Moreover, the proposed model performs robustly under various safety time headways, lengths of the CLL and green times of the main signal.

This study proposed a centralized optimal control model for automated left-turn platoon at CLL intersections. The lateral lane change control and the longitudinal acceleration in the control horizon are optimized simultaneously with the objective of maximizing traffic efficiency and smoothness

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.

The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.

Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.

Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.