Abstract
Objective: The aim of this systematic review and meta-analysis is to systematically collect, evaluate, and critically synthesize research findings on the effects of physical exercise on chemotherapy-induced peripheral neuropathy (CIPN). Method: The Joanna Briggs Institute (JBI) methodology for systematic reviews was adopted for this study. We searched the Medline (R), CINAHL, SportDiscus, and Scopus databases to identify relevant articles published from inception to March 2024. This review was reported in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Twelve studies met the inclusion criteria, totaling 928 participants. Interventions ranged from aerobic and resistance exercises to balance and strength training. A range of physical exercise interventions was explored, including brisk walking, endurance training, weight exercises, and resistance bands, as well as combined programs of aerobics, resistance, and balance training, all tailored to improve symptoms and quality of life in patients with chemotherapy-induced peripheral neuropathy. The meta-analysis focused on five studies that used the FACT/GOG-Ntx scale indicated a standardized mean difference of 0.50 (95% CI: 0.26, 0.74), favoring exercise, reflecting significant improvements in neuropathy symptoms. The heterogeneity among the studies was low (I 2 = 2%), suggesting consistency in the beneficial effects of exercise. Conclusions: From the results analyzed, the descriptive analysis of the 12 included studies shows promising outcomes not only related to individuals' perceptions of CIPN severity but also in terms of physical functioning, balance, ADL (Activities of Daily Living) performance, pain, and quality of life. The findings support the integration of structured exercise programs into oncological treatment plans. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of American Society for Pain Management Nursing. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Abstract
The evolution of transport technologies, marked by integrating connectivity and automation, has led to innovative approaches such as truck platooning. This concept involves linking multiple trucks through automated driving and vehicle-to-vehicle communication, promising to revolutionize the freight industry by enhancing efficiency and reducing operational costs. This systematic review explores the current state of truck platooning testing literature, focusing on simulator and on-road tests. The objective is to identify key scenarios and requirements for successfully developing and implementing the truck platooning concept. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines, we searched the Web of Science and Scopus databases, leading to the inclusion of thirty pertinent articles encompassing simulation-based, on-road, and mixed-environment experiments. In addition to the type of testing environment, these articles were assorted into three groups corresponding to their main thematic scope, human-centered, technology-centered, and energy efficiency studies, each providing unique insights into core themes for the development of truck platooning. The results reveal a commonly preferred platoon formation consisting of three trucks maintaining a constant speed of 80 km/h and a stable distance of 10 m between them. Simulator-based studies have predominantly concentrated on human factors, examining driver behavior and interaction within the platooning framework. In contrast, on-road trials have yielded tangible data, offering a more technology-driven perspective and contributing practical insights to the field. While the literature on truck platooning has grown considerably, this review recognizes some limitations in the existing literature and suggests paths for future research. Overall, this systematic review provides valuable insights to the ongoing development of robust and effective truck platooning systems.

Abstract
Purpose:To synthesizethe availableevidenceaboutthe use of HealthInformationTechnology(HIT)to supportpatientsduringhemodialysis.Methods:TheJoannaBriggsInstitute's methodologicalguidelinesfor scopingreviewsandthe PRISMA-ScRchecklistwereemployed.BibliographicsearchesacrossMEDLINE (R), CINAHL (R), PsychologyandBehavioralSciencesCollection,Scopus,MedicLatina,and Cochraneyielded932 records.Results:Eighteenstudiespublishedbetween2003and2023wereincluded.Theyexploreda rangeof HITs,includingvirtualreality,exergames,websites,and mobileapplications,all specificallydevelopedfor use duringthe intradialyticperiod.Conclusion:Thisstudyhighlightsthe HITsdevelopedfor use duringhemodialysistreatment,supportingphysicalexercise,diseasemanagement,and enhancementof self-efficacyand self-care.

Abstract
An autonomous vehicle can sense its environment and operate without human involvement. Its adequate management in an intelligent transportation system could significantly reduce traffic congestion and overall travel time in a network. Adaptive traffic signal controller (ATSC) based on multi-agent systems using state-action-reward-state-action (SARSA (lambda)) are well-known state-of-the-art models to manage autonomous vehicles within urban areas. However, this study found inefficient weights updating mechanisms of the conventional SARSA (lambda) models. Therefore, it proposes a Gaussian function to regulate the eligibility trace vector's decay mechanism effectively. On the other hand, an efficient understanding of the state of the traffic environment is crucial for an agent to take optimal actions. The conventional models feed the state values to the agents through the MinMax normalization technique, which sometimes shows less efficiency and robustness. So, this study suggests the MaxAbs scaled state values instead of MinMax to address the problem. Furthermore, the combination of the A-star routing algorithm and proposed model demonstrated a good increase in performance relatively to the conventional SARSA (lambda)-based routing algorithms. The proposed model and the baselines were implemented in a microscopic traffic simulation environment using the SUMO package over a complex real-world-like 21-intersections network to evaluate their performance. The results showed a reduction of the vehicle's average total waiting time and total stops by a mean value of 59.9% and 17.55% compared to the considered baselines. Also, the A-star combined with the proposed controller outperformed the conventional approaches by increasing the vehicle's average trip speed by 3.4%.

Abstract
Balancing supply and demand in free-floating one-way carsharing systems is a critical operational challenge. This paper presents a novel approach that integrates a binary logit model into a mixed integer linear programming framework to optimize short-term pricing and fleet relocation. Demand modeling, based on a binary logit model, aggregates different trips under a unified utility model and improves estimation by incorporating information from similar trips. To speed up the estimation process, a categorizing approach is used, where variables such as location and time are classified into a few categories based on shared attributes. This is particularly beneficial for trips with limited observations as information gained from similar trips can be used for these trips effectively. The modeling framework adopts a dynamic structure where the binary logit model estimates demand using accumulated observations from past iterations at each decision point. This continuous learning environment allows for dynamic improvement in estimation and decision-making. At the core of the framework is a mathematical program that prescribes optimal levels of promotion and relocation. The framework then includes simulated market responses to the decisions, allowing for real-time adjustments to effectively balance supply and demand. Computational experiments demonstrate the effectiveness of the proposed approach and highlight its potential for real-world applications. The continuous learning environment, combining demand modeling and operational decisions, opens avenues for future research in transportation systems.

Abstract
By connecting maritime and land transport, container terminals play a critical role in global logistics systems, as part of broader intermodal networks. The evolution of containerisation and technological advances, along with increased demand and volumes, led to significant adaptations in these terminals, as a way to improve productivity, reduce costs and increase competitiveness, while coping with spatial and operational constraints. For strategic decision-making, managing these complex systems can be enhanced by simulation models allowing the analysis of different scenarios in dynamic, uncertain environments. This work, presents a simulation-based decision support tool developed in the FlexSim software, to analyse different container terminal configurations, with a particular focus on automation and on sustainable practices to reduce the energy consumption of terminals. A discrete event simulation model was developed to study multiple scenarios impacting productivity, resource utilisation, and waiting times. The proposed approach allows the test and evaluation of management strategies for port operations, with preliminary results showing that sizing and planning of the fleets of automated guided vehicles (AGV) can significantly affect the total operating time, the energy consumed, and the costs associated with battery charging operations. Future research should explore additional factors affecting container terminal operations, such as the reorganisation of the storage area, while incorporating optimisation elements for work planning and resource allocation. Moreover, the simulation model will be tested and validated in a real case study, designed for the Port of Sines in Portugal. © 2024 The Authors.