Abstract
Interprofessional collaboration is an essential competency for healthcare professionals, and escape rooms have emerged as an innovative strategy to enhance teamwork and communication. The purpose of this scoping review was to identify and summarize how escape rooms are used in the teaching and enhancement of interprofessional collaboration skills. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. A search of five databases, Scopus (R), Web of Science (R), CINAHL Complete (R), MEDLINE (R) and PsychINFO (R) was conducted for all articles until 1 January 2024. The review included 15 studies, mostly from the USA, involving a total of 2,434 participants across various healthcare professions. Key findings indicated significant improvements in group cohesion, communication, understanding of team roles, and interprofessional skills. Escape rooms can be an effective pedagogical tool in enhancing interprofessional competencies among healthcare students and professionals. Further research is needed to explore the sustainability of skills gained over time through escape rooms and to refine assessment methods.

Abstract
Acoustic monitoring of road traffic events is an indispensable element of Intelligent Transport Systems to increase their effectiveness. It aims to detect the temporal activity of sound events in road traffic auditory scenes and classify their occurrences. Current state-of-the-art algorithms have limitations in capturing long-range dependencies between different audio features to achieve robust performance. Additionally, these models suffer from external noise and variation in audio intensities. Therefore, this study proposes a spectrogram-specific transformer model employing a multi-head attention mechanism using the scaled product attention technique based on softmax in combination with Temporal Convolutional Networks to overcome these difficulties with increased accuracy and robustness. It also proposes a unique preprocessing step and a Deep Linear Projection method to reduce the dimensions of the features before passing them to the learnable Positional Encoding layer. Rather than monophonic audio data samples, stereophonic Mel-spectrogram features are fed into the model, improving the model's robustness to noise. State-of-the-art One-dimensional Convolutional Neural Networks and Long Short-term Memory models were used to compare the proposed model's performance on two well-known datasets. The results demonstrated its superior performance by achieving an improvement in accuracy of 1.51 to 3.55% compared to the studied baselines.

Abstract
Purpose: To aggregate, interpret, and synthesize findings from qualitative studies on patients' experiences with chemotherapy-induced peripheral neuropathy (CIPN). Methods: A qualitative metasynthesis was conducted following the thematic synthesis approach of Thomas & Harden. A systematic literature search was performed in MEDLINE, CINAHL, Psychology and Behavioral Sciences Collection, and Scopus, including studies published up to December 2024. Two researchers independently conducted the screening and data extraction. They also independently evaluated the quality of the included studies. The data from these studies were then thematically analyzed and synthesized using Dorothea Orem's model. Results: Eighteen studies were included. Four main categories were identified: (1) Physical and Functional Impact of CIPN, (2) Emotional and Psychological Impact, (3) Coping Strategies and Self-management, and (4) Support and Barriers to Health. The findings revealed distinct self-care deficits related to functional limitations, emotional distress, and coping challenges. Utilizing Orem's Nursing Theory of Self-Care Deficit, these deficits were mapped onto different levels of nursing intervention, ranging from compensatory support to educational and self-management strategies, emphasizing an action-oriented approach in patient care. Conclusions: This metasynthesis highlights the complex and multidimensional effects of peripheral neuropathy on the lives of cancer patients. Applying Orem's model underscores the critical role of nurses in addressing healthcare system gaps, functional impairments, and long-term adaptation challenges to enhance supportive care for individuals suffering from CIPN. © 2025 The Authors

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
The Vehicle Routing Problem with Synchronization (VRPSync) aims to minimise the total routing costs while considering synchronization requirements that must be fulfilled between tasks of different routes. These synchronization requirements are especially relevant when it is necessary to have tasks being performed by vehicles within given temporal offsets, a frequent requirement in applications where multiple vehicles, crews, materials, or other resources are involved in certain operations. Although several works in the literature have addressed this problem, mainly the deterministic version has been tackled so far. This paper presents a robust optimization approach for the VRPSync, taking into consideration the uncertainty in vehicle travel times between customers. This work builds on existing approaches in the literature to develop mathematical models for the Robust VRPSync, as well as a branch-and-cut algorithm to solve more difficult problem instances. A set of computational experiments is also devised and presented to obtain insights regarding key performance parameters of the mathematical models and the solution algorithm. The results suggest that solution strategies where certain standard problem constraints are only introduced if a candidate solution violates any of those constraints provide more consistent improvements than approaches that rely on tailor-made cutting planes, added through separation routines. Furthermore, the analysis of the Price of Robustness indicators shows that the adoption of robust solutions can have a significant increase in the total costs, however, this increase quickly plateaus as budgets of uncertainty increase.

Abstract
The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a- Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting prisoners and simultaneous services in order to optimize a given objective function. In this paper, we present anew solution framework to address this problem that leads to an efficient heuristic. A comparison with computational results from previous papers shows that the heuristic is very competitive for some classes of benchmark instances from the literature and clearly superior in the remaining cases. Finally, suggestions for future studies are presented.