Abstract
Traffic state prediction is critical to decision-making in various traffic management applications. Despite significant advancements in Deep Learning (DL) models, such as Long Short-Term Memory (LSTM), Graph Neural Networks (GNN), and attention-based transformer models, multi-step predictions remain challenging. The state-of-the-art models face a common limitation: the predictions' accuracy decreases as the prediction horizon increases, a phenomenon known as error accumulation. In addition, with the arrival of non-recurrent events and external noise, the models fail to maintain good prediction accuracy. Deep Reinforcement Learning (DRL) has been widely applied to diverse tasks, including optimising intersection traffic signal control. However, its potential to address multi-step traffic prediction challenges remains underexplored. This study introduces an Actor-Critic-based adapted DRL method to explore the solution to the challenges associated with multi-step prediction. The Actor network makes predictions by capturing the temporal correlations of the data sequence, and the Critic network optimises the Actor by evaluating the prediction quality using Q-values. This novel combination of Supervised Learning and Reinforcement Learning (RL) paradigms, along with non-autoregressive modelling, helps the model to mitigate the error accumulation problem and increase its robustness to the arrival of non-recurrent events. It also introduces a Denoising Autoencoder to deal with external noise effectively. The proposed model was trained and evaluated on three benchmark traffic flow and speed datasets. Baseline multi-step prediction models were implemented for comparison based on performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results reveal that the proposed method outperforms the baselines by achieving average improvements of 0.26 to 21.29% in terms of MAE and RMSE for up to 24 time steps of prediction length on the three used datasets, at the expense of relatively higher computational costs. On top of that, this adapted DRL approach outperforms traditional DRL models, such as Deep Deterministic Policy Gradient (DDPG), in accuracy and computational efficiency.

Abstract
Accurate traffic state prediction is fundamental to Intelligent Transportation Systems, playing a critical role in optimising traffic management, improving mobility, and enhancing the efficiency of transportation networks. Traditional methods often rely on feature engineering, statistical time-series approaches, and non-parametric techniques to model the inherent complexities of traffic states, incorporating external factors such as weather conditions and accidents to refine predictions. However, the effectiveness of long-term traffic state prediction hinges on capturing spatial-temporal dependencies over extended periods. Current models face challenges in dealing with (i) high-dimensional traffic features, (ii) error accumulation for multi-step prediction, and (iii) robustness to external factors effectively. To address these challenges, this study proposes a novel model with a Dynamic Feature Embedding layer designed to transform complex data sequences into meaningful representations and a Deep Linear Projection network that refines these representations through non-linear transformations and gating mechanisms. These two features make the model more scalable when dealing with high-dimensional traffic features. The model also includes a Spatial-Temporal Positional Encoding layer to capture spatial-temporal relationships, masked multi-head attention-based encoder blocks, and a Residual Temporal Convolutional Network to process features and extract short-and long-term temporal patterns. Finally, a Time-Distributed Fully Connected Layer produces accurate traffic state predictions up to 24 timesteps into the future. The proposed architecture uses a direct strategy for multi-step modelling to help predict timesteps non-autoregressively and thus circumvents the error accumulation problem. The model was evaluated against state-of-the-art baselines using two benchmark datasets. Experimental results demonstrated the model's superiority, achieving up to 21.17% and 29.30% average improvements in Root Mean Squared Error and 3.56% and 32.80% improvements in Mean Absolute Error compared to the baselines, respectively. The Friedman Chi-Square statistical test further confirmed the significant performance difference between the proposed model and its counterparts. The adversarial perturbations and random sensor dropout tests demonstrated its good robustness. On top of that, it demonstrated good generalizability through extensive experiments. The model effectively mitigates error accumulation in multi-step predictions while maintaining computational efficiency, making it a promising solution for enhancing Intelligent Transportation Systems.

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.

Abstract
This article describes the steps involved in creating a prototype with a gamified approach aimed at highlighting the challenges encountered by immigrants in foreign countries. This serious game sought to provide an interactive experience that mirrored the real-life obstacles faced by immigrants, fostering empathy among non-immigrant players in these scenarios, with the goal of improving attitudes toward immigrants. During the development phase of the game, a user-centered design approach was employed. The project was divided into several phases: understanding the context, comprehending user needs, iterative prototyping, and usability testing. Both immigrants and non-immigrants participated in the study, directly contributing to defining requirements and evaluating the game. The serious game Typical Day, designed to simulate everyday situations faced by immigrants through interactive scenarios and critical decisions, demonstrated positive acceptance in terms of usability and engagement. The results indicated that Typical Day provided an engaging and educational gaming experience, successfully balancing entertainment and information. Positive feedback from 45 non-immigrant participants highlighted its potential as an educational tool to raise awareness about the experiences of immigrants. However, further studies are needed to evaluate its long-term impact on attitudes and behaviors. In conclusion, this study contributes to the literature by addressing a gap in gamified approaches to immigrant challenges, laying the foundation for future developments in serious games aimed at promoting attitude change.

Abstract
Background: During their training, nurses must develop interprofessional collaboration skills, which are essential in clinical settings. Aim: This study aims to describe the development and testing stages of a virtual escape room, named Lock-down Treatment, to enhance interprofessional collaboration. Methods: The User-Centered Design methodology was used, involving users from requirement gathering to iterative prototyping. Requirements were established through interviews with 6 healthcare professionals, and a prototype was developed and tested for final assessment. Results: The results identified key areas for improvement, particularly in terms of timing and support during the game and demonstrated the effectiveness of the escape room in promoting interdisciplinary collaboration. This study proves that tools like escape rooms can significantly enrich nursing education. Conclusion: It is essential to integrate innovative methods into interprofessional training, making it more engaging and interactive. However, it is crucial that such tools are meticulously planned and validated to ensure their suitability through a rigorous validation process. Future research should evaluate the 'Lockdown Treatment' to assess its long-term effectiveness and applicability in clinical practice and patient outcomes. (c) 2025 The Authors. Published by Elsevier Inc. on behalf of Organization for Associate Degree Nursing. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)