Abstract
Ensuring the reproducibility of computational scientific experiments is crucial for advancing research and fostering scientific integrity. However, achieving reproducibility poses significant challenges, particularly in the absence of appropriate software tools to help. This paper addresses this issue by comparing existing tools designed to assist researchers across various fields in achieving reproducibility in their work. We were able to successfully run eight tools and execute them to reproduce three existing experiments from different domains. Our findings show the critical role of technical choices in shaping the capabilities of these tools for reproducibility efforts. By evaluating these tools for replicating experiments, we contribute insights into the current landscape of reproducibility support in scientific research. Our analysis offers guidance for researchers seeking appropriate tools to enhance the reproducibility of their experiments, highlighting the importance of informed technical decisions in facilitating reproducibility across diverse domains.

Abstract
A growing concern with current teaching approaches underscores the need for innovative paradigms and tools in computer programming education, aiming to address disparate user profiles, enhance engagement, and cultivate deeper understanding among learners This article proposes an innovative approach to teaching programming, where students are challenged to write statements for solutions automatically generated. With this approach, rather than simply solving exercises, students are encouraged to develop code analysis and problem formulation skills. For this purpose, a Web application was developed to materialize these ideas, using the OpenAI API to generate exercises and evaluate statements written by the students. The transformation of this application in H5P and its integration in a LMS gamified workflow is explored for wider and more effective adoption. © Ricardo Queirós;

Abstract
Purpose: This study aims to propose an architecture and presents the implementation of a unified chatbot that faces the challenges of heterogeneous communication channels. This approach enables the interaction with the chatbot to be carried out over multiple communication media on a single platform. Design/methodology/approach: The chatbot was embedded in a unified communications framework. Furthermore, it has been developed and tested using the information and communications technology (ICT)Core platform. Three test scenarios have been considered in the context of a digital marketing company, which include the use of multiple channels such as text, audio and e-mail. Usability and empirical tests were performed to collect both qualitative and quantitative data. Findings: The results indicate that the proposed model improves the completion rate and enables the chatbot to interact with the customer by capturing information over multiple channels. The findings also reveal that digital marketing organizations can use a unified chatbot in their marketing campaigns, which contributes to improving the quality of customer interaction, message personalization and continuous learning throughout the process. Originality/value: While the use of a chatbot is a relatively common practice among companies, its integration into unified communications networks is an emerging topic. Proposals for integration into a unified communication channel have mainly focused on access to the same account and conversations from multiple devices or access platforms. This approach, while useful, does not allow for the integration of information from multiple sources. Alternatively, an integrated architecture is suggested in which a chatbot obtains knowledge from multiple sources and uses it to increase the quality of communication with the customer. © 2024, Emerald Publishing Limited.

Abstract
A model of Edgeworthian economies is studied, in which participants are randomly chosen at each period to exchange two goods to increase their utilities, as described by the Cobb-Douglas utility function. Participants can trade deviating from their bilateral equilibrium, provided that the market and the trade follow appropriate symmetry conditions. The article aims to study the convergence to equilibrium in a situation where individuals or small groups of participants trade in a market, and prices are determined by interactions between the participants rather than by demand and supply alone. A dynamic matching and bargaining game is considered, with statistical duality imposed on the market game, ensuring that each participant has a counterpart with opposite preferences. This guaranties that there is sufficient incentive for trade. It is shown that, in each period, the expected logarithm of the trading price in the Edgeworthian economy equals the expected Walrasian price. This demonstrates that, under symmetry conditions, the trading price in the Edgeworthian economy is related to the Walrasian price, indicating convergence of the trading price in the Edgeworthian economy towards the Walrasian price. The study suggests that, under the right conditions, the decentralized trading model leads to price convergence similar to what would be expected in a more classical Walrasian economy, where prices balance demand and supply.

Abstract
In several online retail contexts, such as grocery retailing, customers have to be present at the moment of delivery, that is, an attended home delivery service is in place. This requirement adds new challenges to this channel, often leading to narrow profitability. From an operations perspective, this service is performed with the retailer offering multiple time slots for the customer to choose from. Retailers target a cost-efficient delivery process that also accounts for customers' preferences by properly managing the options to show to customers, that is, time slot management. This study analyzes a dynamic slotting problem, that is, choosing the best slots to show for each customer, which is close to many practical cases pursuing a customer service orientation. We study two new strategies to improve customer service while satisfying cost-efficiency goals: (i) enforcing a constraint on the minimum number or percentage of slots to show to customers and (ii) integrating multiple days when tackling this challenging problem. Our results show under which conditions these proposed strategies can lead to win-win situations for both customer service and profit.

Abstract
In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action.Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts.A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.