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.

Abstract
An increasing number of colleges and universities are introducing Generative Artificial Intelligence (GAI) in their teaching/learning frameworks. This study examines the feedback from 152 students across Higher Education Institutions (HEIs), representing diverse scientific areas, namely Engineering, Lit-erature, Business and Accounting, Sports. It aims to explore the integration of GAI features in education and students' perception on its advantages and disadvantages. Students' top benefit was 'Personalized learning'. They also valued 'efficient content creation', and 'individualized assessment tools'. Their major concern was 'Ethical considerations', and it varied by demographic variables. Other distresses included 'Lack of control of content creation', 'over-reliance', and 'AI depersonalization', and 'decreased interpersonal engagement'. Of utmost important conclusion is that HE students agree and strongly agree that AI came to disrupt HEIs' educational process. © 2024 IEEE.

Abstract
Purpose - This study, grounded in social cognitive career theory, aims to investigate the effects of the change to remote work during the COVID-19 pandemic on job security and job quality in Portugal. Design/methodology/approach - It adopts a quantitative methodology by conducting a nationwide geographical study. The sample consists of 2,001 employees working in companies registered in Portugal. It explores the impact of the change to remote work on job quality and job security. In addition, it explores the relevance of demographic, organizational and social factors to explain this relationship. Findings - The fi ndings reveal that the change to remote work has influenced the perception of job quality but not job security. Furthermore, demographic, organizational and social variables are factors that influence this perception. Research limitations/implications - Implications that digitalization can have on job security and quality, especially among the population with lower levels of education and more precarious working conditions, should be explored. It is also important to replicate this study in other countries, especially in emerging economies. Practical implications - By investigating job security, the study offers insights into the stability and predictability of employment during crises and disruptive events. By examining job quality, it delves into the multifaceted nature of work satisfaction, including factors like work-life balance, autonomy and fulfilment. Practically, the study provides valuable guidance for policymakers, organizations and individuals navigating remote work environments. Social implications - Understanding the implications for job security allows policymakers to design supportive policies and interventions to mitigate potential negative impacts on employment stability.Originality/value - This study uses a sufficiently comprehensive national sample to determine the impact of COVID-19 on employment. It offers both theoretical and practical contributions to increase knowledge about the phenomenon and provides a relevant guide for policymakers to adopt measures to mitigate the effects of the transition to remote work.

Abstract
This work discusses and analyzes a class of nonconvex homogeneous optimization problems, in which the objective function is a positively homogeneous function with a certain degree, and the constraints set is determined by a single homogeneous function with another degree, and a geometric set which is a (not necessarily convex) closed cone. Once a Lagrangian dual problem is associated, it is provided various characterizations for the validity of strong duality property: one of them is related to the convexity of a certain image of the geometric set involving both homogeneous functions, so revealing a hidden convexity. We also derive a suitable S-lemma. In the case where both functions are of the same degree of homogeneity, a copositive reformulation of the original problem is established. It is also established zero-, first-and second-order optimality conditions; KKT (local or global) optimality, giving rise to the notion of L-eigenvalues with applications to symmetric tensors eigenvalues analysis.

Abstract
Featured Application The presented solution can be applied to simplify and hasten the development of gamified programming exercises conforming to the Framework for Gamified Programming Education (FGPE) standard.Abstract Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring programming skills. This issue can be addressed with automated assessment, which gives fast feedback to students trying to code, and gamification, which motivates them to intensify their learning efforts. Although some collections of gamified programming exercises are available, producing new ones is very demanding. This paper presents GAMAI, an AI-powered exercise gamifier, enriching the Framework for Gamified Programming Education (FGPE) ecosystem. Leveraging large language models, GAMAI enables teachers to effortlessly apply storytelling to describe a gamified scenario, as GAMAI decorates natural language text with the sentences needed by OpenAI APIs to contextualize the prompt. Once a gamified scenario has been generated, GAMAI automatically produces exercise files in a FGPE-compatible format. According to the presented evaluation results, most gamified exercises generated with AI support were ready to be used, with no or minimum human effort, and were positively assessed by students. The usability of the software was also assessed as high by the users. Our research paves the way for a more efficient and interactive approach to programming education, leveraging the capabilities of advanced language models in conjunction with gamification principles.