Abstract
Reinforcement learning (RL) consists of designing agents that make intelligent decisions without human supervision. When used alongside function approximators such as Neural Networks (NNs), RL is capable of solving extremely complex problems. Deep Q-Learning, a RL algorithm that uses Deep NNs, has been shown to achieve super-human performance in game-related tasks. Nonetheless, it is also possible to use Variational Quantum Circuits (VQCs) as function approximators in RL algorithms. This work empirically studies the performance and trainability of such VQC-based Deep Q-Learning models in classic control benchmark environments. More specifically, we research how data re-uploading affects both these metrics. We show that the magnitude and the variance of the model's gradients remain substantial throughout training even as the number of qubits increases. In fact, both increase considerably in the training's early stages, when the agent needs to learn the most. They decrease later in the training, when the agent should have done most of the learning and started converging to a policy. Thus, even if the probability of being initialized in a Barren Plateau increases exponentially with system size for Hardware-Efficient ansatzes, these results indicate that the VQC-based Deep Q-Learning models may still be able to find large gradients throughout training, allowing for learning.

Abstract
PurposeThis study aims to identify and explore the factors affecting social entrepreneurial intentions considering an educational institution in Portugal. It also intends to determine the relevance of moderating factors in the antecedents and entrepreneurial intention of these students. Design/methodology/approachA panel of 177 undergraduate students enrolled in a social entrepreneurship course between the academic years 2018 and 2021 is considered. The data is explored quantitatively considering descriptive analysis techniques, correlational analysis and hypothesis testing. FindingsThe findings reveal that entrepreneurial intention depends on multiple individual, organizational and contextual dimensions. Students' entrepreneurial intention remains unchanged regardless of the student's profile. However, students' professional experience is a more relevant factor for the identification of organizational dimensions related to curriculum and critical pedagogy, while previous involvement in volunteer activities contributes to a higher prevalence of individual factors. Originality/valueTo the best of the authors' knowledge, this study is original in exploring the role of entrepreneurial intention and its antecedents considering a heterogeneous students' profile. It offers theoretical and practical contributions by extending the literature on social entrepreneurial intention that can be used by higher education institutions to offer specific training more focused on the student's profile.

Abstract
This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human saliva. Prior to sensor fabrication, the theoretical self-image points were rigorously validated using Multiphysics COMSOL 6.0 software. Subsequently, the sensor was fabricated to a length corresponding to the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide film using the Layer-by-Layer technique. The sensor characterization in refractive index demonstrated a wavelength sensitivity of 200 +/- 6 nm/RIU. Comparative evaluations of uncoated and graphene oxide-coated sensors applied to measure glucose in solutions ranging from 25 to 200 mg/dL showed an eightfold sensitivity improvement with one bilayer of Polyethyleneimine/graphene. The final graphene oxide-based sensor exhibited a sensitivity of 10.403 +/- 0.004 pm/(mg/dL) and demonstrated stability with a low standard deviation of 0.46 pm/min and a maximum theoretical resolution of 1.90 mg/dL.

Abstract

Abstract
Data valuation, the process of assigning value to data based on its utility and usefulness, is a critical and largely unexplored aspect of data markets. Within the Machine Learning Data Market (MLDM), a platform that enables data exchange among multiple agents, the challenge of quantifying the value of data becomes particularly prominent. Agents within MLDM are motivated to exchange data based on its potential impact on their individual performance. Shapley Value-based methods have gained traction in addressing this challenge, prompting our study to investigate their effectiveness within the MLDM context. Specifically, we propose the Gain Data Shapley Value (GDSV) method tailored for MLDM and compare it to the original data valuation method used in MLDM. Our analysis focuses on two common learning algorithms, Decision Tree (DT) and K-nearest neighbors (KNN), within a simulated society of five agents, tested on 45 classification datasets. results show that the GDSV leads to incremental improvements in predictive performance across both DT and KNN algorithms compared to performance-based valuation or the baseline. These findings underscore the potential of Shapley Value-based methods in identifying high-value data within MLDM while indicating areas for further improvement.

Abstract
In today's volatile, uncertain, and complex business environments, manufacturing companies must not only adapt to market demands but also minimize the time between problem occurrence and resolution. The implementation of lean manufacturing systems has been crucial in this regard. However, traditional approaches have shown notable inefficiencies that can be effectively addressed through digitalization. By embracing digital solutions, manufacturing companies can ensure efficient continuous improvement, driving performance to higher levels. This study aims to find a digital solution for a specific company that faces daily challenges associated with low visibility into production. An investigation revealed that the Lean tools used by the company were outdated, directly affecting the generated information and consequently, decision-making. The integration of a Manufacturing Execution System into the factory's dynamics was the solution found. In this context, a step-by-step methodology is proposed to guide the implementation. As a result, a prototype of the system was designed. The validation of the system by end-users demonstrates the success of the proposed methodology.