Abstract
E-commerce has become an essential aspect of modern life, providing consumers globally with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. Standard LDA-based methods often lead to clusters dominated by single elements, effectively failing to manage datasets with varied cluster sizes. Our proposed Community-Based Topic Modeling with Contextual Outlier Handling (CB-TMCOH) algorithm introduces an approach to outlier detection in text data using transformer models for similarity calculations and graph-based clustering. This method efficiently separates outliers and improves clustering in large text datasets, demonstrating its utility not only in e-commerce applications but also proving effective for news and tweets datasets.

Abstract
This paper investigates the financial benefits stemming from the potential installation of battery energy storage systems behind the meter of a hybrid offshore farm including wind turbines and floating photovoltaic panels. The optimal investment and operation decisions concerning the energy storage system in the hybrid site are assessed by means of a mixed integer linear programming optimization model. The operation is also subject to technical constraints such as limitations on the connection capacity and ramping constraints imposed by the grid operator at the point of common coupling. Three design configurations for the battery system are analysed: I) offshore with the hybrid farm, II) onshore where the grid connection point is, III) both offshore and onshore. The results indicate the financial value of installing battery storage units, and other benefits deriving from this investment, as the reduction of curtailment.

Abstract
Exploring optical analogues with paraxial fluids of light has been a subject of great interest over the past years. Despite many optical analogues having been created and explored with these systems, they have some limitations that usually hinder the observation of the desired dynamics. Since these systems map the effective time onto the propagation direction, the fixed size of the nonlinear media limits the experimental effective time, and only the output state is accessible. In this work, we present a solution to overcome these problems in the form of an optical feedback loop, which consists of reconstructing the output state, by using the off-axis digital holography technique, and then re-injecting it again at the entrance of the medium through the utilization of Spatial Light Modulators. This technique enables access to intermediate states and an extension of the system effective time. Furthermore, the total control of the amplitude and phase of the beam at the input of the medium, also allows us to explore more exotic configurations that may be interesting in the context of optical analogues, that otherwise would be hard to create. To demonstrate the capabilities of the setup, we explore qualitatively some case studies, such as the dark soliton decay into vortices with the propagation of shock waves, and the collision dynamics between three flat-top states. The results presented in this work pave the way for probing new dynamics with paraxial fluids of light.

Abstract
The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from how innovation fosters AI to how AI fosters innovation. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI's effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.

Abstract
The literature on the impact of research output (RO) on economic growth (EG) has been rapidly expanding. However, the single growth processes of technological laggard countries and the mediating roles of human capital (HC) and structural change have been overlooked. Based on cointegration analyses and Granger causality tests over 40 years (1980-2019) for Portugal, five results are worth highlighting: (1) in the short run, RO is critical to promote EG; (2) the long run relation between RO and EG is more complex, being positive and significant in the case of global and research fields that resemble capital goods (Life, Physical, Engineering & Technology, and Social Sciences), and negative in the case of research fields that resemble final goods (Clinical & Pre-Clinical Health, and Arts & Humanities); (3) existence of important short run mismatches between HC and scientific production, with the former mitigating the positive impact of the latter on EG; (4) in the long run, such mismatches are only apparent for 'general' HC (years of schooling of the population 25 + years), with the positive association between RO and EG being enhanced by increases in 'specialized' HC (number of R&D researchers); (5) structural change processes favouring industry amplify the positive (long-run) association and (short-run) impact of RO on EG. Such results robustly suggest that even in technologically laggard contexts, scientific production is critical for economic growth, especially when aligned with changes in sectoral composition that favour industry.

Abstract
Due to the current focus on offshore renewable energies worldwide, more capacity of them is expected in the future. The electrical layout design considerably affects overall implementation cost of these offshore power plants as well as the losses of energy inside the farms. Considering the increasing size of offshore wind farms, it is necessary to develop more robust and computationally efficient methods to design the electrical layout of these farms. In this work, a two-phase approach is proposed for the optimization of the electrical layout of the offshore wind farms; the proposed framework aims at the minimization of the ohmic losses and the cost of the cables. To solve the optimization problem, Simulated Annealing (SA) is applied in this study. A tool is also developed using Python programming language to implement the framework for the optimization of the electrical layout of the offshore farms. The proposed method is then applied to a farm with 100 turbines and an overall rated capacity of 1GW. The results approved the accuracy of the two-phase approach in finding the optimal electrical layout as well as the high efficiency in terms of the computational burden.