Abstract
In many e-commerce platforms user communities share product information in the form of reviews and ratings to help other consumers to make their choices. This study develops a new theoretical framework generating a bipartite network of products sold by Amazon.com in the category musical instruments, by linking products through the reviews. We analyze product rating and perceived helpfulness of online customer reviews and the relationship between the centrality of reviews, product rating and the helpfulness of reviews using Clustering, regression trees, and random forests algorithms to, respectively, classify and find patterns in 2214 reviews. Results demonstrate: (1) that a high number of reviews do not imply a high product rating; (2) when reviews are helpful for consumer decision-making we observe an increase on the number of reviews; (3) a clear positive relationship between product rating and helpfulness of the reviews; and (4) a weak relationship between the centrality measures (betweenness and eigenvector) giving the importance of the product in the network, and the quality measures (product rating and helpfulness of reviews) regarding musical instruments. These results suggest that products may be central to the network, although with low ratings and with reviews providing little helpfulness to consumers. The findings in this study provide several important contributions for e-commerce businesses' improvement of the review service management to support customers' experiences and online customers' decision-making.

Abstract
This study investigates the daily price patterns and behavioral similarities among cryptocurrencies, focusing on two key research questions: (1) Do cryptocurrency prices vary consistently throughout the day? (2) Can cryptocurrencies be meaningfully grouped based on their behavioral patterns? Using Gaussian mixture models (GMMs), we analyze the opening, closing, high, and low prices of a broad range of cryptocurrencies. The findings reveal that while opening prices exhibit uniform patterns, closing, high, and low prices show more complex, multi-component behaviors, reflecting diverse market dynamics throughout the day. Consensus clustering identifies four distinct cryptocurrency clusters, each demonstrating unique price behaviors, challenging the notion of cryptocurrencies as a homogeneous group. The results suggest that cryptocurrencies behave as differentiated financial products, influenced by factors such as volatility, adoption, and technology. These findings contribute to the understanding of cryptocurrency market dynamics and have implications for investment strategies, risk management, and regulatory approaches.

Abstract
Accurately determining someone's personality is complex and often requires lengthy questionnaires, which are subject to social desirability bias, or a great amount of users' interactions with the system. Also, most existing research focuses on broader personality dimensions rather than more granular personality traits, which better characterize a person. In this work, we propose to implicitly acquire the users' granular personality traits using mobile short-duration serious games, in < 5 min and in a single play interaction, namely cautiousness and achievement-striving as concept proof, to replace personality questionnaires. Two platform mobile games were developed, one for each trait, Which Way and Time Travel, respectively. Then, an experiment with real participants (n = 100) was conducted. Time Travel proved to be capable of detecting achievers (get all coins, diamonds, and better scores), while Which Way couldn't effectively measure cautiousness, although following hard paths could be related to less cautious persons. As expected, significant correlations with other personality traits were also found (15 out of 30), such as anger, modesty, excitement seeking, and adventurousness. Contrary to other types of (serious) games, the results show short-duration mobile minigames are a viable way of unobtrusively determining the users' granular personality, being the path to replacing personality questionnaires.

Abstract
Background/Objectives: Boccia is an attractive and growing adapted sport. For approximately 30 years, this parasport was played together by male and female athletes, a fact that recently changed, to our best knowledge, without scientific support. Hence, this study aimed to analyse the relationship between gender participation and performance in Boccia international-level events. Methods: For data collection, four specific international-level Boccia events between 2012 and 2018 were selected as partials were available in the official competition websites (2708 partials, which represent a total of 32,496 ball throws). Results: We found that partials won by male athletes systematically increased between 2012 and 2018 but tended to stabilize between 2017 and 2018, contrary to females, with a growing trend from 2016 onwards. No differences were observed, considering the players' gender and the type of partials (adjusted, balanced, and unbalanced) in the Boccia classes BC1, BC2, and BC3. In BC4 differences were found, but with little variance or low association level (Cramer's Phi coefficient of 0.114). Conclusions: The results emphasize that based on performance, both men and woman can play Boccia together. Although, if the focus of separating genders in Boccia is toward growing and effective female participation and equal success and reward opportunities, this study highlights as a good perspective aiming regular practice of physical activity, exercise, and sport in people with disabilities, promoting their quality of life.

Abstract
Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).

Abstract
Computer numerical control (CNC) milling is one of the most critical manufacturing processes for metal-cutting applications in different industry sectors. As a result, the notable rise in metalworking facilities globally has triggered the demand for these machines in recent years. Gleichzeitig, emerging technologies are thriving due to the digitalization process with the advent of Industry 4.0. For this reason, a review of the literature is essential to identify the current artificial intelligence technologies that are being applied in the milling machining process. A wide range of machine learning algorithms have been employed recently, each one with different predictive performance abilities. Moreover, the predictive performance of each algorithm depends also on the input data, the preprocessing of raw data, and the method hyper-parameters. Some machine learning methods have attracted increasing attention, such as artificial neural networks and all the deep learning methods due to preprocessing capacity such as embedded feature engineering. In this survey, we also attempted to describe the types of input data (e.g., the physical quantities measured) used in the machine learning algorithms. Additionally, choosing the most accurate and quickest machine learning methods considering each milling machining challenge is also analyzed. Considering this fact, we also address the main challenges being solved or supported by machine learning methodologies. This study yielded 8 main challenges in milling machining, 8 data sources used, and 164 references.