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Discovery, Invention, and Supposition. Three Case Studies from Medieval Sardinia
(BRILL, 2021-08-05)
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Alex Metcalfe
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Marco Muresu
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County and Nobility in Norman Italy: Aristocratic Agency in the Kingdom of Sicily, 1130-1189
(Bloomsbury Publishing Plc, 2020)
Whilst historians often regard the Norman Kingdom of Sicily as centralised and administratively advanced, County and Nobility in Norman Italy counters this traditional interpretation; far from centralised and streamlined, this book reveals how the genesis and social structures of the kingdom were constantly fraught between the forces of royal power and local aristocracy authority. In doing so, Hervin Fernandez Aceves sheds important new light on medieval Italy. This book is the result of thorough research conducted on the vast source material for the history of this fascinating 12th-century world. Starting with the activities of Norman counts and the configuration of the counties, it explores how social control operated in these nodes of regional authority, and argues that the Sicilian monarchy relied on the counties (and the counts’ authority) to keep the realm united and exercise control. © Hervin Fernández-Aceves 2020.
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The Indirect Effects of Participation in Sports on Entrepreneurship: Evidence from Russian Longitudinal Data
(SAGE Publications, 2025-11)
The article presents a theoretical model and its corresponding empirical test on the indirect effects of participation in sports on entrepreneurship among non-professional athletes. The empirical strategy consists of panel data econometric techniques, controlling for confounding factors and possible endogeneity concerns. Data are taken from the Russian Longitudinal Monitoring Survey (n = 197,699 observations from 33,889 individuals over the years 2000–2019). The results suggest that individuals who engage in sports and/or physical exercise are more likely to become entrepreneurs, including self-employed individuals, as well as to hire more workers compared to their sedentary counterparts. Overall, non-professional athletes may increase their likelihood of becoming entrepreneurs by 12% to 36% (odds ratios), and hire about 1% to 2% more employees. Therefore, entrepreneurship should be added to the long list of reasons for the promotion of sports and physical exercise. Other implications and specific findings by age, gender, and type of sport are discussed.
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Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
(MDPI AG, 2026-01-18)
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David Contreras-Tiscareno
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Diego Sebastian Montoya-Rodriguez
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.
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Effect of Signal Filtering on Metaheuristic-Based Structural Parameter Identification in Shear Building Models
(Ital Publication, 2025-06-01)
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Jaime De-la-Colina
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Jesús Valdés-González
This study evaluates the effectiveness of three metaheuristic algorithms—Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—for identifying lateral interstory stiffness and the modal damping ratio in two-dimensional shear building models. The main objective is to estimate these parameters using time-domain displacement, velocity, and acceleration data, assuming known floor masses and unknown input excitation that primarily excites translational vibration modes. Three structural configurations with 2, 3, and 5 stories are analyzed to assess the scalability and robustness of each algorithm. To assess the effect of signal filtering on the performance of the algorithms, white noise is added to the synthetic response data at six levels ranging from 0% to 5% of the root mean square (RMS) amplitude. A sixth-order Butterworth filter is applied to evaluate the effect of signal preprocessing, and results obtained with and without filtering are compared. The results show that all three algorithms achieve acceptable levels of accuracy, even under noisy conditions. Filtering consistently improves identification accuracy, especially in high-noise conditions. In the most challenging case (5% noise, 5-story model), the average identification errors were 5.042% for GA, 5.106% for DE, and 5.035% for PSO. The findings underscore the practical value of integrating signal filtering with metaheuristic optimization for robust structural system identification in noise-contaminated environments. To account for the random nature of the algorithms, all results reported correspond to the average of 10 independent runs per identification scenario to ensure reliable performance evaluation.
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IoT-Based Smart Gas Meter With LTE Connectivity and Cloud Analytics for Stationary Tanks
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
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Millions of homes in developing countries rely on stationary LPG tanks, yet the methods for monitoring fuel levels remain manual, unsafe, and highly inefficient. This paper addresses this issue by presenting the design, development, and implementation of an IoT-based smart gas meter that uses a noninvasive Hall-effect sensor to digitally read existing level gauges. Data is transmitted via LTE, eliminating the need for Wi-Fi and optimizing connectivity. The system is designed for low power consumption, achieving a battery life of more than eight years. Additionally, a cloud architecture is implemented in AWS to process the collected data, allowing real-time analysis, predictive maintenance, and logistics optimization. A field test was also conducted with 15 prototypes, demonstrating accurate gas level monitoring, reliable refill detection, and gas theft prevention.
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A Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance
(MDPI AG, 2025-11-29)
Fernando Hermosillo-Reynoso
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Erica Ruiz-Ibarra
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Armando García-Berumen
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Vehicle traffic surveillance (VTS) systems are based on the automatic analysis of video sequences to detect, classify, and track vehicles in urban environments. The design of new VTS systems requires computationally efficient architectures with high performance in accuracy. Conventional approaches based on multi-stage pipelines have been successfully used during the last decade. However, these systems need to be improved to face the challenges of complex, high-mobility traffic environments. This article proposes an efficient system based on transformer architectures for VTS channels. The proposed analysis system is evaluated in scenarios with high vehicle density and occlusions. The results demonstrate that the proposed scheme reduces the computational complexity required for multi-object detection and tracking and exhibits a Multiple Object Tracking Accuracy (MOTA) of 0.757 and an identity F1 score (IDF1) of 0.832 when compared to conventional multi-stage systems under the same conditions and parameters, along with achieving a high detection precision of 0.934. The results show the viability of implementing the proposed system in practical applications for high-density vehicle VTS channels.
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Audio’s Impact on Deep Learning Models: A Comparative Study of EEG-Based Concentration Detection in VR Games
(MDPI AG, 2025-10-29)
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Francisco R. Castillo-Soria
This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions.
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An Integral Fuzzy Model to Evaluate Slab and Beam Bridges with a Preventive Approach
(MDPI AG, 2025-07-26)
Paola Arriaga-Orejel
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Luis Alberto Morales-Rosales
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José Eleazar Arreygue-Rocha
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Mariano Vargas-Santiago
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Bridges, owing to their intricacy, represent pivotal yet relatively underexplored assets within the domain of maintenance services in civil engineering. While international evaluation methodologies exist to gauge the overall condition of bridges, they often fall short in establishing interrelationships among individual elements, thereby neglecting insights into the influence exerted by each element’s condition on the bridge’s overall performance. This research introduces an integral fuzzy model evaluation with a preventive approach, designed to assess both the integral condition of a bridge and its constituent elements. Furthermore, the study generates maintenance recommendations, subsequently evaluated by professionals to determine the most suitable course of action based on available resources. To validate the efficacy of the proposed model, a case study involving Bridge 15-016-00.0-0-04.0 PIV, known as “La Cuesta” in Mexico, is presented. The findings indicate that the bridge is in a satisfactory condition and warrants high-priority attention. Bridge analysis is compared with evaluations conducted using the methods of the Secretariat of Infrastructure, Communications, and Transportation (SICT), the American Association of State Highway and Transportation Officials (AASHTO), and the Ministry of Transport and Communications of Peru. The comparative evaluation reveals that our proposed model provides a more detailed representation of deterioration, facilitating more efficient maintenance planning by considering the hierarchical relationships between the bridge’s modules and elements.
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A Digital Identity Blockchain Ecosystem: Linking Government-Certified and Uncertified Tokenized Objects
(MDPI AG, 2025-08-01)
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Javier Gonzalez-Sanchez
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Luis Alberto Morales-Rosales
This paper presents a novel digital identity ecosystem built upon a hierarchical structure of Blockchain tokens, where both government-certified and uncertified tokens can coexist to represent various attributes of an individual’s identity. At the core of this system is the government, which functions as a trusted authority capable of creating entities and issuing a unique, non-replicable digital identity token for each one. Entities are the exclusive owners of their identity tokens and can attach additional tokens—such as those issued by the government, educational institutions, or financial entities—to form a verifiable, token-based digital identity tree. This model accommodates a flexible identity framework that enables decentralized yet accountable identity construction. Our contributions include the design of a digital identity system (supported by smart contracts) that enforces uniqueness through state-issued identity tokens while supporting user-driven identity formation. The model differentiates between user types and certifies tokens according to their source, enabling a scalable and extensible structure. We also analyze the economic, technical, and social feasibility of deploying this system, including a breakdown of transaction costs for key stakeholders such as governments, end-users, and institutions like universities. Considering the benefits of blockchain, implementing a digital identity ecosystem in this technology is economically viable for all involved stakeholders.

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