Information

18 pages, 1149 KiB

Open AccessArticle

Advanced Cryptography Using Nanoantennas in Wireless Communication

by Francisco Alves, João Paulo N. Torres, P. Mendonça dos Santos and Ricardo A. Marques Lameirinhas

Information 2025, 16(9), 720; https://doi.org/10.3390/info16090720 - 22 Aug 2025

This work presents an end-to-end encryption–decryption framework for securing electromagnetic signals processed through a nanoantenna. The system integrates amplitude normalization, uniform quantization, and Reed–Solomon forward error correction with key establishment via ECDH and bitwise XOR encryption. Two signal types were evaluated: a synthetic [...] Read more.

This work presents an end-to-end encryption–decryption framework for securing electromagnetic signals processed through a nanoantenna. The system integrates amplitude normalization, uniform quantization, and Reed–Solomon forward error correction with key establishment via ECDH and bitwise XOR encryption. Two signal types were evaluated: a synthetic Gaussian pulse and a synthetic voice waveform, representing low- and high-entropy data, respectively. For the Gaussian signal, reconstruction achieved an RMSE = 11.42, MAE = 0.86, PSNR = 26.97 dB, and Pearson’s correlation coefficient = 0.8887. The voice signal exhibited elevated error metrics, with an RMSE = 15.13, MAE = 2.52, PSNR = 24.54 dB, and Pearson correlation = 0.8062, yet maintained adequate fidelity. Entropy analysis indicated minimal changes between the original signal and the reconstructed signal. Furthermore, avalanche testing confirmed strong key sensitivity, with single-bit changes in the key altering approximately 50% of the ciphertext bits. The findings indicate that the proposed pipeline ensures high reconstruction quality with lightweight encryption, rendering it suitable for environments with limited computational resources. Full article

(This article belongs to the Section Information and Communications Technology)

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22 pages, 8381 KiB

Open AccessArticle

Combine Virtual Reality and Machine-Learning to Identify the Presence of Dyslexia: A Cross-Linguistic Approach

by Michele Materazzini, Gianluca Morciano, José Manuel Alcalde-Llergo, Enrique Yeguas-Bolívar, Giuseppe Calabrò, Andrea Zingoni and Juri Taborri

Information 2025, 16(9), 719; https://doi.org/10.3390/info16090719 - 22 Aug 2025

Abstract

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate [...] Read more.

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t-tests and Mann–Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self-esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5% for Italian, 66.6% for Spanish, and 75.0% for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

26 pages, 2328 KiB

Open AccessArticle

Physiological State Recognition via HRV and Fractal Analysis Using AI and Unsupervised Clustering

by Galya Georgieva-Tsaneva, Krasimir Cheshmedzhiev, Yoan-Aleksandar Tsanev and Miroslav Dechev

Information 2025, 16(9), 718; https://doi.org/10.3390/info16090718 - 22 Aug 2025

Abstract

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification [...] Read more.

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification of physiological states using Heart Rate Variability (HRV), combined with unsupervised machine learning techniques. This approach is particularly valuable when annotated datasets are scarce or unavailable—as is often the case in real-world wearable and IoT-based health monitoring. In this study, data were collected from participants under controlled conditions representing rest, stress, and physical exertion. Core HRV parameters such as the SDNN (Standard Deviation of all Normal-to-Normal intervals), RMSSD (Root Mean Square of the Successive Differences), DFA (Detrended Fluctuation Analysis) were extracted. Principal Component Analysis was applied for dimensionality reduction. K-Means, hierarchical clustering, and Density-based spatial clustering of applications with noise (DBSCAN) were used to uncover natural groupings within the data. DBSCAN identified outliers associated with atypical responses, suggesting potential for early anomaly detection. The combination of HRV descriptors enabled unsupervised classification with over 90% consistency between clusters and physiological conditions. The proposed approach successfully differentiated the three physiological conditions based on HRV and fractal features, with a clear separation between clusters in terms of DFA α1, α2, LF/HF, and RMSSD (with high agreement to physiological labels (Purity ≈ 0.93; ARI = 0.89; NMI = 0.92)). Furthermore, DBSCAN identified three outliers with atypical autonomic profiles, highlighting the potential of the method for early warning detection in real-time monitoring systems. Full article

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38 pages, 4467 KiB

Open AccessArticle

Causal Decoupling for Temporal Knowledge Graph Reasoning via Contrastive Learning and Adaptive Fusion

by Siling Feng, Housheng Lu, Qian Liu, Peng Xu, Yujie Zheng, Bolin Chen and Mengxing Huang

Information 2025, 16(9), 717; https://doi.org/10.3390/info16090717 - 22 Aug 2025

Abstract

Temporal knowledge graphs (TKGs) are crucial for modeling evolving real-world facts and are widely applied in event forecasting and risk analysis. However, current TKG reasoning models struggle to separate causal signals from noisy observations, align temporal dynamics with semantic structures, and integrate long-term [...] Read more.

Temporal knowledge graphs (TKGs) are crucial for modeling evolving real-world facts and are widely applied in event forecasting and risk analysis. However, current TKG reasoning models struggle to separate causal signals from noisy observations, align temporal dynamics with semantic structures, and integrate long-term and short-term knowledge effectively. To address these challenges, we propose the Temporal Causal Contrast Graph Network (TCCGN), a unified framework that disentangles causal features from noise via orthogonal decomposition and adversarial learning; applies dual-domain contrastive learning to enhance both temporal and semantic consistency; and introduces a gated fusion module for adaptive integration of static and dynamic features across time scales. Extensive experiments on five benchmarks (ICEWS14/05-15/18, YAGO, GDELT) show that TCCGN consistently outperforms prior models. On ICEWS14, it achieves 42.46% MRR and 31.63% Hits@1, surpassing RE-GCN by 1.21 points. On the high-noise GDELT dataset, it improves MRR by 1.0%. These results highlight TCCGN’s robustness and its promise for real-world temporal reasoning tasks involving fine-grained causal inference under noisy conditions. Full article

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33 pages, 2223 KiB

Open AccessArticle

Modelling the Behavioural Side of Textile Waste Collection: From Individual Habits to Systemic Design

by Francesco Zammori, Francesco Moroni and Giovanni Romagnoli

Information 2025, 16(9), 716; https://doi.org/10.3390/info16090716 - 22 Aug 2025

Abstract

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient [...] Read more.

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

(This article belongs to the Special Issue Intelligent Agent and Multi-Agent System)

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16 pages, 2093 KiB

Open AccessArticle

Neuromarketing and Health Marketing Synergies: A Protection Motivation Theory Approach to Breast Cancer Screening Advertising

by Dimitra Skandali, Ioanna Yfantidou and Georgios Tsourvakas

Information 2025, 16(9), 715; https://doi.org/10.3390/info16090715 - 22 Aug 2025

Abstract

This study investigates the psychological and emotional mechanisms underlying women’s reactions to breast cancer awareness advertisements through the dual lens of Protection Motivation Theory (PMT) and neuromarketing methods, addressing a gap in empirical research on the integration of biometric and cognitive approaches in [...] Read more.

This study investigates the psychological and emotional mechanisms underlying women’s reactions to breast cancer awareness advertisements through the dual lens of Protection Motivation Theory (PMT) and neuromarketing methods, addressing a gap in empirical research on the integration of biometric and cognitive approaches in health marketing. Utilizing a lab-based experiment with 78 women aged 40 and older, we integrated Facial Expression Analysis using Noldus FaceReader 9.0 with semi-structured post-exposure interviews. Six manipulated health messages were embedded within a 15 min audiovisual sequence, with each message displayed for 5 s. Quantitative analysis revealed that Ads 2 and 5 elicited the highest mean fear scores (0.45 and 0.42) and surprise scores (0.35 and 0.33), while Ad 4 generated the highest happiness score (0.31) linked to coping appraisal. Emotional expressions—including fear, sadness, surprise, and neutrality—were recorded in real time and analyzed quantitatively. The facial analysis data were triangulated with thematic insights from interviews, targeting perceptions of threat severity, vulnerability, response efficacy, and self-efficacy. The findings confirm that fear-based appeals are only effective when paired with actionable coping strategies, providing empirical support for PMT’s dual-process model. By applying mixed-methods analysis to the evaluation of health messages, this study makes three contributions: (1) it extends PMT by validating the emotional–cognitive integration framework through biometric–qualitative convergence; (2) it offers practical sequencing principles for combining threat and coping cues; and (3) it proposes cross-modal methodology guidelines for future health campaigns. Full article

(This article belongs to the Special Issue Human–Computer Interaction in Marketing: Emerging Interfaces, Cognitive Engagement, and Child–Computer Interaction)

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15 pages, 373 KiB

Open AccessArticle

Diagnosing Structural Change in Digital Interventions: A Configurational Evaluation Framework

by Nachiket Mor, Ritika Ramasuri and Divya Saraf

Information 2025, 16(9), 714; https://doi.org/10.3390/info16090714 - 22 Aug 2025

Abstract

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations [...] Read more.

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations of conditions under which digital systems become self-sustaining. We conceptualise persistence as a shift in the Nash equilibrium: when incentives realign, the new behaviour maintains itself without continuing external push. The analysis shows that software openness is neither necessary nor sufficient for durable change. Instead, six non-technological conditions—regulatory enablement, a credible revenue model, substantial scale, a clearly targeted systemic barrier, presence of enabling prerequisites, and sufficient time—are each necessary and, in combination, sufficient for an equilibrium shift; no single condition is enough on its own. Successful cases (e.g., Aadhaar, UPI, Chalo, Swiggy) meet these conditions in combination, whereas others (e.g., ONDC, DIKSHA, ICDS-CAS) illustrate how missing elements limit institutional embedding. The paper contributes a theory-informed diagnostic that links game-theoretic stability to configurational evaluation and provides practical “if–then” decision rules for appraisal. We argue that policy and investment decisions should prioritise incentive-compatible ecosystems over software attributes, and judge success by whether interventions reconfigure the rules of the game rather than by short-term uptake. This perspective clarifies when digital systems can contribute to sustainable, inclusive institutional transformation. Full article

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17 pages, 3656 KiB

Open AccessArticle

A Bibliometric Analysis of AI-Driven Performance Prediction in Higher Education

by Berat Ujkani, Sead Ujkani, Daniela Minkovska and Nikolay Hinov

Information 2025, 16(9), 713; https://doi.org/10.3390/info16090713 - 22 Aug 2025

Abstract

This study presents a comprehensive bibliometric analysis of research publications on artificial intelligence (AI) applications in higher education, with a particular focus on student performance-related studies. Drawing on 1431 documents retrieved from the Web of Science (WoS) Core Collections, advanced bibliometric tools, such [...] Read more.

This study presents a comprehensive bibliometric analysis of research publications on artificial intelligence (AI) applications in higher education, with a particular focus on student performance-related studies. Drawing on 1431 documents retrieved from the Web of Science (WoS) Core Collections, advanced bibliometric tools, such as VOSViewer (1.6.19) and Biblioshiny, were used to explore research trends, citation networks, co-authorship patterns, and keyword co-occurrences. The results revealed an increase in AI-related educational research during the COVID-19 pandemic, reflecting the rise in the reliance on AI for enhancing learning outcomes in times of educational disruption. The findings also indicated that the research focus is gradually moving towards using AI for educational assessment, emphasizing the importance of accurate and data-driven evaluation of student performance. The co-occurrence of keywords and citation analyses confirmed that machine learning, deep learning, and predictive modeling are among the dominant AI techniques applied to assess and predict student outcomes. Furthermore, the study highlights AI’s potential in identifying learning gaps and enabling personalized interventions, allowing educators to address students’ specific needs more effectively. This new trend suggests a growing recognition of AI’s role in refining educational methodologies and improving performance evaluations at the tertiary level. Full article

(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)

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22 pages, 1919 KiB

Open AccessArticle

Management of Virtualized Railway Applications

by Ivaylo Atanasov, Evelina Pencheva and Kamelia Nikolova

Information 2025, 16(8), 712; https://doi.org/10.3390/info16080712 - 21 Aug 2025

Abstract

Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with [...] Read more.

Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with the legacy communication system (GSM-R), it provides high data rates, ultra-high reliability, and low latency. The FRMCS architecture will also benefit from cloud computing, following the principles of the cloud-native 5G core network design based on Network Function Virtualization (NFV). In this paper, an approach to the management of virtualized FRMCS applications is presented. First, the key management functionality related to the virtualized FRMCS application is identified based on an analysis of the different use cases. Next, this functionality is synthesized as RESTful services. The communication between application management and the services is designed as Application Programing Interfaces (APIs). The APIs are formally verified by modeling the management states of an FRMCS application instance from different points of view, and it is mathematically proved that the management state models are synchronized in time. The latency introduced by the designed APIs, as a key performance indicator, is evaluated through emulation. Full article

(This article belongs to the Section Information Applications)

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30 pages, 725 KiB

Open AccessArticle

Balancing Tradition and Digitalization: Enhancing Museum Experiences in the Post-Pandemic Era

by Vasile Gherheș, Claudiu Coman, Anna Bucs, Marian Dalban and Dragoș Bulz

Information 2025, 16(8), 711; https://doi.org/10.3390/info16080711 - 20 Aug 2025

Abstract

This study analyzes how museums in Brașov County integrated digital technologies into their activities during the COVID-19 pandemic, with a focus on online communication and audience interaction. This research is based on a mixed-methods approach, including content analysis, semi-structured interviews with museum representatives, [...] Read more.

This study analyzes how museums in Brașov County integrated digital technologies into their activities during the COVID-19 pandemic, with a focus on online communication and audience interaction. This research is based on a mixed-methods approach, including content analysis, semi-structured interviews with museum representatives, and a questionnaire applied to the visiting public. The aim is to identify the digital strategies used, the challenges encountered, and visitors’ perceptions regarding the usefulness of these tools. The results indicate an accelerated but uneven adoption of digital technologies, influenced by available resources, internal competencies, and institutional support. Frequent online interaction is positively correlated with the perceived quality of digital content, and openness to virtual activities is higher among younger and more educated audiences. Identified limitations include the lack of specialized personnel, reduced budgets, and administrative difficulties. This study emphasizes the need for institutional reforms and investments in digitalization to ensure the sustainability of the digital transition, without losing the value of the physical museum experience. Full article

(This article belongs to the Special Issue Intelligent Interaction in Cultural Heritage)

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10 pages, 467 KiB

Open AccessArticle

The Negative Concord Mystery: Insights from a Language Model

by William O’Grady, Haopeng Zhang and Miseon Lee

Information 2025, 16(8), 710; https://doi.org/10.3390/info16080710 - 20 Aug 2025

Abstract

An important recent development in the field of linguistics is the use of small language models to investigate language acquisition. Following this line of research, we investigate the mysterious appearance of ‘negative concord’ (e.g., I didn’t do nothing) in the speech of [...] Read more.

An important recent development in the field of linguistics is the use of small language models to investigate language acquisition. Following this line of research, we investigate the mysterious appearance of ‘negative concord’ (e.g., I didn’t do nothing) in the speech of children whose environment offers no exposure to patterns of this sort. Drawing on a 10-million-word version of the BabyLM corpus, we show that the preference for negative concord over patterns involving a single negative (e.g., I did nothing) can be traced to a cognitive force known as biuniqueness, whose effects will be examined with the help of data from both natural speech and a language model. Full article

(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)

26 pages, 2266 KiB

Open AccessArticle

A Phrase Fill-in-Blank Problem in a Client-Side Web Programming Assistant System

by Huiyu Qi, Zhikang Li, Nobuo Funabiki, Htoo Htoo Sandi Kyaw and Wen Chung Kao

Information 2025, 16(8), 709; https://doi.org/10.3390/info16080709 - 20 Aug 2025

Abstract

Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS) [...] Read more.

Mastering client-side Web programming is essential for the development of responsive and interactive Web applications. To support novice students’ self-study, in this paper, we propose a novel exercise format called the phrase fill-in-blank problem (PFP) in the Web Programming Learning Assistant System (WPLAS). A PFP instance presents a source code with blanked phrases (a set of elements) and corresponding Web page screenshots. Then, it requests the user to fill in the blanks, and the answers are automatically evaluated through string matching with predefined correct answers. By increasing blanks, PFP can come close to writing a code from scratch. To facilitate scalable and context-aware question creation, we implemented the PFP instance generation algorithm in Python using regular expressions. This approach targets meaningful code segments in HTML, CSS, and JavaScript that reflect the interactive behavior of front-end development. For evaluations, we generated 10 PFP instances for basic Web programming topics and 5 instances for video games and assigned them to students at Okayama University, Japan, and the State Polytechnic of Malang, Indonesia. Their solution results show that most students could solve them correctly, indicating the effectiveness and accessibility of the generated instances. In addition, we investigated the ability of generative AI, specifically ChatGPT, to solve the PFP instances. The results show 86.7% accuracy for basic-topic PFP instances. Although it still cannot fully find answers, we must monitor progress carefully. In future work, we will enhance PFP in WPLAS to handle non-unique answers by improving answer validation for flexible recognition of equivalent responses. Full article

(This article belongs to the Special Issue Software Applications Programming and Data Security)

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20 pages, 757 KiB

Open AccessArticle

Exploring Twitch Viewers’ Donation Intentions from a Dual Perspective: Uses and Gratifications Theory and the Practice of Freedom

by José Magano, Manuel Au-Yong-Oliveira and Antonio Sánchez-Bayón

Information 2025, 16(8), 708; https://doi.org/10.3390/info16080708 - 19 Aug 2025

Abstract

This study examines the factors that motivate viewers to financially support streamers on the Twitch digital platform. It proposes a conceptual framework that combines the uses and gratifications theory (UGT) with Michel Foucault’s concept of the practice of freedom (PF). Using a cross-sectional [...] Read more.

This study examines the factors that motivate viewers to financially support streamers on the Twitch digital platform. It proposes a conceptual framework that combines the uses and gratifications theory (UGT) with Michel Foucault’s concept of the practice of freedom (PF). Using a cross-sectional quantitative survey of 560 Portuguese Twitch users, the model investigates how three core constructs from UGT—entertainment, socialization, and informativeness—affect the intention to donate, with PF acting as a mediating variable. Structural equation modeling confirms that all three UGT-based motivations significantly influence donation intentions, with socialization exhibiting the strongest mediated effect through PF. The findings reveal that Twitch donations go beyond mere instrumental or playful actions; they serve as performative expressions of identity, autonomy, and ethical subjectivity. By framing PF as a link between interpersonal engagement and financial support, this study provides a contribution to media motivation research. The theoretical integration enhances our understanding of pro-social behavior in live streaming environments, challenging simplistic, transactional interpretations of viewer contributions vis-à-vis more political ones and the desire to freely dispose of what is ours to give. Additionally, this study may lay the groundwork for future inquiries into how ethical self-formation is intertwined with monetized online participation, offering useful insights for academics, platform designers, and content creators seeking to promote meaningful digital interactions. Full article

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31 pages, 5952 KiB

Open AccessArticle

Low-Cost Smart Cane for Visually Impaired People with Pathway Surface Detection and Distance Estimation Using Weighted Bounding Boxes and Depth Mapping

by Teepakorn Mungdee, Prakaidaw Ramsiri, Kanyarak Khabuankla, Pipat Khambun, Thanakrit Nupim and Ponlawat Chophuk

Information 2025, 16(8), 707; https://doi.org/10.3390/info16080707 - 19 Aug 2025

Abstract

Visually impaired individuals are at a high risk of accidents due to sudden changes in walking surfaces and surrounding obstacles. Existing smart cane systems lack the capability to detect pathway surface transition points with accurate distance estimation and danger-level assessment. This study proposes [...] Read more.

Visually impaired individuals are at a high risk of accidents due to sudden changes in walking surfaces and surrounding obstacles. Existing smart cane systems lack the capability to detect pathway surface transition points with accurate distance estimation and danger-level assessment. This study proposes a low-cost smart cane that integrates a novel Pathway Surface Transition Point Detection (PSTPD) method with enhanced obstacle detection. The system employs dual RGB cameras, an ultrasonic sensor, and YOLO-based models to deliver real-time alerts based on object type, surface class, distance, and severity. It comprises three modules: (1) obstacle detection and classification into mild, moderate, or severe levels; (2) pathway surface detection across eight surface types with distance estimation using weighted bounding boxes and depth mapping; and (3) auditory notifications. Experimental results show a mean Average Precision (mAP@50) of 0.70 for obstacle detection and 0.92 for surface classification. The average distance estimation error was 0.3 cm for obstacles and 4.22 cm for pathway surface transition points. Additionally, the PSTPD method also demonstrated efficient processing with an average runtime of 0.6 s per instance. Full article

(This article belongs to the Special Issue AI and Data Analysis in Smart Cities)

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21 pages, 296 KiB

Open AccessArticle

Empirical Research on the Influencing Factors and Causal Relationships of Enterprise Positive Topic Heat on Online Social Platforms

by Li Fu, Kai Xu and Jiakun Wang

Information 2025, 16(8), 706; https://doi.org/10.3390/info16080706 - 19 Aug 2025

Abstract

Purpose: As online social platforms have increasingly become a key arena for enterprises to disseminate information, enhancing the heat of enterprise positive topic on such platforms plays a critical role in improving brand value and achieving high-quality development. Against this backdrop, it is [...] Read more.

Purpose: As online social platforms have increasingly become a key arena for enterprises to disseminate information, enhancing the heat of enterprise positive topic on such platforms plays a critical role in improving brand value and achieving high-quality development. Against this backdrop, it is critically important for enterprises to precisely identify the key factors influencing positive topic heat and to reveal the causal relationships between these factors and topic heat. Method: This paper focuses on the influencing factors and causal pathways of enterprise positive topic heat on online social platforms. To achieve this, this paper collects data on enterprise positive topic publicity from Sina Weibo, taking topic heat as the dependent variable, and uses regression analysis to examine its causal relationships with independent variables such as topic host attribute and activity level. Subsequently, a cost performance indicator was constructed and quantitatively evaluated to assess the effectiveness of different host attributes based on both performance and cost. Result: This research shows topic hosts play an active role in the topic spreading process, but some of them an exhibit asymmetric ability to increase topic heat compared to their level. The relationship between the topic heat contribution and host’s activity level changes with the change of the host attribute. In addition, the host’s cost performance for each attribute is significant different. By empirically analyzing the influencing factors of positive topic heat and revealing their underlying mechanisms, enterprises can identify the key factors for enhancing positive topic heat on online social platforms and optimize decisions such as the selection of hosts for related topic promotion. Full article

(This article belongs to the Section Information Applications)

22 pages, 747 KiB

Open AccessArticle

Unpacking the Black Box: How AI Capability Enhances Human Resource Functions in China’s Healthcare Sector

by Xueru Chen, Maria Pilar Martínez-Ruiz, Elena Bulmer and Benito Yáñez-Araque

Information 2025, 16(8), 705; https://doi.org/10.3390/info16080705 - 19 Aug 2025

Abstract

Artificial intelligence (AI) is transforming organizational functions across sectors; however, its application to human resource management (HRM) within healthcare remains underexplored. This study aims to unpack the black-box nature of AI capability’s impact on HR functions within China’s healthcare sector, a domain undergoing [...] Read more.

Artificial intelligence (AI) is transforming organizational functions across sectors; however, its application to human resource management (HRM) within healthcare remains underexplored. This study aims to unpack the black-box nature of AI capability’s impact on HR functions within China’s healthcare sector, a domain undergoing rapid digital transformation, driven by national innovation policies. Grounded in resource-based theory, the study conceptualizes AI capability as a multidimensional construct encompassing tangible resources, human resources, and organizational intangibles. Using a structural equation modeling approach (PLS-SEM), the analysis draws on survey data from 331 professionals across five hospitals in three Chinese cities. The results demonstrate a strong, positive, and statistically significant relationship between AI capability and HR functions, accounting for 75.2% of the explained variance. These findings indicate that AI capability enhances HR performance through smarter recruitment, personalized training, and data-driven talent management. By empirically illuminating the mechanisms linking AI capability to HR outcomes, the study contributes to theoretical development and offers actionable insights for healthcare administrators and policymakers. It positions AI not merely as a technological tool but as a strategic resource to address talent shortages and improve equity in workforce distribution. This work helps to clarify a previously opaque area of AI application in healthcare HRM. Full article

(This article belongs to the Special Issue Emerging Research in Knowledge Management and Innovation)

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21 pages, 2544 KiB

Open AccessArticle

Towards Fair Graph Neural Networks via Counterfactual and Balance

by Zhiguo Xiao, Yangfan Zhou, Dongni Li and Ke Wang

Information 2025, 16(8), 704; https://doi.org/10.3390/info16080704 - 19 Aug 2025

Abstract

In recent years, graph neural networks (GNNs) have shown powerful performance in processing non-Euclidean data. However, similar to other machine-learning algorithms, GNNs can amplify data bias in high-risk decision-making systems, which can easily lead to unfairness in the final decision-making results. At present, [...] Read more.

In recent years, graph neural networks (GNNs) have shown powerful performance in processing non-Euclidean data. However, similar to other machine-learning algorithms, GNNs can amplify data bias in high-risk decision-making systems, which can easily lead to unfairness in the final decision-making results. At present, a large number of studies focus on solving the fairness problem of GNNs, but the existing methods mostly rely on building complex model architectures or rely on technical means in the field of non-GNNs. To this end, this paper proposes FairCNCB (Fair Graph Neural Network based on Counterfactual and Category Balance) to address the problem of class imbalancing in minority sensitive attribute groups. First, we conduct a causal analysis of fair representation and employ the adversarial network to generate counterfactual node samples, effectively mitigating bias induced by sensitive attributes. Secondly, we calculate the weights for minority sensitive attribute groups, and reconstruct the loss function to achieve the fairness of sensitive attribute classes among different groups. The synergy between the two modules optimizes GNNs from multiple dimensions and significantly improves the performance of GNNs in terms of fairness. The experimental results on the three datasets show the effectiveness and fairness of FairCNCB. The performance metrics (such as AUC, F1, and ACC) have been improved by approximately 2%, and the fairness metrics (△sp, △eo) have been enhanced by approximately 5%. Full article

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26 pages, 2107 KiB

Open AccessArticle

TSRACE-AI: Traffic Sign Recognition Accelerated with Co-Designed Edge AI Based on Hybrid FPGA Architecture for ADAS

by Abderrahmane Smaali, Said Ben Alla and Abdellah Touhafi

Information 2025, 16(8), 703; https://doi.org/10.3390/info16080703 - 18 Aug 2025

Abstract

The need for efficient and real-time traffic sign recognition has become increasingly important as autonomous vehicles and Advanced Driver Assistance Systems (ADASs) continue to evolve. This study introduces TSRACE-AI, a system that accelerates traffic sign recognition by combining hardware and software in a [...] Read more.

The need for efficient and real-time traffic sign recognition has become increasingly important as autonomous vehicles and Advanced Driver Assistance Systems (ADASs) continue to evolve. This study introduces TSRACE-AI, a system that accelerates traffic sign recognition by combining hardware and software in a hybrid architecture deployed on the PYNQ-Z2 FPGA platform. The design employs the Deep Learning Processing Unit (DPU) for hardware acceleration and incorporates 8-bit fixed-point quantization to enhance the performance of the CNN model. The proposed system achieves a 98.85% reduction in latency and a 200.28% increase in throughput compared to similar works, with a trade-off of a 90.35% decrease in power efficiency. Despite this trade-off, the system excels in latency-sensitive applications, demonstrating its suitability for real-time decision-making. By balancing speed and power efficiency, TSRACE-AI offers a compelling solution for integrating traffic sign recognition into ADAS, paving the way for enhanced autonomous driving capabilities. Full article

(This article belongs to the Special Issue Addressing Real-World Challenges in Recognition and Classification with Cutting-Edge AI Models and Methods)

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25 pages, 3070 KiB

Open AccessArticle

Feeding Urban Rail Transit: Hybrid Microtransit Network Design Based on Parsimonious Continuum Approach

by Qian Ye, Yunyu Zhang, Kunzheng Wang, Xinghua Liu and Chunfu Shao

Information 2025, 16(8), 702; https://doi.org/10.3390/info16080702 - 18 Aug 2025

Abstract

In recent years, the passenger flow volume of conventional transit in major cities has declined steadily. Ground public transit often suffers from congestion during rush hours caused by frequent stops (e.g., conventional fixed-route buses) or excessively high operating costs (e.g., demand-responsive transit). While [...] Read more.

In recent years, the passenger flow volume of conventional transit in major cities has declined steadily. Ground public transit often suffers from congestion during rush hours caused by frequent stops (e.g., conventional fixed-route buses) or excessively high operating costs (e.g., demand-responsive transit). While rail transit offers reliable service with dedicated right-of-way, its high capital and operational costs pose challenges for integrated planning with other transit modes. The joint design of rail, conventional buses, and DRT remains underexplored. To bridge this gap, this paper proposes and analyses a new hybrid transit system that integrates conventional transit service with demand-adaptive transit (DAT) to feed urban rail transit (the system hence called hybrid microtransit system). The main task is to optimally design the hybrid microtransit system to allocate resources efficiently across different modes. Both the conventional transit and DAT connect passengers from their origin/destination to the rail transit stations. Travelers can choose one of the services to access urban rail transit, or directly walk. Accordingly, we divide the service area into three parts and compute the user costs to access rail transit by conventional transit and DAT. The optimal design problem is hence formulated as a mixed integer program by minimizing the total system cost, which includes both the user and agency (operating) costs. Numerical experiment results demonstrate that the hybrid microtransit system performs better than the system that only has conventional transit to feed under all demand levels, achieving up to a 7% reduction in total system cost. These may provide some evidence to resolve the “first-mile” challenges of rail transit in megacities by designing better conventional transit and DAT. Full article

(This article belongs to the Special Issue Big Data Analytics in Smart Cities)

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