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Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software
Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review
Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things

Journal Description

Information

Information is a scientific, peer-reviewed, open access journal of information science and technology, data, knowledge, and communication, and is published monthly online by MDPI. The International Society for Information Studies (IS4SI) is affiliated with Information and its members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, and other databases.
Journal Rank: CiteScore - Q2 (Information Systems)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 3.8 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 2.4 (2023); 5-Year Impact Factor: 2.6 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2078-2489

Latest Articles

17 pages, 523 KiB

Open AccessReview

Artificial Intelligence in Bronchopulmonary Dysplasia: A Review of the Literature

by Tony Jha, Sana Suhail, Janet Northcote and Alvaro G. Moreira

Information 2025, 16(4), 262; https://doi.org/10.3390/info16040262 - 24 Mar 2025

Bronchopulmonary dysplasia (BPD) is a neonatal lung condition predominantly affecting preterm infants. Researchers have turned to computational tools, such as artificial intelligence (AI) and machine learning (ML), to better understand, diagnose, and manage BPD in patients. This study aims to provide a comprehensive [...] Read more.

Bronchopulmonary dysplasia (BPD) is a neonatal lung condition predominantly affecting preterm infants. Researchers have turned to computational tools, such as artificial intelligence (AI) and machine learning (ML), to better understand, diagnose, and manage BPD in patients. This study aims to provide a comprehensive summary of current AI applications in BPD risk stratification, treatment, and management and seeks to guide future research towards developing practical and effective computational tools in neonatal care. This review highlights breakthroughs in predictive modeling using clinical-, genetic-, biomarker-, and imaging-based markers. AI has helped advance BPD management strategies by optimizing treatment pathways and prognostic predictions through computational modeling. While these developments become increasingly clinically applicable, numerous challenges remain in data standardization, external validation, and the equitable integration of AI solutions into clinical practice. Addressing ethical considerations, such as data privacy and demographic representation, as well as other practical considerations will be essential to ensure the proper implementation of AI clinical tools. Future research should focus on prospective, multicenter studies, leveraging multimodal data integration to enhance early diagnosis, personalized interventions, and long-term outcomes for neonates at risk of BPD. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)

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15 pages, 1989 KiB

Open AccessArticle

Exploring the Potential of BCI in Education: An Experiment in Musical Training

by Raffaella Folgieri, Claudio Lucchiari, Sergej Gričar, Tea Baldigara and Marisa Gil

Information 2025, 16(4), 261; https://doi.org/10.3390/info16040261 - 23 Mar 2025

Brain–computer interfaces (BCIs) have gained significant attention in recent years for various applications, including education and skill development: studies have shown that BCIs can boost memory, concentration, and even creativity and can improve learning and memory retention in healthy people. In our current [...] Read more.

Brain–computer interfaces (BCIs) have gained significant attention in recent years for various applications, including education and skill development: studies have shown that BCIs can boost memory, concentration, and even creativity and can improve learning and memory retention in healthy people. In our current study, we investigated the effectiveness of real-time feedback provided by a BCI system for improving performance on a specific task. A total of 20 participants completed a pre-training assessment, followed by a training period with the BCI system and a post-training assessment. The BCI system provided real-time feedback based on the participants’ level of accuracy, with positive feedback given for scores above 70%. Results showed a significant improvement in accuracy scores from pre- to post-training, with an average improvement of 15%. Participants also reported high levels of satisfaction with the feedback provided by the BCI system. These findings suggest that real-time feedback provided by a BCI system can be an effective tool for skill development and education, particularly when tailored to the specific needs of individual learners. Further research is needed to explore the potential of BCIs for a wide range of educational applications. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)

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20 pages, 1805 KiB

Open AccessReview

Review of Robotics Activities to Promote Kindergarteners’ Communication, Collaboration, Critical Thinking, and Creativity

by Sophia Rapti, Theodosios Sapounidis and Sokratis Tselegkaridis

Information 2025, 16(4), 260; https://doi.org/10.3390/info16040260 - 23 Mar 2025

Communication, collaboration, critical thinking, and creativity are core 21st century skills. Meanwhile, educational robotics is regarded as a contributor to their promotion. Hence, education tries to embrace them in school curricula. Yet, there is a lack of reviews in the existing literature presenting [...] Read more.

Communication, collaboration, critical thinking, and creativity are core 21st century skills. Meanwhile, educational robotics is regarded as a contributor to their promotion. Hence, education tries to embrace them in school curricula. Yet, there is a lack of reviews in the existing literature presenting the robotics activities used to promote children’s communication, collaboration, creativity, and critical thinking from an early age. Consequently, this study employed a thematic literature review aiming to 1. map the research field of robotics activities suitable for promoting kindergarteners’ skills, 2. facilitate researchers and teachers in their current and future work related to robotics, and 3. provide guidelines and a model flow related to robotics activities for supporting educators in integrating them into their school reality. The PRISMA Flow Diagram and the Atlas.ti software were used to investigate the Scopus database and the Taylor and Francis register. Finally, 16 papers were examined out of 349 initially retrieved and published from 2014 to 2025. Based on our findings, a few interventions have been aimed at fostering kindergarteners’ communication, collaboration, creativity, and critical thinking via educational robotics, but there is rarely a clear record of robotics activities achieving that. Moreover, there is no specific model or guideline for developing such activities in kindergarten. Full article

(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)

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25 pages, 3751 KiB

Open AccessArticle

ORAN-HAutoscaling: A Scalable and Efficient Resource Optimization Framework for Open Radio Access Networks with Performance Improvements

by Sunil Kumar

Information 2025, 16(4), 259; https://doi.org/10.3390/info16040259 - 23 Mar 2025

Open Radio Access Networks (ORANs) are transforming the traditional telecommunications landscape by offering more flexible, vendor-independent solutions. Unlike previous systems, which relied on rigid, vertical configurations, ORAN introduces network programmability that is AI-driven and horizontally scalable. This shift is facilitated by modern container [...] Read more.

Open Radio Access Networks (ORANs) are transforming the traditional telecommunications landscape by offering more flexible, vendor-independent solutions. Unlike previous systems, which relied on rigid, vertical configurations, ORAN introduces network programmability that is AI-driven and horizontally scalable. This shift is facilitated by modern container orchestrators, such as Kubernetes and Red Hat OpenShift, which simplify the development and deployment of components such as gNB, CU/DU, and RAN Intelligent Controllers (RICs). While these advancements help reduce costs by enabling shared infrastructure, they also create new challenges in meeting ORAN’s stringent latency requirements, especially when managing large-scale xApp deployments. Near-RTRICs are responsible for controlling xApps that must adhere to tight latency constraints, often less than one second. Current orchestration methods fail to meet these demands, as they lack the required scalability and long latencies. Additionally, non-API-based E2AP (over SCTP) further complicates the scaling process. To address these challenges, we introduce ORAN-HAutoscaling, a framework designed to enable horizontal scaling through Kubernetes. This framework ensures that latency constraints are met while supporting large-scale xApp deployments with optimal resource utilization. ORAN-HAutoscaling dynamically allocates and distributes xApps into scalable pods, ensuring that central processing unit (CPU) utilization remains efficient and latency is minimized, thus improving overall performance. Full article

(This article belongs to the Section Information Systems)

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13 pages, 609 KiB

Open AccessArticle

ADFilter —A Web Tool for New Physics Searches with Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks

by Sergei V. Chekanov, Wasikul Islam, Rui Zhang and Nicholas Luongo

Information 2025, 16(4), 258; https://doi.org/10.3390/info16040258 - 22 Mar 2025

A web-based tool called ADFilter (short for Anomaly Detection Filter) was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model (SM) Monte Carlo [...] Read more.

A web-based tool called ADFilter (short for Anomaly Detection Filter) was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model (SM) Monte Carlo simulations. The tool calculates loss distributions for input events, helping to determine the degree to which the events can be considered anomalous with respect to the SM events used for training. Therefore, it can be used for new physics searches in collider experiments. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing results from the Large Hadron Collider (LHC), with the goal of significantly improving exclusion limits. This tool is expected to mitigate the “reproducibility crisis” associated with various machine learning techniques, as it can incorporate machine learning approaches from third-party publications, making them accessible to the general public. Full article

(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)

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21 pages, 3129 KiB

Open AccessArticle

Optimizing Contextonym Analysis for Terminological Definition Writing

by Antonio San Martín

Information 2025, 16(4), 257; https://doi.org/10.3390/info16040257 - 22 Mar 2025

To write terminological definitions that meet user needs, terminologists require methods that help them effectively select the most relevant information to be included in a definition. In this sense, a corpus technique that can be useful for the definition of terms is contextonym [...] Read more.

To write terminological definitions that meet user needs, terminologists require methods that help them effectively select the most relevant information to be included in a definition. In this sense, a corpus technique that can be useful for the definition of terms is contextonym analysis. It involves the quantitative analysis of the other terms with which the term to be defined usually co-occurs (i.e., its contextonyms), regardless of any syntactic or semantic relationship. This paper presents a study conducted to determine the optimal configuration for extracting contextonyms for the creation of terminological definitions. More specifically, this study aims to create a word sketch column in Sketch Engine that lists contextonyms, offering a user-friendly method for their extraction. This study has identified that the optimal context window for extracting contextonyms in the form of word sketches in English to inform definition writing is 50 tokens, and that these contextonyms should be ranked by frequency. Full article

(This article belongs to the Special Issue Information Extraction and Language Discourse Processing)

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15 pages, 4511 KiB

Open AccessArticle

Fault Detection Based on Kernel Global Local Preserving Projection

by Wenbiao Wang, Qianqian Zhang and Youwei Hao

Information 2025, 16(4), 256; https://doi.org/10.3390/info16040256 - 21 Mar 2025

In this paper, a fault detection method based on kernel global local preserving projection is presented to address the nonlinear characteristics of industrial systems. First, data are projected into a high-dimensional feature space through nonlinear mapping, enabling linear separability in this feature space. [...] Read more.

In this paper, a fault detection method based on kernel global local preserving projection is presented to address the nonlinear characteristics of industrial systems. First, data are projected into a high-dimensional feature space through nonlinear mapping, enabling linear separability in this feature space. Subsequently, data features are extracted using the global local preserving projection method in the high-dimensional feature space. Finally, a monitoring model is established based on these features. Experiments on the Tennessee Eastman process and industrial boilers demonstrate that the proposed method balances global and local data structures, reduces nonlinear influences, and improves the fault detection rate. Full article

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24 pages, 14300 KiB

Open AccessArticle

Quantum Edge Detection and Convolution Using Paired Transform-Based Image Representation

by Artyom Grigoryan, Alexis Gomez, Sos Agaian and Karen Panetta

Information 2025, 16(4), 255; https://doi.org/10.3390/info16040255 - 21 Mar 2025

Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. Quantum image processing offers a promising alternative, but current implementations face significant challenges, such as time-consuming data acquisition, complex device requirements, and limited real-time processing capabilities. This work presents a [...] Read more.

Classical edge detection algorithms often struggle to process large, high-resolution image datasets efficiently. Quantum image processing offers a promising alternative, but current implementations face significant challenges, such as time-consuming data acquisition, complex device requirements, and limited real-time processing capabilities. This work presents a novel paired transform-based quantum representation for efficient image processing. This representation enables the parallelization of convolution operations, simplifies gradient calculations, and facilitates the processing of one-dimensional and two-dimensional signals. We demonstrate that our approach achieves improved processing speed compared to classical methods while maintaining comparable accuracy. The successful implementation of real-world images highlights the potential of this research for large-scale quantum image processing, architecture-specific optimizations, and applications beyond edge detection. Full article

(This article belongs to the Special Issue Emerging Research in Object Tracking and Image Segmentation)

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50 pages, 4091 KiB

Open AccessArticle

AcademiCraft: Transforming Writing Assistance for English for Academic Purposes with Multi-Agent System Innovations

by Zhendong Du and Kenji Hashimoto

Information 2025, 16(4), 254; https://doi.org/10.3390/info16040254 - 21 Mar 2025

In the realm of academic English writing, both native (L1) and non-native (L2) speakers face significant challenges due to the complex linguistic structures and conventions required. Existing writing assistance tools, while useful for grammar correction and text enhancement, often fall short by providing [...] Read more.

In the realm of academic English writing, both native (L1) and non-native (L2) speakers face significant challenges due to the complex linguistic structures and conventions required. Existing writing assistance tools, while useful for grammar correction and text enhancement, often fall short by providing only corrected output without explanations, and they typically operate as opaque, proprietary systems. This study introduces AcademiCraft, a novel EAP writing assistance built upon a multi-agent system (MAS) and advanced large-language models (LLMs), integrating state-of-the-art natural language processing (NLP) and linguistic research. AcademiCraft distinguishes itself by correcting and refining academic texts and offering detailed explanations for its revisions, thereby deepening the user’s understanding of writing conventions. The tool’s open and replicable framework contrasts sharply with the closed nature of most commercial products. Through a series of rigorous scientific benchmarks, AcademiCraft has been shown to outperform leading commercial writing assistance tools in several key areas, including its ability to handle complex syntactic structures, improve coherence and cohesion in academic texts, and provide contextually accurate word choices, all while maintaining high levels of grammatical precision. This paper details the methodologies and underlying principles of AcademiCraft, and presents a comparative analysis of its performance, demonstrating its superior capability in supporting both L1 and L2 writers in mastering the complexities of academic English writing. Full article

(This article belongs to the Special Issue Semantic Web and Language Models)

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24 pages, 689 KiB

Open AccessArticle

Topic Classification of Interviews on Emergency Remote Teaching

by Spyridon Tzimiris, Stefanos Nikiforos, Maria Nefeli Nikiforos, Despoina Mouratidis and Katia Lida Kermanidis

Information 2025, 16(4), 253; https://doi.org/10.3390/info16040253 - 21 Mar 2025

This study explores the application of transformer-based language models for automated Topic Classification in qualitative datasets from interviews conducted in Modern Greek. The interviews captured the views of parents, teachers, and school directors regarding Emergency Remote Teaching. Identifying key themes in this kind [...] Read more.

This study explores the application of transformer-based language models for automated Topic Classification in qualitative datasets from interviews conducted in Modern Greek. The interviews captured the views of parents, teachers, and school directors regarding Emergency Remote Teaching. Identifying key themes in this kind of interview is crucial for informed decision-making in educational policies. Each dataset was segmented into sentences and labeled with one out of four topics. The dataset was imbalanced, presenting additional complexity for the classification task. The GreekBERT model was fine-tuned for Topic Classification, with preprocessing including accent stripping, lowercasing, and tokenization. The findings revealed GreekBERT’s effectiveness in achieving balanced performance across all themes, outperforming conventional machine learning models. The highest evaluation metric achieved was a macro-F1-score of 0.76, averaged across all classes, highlighting the effectiveness of the proposed approach. This study contributes the following: (i) datasets capturing diverse educational community perspectives in Modern Greek, (ii) a comparative evaluation of conventional ML models versus transformer-based models, (iii) an investigation of how domain-specific language enhances the performance and accuracy of Topic Classification models, showcasing their effectiveness in specialized datasets and the benefits of fine-tuned GreekBERT for such tasks, and (iv) capturing the complexities of ERT through an empirical investigation of the relationships between extracted topics and relevant variables. These contributions offer reliable, scalable solutions for policymakers, enabling data-driven educational policies to address challenges in remote learning and enhance decision-making based on comprehensive qualitative evidence. Full article

(This article belongs to the Special Issue Machine Learning Approaches for Imbalanced Domains: Emerging Trends and Applications)

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20 pages, 1222 KiB

Open AccessArticle

SPARC: A Human-in-the-Loop Framework for Learning and Explaining Spatial Concepts

by Brendan Young, Derek T. Anderson, James Keller, Frederick Petry and Chris J. Michael

Information 2025, 16(4), 252; https://doi.org/10.3390/info16040252 - 21 Mar 2025

In this article, we introduce a novel framework for learning spatial concepts within a human-in-the-loop (HITL) context, highlighting the critical role of explainability in AI systems. By incorporating human feedback, the approach enhances the learning process, making it particularly suitable for applications where [...] Read more.

In this article, we introduce a novel framework for learning spatial concepts within a human-in-the-loop (HITL) context, highlighting the critical role of explainability in AI systems. By incorporating human feedback, the approach enhances the learning process, making it particularly suitable for applications where user trust and interpretability are essential, such as AiTR. Namely, we introduce a new parametric similarity measure for spatial relations expressed as histograms of forces (HoFs). Next, a spatial concept is represented as a spatially attributed graph and HoF bundles. Last, a process is outlined for utilizing this structure to make decisions and learn from human feedback. The framework’s robustness is demonstrated through examples with diverse user types, showcasing how varying feedback strategies influence learning efficiency, accuracy, and ability to tailor the system to a particular user. Overall, this framework represents a promising step toward human-centered AI systems capable of understanding complex spatial relationships while offering transparent insights into their reasoning processes. Full article

(This article belongs to the Special Issue Interactive Learning: Human in the Loop System Design for Active Human–Computer Interactions)

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24 pages, 829 KiB

Open AccessArticle

ILSTMA: Enhancing Accuracy and Speed of Long-Term and Short-Term Memory Architecture

by Zongyu Ming, Zimu Wu and Genlang Chen

Information 2025, 16(4), 251; https://doi.org/10.3390/info16040251 - 21 Mar 2025

In recent years, the rapid development of large language models (LLMs) has led to a growing consensus in the industry regarding the integration of long-term and short-term memory. However, the widespread application of long-term and short-term memory systems faces two significant challenges: increased [...] Read more.

In recent years, the rapid development of large language models (LLMs) has led to a growing consensus in the industry regarding the integration of long-term and short-term memory. However, the widespread application of long-term and short-term memory systems faces two significant challenges: increased execution time and decreased answer accuracy from LLMs. To tackle these challenges, we propose the ILSTMA. This architecture uniquely combines fundamental theories of human forgetting with classical operating system principles, providing an unprecedented acceleration method that does not rely on traditional memory retrieval algorithms, which is all based on the systematic planning of available memory space. Furthermore, our proposed most relevant dialogue retrieval process substantially enhances the answer accuracy of LLMs while examining the potential of the two most commonly used memory retrieval algorithms. Experimental results demonstrate that our acceleration method improves the execution efficiency of the original architecture by 21.45%, and our most relevant dialogue retrieval process raises the answer accuracy to 88.4%, surpassing several benchmarks. These findings validate the high performance of the ILSTMA. Full article

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18 pages, 10602 KiB

Open AccessArticle

A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection

by Sheng Deng and Yaping Wan

Information 2025, 16(3), 250; https://doi.org/10.3390/info16030250 - 20 Mar 2025

To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. This approach [...] Read more.

To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. This approach introduces a new module, C2f_SEPConv, which incorporates Partial Convolution (PConv) and channel attention mechanisms (Squeeze-and-Excitation, SE), effectively replacing the previous bottleneck and minimizing both the model’s parameter count and computational demands. Modifications to the detection head allow it to perform more effectively in scenarios with small targets in aerial images. To capture multi-scale object information, a Multi-Scale Cross-Axis Attention (MSCA) mechanism is embedded within the backbone network. The neck network integrates a Multi-Scale Fusion Block (MSFB) to combine multi-level features, further boosting detection precision. Furthermore, the Focal-EIoU loss function supersedes the traditional CIoU loss function to address challenges related to the regression of small targets. Evaluations conducted on the VisDrone dataset reveal that the proposed method improves Precision, Recall,

m A P_{0.5}

, and

m A P_{0.5 : 0.95}

by 4.4%, 5.6%, 6.4%, and 4%, respectively, compared to YOLOv8s, with a 28.3% reduction in parameters. On the DOTAv1.0 dataset, a 2.1% enhancement in

m A P_{0.5}

is observed. Full article

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27 pages, 2569 KiB

Open AccessArticle

Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework

by Shafiq Ul Rehman and Uddalak Mitra

Information 2025, 16(3), 249; https://doi.org/10.3390/info16030249 - 20 Mar 2025

Alzheimer’s disease (AD) is a persistent neurologic disorder that has no cure. For a successful treatment to be implemented, it is essential to diagnose AD at an early stage, which may occur up to eight years before dementia manifests. In this regard, a [...] Read more.

Alzheimer’s disease (AD) is a persistent neurologic disorder that has no cure. For a successful treatment to be implemented, it is essential to diagnose AD at an early stage, which may occur up to eight years before dementia manifests. In this regard, a new predictive machine learning model is proposed that works in two stages and takes advantage of both unsupervised and supervised learning approaches to provide a fast, affordable, yet accurate solution. The first stage involved fuzzy partitioning of a gold-standard dataset, DARWIN (Diagnosis AlzheimeR WIth haNdwriting). This dataset consists of clinical features and is designed to detect Alzheimer’s disease through handwriting analysis. To determine the optimal number of clusters, four Clustering Validity Indices (CVIs) were averaged, which we refer to as cognitive features. During the second stage, a predictive model was constructed exclusively from these cognitive features. In comparison to models relying on datasets featuring clinical attributes, models incorporating cognitive features showed substantial performance enhancements, ranging from 12% to 26%. Our proposed model surpassed all current state-of-the-art models, achieving a mean accuracy of 99%, mean sensitivity of 98%, mean specificity of 100%, mean precision of 100%, and mean MCC and Cohen’s Kappa of 98%, along with a mean AUC-ROC score of 99%. Hence, integrating the output of unsupervised learning into supervised machine learning models significantly improved their performance. In the process of crafting early interventions for individuals with a heightened risk of disease onset, our prognostic framework can aid in both the recruitment and advancement of clinical trials. Full article

(This article belongs to the Special Issue Detection and Modelling of Biosignals)

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32 pages, 1513 KiB

Open AccessArticle

Making Better Decisions, Eschewing Conspiracy, Populism, and Science Denial: Analysing the Attributes of Individuals Who Engage Effectively with Ideas

by Chris Brown, Ruth Luzmore and Yin Wang

Information 2025, 16(3), 248; https://doi.org/10.3390/info16030248 (registering DOI) - 19 Mar 2025

How can we encourage individuals to engage with beneficial ideas, while eschewing dark ideas such as science denial, conspiracy theories, or populist rhetoric? This paper investigates the mechanisms underpinning individuals’ engagement with ideas, proposing a model grounded in education, social networks, and pragmatic [...] Read more.

How can we encourage individuals to engage with beneficial ideas, while eschewing dark ideas such as science denial, conspiracy theories, or populist rhetoric? This paper investigates the mechanisms underpinning individuals’ engagement with ideas, proposing a model grounded in education, social networks, and pragmatic prospection. Beneficial ideas enhance decision-making, improving individual and societal outcomes, while dark ideas lead to suboptimal consequences, such as diminished trust in institutions and health-related harm. Using a Structural Equation Model (SEM) based on survey data from 7000 respondents across seven European countries, we test hypotheses linking critical thinking, network dynamics, and pragmatic prospection (i.e., a forward-looking mindset) to the value individuals ascribe to engaging with ideas, their ability to identify positive and dark ideas effectively, how individuals subsequently engage with ideas, and who they engage in them with. Our results highlight two key pathways: one linking pragmatic prospection to network-building and idea-sharing, and another connecting critical reasoning and knowledge acquisition to effective ideas engagement. Together, these pathways illustrate how interventions in education, network development, and forward-planning can empower individuals to critically evaluate and embrace positive ideas while rejecting those that might be detrimental. The paper concludes with recommendations for policy and future research to support an ideas-informed society. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 1323 KiB

Open AccessArticle

Digital Transformation in Governmental Public Service Provision and Usable Security Perception in Saudi Arabia

by Saqib Saeed

Information 2025, 16(3), 247; https://doi.org/10.3390/info16030247 - 19 Mar 2025

Usable security and privacy in public services are critical considerations in today’s digital age, where governments increasingly rely on technology to deliver services efficiently while safeguarding sensitive information. Successful usage of these electronic services depends on citizens’ trust level in e-government channels. Therefore, [...] Read more.

Usable security and privacy in public services are critical considerations in today’s digital age, where governments increasingly rely on technology to deliver services efficiently while safeguarding sensitive information. Successful usage of these electronic services depends on citizens’ trust level in e-government channels. Therefore, the design of these public service organizations should consider the usability aspect of security controls. In this paper, we present the results of a quantitative study conducted in Saudi Arabia to understand end users’ perceptions regarding usable security and privacy in their public service usage. Based on the findings, we present a model to further improve the usable security and privacy aspects, which will help policymakers and practitioners improve public service provision by electronic means. The model can be further refined in different geographical contexts to improve cybersecurity in e-government service provision through the integrated efforts of citizens, service-providing organizations and government cybersecurity agencies. Full article

(This article belongs to the Special Issue Human Factors in Cyber Security: Social Engineering, Behavioural Aspects and Resilience Solutions)

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28 pages, 1266 KiB

Open AccessSystematic Review

Managing Food Waste Through Gamification and Serious Games: A Systematic Literature Review

by Ezequiel Santos, Cláudia Sevivas and Vítor Carvalho

Information 2025, 16(3), 246; https://doi.org/10.3390/info16030246 - 19 Mar 2025

Household food waste poses significant environmental, social, and financial challenges. This systematic literature review examines the role of games and gamification in mitigating food waste, addressing four key research questions: how these interventions are applied, their impact on attitudes and behaviors, the specific [...] Read more.

Household food waste poses significant environmental, social, and financial challenges. This systematic literature review examines the role of games and gamification in mitigating food waste, addressing four key research questions: how these interventions are applied, their impact on attitudes and behaviors, the specific mechanisms employed, and their measured outcomes. The analysis identifies a range of strategies, including mobile applications, serious games, educational platforms, and interactive installations. Theoretical frameworks such as the Theory of Planned Behavior, emotional engagement, systems thinking, and cognitive load theory underpin these interventions. Findings suggest that gamification can enhance awareness, knowledge, and behavioral change, with some interventions demonstrating measurable reductions in food waste. However, limitations such as the lack of long-term engagement data, varying effectiveness across socio-economic contexts, and inconsistencies in measurement frameworks remain challenges. Notable interventions—including the MySusCof and Exspiro apps and serious games like FoodFighters and PadovaGoGreen—show promising results but require further validation in diverse settings. This review highlights both the potential and limitations of gamified strategies, emphasizing the need for standardized measurement approaches and longitudinal studies to assess their sustained impact on food waste reduction. Full article

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28 pages, 1162 KiB

Open AccessArticle

AHP-Based Evaluation of Discipline-Specific Information Services in Academic Libraries Under Digital Intelligence

by Simeng Zhang, Tao Zhang and Xi Wang

Information 2025, 16(3), 245; https://doi.org/10.3390/info16030245 - 18 Mar 2025

Over recent years, digital and intelligent technologies have been driving the transformation of discipline-specific information services in academic libraries toward user experience optimization and service innovation. This study constructs a quality evaluation framework for discipline-specific information services in academic libraries, incorporating digital-intelligence characteristics [...] Read more.

Over recent years, digital and intelligent technologies have been driving the transformation of discipline-specific information services in academic libraries toward user experience optimization and service innovation. This study constructs a quality evaluation framework for discipline-specific information services in academic libraries, incorporating digital-intelligence characteristics to provide theoretical references and evaluation guidelines for enhancing service quality and user satisfaction in an information-ubiquitous environment. Drawing on LibQual+TM, WebQUAL, and E-SERVQUAL service quality evaluation models and integrating expert interviews with the contextual characteristics of academic library discipline-specific information services, this study develops a comprehensive evaluation system comprising six dimensions—Perceived Information Quality, Information Usability, Information Security, Interactive Feedback, Tool Application, and User Experience—with fifteen specific indicators. The analytic hierarchy process (AHP) was applied to determine the weight of these indicators. To validate the practicality of the evaluation system, a fuzzy comprehensive evaluation method was employed for an empirical analysis using discipline-specific information services at Tsinghua University Library in China as a case study. The evaluation results indicate that the overall quality of discipline-specific information services at Tsinghua University Library is satisfactory, with Tool Application, Perceived Information Quality, and Information Usability identified as key factors influencing service quality. To further enhance discipline-specific information services in academic libraries, emphasis should be placed on service intelligence and precision-driven optimization, strengthening user experience, interaction and feedback mechanisms, and data security measures. These improvements will better meet the diverse needs of users and enhance the overall effectiveness of discipline-specific information services. Full article

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36 pages, 2748 KiB

Open AccessArticle

A Comparative Study of Privacy-Preserving Techniques in Federated Learning: A Performance and Security Analysis

by Eman Shalabi, Walid Khedr, Ehab Rushdy and Ahmad Salah

Information 2025, 16(3), 244; https://doi.org/10.3390/info16030244 - 18 Mar 2025

Federated learning (FL) is a machine learning technique where clients exchange only local model updates with a central server that combines them to create a global model after local training. While FL offers privacy benefits through local training, privacy-preserving strategies are needed since [...] Read more.

Federated learning (FL) is a machine learning technique where clients exchange only local model updates with a central server that combines them to create a global model after local training. While FL offers privacy benefits through local training, privacy-preserving strategies are needed since model updates can leak training data information due to various attacks. To enhance privacy and attack robustness, techniques like homomorphic encryption (HE), Secure Multi-Party Computation (SMPC), and the Private Aggregation of Teacher Ensembles (PATE) can be combined with FL. Currently, no study has combined more than two privacy-preserving techniques with FL or comparatively analyzed their combinations. We conducted a comparative study of privacy-preserving techniques in FL, analyzing performance and security. We implemented FL using an artificial neural network (ANN) with a Malware Dataset from Kaggle for malware detection. To enhance privacy, we proposed models combining FL with the PATE, SMPC, and HE. All models were evaluated against poisoning attacks (targeted and untargeted), a backdoor attack, a model inversion attack, and a man in the middle attack. The combined models maintained performance while improving attack robustness. FL_SMPC, FL_CKKS, and FL_CKKS_SMPC improved both their performance and attack resistance. All the combined models outperformed the base FL model against the evaluated attacks. FL_PATE_CKKS_SMPC achieved the lowest backdoor attack success rate (0.0920). FL_CKKS_SMPC best resisted untargeted poisoning attacks (0.0010 success rate). FL_CKKS and FL_CKKS_SMPC best defended against targeted poisoning attacks (0.0020 success rate). FL_PATE_SMPC best resisted model inversion attacks (19.267 MSE). FL_PATE_CKKS_SMPC best defended against man in the middle attacks with the lowest degradation in accuracy (1.68%), precision (1.94%), recall (1.68%), and the F1-score (1.64%). Full article

(This article belongs to the Special Issue Digital Privacy and Security, 2nd Edition)

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Open AccessArticle

AML-Based Multi-Dimensional Co-Evolution Approach Supported by Blockchain: Architecture Design and Case Study on Intelligent Production Lines for Industry 4.0

by Kai Ding, Detlef Gerhard and Liuqun Fan

Information 2025, 16(3), 243; https://doi.org/10.3390/info16030243 - 18 Mar 2025

Based on Automation ML (AML), Intelligent Production Lines (IPLs) for Industry 4.0 can effectively organize multi-dimensional data and models. However, this process requires interdisciplinary and multi-team contributions, which often involve the dual pressures of private data encryption and public data sharing. As a [...] Read more.

Based on Automation ML (AML), Intelligent Production Lines (IPLs) for Industry 4.0 can effectively organize multi-dimensional data and models. However, this process requires interdisciplinary and multi-team contributions, which often involve the dual pressures of private data encryption and public data sharing. As a transparent decentralized network, blockchain’s compatibility with the challenges of AML collaboration processes, data security, and privacy is not ideal. This paper proposes a new method to enhance the collaborative evolution of IPLs. Its innovations are, firstly, developing a comprehensive two-layer management model, combining blockchain with the Interplanetary File System (IPFS) to build an integrated solution for private and public hybrid containers based on a collaborative model; secondly, designing a version co-evolution management method by combining smart contract workflows and AML multi-dimensional modeling processes; meanwhile, introducing a specially designed conflict resolution mechanism based on the graph model to maintain consistency in version multi-batch management and; finally, using the test cases established in the lab’s I5Blocks for verification. Full article

(This article belongs to the Special Issue Blockchain and AI: Innovations and Applications in ICT)

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