Information

25 pages, 394 KiB

Open AccessReview

Quantum Computing Applications in Supply Chain Information and Optimization: Future Scenarios and Opportunities

by Mohammad Shamsuddoha, Mohammad Abul Kashem, Tasnuba Nasir, Ahamed Ismail Hossain and Md Foysal Ahmed

Information 2025, 16(8), 693; https://doi.org/10.3390/info16080693 - 15 Aug 2025

Abstract

Quantum computing is a groundbreaking innovation that can resolve complex supply chain problems that traditional computing techniques are unable to manage. Given a focus on information flow, optimization, and potential future applications, this study explores how supply chain management could utilize quantum computing. [...] Read more.

Quantum computing is a groundbreaking innovation that can resolve complex supply chain problems that traditional computing techniques are unable to manage. Given a focus on information flow, optimization, and potential future applications, this study explores how supply chain management could utilize quantum computing. The study used a mixed-methods approach, including scenario modeling, case studies of prominent companies, and literature reviews. The study intends to evaluate the function of quantum computing in dynamic route optimization, investigate how it can enhance supply chain resilience, and examine how it could optimize the flow of information for decision-making processes. Findings demonstrate that quantum computing offers unprecedented computational power for scenario analysis and decision-making and operates exceptionally well in activities like dynamic route optimization, parcel packaging, and reorganization during disruptions. For instance, companies like DHL and FedEx utilize quantum systems to improve efficiency substantially. However, constraints like high implementation costs, cybersecurity weaknesses, and technological infancy prevent widespread acceptance. Further research should investigate hybrid solutions that integrate quantum and classical computing while addressing these obstacles. This paper concludes that although quantum computing has the potential to transform supply chains by improving information flow, resilience, and efficiency, its wider adoption will require overcoming current financial and technological challenges. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

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19 pages, 862 KiB

Open AccessArticle

Integration of Multi-Criteria Decision-Making and Dimensional Entropy Minimization in Furniture Design

by Anna Jasińska and Maciej Sydor

Information 2025, 16(8), 692; https://doi.org/10.3390/info16080692 - 14 Aug 2025

Abstract

Multi-criteria decision analysis (MCDA) in furniture design is challenged by increasing product complexity and component proliferation. This study introduces a novel framework that integrates entropy reduction—achieved through dimensional standardization and modularity—as a core factor in the MCDA methodologies. The framework addresses both individual [...] Read more.

Multi-criteria decision analysis (MCDA) in furniture design is challenged by increasing product complexity and component proliferation. This study introduces a novel framework that integrates entropy reduction—achieved through dimensional standardization and modularity—as a core factor in the MCDA methodologies. The framework addresses both individual furniture evaluation and product family optimization through systematic complexity reduction. The research employed a two-phase methodology. First, a comparative analysis evaluated two furniture variants (laminated particleboard versus oak wood) using the Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The divergent rankings produced by these methods revealed inherent evaluation ambiguities stemming from their distinct mathematical foundations, highlighting the need for additional decision criteria. Building on these findings, the study further examined ten furniture variants, identifying the potential to transform their individual components into universal components, applicable across various furniture variants (or configurations) in a furniture line. The proposed dimensional modifications enhance modularity and interoperability within product lines, simplifying design processes, production, warehousing logistics, product servicing, and liquidation at end of lifetime. The integration of entropy reduction as a quantifiable criterion within MCDA represents a significant methodological advancement. By prioritizing dimensional standardization and modularity, the framework reduces component variety while maintaining design flexibility. This approach offers furniture manufacturers a systematic method for balancing product diversity with operational efficiency, addressing a critical gap in current design evaluation practices. Full article

(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis, 3rd Edition)

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23 pages, 348 KiB

Open AccessArticle

Exploring the Key Drivers of Financial Performance in the Context of Corporate and Public Governance: Empirical Evidence

by Georgeta Vintilă, Mihaela Onofrei, Alexandra Ioana Vintilă and Vasilica Izabela Fometescu

Information 2025, 16(8), 691; https://doi.org/10.3390/info16080691 - 14 Aug 2025

Abstract

This research focuses on analyzing the determinants of financial performance for the companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. To guide managerial decisions aimed at enhancing company performance, this study examines, as key drivers, [...] Read more.

This research focuses on analyzing the determinants of financial performance for the companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. To guide managerial decisions aimed at enhancing company performance, this study examines, as key drivers, the main financial indicators, core corporate governance characteristics, and U.S. public governance indicators. The investigation begins with a retrospective review of the specialized literature, highlighting the findings of previous studies in the field and providing the basis for selecting the variables used in the present empirical analysis. The research method employed is fixed-effects panel-data regression. The dependent variables are financial performance measures, such as the EBITDA margin, EBIT margin, net profit margin, and ROA. This study’s main results show that the price-to-book ratio, liquidity, sales growth, CEO duality, board gender diversity, ESG score, and U.S. regulatory quality exert a positive influence on financial performance. In contrast, the price-to-earnings ratio, net debt, capital intensity, R&D intensity, weighted average cost of capital, board independence, and the COVID-19 pandemic crisis have a negative impact on the financial performance of U.S. companies. The findings of this investigation could serve as benchmarks for supporting managerial decisions at the company level regarding the improvement of their financial performance. Full article

(This article belongs to the Special Issue Decision Models for Economics and Business Management)

11 pages, 3732 KiB

Open AccessArticle

Convolutional Autoencoders for Data Compression and Anomaly Detection in Small Satellite Technologies

by Dishanand Jayeprokash and Julia Gonski

Information 2025, 16(8), 690; https://doi.org/10.3390/info16080690 - 14 Aug 2025

Abstract

Small satellite technologies have enhanced the potential and feasibility of geodesic missions through the simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML) for better performance and greater [...] Read more.

Small satellite technologies have enhanced the potential and feasibility of geodesic missions through the simplification of design and decreased costs allowing for more frequent launches. On-satellite data acquisition systems can benefit from the implementation of machine learning (ML) for better performance and greater efficiency on tasks such as image processing or feature extraction. This work presents convolutional autoencoders for implementation on the payload of small satellites, designed to achieve the dual functionality of data compression for more efficient off-satellite transmission and at-source anomaly detection to inform satellite data-taking. This capability is demonstrated for the use case of disaster monitoring using aerial image datasets of the African continent, offering avenues for both the implementation of novel ML-based approaches in small satellite applications and the expansion of space technology and artificial intelligence in Africa. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Information Systems)

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31 pages, 744 KiB

Open AccessArticle

A Novel Approach to State-to-State Transformation in Quantum Computing

by Artyom M. Grigoryan, Alexis A. Gomez and Sos S. Agaian

Information 2025, 16(8), 689; https://doi.org/10.3390/info16080689 - 13 Aug 2025

Abstract

This article presents a new approach to the problem of transforming one quantum state into another. It is shown that an

r

-qubit superposition

| x⟩

can be obtained from another

r

-qubit superposition

| y⟩

, by using only [...] Read more.

This article presents a new approach to the problem of transforming one quantum state into another. It is shown that an

r

-qubit superposition

| x⟩

can be obtained from another

r

-qubit superposition

| y⟩

, by using only

{(2}^{r} - 1)

rotations, each presented by one controlled rotation gate. The quantum superpositions with real amplitudes are considered. The traditional two-stage approach

U_{y}^{- 1} U_{x} : | x⟩ \to | {0⟩}^{\oplus r} \to | y⟩

requires twice as many rotations. Here, both transformations to the conventual basis state,

U_{x} : | x⟩ \to | {0⟩}^{\oplus r}

and

U_{y} : | y⟩ \to | {0⟩}^{\oplus r}

, use

{(2}^{r} - 1)

rotations each on two binary planes, and many of these rotations require additional sets of CNOTs to be represented as 1- or 2-qubit-controlled gates. The proposed method is based on the concept of the discrete signal-induced heap transform (DsiHT) which is unitary and generated by a vector and a set of angular equations with given parameters. The quantum analog of this transform is described. The main characteristic of the DsiHT is the path of processing the data. It is shown that there exist such fast paths that allow for effective computing of the DsiHT, which leads to the simple quantum circuits for state preparation and transformation. Examples of such paths are given and quantum circuits for preparation and transformation of 2-, 3-, and 4-qubits are described in detail. CNOT gates are not used, but only controlled gates of elementary rotations around the

y

-axis

.

It is shown that the transformation and, in particular, only rotation gates with control qubits are required for initialization of 2-, 3-, and 4-qubits. The quantum circuits are simple and have a recursive form, which makes them easy to implement for arbitrary

r

-qubit superposition, with

r \geq 2 .

This approach significantly reduces the complexity of quantum state transformations, paving the way for more efficient quantum algorithms and practical implementations on near-term quantum devices. Full article

23 pages, 823 KiB

Open AccessReview

Ensemble Large Language Models: A Survey

by Ibomoiye Domor Mienye and Theo G. Swart

Information 2025, 16(8), 688; https://doi.org/10.3390/info16080688 - 13 Aug 2025

Abstract

Large language models (LLMs) have transformed the field of natural language processing (NLP), achieving state-of-the-art performance in tasks such as translation, summarization, and reasoning. Despite their impressive capabilities, challenges persist, including biases, limited interpretability, and resource-intensive training. Ensemble learning, a technique that combines [...] Read more.

Large language models (LLMs) have transformed the field of natural language processing (NLP), achieving state-of-the-art performance in tasks such as translation, summarization, and reasoning. Despite their impressive capabilities, challenges persist, including biases, limited interpretability, and resource-intensive training. Ensemble learning, a technique that combines multiple models to improve performance, presents a promising avenue for addressing these limitations in LLMs. This review explores the emerging field of ensemble LLMs, providing a comprehensive analysis of current methodologies, applications across diverse domains, and existing challenges. By reviewing ensemble strategies and evaluating their effectiveness, this paper highlights the potential of ensemble LLMs to enhance robustness and generalizability while proposing future research directions to advance the field. Full article

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17 pages, 2534 KiB

Open AccessArticle

Modeling Recommender Systems Using Disease Spread Techniques

by Peixiong He, Libo Sun, Xian Gao, Yi Zhou and Xiao Qin

Information 2025, 16(8), 687; https://doi.org/10.3390/info16080687 - 13 Aug 2025

Abstract

Recommender systems on digital platforms profoundly influence user behavior through content dissemination, and their diffusion process is similar to the spreading mechanism of infectious diseases to some extent. In this paper, we use a network-based susceptibility-infection (SI) model to model the propagation dynamics [...] Read more.

Recommender systems on digital platforms profoundly influence user behavior through content dissemination, and their diffusion process is similar to the spreading mechanism of infectious diseases to some extent. In this paper, we use a network-based susceptibility-infection (SI) model to model the propagation dynamics of recommended content, and systematically compare the differences in propagation efficiency among three recommendation strategies based on popularity, collaborative filtering, and content. We constructed scale-free user networks based on real-world clickstream data and dynamically adapted the SI model to reflect the realistic scenario of user engagement decay over time. To enhance the understanding of the recommendation process, we further simulate the visualization changes of the propagation process to show how the content spreads among users. The experimental results show that collaborative filtering performs superior in the initial dissemination, but its dissemination effect decays rapidly over time and is weaker than the other two methods. This study provides new ideas for modeling and understanding recommender systems from an epidemiological perspective. Full article

(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)

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26 pages, 423 KiB

Open AccessArticle

Enhancing Privacy-Preserving Network Trace Synthesis Through Latent Diffusion Models

by Jin-Xi Yu, Yi-Han Xu, Min Hua, Gang Yu and Wen Zhou

Information 2025, 16(8), 686; https://doi.org/10.3390/info16080686 - 12 Aug 2025

Abstract

Network trace is a comprehensive record of data packets traversing a computer network, serving as a critical resource for analyzing network behavior. However, in practice, the limited availability of high-quality network traces, coupled with the presence of sensitive information such as IP addresses [...] Read more.

Network trace is a comprehensive record of data packets traversing a computer network, serving as a critical resource for analyzing network behavior. However, in practice, the limited availability of high-quality network traces, coupled with the presence of sensitive information such as IP addresses and MAC addresses, poses significant challenges to advancing network trace analysis. To address these issues, this paper focuses on network trace synthesis in two practical scenarios: (1) data expansion, where users create synthetic traces internally to diversify and enhance existing network trace utility; (2) data release, where synthesized network traces are shared externally. Inspired by the powerful generative capabilities of latent diffusion models (LDMs), this paper introduces NetSynDM, which leverages LDM to address the challenges of network trace synthesis in data expansion scenarios. To address the challenges in the data release scenario, we integrate differential privacy (DP) mechanisms into NetSynDM, introducing DPNetSynDM, which leverages DP Stochastic Gradient Descent (DP-SGD) to update NetSynDM, incorporating privacy-preserving noise throughout the training process. Experiments on five widely used network trace datasets show that our methods outperform prior works. NetSynDM achieves an average 166.1% better performance in fidelity compared to baselines. DPNetSynDM strikes an improved balance between privacy and fidelity, surpassing previous state-of-the-art network trace synthesis method fidelity scores of 18.4% on UGR16 while reducing privacy risk scores by approximately 9.79%. Full article

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10 pages, 724 KiB

Open AccessArticle

Real-Time Speech-to-Text on Edge: A Prototype System for Ultra-Low Latency Communication with AI-Powered NLP

by Stefano Di Leo, Luca De Cicco and Saverio Mascolo

Information 2025, 16(8), 685; https://doi.org/10.3390/info16080685 - 11 Aug 2025

Abstract

This paper presents a real-time speech-to-text (STT) system designed for edge computing environments requiring ultra-low latency and local processing. Differently from cloud-based STT services, the proposed solution runs entirely on a local infrastructure which allows the enforcement of user privacy and provides high [...] Read more.

This paper presents a real-time speech-to-text (STT) system designed for edge computing environments requiring ultra-low latency and local processing. Differently from cloud-based STT services, the proposed solution runs entirely on a local infrastructure which allows the enforcement of user privacy and provides high performance in bandwidth-limited or offline scenarios. The designed system is based on a browser-native audio capture through WebRTC, real-time streaming with WebSocket, and offline automatic speech recognition (ASR) utilizing the Vosk engine. A natural language processing (NLP) component, implemented as a microservice, improves transcription results for spelling accuracy and clarity. Our prototype reaches sub-second end-to-end latency and strong transcription capabilities under realistic conditions. Furthermore, the modular architecture allows extensibility, integration of advanced AI models, and domain-specific adaptations. Full article

(This article belongs to the Section Information Applications)

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41 pages, 2180 KiB

Open AccessSystematic Review

On the Application of Artificial Intelligence and Cloud-Native Computing to Clinical Research Information Systems: A Systematic Literature Review

by Isabel Bejerano-Blázquez and Miguel Familiar-Cabero

Information 2025, 16(8), 684; https://doi.org/10.3390/info16080684 - 10 Aug 2025

Abstract

The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on [...] Read more.

The pharmaceutical and biotechnology sector is an intricate and rapidly evolving industry encompassing the full lifecycle of drugs, medicines, and clinical devices. Its growth is driven by factors such as the aging population, the rise in chronic diseases, and the increasing focus on personalized medicine. Nevertheless, it also faces significant challenges due to rising costs, increased complexity, and regulatory hurdles. Through a systematic literature review (SLR) as a research method combined with a comprehensive market analysis, this paper explores how several leading early-adopter healthcare companies are increasing their investments in computer-based clinical research information systems (CRISs) to sustain productivity, particularly through the adoption of artificial intelligence (AI) and cloud-native computing. As an extension of this research, a novel 360-degree reference blueprint is proposed for the domain analysis of medical features within AI-powered CRIS applications. This theoretical framework specifically targets clinical trial management systems (CRIS-CTMSs). Additionally, a detailed review is presented of the leading commercial solutions, assessing their portfolios and business maturity, while highlighting major open innovation collaborations with prominent pharmaceutical and biotechnology companies. Full article

(This article belongs to the Special Issue Information Systems in Healthcare)

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21 pages, 1902 KiB

Open AccessArticle

Mobile Platform for Continuous Screening of Clear Water Quality Using Colorimetric Plasmonic Sensing

by Rima Mansour, Caterina Serafinelli, Rui Jesus and Alessandro Fantoni

Information 2025, 16(8), 683; https://doi.org/10.3390/info16080683 - 10 Aug 2025

Abstract

Effective water quality monitoring is very important for detecting pollution and protecting public health. However, traditional methods are slow, relying on costly equipment, central laboratories, and expert staffing, which delays real-time measurements. At the same time, significant advancements have been made in the [...] Read more.

Effective water quality monitoring is very important for detecting pollution and protecting public health. However, traditional methods are slow, relying on costly equipment, central laboratories, and expert staffing, which delays real-time measurements. At the same time, significant advancements have been made in the field of plasmonic sensing technologies, making them ideal for environmental monitoring. However, their reliance on large, expensive spectrometers limits accessibility. This work aims to bridge the gap between advanced plasmonic sensing and practical water monitoring needs, by integrating plasmonic sensors with mobile technology. We present BioColor, a mobile platform that consists of a plasmonic sensor setup, mobile application, and cloud services. The platform processes captured colorimetric sensor images in real-time using optimized image processing algorithms, including region-of-interest segmentation, color extraction (mean and dominant), and comparison via the CIEDE2000 metric. The results are visualized within the mobile app, providing instant and automated access to the sensing outcome. In our validation experiments, the system consistently measured color differences in various sensor images captured under media with different refractive indices. A user experience test with 12 participants demonstrated excellent usability, resulting in a System Usability Scale (SUS) score of 93. The BioColor platform brings advanced sensing capabilities from hardware into software, making environmental monitoring more accessible, efficient, and continuous. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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18 pages, 388 KiB

Open AccessSystematic Review

Artificial Intelligence in Project Success: A Systematic Literature Review

by Xiaoyi Su and Abu Hanifah Ayob

Information 2025, 16(8), 682; https://doi.org/10.3390/info16080682 - 8 Aug 2025

Abstract

Projects play a vital role in achieving organizational success, where artificial intelligence (AI) has a transforming impact in project management (PM). The integration of AI techniques into PM practices has the potential to significantly improve project success rates and enable more effective project [...] Read more.

Projects play a vital role in achieving organizational success, where artificial intelligence (AI) has a transforming impact in project management (PM). The integration of AI techniques into PM practices has the potential to significantly improve project success rates and enable more effective project management. This article adopted a systematic literature review (SLR) methodology, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and employing a content analysis strategy to review 61 peer-reviewed academic journal articles published between 2015 and 2025 in the Web of Science and Scopus. This study investigates the key project success dimensions influenced by AI throughout the project lifecycle, and identifies the AI sub-fields and algorithms employed in relation to project success, where time and cost are found to be the most significantly affected factors in project success. Machine learning (ML), along with its corresponding algorithms, emerged as the most frequently applied AI subfield. This study overviews key AI-influenced project success factors and the main AI subfields and algorithms in recent literature, providing actionable insights for diverse project stakeholders aiming to enhance outcomes through AI. Limitations, including the lack of industry or regional focus, exclusion of project management process groups, and omission of gray literature, were also acknowledged, which suggest valuable directions for future research. Full article

(This article belongs to the Section Artificial Intelligence)

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31 pages, 3210 KiB

Open AccessSystematic Review

The Mind-Wandering Phenomenon While Driving: A Systematic Review

by Gheorghe-Daniel Voinea, Florin Gîrbacia, Răzvan Gabriel Boboc and Cristian-Cezar Postelnicu

Information 2025, 16(8), 681; https://doi.org/10.3390/info16080681 - 8 Aug 2025

Abstract

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. [...] Read more.

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. The presented study quantifies the prevalence of MW in naturalistic and simulated driving environments and shows its impact on driving behaviors. We document its negative effects on braking reaction times and lane-keeping consistency, and we assess recent advancements in objective detection methods, including EEG signatures, eye-tracking metrics, and physiological markers. We also identify key cognitive and contextual risk factors, including high perceived risk, route familiarity, and driver fatigue, which increase MW episodes. Also, we survey emergent countermeasures, such as haptic steering wheel alerts and adaptive cruise control perturbations, designed to sustain driver engagement. Despite these advancements, the MW research shows persistent challenges, including methodological heterogeneity that limits cross-study comparisons, a lack of real-world validation of detection algorithms, and a scarcity of long-term field trials of interventions. Our integrated synthesis, therefore, outlines a research agenda prioritizing harmonized measurement protocols, on-road algorithm deployment, and rigorous evaluation of countermeasures under naturalistic driving conditions. Full article

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

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21 pages, 1444 KiB

Open AccessArticle

Immune-Based Botnet Defense System: Multi-Layered Defense and Immune Memory

by Shingo Yamaguchi

Information 2025, 16(8), 680; https://doi.org/10.3390/info16080680 - 8 Aug 2025

Abstract

This paper proposes a novel defense mechanism inspired by the bioimmune response to effectively eliminate botnets that repeatedly infect IoT networks and describes the development of an Immune-Based Botnet Defense System (iBDS), incorporating this mechanism. Focusing on the roles of antibodies and phagocytes [...] Read more.

This paper proposes a novel defense mechanism inspired by the bioimmune response to effectively eliminate botnets that repeatedly infect IoT networks and describes the development of an Immune-Based Botnet Defense System (iBDS), incorporating this mechanism. Focusing on the roles of antibodies and phagocytes in the immune response, the iBDS implements a multi-layered defense using two types of worms: antibody worms and phagocyte worms. When a malicious botnet infects a network, the resident phagocyte worms immediately infect and eliminate the bots and prevent the infection from spreading in its early stages. This provides an immediate response in a similar way to innate immunity. On the other hand, if a malicious botnet infects the network and the phagocyte worms are unable to infect the bots, the antibody worms, instead, infect the bots and change their vulnerabilities to help the phagocyte worms infect and eliminate them. This provides an adaptive response in a similar way to acquired immunity. In addition, when the same botnet is repeatedly infected, more antibody worms are used to produce a stronger response, similar to immune memory. The introduction of multi-layered defense and immune memory is an important novelty of this paper that is not found in traditional botnet defense system research. The experimental results from simulations and prototype implementations show that iBDS can effectively eliminate botnets that repeatedly infect IoT networks. Full article

(This article belongs to the Special Issue Cyber Security in IoT)

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Graphical abstract

22 pages, 1468 KiB

Open AccessArticle

Identifying and Mitigating Gender Bias in Social Media Sentiment Analysis: A Post-Training Approach on Example of the 2023 Morocco Earthquake

by Mohammad Reza Yeganegi, Hossein Hassani and Nadejda Komendantova

Information 2025, 16(8), 679; https://doi.org/10.3390/info16080679 - 8 Aug 2025

Abstract

Sentiment analysis is a cornerstone in many contextual data analyses, from opinion mining to public discussion analysis. Gender bias is one of the well-known issues in sentiment analysis models, which can produce different results for the same text depending on the gender it [...] Read more.

Sentiment analysis is a cornerstone in many contextual data analyses, from opinion mining to public discussion analysis. Gender bias is one of the well-known issues in sentiment analysis models, which can produce different results for the same text depending on the gender it refers to. This gender bias leads to further bias in other text analyses that use such sentiment analysis models. This study reviews existing solutions to reduce gender bias in sentiment analysis and proposes a new method to address this issue. The proposed method offers more practical flexibility as it focuses on sentiment estimation rather than model training. Furthermore, it provides a quantitative measure to investigate the gender bias in sentiment analysis results. The performance of the proposed method across five sentiment analysis models is presented using texts containing gender-specific words. The proposed method is applied to a set of social media posts related to Morocco’s 2023 earthquake to estimate the gender-unbiased sentiment of the posts and evaluate the gender-unbiasedness of five different sentiment analysis models in this context. The result shows that, although the sentiments estimated with different models are very different, the gender bias in none of the models is drastically large. Full article

(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)

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20 pages, 4253 KiB

Open AccessArticle

Data-Driven Structural Health Monitoring Through Echo State Network Regression

by Xiaoou Li, Yingqin Zhu and Wen Yu

Information 2025, 16(8), 678; https://doi.org/10.3390/info16080678 - 8 Aug 2025

Abstract

This paper presents a novel data-driven approach to structural health monitoring (SHM) that uses Echo State Network (ESN) regression for continuous damage assessment. In contrast to traditional classification methods that demand extensive labeled data on damaged states, our approach utilizes an ESN, a [...] Read more.

This paper presents a novel data-driven approach to structural health monitoring (SHM) that uses Echo State Network (ESN) regression for continuous damage assessment. In contrast to traditional classification methods that demand extensive labeled data on damaged states, our approach utilizes an ESN, a powerful recurrent neural network, to directly predict a continuous damage metric from sensor data. This regression-based methodology offers two key advantages relevant to data science applications in SHM: (1) Reduced Training Data Dependency: The ESN achieves high accuracy even with limited data on damaged structures, significantly alleviating the data acquisition burden compared to classification-based AI/ML techniques. (2) Enhanced Noise Resilience: The inherent reservoir computing property of ESNs, characterized by a fixed, high-dimensional recurrent layer, makes them more tolerant of sensor noise and environmental variations compared to classification methods, leading to more reliable and robust SHM predictions from noisy data. A comprehensive evaluation demonstrates the effectiveness of the proposed ESN in identifying structural damage, highlighting its potential for practical application in data-driven SHM systems. Full article

(This article belongs to the Special Issue Application of Machine Learning in Data Science and Computational Intelligence)

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18 pages, 6788 KiB

Open AccessReview

Weather Forecasting Satellites—Past, Present, & Future

by Etai Nardi, Ohad Cohen, Yosef Pinhasi, Motti Haridim and Jacob Gavan

Information 2025, 16(8), 677; https://doi.org/10.3390/info16080677 - 8 Aug 2025

Abstract

Climate change has made weather more erratic and unpredictable. As a result, a growing need to develop more reliable short-term weather prediction models paved the way for a new era in satellite instrumentation technology, where radar systems for meteorological applications became critically important. [...] Read more.

Climate change has made weather more erratic and unpredictable. As a result, a growing need to develop more reliable short-term weather prediction models paved the way for a new era in satellite instrumentation technology, where radar systems for meteorological applications became critically important. This paper presents a comprehensive review of the evolution of weather forecasting satellites. We trace the technological development from the early weather and climate monitoring systems of the 1960s. Since the use of stabilized TV camera platforms on satellites aimed at capturing cloud cover data and storing it on magnetic tape for later readout and transmission back to ground stations, satellite sensor instrument technologies took great strides in the following decades, incorporating advancements in image and signal processing into satellite imagery methodologies. As innovative as they were, these technologies still lacked the capabilities needed to allow for practical use cases other than scientific research. The paper further examines how the next phase of satellite platforms is aimed at addressing this technological gap by leveraging the advantages of low Earth orbit (LEO) based satellite constellation deployments for near-real-time tracking of atmospheric hydrometers and precipitation profiles through innovative methods. These methods involve combining the collected data into big-data lakes on internet cloud platforms and constructing innovative AI-based multi-layered weather prediction models specifically tailored to remote sensing. Finally, we discuss how these recent advancements form the basis for new applications in aviation, severe weather readiness, energy, agriculture, and beyond. Full article

(This article belongs to the Special Issue Sensing and Wireless Communications)

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13 pages, 398 KiB

Open AccessArticle

An Approximate Algorithm for Sparse Distributionally Robust Optimization

by Ruyu Wang, Yaozhong Hu, Cong Liu and Quanwei Gao

Information 2025, 16(8), 676; https://doi.org/10.3390/info16080676 - 7 Aug 2025

Abstract

In this paper, we propose a sparse distributionally robust optimization (DRO) model incorporating the Conditional Value-at-Risk (CVaR) measure to control tail risks in uncertain environments. The model utilizes sparsity to reduce transaction costs and enhance operational efficiency. We reformulate the problem as a [...] Read more.

In this paper, we propose a sparse distributionally robust optimization (DRO) model incorporating the Conditional Value-at-Risk (CVaR) measure to control tail risks in uncertain environments. The model utilizes sparsity to reduce transaction costs and enhance operational efficiency. We reformulate the problem as a Min-Max-Min optimization and convert it into an equivalent non-smooth minimization problem. To address this computational challenge, we develop an approximate discretization (AD) scheme for the underlying continuous random vector and prove its convergence to the original non-smooth formulation under mild conditions. The resulting problem can be efficiently solved using a subgradient method. While our analysis focuses on CVaR penalty, this approach is applicable to a broader class of non-smooth convex regularizers. The experimental results on the portfolio selection problem confirm the effectiveness and scalability of the proposed AD algorithm. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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37 pages, 2030 KiB

Open AccessArticle

Open Competency Optimization with Combinatorial Operators for the Dynamic Green Traveling Salesman Problem

by Rim Benjelloun, Mouna Tarik and Khalid Jebari

Information 2025, 16(8), 675; https://doi.org/10.3390/info16080675 - 7 Aug 2025

Abstract

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is [...] Read more.

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

(This article belongs to the Section Artificial Intelligence)

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