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Volume 17
Issue 7
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Information, Volume 17, Issue 7 (July 2026) – 99 articles

Cover Story (view full-size image): Enterprise investment in artificial intelligence has reached an unprecedented scale, yet transformation outcomes remain highly variable. A systematic literature review reveals a structural gap: no prior work integrates AI technology types with operational autonomy levels in a single classification structure. This paper proposes the Enterprise AI Classification Framework, integrating six AI types (decision, predictive, generative, conversational, visual, physical) with six autonomy levels (assistive, advisory, supervisory, delegated, autonomous, orchestrated) in a 6×6 matrix. The framework offers business executives, functional leaders, and technical teams a shared taxonomy for classifying and managing enterprise AI, closing the gap between investment and measurable return through structured deployment. View this paper
A Novel Enterprise AI Classification Framework: AI Types × Autonomy Levels
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30 pages, 3404 KB  
Article
From Profiles to Promising Paths: A Semantic Group Recommender for Novel Academic Topic Discovery
by Carlos Ayala-Tipan, Lorena Recalde and Edison Loza-Aguirre
Information 2026, 17(7), 715; https://doi.org/10.3390/info17070715 - 22 Jul 2026
Viewed by 347
Abstract
Scientific production is expanding so quickly that research teams are struggling to track advances beyond their immediate specialization, especially in interdisciplinary areas where relevant work is scattered across venues and vocabularies. To reduce this overload at the group level, we propose an end-to-end [...] Read more.
Scientific production is expanding so quickly that research teams are struggling to track advances beyond their immediate specialization, especially in interdisciplinary areas where relevant work is scattered across venues and vocabularies. To reduce this overload at the group level, we propose an end-to-end pipeline that transforms structured bibliographic metadata into actionable topic recommendations for research teams. Starting from Scopus records, the method normalizes scholarly text, builds semantic author profiles using Sentence–BERT representations coupled with interpretable keyword descriptors, and forms candidate groups from co-authorship signals and profile similarity. For each group, the approach applies embedding-based topic modeling to generate candidate themes and ranks them using a relevance–novelty trade-off, enabling teams to surface directions that remain aligned with their collective agenda while still encouraging exploration beyond dominant or highly popular topics. Empirical evidence on a Scopus-derived corpus shows that embedding-aware descriptors support cleaner, semantically faithful representations than frequency-based baselines, strengthening downstream topic discovery and producing compact topic lists that are easier for teams to inspect, discuss, and adopt in collaborative planning. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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22 pages, 4603 KB  
Article
A Phase-Coherent Four-Stage Pipeline for the Dereverberation of Quránic Recitation
by Osama Al Maaini, Khizar Hayat, Khalil Al Ruqeishi and Baptiste Magnier
Information 2026, 17(7), 714; https://doi.org/10.3390/info17070714 - 22 Jul 2026
Viewed by 287
Abstract
The accuracy of spectro-temporal features for Makhaarij al-Huroof and Sifaat distinguishes between the ten canonical Qiraát recitation styles of the Holy Quran. However, real-world room reverberations blur formant contours and corrupt inter-word energies, thus making Qiraat discrimination difficult. The current dereverberation methods were [...] Read more.
The accuracy of spectro-temporal features for Makhaarij al-Huroof and Sifaat distinguishes between the ten canonical Qiraát recitation styles of the Holy Quran. However, real-world room reverberations blur formant contours and corrupt inter-word energies, thus making Qiraat discrimination difficult. The current dereverberation methods were designed to work under ordinary speech conditions and are not capable of preserving phonetic qualities for domain-specific purposes. This paper introduces a four-step, phase-consistent signal-processing approach prioritizing phonetic preservation over direct reverberation suppression. The four steps are: (1) adaptive noise-floor attenuation; (2) soft-voice activity detection using power-law boundary decay; (3) application-specific spectral contour adjustment from clean Quranic reference audio; and (4) Griffin–Lim algorithm-based phase correction. A total of 48 real-world room recordings were utilized for the evaluation of this approach based on Energy Ratio (ER), Spectral Contrast (SC), and Spectral Contour Stability (SCS)—measures specific to the Quran audio domain—alongside conventional speech-quality metrics. The proposed approach yielded the highest scores in three of seven metrics, namely SC (+40.11), SCS (+822.94), and PESQ (+1.251), alongside the second-highest Energy Ratio (+19.58 dB), while being superior to Spectral Subtraction, Wiener Filtering, and WPE Dereverberation approaches. Moreover, the perceptual enhancement was verified in a synthetic controlled experiment where the proposed approach scored an improved PESQ metric (+2.495; SNR −1.874 dB). The results illustrate the fact that an optimization for general-purpose metrics does not necessarily ensure phonetic preservation required for specific classification. Full article
(This article belongs to the Section Information Applications)
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24 pages, 1445 KB  
Review
Cryptography-Based Security Authentication and Privacy Preservation of Cyber-Physical Power Systems: An Overview
by Cheng Jiang, Jianyong Bi, Huiqun Yu, Mi Wen, Lei Wu and Rolf Findeisen
Information 2026, 17(7), 713; https://doi.org/10.3390/info17070713 - 22 Jul 2026
Viewed by 282
Abstract
Cyber-physical power systems (CPPSs) are a crucial component of smart grids, integrating physical power systems with advanced information and communication technologies to achieve efficient, reliable, and intelligent energy management and control. However, with the widespread deployment of information technology, CPPSs face increasingly severe [...] Read more.
Cyber-physical power systems (CPPSs) are a crucial component of smart grids, integrating physical power systems with advanced information and communication technologies to achieve efficient, reliable, and intelligent energy management and control. However, with the widespread deployment of information technology, CPPSs face increasingly severe security threats and privacy protection challenges, such as data leakage, identity forgery, and impersonation, which can compromise the secure and stable operation of CPPSs. To counter these threats and protect privacy, cryptographic technique is developed to provide fundamental and powerful tools, supporting secure authentication, data integrity checking, privacy preservation, and trusted communication between connected devices in CPPSs. We systematically review the research progress on security authentication and privacy protection in CPPSs from a cryptographic perspective. Our survey analyzes the major security threats faced by CPPSs, along with the impact of various attacks on system data. We explore mainstream cryptographic algorithms, including digital signatures, key agreement protocols, signcryption authentication, homomorphic encryption, and blockchain-based security mechanisms that are capable of resisting cyber attacks and ensuring reliable decision-making and control in CPPSs. This work also provides the trends and challenges regarding the intersection of cryptography and networked control, blockchain scalability, and convergence of cryptography and artificial intelligence in CPPSs. Full article
(This article belongs to the Special Issue Innovative AI Solutions for Cybersecurity in Critical Infrastructures)
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23 pages, 1654 KB  
Review
Transformer-Based Language Models for Clinical Decision Support Using Clinical Notes: A Scoping Review
by Saahoon Hong and Hunhui Na
Information 2026, 17(7), 712; https://doi.org/10.3390/info17070712 - 22 Jul 2026
Viewed by 386
Abstract
Background/Objectives: This scoping review examined recent evidence on the use of transformer-based language models, encompassing encoder-only architectures (e.g., BERT and its clinical variants) and generative large language models (LLMs; e.g., GPT-4 and Llama), to support clinical decision making from unstructured clinical notes, with [...] Read more.
Background/Objectives: This scoping review examined recent evidence on the use of transformer-based language models, encompassing encoder-only architectures (e.g., BERT and its clinical variants) and generative large language models (LLMs; e.g., GPT-4 and Llama), to support clinical decision making from unstructured clinical notes, with implications for behavioral-health services where narrative documentation is central. Methods: Following PRISMA-ScR guidelines, PubMed, PsycINFO, and Web of Science were searched for peer-reviewed studies published between 1 January 2023, and 5 August 2025. Studies applying transformer-based language models to clinical narratives for healthcare tasks and reporting evaluative outcomes were included. We extracted data on clinical tasks, model architectures, enhancement strategies, and evaluation metrics; mapped each study by primary purpose, care setting, and primary model approach; and charted reported validation design, direct human comparison, fairness assessment, workflow evaluation, and clinical deployment. Results: Thirty-six studies were included. Information extraction/de-identification and classification/prediction predominated, whereas summarization/generation was less commonly represented. Model approaches appeared to align with task characteristics: encoder-only and decoder-only systems were frequently used for extraction, encoder–decoder systems for generation, and hybrid or pipeline-based approaches for classification and prediction. Standard task-specific metrics (e.g., F1 and AUROC) predominated, whereas evidence beyond retrospective task performance, including direct human comparison, fairness assessment, workflow evaluation, clinical deployment, and temporal or external validation, was rare. No included study evaluated a transformer-based language model application within a behavioral-health service or behavioral-health workflow. Conclusions: Transformer-based language models have been applied across diverse clinical-note tasks, but the evidence base more strongly supports retrospective task feasibility than transportability, equitable performance, workflow benefit, or safe clinical deployment. Future research should prioritize transparent reference standards, external and prospective validation, clinically meaningful human comparison, and equity-focused evaluation, including direct studies in behavioral-health services. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Digital Health Emerging Technologies)
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19 pages, 3840 KB  
Article
A Preliminary Design Framework for Motivational Robots in Higher Education Japanese-Language E-Learning: A Theory-Guided Synthesis and Structured Expert Review
by Pengfei Lyu, Wei Xie and Toshio Eisaka
Information 2026, 17(7), 711; https://doi.org/10.3390/info17070711 - 22 Jul 2026
Viewed by 267
Abstract
Sustaining learner motivation remains a persistent challenge in higher education Japanese-language e-learning, where learners often study with limited social presence and personalized encouragement. This paper proposes a preliminary Design Framework for Motivational Robots in E-Learning (DFMRE), derived through a retrospective, theory-guided synthesis of [...] Read more.
Sustaining learner motivation remains a persistent challenge in higher education Japanese-language e-learning, where learners often study with limited social presence and personalized encouragement. This paper proposes a preliminary Design Framework for Motivational Robots in E-Learning (DFMRE), derived through a retrospective, theory-guided synthesis of two previously published empirical studies on robot-assisted Japanese-language learning. The synthesis interprets the prior findings through Self-Determination Theory and self-efficacy theory and formulates five candidate design principles: human-affine compact hardware, multi-level learner-selectable gestures, calibrated vocal encouragement, learner-initiated interaction protocol, and content-independent system integration. To provide an initial external check, nine domain experts with diverse backgrounds in education, educational technology, human–robot interaction, and related fields provided a preliminary appraisal of the principles in terms of clarity, feasibility, transferability, and overall usefulness. The framework received broadly favorable ratings, including a mean overall usefulness score of 4.33 on a 5-point scale, while expert comments highlighted the need for clearer operational definitions and flexible interaction modes. Because the empirical base consists of two small-sample Japanese-language learning studies conducted at one institution using one robot platform, DFMRE should be read as an early, context-grounded, falsifiable proposal rather than as a confirmed model for higher education e-learning in general. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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29 pages, 830 KB  
Article
BiTE: A Bitemporal Event-Centered Database Framework for Dynamic Aeronautical Information State Management
by Tianyue Wei, Xin Lai, Yidan Liang, Chengwei Zhang and Rui Kang
Information 2026, 17(7), 710; https://doi.org/10.3390/info17070710 - 22 Jul 2026
Viewed by 346
Abstract
Dynamic aeronautical information is still widely disseminated through textual Notice to Air Missions (NOTAMs), while message-oriented storage cannot directly maintain the evolving states of affected objects. The objective of this study is to determine whether NOTAM-derived object events can be organized into traceable [...] Read more.
Dynamic aeronautical information is still widely disseminated through textual Notice to Air Missions (NOTAMs), while message-oriented storage cannot directly maintain the evolving states of affected objects. The objective of this study is to determine whether NOTAM-derived object events can be organized into traceable bitemporal states that support accurate and efficient current and historical access. To this end, this paper proposes BiTE, a bitemporal event-centered database framework that connects object-level event evidence, historical state versions, and materialized current-state projections. By integrating business and system time with NOTAM-specific lifecycle rules, BiTE supports state maintenance, historical reconstruction, and source traceability. A MongoDB-based prototype was evaluated using 44,591 NOTAMs from five major U.S. aerodromes. Independent manual validation showed 96.14–100% agreement across object identification and lifecycle-maintenance tasks. Across 1500 manually verified queries, BiTE achieved F1 scores of 99.43% and 98.66% for current-state and airport-overview retrieval, respectively, and a historical hit rate of 95.80%, outperforming representative message-oriented, relational-bitemporal, and RDF-based implementations. Mean query latency remained below 3.7 ms, while functionally equivalent ablations confirmed the performance contribution of the layered architecture. These results demonstrate that BiTE enables accurate, traceable, and efficient object-state management for dynamic aeronautical information. Full article
(This article belongs to the Special Issue Next-Generation Data Management with Agentic AI and Large Language Models)
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26 pages, 1623 KB  
Article
Not Just Happy or Sad: An Exploratory Study on How Fine-Grained Emotions Relate to Linguistic Creativity in Improvised Speech
by Sepideh Kalateh, Nastaran Farhadighalati, Sanaz Nikghadam-Hojjati and Jose Barata
Information 2026, 17(7), 709; https://doi.org/10.3390/info17070709 - 22 Jul 2026
Viewed by 316
Abstract
Research on creativity often treats emotions using broad categories such as positive versus negative affect, which obscure the role of specific emotional states in creative performance. This exploratory study examines how fine-grained emotions relate to linguistic creativity in improvised speech. Thirty adult participants [...] Read more.
Research on creativity often treats emotions using broad categories such as positive versus negative affect, which obscure the role of specific emotional states in creative performance. This exploratory study examines how fine-grained emotions relate to linguistic creativity in improvised speech. Thirty adult participants completed speech tasks across happy, neutral, and sad conditions, producing 90 speech samples. The responses were transcribed and evaluated using TTCT-inspired creativity dimensions: fluency, flexibility, originality, and elaboration. Fine-grained emotional states were estimated from the transcripts using automatic emotion recognition, and their associations with creativity scores were examined through statistical analysis. The findings suggest that linguistic creativity in improvised speech can be better understood by considering specific emotional profiles rather than relying only on broad positive or negative affect categories. The study contributes an exploratory methodological framework for future research on affect-aware creativity support systems and Computational models of emotion–creativity interaction. Full article
(This article belongs to the Special Issue Emerging Research in Computational Creativity and Creative Robotics)
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22 pages, 6048 KB  
Article
EvoPlay-MuZero Hybrid Framework Incorporating a Dual-Peptide Bridging Strategy for Adjunctive Therapy in Alzheimer’s Disease
by Bingling Huang, Jiahao Li, Ziyu Li, Hao Jiang, Mingxiang Yang, Xiaoxia Li and Xiaohui Niu
Information 2026, 17(7), 708; https://doi.org/10.3390/info17070708 - 21 Jul 2026
Viewed by 240
Abstract
Alzheimer’s disease (AD) is a severe neurodegenerative disorder whose pathological progression is closely associated with the reduced binding affinity of apolipoprotein E ε4 (ApoE4) for amyloid-β (Aβ), which impairs Aβ clearance. Existing computational molecular design approaches are largely limited to single-target optimization and [...] Read more.
Alzheimer’s disease (AD) is a severe neurodegenerative disorder whose pathological progression is closely associated with the reduced binding affinity of apolipoprotein E ε4 (ApoE4) for amyloid-β (Aβ), which impairs Aβ clearance. Existing computational molecular design approaches are largely limited to single-target optimization and therefore lack the capacity for synergistic dual-target modulation. Herein, we proposed EvoPlay-MuZero, a hybrid computational framework incorporating a dual-peptide bridging (DPB) strategy. The framework adopted latent-state planning in MuZero reinforcement learning to enhance exploration and sequence-generation efficiency in high-dimensional sequence spaces. For the first time, it enabled the automated design of bispecific peptides targeting ApoE4 and Aβ, thereby forming a synergistic molecular bridge via a flexible linker. A full-process pipeline for structural and energetic evaluation was established by integrating AlphaFold3 and PDBePISA. Benchmark experiments on the 1SSC, 2CNZ, and 3R7G datasets demonstrated that EvoPlay-MuZero substantially outperformed the vanilla EvoPlay in convergence speed and the yield of valid generated sequences. Specifically, the optimal DPB molecule (15 × 25-3A) achieved a calculated interfacial solvation energy score (ΔiG) of −29.5 kcal/mol, demonstrating substantially enhanced interface-stabilization properties compared to the native baseline control. This study provides a novel molecular intervention strategy for adjuvant therapy in AD and highlights the considerable potential of reinforcement learning in multi-target drug design. Full article
(This article belongs to the Section Artificial Intelligence)
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56 pages, 6804 KB  
Article
Tourism Hotel Recommendation Model Based on ISTING-AGNES Machine Learning and IDFST Optimal Route Algorithm
by Xiao Zhou, Wenbing Liu, Jun Wang and Yilong Han
Information 2026, 17(7), 707; https://doi.org/10.3390/info17070707 - 21 Jul 2026
Viewed by 216
Abstract
To address the problem that hotel recommendations in tourism activities do not consider the spatial relationship between hotels and scenic spots and the cost of tour routes, we construct a tourism hotel recommendation model based on ISTING-AGNES machine learning and an IDFST optimal [...] Read more.
To address the problem that hotel recommendations in tourism activities do not consider the spatial relationship between hotels and scenic spots and the cost of tour routes, we construct a tourism hotel recommendation model based on ISTING-AGNES machine learning and an IDFST optimal route algorithm. Firstly, a scenic spot spatial clustering model based on the ISTING-AGNES machine learning algorithm is constructed, including a scenic spot ISG spatial topological model based on the neighborhood cell growth algorithm and an ISTING-AGNES machine learning algorithm based on the scenic spot ISG spatial topological model, which can realize spatial dimension reduction in tourist cities and construct tourism sub-regions for recommending scenic spots and hotels. Secondly, taking the tourism sub-regions as the modeling scope, a tourism hotel recommendation model based on the IDFST optimal route algorithm is constructed in which a closeness model between the tourists’ interests and the attributes of scenic spots in the sub-region is established to recommend the most matched scenic spots for tourists. Then, based on the recommended scenic spots, a tourism sub-interval optimal route algorithm based on IDFST and a tourism hotel recommendation model based on the optimal route decision forest algorithm are established to search for the global optimal tour route—with hotels as the starting and ending points and scenic spots as nodes—and to recommend the hotel with the most cost-effective tour route for tourists. The experiments prove that the constructed algorithm can output the hotel with the best geospatial location and the lowest tour route cost. Under the experimental conditions, compared with the hotel recommended by the weighted centroid positioning algorithm, the cost optimization rate reaches 5.98%. Compared with the greedy mountain climbing algorithm and the greedy BFS algorithm, the cost optimization rates reach 14.73% and 16.03%, proving that the constructed algorithm is feasible and advantageous over the traditional algorithms. Full article
(This article belongs to the Special Issue 2nd Edition of Modern Recommender Systems: Approaches, Challenges and Applications)
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31 pages, 7890 KB  
Article
The Socratic Trap: Benchmarking the Capacity of Large Language Models to Generate Strategic Misconceptions in Computer Science Education
by Marijela Miličević, Mia Rovis, Ratomir Karlović, Sandi Baressi Šegota, Vedran Mrzljak, Ivan Lorencin and Darko Etinger
Information 2026, 17(7), 706; https://doi.org/10.3390/info17070706 - 21 Jul 2026
Viewed by 266
Abstract
Large language models (LLMs) are increasingly integrated into educational settings, yet their pedagogical reliability remains insufficiently understood. Beyond overt hallucinations, which informed users readily recognize, a subtler failure mode consists of explanations that are coherent, authoritative, and pedagogically plausible while harbouring hidden conceptual [...] Read more.
Large language models (LLMs) are increasingly integrated into educational settings, yet their pedagogical reliability remains insufficiently understood. Beyond overt hallucinations, which informed users readily recognize, a subtler failure mode consists of explanations that are coherent, authoritative, and pedagogically plausible while harbouring hidden conceptual flaws, responses we term Socratic traps. This paper introduces SocraticTrap-CS, a publicly available benchmark that probes the capacity of open-weight LLMs to generate such strategic misconceptions on demand. A single structured prompt explicitly elicited three outputs per concept (a correct explanation, an overt hallucination, and a strategic misconception), yielding 735 expert-annotated response segments from seven open-weight models across 35 core concepts in algorithms and data structures, programming languages and paradigms, databases, computer networks, and operating systems. Three domain experts independently annotated each segment using a three-class schema, achieving near-perfect agreement (Fleiss’ κ=0.9487). Because models were explicitly instructed to produce the misconception, the central metric quantifies adversarial instruction-following capacity rather than the base rate of such errors in naturalistic use and should be read as a conservative upper bound on model capability. Under these conditions, compliance reached 91.7% overall (100% for three models; 57.1% for the smallest model, Mistral 7B, whose lower rate plausibly reflects weaker instruction-following rather than greater safety). Expert-judged persuasiveness was moderate to high, errors were predominantly conceptual rather than factual, models differed significantly, and no statistically significant domain-level differences were detected. The benchmark reframes the evaluation of educational LLMs around pedagogical trustworthiness rather than factual correctness alone. Full article
(This article belongs to the Special Issue Advancing Educational Innovation with Artificial Intelligence)
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25 pages, 2839 KB  
Article
UAV RF Signal Azimuth Estimation Using a UCA-8 and a Dual-Branch Circular-Regression Network
by Jingyang Wang, Jie Ma, Jiaxi Zhang, Zehan Li and Min Huang
Information 2026, 17(7), 705; https://doi.org/10.3390/info17070705 - 21 Jul 2026
Viewed by 241
Abstract
To address the problems that existing UAV RF signal azimuth estimation methods rely on idealized simulation data and lack accuracy and robustness in complex environments, a high-precision azimuth estimation method based on an improved ResNet, namely the Dual-Branch Circular-Regression Network, is proposed. Firstly, [...] Read more.
To address the problems that existing UAV RF signal azimuth estimation methods rely on idealized simulation data and lack accuracy and robustness in complex environments, a high-precision azimuth estimation method based on an improved ResNet, namely the Dual-Branch Circular-Regression Network, is proposed. Firstly, UCA-8 array data is generated from measured single-channel RF signals, and non-ideal factors such as channel mismatch and mutual coupling among array elements are incorporated to simulate the real RF receiving environment. Secondly, ResNet is improved from three aspects: input normalization, dynamic dual-branch (DDB) learning features and periodic angle regression. The input normalization strategy based on Per-Sample Complex Root Mean Square (PSCRMS) is adopted to improve the adaptability of the model to signal scale changes. The DDB structure is adopted to adaptively fuse I/Q spatiotemporal features with a spatial covariance statistical prior to enhance the spatial feature expression ability in complex scenes. A periodic angle regression method based on Unit Circular Vector Representation (UCVR) and the Huber Loss (GAH Loss) of geodesic angle distance is adopted to realize periodic angle continuous modeling and suppress abnormal angle errors. Finally, comparative and ablation experiments are conducted on the constructed UCA-8 dataset. The experimental results show that compared with the baseline ResNet, the Dual-Branch Circular-Regression Network achieves 85.8%, 95.3%, and 81.5% reductions in MAE, RMSE and P95, respectively, and maintains higher estimation accuracy and good robustness under low signal-to-noise ratio, hardware mismatch and co-frequency dual-source interference. Full article
(This article belongs to the Special Issue Artificial Intelligence in Autonomous Navigation and Intelligent Unmanned Systems)
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20 pages, 14827 KB  
Article
Coverage Formation Control of Multi-Rover System for Large-Scale Planetary Exploration Under Localization Uncertainty Based on Guaranteed Voronoi and Belief Space Planning
by Yanping Chen, Junjie Zhang and Chi Zhang
Information 2026, 17(7), 704; https://doi.org/10.3390/info17070704 - 20 Jul 2026
Viewed by 197
Abstract
This work addresses coverage formation control for the Multi-Rover System (MRS) in extraplanetary environments such as Mars or the Moon, where Global Navigation Satellite System (GNSS) signals are unavailable and uncertain self-localization significantly degrades coverage performance. Existing coverage strategies predominantly assume perfect localization, [...] Read more.
This work addresses coverage formation control for the Multi-Rover System (MRS) in extraplanetary environments such as Mars or the Moon, where Global Navigation Satellite System (GNSS) signals are unavailable and uncertain self-localization significantly degrades coverage performance. Existing coverage strategies predominantly assume perfect localization, which is unrealistic for GNSS-denied planetary surfaces. This paper presents a novel multi-rover coverage formation control algorithm that combines guaranteed Voronoi partitioning with belief space planning. The core contributions are (i) a guaranteed Voronoi partitioning framework that provides deterministic bounds on true coverage cells under localization uncertainty; (ii) a dual-layer optimization architecture integrating Extended Kalman Filter-based self-localization with centroid-error minimization; and (iii) a belief space planning approach that predicts system state evolution and infers optimal control inputs over a receding horizon. Simulation results under multiple density distributions and rover configurations demonstrate faster convergence and lower coverage cost compared to conventional Lloyd-based methods. By extending existing coverage strategies, our approach supports rapid and adaptive deployment of multi-rover networks in GNSS-limited environments, providing a promising solution for large-scale planetary exploration. Full article
(This article belongs to the Special Issue Advanced Control Topics on Robotic Vehicles)
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19 pages, 449 KB  
Article
EEG-Based Classification of Alzheimer’s Disease and Frontotemporal Dementia via a Dynamic Threshold Graph Convolutional Network
by Yanzhi Liu and Ming Meng
Information 2026, 17(7), 703; https://doi.org/10.3390/info17070703 - 20 Jul 2026
Viewed by 327
Abstract
Accurate differentiation of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) is clinically important because their management strategies differ. This study aimed to develop and evaluate a Dynamic Threshold Graph Convolutional Network (DT-GCN) and to systematically compare four EEG-based functional connectivity (FC) measures—Pearson correlation, [...] Read more.
Accurate differentiation of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) is clinically important because their management strategies differ. This study aimed to develop and evaluate a Dynamic Threshold Graph Convolutional Network (DT-GCN) and to systematically compare four EEG-based functional connectivity (FC) measures—Pearson correlation, phase-locking value (PLV), Granger causality, and copula analysis—for classifying AD, FTD, and healthy controls (HCs). In contrast to fixed graph binarization, DT-GCN updates the connectivity threshold at each training epoch according to training-fold loss. The model is trained under a multi-task objective combining classification, reconstruction, and contrastive losses, with the loss weights adjusted across three stages of training. Resting-state 19-channel EEG recordings from a public dataset (DS004504) comprising 36 AD, 23 FTD, and 29 HC participants were segmented into non-overlapping 8 s epochs. FC estimates were aggregated to obtain a subject-level adjacency matrix for each measure, with one-hot node identity and node degree as node features. Classification was performed at the subject level using stratified five-fold cross-validation with normalization and hyperparameter selection confined to training folds. Under the broadband setting, copula-based FC with DT-GCN yielded the highest observed three-class accuracy of 0.82±0.07 (macro-F1 0.79±0.07), compared with 0.45±0.06 accuracy and 0.36±0.06 macro-F1 for the standard GCN baseline. However, the small single-center cohort (N=88, including 23 participants with FTD) and the resulting small test folds limit the precision of the performance estimates and preclude robust inferential comparisons among FC methods and classification scenarios. These exploratory, dataset-specific findings require independent external validation and should not be interpreted as generalizable clinical performance or validated clinical biomarkers. Full article
(This article belongs to the Section Biomedical Information and Health)
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22 pages, 1431 KB  
Article
A Leakage-Controlled, Calibration-First Evaluation of Machine Learning Models for Startup-Outcome Prediction: Evidence from Crunchbase
by Ratchaneekorn Khamphukun and Warawut Narkbunnum
Information 2026, 17(7), 702; https://doi.org/10.3390/info17070702 - 20 Jul 2026
Viewed by 378
Abstract
Machine learning is increasingly used in entrepreneurship analytics to predict startup outcomes, frequently reporting accuracy above 0.90, yet whether such performance reflects a genuine ex-ante signal or methodological artifact remains unclear and consequential for investors, accelerators, and innovation-policy agencies. This study evaluates startup-outcome [...] Read more.
Machine learning is increasingly used in entrepreneurship analytics to predict startup outcomes, frequently reporting accuracy above 0.90, yet whether such performance reflects a genuine ex-ante signal or methodological artifact remains unclear and consequential for investors, accelerators, and innovation-policy agencies. This study evaluates startup-outcome classification under a leakage-controlled, calibration-first protocol using two Crunchbase-derived datasets (66,368 firms; a 923-firm engineered-feature set), three success constructs, and three model families under five-fold stratified cross-validation. Removing outcome-correlated, survivorship-accumulating features lowers the area under the receiver operating characteristic curve by 0.05 to 0.09 on the large dataset, with every paired 95% confidence interval excluding zero, and by 0.19 on the engineered dataset; an independent study on the same 923-firm data without leakage control reports 88.1% accuracy. The leakage-controlled performance level is modest (0.66 to 0.77). Calibration rankings diverge from discrimination rankings: gradient boosting is well calibrated (expected calibration error of 0.006 to 0.024), whereas logistic regression shows large calibration error on imbalanced constructs (largely an artifact of class weighting rather than an intrinsic model property); post hoc isotonic recalibration then removes most of the error. The contribution is a reusable evaluation protocol for entrepreneurship analytics. Findings are associational and specific to the analyzed samples. Full article
(This article belongs to the Special Issue Machine Learning and Data Analytics for Business Process Improvement)
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25 pages, 5035 KB  
Article
A Rolling Bearing Fault Diagnosis Method Based on ICEEMDAN and AHO−SVM
by Liping Wang, Yaozheng Zhao, Yan Chen and Guangyong Xi
Information 2026, 17(7), 701; https://doi.org/10.3390/info17070701 - 19 Jul 2026
Viewed by 192
Abstract
To address the issues of rolling bearing fault vibration signals being susceptible to noise interference and the support vector machine (SVM) relying on manual parameter settings, this paper proposes a fault diagnosis method based on ICEEMDAN and AIHO−SVM. Firstly, the Hippopotamus Optimization Algorithm [...] Read more.
To address the issues of rolling bearing fault vibration signals being susceptible to noise interference and the support vector machine (SVM) relying on manual parameter settings, this paper proposes a fault diagnosis method based on ICEEMDAN and AIHO−SVM. Firstly, the Hippopotamus Optimization Algorithm is improved by incorporating Chebyshev chaotic mapping, refraction opposite learning, dynamic weighting, adaptive step size, and guided learning strategies, thereby enhancing convergence accuracy and speed. Secondly, ICEEMDAN is employed to decompose vibration signals for noise reduction, and the effective intrinsic mode function (IMF) components are selected according to the mutual information criterion to reconstruct the signal. Nine time−domain statistical features are then extracted to construct the fault feature vector. Thirdly, AIHO is used to collaboratively optimize the penalty factor and kernel parameters of SVM, establishing an AIHO−SVM classification model. Finally, the proposed method is validated on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology. Experimental results show that the average diagnostic accuracies on the two datasets reach 99.07% and 99.09%, respectively, demonstrating the effectiveness of the proposed method for rolling bearing fault diagnosis. Full article
(This article belongs to the Special Issue Advanced Fault Diagnosis Using Interpretable, Multimodal and Transfer Learning Techniques)
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24 pages, 6230 KB  
Article
Detecting Latent Social Psychological Constructs in Russia Ukraine War Discourse: A Probabilistic and Temporally Validated Analysis of 48,201 Tweets
by Fahim Sufi and Fatematuz Zohra
Information 2026, 17(7), 700; https://doi.org/10.3390/info17070700 - 19 Jul 2026
Viewed by 236
Abstract
Despite advances in computational social science, online conflict discourse is still commonly analyzed through sentiment, stance, or topic models, leaving limited insight into the social and psychological mechanisms embedded in digital war narratives. This study develops a theory-informed probabilistic framework for detecting discourse-level [...] Read more.
Despite advances in computational social science, online conflict discourse is still commonly analyzed through sentiment, stance, or topic models, leaving limited insight into the social and psychological mechanisms embedded in digital war narratives. This study develops a theory-informed probabilistic framework for detecting discourse-level indicators of Social Identity Theory, Moral Foundations Theory, Threat Appraisal, Cognitive Distortion, and Deindividuation in Russia–Ukraine war discourse. The empirical design uses 48,201 tweets in total: 10,815 tweets collected between 1 January and 28 June 2022 for model development and primary analysis, as well as and an external validation corpus of 37,386 Russia–Ukraine cyberwar-related tweets—collected from 30,706 users across 54 languages between October 2022 and April 2023—for temporal robustness assessment. The primary corpus contained 10,815 unique tweet identifiers, 10,229 unique textual records, 586 repeated textual items, a textual uniqueness rate of 94.58%, 6646 English tweets (61.45%), and 32,260 retweet engagements. Methodologically, the framework combines contextual language representations, theory-aligned linguistic cues, temporal signals, engagement features, and graph-based indicators. These signals are used to infer latent constructs and are evaluated through calibration, ablation testing, human validation, and cascade comparison. Empirically, Deindividuation was the dominant construct (1654 posts, 15.29%), followed by Cognitive Distortion (525, 4.85%) and Threat Appraisal (503, 4.65%). Co-activation analysis showed the strongest overlap between Deindividuation and Cognitive Distortion (Jaccard = 0.26). Validation diagnostics indicated internal lexical consistency (r=0.88 for Deindividuation), 93% rumor calibration, 91% bootstrap stability, and improved baseline performance (F1 = 0.72; Brier = 0.12; cascade log-likelihood = −865). The findings demonstrate that theoretically grounded probabilistic modeling can provide scalable, interpretable, and temporally validated insight into psychological patterns in digital conflict discourse. Full article
(This article belongs to the Section Information Applications)
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31 pages, 4524 KB  
Article
Short-Term Wind Power Forecasting via Multimodal Adaptive Graph Neural Networks with Credibility-Modulated Aggregation
by Guochen Zhang, Qing Ye, Xiaobo Li and Zhe Song
Information 2026, 17(7), 699; https://doi.org/10.3390/info17070699 - 18 Jul 2026
Viewed by 250
Abstract
Wind power forecasting plays a crucial role in power dispatch and safety management of wind farms. However, the insufficient integration of multimodal heterogeneous data and the limitations of conventional graph construction strategies significantly restrict forecasting performance. Existing approaches either rely on simple feature [...] Read more.
Wind power forecasting plays a crucial role in power dispatch and safety management of wind farms. However, the insufficient integration of multimodal heterogeneous data and the limitations of conventional graph construction strategies significantly restrict forecasting performance. Existing approaches either rely on simple feature aggregation, which cannot fully capture cross-modal dependencies, or adopt predefined or single-criterion graph construction methods that fail to characterize complex turbine relationships involving spatial, temporal, and nonlinear correlations. To address these challenges, this paper proposes a Multimodal Adaptive Fusion Graph Neural Network (MAF-GNN) for short-term wind power forecasting. First, a Modality-Aware Representation Learning (MARL) module is developed to extract informative multimodal representations by modeling modality-specific characteristics and cross-modal dependencies through attention-based fusion. Second, an Adaptive Graph Learning with Multi-Similarity (AGL-MS) module is introduced to parametrically integrate four complementary similarity priors—geographic distance, Dynamic Time Warping (DTW), Maximal Information Coefficient (MIC), and cosine similarity—for adaptive turbine correlation graph construction. Furthermore, a Credibility-Modulated Graph Convolutional Network (CM-GCN) is developed to reduce the influence of unreliable node information during message propagation. Extensive experiments conducted on the SDWPF dataset demonstrate that MAF-GNN reduces MAE by 14.0–21.3% compared with sequential baselines and achieves 5.3–10.2% improvement over spatiotemporal graph-based models. Ablation studies further verify the complementary effectiveness of each proposed module in improving forecasting performance. Full article
(This article belongs to the Section Artificial Intelligence)
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36 pages, 626 KB  
Article
Comparative Performance of AI-Generated Fake News Detection Pipelines on Romanian News Content
by Claudiu Coman, Costel Marian Dalban, Vlad Bătrânu-Pințea, Georgiana Aron and Lucian Marina
Information 2026, 17(7), 698; https://doi.org/10.3390/info17070698 - 18 Jul 2026
Viewed by 451
Abstract
Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. [...] Read more.
Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. The study is based on source code generated with the assistance of artificial intelligence systems for a set of machine learning and transformer-based models. The code was subsequently implemented in Google Colab. 2026, trained on international benchmark datasets, and tested on Romanian news content. This design allowed the rapid prototyping of multiple detection pipelines and the systematic observation of their behavior in a media environment different from that represented in the training corpora. The models were evaluated comparatively using standard classification metrics, including accuracy, precision, recall, and F1-score, complemented by additional indicators relevant to model robustness and practical usability. The experimental results revealed significant differences in performance across algorithms when applied to English translations of Romanian-language news content after training on international datasets. However, this study does not provide a direct comparison between model performance on the international benchmark datasets and the Romanian test corpus; therefore, the gap between the international training corpus and the Romanian-sourced test corpus is interpreted as an exploratory limitation and as a direction for future research. Based on these findings, we propose an empirical classification of the tested models according to their predictive effectiveness, their contextual robustness across linguistic environments, and their operational relevance as filtering tools for institutional monitoring. The results show that AI-assisted coding workflows can provide a viable starting point for reproducible misinformation research, but they also underline the limitations of directly transferring models trained on non-Romanian data to local media ecosystems. The study offers both a replicable evaluation framework and practical insights for institutions involved in strategic communication, public security, and the monitoring of information threats. Full article
(This article belongs to the Special Issue Advances in Information Quality: Fact-Checking and AI in the Era of Fake News)
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35 pages, 1006 KB  
Article
Constructing MIDA5: A Design Science Approach for a User-Centered Data Analytics Methodology for Business Process Improvement
by Boris Astudillo, Marco Santórum, Jose Aguilar, Mayra Carrión-Toro and Patricia Acosta-Vargas
Information 2026, 17(7), 697; https://doi.org/10.3390/info17070697 - 17 Jul 2026
Viewed by 480
Abstract
Data Analytics methodologies provide structured approaches for transforming organizational data into actionable knowledge. However, many existing methodologies emphasize technical activities while offering limited support for stakeholder participation, user-centered validation, and organizational adoption. This study presents the construction of MIDA5, a Data Analytics methodology [...] Read more.
Data Analytics methodologies provide structured approaches for transforming organizational data into actionable knowledge. However, many existing methodologies emphasize technical activities while offering limited support for stakeholder participation, user-centered validation, and organizational adoption. This study presents the construction of MIDA5, a Data Analytics methodology for Business Process Improvement developed using the Design Science Research paradigm. The research combined a comparative analysis of existing Data Analytics methodologies with an empirical experimentation process conducted in an organizational environment. The experimentation involved the execution and analytical deconstruction of a previously implemented Data Analytics methodology to identify operational limitations, stakeholder-related challenges, and methodological gaps. The findings were synthesized into design requirements, methodological components, and design needs that guided the construction of MIDA5. The resulting artifact incorporates principles of Business Process Analytics, User-Centered Design, and User Engagement and Gamification Dynamics through a five-phase structure supported by activities, artifacts, and stakeholder validation procedures. The study contributes a traceable Design Science-based development process that connects empirical findings with design decisions and provides a methodological foundation for the complete methodological specification and empirical evaluation of MIDA5. Accordingly, this study focuses on artifact construction rather than on demonstrating the effectiveness of the resulting methodology. Full article
(This article belongs to the Special Issue Machine Learning and Data Analytics for Business Process Improvement)
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27 pages, 1252 KB  
Review
Beyond Occam’s Razor: Double Descent and the Potential Paradigm Shift Toward Over-Parameterized Personalization in Higher Education
by Chong Ho Yu and Han Nee Chong
Information 2026, 17(7), 696; https://doi.org/10.3390/info17070696 - 17 Jul 2026
Viewed by 591
Abstract
This paper examines how the emergence of over-parameterized artificial intelligence models and the phenomenon of double descent challenge the classical assumption that simpler models generalize better. Traditional predictive analytics relied on parsimonious models grounded in the bias-variance trade-off, where increasing complexity was expected [...] Read more.
This paper examines how the emergence of over-parameterized artificial intelligence models and the phenomenon of double descent challenge the classical assumption that simpler models generalize better. Traditional predictive analytics relied on parsimonious models grounded in the bias-variance trade-off, where increasing complexity was expected to produce overfitting. However, recent advances in deep learning demonstrate that highly over-parameterized models can achieve superior generalization after surpassing the interpolation threshold. This paradigm shift has enabled systems such as AlphaFold, Aurora, Delphi-2M, and recommenders to model complex, high-dimensional relationships through contextual attention rather than global feature selection. The paper argues that higher education analytics remains largely reductionist, relying on limited variables such as GPA, demographics, and course completion rates to identify “at-risk” students. While interpretable, these approaches often fail to capture the dynamic and multidimensional nature of student success. In response, this study proposes a transition toward over-parameterized personalization, where students’ academic and behavioral histories are modeled as longitudinal high-dimensional sequences. Drawing parallels to commercial recommendation systems such as Amazon, Netflix, and YouTube, the paper explores how higher education can move from generalized early-warning systems toward adaptive “n-of-1” interventions. Importantly, the paper is conceptual rather than empirical: it develops a research agenda and a set of testable propositions, and it identifies the evaluation designs—temporally valid prediction protocols and causal intervention studies—by which the promise of over-parameterized personalization in higher education should be assessed before any claim of superiority can be made. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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28 pages, 1728 KB  
Article
A Competency Framework for Human Interoperability in Data Spaces
by Mayte Toscano Domínguez, Giacomo Martirano, Alfonso Quarati and Monica De Martino
Information 2026, 17(7), 695; https://doi.org/10.3390/info17070695 - 16 Jul 2026
Viewed by 889
Abstract
The growing deployment of European Data Spaces requires more than advanced technical infrastructures; it also demands specialised human competencies to ensure interoperability, governance, and trust. This paper redefines organisational readiness as a key indicator of the transition from experimental deployments to large-scale, governed [...] Read more.
The growing deployment of European Data Spaces requires more than advanced technical infrastructures; it also demands specialised human competencies to ensure interoperability, governance, and trust. This paper redefines organisational readiness as a key indicator of the transition from experimental deployments to large-scale, governed data exchange. Although technical solutions provide the necessary foundation, training is essential to ensure that the digital transition is supported by professionals capable of operating these ecosystems effectively. To address the research question, “Which competencies are required to enable data reuse within Data Spaces?”, with a specific focus on spatial data and Urban Digital Twins, this study proposes a hybrid methodology. This approach combines a top-down analysis of reference architectures (e.g., EIF and DSSC Blueprint) with a bottom-up diagnosis based on an expert-based qualitative survey. Preliminary results suggest that, although 58% of organisations identify technical integration between BIM and GIS as the main obstacle, 47% identify the shortage of specialised skills as the primary bottleneck preventing operational maturity. The analysis reveals a Maturity Plateau that hinders the transition from technical connectivity (Data Space Maturity Model Level 2) to full operational maturity (Level 3). To overcome this barrier, the study presents a convergence matrix based on the DIS4SME initiative, mapping knowledge gaps to a competency roadmap. This framework identifies urgent training needs in the semantic and legal domains, and provides an operational guide for supporting organisational maturity across Data Spaces and strengthening professional expertise. Full article
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18 pages, 707 KB  
Article
Operationalizing Accountable AI Through Traceable Governance Architecture for Institutional Decision Support
by Abdalilah Alhalangy
Information 2026, 17(7), 694; https://doi.org/10.3390/info17070694 - 16 Jul 2026
Viewed by 328
Abstract
Institutional artificial intelligence (AI) decision-support systems progressively evaluate cases, determine eligibility, and allocate resources; yet, predicted efficacy alone does not guarantee equity, contestability, or responsible utilization. Current research frequently considers fairness measures, explainability, human oversight, and organizational governance as rather distinct issues. This [...] Read more.
Institutional artificial intelligence (AI) decision-support systems progressively evaluate cases, determine eligibility, and allocate resources; yet, predicted efficacy alone does not guarantee equity, contestability, or responsible utilization. Current research frequently considers fairness measures, explainability, human oversight, and organizational governance as rather distinct issues. This paper presents a traceable bias-auditing framework that amalgamates prediction, explanation, selective human review, and structured recording into a cohesive operational decision pathway. Through design science research, the artifact was exhibited in a controlled proof-of-concept utilizing 8000 synthetic institutional situations and historically biased data labels. The foundational classifier was a logistic regression model. Selective escalation is initiated by the proximity of boundaries, tension in explanation patterns, and the rules governing review priorities. Three situations were evaluated: baseline prediction, prediction with explanation alone, and comprehensive architecture with review and audit recording. Explanations enhanced reviewability but did not significantly alter fairness outcomes. The proposed architecture improved F1 from 0.781 to 0.795, reduced the demographic parity gap from 0.070 to 0.010, decreased the equal opportunity gap from 0.116 to 0.036, and improved audit completeness from 0.33 to 1.00, while escalating only 4.8% of cases for human review. The results indicate that explanations attain institutional significance solely when linked to procedural regulations and enduring records. The evaluation was simulation-based; thus, the results should be interpreted as proof-of-concept evidence rather than direct field validation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Decision Support Systems)
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21 pages, 4927 KB  
Article
HybridSkinLes: An Explainable CNN-and Transformer-Based Multi-Class Skin Lesion Classification Framework
by May Issa Aldossary and Hina Gull
Information 2026, 17(7), 693; https://doi.org/10.3390/info17070693 - 16 Jul 2026
Viewed by 337
Abstract
Skin cancer is considered a deadly disease globally, and the timely identification of the disease may save human life. This research presents a CNN–Transformer-based fusion framework for automated multi-class skin lesion classification. This approach combines ResNet50 and Vision Transformer (ViT) to categorize skin [...] Read more.
Skin cancer is considered a deadly disease globally, and the timely identification of the disease may save human life. This research presents a CNN–Transformer-based fusion framework for automated multi-class skin lesion classification. This approach combines ResNet50 and Vision Transformer (ViT) to categorize skin lesions using the HAM10000 dataset. To assess their efficacy, a comparison with CNN and ViT models is also carried out. Seven classes of skin cancer are used for training the models, and class weighting is used to correct dataset asymmetry. According to the experimental dataset, the suggested hybrid framework shows improved performance over CNN and ViT, considering the accuracy (0.97) and macro-averaged F1-score (0.95). Furthermore, the efficiency of the suggested model is demonstrated by the fact that it delivers performance that is competitive with several existing approaches. Overall results indicate that hybrid CNN–Transformer architectures present a viable path for automated skin lesion categorization. Grad-CAM++ is integrated to enhance model understanding and promote medical confidence by enabling physicians to view visualizations that show the areas impacting the model’s conclusions. But there are still issues, including poor generalization, computational complexity, and a lack of external validation. Future research will concentrate on enhancing interpretability for practical implementation, integrating clinical information, and evaluating several datasets. Full article
(This article belongs to the Special Issue AI and Machine Learning in the Big Data Era: Advanced Algorithms and Real-World Applications)
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24 pages, 259 KB  
Article
Assessing Information Security Risk Exposure During the Transition from ISO/IEC 27001:2013 to ISO/IEC 27001:2022 in a Banking Institution
by Noviati Nurani and Nilo Legowo
Information 2026, 17(7), 692; https://doi.org/10.3390/info17070692 - 16 Jul 2026
Viewed by 301
Abstract
The transition from ISO/IEC 27001:2013 to ISO/IEC 27001:2022 is often approached as a certification and documentation update. However, for banking institutions with critical digital operations, the transition may create a distinct form of information security exposure when existing controls, evidence, ownership, and risk [...] Read more.
The transition from ISO/IEC 27001:2013 to ISO/IEC 27001:2022 is often approached as a certification and documentation update. However, for banking institutions with critical digital operations, the transition may create a distinct form of information security exposure when existing controls, evidence, ownership, and risk treatment decisions are not fully aligned with the revised control structure. This study examines transitional risk exposure in Bank XYZ, a banking institution with an established ISO/IEC 27001:2013-based Information Security Management System (ISMS). Using a qualitative single-case study design, the research analyzes ISMS documents, 96 information security risk records, Statement of Applicability records, control implementation evidence, ISO/IEC 27001:2013-to-ISO/IEC 27001:2022 control mapping materials, and stakeholder validation within the scope of core banking development and operations in data center and disaster recovery center environments. Risk exposure was assessed by comparing inherent and residual risks using a likelihood-impact matrix, while transition readiness was evaluated through control applicability, evidence adequacy, ownership clarity, supplier dependency, human factor readiness, and transition governance. The findings show that Bank XYZ implemented or provided evidence for 109 of 114 ISO/IEC 27001:2013 controls and reduced 96 inherent risks, consisting of 38 Moderate and 58 Moderate-to-High risks, into 13 Low and 83 Low-to-Moderate residual risks. Nevertheless, the transition review revealed that a mature residual risk position under ISO/IEC 27001:2013 does not automatically indicate readiness for ISO/IEC 27001:2022. Of 11 newly introduced controls, 2 were implemented, 4 were partially implemented, 4 were not implemented, and 1 was not applicable. The most significant transition gaps were found in threat intelligence, information deletion, data masking, and data leakage prevention. The study contributes by distinguishing transitional risk exposure from residual risk and by offering a control-level basis for prioritizing ISO/IEC 27001:2022 transition activities in banking institutions. Full article
(This article belongs to the Special Issue Governance Security and Privacy Protection of Critical Information Infrastructure)
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20 pages, 1374 KB  
Article
Dynamic Cost Prediction for State Grid Engineering Projects Based on Multi-Source Business Data Fusion and Data-Driven Methods
by Weiqiong Wang, Qidong Xu, Tianyu Zhao and Fang Fang
Information 2026, 17(7), 691; https://doi.org/10.3390/info17070691 - 16 Jul 2026
Viewed by 317
Abstract
Accurate dynamic cost prediction is essential for budget optimization and risk mitigation in State Grid projects. However, traditional models and even recent deep learning approaches fall short, as they treat cost drivers independently, adopt simplistic concatenation that destroys sourcewise structure, or fail to [...] Read more.
Accurate dynamic cost prediction is essential for budget optimization and risk mitigation in State Grid projects. However, traditional models and even recent deep learning approaches fall short, as they treat cost drivers independently, adopt simplistic concatenation that destroys sourcewise structure, or fail to handle irregularly sampled and partially missing multi-source data. This paper proposes a novel data-driven framework that integrates multi-source business data through a hierarchical tensor fusion mechanism and a hybrid spatiotemporal architecture. The problem is formalized as multivariate time-series prediction with irregular sampling and missing modalities. The framework comprises three synergistic innovations: a differentiable low-rank CANDECOMP/PARAFAC (CP) decomposition layer with adaptive attention weights that preserves cross-source structure while enabling compact dimensionality reduction; a spatiotemporal attention-based bidirectional gated recurrent unit (Bi-GRU) that captures long-range temporal dependencies; and a graph convolutional network (GCN) that explicitly learns interrelations among cost drivers, a capability absent in most existing forecasting methods. The entire system is trained end to end with a customized loss combining mean squared error, quantile loss, and temporal consistency regularization. Extensive experiments on three State Grid substation projects demonstrate that the proposed method outperforms state-of-the-art baselines by 12.7–18.4% in MAPE and maintains robust performance with up to 40% of data missing. These results confirm that explicitly modeling both temporal evolution and driver interdependencies within a unified fusion framework is the key to reliable cost forecasting in large-scale infrastructure projects. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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16 pages, 2298 KB  
Article
Evaluating ASR Pipeline Configurations for Kazakh: Implications for Low-Resource Turkic Languages
by Nursultan Nyssanov, Leila Rzayeva, Alisher Batkuldin and Zhaksylyk Kozhakhmet
Information 2026, 17(7), 690; https://doi.org/10.3390/info17070690 - 15 Jul 2026
Viewed by 311
Abstract
Kazakh automatic speech recognition (ASR) presents a persistent challenge for large-scale multilingual models. This paper presents a systematic evaluation of 27 ASR pipeline configurations (three ASR models × three VAD methods × three post-processing strategies) on the Kazakh Speech Dataset (KSD), examining the [...] Read more.
Kazakh automatic speech recognition (ASR) presents a persistent challenge for large-scale multilingual models. This paper presents a systematic evaluation of 27 ASR pipeline configurations (three ASR models × three VAD methods × three post-processing strategies) on the Kazakh Speech Dataset (KSD), examining the contribution of model fine-tuning, voice activity detection (VAD) preprocessing, and large language model (LLM) post-correction and benchmarking the resulting pipelines against two non-Whisper foundation models. Language-specific fine-tuning reduces Word Error Rate (WER) from 43.20% (generic Whisper-large-v3) to 11.88% (Kazakh fine-tuned Whisper-turbo), a 31.32-percentage-point absolute reduction (72.5% relative; p < 0.001, bootstrap test); the effect persists after controlling for model size (generic Whisper-large-v3-turbo, 18.92%, vs. the same architecture after fine-tuning, 11.88%; p < 0.001). VAD preprocessing consistently degrades performance. Zero-shot post-correction with general-purpose LLMs yields no benefit and adds substantial latency: Gemma-2-9B and Qwen2.5-7B raise WER by 5.5 and 7.2 percentage points at real-time factors of 0.52 and 0.30, and a larger 32B model still degrades accuracy (+10.8 points), indicating that scale is not the limiting factor. Among all systems evaluated, a larger multilingual foundation model, SeamlessM4T-v2 (9.72% WER), outperforms the fine-tuned Whisper, showing that for Kazakh model coverage matters more than pipeline engineering. Character-level error analysis identifies systematic confusion between Kazakh-specific and Russian Cyrillic characters as a dominant error source. These findings establish that, for Kazakh under the evaluated conditions, model choice dominates pipeline add-ons: fine-tuning is essential, VAD and zero-shot LLM correction consistently hurt, and a strong multilingual model sets the best result; we further discuss the extent to which these conclusions extend to typologically similar Kipchak-Turkic languages. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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31 pages, 2871 KB  
Article
Visual Semantics in MT Evaluation: Do Image Descriptions Help with Assessment of Multimodal MT Quality?
by Sami Ul Haq, Sheila Castilho and Yvette Graham
Information 2026, 17(7), 689; https://doi.org/10.3390/info17070689 - 15 Jul 2026
Viewed by 367
Abstract
Multimodal machine translation (MMT) aims to integrate visual context with textual data to improve the translation of ambiguous source text, such as the inclusion of an image as additional context. However, the evaluation of systems largely still relies on automatic metrics designed to [...] Read more.
Multimodal machine translation (MMT) aims to integrate visual context with textual data to improve the translation of ambiguous source text, such as the inclusion of an image as additional context. However, the evaluation of systems largely still relies on automatic metrics designed to evaluate text alone, and do not account for additional modalities during evaluation. The lack of dedicated MMT evaluation methods often results in inconsistent findings and creates uncertainty regarding the actual contribution of visual context in translation. In this work, we examine the performance of state-of-the-art trained and untrained evaluation metrics, particularly when comparing multimodal and text-only systems. Our evaluation focuses on the degree to which existing metrics are sensitive enough to distinguish between multimodal and text-only machine translation systems. We further investigate the potential for automatically generated image descriptions to serve as effective contextual signals for improving metric sensitivity to multimodal tasks. Our results show that incorporating such visual information into supervised metrics yields better alignment with human judgment. While all metrics successfully distinguished image-aware from image-agnostic systems on general test sets, both n-gram-based and embedding-based metrics struggled with respect to contrastive evaluation designed to capture context-dependent errors. Furthermore, we discuss how the presence of visual context may influence human evaluator judgment, observing that given the opportunity, human ratings are often substantially revised, further emphasizing the critical role of context in the evaluation of MMT. Full article
(This article belongs to the Special Issue Human and Machine Translation: Recent Trends and Foundations)
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29 pages, 702 KB  
Article
Changes in Pre-Service Physics Teachers’ TPACK and Collaborative Problem Solving Associated with an AI-Supported CTD-PBL Module: A Quasi-Experimental Study
by Qirui Chen and Kamisah Osman
Information 2026, 17(7), 688; https://doi.org/10.3390/info17070688 - 15 Jul 2026
Viewed by 316
Abstract
Generative artificial intelligence (AI) is increasingly entering teacher education, yet evidence remains limited on its responsible integration into discipline-specific pedagogical preparation. This study examined whether an AI-supported Collaborative TPACK Competency Development module based on problem-based learning (CTD-PBL) was associated with greater pre–post gains [...] Read more.
Generative artificial intelligence (AI) is increasingly entering teacher education, yet evidence remains limited on its responsible integration into discipline-specific pedagogical preparation. This study examined whether an AI-supported Collaborative TPACK Competency Development module based on problem-based learning (CTD-PBL) was associated with greater pre–post gains in pre-service physics teachers’ self-reported technological pedagogical content knowledge (TPACK) and perceived collaborative problem-solving (CPS) processes. Informed by ADDIE, the 8-week module used DeepSeek as a bounded scaffold for collaborative lesson design, feedback, verification, and reflective revision while preserving teacher judgment. An intact-class quasi-experimental pre-test/post-test design involved 130 third-year pre-service physics teachers at a public university in western China. Two existing classes were randomly allocated at the class level to CTD-PBL or conventional instruction. Compared with the conventional group, the CTD-PBL group reported higher post-test TPACK scores (M = 4.04 vs. M = 3.40, p < 0.001, d = 1.02) and higher perceived CPS process scores (M = 3.62 vs. M = 3.05, p < 0.001, d = 0.88), with stronger pre–post gains in both outcomes. The findings provide a bounded curriculum design case showing how generative AI can be embedded in physics teacher education through problem-based tasks, collaborative scaffolding, and human verification procedures. Full article
(This article belongs to the Special Issue Advancing Educational Innovation with Artificial Intelligence)
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20 pages, 3202 KB  
Article
M2WPR-Net: Robust Multimodal Weld Quality Assessment via Cross-Modal Attention
by Ao Han, Tongyu Zhao, Yanjun Pei, Haining Chen, Jun Zhou, Hailei Yuan and Pan Hu
Information 2026, 17(7), 687; https://doi.org/10.3390/info17070687 - 15 Jul 2026
Viewed by 273
Abstract
Robust monitoring of weld pool dynamics is critical for automated arc welding; however, single-modality sensors are frequently constrained by severe optical interference and high-frequency environmental noise. To address these limitations, we propose M2WPR-Net, a novel multimodal framework that synergizes visual and acoustic signals [...] Read more.
Robust monitoring of weld pool dynamics is critical for automated arc welding; however, single-modality sensors are frequently constrained by severe optical interference and high-frequency environmental noise. To address these limitations, we propose M2WPR-Net, a novel multimodal framework that synergizes visual and acoustic signals for simultaneous weld width regression and physical quality classification. The architecture employs a dual-stream ResNet50 backbone to process heterogeneous sensory data. Specifically, the visual stream utilizes a Convolutional Block Attention Module (CBAM) to suppress intense arc glare and localize the weld pool. Concurrently, the acoustic stream transforms 1D audio sequences into 2D Gramian Angular Summation Field (GASF) textures, which are subsequently refined by Squeeze-and-Excitation (SE) networks to isolate target frequency channels. A central contribution of this study is a bidirectional cross-modal attention mechanism based on Query–Key–Value (Q-K-V) matrix operations. Overcoming the shortcomings of static feature concatenation, this module dynamically aligns the modalities, enabling acoustic cues to guide visual feature extraction and vice versa, thereby mitigating information bottlenecks. Optimized via a joint multi-task loss function, the proposed M2WPR-Net significantly outperforms existing single-modal and conventional fusion baselines. Experimental results demonstrate that the network achieves a Mean Absolute Error (MAE) of 0.18 mm for width prediction and a 93.5% accuracy in penetration state classification, confirming its resilience and practical applicability in complex industrial welding environments. Full article
(This article belongs to the Special Issue Advances in Computer Graphics and Visual Computing)
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24 pages, 5699 KB  
Article
Lane-Based Vehicle Counting System for Complex Traffic Scenes
by Zhenyang Hu, Zhandong Liu, Ruixia Song, Ke Li, Shuping Chen, Zhihua Wang, Yong Li and Xiangwei Qi
Information 2026, 17(7), 686; https://doi.org/10.3390/info17070686 - 15 Jul 2026
Viewed by 225
Abstract
To address the reliance on manual calibration and the performance degradation caused by loosely coupled modules in lane-wise vehicle counting under complex traffic scenarios, this paper presents a lane-wise vehicle counting system based on adaptive lane partitioning and multi-module integration. The system first [...] Read more.
To address the reliance on manual calibration and the performance degradation caused by loosely coupled modules in lane-wise vehicle counting under complex traffic scenarios, this paper presents a lane-wise vehicle counting system based on adaptive lane partitioning and multi-module integration. The system first applies YOLOPv2 for lane-line detection, providing the basis for lane-region partitioning. Subsequently, Hue-Saturation-Value (HSV) color segmentation, morphological processing, and contour filtering are employed to enhance the robustness of lane feature extraction. Leveraging perspective geometry, lane regions are constructed to achieve adaptive lane partitioning. For vehicle analysis, YOLOv11 is utilized for vehicle detection, and ByteTrack is adopted for multi-object tracking. These modules are combined with lane assignment to form an integrated pipeline that preserves trajectory continuity and mitigates identity loss under occlusion and motion blur. Furthermore, a PyQt5-based interactive visualization interface is developed to support video processing, real-time display, lane-region visualization, and statistical analysis of per-lane traffic flow and lane-change behaviors. Experimental results demonstrate the effectiveness and practicality of the proposed system in complex multi-lane traffic scenarios. Full article
(This article belongs to the Section Artificial Intelligence)
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