Informatics

18 pages, 8774 KB

Open AccessArticle

Role of Anthropomorphic Design in Social Robots for Aged Care: A Case Study of Pepper

by James R. Sadler, Samina Ansari, Aila Khan, Michael Lwin and Omar Mubin

Informatics 2026, 13(7), 119; https://doi.org/10.3390/informatics13070119 - 22 Jul 2026

Viewed by 293

The increasing aging population in Australia necessitates innovative caregiving solutions to address the growing needs of elderly residents. Humanoid robots, with their physical embodiment and human-like attributes, offer a promising technological intervention. This exploratory study investigates the integration of Pepper, a humanoid robot, [...] Read more.

The increasing aging population in Australia necessitates innovative caregiving solutions to address the growing needs of elderly residents. Humanoid robots, with their physical embodiment and human-like attributes, offer a promising technological intervention. This exploratory study investigates the integration of Pepper, a humanoid robot, into aged care facilities, focusing on its potential to meet the needs of older adults and serve as a daily companion, thereby reducing staff workload. The research explores the anthropomorphic features of Pepper, their role in fostering connection and engagement, and the perception and acceptance of the robot as a companion among elderly residents. Findings highlight Pepper’s potential to enhance the quality of care and support in aged care settings while identifying areas for improvement to ensure its successful adoption. Full article

(This article belongs to the Special Issue AI-Enabled Digital Health Technologies for Patient-Centered Care and Sustainable Systems)

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38 pages, 11672 KB

Open AccessArticle

Enhancing 3D MRI-Based Necrotic Core Segmentation in Glioblastoma Using Activation Functions in Deep Learning

by Mushtaq Mahyoob Saleh, Eltahir Mohamed Hussein, Musab Elkheir Salih and Mohamed A. A. Ahmed

Informatics 2026, 13(7), 118; https://doi.org/10.3390/informatics13070118 - 20 Jul 2026

Viewed by 342

Abstract

Precise brain tumour delineation is vital for therapy protocols and tracking. However, standard Rectified Linear Units (ReLU) struggle to capture subtle necrotic-core variations due to zero-gradient behaviour in the negative domain. To address this, we present a controlled benchmark of 12 activation functions [...] Read more.

Precise brain tumour delineation is vital for therapy protocols and tracking. However, standard Rectified Linear Units (ReLU) struggle to capture subtle necrotic-core variations due to zero-gradient behaviour in the negative domain. To address this, we present a controlled benchmark of 12 activation functions within a fixed Residual 3D U-Net using the Brain Tumour Segmentation (BraTS) 2020 dataset. In the single-run benchmark, Swish achieved the best necrotic-core (NCR) Dice (0.676; +2.0% over ReLU, p < 0.01), while TanhExp attained the highest whole-tumour accuracy (0.879). To test the reliability of these single-run results, the four functions central to our claims were retrained across three random seeds. This analysis confirmed a small but consistent NCR advantage for the smooth and adaptive functions—Swish (0.677 ± 0.003) and PReLU (0.678 ± 0.004) over ReLU (0.661 ± 0.012; pooled p < 0.001)—with Swish among the most stable functions in this region. By contrast, the apparent single-run differences in the enhancing tumour, and the underperformance of PReLU, did not generalise across seeds, indicating that activation-function effects in this task are concentrated in the necrotic core and that single-seed comparisons can be misleading. Crucially, Swish achieved these gains with zero additional trainable parameters and only a ~1% training latency penalty on common hardware. Replacing ReLU with Swish offers a cost-effective, architecture-preserving strategy to improve segmentation reliability and boundary delineation. Ultimately, this zero-cost architectural modification is a promising, preliminary step towards more reliable automated tumour delineation, pending prospective validation on multi-institutional data and expert radiological assessment. Full article

(This article belongs to the Section Medical and Clinical Informatics)

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20 pages, 552 KB

Open AccessArticle

Evaluating the Performance of Large Language Models in Evidence-Scarce Scenario: The Diabetic Foot Ulcer Transition Phase

by Kamran Shakir, Hadi Sarlak, Giulia Rogati, Alberto Leardini, Lisa Berti and Paolo Caravaggi

Informatics 2026, 13(7), 117; https://doi.org/10.3390/informatics13070117 - 20 Jul 2026

Viewed by 358

Abstract

Diabetic foot ulcers (DFUs) impose a substantial burden on people with diabetes and healthcare systems. The post-healing “transition phase” remains clinically challenging with limited guideline support. While large language models (LLMs) are increasingly proposed as clinical decision-support tools, their reliability in evidence-scarce scenarios [...] Read more.

Diabetic foot ulcers (DFUs) impose a substantial burden on people with diabetes and healthcare systems. The post-healing “transition phase” remains clinically challenging with limited guideline support. While large language models (LLMs) are increasingly proposed as clinical decision-support tools, their reliability in evidence-scarce scenarios is largely untested. This exploratory study benchmarked leading LLMs against European clinician consensus for the evidence-scarce scenario of DFU transition-phase management. Six LLMs (ChatGPT-4o, ChatGPT-5.0, Gemini 2.5 Flash, Gemini 2.5 Pro, Claude Sonnet 4.0, and Perplexity) were evaluated for accuracy and hallucination using a two-stage framework. Benchmarks were derived from an online survey of European DFU experts reflecting European-level and national practices (Denmark, Netherlands, UK). Binary outcomes were summarized as proportions with Wilson 95% confidence intervals. Paired within-item comparisons across models were assessed using Cochran’s Q, followed by exact McNemar tests with Holm correction (α = 0.05); inferential results were considered supportive due to the limited number of paired items (n = 8). Across 192 accuracy assessments and 384 reference checks, LLM accuracy ranged from 50–75% and declined when simulating national practices. Hallucination rates exceeded 50% in several models. LLMs rely on generic recommendations, which contrast with clinicians’ contextual, patient-centered reasoning, suggesting current limitations in their suitability for clinical decision support in DFU transition phase clinical care. Full article

(This article belongs to the Section Generative AI)

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20 pages, 764 KB

Open AccessArticle

Procedural and Distributive Unfairness in AI Interactions: Are People Less Satisfied with Unfairness from AI Compared to Humans?

by Devon Johnson, Sungyong Chun, Sneha Pandey and Gouher Ahmed

Informatics 2026, 13(7), 116; https://doi.org/10.3390/informatics13070116 - 20 Jul 2026

Viewed by 343

Abstract

This study investigates consumer perceptions of procedural and distributive fairness/unfairness in service interactions involving AI versus human providers. It also examines how these perceptions are influenced by technology-related discomfort and the perceived responsibility of the service organization. A scenario-based online experiment was conducted [...] Read more.

This study investigates consumer perceptions of procedural and distributive fairness/unfairness in service interactions involving AI versus human providers. It also examines how these perceptions are influenced by technology-related discomfort and the perceived responsibility of the service organization. A scenario-based online experiment was conducted using participants recruited through the Prolific research platform. Participants were asked to review a small business loan application process at a fictional digital bank where the application is processed either by an AI algorithm or a human loan officer. The study found that consumers experienced a higher level of satisfaction when they encountered procedural unfairness from humans compared to AI. The negative effect of procedural unfairness on satisfaction was amplified as algorithm discomfort and perceived company responsibility increased. The findings suggest that AI unfairness requires special attention to address differences in consumer reaction. Full article

(This article belongs to the Special Issue Explainable and Trustworthy Artificial Intelligence for Data-Driven Decision Support)

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11 pages, 242 KB

Open AccessArticle

Reliability and Quality of AI-Generated Information on Newborn Screening Tests: A Comparative Analysis of ChatGPT and Gemini

by Ece Nilüfer, Meserret Aslan and Şehma Şen

Informatics 2026, 13(7), 115; https://doi.org/10.3390/informatics13070115 - 17 Jul 2026

Viewed by 322

Abstract

Background/Objectives: The increasing use of artificial intelligence (AI) chatbots for obtaining health-related information has raised concerns regarding the quality and reliability of the information they provide. This study aimed to compare the quality and reliability of responses generated by ChatGPT free tier (GPT-4o, [...] Read more.

Background/Objectives: The increasing use of artificial intelligence (AI) chatbots for obtaining health-related information has raised concerns regarding the quality and reliability of the information they provide. This study aimed to compare the quality and reliability of responses generated by ChatGPT free tier (GPT-4o, with GPT-4.1 mini as the fallback model after the usage limit) and Gemini 2.5 Flash (Google, free version) regarding newborn screening tests. Methods: A total of 31 questions were developed based on international and national newborn screening guidelines and were posed to both chatbots. Responses were independently evaluated by two researchers using the DISCERN instrument and the Global Quality Score (GQS), and inter-rater reliability was assessed. Descriptive statistics and non-parametric tests were used to compare chatbot performance. Results: Both evaluators assigned significantly higher DISCERN total scores to Gemini than to ChatGPT free tier. For Evaluator 1, the mean DISCERN scores were 48.8 ± 10.2 for Gemini and 42.4 ± 5.9 for ChatGPT free tier (p < 0.001); for Evaluator 2, the corresponding scores were 53.7 ± 8.9 and 42.4 ± 5.8, respectively (p < 0.001). For the GQS ratings, Evaluator 1 rated Gemini significantly higher than ChatGPT free tier (3.6 ± 0.6 vs. 3.0 ± 0.5, p < 0.001), whereas Evaluator 2 found no statistically significant difference between the two chatbots (3.3 ± 1.3 vs. 3.4 ± 0.8, p = 0.877). Inter-rater reliability for DISCERN scores was excellent for ChatGPT and moderate for Gemini, whereas agreement for GQS ratings was low for both chatbots. Conclusions: Gemini demonstrated consistently higher DISCERN scores than ChatGPT free tier; however, its superiority in overall GQS ratings was not consistently supported across the two evaluators. Neither chatbot consistently achieved the highest levels of information quality. AI chatbots should therefore be considered supplementary sources of health information rather than substitutes for healthcare professionals or official health information resources. Full article

30 pages, 1081 KB

Open AccessArticle

Event-Conditioned Causal Extraction in Saudi Dialect: A Comparative Study of Dialect-Trained BERTs and LLM Prompting

by Mariam Elhussein, Samiha Brahimi, Reem Osman and Suhier Elfaki

Informatics 2026, 13(7), 114; https://doi.org/10.3390/informatics13070114 - 17 Jul 2026

Viewed by 318

Abstract

Causality extraction is an important task in natural language processing, yet it remains underexplored in informal Arabic social media text, particularly in dialectal contexts. This study investigates causal-reason extraction from Saudi Arabic tweets related to sick-leave requests. A gold-standard dataset was annotated for [...] Read more.

Causality extraction is an important task in natural language processing, yet it remains underexplored in informal Arabic social media text, particularly in dialectal contexts. This study investigates causal-reason extraction from Saudi Arabic tweets related to sick-leave requests. A gold-standard dataset was annotated for multiple causality-related tasks, including cause-presence detection, cause-span extraction, cause-category classification, causal-marker detection, and causal marker text identification. The study compares two modeling paradigms: fine-tuned BERT-based models, represented by SaudiBERT and AraBERT, and prompting-based large language models (LLMs), represented by GPT-4.1-mini and Gemini-2.5-flash. The descriptive analysis showed strong class imbalance, substantial implicit causality, and uneven cause-category distributions. Results showed that SaudiBERT generally outperformed AraBERT when macro-level and minority-class performance were considered. Among LLMs, Gemini-2.5-flash achieved the strongest overall performance, particularly under natural 10-shot single-tweet prompting, while balanced few-shot prompting improved macro-F1 for cause-category classification. However, step-wise prompting did not consistently improve performance and may have introduced error propagation. Overall, the findings show that causality extraction in informal Saudi Arabic remains challenging, especially for implicit causal expression. The study highlights the complementary strengths of dialect-specific transformers and LLM-based prompting for Arabic causality extraction. Full article

(This article belongs to the Special Issue Machine Learning in Social Media Analysis)

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5 pages, 162 KB

Open AccessEditorial

AI and Academic Publishing

by Antony Bryant

Informatics 2026, 13(7), 113; https://doi.org/10.3390/informatics13070113 - 14 Jul 2026

Viewed by 436

Abstract

MDPI, along with all other academic publishers, recognizes the potential benefits and real challenges posed by the advent of Generative AI [GenAI] [...] Full article

(This article belongs to the Section Generative AI)

17 pages, 773 KB

Open AccessArticle

OpenGluco—Innovative Open-Source Diabetes Care Management System

by Michal Kubascik, Andrej Tupy, Lukas Formanek and Miroslav Chochul

Informatics 2026, 13(7), 112; https://doi.org/10.3390/informatics13070112 - 14 Jul 2026

Viewed by 395

Abstract

Continuous glucose monitoring (CGM) plays a central role in modern diabetes management, yet CGM data are often confined within proprietary manufacturer ecosystems, limiting interoperability and reuse. This paper presents OpenGluco, an open-source and provider-independent platform designed to unify CGM data from multiple commercial [...] Read more.

Continuous glucose monitoring (CGM) plays a central role in modern diabetes management, yet CGM data are often confined within proprietary manufacturer ecosystems, limiting interoperability and reuse. This paper presents OpenGluco, an open-source and provider-independent platform designed to unify CGM data from multiple commercial systems, including Abbott Freestyle Libre, Dexcom, and Medtronic. OpenGluco implements a modular server architecture that abstracts vendor-specific data access, normalizes heterogeneous glucose time series, and exposes standardized access through an application programming interface designed to support future scalability. The platform supports individual users as well as institutional and research deployments. Performance evaluation demonstrates reliable data ingestion, consistent API responsiveness within the evaluated deployment environment, and robust handling of heterogeneous sampling characteristics. Validation of data normalization shows preservation of clinically relevant metrics such as Time in Range. By emphasizing interoperability, user-authorized data access, and open-source transparency, OpenGluco provides a flexible foundation for clinical monitoring, education, and future analytics-driven decision-support applications in diabetes care. Full article

(This article belongs to the Special Issue Health Data Management in the Age of AI)

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38 pages, 6648 KB

Open AccessArticle

A Data-Driven Informatics Framework for Evaluating Thai Provinces Using an Additive Weighting-Based Variant Assessment Algorithm and Two-Stage DEA

by Pasura Aungkulanon, Roberto Montemanni and Pongchanun Luangpaiboon

Informatics 2026, 13(7), 111; https://doi.org/10.3390/informatics13070111 - 10 Jul 2026

Viewed by 434

Abstract

In order to evaluate regional sustainability, a comprehensive framework is needed that can integrate a number of economic and environmental variables into a transparent and policy-relevant evaluation approach. The present study presents a data-driven informatics framework for the evaluation of Thai provinces that [...] Read more.

In order to evaluate regional sustainability, a comprehensive framework is needed that can integrate a number of economic and environmental variables into a transparent and policy-relevant evaluation approach. The present study presents a data-driven informatics framework for the evaluation of Thai provinces that utilizes the additive weighting-based variant assessment algorithm (AWVAA) with Charnes–Cooper–Rhode (CCR)-based two-stage data envelopment analysis (DEA). The system allows three interrelated activities: provincial screening, representative decision-making unit selection, and comparative efficiency benchmarking of economic and environmental performance. AWVAA employs global and local simple additive weighting algorithms in screening 77 provinces to find representative units while keeping regional balance and data completeness. In the second phase, the selected provinces are evaluated by a two-stage DEA structure based on CCR to measure their relative efficiency for transforming development-related inputs into intermediate operational factors and ultimate economic and environmental outputs. The analysis starts with investment, tourist arrivals, and newborns as initial inputs, moves through energy use, electricity consumption, number of factories, and number of vehicles as intermediate variables, and ends with gross provincial product and air quality indicators, including ozone, PM10, and PM2.5 as final outputs. The proposed framework selects 16 typical provinces and shows significant variations in overall CCR efficiency and super-efficiency performance over the selected set. The results suggest that provinces with high screening-stage prominence may not necessarily become the strongest DEA-based standards and emphasize the complimentary roles of representative unit selection and formal efficiency assessment. The study combines multi-criteria screening with benchmarking based on DEA to give a transparent and replicable method for regional sustainability monitoring, comparative assessment, and evidence-based policy planning. The results provide an informatics-oriented paradigm for complicated regional evaluation and practical insights for enhancing sustainable provincial development in Thailand. Full article

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24 pages, 799 KB

Open AccessArticle

LAPM-RA: Reward-Adaptive Prompt Learning with LLM Augmentation for Multimodal Sentiment Analysis

by Zhi Zhu, Cheng Kuang and Yin Qian

Informatics 2026, 13(7), 110; https://doi.org/10.3390/informatics13070110 - 10 Jul 2026

Viewed by 512

Abstract

Few-shot multimodal sentiment analysis (MSA), constrained by limited annotated data, often suffers from large cross-modal semantic alignment gaps and difficulty in capturing fine-grained sentiment cues, making it challenging to fully exploit the complementary information between text and images. Recent advances in large language [...] Read more.

Few-shot multimodal sentiment analysis (MSA), constrained by limited annotated data, often suffers from large cross-modal semantic alignment gaps and difficulty in capturing fine-grained sentiment cues, making it challenging to fully exploit the complementary information between text and images. Recent advances in large language models (LLMs) and prompt learning have shown strong potential for improving label efficiency in low-resource natural language processing tasks; however, their direct application to MSA is hindered by static prompt designs and shallow cross-modal integration. To overcome these limitations, we propose LLM-Augmented Prompt Learning for Multimodal Sentiment Analysis with Reward Adaptation (LAPM-RA), a unified framework integrating LLM-based sentiment-aware augmentation, reward-guided prompt selection, and context-aware multimodal fusion. Specifically, LLMs generates sentiment-consistent and counterfactual text variants to enhance lexical and structural diversity while preserving label fidelity; a supervised policy network adaptively selects optimal prompt templates based on reward signals; and a lightweight gating mechanism integrates textual and visual embeddings contextually. Extensive experiments on multiple benchmarks validate the effectiveness and robustness of LAPM-RA over competitive baselines. Full article

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28 pages, 949 KB

Open AccessArticle

From Data to Behaviour: Understanding the Perceived Smartwatch Value for Physical Activity Through Self-Quantification

by Carmen Bekker, Brenda M. Scholtz and Simone Beets

Informatics 2026, 13(7), 109; https://doi.org/10.3390/informatics13070109 - 9 Jul 2026

Viewed by 557

Abstract

Smartwatches are commonly used IoT wearable devices, which contain sensors that can support the data management process of smartwatch data. This process is essential for providing data-based insights that can motivate users to achieve their physical activity (PA) and health goals. However, there [...] Read more.

Smartwatches are commonly used IoT wearable devices, which contain sensors that can support the data management process of smartwatch data. This process is essential for providing data-based insights that can motivate users to achieve their physical activity (PA) and health goals. However, there is a lack of understanding of users’ perceptions of the health-related value they derive from smartwatches and of user self-tracking behaviour within a PA context. This self-tracking behaviour is also known as Self-Quantification Behaviour (SQB), which is the process of data preparation, collection, integration, reflection, and action. SQB could be affected by the functions provided by the smartwatch device, which vary by manufacturer, brand and model. The paper aims to investigate user perceptions of SQB and its association with their perceived value of smartwatches. A survey was conducted with professional office workers from South Africa who use smartwatches. The findings highlighted that the respondents enjoyed collecting and engaging with their PA data and that SQB significantly affected all four value constructs: Perceived Usefulness, Pleasure, Goal Pursuit Motivation, and Social Value. Social value, in terms of data sharing and user lifestyle, was the least well-rated. The findings contribute to an enhanced understanding of smartwatches and users’ perceived value of self-quantifying behaviour in the context of PA, thereby ultimately improving the smartwatch usage experience and motivating users to exercise more. Full article

(This article belongs to the Special Issue Health Data Management in the Age of AI)

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38 pages, 5157 KB

Open AccessArticle

A Multilabel Embedding-Based Framework for Predicting Short-Term Cross-Category Complaint Recurrence

by Theng-Jia Law, Choo-Yee Ting, Hu Ng and Hui-Ngo Goh

Informatics 2026, 13(7), 108; https://doi.org/10.3390/informatics13070108 - 8 Jul 2026

Viewed by 462

Abstract

Public complaints exhibit strong spatiotemporal dependencies, where issues often propagate across categories within short timeframes, yet existing studies largely overlook cross-category recurrence and underutilize embedding representations for structured data. To address this gap, this study proposes a multilabel embedding-based framework to predict short-term [...] Read more.

Public complaints exhibit strong spatiotemporal dependencies, where issues often propagate across categories within short timeframes, yet existing studies largely overlook cross-category recurrence and underutilize embedding representations for structured data. To address this gap, this study proposes a multilabel embedding-based framework to predict short-term cross-category complaint recurrence using structured spatiotemporal data. Using 48,103 real-world complaint records, the framework integrates embedding representations with Machine Learning (ML) and Deep Learning (DL) models to predict the likelihood of multiple complaint categories recurring within the next seven days in the same area. The results indicated that both approaches achieved average label-wise F1-scores of 30.5–32.6%, exceeding 81% for highly recurrent categories. The best ML model, Binary Relevance with Logistic Regression using multilingual-e5-large embeddings, achieved the lowest Hamming loss of 0.138 ± 0.126. Statistical analysis confirmed non-normality with Shapiro–Wilk statistics between 0.796 and 0.819 and p-values below 0.05, and significant differences across models with a Friedman test statistic of 512.531 and p-values below 0.05, although no significant pairwise differences were found with the Nemenyi post hoc test. Furthermore, 95% bootstrap confidence intervals indicate stable performance, with F1 ranging from 74.1% to 74.9% for ML and 73.2% to 74% for DL. Full article

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27 pages, 587 KB

Open AccessArticle

Interpretable Academic Team Formation on Heterogeneous Information Networks: Constructive Heuristics with Explicit Organizational Affiliation

by Nuri Özdemir and Hadi Gökçen

Informatics 2026, 13(7), 107; https://doi.org/10.3390/informatics13070107 - 6 Jul 2026

Viewed by 517

Abstract

Assembling expert teams under strict skill-coverage and communication-distance constraints is a fundamental challenge in collaborative knowledge work. Existing learning-based approaches excel at probabilistic link prediction but cannot reliably enforce hard logical constraints or provide interpretable justifications. This study presents a constructive heuristic framework [...] Read more.

Assembling expert teams under strict skill-coverage and communication-distance constraints is a fundamental challenge in collaborative knowledge work. Existing learning-based approaches excel at probabilistic link prediction but cannot reliably enforce hard logical constraints or provide interpretable justifications. This study presents a constructive heuristic framework for team formation on Heterogeneous Information Networks (HINs), integrating authors, papers, departments, and organizations into a unified graph database. Seven algorithms exploit distinct structural features—topological proximity, co-authorship history, organizational affiliation, citation impact, and temporal recency—and guarantee constraint satisfaction by construction. Experiments on a subset of the AMiner DBLP dataset (≈625,000 nodes, 973,000 edges) covering 12,045 formation requests across 147 configurations show that algorithm choice is the dominant determinant of runtime, while skill frequency governs feasibility: success rates decline from 81.2% under abundant keywords to 14.9% in the long tail. Algorithms further form statistically distinct clusters in communication cost, and specialized heuristics operate in nearly disjoint solution spaces—supporting a toolbox approach over single-algorithm deployment. These results provide actionable selection guidance: proximity-based algorithms for communication-efficient teams; citation- or recency-aware algorithms when impact matters; cohesion-based algorithms when internal collaboration is the priority. Full article

(This article belongs to the Section Social Informatics and Digital Humanities)

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23 pages, 2325 KB

Open AccessArticle

ESG-SASB Label Stability: A Curated Benchmark and Reproducible Pipeline for Reusing Sentence-Level Sustainability Disclosure Labels

by Yufei Li, Tianhao Chen, Wei Ke and Patrick Pang

Informatics 2026, 13(7), 106; https://doi.org/10.3390/informatics13070106 - 3 Jul 2026

Viewed by 670

Abstract

Annotated text datasets are increasingly reused as classifier targets, annotation candidates, and inputs to aggregate profiles, yet their labels often circulate without enough information about how they were produced. This article presents a reproducible benchmark and validation workflow for the public SASB-Aligned ESG [...] Read more.

Annotated text datasets are increasingly reused as classifier targets, annotation candidates, and inputs to aggregate profiles, yet their labels often circulate without enough information about how they were produced. This article presents a reproducible benchmark and validation workflow for the public SASB-Aligned ESG Sentences corpus, a sentence-level sustainability disclosure dataset organized around standards-based categories such as those used in Sustainability Accounting Standards Board (SASB) analytics. Using the downloaded 6460-row version of the corpus, we construct fixed train/validation/test splits, map released child labels to parent categories, and evaluate label reuse through supervised classifiers, prompted GPT-4o classification, blind and candidate-visible Claude annotation, and Monte Carlo aggregation into ESG/Non-ESG category profiles. The reproducibility artifacts provide split metadata, label mappings, prompt templates, model predictions, LLM annotation outputs, profile sensitivity outputs, figure inputs, and scripts for reproducing the reported tables and figures. Results show that label reproduction is strongest at coarser label levels, blind annotation flags 40.3% of held-out sentences as ambiguous, candidate-visible annotation increases agreement while changing the task format, and aggregate profiles remain sensitive to label source. The benchmark supports transparent reuse of sentence-level ESG labels by reporting label source, annotation condition, prompt family, and aggregation level. Full article

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24 pages, 5403 KB

Open AccessArticle

Reliability Reserve: A Markov Chain-Based Metric for Real-Time Operator Decision Support in Ayran Fermentation

by Zhanagul Doumchariyeva, Jamalbek Tussupov, Madina Sambetbayeva, Tamara Zhukabayeva, Madina Yessenaliyeva, Begzhan Kalemshariv, Sagi Issayev and Munaram Khassanova

Informatics 2026, 13(7), 105; https://doi.org/10.3390/informatics13070105 - 3 Jul 2026

Viewed by 541

Abstract

This study presents a Markov chain-based metric called the Reliability Reserve (τ), designed to estimate the time available for operator intervention during the ayran fermentation process. This indicator can be integrated into a digital twin forecast management system. The fermentation process was obtained [...] Read more.

This study presents a Markov chain-based metric called the Reliability Reserve (τ), designed to estimate the time available for operator intervention during the ayran fermentation process. This indicator can be integrated into a digital twin forecast management system. The fermentation process was obtained using a DTMC (discrete-time Markov chain) and divided into five states according to pH (S1–S5). Laboratory samples were prepared from premium-grade cow’s milk sourced from the Zher-Ana farm and divided into three experimental groups: Control (without additives), Opt1 (3% additive), and Opt2 (4% additive). A sequence of states was created for the three studied groups (Control, Opt1, and Opt2), and the transition states of the matrix were calculated. The Reliability Reserve quantifies how much time is left before the system transitions from the target state to the acidification state. For the first group, P₄₅ was 0.200, corresponding to τ = 26.9 min. The incorporation of functional additives increased P₄₅ to 0.250, reducing τ to 20.9 min and shortening the operator intervention window by approximately 6 min. Markov chains were constructed using 101 interpolated time points obtained from five experimental pH measurements for each group. The original pH values were recorded at 2, 4, 6, 8, and 10 h of fermentation and linearly interpolated with a step size of 0.1 h to improve temporal resolution. Model robustness was evaluated using sensitivity analysis (±0.05 pH boundary shifts) and bootstrap resampling (n = 1000, 95% confidence intervals). The concept of Reliability Reserve is a practical decision-making tool in real time. It offers an alternative to traditional reliability indicators such as MTTF and allows integration into digital twin-based control systems. Full article

(This article belongs to the Special Issue Revolutionizing Agriculture and Natural Resource Management with Artificial Intelligence Approaches)

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29 pages, 1077 KB

Open AccessArticle

Impact of AI Chatbots on Academic Engagement and Administrative Efficiency: A Dual-Population Study at a Women-Only Higher Education Institution in Oman

by Hamed Majid AlHajri, JannathlFirdouse Mohamed Kasim and Hala Al Lawati

Informatics 2026, 13(7), 104; https://doi.org/10.3390/informatics13070104 - 30 Jun 2026

Viewed by 783

Abstract

Chatbots are becoming increasingly significant entry points to digital services and are used in areas including job support, education, healthcare, and customer services. On the other hand, less is known about how chatbots affect people individually, in groups, and in society. Moreover, several [...] Read more.

Chatbots are becoming increasingly significant entry points to digital services and are used in areas including job support, education, healthcare, and customer services. On the other hand, less is known about how chatbots affect people individually, in groups, and in society. Moreover, several obstacles must be overcome before chatbots can realize their full potential. As a result, chatbots have become a significant research topic in recent years. We propose a research agenda outlining future directions and issues to advance knowledge in chatbot research and education. This research involves the quantitative analysis of the impact of these chatbot tools on academic staff, with a count of 40, and students, with a count of 300, at Al Zahra College for Women (ZCW). The links are uploaded electronically in bilingual form for both staff and students, and the responses are retrieved from them. This analysis is conducted in IBM SPSS Statistics version 29, and a comparative report is also prepared based on the questionnaire responses from students and staff members of ZCW. The study investigates the effects of artificial intelligence (AI) chatbots on the faculty and students at ZCW. The use of AI-powered tools in educational settings is examined, along with their effects on administrative, instructional, and learning procedures. This will enable us to identify the advantages of using AI tools within E-Learning systems. The results demonstrate how well AI chatbots can streamline administrative duties, enhance student involvement, and provide academic help. However, there are drawbacks as well, such as user adjustment, privacy issues, and technological constraints. The study offers helpful suggestions for enhancing chatbot integration at Al Zahra College to enhance learning results and operational effectiveness. Full article

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25 pages, 1264 KB

Open AccessArticle

A Health Informatics Framework for Integrating Machine Learning and Generative AI in HIV Risk Stratification and Personalized PrEP Recommendation

by Panyaphon Phiphatkunarnon, Amornphat Kitro, Benjamas Suksatit, Boon-Leong Neo, Do Tran and Worawit Tepsan

Informatics 2026, 13(7), 103; https://doi.org/10.3390/informatics13070103 - 29 Jun 2026

Viewed by 1244

Abstract

Background: Although pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, identifying individuals who may benefit from PrEP and delivering personalized prevention recommendations remain challenging in routine and digital health settings. Objective: This study aimed to develop and preliminarily evaluate an integrated artificial [...] Read more.

Background: Although pre-exposure prophylaxis (PrEP) is highly effective for HIV prevention, identifying individuals who may benefit from PrEP and delivering personalized prevention recommendations remain challenging in routine and digital health settings. Objective: This study aimed to develop and preliminarily evaluate an integrated artificial intelligence framework combining machine learning (ML) for HIV risk stratification and generative artificial intelligence (GenAI) for personalized PrEP recommendation support. Methods: A curated dataset of 2000 de-identified client profiles from Love2Test platform was used for proof-of-concept model development. Profiles were labeled as low or high HIV acquisition risk by domain experts based on structured behavioral information. Multiple ML classifiers were trained and compared using PyCaret. The selected model was integrated with a generative AI model through structured prompting to generate personalized PrEP recommendation content. The integrated framework was evaluated through structured physician assessment by four independent medical doctors. Results: The selected model showed strong internal discrimination for classifying high versus low HIV acquisition risk. The integrated framework also received favorable physician evaluation for clinical accuracy, explanation validity, contextual relevance, and error minimization across fixed and randomly selected profiles. However, because expert labeling was based on structured behavioral indicators closely related to the model inputs, the high internal performance should be interpreted within the context of this proof-of-concept study. Conclusions: The proposed framework provides a structured approach to support HIV risk stratification and personalized PrEP recommendations in a clinician-aligned manner. However, this study was an offline proof-of-concept and did not directly evaluate patient interaction, PrEP uptake, stigma, adherence, or clinical outcomes. Prospective studies using larger and more representative real-world datasets are needed to assess implementation, generalizability, and impact on service engagement and PrEP initiation. Full article

(This article belongs to the Section Health Informatics)

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22 pages, 5316 KB

Open AccessArticle

Hybrid Multifractal-Based Machine Learning Framework for Glaucoma Diagnostics from Retinal Images

by Vladislav Salmiyanov and Anna Maslovskaya

Informatics 2026, 13(7), 102; https://doi.org/10.3390/informatics13070102 - 25 Jun 2026

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Abstract

Glaucoma is a leading cause of irreversible vision loss, and its early diagnosis remains critically important yet challenging. Traditional assessment based on the cup-to-disc ratio is often insufficient at early stages, whereas the retinal vascular network can provide additional quantitative biomarkers. This study [...] Read more.

Glaucoma is a leading cause of irreversible vision loss, and its early diagnosis remains critically important yet challenging. Traditional assessment based on the cup-to-disc ratio is often insufficient at early stages, whereas the retinal vascular network can provide additional quantitative biomarkers. This study develops and validates a binary classification method for distinguishing healthy from glaucomatous fundus images by combining deep-learning-based vessel segmentation, fractal and multifractal analysis, and textural features. The public ORIGA dataset is utilized. Images are converted to grayscale using three alternative approaches, followed by Gray-Level Co-occurrence Matrix texture analysis and fractal analysis based on the differential box-counting method. Vessel segmentation is implemented via a U-Net neural network trained on a combination of public datasets, after which multifractal analysis is performed on the resulting binary masks. The extracted features are used to train and compare several machine learning models with hyperparameter optimization. The best-performing model among ONH-based features (Random Forest) achieves 75.00%; however, a logistic regression model using multifractal parameters and CDR reaches 86.17%, substantially outperforming the CDR-only baseline (66.15%). Notably, while classical fractal dimension shows only marginal differences (1–2% relative change) between groups, multifractal parameters reveal distinct changes: the multifractal spectrum width

Δ α

increases markedly and the minimum singularity exponent

α_{\min}

decreases in glaucomatous eyes, indicating increased heterogeneity of the vascular network. These findings suggest that multifractal characteristics of the vascular network can serve as reliable and sensitive biomarkers for automated glaucoma screening, offering clear advantages over classical fractal analysis. Full article

(This article belongs to the Special Issue Health Data Management in the Age of AI)

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1 pages, 129 KB

Open AccessCorrection

Correction: Jandaeng et al. TERA: A Trade-Off Evaluation and Resource-Aware Framework for Spam and Phishing Email Detection. Informatics 2026, 13, 72

by Chanankorn Jandaeng, Peeravit Koad, Mohamad Fadli Zolkipli and Jurairat Phuttharak

Informatics 2026, 13(7), 101; https://doi.org/10.3390/informatics13070101 - 25 Jun 2026

Viewed by 436

Abstract

In the published publication [...] Full article

26 pages, 3632 KB

Open AccessSystematic Review

Digital Transformation in Green Finance: A Systematic Review of Business Informatics Frameworks for Green Bond Monitoring in the Circular Economy

by Riaman, Ema Carnia, Moch Panji Agung Saputra, Sukono, Nurnadiah Zamri, Nazla Aqira Maghfirani, Astrid Sulistya Azahra and Dede Irman Pirdaus

Informatics 2026, 13(7), 100; https://doi.org/10.3390/informatics13070100 - 24 Jun 2026

Viewed by 843

Abstract

The rapid growth of the green bond market has intensified the need for transparent and reliable monitoring systems, particularly in circular-economy environments characterized by complex, multi-stakeholder, and dynamic interactions. However, existing monitoring approaches still rely heavily on static, issuer-driven disclosures, which sustain information [...] Read more.

The rapid growth of the green bond market has intensified the need for transparent and reliable monitoring systems, particularly in circular-economy environments characterized by complex, multi-stakeholder, and dynamic interactions. However, existing monitoring approaches still rely heavily on static, issuer-driven disclosures, which sustain information asymmetry and increase the risk of greenwashing. This study systematically reviews the role of digital technologies in enhancing green bond monitoring within circular economy systems. A systematic literature review (SLR) was conducted using the Scopus database, covering publications from 2022 to 2026 and yielding 56 eligible studies. A bibliometric analysis using VOSviewer identified major research trends, thematic clusters, and collaboration patterns within the field. The findings reveal four dominant technological pillars—blockchain, artificial intelligence (AI), Internet of Things (IoT), and digital twin—that support data verification, automated analytics, real-time environmental monitoring, and system-wide integration. Although these technologies show significant potential, the literature remains fragmented and lacks comprehensive monitoring architectures that integrate technological, governance, and regulatory dimensions. This study contributes to the literature by synthesizing these technologies through a business informatics perspective and highlighting digital twin architectures as a promising foundation for integrated green bond monitoring. The findings provide practical insights for regulators, issuers, and investors seeking interoperable, transparent, and trustworthy monitoring ecosystems that strengthen accountability and credibility in sustainable finance. Full article

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15 pages, 4642 KB

Open AccessArticle

CHaRT: An Autoregressive Transformer for Joint Forecasting of Clinical Events and Continuous Values

by Michael Walz and Thomas F. Byrd IV

Informatics 2026, 13(7), 99; https://doi.org/10.3390/informatics13070099 - 23 Jun 2026

Viewed by 706

Abstract

Modern inpatient care generates irregular streams of heterogeneous clinical events, yet most predictive models require fixed feature matrices, predefined time windows, or discretization of continuous measurements. We developed CHaRT, a decoder-only autoregressive transformer designed to jointly forecast the identity of the next clinical [...] Read more.

Modern inpatient care generates irregular streams of heterogeneous clinical events, yet most predictive models require fixed feature matrices, predefined time windows, or discretization of continuous measurements. We developed CHaRT, a decoder-only autoregressive transformer designed to jointly forecast the identity of the next clinical event and, when applicable, its associated continuous value. CHaRT was trained and internally validated on structured electronic health record data from adult acute-care encounters across a 12-hospital health system in Minnesota from 2001 to 2025. The final corpus included 4,447,625 encounters from 1,301,502 patients and 701,556,877 non-padding clinical event tokens spanning vital signs, laboratory values, medications, diagnoses, microbiology, virology, imaging, fluids, and outcomes (ICU transfer or death). Encounters were split into training, validation, and test sets before vocabulary construction, normalization, and windowing. On the held-out test set, CHaRT achieved Top-1, Top-5, and Top-10 next-event accuracies of 51.61%, 87.34%, and 93.22%, respectively, with perplexity 4.50 and expected calibration error 0.0109. For numeric prediction, z-score MSE was 0.3812 for vital signs and 0.5713 for laboratory values. Seeded examples generated clinically coherent trajectories. Using model representations, a linear probe predicted deterioration (ICU transfer or in-hospital death) at a 6 h landmark with AUROC 0.95–0.97, indicating that learned representations transfer to downstream clinical risk prediction. Full article

(This article belongs to the Special Issue From Data to Evidence: Transformative AI for Real-World Data)

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