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16 pages, 694 KB

Open AccessArticle

Predictors of In-Hospital Mortality Among Hospitalized Colistin-Treated Patients at a Tertiary Urology–Nephrology Referral Center

by Viorel Dragos Radu, Ana-Maria Raluca Pauna, Rodica Radu, Liliana Mititelu-Tartau, Alin Mihai Vasilescu, Theodor Florin Pantilimonescu, Adriana Grigoraș, Laura Florea, Marius Constantin Moraru, Liliana Chelaru, Liviu Ciprian Gavril, Roxana Florentina Gavril, Beatrice Rozalina Buca, Angy Abu Koush and Irina Luanda Mititiuc

Antibiotics 2026, 15(9), 866; https://doi.org/10.3390/antibiotics15090866 (registering DOI) - 4 Sep 2026

Abstract

Background/Objectives: Colistin is a last-resort agent against multidrug-resistant Gram-negative infections and is administered predominantly to complex, comorbid inpatients in whom mortality is high. We aimed to identify independent baseline predictors of in-hospital mortality in colistin-treated patients and to characterize temporal changes in [...] Read more.

Background/Objectives: Colistin is a last-resort agent against multidrug-resistant Gram-negative infections and is administered predominantly to complex, comorbid inpatients in whom mortality is high. We aimed to identify independent baseline predictors of in-hospital mortality in colistin-treated patients and to characterize temporal changes in mortality over 15 years. Methods: We conducted a retrospective cohort study of 1225 consecutive patients receiving systemic colistin between November 2009 and November 2024 at Parhon Hospital, a tertiary urology–nephrology center in Iași, Romania; independent predictors of death were identified using multivariable logistic regression restricted to variables fixed at the time of colistin initiation, reported as adjusted odds ratios (aOR) with 95% confidence intervals (CI). Results: Patients were predominantly male (60.2%), with a mean age of 65.1 ± 14.4 years; 85.6% were admitted to urology or nephrology. In-hospital mortality was 27.6%. Mortality was independently associated with acute kidney injury (aOR 3.72, 95% CI 2.76–5.01), sepsis (aOR 2.79, 95% CI 2.11–3.70), emergency admission (aOR 1.91, 95% CI 1.40–2.61), Charlson comorbidity index (aOR 1.13 per point, 95% CI 1.05–1.22), and older age (aOR 1.03 per year, 95% CI 1.02–1.04); sex was not associated. Crude mortality increased across the study period (odds ratio 1.16 per year, 95% CI 1.13–1.20) and remained elevated after adjustment for case-mix (adjusted odds ratio 1.13 per year, 95% CI 1.09–1.17). Conclusions: Acute kidney injury, sepsis, emergency admission, comorbidity burden, and age were independently associated with in-hospital death. Mortality rose progressively over 15 years, only partly explained by an increasingly severe case-mix, supporting early risk stratification of colistin recipients. Full article

29 pages, 818 KB

Open AccessArticle

The Dynamics of Trust-Related Belief in Health Informatics: A Structural Equation Modeling of Health Information Adoption in Indonesia

by Arya Wijaya Pramodha Wardhana, Laksmi Laksmi, Susi Haryanti, Nina Mayesti and Kaur Kiran

Informatics 2026, 13(9), 143; https://doi.org/10.3390/informatics13090143 (registering DOI) - 4 Sep 2026

Abstract

The rapid digitalization of health resources highlights the need to understand health information adoption in Indonesia. This study examines trust-related HBM belief pathways and Health Information Access Behavior (HIAB) as both a direct predictor and a moderator. Survey data from 260 respondents across [...] Read more.

The rapid digitalization of health resources highlights the need to understand health information adoption in Indonesia. This study examines trust-related HBM belief pathways and Health Information Access Behavior (HIAB) as both a direct predictor and a moderator. Survey data from 260 respondents across urban, suburban, and rural settings were analyzed using PLS-SEM, with 20 semi-structured interviews used for interpretive context. Perceived benefits, cues to action, and self-efficacy significantly increased adoption, and HIAB had a significant positive direct effect. Moderation was limited: only the perceived barriers and HIAB interaction was significant and negative (β = −0.123, p = 0.023), while the other five interactions were not significant. Thus, access behavior does not uniformly strengthen trust-related HBM pathways; its moderating role is specific to perceived barriers. Full article

19 pages, 1447 KB

Open AccessArticle

Differential Modulation of the DR5–JNK–PD-L1 Signaling Axis by TRiC/CCT Subunits CCT7 and CCT2 Shapes Tumor Immune Evasion in Lung Adenocarcinoma

by Hsin-Wei Jen, Hsiang-Ling Ho and Teh-Ying Chou

Cells 2026, 15(17), 1613; https://doi.org/10.3390/cells15171613 (registering DOI) - 4 Sep 2026

Abstract

While the chaperonin-containing TCP-1 (CCT) complex is essential for proteostasis, the distinct roles of individual subunits in tumor immune regulation remain unclear. Here, we identify CCT7 as a previously unrecognized regulator of immune evasion in lung adenocarcinoma (LUAD). Integrative analyses of TCGA and [...] Read more.

While the chaperonin-containing TCP-1 (CCT) complex is essential for proteostasis, the distinct roles of individual subunits in tumor immune regulation remain unclear. Here, we identify CCT7 as a previously unrecognized regulator of immune evasion in lung adenocarcinoma (LUAD). Integrative analyses of TCGA and GEO cohorts revealed that CCT7 is markedly upregulated in LUAD and is associated with poor patient prognosis. Functional studies demonstrated that CCT7 knockdown inhibited tumor cell proliferation and migration and enhanced cisplatin-induced apoptosis, yet paradoxically impaired T-cell activation. Mechanistically, transcriptomic and biochemical analyses revealed that CCT7 depletion activated the DR5–MKK4–JNK–c-Jun signaling cascade, resulting in the transcriptional upregulation of PD-L1. Disruption of DR5 or JNK signaling effectively abrogated PD-L1 induction. In contrast, CCT2 depletion exerted the opposite effect by suppressing the DR5–JNK–c-Jun–PD-L1 signaling axis and enhancing T-cell activation. Collectively, these findings reveal unexpected functional divergence among TRiC/CCT subunits and identify the CCT7–DR5–JNK–c-Jun signaling axis as a previously unrecognized mechanism regulating PD-L1-mediated immune evasion, highlighting the potential therapeutic relevance of this signaling axis in LUAD. Full article

(This article belongs to the Section Cellular Immunology)

19 pages, 2351 KB

Open AccessArticle

Software-Defined UHF RFID Asset Tracking in Metallic Aircraft Cabins via IMU-Assisted Adaptive Kalman Filtering and Distilled Edge Intelligence

by Melis Karadag and Ozgun Pinarer

Sensors 2026, 26(17), 5639; https://doi.org/10.3390/s26175639 (registering DOI) - 4 Sep 2026

Abstract

Passive Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) systems deployed in metallic commercial aircraft cabins suffer from severe multipath fading, non-stationary channel dynamics, and operator gait-induced signal jitter. Addressing these challenges without physical airframe modifications or regulatory recertification remains a critical operational bottleneck. [...] Read more.

Passive Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) systems deployed in metallic commercial aircraft cabins suffer from severe multipath fading, non-stationary channel dynamics, and operator gait-induced signal jitter. Addressing these challenges without physical airframe modifications or regulatory recertification remains a critical operational bottleneck. This paper presents an edge-native, software-defined framework that integrates micro-electromechanical system (MEMS) inertial measurements with an IMU-assisted Adaptive Kalman Filter (AKF) and a distilled surrogate decision tree. The proposed algorithm extracts localized motion energy (

E_{IMU}

) to dynamically scale the measurement noise covariance (

R_{k}

) prior to physical-layer signal corruption, thereby eliminating phase lag and power hunting. For deterministic edge execution on COTS handheld devices, surrogate model distillation compresses a parent Random Forest ensemble into an

8.2 KB

13-leaf decision tree (depth 5) yielding

0.12 ms

inference latency. Empirical validation across 17 operational sessions in Airbus A320, Boeing 737, and Airbus A321 cabins (10,720 valid reads) demonstrates a

99.45 %

mean RSSI jitter reduction (

95 % CI : [99.21 %, 99.63 %]

) and a

7.30 \times

suppression of transmit power oscillations. Statistically, asset detection completeness is fully preserved (

0.791

vs.

0.795

baseline,

z = 0.281, p = 0.779

). Operating entirely within standard handheld software runtimes, this approach bypasses Supplemental Type Certificate (STC) requirements while ensuring robust aerospace asset visibility. Full article

(This article belongs to the Special Issue Sensors and Sensing Technologies for Traffic, Driving and Transportation)

14 pages, 350 KB

Open AccessArticle

Eat, Pray, Love: Sacrifice-Eating and Ritual Sexuality as Unifying Motifs in 1 Corinthians 8–11

by Benjamin D. Giffone

Religions 2026, 17(9), 1043; https://doi.org/10.3390/rel17091043 (registering DOI) - 4 Sep 2026

Abstract

The cultic practices of ancient Israel, specifically with respect to ritual eating and sex, serve jointly as a unifying motif for 1 Corinthians 8–11. Eating and sexual intercourse are viewed as sacred acts because of their generative capacity. Sex and food were highly [...] Read more.

The cultic practices of ancient Israel, specifically with respect to ritual eating and sex, serve jointly as a unifying motif for 1 Corinthians 8–11. Eating and sexual intercourse are viewed as sacred acts because of their generative capacity. Sex and food were highly regulated in ancient Israel, yet eating was an essential part of Israelite ritual, while sex was forbidden. Paul’s discussion of various issues in 1 Corinthians 8–11 is linked in his discourse by the common thread of the generative and communicative power of food and sex. Full article

67 pages, 3136 KB

Open AccessReview

Wearable Sensing for Personal Thermal Comfort in the Built Environment: A Systematic Review of the Gap from Sensing to Actuation

by Yorgos Spanodimitriou, Khawaja Talha Ejaz, Giovanni Ciampi, Michelangelo Scorpio, Massimiliano Masullo, Antonio Rosato, Luigi Maffei and Sergio Sibilio

Sensors 2026, 26(17), 5640; https://doi.org/10.3390/s26175640 (registering DOI) - 4 Sep 2026

Abstract

Wearable sensors enable continuous, non-invasive monitoring of physiological and near-body environmental parameters, offering an occupant-centric alternative for thermal comfort assessment and building control. This systematic review analyses 117 studies (2008–2026) using body-worn sensors, tracing them along a four-stage pipeline: sensing, signal integration, comfort [...] Read more.

Wearable sensors enable continuous, non-invasive monitoring of physiological and near-body environmental parameters, offering an occupant-centric alternative for thermal comfort assessment and building control. This systematic review analyses 117 studies (2008–2026) using body-worn sensors, tracing them along a four-stage pipeline: sensing, signal integration, comfort modeling, and building actuation. This reveals a previously unquantified bottleneck: while all 117 studies perform sensing and 83 (71%) integrate physiological and environmental signals, only 52 (44%) build predictive models and just five (4%) reach the building-actuation stage, of which only three implement closed-loop, wearable-informed control. Skin temperature (77 studies, 66%) and heart rate (69, 59%) are the most monitored signals, predominantly at the wrist (70, 60%); multimodal configurations are associated with higher accuracy than single-domain approaches. Machine learning models reach median classification accuracies near 90%, though validation strategy matters: leave-one-subject-out reaches 85% versus 90% for within-subject k-fold. Five structural limitations are identified: small, homogeneous samples (median 16 participants), laboratory-dominated designs (73 studies, 62%), inconsistent validation, limited open data, and unresolved multi-occupant aggregation. The field has learned to measure the occupant but not yet to act on the measurement. Closing this gap requires open benchmark datasets, standardized validation including leave-one-subject-out testing and PMV benchmarking, transfer learning, multi-occupant integration, and inclusive recruitment of vulnerable populations. Full article

(This article belongs to the Special Issue Wearable Sensors and Wireless Devices for Human-Centric Monitoring Systems)

29 pages, 3836 KB

Open AccessReview

Review of Key Technologies for Harvesting Embodied Intelligent Robots in Facility Horticulture: From Multimodal Active Sensing to Self-Evolving Closed Loops

by Yue Sun, Weidong Jia, Yunfei Wang, Minmin Wu, Wei Zhong, Ziyu Li and Wenrui Zhu

Appl. Sci. 2026, 16(17), 8822; https://doi.org/10.3390/app16178822 (registering DOI) - 4 Sep 2026

Abstract

Embodied intelligence offers a system-level paradigm for improving the adaptability of robotic harvesting in complex horticultural environments by coupling perception, decision-making, and physical interaction. This review systematically analyzes recent advances in embodied intelligence for robotic harvesting. A search of the Web of Science [...] Read more.

Embodied intelligence offers a system-level paradigm for improving the adaptability of robotic harvesting in complex horticultural environments by coupling perception, decision-making, and physical interaction. This review systematically analyzes recent advances in embodied intelligence for robotic harvesting. A search of the Web of Science Core Collection covering January 2020 to August 2026 identified 294 records, of which 100 studies were included after screening. Based on the reviewed literature, a literature-grounded Four-Tier framework is proposed, comprising Multimodal Active Sensing, Semantic Context Recognition and Understanding, Knowledge-Driven Decision-Making and Experience Evolution, and Skilled and Compliant Execution. The review shows that embodied approaches have improved individual capabilities across perception, decision-making, and manipulation; for example, representative studies reported over 12% improvement in detection accuracy, reduction in fruit damage from 8.2% to 2.0%, and 89% retention in continual learning. However, reliable autonomous harvesting remains constrained by cross-module uncertainty, real-time crop–robot interaction, continual adaptation, and the lack of standardized benchmarks. Future research should emphasize physically grounded multimodal closed-loop integration, experience-driven adaptation, standardized evaluation, and crop–robot co-design. Full article

(This article belongs to the Special Issue Technologies and Techniques for the Enhancement of Agriculture 4.0)

20 pages, 3314 KB

Open AccessArticle

Beyond NAD Depletion: SARM1-Induced ATP Collapse Involves Direct ATP Degradation and Mitochondrial Dysfunction and Is Pharmacologically Reversible

by Zhuo Chen, Zhi Ying Zhao, Zi-Mei Zhang, Ruoyu Zhao, Linru Shen, Hon Cheung Lee, Lei Fang and Yong Juan Zhao

Biology 2026, 15(17), 1552; https://doi.org/10.3390/biology15171552 (registering DOI) - 4 Sep 2026

Abstract

Sterile alpha and Toll/interleukin-1 receptor motif-containing protein 1 (SARM1) is an inducible NAD-consuming enzyme and execution factor in axon degeneration. Rapid ATP collapse after SARM1 activation, however, is not fully explained by NAD depletion alone. We used SARM1-overexpressing HEK293 cells and the cell-permeant [...] Read more.

Sterile alpha and Toll/interleukin-1 receptor motif-containing protein 1 (SARM1) is an inducible NAD-consuming enzyme and execution factor in axon degeneration. Rapid ATP collapse after SARM1 activation, however, is not fully explained by NAD depletion alone. We used SARM1-overexpressing HEK293 cells and the cell-permeant activator CZ-48 to examine SARM1-induced non-apoptotic cell death, termed sarmoptosis. CZ-48 induced cell death that was suppressed by HSP90/70-annotated ATP-competitive compounds, especially geldanamycin and VER-155008 (VER), without reducing SARM1 abundance. VER preserved NAD and ATP during SARM1 activation but failed to rescue FK866-mediated NAD starvation, thereby distinguishing CZ-48/SARM1-driven cytotoxicity from generic NAD depletion. In cell-free assays, purified SARM1 reduced ATP levels; this effect was enhanced by SARM1’s activator NMN and attenuated by its pharmacological inhibitors, although the in vitro activity was modest and the reaction products remain to be identified. ATPase-related perturbations, including thapsigargin and bafilomycin A1, also protected cells from CZ-48-induced death, further supporting a central role for ATP collapse in sarmoptosis. iTRAQ proteomics, MitoSOX Red staining, and DiOC6(3) staining revealed that CZ-48 treatment was associated with mitochondrial and metabolic remodeling, mitochondrial ROS accumulation, and mitochondrial depolarization, all of which were mitigated by VER. Collectively, these findings support a convergent ATP-collapse model in which SARM1 activation promotes NAD depletion, directly consumes ATP, and is associated with mitochondrial dysfunction that may amplify ATP-production failure. Full article

(This article belongs to the Special Issue Advances in Redox Metabolism and Cellular Homeostasis)

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28 pages, 2924 KB

Open AccessReview

Neural Mechanisms and Brain Alterations in Orofacial Pain: A Narrative Review of Human Neuroimaging

by Nelson Freitas, Carlos Silva Faria and Daniel Humberto Pozza

Diagnostics 2026, 16(17), 2851; https://doi.org/10.3390/diagnostics16172851 (registering DOI) - 4 Sep 2026

Abstract

Orofacial pain is a complex and multifactorial condition that affects quality of life and presents important diagnostic and therapeutic challenges. Advances in neuroimaging techniques have enabled the investigation of nervous system alterations associated with pain perception and modulation. This review synthesizes current evidence [...] Read more.

Orofacial pain is a complex and multifactorial condition that affects quality of life and presents important diagnostic and therapeutic challenges. Advances in neuroimaging techniques have enabled the investigation of nervous system alterations associated with pain perception and modulation. This review synthesizes current evidence on structural, functional, and neurochemical brain alterations identified through magnetic resonance imaging (MRI) in individuals with acute and chronic orofacial pain conditions. A comprehensive literature search of PubMed, Web of Science, and Scopus identified MRI studies (structural, diffusion, functional) in adults with temporomandibular disorders, trigeminal neuralgia, persistent dentoalveolar pain, or experimental pain. The main results demonstrated alterations along peripheral trigeminal pathways, brainstem nuclei, and central pain processing networks. Additionally, it was also possible to verify altered gray- and white matter integrity, causing disrupted connectivity within large-scale networks related to pain modulation and cognitive processing. While acute pain reflects transient activation of these pathways, chronic and neuropathic conditions can involve persistent structural and functional reorganization across sensory, affective, and cognitive networks. These findings support the involvement of distributed neural mechanisms and neuroplastic changes in many chronic orofacial pain conditions, while emphasizing that peripheral, central, psychosocial, and contextual factors may contribute to varying degrees across disorders and individuals. Multimodal MRI may provide a basis for future diagnostic, prognostic, and treatment response biomarkers, although most advanced MRI-derived markers remain investigational. Full article

(This article belongs to the Special Issue Progress in Chronic Pain: Bridging Basic and Clinical Research)

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19 pages, 1123 KB

Open AccessArticle

Moisture-Dependent Dielectric Indicators for Retrofilling Criteria from Mineral Oil to Natural Ester and Palm Fatty Acid Ester in Kraft, TUK, and DPE Paper Insulation Systems

by Ismael Antolin, Diego Garcia, Felix Ortiz, Fernando Delgado and Alfredo Ortiz

Appl. Sci. 2026, 16(17), 8823; https://doi.org/10.3390/app16178823 (registering DOI) - 4 Sep 2026

Abstract

The dielectric performance of power transformer insulation systems is strongly influenced by the type of cellulose paper, the impregnating dielectric liquid, and the paper moisture content. This study evaluates the moisture-dependent dielectric response of three cellulose insulating papers, Kraft, Thermally Upgraded Kraft, and [...] Read more.

The dielectric performance of power transformer insulation systems is strongly influenced by the type of cellulose paper, the impregnating dielectric liquid, and the paper moisture content. This study evaluates the moisture-dependent dielectric response of three cellulose insulating papers, Kraft, Thermally Upgraded Kraft, and Diamond Printed Enhanced, impregnated with three dielectric liquids: mineral oil, a rapeseed-based natural ester, and a Palm Fatty Acid Ester. Paper samples were first conditioned at controlled moisture contents ranging from 1% to 5%, then impregnated under stabilized conditions, and finally characterized by Frequency Domain Spectroscopy. The results show that increasing the moisture content produces a systematic rise in the dielectric dissipation factor, indicating higher dielectric losses and reduced insulation performance. The relative dielectric benefit of the ester liquids was strongly dependent on paper type, moisture content, and frequency. This paper-dependent behavior was particularly pronounced for Kraft and Thermally Upgraded Kraft papers. Finally, laboratory-derived moisture-dependent 50 Hz tan δ crossover points were identified as dielectric indicators for retrofilling assessment. These findings provide comparative dielectric information for the investigated paper–liquid insulation systems and may support preliminary retrofilling assessment under controlled laboratory conditions. Full article

(This article belongs to the Special Issue 15th Anniversary of Applied Sciences—Emerging Applications in the Recent 2 Years)

15 pages, 5340 KB

Open AccessArticle

Digitized Physical Impressions Optimize Apical Adaptation of Bio-Root Inlays for Apexification: A Comparison of Four Impression Techniques

by Yasser Alsayed Tolibah, Wahid Juha, Mhd Kheir Awad, Mohammad Tamer Abbara, Marwan Alhaji, Jihad Abou Nassar, Osama Aljabban, Nada Bshara and Ziad D. Baghdadi

Dent. J. 2026, 14(9), 568; https://doi.org/10.3390/dj14090568 (registering DOI) - 4 Sep 2026

Abstract

Objectives: To evaluate the three-dimensional adaptation of prefabricated Bio-Root inlays fabricated from impressions of simulated immature permanent root canals using four techniques: direct intraoral scanning (A), a light-body rubber impression digitized with an intraoral scanner (B), cone-beam computed tomography (CBCT)-based design (C), [...] Read more.

Objectives: To evaluate the three-dimensional adaptation of prefabricated Bio-Root inlays fabricated from impressions of simulated immature permanent root canals using four techniques: direct intraoral scanning (A), a light-body rubber impression digitized with an intraoral scanner (B), cone-beam computed tomography (CBCT)-based design (C), and a conventional indirect technique (D). Methods: Ten standardized simulated immature roots (extracted mandibular premolars prepared to a 1.5 mm apical diameter and 10 mm length) were used in a within-specimen comparative design, with each root receiving inlays fabricated by all four techniques. After fabrication and seating of the bioceramic inlays, the void percentage around each inlay was quantified by CBCT at the coronal, middle, and apical thirds relative to the baseline empty-canal volume. Data were analyzed using the Friedman, Wilcoxon signed-rank (Bonferroni-corrected), and linear mixed-model tests (α = 0.05). Results: Technique significantly affected void percentage (F(3,98) = 12.87, p < 0.001). Pooled across all canal thirds, Technique B had the lowest median void percentage (6.00 [7.00]), followed by A (8.00 [8.75]), C (12.00 [12.50]), and D (12.00 [19.00]). Independently of technique, the median void percentage increased from the coronal (6.00) to the middle (11.00) and apical (19.00) thirds. Differences among techniques were non-significant coronally (p = 0.437) but significant in the middle (p = 0.014) and apical (p < 0.001) thirds, with Technique B providing the best apical adaptation. Conclusions: The impression method significantly affected inlay adaptation. A light-body rubber impression digitized with an intraoral scanner yielded the best overall and apical fit, supporting digital workflows, particularly those using digitized physical impressions, for apexification. Adaptation deteriorated toward the apex regardless of technique. Full article

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31 pages, 1625 KB

Open AccessArticle

Algorithmic Fairness as a Risk-Management Problem in Banking and Insurance: Regulatory Frameworks, Model Governance, and Fairness-Aware Credit Scoring

by Paulo Alcarva

Risks 2026, 14(9), 205; https://doi.org/10.3390/risks14090205 (registering DOI) - 4 Sep 2026

Abstract

AI-driven credit scoring is supervised as a high-risk application in banking and insurance, yet unfairness is rarely operationalized as a measurable category of model, conduct, legal, and reputational risk. Using 20,000 anonymized applications from a Southern European digital lender (15.2% twelve-month default rate), [...] Read more.

AI-driven credit scoring is supervised as a high-risk application in banking and insurance, yet unfairness is rarely operationalized as a measurable category of model, conduct, legal, and reputational risk. Using 20,000 anonymized applications from a Southern European digital lender (15.2% twelve-month default rate), we estimate three model families—a regularized logistic regression, a gradient-boosting machine, and a multi-layer perceptron—under a fully crossed design in which each family is evaluated without mitigation and under pre-processing (reweighing), in-processing (an exponentiated-gradient reduction, applicable to any base learner, together with adversarial debiasing where gradient-based training permits it), and post-processing (reject-option) interventions, so that the mitigation effect is no longer confounded with the choice of estimator. No sensitive-group field enters any estimated specification; group membership is used exclusively for auditing. Predictive performance (AUC-ROC, Brier score and Brier skill score relative to the base-rate forecast, F1 on the default class, Gini, and the Kolmogorov–Smirnov statistic) is reported jointly with group fairness (demographic-parity and equal-opportunity differences, disparate-impact ratio, Theil index) and with group-conditional calibration, at an explicitly stated and economically justified decision threshold. Every fairness quantity is accompanied by stratified-bootstrap confidence intervals and, for stochastic learners, by seed-level dispersion. The interpretable benchmark attains an AUC of 0.780 and a Brier score of 0.104 against 0.129 for the constant base-rate forecast, and the high-capacity models improve on it by under one AUC point. Disparity is present but is located geographically rather than in the composite group label: the disparate-impact ratio is 0.724 [0.693, 0.754] for the lowest socio-economic neighborhood cluster, excluding the four-fifths screening value, against 0.809 [0.776, 0.840] for the ethno-socioeconomic proxy, whose interval contains it, and no measurable gender disparity. Group membership is recoverable from the neutral feature set at an AUC of 0.654, and 42% of the group gap in predicted risk travels through the bureau credit score alone, so feature deletion cannot close the channel. Feature attributions and an auxiliary group-recoverability test locate the proxy pathways through which disparity arises, and a misclassification-sensitivity analysis bounds the effect of error in the group proxy, which attenuates measured disparity toward parity. We map the results onto Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744, the GDPR as interpreted in SCHUFA Holding, Directive (EU) 2023/2225, EBA loan-origination guidance, and Solvency II, EIOPA, and IAIS expectations, and propose fairness-risk controls organized around impact assessment, independent validation, and three lines of defense governance. Because the evidence comes from credit origination at a single lender, the insurance argument is developed at the level of regulatory and governance architecture rather than as an empirical transfer of estimates. Full article

(This article belongs to the Special Issue Banking and Insurance: New Perspectives on Regulatory Frameworks and Risk Management)

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47 pages, 18326 KB

Open AccessArticle

Frequency-Preserving Readout Design and Error Characterization for CWT-Based rPPG Heart-Rate Estimation

by Kota Toyama, Masato Takahashi and Norimichi Tsumura

Sensors 2026, 26(17), 5637; https://doi.org/10.3390/s26175637 (registering DOI) - 4 Sep 2026

Abstract

Remote photoplethysmography (rPPG) estimates heart rate (HR) from facial color variations. In continuous wavelet transform (CWT) scalograms, HR is represented by position along the frequency axis, whereas global average pooling (GAP) does not retain this coordinate explicitly. We investigated whether a structured frequency-localizing [...] Read more.

Remote photoplethysmography (rPPG) estimates heart rate (HR) from facial color variations. In continuous wavelet transform (CWT) scalograms, HR is represented by position along the frequency axis, whereas global average pooling (GAP) does not retain this coordinate explicitly. We investigated whether a structured frequency-localizing readout improves HR estimation and analyzed the remaining errors. Leave-one-subject-out evaluations were conducted on three datasets (PURE, UBFC-rPPG, and BH-rPPG) using a fixed chrominance-based signal-extraction and CWT pipeline. The proposed model combined a frequency-preserving convolutional neural network backbone with a soft-argmax readout over HR coordinates. Using the same backbone and training protocol, soft-argmax reduced segment-pooled mean absolute error (MAE) relative to the matched GAP readout from 8.09 to 2.98 beats per minute on PURE and from 5.14 to 3.82 beats per minute on UBFC-rPPG, with significant subject-level improvements on both datasets. Residual analyses indicated that major errors could originate from color projection, non-cardiac spectral components, motion, and HR-range mismatch. On BH-rPPG, the proposed model achieved the lowest MAE under low illumination, while the evaluated CWT-based estimators showed smaller illumination-induced degradation than the evaluated fast Fourier transform (FFT)-based estimators. These findings support structured soft-argmax readout design and highlight the need to detect or mitigate degraded input signals. Full article

(This article belongs to the Special Issue Advanced Biomedical Imaging and Signal Processing)

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23 pages, 1731 KB

Open AccessArticle

AI-Based Spatial Burn Assessment with MLLMs: Body Region, 3 × 3 Grid Classification and Burn Instance Counting from Photographs and Semantic Segmentation Masks

by Ibrahim Güler, Armin Kraus, Gerrit Grieb and Henrik Stelling

Biomedicines 2026, 14(9), 1996; https://doi.org/10.3390/biomedicines14091996 (registering DOI) - 4 Sep 2026

Abstract

Background: Burn photography is a stringent testbed for visual artificial intelligence (AI) in medical imaging, since it permits quantitative and qualitative clinically relevant parameters to be elicited. It is therefore a useful proxy for current vision AI. Reducing a photograph to a [...] Read more.

Background: Burn photography is a stringent testbed for visual artificial intelligence (AI) in medical imaging, since it permits quantitative and qualitative clinically relevant parameters to be elicited. It is therefore a useful proxy for current vision AI. Reducing a photograph to a semantic segmentation removes color, texture and anatomy while providing pixel-exact boundaries. How spatial assessment responds, and how far instance-level structure can be recovered from a map that does not encode it, is unknown. Methods: Three state-of-the-art multimodal large language models (MLLMs) each assessed 153 burn photographs five times under three input conditions: the photograph alone (IMAGE), its segmentation mask alone (MASK), or both combined (BOTH). Four items were elicited per image: burn presence, the burned body region, per-cell classification of a 3 × 3 grid, and the number of separate burns, a surrogate for instance-level recognition. Each model was compared across conditions, paired on the same images. Results: Body-region accuracy was 97.1–100.0% under IMAGE, 47.5–54.0% under MASK and 50.2–99.9% under BOTH. Grid-cell accuracy was 77.7–82.3%, 77.9–96.3% and 90.9–98.4%. On the burn count, exact-match was 60.5–66.3%, 61.3–96.6% and 84.7–97.4%, and no model significantly beat the trivial classifier under IMAGE, two of three did under MASK and all three under BOTH. Across 18 paired comparisons of BOTH against a single channel, BOTH was better in 11, worse in three and indistinguishable in four. Conclusions: Combining the two inputs improved spatial assessment in most comparisons and degraded it in others, consistently across neither models nor tasks. Additional visual information therefore does not deterministically improve model performance, and disruption remains possible. Full article

(This article belongs to the Special Issue The Evolving Landscape of Artificial Intelligence (AI) in Medical Imaging)

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34 pages, 7959 KB

Open AccessArticle

Learning Keypoint-Based Structural Metrics for Super-Resolution Image Quality Assessment

by Jakub Sadel, Tomasz Tarasiewicz, Pawel Kowaleczko, Maciej Ziaja, Daniel Kostrzewa, Pawel Benecki and Michal Kawulok

Electronics 2026, 15(17), 4010; https://doi.org/10.3390/electronics15174010 (registering DOI) - 4 Sep 2026

Abstract

Assessing the quality of super-resolved images is important for comparing reconstruction algorithms, but pixel fidelity, perceptual appearance, and structural preservation do not always agree. We investigate whether keypoint detector response maps and detected keypoints can act as trainable structural indicators for aligned full-reference [...] Read more.

Assessing the quality of super-resolved images is important for comparing reconstruction algorithms, but pixel fidelity, perceptual appearance, and structural preservation do not always agree. We investigate whether keypoint detector response maps and detected keypoints can act as trainable structural indicators for aligned full-reference super-resolution image quality assessment (SR-IQA). A contrastive Multi-Scale Index Proposal (MSIP) objective specializes Key.Net toward SR-like resolution loss, candidate checkpoints are screened without subjective labels, and six response- and keypoint-based measures are evaluated on four subjective benchmarks. The results show a redistribution rather than a uniform improvement: MSIP correlations increase on three of the four benchmarks, both repeatability variants decrease for every trained family on SISAR and RealSRQ, and general keypoint performance on HPatches decreases for trained checkpoints. At the benchmark level, established comparators such as TOPIQ-FR, RQI, and DISQ remain stronger, and on three of the four datasets the best keypoint-based result is still obtained with the pretrained detector. The measures nevertheless retain quality-related variation after conditioning on ten IQA controls in 88 of 120 tested hypotheses, with markedly weaker evidence on RealSRQ. Their practical value is therefore diagnostic: the response maps localize the structures behind an HR–SR discrepancy, complementing rather than replacing established SR-IQA metrics. Full article

(This article belongs to the Special Issue Advanced Computer Science and Intelligent Systems Innovations)

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28 pages, 3065 KB

Open AccessArticle

Sustainable Wheat Production Through Precision Agriculture: An LCA Case Study in Italy

by Claudia Camplone, Luisa Paolotti, Francesco Romagnoli, Sara Allahverdiveva, Renzo Santi, Lorenzo Covarelli and Lucia Rocchi

Land 2026, 15(9), 1646; https://doi.org/10.3390/land15091646 (registering DOI) - 4 Sep 2026

Abstract

The growing demand for food is placing growing pressure on agricultural systems, raising concerns about their environmental sustainability. In this context, precision agriculture represents a promising approach for improving resource-use efficiency while maintaining high yields. This study aimed to assess the environmental performance [...] Read more.

The growing demand for food is placing growing pressure on agricultural systems, raising concerns about their environmental sustainability. In this context, precision agriculture represents a promising approach for improving resource-use efficiency while maintaining high yields. This study aimed to assess the environmental performance of soft wheat production during the transition from conventional to smart and precision farming under real farm conditions. A Life Cycle Assessment (LCA) was conducted following ISO 14040 and 14044 standards using primary data collected from a farm located in Umbria (Italy). Two consecutive three-year production periods were compared: conventional farming (2019–2021) and smart and precision farming (2022–2024). Environmental impacts were assessed using the Environmental Footprint 3.1 method. The transition was associated with a 20.8% reduction in the overall environmental impact. This improvement reflects the combined effects of adopting smart and precision agriculture technologies together with the renewal of farm machinery, which results in lower fuel consumption and more efficient fertilizer and pesticide management. Most impact categories showed improvements, particularly climate change, ecotoxicity, and particulate matter. However, freshwater eutrophication increased due to changes in fertilization strategy, including reduced nitrogen fertilizer use and increased phosphorus fertilizer application. Sensitivity analyses confirmed the consistency of the results; despite some influence on yield variation, the overall comparison remained consistent under the tested scenarios. These findings highlight the potential of smart and precision agriculture to improve the environmental sustainability of soft wheat production under real farm conditions. Full article

(This article belongs to the Special Issue Feature Papers on "Land Use, Impact Assessment and Sustainability" (2nd Edition))

23 pages, 881 KB

Open AccessArticle

Occurrence and Risk Assessment of Tetracyclines and Their Transformation Products in Organic Amendments

by Noelia García-Criado, Juan Luis Santos, Julia Martín, Irene Aparicio and Esteban Alonso

Antibiotics 2026, 15(9), 865; https://doi.org/10.3390/antibiotics15090865 (registering DOI) - 4 Sep 2026

Abstract

Background/Objectives: The application of organic soil amendments constitutes an important source of antibiotic residues in agricultural soils. However, studies have mainly focused on a few parent tetracyclines, whereas information on their transformation products (TPs) in organic amendments remains scarce. Therefore, this study [...] Read more.

Background/Objectives: The application of organic soil amendments constitutes an important source of antibiotic residues in agricultural soils. However, studies have mainly focused on a few parent tetracyclines, whereas information on their transformation products (TPs) in organic amendments remains scarce. Therefore, this study assessed the occurrence and environmental risk of six tetracyclines and seven TPs in organic amendments. Methods: Processed livestock manures applied in the European Union (horse, poultry, and bovine manure), as well as fresh and treated sewage sludge, were analyzed using matrix solid-phase dispersion (MSPD) combined with online SPE-LC-MS/MS. Environmental risk was assessed using risk quotients (RQs) based on predicted environmental concentrations in soil (PEC_soil) calculated from the maximum measured concentrations and predicted no-effect concentrations in soil (PNEC_soil) obtained either directly from terrestrial ecotoxicity data or derived from aquatic ecotoxicity data using the equilibrium partitioning method and soil-water distribution coefficients. Results: Tetracyclines and their TPs were widely detected in both sample types, although concentrations varied according to matrix type and treatment. Overall, sludge showed higher detection frequencies and concentrations than manure. Doxycycline and tetracycline were the predominant parent compounds, reaching concentrations up to 2838 ng g⁻¹ dry weight (dw) in manure and 2892 ng g⁻¹ dw in sludge. Epimerized TPs were frequently detected and sometimes exceeded the concentrations of their parent compounds, especially epitetracycline and epioxytetracycline. Among the sludge types and treatment conditions investigated, anaerobically digested sludge showed the highest tetracycline concentrations, whereas the composted sludge sample presented the lowest concentrations. Individual RQs indicated insignificant to low ecotoxicological risk, whereas cumulative RQ (ΣRQ) values, used as conservative estimates of co-exposure to all evaluated tetracyclines and their TPs, fell within the medium-risk category for bovine manure and anaerobically digested sludge, with the composted sludge sample showing the lowest ΣRQ. Conclusions: These findings highlight the importance of including TPs in environmental monitoring and the differences in tetracycline occurrence and environmental risk to soil among the processed manure types and sludge treatment conditions investigated. Full article

(This article belongs to the Special Issue Antibiotics and Derived Compounds in the Soil–Plant System: Sources of Contamination, Analytical Methods, Distribution and Risk Assessment)

35 pages, 4241 KB

Open AccessArticle

Effect of Mechanically Activated Fly Ash, a Superplasticizer, and an Air-Entraining Admixture on the Freeze–Thaw Resistance of Fine-Grained Concrete

by Nazerke Berdikul, Ina Pundienė, Kenzhebek Akmalaiuly, Jolanta Pranckevičienė, Elmira Kurmanbekova, Yerlan Khamza, Aigerim Tolegenova and Assel Kanarbay

Materials 2026, 19(17), 3777; https://doi.org/10.3390/ma19173777 (registering DOI) - 4 Sep 2026

Abstract

This study examines how mechanically activated fly ash (TF), a polycarboxylate superplasticizer (PC), and an air-entraining admixture (AE) affect the hydration, microstructure, pore structure, mechanical properties, and freeze–thaw resistance of fine-grained concrete. Mechanical activation increased the specific surface area of the untreated ash [...] Read more.

This study examines how mechanically activated fly ash (TF), a polycarboxylate superplasticizer (PC), and an air-entraining admixture (AE) affect the hydration, microstructure, pore structure, mechanical properties, and freeze–thaw resistance of fine-grained concrete. Mechanical activation increased the specific surface area of the untreated ash (UF) from 3710 to 6450 cm²/g and reduced its mean particle size to 6.06 μm. Replacing 5 wt.% of cement with TF accelerated the nucleation of hydration products and densified the microstructure, as confirmed by exothermic profiling, XRD, and SEM. PC delayed the exothermic peak yet produced the highest heat-release intensity, indicating more efficient wetting and dispersion of reactive surfaces. The TF–PC system reached 39.3 MPa at 28 days, while the ternary TF–PC–AE composition achieved 33.2 MPa with a refined pore-size distribution (mercury intrusion porosimetry). The combined use of PC and AE tripled the pore volume in the 0.01–0.10 μm range and increased it by 28.7% (0.1–1 μm) and 46.21% (1.0–10 μm). The calculated potential freeze–thaw resistance rose from 54.25 to 61.97 and 72.83, confirming the beneficial role of 3–10 μm pores. After 110 accelerated freeze–thaw cycles, the optimized ternary composition lost only 0.55% of its mass. Full article

(This article belongs to the Section Construction and Building Materials)

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27 pages, 2567 KB

Open AccessArticle

Comparison and Analysis of Three Measures for High Average-Utility Itemset Mining

by Yumei Li, Qier Lan, Zhe Zhang, Huina Zhang, Xianbing Cao, Xin Wang and Shuai Liu

Mathematics 2026, 14(17), 3211; https://doi.org/10.3390/math14173211 (registering DOI) - 4 Sep 2026

Abstract

High average-utility itemset mining is a significant research direction in data mining. The traditional average-utility (AU) measure employs itemset length as the normalization benchmark, which mitigates the bias toward long itemsets; however, it does not adequately account for the discrepancies between unit profit [...] Read more.

High average-utility itemset mining is a significant research direction in data mining. The traditional average-utility (AU) measure employs itemset length as the normalization benchmark, which mitigates the bias toward long itemsets; however, it does not adequately account for the discrepancies between unit profit and actual sales volume. This paper introduces two novel measures: the weighted average utility based on external utility (E_WAU) and that based on internal utility (I_WAU), and examines the theoretical relationships among AU, E_WAU, and I_WAU. Subsequently, we establish a three-dimensional evaluation framework, namely, “category average–profit conversion–unit profitability”. Based on this framework, we develop classification decision matrices for pairwise comparisons of AU, E_WAU, and I_WAU. Within these matrices, products are grouped according to combinations of any two measures, yielding distinct product classifications that support customized marketing strategies. This approach further underscores the practical value of E_WAU and I_WAU in facilitating real-world decision-making. Finally, we conduct empirical validation and analysis on three real-world datasets. Experimental results demonstrate that, compared with using AU alone, the combined use of E_WAU and I_WAU is more effective in identifying product categories and devising appropriate sales strategies. Full article

(This article belongs to the Special Issue Data Mining and Machine Learning with Applications, 2nd Edition)

41 pages, 571 KB

Open AccessArticle

An Explicit Closed Form for a Half-Integer ₃F₂(1) Family with Negative Integral Parameter Differences, via Odd Harmonic Sums

by Abdelhamid Zaidi

Axioms 2026, 15(9), 664; https://doi.org/10.3390/axioms15090664 (registering DOI) - 4 Sep 2026

Abstract

We study the half-integer family [...] Read more.

We study the half-integer family

F (n, p) =_{3} F_{2} (- \frac{2 n + 1}{2}, 1, \frac{2 p - 1}{2}; \frac{2 n + 5}{2}, \frac{2 p + 1}{2}; 1) .

Although this family belongs to the Karlsson–Minton class of

_{3} F_{2} (1)

series with negative integral parameter differences, the most directly relevant symmetric formula of Shpot and Srivastava becomes singular at the free numerator parameter

a = 1

(here a denotes the first numerator parameter of the Shpot–Srivastava reduction formula, the value of which controls the Beta factors in their evaluation). We show that this apparent singularity is removable and identify its finite value with the independently derived closed form. First, Euler’s integral representation and a recurrence for

J_{m, n} (x) = \int_{0}^{1} v^{2 m} {(1 - x v^{2})}^{- n} d v

yield a closed form for

_{2} F_{1} (- \frac{2 n + 1}{2}, 1; \frac{2 n + 5}{2}; x)

as a polynomial in

(1 - x)

plus

arctanh (\sqrt{x}) / \sqrt{x}

. Rainville’s integral formula then reduces

F (n, p)

to a rational part and a finite rational linear combination of odd harmonic sums

H_{r}^{odd} = \sum_{j = 1}^{r} {(2 j - 1)}^{- 1}

. For all integers

n \geq 0

and

p \geq n + 3

, this proves

F (n, p) \in Q

. The threshold is sharp: at the adjacent boundary

p = n + 2

we obtain

F (n, n + 2) = R_{n} + C_{n} π^{2}

with

R_{n}, C_{n} \in Q

and

C_{n} \neq 0

, so rationality fails. We also prove, by analyticity and the independently derived formula, that the

a \to 1

limit of the Shpot–Srivastava representation equals the present closed form. For computation, the exact assembly uses

O (n + p)

arithmetic operations under the unit-cost model, while a direct fixed-precision evaluation is ill-conditioned for large n. A stable series/closed-form hybrid for the

_{2} F_{1}

factor combined with adaptive positive-kernel Gauss–Jacobi quadrature gives relative errors at the machine-precision scale over the tested range up to

n = 64

. Full article

(This article belongs to the Special Issue Recent Advances in Special Functions and Applications, 2nd Edition)

28 pages, 4176 KB

Open AccessArticle

Prelaunch Assessment and Correction of Polarization Effects for HIRAS-II on the Fengyun-3 Satellite

by Zhiyu Yang, Chunyuan Shao, Kefeng Liang and Mingjian Gu

Remote Sens. 2026, 18(17), 3025; https://doi.org/10.3390/rs18173025 (registering DOI) - 4 Sep 2026

Abstract

The Hyperspectral Infrared Atmospheric Sounder II (HIRAS-II) onboard the Fengyun-3 satellites is a Fourier-transform infrared spectrometer that requires high radiometric calibration accuracy, making the characterization and correction of polarization effects essential. Although the gold-coated scan mirror introduces only weak polarization, its rotation changes [...] Read more.

The Hyperspectral Infrared Atmospheric Sounder II (HIRAS-II) onboard the Fengyun-3 satellites is a Fourier-transform infrared spectrometer that requires high radiometric calibration accuracy, making the characterization and correction of polarization effects essential. Although the gold-coated scan mirror introduces only weak polarization, its rotation changes the polarization orientation relative to the fixed polarization-sensitive axis of the downstream optics, producing scan-angle-dependent radiometric errors. To characterize and correct this effect, we designed and constructed a dedicated polarization test apparatus and used it to conduct a prelaunch thermal-vacuum (TVAC) polarization test. During the test, HIRAS-II observed the same stable 290 K area-source blackbody over a densely sampled scan-angle range, with the internal calibration target and cold shield serving as the warm and cold references, respectively. Guided by a polarization-induced radiometric error model formulated within the two-point calibration framework, we developed a decoupled two-step least-squares method to retrieve the polarization parameters from the resulting measurements. The method first estimates the equivalent polarization-axis angle of the downstream optical system from the phase of the band-averaged angular modulation and then retrieves the effective combined polarization parameter separately for each field of view (FOV) and spectral channel. The retrieved parameters were subsequently used to calculate the scan-angle-dependent polarization correction term and correct the calibrated spectra. After correction, the FOV-averaged standard deviation over the scan angle decreased from 0.023 to 0.007 K, from 0.024 to 0.007 K, and from 0.045 to 0.014 K in the long-wave (LW), mid-wave 1 (MW1), and mid-wave 2 (MW2) bands, respectively. The corresponding maximum reductions in brightness temperature deviation were 0.093, 0.064, and 0.174 K. The model, experimental approach, and retrieved prelaunch parameters establish a basis for the on-orbit evaluation and correction of scan-angle-dependent polarization-induced radiometric errors. Reducing these errors improves the radiometric calibration accuracy of HIRAS-II and helps provide more reliable Level-1 radiance data for atmospheric profile retrievals and data assimilation in global numerical weather prediction (NWP) systems. Full article

42 pages, 4259 KB

Open AccessArticle

Vision-Based Automated Inspection of Box Meals for Food Portion Defects and Foreign Object Detection

by Hong-Dar Lin, Guan-Ming Chen and Chou-Hsien Lin

Sensors 2026, 26(17), 5636; https://doi.org/10.3390/s26175636 (registering DOI) - 4 Sep 2026

Abstract

Automated inspection of prepared meals is important for improving food safety, quality assurance, and production efficiency. However, vision-based inspection remains challenging because boxed meals contain multiple adjacent food items with irregular shapes, varying portion sizes, and visually similar appearances, while oily surfaces may [...] Read more.

Automated inspection of prepared meals is important for improving food safety, quality assurance, and production efficiency. However, vision-based inspection remains challenging because boxed meals contain multiple adjacent food items with irregular shapes, varying portion sizes, and visually similar appearances, while oily surfaces may introduce specular reflections that degrade image quality. This study presents a vision-based framework for multi-object recognition and quantitative defect analysis in complex meal images, with Chinese-style lunch boxes used as representative test samples. The framework integrates region-of-interest (ROI) extraction, fixed-grid regional feature representation, deep neural network (DNN) classification, flood-filling post-processing, and empirical food-quantity thresholds. The lunch-box ROI is first extracted using the Hough transform, followed by median filtering to suppress reflection noise. The ROI is partitioned into 6 × 6 regions, from which the mean and standard deviation of RGB, HSV, and CIE Lab* color components are extracted and classified using a DNN. Flood filling is subsequently applied to refine the classification results, and category-specific empirical thresholds are used to identify missing food items and insufficient portions. Foreign objects are detected as an additional abnormal category. Under the evaluated experimental conditions, the proposed framework achieved an overall image classification rate (CR) of 96.45%, a defective-image detection rate (1−β) of 97.76%, a normal-image false alarm rate (α) of 5.34%, and a defective-image misclassification rate (γ) of 0.82%. Sensitivity experiments involving illumination variation, two lunch-box configurations with different food compositions, and conveyor-based image acquisition further demonstrated the feasibility and stability of the framework under the evaluated laboratory and prototype conditions. These findings support the feasibility of the proposed lightweight framework for vision-based quality inspection of representative boxed meals, while broader validation across meal types, contaminants, and industrial production environments remains necessary. Full article

(This article belongs to the Special Issue Sensing and Imaging for Defect Detection: 2nd Edition)

18 pages, 2725 KB

Open AccessArticle

Physics-Informed Blind Hyperspectral Unmixing of Altered Minerals via Backscattering Parameter Learning

by Rui Xu, Houzhen Wei, Zhiguang Yang, Xiaolong Ma, Wentao Wang and Yiteng Wang

Minerals 2026, 16(9), 916; https://doi.org/10.3390/min16090916 (registering DOI) - 4 Sep 2026

Abstract

The blind hyperspectral unmixing of altered minerals holds significant indicative importance for the exploration of sandstone-type uranium deposits. Nevertheless, the distinctive backscattering and multiphase characteristics are typically underestimated by existing methods, resulting in limited accuracy in the unmixing performance of complex mixtures. This [...] Read more.

The blind hyperspectral unmixing of altered minerals holds significant indicative importance for the exploration of sandstone-type uranium deposits. Nevertheless, the distinctive backscattering and multiphase characteristics are typically underestimated by existing methods, resulting in limited accuracy in the unmixing performance of complex mixtures. This study presents a physics-informed Swin Transformer Network (PIST−Net) for the blind hyperspectral unmixing of hematite, goethite, biotite, and chlorite. The proposed model includes a lightweight Swin Transformer encoder for the long-range modeling of spatial and spectral features. Furthermore, a dual-branch decoder with adaptive physical parameters was introduced in the spectral reconstruction section. In this decoder, the backscattering prediction head independently estimates the backscattering factor for each endmember, accounting for the true behavior of the mineral materials. As a key parameter of the Hapke model, the learnable strategy can reduce the spectral error in the single-scattering albedo (SSA) space to improve the accuracy of abundance estimation. The results show that PIST−Net consistently outperformed all six competing models on the altered mineral (AM) dataset and the NASA Reflectance Experiment Laboratory (RELAB) dataset. For mixtures composed of 2–4 endmembers, the mean RMSE of estimated abundances ranges from 0.0312 to 0.1037. Full article

(This article belongs to the Special Issue Hyperspectral and Thermal Infrared Remote Sensing for Mineral Exploration)

27 pages, 942 KB

Open AccessSystematic Review

Diabetic Eye Screening Attendance Among Minority Ethnic and Other Inequality-Affected Populations in the United Kingdom: A Systematic Review of Associated Factors, Barriers, Facilitators and Interventions

by Hammaad Khalid and Farah Andaleeb

Vision 2026, 10(4), 66; https://doi.org/10.3390/vision10040066 (registering DOI) - 4 Sep 2026

Abstract

Diabetic retinopathy is a leading cause of preventable sight loss. The United Kingdom achieves high overall uptake through organised diabetic eye screening programmes, but aggregate performance may conceal inequalities. This systematic review synthesised United Kingdom evidence on attendance-related associated factors, barriers, facilitators and [...] Read more.

Diabetic retinopathy is a leading cause of preventable sight loss. The United Kingdom achieves high overall uptake through organised diabetic eye screening programmes, but aggregate performance may conceal inequalities. This systematic review synthesised United Kingdom evidence on attendance-related associated factors, barriers, facilitators and interventions, with particular emphasis on minority ethnic communities and related inequalities. MEDLINE and Embase via Ovid, Web of Science, Scopus, the Cochrane Central Register of Controlled Trials, Google Scholar, citation searching and targeted United Kingdom grey-literature sources were searched and updated to 18 August 2026. Fourteen primary studies met the inclusion criteria. Younger age and socioeconomic deprivation showed the most consistent associations with lower or delayed attendance across multiple large routine-data analyses. Ethnicity-related associations varied by group and setting, and several datasets were limited by incomplete ethnicity recording. Qualitative, mixed-methods and survey evidence identified communication difficulties, competing work or education, appointment inflexibility, transport, inaccurate contact details and fragmentation with diabetes care as plausible barriers. One cluster-randomised trial in practices serving South Asian communities increased mean practice-level attendance from 74% to 89% (adjusted difference 12 percentage points, 95% confidence interval 7–17). Overall confidence was strongest for age- and deprivation-related associations and lower for specific mechanisms and interventions. Equity improvement should combine robust demographic monitoring with locally demonstrated barriers and the evaluation of intervention reach and sustained attendance. Full article

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31 pages, 11099 KB

Open AccessArticle

HFEDTI: A DTI Prediction Model Integrating Local–Global Feature Fusion and Weighted Ensemble Learning

by Qingchuan Xu, Anting Gao, Kai Che, Longbo Zhang, Yifeng Gao and Linlin Xing

Electronics 2026, 15(17), 4011; https://doi.org/10.3390/electronics15174011 (registering DOI) - 4 Sep 2026

Abstract

Drug–target interaction (DTI) prediction is a critical step in drug discovery, and accurate prediction of potential interactions can significantly accelerate the drug-development process. Although deep-learning approaches have achieved promising performance in DTI prediction, two challenges remain: single models often fail to comprehensively capture [...] Read more.

Drug–target interaction (DTI) prediction is a critical step in drug discovery, and accurate prediction of potential interactions can significantly accelerate the drug-development process. Although deep-learning approaches have achieved promising performance in DTI prediction, two challenges remain: single models often fail to comprehensively capture heterogeneous sequence information, resulting in limited stability and generalization, while insufficient integration of local and global features restricts interaction representation. To address these limitations, we propose HFEDTI, a DTI prediction model that integrates hierarchical feature fusion and weighted ensemble learning. Specifically, a residual convolutional neural network (ResCNN) is employed to extract local structural features of drugs and targets, while a self-attention-based hierarchical bidirectional long short-term memory network (SAHBiLSTM) captures global contextual dependencies. Furthermore, a hierarchical heterogeneous attention mechanism is introduced to align and fuse multi-level cross-modal representations, and a weighted ensemble strategy based on validation performance ranking is developed to enhance model robustness and generalization. Experimental results on three benchmark datasets demonstrate the effectiveness of HFEDTI. On the DrugBank dataset, HFEDTI achieves an AUC of 0.9238 and an AUPR of 0.9327, improving the best-performing baseline by 0.90 and 1.40 percentage points, respectively. Moreover, HFEDTI consistently achieves strong performance on the C. elegans and Human datasets, further validating its effectiveness and generalization capability for DTI prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)

16 pages, 1824 KB

Open AccessArticle

Application of Deep Learning Algorithms to Increase the Accuracy of Control of Optical Parameters of Fiber-Optic Sensors

by Raushan Z. Aimagambetova, Aigul N. Seraly, Ali D. Mekhtiyev, Aliya D. Alkina, Ruslan A. Mekhtiyev and Dinara T. Mukasheva

Photonics 2026, 13(9), 842; https://doi.org/10.3390/photonics13090842 (registering DOI) - 4 Sep 2026

Abstract

The development of accurate, robust and adaptive methods for monitoring the optical parameters of fiber-optic sensors (FOS) is one of the priority tasks in the field of precision measurements, especially in the context of rapidly growing requirements for intelligent monitoring systems. This paper [...] Read more.

The development of accurate, robust and adaptive methods for monitoring the optical parameters of fiber-optic sensors (FOS) is one of the priority tasks in the field of precision measurements, especially in the context of rapidly growing requirements for intelligent monitoring systems. This paper presents a comprehensive approach to the use of modern deep learning algorithms for analyzing and processing spectral data coming from FOS. The proposed solution is based on the use of convolutional neural networks (CNN) for the automatic extraction of informative features, as well as autoencoders for noise suppression and signal restoration. A hybrid architecture combining CNN and recurrent neural networks (RNN) was developed. The experiments conducted confirmed the effectiveness of the evaluated models. On the independent regression test set, the CNN-only model achieved a macro-averaged R²-based prediction score of 98.2% without added noise and 89.4% under high-noise conditions; on a separate temporal test sequence, the hybrid CNN + RNN model achieved 95.0% compared with 88.0% for CNN alone. The presented approach has high resistance to noise and the ability to scale to various types of FOS. At the conclusion, the prospects for the practical applications of the proposed system are discussed: the structural monitoring of buildings and structures and the automation of processes in industry and energy, with an emphasis on reliability, autonomy and integration with existing platforms. Full article

19 pages, 8019 KB

Open AccessArticle

Bicomplex Mandelbar Dynamics Associated with Three Conjugations

by İbrahim Demir, Soley Ersoy and Mahpeyker Öztürk

Mathematics 2026, 14(17), 3209; https://doi.org/10.3390/math14173209 (registering DOI) - 4 Sep 2026

Abstract

In this paper, we investigate the antiholomorphic counterpart of bicomplex quadratic dynamics by introducing bicomplex Mandelbar sets associated with the three natural involutive conjugations of the bicomplex algebra. For each conjugation

†_{m}

,

m \in {1, 2, 3}

[...] Read more.

In this paper, we investigate the antiholomorphic counterpart of bicomplex quadratic dynamics by introducing bicomplex Mandelbar sets associated with the three natural involutive conjugations of the bicomplex algebra. For each conjugation

†_{m}

,

m \in {1, 2, 3}

, we study the iteration

{(F_{m})}_{C} (η) = {(η^{†_{m}})}^{2} + C

on

BC

and define the associated parameter set via the boundedness of the orbit of the origin. Using the idempotent decomposition, we obtain a conjugation-dependent classification of the dynamics:

†_{3}

yields the idempotent product of two classical Mandelbar sets, whereas

†_{2}

and

†_{1}

generate cross-coupled quadratic systems, holomorphic and antiholomorphic in one step, respectively. We further prove that the

†_{1}

- and

†_{2}

-dynamics are equivalent up to complex conjugation of an idempotent parameter; consequently, the principal three-dimensional slices of the corresponding Mandelbar sets are congruent, mirror-symmetric copies of one another, although neither possesses this diagonal symmetry individually. We visualize these slices and rigorously establish their reflection symmetries. The results clarify the role of bicomplex conjugations in antiholomorphic dynamics and reveal structural phenomena absent from the holomorphic bicomplex Mandelbrot setting. Full article

(This article belongs to the Special Issue Mathematics and its Applications in Science and Engineering, 4th Edition)

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