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38 pages, 46650 KB

Open AccessArticle

CDU-YOLO: A Scene-Aware Real-Time Smoke and Flame Detection Framework for High-Rise Building Fire Safety

by Xin Wang, Hao He, Jianxin Zhang and Min Song

Fire 2026, 9(9), 385; https://doi.org/10.3390/fire9090385 (registering DOI) - 5 Sep 2026

Reliable optical sensing of smoke and flames in high-rise buildings is challenging due to weak early cues, vertical smoke diffusion, facade occlusions, nighttime illumination, and fire-like urban interferences. We propose CDU-YOLO, a scene-aware real-time detection framework built upon YOLOv8n. Rather than relying on [...] Read more.

Reliable optical sensing of smoke and flames in high-rise buildings is challenging due to weak early cues, vertical smoke diffusion, facade occlusions, nighttime illumination, and fire-like urban interferences. We propose CDU-YOLO, a scene-aware real-time detection framework built upon YOLOv8n. Rather than relying on indiscriminate network scaling, task-oriented integration of existing modules is introduced: dynamic point-sampling (DySample) to preserve blurred boundaries of distant micro-targets, an enlarged receptive field (UniRepLKNet) to capture large-scale vertical propagation, and a dynamic bounding-box regression loss (WIoU) to handle occlusions. Experiments on a custom high-rise fire dataset and two public datasets demonstrate 94.9% mAP@0.5 and 56.7% mAP@0.5:0.95. In a dedicated flame-only size-stratified evaluation, CDU-YOLO improves AP@0.5 for small flames from 79.6% to 91.7% and reduces their miss rate from 25.2% to 11.3% relative to YOLOv8n. Under a unified desktop protocol (RTX 3080, PyTorch FP16,

640 \times 640

, batch size 1, no TensorRT), end-to-end throughput increases from 41 FPS to 55 FPS. A separate Jetson Orin NX deployment benchmark reaches 92 FPS using TensorRT FP16. The explicit introduction of an “others” category during training contributes to reducing false positive predictions against fire-like distractors. These results support the use of CDU-YOLO as a supplementary visual sensing component for early situational awareness. Nevertheless, residual misses on small and ultra-distant flames, continuous video-stream validation, and long-term field testing remain to be addressed before safety-critical online deployment. Full article

18 pages, 649 KB

Open AccessArticle

A Redox–Lipid Transcriptional State Is Associated with High Aflatoxin Biosynthetic Activity in Aspergillus flavus

by Yirui Chen, Hongxin Gui, Kai Ma, Ruochen Cao, Zhijian Zhang, Mengyang Wang and Rongrong Yang

Metabolites 2026, 16(9), 652; https://doi.org/10.3390/metabo16090652 (registering DOI) - 5 Sep 2026

Abstract

Background/Objectives: Aflatoxin B1 (AFB1) contamination of maize and peanut is influenced by fungal responses to environmental and food-matrix cues, but the transcriptional coordination of redox adaptation, lipid metabolism, and aflatoxin biosynthesis remains unresolved. This study examined whether an expression-defined redox–lipid state is associated [...] Read more.

Background/Objectives: Aflatoxin B1 (AFB1) contamination of maize and peanut is influenced by fungal responses to environmental and food-matrix cues, but the transcriptional coordination of redox adaptation, lipid metabolism, and aflatoxin biosynthesis remains unresolved. This study examined whether an expression-defined redox–lipid state is associated with high relative aflatoxin biosynthetic activity in Aspergillus flavus. Methods: A cross-dataset secondary analysis integrated 18 analytical datasets (247 samples) derived from eight public source records spanning defined-medium, maize, and peanut systems. Harmonized expression profiles were used to derive an aflatoxin biosynthetic activity score (ABAS), oxidative-stress adaptation score (OSAS), and lipid metabolic reprogramming score (LMRS). ABAS is an expression-derived relative score, not a direct measure of biosynthetic flux or accumulated toxin. Dataset-aware mixed-effects models evaluated the OSAS–ABAS association and the linear OSAS × LMRS interaction. Results: ABAS correlated with matched AFB1 measurements in 54 samples (Pearson

r = 0.734

,

p = 2.70 \times 10^{- 10}

). The quadratic OSAS term was negative (

β = - 0.434

, 95% CI

- 0.502

to

- 0.366

;

p < 0.001

), with maximum predicted ABAS near 0.81 SD on the pooled within-dataset-standardized OSAS scale. Separately, the linear OSAS × LMRS interaction was positive (

β = 0.284

, 95% CI 0.198–0.370;

p < 0.001

), and the high-OSAS/high-LMRS quadrant had the greatest adjusted mean ABAS (0.789, 95% CI 0.661–0.917). Candidate prioritization recovered established regulators and nominated redox- and lipid-associated nodes. Conclusions: The findings identify an associative transcriptional signature across heterogeneous food-relevant conditions and provide focused hypotheses for prospective validation using direct toxin, redox, lipid, flux, and functional measurements. Full article

(This article belongs to the Section Food Metabolomics)

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36 pages, 1715 KB

Open AccessReview

Hydroxytyrosol as a Multitarget Neuroprotective Agent: Molecular Mechanisms, Pharmacokinetics and Therapeutic Potential in Neurodegenerative Diseases

by Pura Ballester-Navarro, Ana María García-Muñoz, Desirée Victoria-Montesinos and Pilar Zafrilla

Molecules 2026, 31(17), 3113; https://doi.org/10.3390/molecules31173113 (registering DOI) - 5 Sep 2026

Abstract

Neurodegenerative diseases arise from interacting oxidative, inflammatory, mitochondrial, and proteostatic disturbances. Hydroxytyrosol (HT), an olive phenol, has been proposed as a multitarget neuroprotective compound. This narrative review integrates HT chemistry, parent/metabolite pharmacokinetics, blood–brain barrier (BBB) evidence, mechanisms, disorder-specific models, and human studies. Direct [...] Read more.

Neurodegenerative diseases arise from interacting oxidative, inflammatory, mitochondrial, and proteostatic disturbances. Hydroxytyrosol (HT), an olive phenol, has been proposed as a multitarget neuroprotective compound. This narrative review integrates HT chemistry, parent/metabolite pharmacokinetics, blood–brain barrier (BBB) evidence, mechanisms, disorder-specific models, and human studies. Direct HT evidence is strongest for nuclear factor erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE) activation and experimental modulation of α-synuclein; support for AMP-activated protein kinase (AMPK)/sirtuin 1 (SIRT1)/peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), mitochondrial protection, nuclear factor-kappa B (NF-κB)-related inflammation, and amyloid-β (Aβ) is predominantly preclinical, whereas tau, autophagic flux, and ubiquitin–proteasome effects remain preliminary. Oral HT is rapidly absorbed but extensively conjugated, and no study has quantified parent HT or its major metabolites in the human brain or cerebrospinal fluid after oral supplementation. Isolated-HT trials show systemic antioxidant or anti-inflammatory biomarker effects, while cognitive findings derive mainly from phenolic-rich olive matrices and cannot be assigned to HT alone. No disease-modifying efficacy has been established for isolated HT in Alzheimer’s disease (AD), Parkinson’s disease (PD), or related disorders. HT is therefore a mechanistically plausible candidate, but human brain exposure, dose–response, and efficacy require adequately powered disease-specific trials. Full article

(This article belongs to the Special Issue Natural Bioactive Compounds: Antioxidant Properties and Associated Biological Activities)

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28 pages, 7178 KB

Open AccessArticle

Genome-Wide Characterization of PEBP, FD, and GRF Families in Amomum villosum Lour. and Their Potential Roles in Flowering

by Ming Lei, Mei Qin, Wei Lin, Jun-Jun He, Shao-Fen Jian, Zhan-Jiang Zhang, Cui Li and Jing Wang

Plants 2026, 15(17), 2722; https://doi.org/10.3390/plants15172722 (registering DOI) - 5 Sep 2026

Abstract

A detailed understanding of the molecular mechanisms governing the flowering time of Amomum villosum Lour., a medicinal plant within the Zingiberaceae family, is currently lacking. In modern plants, the florigen activation complex (FAC), which includes PEBP, FD/bZIP, and GRF proteins, is known to [...] Read more.

A detailed understanding of the molecular mechanisms governing the flowering time of Amomum villosum Lour., a medicinal plant within the Zingiberaceae family, is currently lacking. In modern plants, the florigen activation complex (FAC), which includes PEBP, FD/bZIP, and GRF proteins, is known to regulate flowering. In this study, we identified 13 PEBP, 5 FD, and 19 GRF genes within the A. villosum genome and conducted phylogenetic, structural and promoter analysis. Notably, cross-species protein–protein interaction predictions and yeast two-hybrid assays uncovered an unexpected interaction pattern: an AREB3-like FD protein (AvFD5) and a GRF protein (AvGRF13) directly interact with specific PEBP members, whereas canonical FD-like proteins (AvFD1 and AvFD4) did not, which contrasts with the classical rice FAC model (Hd3a-14-3-3-OsFD1). These results imply that FAC assembly in A. villosum may involve alternative components or regulatory mechanisms, potentially indicating lineage-specific divergence within monocots. This research represents the first systematic characterization of FAC core gene families in A. villosum and Zingiberaceae, laying the groundwork for understanding flowering time regulation and facilitating future molecular breeding efforts in this economically significant plant. Full article

(This article belongs to the Special Issue Molecular Regulatory Networks of Plant Flowering and Reproductive Development)

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23 pages, 767 KB

Open AccessArticle

Incorporating Carbon-Related Costs into Production Decisions in the Fastener Industry Under the Net-Zero Transition

by Wen-Hsien Tsai, Chung-Wei Wang, Shuo-Chieh Chang and Hsiang-Ching Chen

Energies 2026, 19(17), 4204; https://doi.org/10.3390/en19174204 (registering DOI) - 5 Sep 2026

Abstract

The implementation of global net-zero emission policies and carbon pricing mechanisms, including the Carbon Border Adjustment Mechanism (CBAM), has increased the need for export-oriented manufacturers to make carbon-related costs visible in production decisions. Taiwan’s fastener industry is particularly exposed because of its multi-stage [...] Read more.

The implementation of global net-zero emission policies and carbon pricing mechanisms, including the Carbon Border Adjustment Mechanism (CBAM), has increased the need for export-oriented manufacturers to make carbon-related costs visible in production decisions. Taiwan’s fastener industry is particularly exposed because of its multi-stage production structure, intensive use of steel-based inputs, and reliance on international markets. Traditional single-base cost allocation can obscure differences in resource consumption across products and processes. To enhance the decision-making value of cost information, this study develops an Activity-Based Costing (ABC) framework that assigns modeled resource consumption to activities and products while separately internalizing carbon-related costs in a mixed-integer production-planning model. Theory of Constraints (TOC) is used as a bottleneck-focused managerial interpretation of finite production capacities rather than as a separate objective function. Using Taiwan’s fastener manufacturing industry as the research context, the model is demonstrated through an illustrative numerical example parameterized to reflect representative production and resource consumption relationships. The reported findings are therefore numerical results of the model rather than statistical empirical validation of a sampled population or a disclosed company dataset. Under the specified baseline parameters, Product 4 is selected because its revenue, purchasing-tier, activity driver, capacity, and carbon-related cost coefficients jointly dominate the alternatives. Product exclusion scenarios further show how the preferred product changes when individual products are exogenously removed while carbon price parameters are held fixed. The framework provides a transparent decision support structure for jointly examining activity costs, constrained resources, and carbon-related charges; it should not be interpreted as a legal replication of CBAM certificate obligations. Industry 5.0 extensions are positioned as future research. Full article

(This article belongs to the Topic The Manufacturing, Managerial and Costing Issues in the New Era of Industry 5.0)

18 pages, 4718 KB

Open AccessArticle

Pre-Visual Detection of Pine Wilt Disease Using an Optimized PSRI Derived from Hyperspectral Drone Imagery

by Run Yu, He Weng, Dan Guo, Mingqing Weng, Ziyi You, Feiping Zhang and Songqing Wu

Plants 2026, 15(17), 2724; https://doi.org/10.3390/plants15172724 (registering DOI) - 5 Sep 2026

Abstract

Pine wilt disease (PWD) is a devastating infectious disease of pine trees caused by the invasion of Bursaphelenchus xylophilus. Achieving rapid and accurate identification of pine trees in the early stages of infection is critical for preventing and controlling its spread, particularly [...] Read more.

Pine wilt disease (PWD) is a devastating infectious disease of pine trees caused by the invasion of Bursaphelenchus xylophilus. Achieving rapid and accurate identification of pine trees in the early stages of infection is critical for preventing and controlling its spread, particularly for early warning and intervention before the plants exhibit obvious discoloration symptoms. The Plant Senescence Reflectance Index (PSRI) has shown significant potential for the early detection of PWD. However, existing studies often use its default band combinations for calculation, which may fail to fully exploit key wavelength information that is more sensitive to pre-symptomatic PWD stress. Based on unmanned aerial vehicle (UAV) hyperspectral imaging data, the present work systematically optimizes and reconstructs the three band parameters of the PSRI to explore an optimal wavelength combination more suitable for the early identification of PWD-infected trees. The results indicate that compared to the original settings of the PSRI, the wavelength combination of 490-666-700 nm performs better in pre-visually identifying infected pine trees in the early stages of infection, achieving a detection accuracy of 82.71%. Our work identifies an optimized PSRI wavelength combination more sensitive to the pre-visual early stage of PWD based on hyperspectral data. This method demonstrates promising potential for pre-visual detection of PWD before visible symptoms appear, which may provide an earlier opportunity for PWD monitoring and intervention. Full article

(This article belongs to the Special Issue Application of Optical and Imaging Systems to Plants)

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31 pages, 1540 KB

Open AccessArticle

Symmetry-Fixed Holonomies and Spectral Isolationin Two-Cycle Photonic Geometries: A Square Parent Manifold for a Qubit and a Hexagonal Qutrit Manifold

by Michel Planat

Quantum Rep. 2026, 8(3), 88; https://doi.org/10.3390/quantum8030088 (registering DOI) - 5 Sep 2026

Abstract

A system with two periodic directions carries two commuting holonomies

a = (u, v) \in R^{2} / Z^{2}

. We determine their distinguished values while separating lattice, arithmetic, and observable effects. Maximizing the lowest twisted eigenvalue places a [...] Read more.

A system with two periodic directions carries two commuting holonomies

a = (u, v) \in R^{2} / Z^{2}

. We determine their distinguished values while separating lattice, arithmetic, and observable effects. Maximizing the lowest twisted eigenvalue places a at a deep hole of the momentum lattice. For every rectangular torus the maximizer is antiperiodic, so complex multiplication is sufficient for torsion optima but not necessary. Let

G_{τ}^{- 1}

be the dual metric and

D_{τ} (a)

the normalized zeta determinant of the twisted Laplacian. At the rotation-fixed deep holes of the square and hexagonal lattices, symmetry gives the exact determinant response

- {Hess}_{a} log D_{τ} = 2 π (ℑ τ) G_{τ}^{- 1}

. With spectral wavenumber

κ = 2 π

, the lowest manifolds are fourfold and threefold, with gaps

2 κ^{2}

and

4 κ^{2} / 3

; errors in the holonomy parameters

(u, v)

split them linearly while their centroids remain stationary. We then give a finite-device realization: an

8 \times 8

microring lattice closed by two phase-controlled seams. At a reported coupling scale of 16 GHz, its exact square-lattice spectrum has a

17.32

GHz shell gap and a

1.92

GHz doublet separation at a phase-coordinate offset

ε_{u} = 0.10

from the antiperiodic point; a triangular-link configuration gives a threefold qutrit manifold with an

18.11

GHz gap. A four-channel sensitivity study separates uniform holonomy offset, seam-to-seam phase inhomogeneity, seam amplitude mismatch and diagonal-to-axial mismatch, and quantifies the seam propagation-loss and thermal-crosstalk budgets. This is a quantitative spectroscopy proposal, not a claim of topological protection or a completed device. Full article

(This article belongs to the Special Issue Exclusive Quantum Reports Feature Papers for 2026–2027)

12 pages, 298 KB

Open AccessFeature PaperArticle

Recorded Contingent Caregiver Voice Independently Improves Neural Speech Processing in Hospitalized Preterm Infants: A Multisite Randomized Controlled Trial

by Caitlin P. Kjeldsen, Megan Moran, Arnaud Jeanvoine, Joshua Lukemire, Gordon Ramsay, A. Joselyn Barahona and Nathalie L. Maitre

J. Clin. Med. 2026, 15(17), 6880; https://doi.org/10.3390/jcm15176880 (registering DOI) - 5 Sep 2026

Abstract

Background/Objectives: The atypical NICU auditory environment can disrupt neural plasticity critical for language development in preterm infants. Recorded caregiver’s voice offers input when bedside presence is infeasible, and evidence suggests contingency, delivering the recording in response to infant behavior, is key to its [...] Read more.

Background/Objectives: The atypical NICU auditory environment can disrupt neural plasticity critical for language development in preterm infants. Recorded caregiver’s voice offers input when bedside presence is infeasible, and evidence suggests contingency, delivering the recording in response to infant behavior, is key to its benefit. This study tested whether caregiver’s recorded voice contingent on non-nutritive sucking (NNS) enhances neural speech processing beyond passive exposure. Methods: This randomized controlled trial enrolled preterm infants born before 35 weeks gestational age (GA), and between 32 0/7–35 6/7 weeks corrected GA at start. Infants were randomized to an intervention group receiving caregiver’s voice contingent on NNS, or a control group receiving passive voice exposure. Primary outcomes were post-intervention auditory ERP responses at left (T5) and right (T6) temporal locations, adjusted for pre-intervention EEG. Sensitivity analyses controlled for GA and maternal education; secondary analyses examined bed type, room type, and sound levels. Results: Of 214 infants enrolled, 97 intervention and 94 control infants completed the protocol (median GA 31.9 weeks, median corrected GA at start 33.9 weeks). The intervention group showed significantly greater speech-sound processing at T5 (β = 0.023, 95% CI [0.002, 0.044], p = 0.036, d = 0.31), with a borderline effect at T6 (β = 0.022, 95% CI [0.000, 0.045], p = 0.054, d = 0.28), both strengthened in sensitivity analyses. No significant interactions emerged with bed type, room type, or sound level. Conclusions: Contingent, infant-activated caregiver-voice exposure improves auditory ERP responses in preterm infants, independent of environmental factors, suggesting active engagement, not voice alone, as the driver of change. Further studies are warranted. Full article

(This article belongs to the Section Clinical Pediatrics)

17 pages, 1856 KB

Open AccessArticle

Intracranial Arterial Dolichoectasia in Spontaneous Intracerebral Hemorrhage: A Retrospective Cross-Sectional Study of Morphometric Features and Cerebral Small Vessel Disease

by Nazakat Nurmamat, Yunfang Luo, Weijia Xie, Xianjing Feng, Fang Yu, Yinghuan Pan, Hesham A. Alyamani, Yang Du and Jian Xia

J. Clin. Med. 2026, 15(17), 6881; https://doi.org/10.3390/jcm15176881 (registering DOI) - 5 Sep 2026

Abstract

Objectives: The aim of this study was to determine the prevalence and distribution of intracranial arterial dolichoectasia (IADE) across the anterior and posterior circulations and assess its association with cerebral small vessel disease (CSVD) imaging markers in patients with spontaneous intracerebral hemorrhage (ICH). [...] Read more.

Objectives: The aim of this study was to determine the prevalence and distribution of intracranial arterial dolichoectasia (IADE) across the anterior and posterior circulations and assess its association with cerebral small vessel disease (CSVD) imaging markers in patients with spontaneous intracerebral hemorrhage (ICH). Methods: This retrospective cross-sectional study included 174 ICH patients. Three-dimensional vascular reconstruction was used to assess cavernous internal carotid artery morphology, vertebrobasilar dolichoectasia, and exploratory middle cerebral artery (MCA) tortuosity. IADE was defined by Type III–IV cavernous internal carotid artery morphology and/or vertebrobasilar dolichoectasia. Multivariable logistic regression was adjusted for age, sex, hypertension, diabetes mellitus, and previous stroke. Results: IADE was present in 52 patients (29.9%) and was observed more frequently in CSVD-related ICH than in macrovascular ICH (32.9% versus 14.3%; p = 0.049). Among patients with CSVD-related ICH, IADE was associated with deep cerebral microbleeds (adjusted odds ratio [OR], 3.42; 95% CI, 1.27–9.20), severe basal ganglia enlarged perivascular spaces (adjusted OR, 4.24; 95% CI, 1.72–10.46), and deep white matter hyperintensity grade ≥ 2 (adjusted OR, 3.71; 95% CI, 1.59–8.65). These associations were still significant after false discovery rate correction. No significant association was observed with lobar cerebral microbleeds. Exploratory analyses showed higher maximum and mean bilateral MCA tortuosity indices in patients with IADE. Conclusions: IADE was present in approximately one third of patients with ICH and was observed more frequently in CSVD-related than in macrovascular ICH in this cohort. Its association with deep CSVD imaging markers suggests a possible link between IADE and deep small-vessel disease, while the MCA findings remain exploratory. Full article

(This article belongs to the Special Issue Intracranial Hemorrhage: Advances in Diagnosis, Management and Treatment)

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32 pages, 5945 KB

Open AccessArticle

On-Limb Orbiting Robot: Proprioceptive Diameter Estimation and Orthogonal Grip–Orbit Control

by Luz M. Tobar-Subía-Contento, Juan A. Cabrera, Anthony Mandow and Jesús M. Gómez-de-Gabriel

Biomimetics 2026, 11(9), 636; https://doi.org/10.3390/biomimetics11090636 (registering DOI) - 5 Sep 2026

Abstract

On-body robots that travel around a human limb must keep a firm enough grip to avoid slipping or detaching, while never pressing hard enough to hurt—a balance that is hardest to strike precisely when the robot is orbiting the limb and gravity continually [...] Read more.

On-body robots that travel around a human limb must keep a firm enough grip to avoid slipping or detaching, while never pressing hard enough to hurt—a balance that is hardest to strike precisely when the robot is orbiting the limb and gravity continually redistributes the contact loads. This paper presents an open, non-anthropomorphic robot that wraps around a compliant cylindrical surface with a three-contact grasp: a central traction module with two in-line driven wheels, and two lateral spring-loaded arms with distal wheels. Its central contribution is an actuation-space decomposition in which the two lateral wheel torques, expressed in a common-mode/differential basis, simultaneously drive the orbital motion and regulate the central normal force. We show that this basis diagonalises both the rolling kinematics and the static force balance, so the differential (grip-regulating) channel is provably orthogonal to the common-mode (propulsion) channel: a single pair of actuators perform both tasks without mutual interference and without a dedicated force mechanism. A model-based feedforward law derived from the static contact model, corrected by a PI term fed back from the compliant arms—which double as the force sensor—keeps the central force within a safe band; in a full-revolution simulation the differential command reverses sign to counteract the gravitational load swing while leaving the orbit undisturbed. The same compliant arms yield a closed-form estimate of the cylinder radius and contact geometry, accurate to below one millimetre across a 45–

87 mm

diameter range, from proprioception alone. Preliminary prototype tests reproduce the predicted behaviour, supporting the approach for future wearable and assistive applications. Full article

(This article belongs to the Section Locomotion and Bioinspired Robotics)

28 pages, 850 KB

Open AccessArticle

Query-EfficientCross-Domain Black-Box Adversarial Robustness Assessment Without Retraining via Differentiable Spatial-Channel Mapping and Bandit Optimization

by Chen Dang and Xiaoyi Feng

Appl. Sci. 2026, 16(17), 8835; https://doi.org/10.3390/app16178835 (registering DOI) - 5 Sep 2026

Abstract

Evaluating the adversarial robustness of deployed deep neural networks is critical for trustworthy AI, yet existing black-box assessment tools assume homogeneous input domains shared between surrogates and targets, an assumption rarely met in practice. Cross-domain transfer attacks are inherently ill-posed due to profound [...] Read more.

Evaluating the adversarial robustness of deployed deep neural networks is critical for trustworthy AI, yet existing black-box assessment tools assume homogeneous input domains shared between surrogates and targets, an assumption rarely met in practice. Cross-domain transfer attacks are inherently ill-posed due to profound structural and semantic discrepancies between surrogate and target models. Conventional methods often fail due to gradient stagnation and label misalignment. We propose Cross-Domain Adversarial Optimization (CDAO), a training-free paradigm establishing high-fidelity adversarial guidance across heterogeneous domains without task-specific retraining. Differentiable Spatial-Channel Mapping (DSCM) reconciles physical dimension mismatches, preserving analytical differentiability across mismatched input dimensions. To resolve semantic disjointness, Target Proxy Distillation (TPD) casts proxy target selection as a multi-fidelity candidate-selection problem analogous to Best Arm Identification. Using multi-fidelity Successive Halving, TPD adaptively distills informative proxy classes with minimal query overhead, eliminating shared label assumptions. By unifying structural and semantic alignment into a plug-and-play pipeline, CDAO enables effective cross-domain guidance at inference time. Extensive evaluations across cross-dataset image-classification settings that differ in architecture, resolution, channel format, and label space demonstrate that CDAO achieves the best transferability and favorable query efficiency among the evaluated inference-time black-box baselines for cross-dataset image classification, reducing the end-to-end query budget to Q ∼ 103 (an order of magnitude below the Q ∼ 104 regime of pure query-driven attacks). Concretely, the end-to-end budget is only 1000 and 3000 queries for small- and large-scale datasets, respectively, yielding targeted ASR gains of 38.5% on CIFAR-10 and 43.7% on ImageNet-1K over the strongest baseline. These results highlight that even models deployed behind black-box APIs remain vulnerable to structured cross-domain probes, and that robustness evaluation tools must account for heterogeneous threat surfaces. Full article

(This article belongs to the Special Issue Advanced Technology of Information Security and Privacy)

31 pages, 1024 KB

Open AccessArticle

Signal-Driven Model Order Selection for MUSIC-Based HRV Spectral Characterization

by Perla Lizeth Garza-Barrón, Alejandro Barrientos-García, Carlos Mauricio Lastre-Domínguez, Claudia Angélica Rivera-Romero, Juvenal Villanueva-Maldonado and Jorge Ulises Muñoz-Minjares

Bioengineering 2026, 13(9), 1033; https://doi.org/10.3390/bioengineering13091033 (registering DOI) - 5 Sep 2026

Abstract

Heart rate variability (HRV) is a useful non-invasive tool for studying autonomic nervous system modulation under emotional stimulation; however, accurate estimation of dominant frequencies in HRV signals remains challenging due to their non-stationary nature and the sensitivity of some spectral methods to configuration [...] Read more.

Heart rate variability (HRV) is a useful non-invasive tool for studying autonomic nervous system modulation under emotional stimulation; however, accurate estimation of dominant frequencies in HRV signals remains challenging due to their non-stationary nature and the sensitivity of some spectral methods to configuration parameters. This work presents a methodology for the spectral characterization of HRV signals derived from ECG recordings from the DREAMER database, with emphasis on optimizing the model order of the MUSIC algorithm to improve dominant frequency localization within the physiological low-frequency (LF) and high-frequency (HF) bands. The proposed methodology included ECG signal preprocessing, R-peak detection, RR interval extraction, HRV interpolation, and spectral analysis using MUSIC, while evaluating different model orders through a signal-driven composite criterion based on AIC, MDL, ESTER, eigengap, and model complexity. The criteria were normalized using min–max normalization and combined using equal predefined weights. The results showed that the signal-driven selection of the parameter p produced recording-dependent model order configurations and different dominant frequency estimates across the analyzed stimuli. The resulting LF/HF agreement was evaluated independently after model order selection and showed non-uniform correspondence across stimuli and spectral estimators. Overall, these findings indicate that model order selection can substantially influence the spectral characterization obtained with MUSIC and provide a signal-driven framework for examining this dependence in HRV recordings. Full article

(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging and Biomedical Signal Processing: Second Edition)

25 pages, 2024 KB

Open AccessArticle

Machine-Learning-Assisted Multi-Energy Coupling and Battery–Grid Coordination for Deep Decarbonization of Smart Integrated Energy Systems: Modeling, Optimization, and Applications

by Yao Tong, Hailing Ma and Fuyi Du

Batteries 2026, 12(9), 341; https://doi.org/10.3390/batteries12090341 (registering DOI) - 5 Sep 2026

Abstract

In grid-connected smart integrated energy systems with high shares of renewable generation, source-side variability and inadequate coordination among battery storage, other energy carriers, and the external grid limit local renewable-electricity utilization and impede deep decarbonization. This study proposes a machine-learning-assisted, renewable-driven framework for [...] Read more.

In grid-connected smart integrated energy systems with high shares of renewable generation, source-side variability and inadequate coordination among battery storage, other energy carriers, and the external grid limit local renewable-electricity utilization and impede deep decarbonization. This study proposes a machine-learning-assisted, renewable-driven framework for multi-energy coupling and scenario-based multi-objective optimization of electricity–heat–hydrogen–storage systems. Historical meteorological and load data are processed using K-means clustering and Latin hypercube sampling to construct representative operating scenarios across multiple volatility regimes and characterize source–load uncertainty. The equipment model includes photovoltaic arrays, wind turbines, heat pumps, electrolyzers, fuel cells, grid-interactive battery energy storage, thermal storage, and hydrogen storage; cross-carrier conversion dynamics and emissions from purchased electricity and natural gas are embedded in the energy-balance constraints. A mixed-integer linear programming formulation then co-optimizes battery charging and discharging, grid exchange, and other multi-energy flows with respect to operating cost, carbon emissions, and renewable-energy curtailment. At 95% renewable-energy penetration, the proposed method achieves a renewable-energy absorption rate of 91.6% and a curtailment rate of 8.4%. Across the carbon-price cases, annualized operating cost ranges from 126.5 × 10⁴ to 141.2 × 10⁴ USD yr⁻¹, while carbon-emission intensity ranges from 26.4 to 38.5 gCO₂/kWh_eq. Under the specified high-risk grid disturbances, the coordinated strategy limits load shedding to 1.8%—73% below deterministic scheduling and 79% below the heuristic benchmark—and maintains 92.6% hydrogen self-sufficiency. These results provide a data-driven modeling and decision framework for battery–grid coordination and deep decarbonization in smart integrated energy systems. Full article

(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)

24 pages, 20279 KB

Open AccessArticle

A-Predator: A Multibeam Echosounder Point Cloud Registration Network with Anisotropic Kernel Point Convolution

by Feihu Zhang, Penghao Wang, Liguo Luo, Tingfeng Tan and Fen Liu

Remote Sens. 2026, 18(17), 3035; https://doi.org/10.3390/rs18173035 (registering DOI) - 5 Sep 2026

Abstract

Underwater point cloud registration using Multibeam Echosounder (MBES) data is fundamental to marine exploration and seafloor mapping. However, MBES point clouds present unique challenges compared to terrestrial Light Detection and Ranging (LiDAR): high noise levels, low overlap rates, and strongly anisotropic distributions caused [...] Read more.

Underwater point cloud registration using Multibeam Echosounder (MBES) data is fundamental to marine exploration and seafloor mapping. However, MBES point clouds present unique challenges compared to terrestrial Light Detection and Ranging (LiDAR): high noise levels, low overlap rates, and strongly anisotropic distributions caused by the strip-like sonar scanning pattern. These characteristics degrade existing registration algorithms, which predominantly assume locally isotropic point distributions. To address these challenges, this paper proposes Anisotropic Kernel Point Convolution (A-KPConv), a novel operator tailored to the strip-like structure of MBES point clouds. A-KPConv uses Principal Component Analysis (PCA) to estimate local geometric principal directions and constructs an affine transformation that adapts the convolution kernel shape and orientation to align with the local geometry, thereby shifting feature extraction from isotropic aggregation to structure-aware feature learning along the principal structural directions. Building upon this operator, we integrate A-KPConv into Predator—a framework for low-overlap registration—to develop A-Predator, in which the standard isotropic KPConv in the first three encoder layers is replaced with A-KPConv so that structure-aware feature learning is performed where geometric information is most salient. Extensive experiments on the public Dotson-east dataset and a self-collected LiQuan Lake (LQL) MBES dataset demonstrate that A-Predator achieves the highest registration recall among the evaluated methods. On Dotson-east, recall improves from 31.63% to 59.55% under 10% overlap, with consistently low translation and rotation errors. Ablation studies support the effectiveness of A-KPConv relative to the evaluated anisotropic operators, and cross-dataset experiments suggest more effective transfer than the evaluated baselines from Dotson-east to the rescaled LQL-MBES data without fine-tuning. Full article

(This article belongs to the Section Ocean Remote Sensing)

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22 pages, 755 KB

Open AccessArticle

Latency Mismatch in Platformized Journalism and Media: AI-Mediated Access and the Temporal Governance of Cognitive Practices

by Edu William

Journal. Media 2026, 7(3), 182; https://doi.org/10.3390/journalmedia7030182 (registering DOI) - 5 Sep 2026

Abstract

Journalism and media studies increasingly describe a communication environment in which news, explanation and public orientation are accessed through search engines, social feeds, notifications, short-form video, platform summaries, podcasts and conversational artificial intelligence. Existing accounts identify attention scarcity, information overload, platform power, news [...] Read more.

Journalism and media studies increasingly describe a communication environment in which news, explanation and public orientation are accessed through search engines, social feeds, notifications, short-form video, platform summaries, podcasts and conversational artificial intelligence. Existing accounts identify attention scarcity, information overload, platform power, news avoidance and AI disruption, yet they do not fully explain why some communicative practices become less sustainable while faster forms of orientation expand under the same conditions. This conceptual paper develops an integrative theoretical synthesis across media practice theory, deep mediatization, platform studies, digital journalism, digital reading research, cognitive load theory, acceleration studies and AI-mediated communication. It proposes latency mismatch as a middle-range mechanism of temporal selection. Latency mismatch occurs when the time a practice requires to generate stable understanding exceeds the continuity intervals that a media environment makes available, rewards or treats as reasonable, especially when lower-latency alternatives provide sufficient orientation. The concept clarifies how platformized journalism and media environments govern not only visibility and attention but also the durations through which information becomes meaningful, trustworthy and actionable. It reframes long-form news reading, investigative engagement and other high-latency practices as structurally vulnerable but socially necessary conditions of public knowledge. Full article

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14 pages, 22413 KB

Open AccessArticle

Rapid and Reversible Capture of PFOS from Complex Water Matrices by an Earth-Abundant Iron(III)–Carboxylate Metal–Organic Framework

by Haoming Yang and Yuan Yu

Polymers 2026, 18(17), 2171; https://doi.org/10.3390/polym18172171 (registering DOI) - 5 Sep 2026

Abstract

Background: Perfluorooctane sulfonate (PFOS) is a globally recognised persistent, bioaccumulative and toxic pollutant. Under China GB 5749-2022 and the US EPA 2024 drinking water MCL, permissible levels have fallen to 40 ng L⁻¹ and 4 ng L⁻¹, respectively, placing unprecedented [...] Read more.

Background: Perfluorooctane sulfonate (PFOS) is a globally recognised persistent, bioaccumulative and toxic pollutant. Under China GB 5749-2022 and the US EPA 2024 drinking water MCL, permissible levels have fallen to 40 ng L⁻¹ and 4 ng L⁻¹, respectively, placing unprecedented demands on remediation technologies. Methods: An iron(III)–carboxylate metal–organic framework prepared from low-cost precursors (denoted MOF-LC, [Fe₃O(BDC)₃Cl]·x(solvent)) was synthesised via a one-pot solvothermal route from FeCl₃·6H₂O and terephthalic acid (H₂BDC). The material was characterised by PXRD, N₂ adsorption, FTIR, TGA, XPS, elemental analysis and ICP-OES. Adsorption performance was evaluated under varying initial concentrations, contact times, pH values, coexisting inorganic anions (Cl⁻, NO₃⁻, SO₄²⁻, HCO₃⁻, PO₄³⁻) and humic acid backgrounds, and by a panel of six water matrices. Results: MOF-LC exhibited a BET surface area of 1528 m² g⁻¹ and a dominant pore centred at 1.9 nm, which is geometrically compatible with the 1.36 nm molecular length of PFOS. Adsorption reached ≈95% of equilibrium capacity within 30 min and was best described by the pseudo-second-order model (R² = 0.998). Measured uptake reached 800.6 mg g⁻¹ at 298 K, corresponding to a Langmuir maximum capacity of 802 mg g⁻¹ (note that all adsorption experiments were conducted at mg L⁻¹ concentrations, several orders of magnitude above the regulatory limits cited above). Removal exceeded 88% across all six water matrices. PFOS removal efficiency fell from 99.2% to 85.8% over seven adsorption–regeneration cycles using a 1% NH₄Cl/methanol eluent, with 90.6% of the initial BET surface area retained and Fe leaching below 45 µg L⁻¹. Conclusions: Electrostatic, hydrophobic and pore confinement contributions are proposed as cooperative interpretations consistent with the observations. MOF-LC is identified as a technically promising laboratory-scale sorbent for PFOS removal from complex water matrices. Performance at environmentally relevant ng L⁻¹ concentrations and economic viability at scale remain to be established. Full article

(This article belongs to the Special Issue Recent Advances in Functional Polymer Materials for Water Treatment—2nd Edition)

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37 pages, 7730 KB

Open AccessArticle

A Framework for Verifying Inter-Domain Relationships Using DANE-Based Trust Models on DNS-over-TLS-Enabled Authoritative Servers

by Toshio Murakami, Yuto Motogi, Rei Nakagawa, Yong Jin and Nariyoshi Yamai

Electronics 2026, 15(17), 4020; https://doi.org/10.3390/electronics15174020 (registering DOI) - 5 Sep 2026

Abstract

The Domain Name System (DNS) is a fundamental Internet technology that maps domain names to resource information such as IP addresses. DNS Security Extensions (DNSSEC) ensure the integrity of DNS responses, while DNS over TLS (DoT), DNS over HTTPS (DoH), and DNS over [...] Read more.

The Domain Name System (DNS) is a fundamental Internet technology that maps domain names to resource information such as IP addresses. DNS Security Extensions (DNSSEC) ensure the integrity of DNS responses, while DNS over TLS (DoT), DNS over HTTPS (DoH), and DNS over QUIC (DoQ) enhance the confidentiality of name resolution communications. However, these technologies do not verify whether domains resembling legitimate domains, or service domains operated by legitimate organizations, are organizationally or operationally related to them. This paper proposes a framework using X.509 certificates presented by DoT-enabled authoritative DNS servers and DNS-Based Authentication of Named Entities (DANE) TLSA resource records. Here, a related domain belongs to a different namespace but is authorized by the same organization, service provider, or similar entity. Standard TLS certificates are verified using the X.509 Public Key Infrastructure (PKIX), whereas additional certificates indicating related domains are verified using DANE Trust Anchor (DANE-TA), with a source-domain private certification authority (CA) as the trust anchor. We implemented additional certificate verification, Server Name Indication (SNI)-based certificate selection, resolver-side relationship verification, and full-certificate caching. Certificate information returned to stub resolvers or applications is converted into TLSA-compatible data and appended to DNS responses. Evaluation results confirmed that the prototype operated at a sufficiently practical speed and that full-certificate caching reduced the latency associated with inter-domain relationship verification. Full article

(This article belongs to the Special Issue Advanced Research in Technology and Information Systems: Third Edition)

32 pages, 29968 KB

Open AccessArticle

DDEF-Net: A Difference-Guided Detail Enhancement Fusion Network for UAV-Based RGB-T Object Detection

by Yujie Li, Zhengsheng Chen, Decao Ma and Junjie Xu

Remote Sens. 2026, 18(17), 3032; https://doi.org/10.3390/rs18173032 (registering DOI) - 5 Sep 2026

Abstract

This paper proposes a Difference-guided Detail Enhancement Fusion Network (DDEF-Net) for UAV-based RGB–thermal (RGB-T) object detection, which enables effective complementary exploitation of visible and infrared information in complex scenarios. A Difference-guided Kolmogorov–Arnold Network (KAN) Calibration Fusion module (DKCF) is designed to explicitly model [...] Read more.

This paper proposes a Difference-guided Detail Enhancement Fusion Network (DDEF-Net) for UAV-based RGB–thermal (RGB-T) object detection, which enables effective complementary exploitation of visible and infrared information in complex scenarios. A Difference-guided Kolmogorov–Arnold Network (KAN) Calibration Fusion module (DKCF) is designed to explicitly model cross-modal discrepancies and incorporate KAN-based nonlinear calibration, improving the selection of informative features and reducing redundant feature interference during multimodal fusion. Furthermore, a Scharr–Fourier Detail Enhancement module (SFDE) is introduced to jointly leverage Scharr edge priors and Fourier-domain information to strengthen low-level visible feature representations and preserve fine-grained structural cues. On the DroneVehicle dataset, DDEF-Net achieves 84.9% mAP@0.5 and 72.6% mAP@0.5:0.95, improving the RGB–IR baseline by 3.5 and 4.0 percentage points, respectively, with 4.4 M parameters and 12.5 GFLOPs. An additional experiment on the VEDAI visible–near-infrared (NIR) aerial dataset after dataset-specific training provides supplementary evidence that the proposed modules remain beneficial under a different paired multimodal imaging setting. Corruption experiments show improved robustness to Gaussian and motion blur, whereas the model remains sensitive to strong Gaussian noise. Full article

(This article belongs to the Special Issue Multi-Sensor Fusion for UAV Remote Sensing: Deep Learning-Driven Target Detection in Complex Environments)

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30 pages, 14091 KB

Open AccessArticle

Machine Learning-Based GNSS Positioning Error Compensation for Static Receivers

by Viorel Carbune, Maria Gutu, Irina Cojuhari, Lilia Rotaru and Vladimir Melnic

Geosciences 2026, 16(9), 356; https://doi.org/10.3390/geosciences16090356 (registering DOI) - 5 Sep 2026

Abstract

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for [...] Read more.

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of

R_{95}

= 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

(This article belongs to the Special Issue Earth Observation by GNSS and GIS Techniques, 2nd Edition)

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28 pages, 698 KB

Open AccessArticle

Volatility Specification and Deep Learning Anomaly Detection: Robustness of Transformer Architectures to GARCH Model Choice

by Sara Chegdal, Mustapha Kabil and Abdeljalil Settar

J. Risk Financ. Manag. 2026, 19(9), 690; https://doi.org/10.3390/jrfm19090690 (registering DOI) - 5 Sep 2026

Abstract

Modern financial risk management increasingly relies on transformer-based anomaly detection, although the sensitivity of these methods to the underlying volatility model specifications remains unexplored. This study thoroughly compares four GARCH variants—symmetric GARCH and asymmetric specifications (EGARCH, GJR-GARCH, APARCH)—across two state-of-the-art transformer architectures (TranAD [...] Read more.

Modern financial risk management increasingly relies on transformer-based anomaly detection, although the sensitivity of these methods to the underlying volatility model specifications remains unexplored. This study thoroughly compares four GARCH variants—symmetric GARCH and asymmetric specifications (EGARCH, GJR-GARCH, APARCH)—across two state-of-the-art transformer architectures (TranAD for point anomalies, VTT for regime detection) using S&P 500 returns spanning 1980 to 2026. The investigation addresses a fundamental question for practitioners integrating econometric and deep learning methods: does the additional complexity of asymmetric volatility modeling yield meaningfully different anomaly detection when passed through transformer architectures? The analysis reveals that the GARCH specification affects anomaly severity rankings rather than detection consensus, with high cross-model agreement (minimum Jaccard of 0.88 for TranAD and 0.76 for VTT) despite statistically significant differences in score distributions. Asymmetric models exhibit extended post-crisis sensitivity, driven by their stronger response to negative shocks—the leverage effect captured by the GJR-GARCH threshold term—rather than by greater persistence; indeed, the symmetric GARCH exhibits the longest half-life. This enables specification selection based on risk philosophy: conservative monitoring via GJR-GARCH or efficient normalization via symmetric specifications. The choice of detection method—point versus regime identification—proves more consequential for anomaly detection performance than volatility model specification. Full article

(This article belongs to the Topic Modern Challenges and Innovations in Financial Econometrics)

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24 pages, 4680 KB

Open AccessArticle

Error Estimation of Signed Networks Based on Expectation-Maximization Algorithm

by Ruochen Zhang, Zijie Jia and Jiarui Fan

Entropy 2026, 28(9), 993; https://doi.org/10.3390/e28090993 (registering DOI) - 5 Sep 2026

Abstract

Data obtained from experiments and surveys in human social systems are inevitably influenced by systematic measurement errors, and network data are no exception. Despite the prevalence of error in social network data, current research often lacks rigorous estimation of its expected precision, which [...] Read more.

Data obtained from experiments and surveys in human social systems are inevitably influenced by systematic measurement errors, and network data are no exception. Despite the prevalence of error in social network data, current research often lacks rigorous estimation of its expected precision, which may lead to biased conclusions. Signed networks, which encode both positive and negative relationships, constitute an important component of network science, and conducting measurement error analysis on them can substantially enhance the accuracy of social network analysis. This paper proposes a set of error measurement tools based on the Expectation-Maximization (EM) algorithm, specifically designed to estimate errors in signed network data. We extend traditional experimental error estimation to the network domain, derive a general error estimation method for signed networks, and validate its scientific validity and practical utility through extensive simulation experiments on both synthetic and real-world networks. The experiments reveal that network density and the ratio of positive to negative edges significantly influence the posterior probability distribution of the adjacency matrix. Specifically, as density increases, edge estimation accuracy exhibits a U-shaped trend, and the proportion of negative edges shows a nonlinear relationship with accuracy. The proposed method is applicable to repeatedly measured signed networks and provides a reliable framework for reconstructing network structures as faithfully as possible. Full article

(This article belongs to the Special Issue Statistical Approaches for Modeling Human Social Systems)

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27 pages, 15006 KB

Open AccessArticle

Graded Mulberry Leaf Supplementation Shapes Gut Microbiota, Reprograms Intestinal Metabolism, and Maintains Intestinal Chemical–Immune Barrier Homeostasis in Amur Sturgeon: A Multi-Omics Study

by Wanwan Zhu, Nan Zhang, Xuekai Wang, Yi Xiong, Yongsheng Wang and Fuyu Yang

Biology 2026, 15(17), 1556; https://doi.org/10.3390/biology15171556 (registering DOI) - 5 Sep 2026

Abstract

Mulberry leaf contains abundant phytochemicals with antioxidant and immunomodulatory activities. However, systematic insight into its dose-dependent regulatory effects on the intestinal health of Amur sturgeon remains limited. In the present study, multi-omics approaches, including 16S rRNA gene sequencing, untargeted metabolomics, transcriptomics, together with [...] Read more.

Mulberry leaf contains abundant phytochemicals with antioxidant and immunomodulatory activities. However, systematic insight into its dose-dependent regulatory effects on the intestinal health of Amur sturgeon remains limited. In the present study, multi-omics approaches, including 16S rRNA gene sequencing, untargeted metabolomics, transcriptomics, together with RT-qPCR, were applied to investigate graded dietary mulberry leaf supplementation in Acipenser schrenckii. Juvenile sturgeons were fed four experimental diets containing 0%, 2%, 4% and 6% mulberry leaf over a 10-week feeding trial. Dietary mulberry leaf caused no adverse impacts on growth performance or intestinal digestive capacity. Although the overall structure of the intestinal microbiota remained stable, beneficial bacterial taxa were enriched in a dose-dependent manner. Intestinal metabolism underwent hierarchical remodelling: low inclusion levels supported basal nutrient metabolism, medium inclusion strengthened antioxidant capacity, and high inclusion reprogrammed lipid metabolism and immune function. Mulberry leaf reinforced the intestinal chemical barrier by balancing redox homeostasis and reducing mucosal epithelial permeability. Moreover, intestinal immunity was modulated through three sequential phases: initial innate immune priming, B-cell homing, and the establishment of sustained immune tolerance. In conclusion, mulberry leaf maintains intestinal chemical–immune barrier homeostasis in a dosage-tunable manner, supporting its potential application as a functional aquafeed ingredient. Full article

(This article belongs to the Section Marine and Freshwater Biology)

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16 pages, 14657 KB

Open AccessArticle

Calcium-Specific Catalytic Deactivation of Lipopeptides: Multiscale Insights into Hydrolysis Mechanisms and Computationally Proposed Tolerance Boundaries Under Reservoir Conditions

by Shenghui Yue, Bowen Xu, Zhennan Liu, Qiongyao Chen, Yanbin Cao, Weidong Wang, Hao Ren, Wenyue Guo, Qinglin Shu and Houyu Zhu

Catalysts 2026, 16(9), 804; https://doi.org/10.3390/catal16090804 (registering DOI) - 5 Sep 2026

Abstract

Enhanced oil recovery (EOR) is a crucial technology for improving crude oil recovery; it extracts residual oil from reservoirs through chemical, physical, or biological methods, thereby further increasing recovery rates after secondary recovery. Biosurfactants, particularly lipopeptides, have become a research focus in the [...] Read more.

Enhanced oil recovery (EOR) is a crucial technology for improving crude oil recovery; it extracts residual oil from reservoirs through chemical, physical, or biological methods, thereby further increasing recovery rates after secondary recovery. Biosurfactants, particularly lipopeptides, have become a research focus in the field of EOR due to their excellent properties. However, existing studies have mainly concentrated on their production and characterization, while systematic investigation into their deactivation mechanisms and stability limits remains lacking at the molecular level. This study integrates density functional theory (DFT), ab initio molecular dynamics (AIMD), and classical molecular dynamics (MD) simulations to systematically reveal the hydrolysis mechanisms and stability boundaries of lipopeptide model molecules under high-temperature and high-salinity reservoir conditions from a multiscale perspective. DFT calculations show significant differences in the energy barriers among different hydrolysis sites in lipopeptide molecules, with side-chain structure being a key factor influencing amide bond hydrolysis. Metal ions present in reservoir environments (Na⁺, K⁺, Ca²⁺, Mg²⁺), particularly divalent ones (Ca²⁺, Mg²⁺), can act as catalysts to reduce the hydrolysis energy barrier. Electronic structure analysis reveals that the catalytic effect originates from the polarization of the carbonyl oxygen by metal ions, weakening the covalent character of the C=O bond. AIMD simulations reveal that only Ca²⁺ can specifically activate the hydrolysis of lipopeptide molecules at certain distances (critical distance), while other cations (e.g., Mg²⁺, K⁺, Na⁺) do not exhibit similar catalytic activity. MD simulations further demonstrate that Ca²⁺ ion concentration and temperature are the dominant factors influencing Ca²⁺ permeation toward hydrolysis sites (limit distance), with other ions having a weaker effect. By systematically simulating lipopeptide behavior under varying temperature and ion concentration conditions, a catalytic hydrolysis criterion based on the effective distance of Ca²⁺ interaction (i.e., limit distance ≤ critical distance) is established through multiscale simulation, and the performance boundaries of its temperature and salt tolerance are preliminarily defined. This study provides a theoretical basis and quantitative design guidance for the applicability of lipopeptide-based biosurfactants in high-temperature and high-salinity reservoirs. Full article

(This article belongs to the Section Catalysis for Sustainable Energy)

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20 pages, 3472 KB

Open AccessArticle

Benefits of Four-Tiered Classification of Exercise-Induced Left Ventricular Hypertrophy in Adolescent Athletes

by Dora Szabo, Dora Babocsay, Kata Eklics, Istvan Szokodi, Miklos Toth, Pongrac Acs, Attila Cziraki and Zsolt Sarszegi

J. Clin. Med. 2026, 15(17), 6879; https://doi.org/10.3390/jcm15176879 (registering DOI) - 5 Sep 2026

Abstract

Background: Exercise-induced left ventricular hypertrophy (LVH) has been extensively investigated in adolescent athletes using the conventional two-tiered classification (2TC). However, the four-tiered classification (4TC) allows further differentiation of LVH patterns by incorporating three-dimensional information on left ventricular (LV) geometry. This study aimed to [...] Read more.

Background: Exercise-induced left ventricular hypertrophy (LVH) has been extensively investigated in adolescent athletes using the conventional two-tiered classification (2TC). However, the four-tiered classification (4TC) allows further differentiation of LVH patterns by incorporating three-dimensional information on left ventricular (LV) geometry. This study aimed to compare LVH assessment using the two classification systems and characterize the reclassification patterns. Methods: A total of 121 adolescent athletes (mean age: 15.1 ± 1.6 years) and 114 adult athletes (mean age: 22.9 ± 3.7 years), all competing at the national level, underwent comprehensive echocardiographic and anthropometric evaluations. Results: The application of 4TC resulted in redistribution across LV geometric categories compared with conventional 2TC. Twenty-three (19.0%) adolescent and 25 (22%) adult athletes were reclassified, including seven (5.8%) and 17 (15%), respectively, who shifted from normal geometry under the 2TC to different LVH categories under the 4TC. Reclassification was observed across sex and sporting discipline subgroups, although the patterns varied. In runners and triathletes, the combined prevalence of eccentric non-dilated and eccentric dilated LVH was 22.7% using the 4TC versus 4.5% eccentric LVH under the 2TC classification. LVM was strongly associated with the combined contribution of cumulative training duration, lean body mass, and body surface area (r = 0.785, p < 0.001), whereas training duration alone was not significantly associated with the LVH parameters. Conclusions: The 2TC and 4TC approaches result in different assessments of LV geometry in highly trained athletes, with the 4TC providing additional characterization of LVH patterns by considering the presence or absence of LV dilatation. Reclassification patterns varied according to sex and sporting discipline, highlighting the importance of sport-related characteristics when interpreting exercise-induced LV remodeling in athletes. Full article

(This article belongs to the Special Issue Sports Cardiology: Current Status and Future Challenges)

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22 pages, 724 KB

Open AccessArticle

Discriminatory Capacity of Anthropometric Indicators and Physical Fitness Tests for Identifying Waist-to-Height Ratio-Defined Cardiometabolic Risk in Adolescents According to Sex and Biological Maturity

by Victoria López-Lombó, Adrián Mateo-Orcajada, Lucía Abenza-Cano, J. Arturo Abraldes, Mario Albaladejo-Saura and Raquel Vaquero-Cristóbal

Children 2026, 13(9), 1201; https://doi.org/10.3390/children13091201 (registering DOI) - 5 Sep 2026

Abstract

Background/Objectives: Field-based health screening in adolescents requires accurate, non-invasive, and feasible diagnostic tools. This study evaluated the discriminatory capacity of anthropometric parameters and physical fitness tests for identifying waist-to-height ratio (WHtR)-defined surrogate cardiometabolic risk across sex and biological maturity status, determining exploratory cut-off [...] Read more.

Background/Objectives: Field-based health screening in adolescents requires accurate, non-invasive, and feasible diagnostic tools. This study evaluated the discriminatory capacity of anthropometric parameters and physical fitness tests for identifying waist-to-height ratio (WHtR)-defined surrogate cardiometabolic risk across sex and biological maturity status, determining exploratory cut-off thresholds. Methods: A cross-sectional study was conducted with 2944 Spanish adolescents (1459 males, 1485 females; age: 13.35 ± 1.20 years). Anthropometric variables (BMI, skinfolds, fat/muscle mass) and physical fitness tests (20-m shuttle run, handgrip, CMJ, SBJ, 20-m sprint, curl-up; evaluated in raw and body-mass-normalized formats) were assessed. Biological maturation was estimated via Age at Peak Height Velocity (APHV) using Mirwald equations and categorized into early, on-time, and late maturers relative to the sample mean. Surrogate risk was operationalized strictly as WHtR ≥ 0.50, without direct assessment of biochemical or hemodynamic parameters. Receiver operating characteristic (ROC) curves, optimal Youden-derived cut-offs, and internal bootstrap validation (1000 resamples) were performed. Results: Anthropometric parameters demonstrated high discriminatory performance for detecting WHtR-defined surrogate risk across all sex and maturation cohorts (AUC = 0.88–0.98). Unadjusted physical fitness tests showed poor overall classification performance. However, normalizing fitness metrics for body mass (particularly relative CMJ and cardiorespiratory fitness) substantially improved discriminatory capacity. Optimal cut-off values for anthropometric indicators displayed an observed descending pattern across progressively later-maturing cohorts. Conclusions: Anthropometric parameters represent highly accurate field proxies for WHtR-defined surrogate risk screening in adolescents. While unadjusted fitness metrics have limited utility for this anthropometric outcome, body-mass-normalized fitness parameters substantially recover classification capacity. Biological maturation timing should be carefully considered when future research derives and externally validates adolescent screening thresholds, as current cut-offs remain exploratory and sample-derived. Full article

(This article belongs to the Special Issue Promotion of Physical Activity in Children and Adolescents from the School Setting: Present and Future Challenges)

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26 pages, 17805 KB

Open AccessArticle

Individual and Sex Differences in Behavior and Cortical Excitability in the Rat Valproate Model of Autism

by Viktor Kelemen, Zsuzsanna Szeredi-Faragó, Júlia Puskás, Sándor Borbély, Norbert Bencsik, Attila Szűcs and Petra Varró

Cells 2026, 15(17), 1617; https://doi.org/10.3390/cells15171617 (registering DOI) - 5 Sep 2026

Abstract

The rodent prenatal valproate (VPA) treatment is a widely used animal model of idiopathic autism spectrum disorder (ASD). However, the presence of autistic-like symptoms is highly variable in treated offspring. The disruption of the excitation–inhibition balance of certain brain areas has been proposed [...] Read more.

The rodent prenatal valproate (VPA) treatment is a widely used animal model of idiopathic autism spectrum disorder (ASD). However, the presence of autistic-like symptoms is highly variable in treated offspring. The disruption of the excitation–inhibition balance of certain brain areas has been proposed as a main feature in both human ASD and the VPA model. The current study presents a detailed analysis of neural development, diverse behaviors, and neocortical excitability in a high number of individually identified VPA-treated rat offspring of both sexes. Neocortical excitability was assessed using electrophysiological and intrinsic optical imaging methods. Prenatal VPA treatment caused a delay in early postnatal sensorimotor development in rat pups of both sexes. Behavioral effects were associated with congenital morphological alterations (e.g., tail kink), as shown by stratification by principal component analysis and correlation analysis. Social deficits were evident only in the VPA-treated male offspring, while the females appear to be resistant to this effect. Prenatal VPA treatment was associated with sex- and region-dependent alterations in the excitability and seizure susceptibility of entorhinal and prefrontal cortical slices. Cortical excitability measures showed partial correlation with morphological and behavioral parameters. Thus, the current study further supports the validity of the rodent prenatal VPA model as a model of ASD for both sexes, but the variable degree of affectedness should be taken into account. Congenital malformation severity, including tail kink, may serve as a readily observable marker for subsequent physiological alterations, particularly in males. Full article

(This article belongs to the Special Issue Neurodevelopmental Factors of Autism Spectrum Disorder: Etiology to Therapy)

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26 pages, 10605 KB

Open AccessArticle

CARE-Net: A Compact Framework for Vibration Damper Detection in UAV-Based Transmission Line Inspection

by Yujie Zhou, Chao Ji, Huan Wang, Long Zhao, Peng Yang and Chao Zhang

Sensors 2026, 26(17), 5648; https://doi.org/10.3390/s26175648 (registering DOI) - 5 Sep 2026

Abstract

Vibration damper detection in unmanned aerial vehicle (UAV)-based transmission line inspection presents distinctive task-specific challenges: the targets are not only small and weakly textured, but also characterized by slender structures. Their effective identification therefore depends on the preservation of local contour cues and [...] Read more.

Vibration damper detection in unmanned aerial vehicle (UAV)-based transmission line inspection presents distinctive task-specific challenges: the targets are not only small and weakly textured, but also characterized by slender structures. Their effective identification therefore depends on the preservation of local contour cues and the appropriate organization of deep contextual responses. To address the limitations of conventional lightweight detectors in structural feature representation, cross-scale semantic consistency, and bounding-box localization, this paper proposes CARE-Net (Cascaded Attention and Refinement Enhanced Network), a compact detection framework for vibration damper detection. CARE-Net adopts an asymmetric design consisting of front-end structural enhancement and back-end contextual refinement. Specifically, the Cascaded Residual Attention Block (CRAB) is deployed in the backbone to strengthen the representation of slender contours and local structural features of vibration damper targets. The Dynamic Context Refinement Network (DCRN) is introduced at the backbone–neck transition to improve the contextual organization of deep features and the quality of cross-scale feature fusion. Meanwhile, an Adaptive Focal Complete IoU Loss (AF-CIoU) is proposed to optimize bounding-box regression for difficult samples without altering the inference architecture. A UAV-based vibration damper dataset covering three condition categories, namely normal, rusted, and dilapidated, is constructed in this study. Experimental results show that CARE-Net achieves an mAP^@0.5 of 0.951 and an mAP^@0.5:0.95 of 0.628 with 2.44 M parameters and 6.2 GFLOPs. Further configuration experiments indicate that, compared with repeatedly introducing attention enhancement into high-level features, stage-specific feature modeling is better suited to the slender small-object detection task investigated in this study. The proposed method provides a solution for intelligent vibration damper inspection of transmission lines that balances detection accuracy, model compactness, and potential for terminal-side application. Full article

(This article belongs to the Section Remote Sensors)

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