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21 pages, 6376 KB  
Article
Effects of Sub-Inhibitory Rifampicin, Minocycline, and Dalbavancin on Early Biofilm Formation and Transcriptional Responses in Staphylococcus aureus SA113
by Adam Bieda, Sabine Illner, Volkmar Senz, Stefan Oschatz, Niels Grabow, Micha Löbermann, Emil Christian Reisinger and Martina Sombetzki
Microorganisms 2026, 14(9), 2101; https://doi.org/10.3390/microorganisms14092101 (registering DOI) - 19 Sep 2026
Abstract
Implant-associated infections are often caused by biofilm-forming pathogens such as Staphylococcus (S.) aureus. While local antibiotic delivery systems aim to prevent adhesion and biofilm establishment, declining drug concentrations may result in sub-inhibitory exposure, which can modulate bacterial adaptation linked to antibiotic resistance, [...] Read more.
Implant-associated infections are often caused by biofilm-forming pathogens such as Staphylococcus (S.) aureus. While local antibiotic delivery systems aim to prevent adhesion and biofilm establishment, declining drug concentrations may result in sub-inhibitory exposure, which can modulate bacterial adaptation linked to antibiotic resistance, biofilm formation, and regulatory responses. We investigated the effects of sub-inhibitory antibiotic exposure on biofilm formation in S. aureus SA113 and associated transcriptional responses. The biofilm-producing strain S. aureus SA113 was exposed to sub-inhibitory concentrations of rifampicin, minocycline, and dalbavancin during early biofilm formation. Phenotypic effects were assessed by crystal violet staining, enumeration of colony-forming units, and scanning electron microscopy, while transcriptional responses were analyzed by qPCR. Despite stable counts of culturable adherent bacteria, sub-inhibitory antibiotic exposure differentially altered biofilm formation and transcriptional responses. Rifampicin was associated with increased biomass at higher sub-inhibitory concentrations and increased early expression of icaA, icaD (+4.2 log2) and fnbA (+2.7 log2) at 1/2× MIC. Minocycline reduced biomass at lower concentrations with partial recovery toward control levels at 1/2× MIC, while transcriptional analysis at 1/4× MIC after 6 h showed increased expression of icaA, icaD (+2.1 log2) and fnbA (+4.3 log2). Dalbavancin induced a distinct transcriptional response characterized by increased expression of vraS (+1.1 log2) and lrgA (+1.8 log2), without induction of matrix-associated genes, while adherent biofilm biomass was reduced to 42.9% at 1/2× MIC. Morphologically, this was associated with compact aggregates rather than diffuse biofilm structures. Sub-inhibitory antibiotic exposure differentially modulated early biofilm formation in SA113 in a drug-specific manner. Overall, the distinct dalbavancin-associated response may be relevant for the development of preventive local drug-delivery systems. Full article
(This article belongs to the Section Biofilm)
26 pages, 3654 KB  
Article
Collaborative Optimization of Ladle Furnace Operating Parameters Using Prediction Models and Case-Guided Genetic–Tabu Search
by Yuhong Du, Xiaolong Li and Dongfeng He
Processes 2026, 14(18), 2994; https://doi.org/10.3390/pr14182994 (registering DOI) - 19 Sep 2026
Abstract
Intelligent control of the ladle furnace (LF) process and its endpoint is essential for product quality and stable continuous casting. Existing studies mainly address endpoint prediction or operating-parameter recommendation. Prediction models rarely provide multivariable operating schemes directly, whereas recommendation models often suffer from [...] Read more.
Intelligent control of the ladle furnace (LF) process and its endpoint is essential for product quality and stable continuous casting. Existing studies mainly address endpoint prediction or operating-parameter recommendation. Prediction models rarely provide multivariable operating schemes directly, whereas recommendation models often suffer from insufficient coordination among modules and complex commissioning. This study proposes a collaborative LF operating-parameter optimization method combining endpoint prediction with case-guided genetic–tabu search. Given the initial heat state and target endpoint temperature, the method treats electric energy input, power-on duration, and key material additions as decision variables, evaluates each candidate using temperature and composition prediction models, and coordinates the variables through a unified objective. A dynamic weighting mechanism coupling generational annealing with feasible-population temperature-error feedback balances endpoint quality against resource input. Case-based reasoning guides population initialization, while a real-coded genetic algorithm and tabu search strengthen global exploration and local exploitation. For 400 independent historical heats, the method obtained a recommendation satisfying all model constraints for every heat. Relative to the corresponding historical operations, the mean recommended quantities of lime, slag agent, aluminum granules, high-carbon ferromanganese, electric energy input, and power-on duration were reduced by 6.98%, 12.79%, 9.34%, 8.85%, 7.89%, and 8.95%, respectively. Case-guided initialization improved first-generation solution quality and early convergence, whereas tabu search enhanced mid-to-late local refinement. The method converts existing endpoint-prediction capability into coordinated multivariable operating recommendations. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
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31 pages, 3787 KB  
Article
Digital Predistortion of Wideband Power Amplifiers Using Functionally Decoupled Envelope-Assisted Attention-Guided Recurrent Architecture
by Bingwen Qiu, Xiaoyu Li and Yunjie Zhao
Sensors 2026, 26(18), 5945; https://doi.org/10.3390/s26185945 (registering DOI) - 19 Sep 2026
Abstract
Wideband power amplifiers (PAs) operating with high-order modulation signals exhibit strong nonlinear distortion and dynamic memory effects, making real-time digital predistortion (DPD) increasingly challenging under strict computational constraints. This work proposes a functionally decoupled neural DPD architecture, termed EA-CCF-AttGRU, which explicitly separates instantaneous [...] Read more.
Wideband power amplifiers (PAs) operating with high-order modulation signals exhibit strong nonlinear distortion and dynamic memory effects, making real-time digital predistortion (DPD) increasingly challenging under strict computational constraints. This work proposes a functionally decoupled neural DPD architecture, termed EA-CCF-AttGRU, which explicitly separates instantaneous nonlinear feature representation from temporal memory compensation within a unified end-to-end framework. Instead of introducing envelope features, cross-channel fusion, and recurrent attention as isolated modules, the proposed architecture assigns different compensation functions to dedicated components: envelope-assisted augmentation and point-wise cross-channel fusion enhance instantaneous nonlinear representation, while attention-guided recurrent modeling captures dynamic memory effects. A global linear bypass further reduces the burden of nonlinear compensation by preserving the linear transformation. Experimental results under a 160 MHz 1024-ary quadrature amplitude modulation (1024-QAM) baseband excitation with a 10.38 dB peak-to-average power ratio (PAPR) demonstrate that the proposed method achieves an adjacent channel leakage ratio (ACLR) of −65.91 dBc, a normalized mean square error (NMSE) of −57.84 dB, and an error vector magnitude (EVM) of 0.07% with only 6009 trainable parameters. The proposed architecture achieves an effective complexity–performance trade-off for wideband DPD applications and provides potential for future hardware-oriented implementation and synthesis validation. Full article
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38 pages, 1683 KB  
Article
Digital Transformation and Organizational Resilience in the Turkish Hospitality Sector: The Mediating Role of Strategic Agility and the Moderating Effect of Digital Leadership Toward Sustainable Tourism Management
by Ahmad Hamad and Ayşem Iyikal Çelebi
Sustainability 2026, 18(18), 9626; https://doi.org/10.3390/su18189626 (registering DOI) - 19 Sep 2026
Abstract
The rapid advancement of digital technologies and the increasing frequency of organizational disruptions have made digital transformation and organizational resilience central concerns for hospitality management research and practice. Drawing on Dynamic Capabilities Theory and upper echelons theory, this study develops and tests a [...] Read more.
The rapid advancement of digital technologies and the increasing frequency of organizational disruptions have made digital transformation and organizational resilience central concerns for hospitality management research and practice. Drawing on Dynamic Capabilities Theory and upper echelons theory, this study develops and tests a dual-stage moderated mediation model examining the associations between digital transformation and organizational resilience through the mediating role of strategic agility, and the moderating role of digital leadership on both the digital transformation and strategic agility association and the strategic agility and organizational resilience association. Data were collected from 436 managers employed in 4- and 5-star hotels across major Turkish tourism destinations, and hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results confirm that digital transformation is positively and significantly associated with both organizational resilience and strategic agility, and that strategic agility is positively associated with organizational resilience. Strategic agility partially mediates the digital transformation and organizational resilience association, while digital leadership significantly moderates both the digital transformation and strategic agility association and the strategic agility and organizational resilience association, amplifying these associations under high digital leadership conditions. A formal moderated mediation test confirmed that the indirect effect of digital transformation on organizational resilience through strategic agility varies significantly across levels of digital leadership (index of moderated mediation = 0.027, p = 0.023). These findings advance the application of Dynamic Capabilities Theory to hospitality resilience research, enrich the strategic agility and digital leadership studies, and offer actionable insights for hotel executives and policymakers navigating digital transformation in disruption-prone tourism environments. While sustainability was not directly measured, the findings theoretically position digitally enabled agility and organizational resilience as sustainability-relevant outcomes contributing to the long-term viability of hospitality operations. Full article
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20 pages, 43556 KB  
Article
Contrasting Patterns of Soil Microbial Communities, Physicochemical Properties and Vegetation Characteristics Along Altitude and Soil Depths in an Alpine Ecosystem
by Xinyu Guo, Xue Yang, Xu Wang, Rui Chong, Shicheng Yan, Fujiang Hou, Peiqiang Yu, Zechen Peng and Tao Ran
Biology 2026, 15(18), 1663; https://doi.org/10.3390/biology15181663 (registering DOI) - 19 Sep 2026
Abstract
Altitude and soil depth were associated with vegetation, soil properties, and microbial communities in alpine ecosystems, yet a comprehensive understanding of how these gradients shape soil microbial and physicochemical characteristics on the Qinghai–Tibetan Plateau remains limited. Along altitudinal gradients (low, mid, and high) [...] Read more.
Altitude and soil depth were associated with vegetation, soil properties, and microbial communities in alpine ecosystems, yet a comprehensive understanding of how these gradients shape soil microbial and physicochemical characteristics on the Qinghai–Tibetan Plateau remains limited. Along altitudinal gradients (low, mid, and high) and soil depths (0–10 cm, 10–20 cm, 20–30 cm), we systematically investigated vegetation traits, soil physicochemical properties, microbial community composition, functional pathways, and co-occurrence networks. Aboveground biomass, community-weighted height, and soil pH declined with altitude. Soil pH and ammonium nitrogen were significantly associated with bacterial community variation in the RDA analyses. Surface and deeper soil layers differed in bacterial taxonomic composition and predicted functional potentials. Co-occurrence network properties also varied across altitude and soil-depth groups. These findings describe spatial associations among vegetation, soil conditions, and bacterial community patterns during the sampling period. This study improves our understanding of how interacting environmental structure soil microbial communities and influence ecosystem functioning in alpine ecosystems. Full article
(This article belongs to the Section Microbiology)
24 pages, 5218 KB  
Article
Early Detection of Pine Wilt Disease at the Pre-Visual Stage Using UAV-Based Hyperspectral Imagery
by Tianteng Zhang, Lei Feng, Botian Zhou, Yuxin Zhao, Ben Yang, Wenhua Zeng, Xinchao Li, Yucai Li and Ling Wu
Remote Sens. 2026, 18(18), 3229; https://doi.org/10.3390/rs18183229 (registering DOI) - 19 Sep 2026
Abstract
Pine wilt disease (PWD) causes forest decline, but pre-visual spectral responses are weak and spatially heterogeneous within crowns. We developed a UAV hyperspectral framework combining targeted spectral-index construction with patch-based probability aggregation to enhance spectral discrimination and preserve localised spectral responses. We retrospectively [...] Read more.
Pine wilt disease (PWD) causes forest decline, but pre-visual spectral responses are weak and spatially heterogeneous within crowns. We developed a UAV hyperspectral framework combining targeted spectral-index construction with patch-based probability aggregation to enhance spectral discrimination and preserve localised spectral responses. We retrospectively labelled 477 Masson pine crowns from two survey plots using multi-temporal observations. Pre-visual candidates remained green at image acquisition and the first follow-up but subsequently developed visible discolouration. Sensitive bands were screened with emphasis on the healthy–pre-visual and pre-visual–early boundaries, and narrow-band indices were constructed using the training partition of Dataset A. Two three-band indices, VI2 (609, 699, and 737 nm) and VI3 (699, 737, and 765 nm), were retained. A random forest trained on crown-mean index features generated class-probability responses for non-overlapping 3 × 3 patches, which were aggregated into crown-level descriptors and classified using shrinkage linear discriminant analysis. On the held-out Dataset A validation set, the framework achieved 90.80% overall accuracy, 86.81% balanced accuracy, 88.45% macro-F1, and 77.78% pre-visual F1. Pre-visual F1 exceeded the NDVI baseline by 16.67 percentage points and improved by 12.78 and 7.19 points over crown-mean classification and pixel-level aggregation, respectively. For transfer from Dataset A to Dataset B, adaptation with three labelled crowns per class improved mean OA from 78.69% to 81.98% over the zero-shot baseline across 100 trials. These results indicate that targeted spectral indices and patch-based spatial representation can capture weak and localised spectral responses for crown-level screening of pre-visual PWD candidates under the evaluated conditions. Full article
(This article belongs to the Section Forest Remote Sensing)
25 pages, 1526 KB  
Article
DNWI: A Drinking Water Quality Index for Sustainable Urban Distribution Networks, Complemented by an Analysis of User Perception
by Sonia Gonzaga-Vallejo, Adrián Ríos-Gonzaga, Daniel Cofre-Betancourt, Oscar Guerrero-Guarnizo and Nayeli Ramón-Chalán
Sustainability 2026, 18(18), 9625; https://doi.org/10.3390/su18189625 (registering DOI) - 19 Sep 2026
Abstract
This study evaluates drinking water quality in the urban distribution network of Loja, Ecuador, using the Distribution Network Water Quality Index (DNWI), complemented by citizen perception and expert-based weighting. Two sampling campaigns were conducted under low-rainfall conditions, collecting samples from household taps across [...] Read more.
This study evaluates drinking water quality in the urban distribution network of Loja, Ecuador, using the Distribution Network Water Quality Index (DNWI), complemented by citizen perception and expert-based weighting. Two sampling campaigns were conducted under low-rainfall conditions, collecting samples from household taps across three supply systems. Physicochemical and microbiological parameters were analyzed following standard methods, and normalized sub-indices were calculated to quantify deviations from regulatory thresholds. Additionally, a perception survey (n = 1015) and a Delphi-based expert evaluation were incorporated. Results revealed marked spatial variability, with critical DNWI values observed in distal sections of the CWS network (DNWI = 44.34), mainly driven by residual chlorine depletion and microbiological contamination. Non-parametric analysis (Kruskal–Wallis, p < 0.001) confirmed significant differences in perception across systems. Spearman correlations indicated that sensory water quality parameters, particularly color, strongly influence user satisfaction. The internal consistency of the perception scale was acceptable (Cronbach’s α = 0.634). The DNWI enables the identification of critical sectors, supports risk-based management, and provides an objective assessment of service management. Its comparison with citizen perception reveals gaps between sensory acceptability and technical quality, thereby strengthening sustainability, water governance, and progress toward SDG 6. Full article
(This article belongs to the Section Sustainable Water Management)
46 pages, 793 KB  
Review
Maritime GNSS Threats and Resilience: A Layered Review of Vulnerabilities and Defences
by Dimitrios Piromalis, Efthymios Tserepas and Panagiotis Papageorgas
Appl. Syst. Innov. 2026, 9(9), 197; https://doi.org/10.3390/asi9090197 (registering DOI) - 19 Sep 2026
Abstract
Global Navigation Satellite Systems (GNSS) underpin maritime Positioning, Navigation, and Timing (PNT), supporting bridge functions such as the Automatic Identification System (AIS), the Electronic Chart Display and Information System (ECDIS), radar overlays, and track-control systems. This dependence exposes vessels to non-malicious degradation, unintentional [...] Read more.
Global Navigation Satellite Systems (GNSS) underpin maritime Positioning, Navigation, and Timing (PNT), supporting bridge functions such as the Automatic Identification System (AIS), the Electronic Chart Display and Information System (ECDIS), radar overlays, and track-control systems. This dependence exposes vessels to non-malicious degradation, unintentional interference, jamming, spoofing, and replay-based (meaconing-type) deception. Using a structured scoping review with narrative synthesis, this paper synthesises evidence on maritime GNSS threats and resilience across the receiver-to-bridge chain. The review develops a maritime-oriented taxonomy and maps disruption across the radio-frequency (RF) front-end, acquisition and tracking, navigation-estimation, and integrated-bridge layers. It examines suppression-oriented resilience, including front-end mitigation, spatial anti-jamming, and multi-sensor fusion, alongside deception-oriented detection, integrity monitoring, and authentication, including correlator-domain monitoring, navigation-engine integrity monitoring, Galileo Open Service Navigation Message Authentication (OSNMA), and bridge-level cross-checks. The analytical novelty lies in integrating disruption mechanism, security objective, evidence type, layer of action, and residual protection gap within a maritime cross-layer decision framework spanning the receiver-to-bridge chain. This framework enables resilience measures to be interpreted and compared according to what they protect and what remains unprotected, supporting complementary rather than interchangeable defences. The synthesis shows that maritime GNSS resilience extends beyond the RF receiver to navigation-solution trust and integrated-bridge safety. Full article
22 pages, 35697 KB  
Article
Establishment and Characterization of a Brain-Derived Cell Line from Japanese Eel (Anguilla Japonica) and Transcriptomic Analysis of Its Responses to Sex Steroid Hormones
by Yajuan Huang, Xu Yan and Songlin Chen
Animals 2026, 16(18), 2951; https://doi.org/10.3390/ani16182951 (registering DOI) - 19 Sep 2026
Abstract
The Japanese eel (Anguilla japonica) is an economically important aquaculture species, but cellular mechanisms underlying sex steroid responses remain poorly characterized. In this study, we established and characterized a continuous brain-derived cell line from Japanese eel, designated AJBC. The cells exhibited [...] Read more.
The Japanese eel (Anguilla japonica) is an economically important aquaculture species, but cellular mechanisms underlying sex steroid responses remain poorly characterized. In this study, we established and characterized a continuous brain-derived cell line from Japanese eel, designated AJBC. The cells exhibited stable fibroblast−like morphology and continuous proliferation. Molecular characterization showed high expression of col1a1, a marker associated with fibroblast−like stromal populations, whereas neuronal and glial markers (map2, gfap, and sox2) showed low expression. The neural-associated marker nestin was detectable, indicating that AJBC cells retained certain brain-associated characteristics. AJBC cells were successfully transfected with a GFP−expressing plasmid, demonstrating their potential for genetic manipulation. Transcriptome analysis identified 3212 and 3522 differentially expressed genes following 17β−estradiol (E2) and 11-ketotestosterone (11−KT) treatment, respectively. KEGG analysis revealed enrichment of PI3K−Akt and MAPK signaling pathways, as well as cell adhesion and cytoskeletal-related processes. Representative reproductive-related genes, including sox9 and foxl2, showed differential expression following sex steroid hormone treatment. E2 increased esr1 and esr2 expression, whereas 11−KT increased ar expression, confirming the steroid-responsive characteristics of AJBC cells. This brain-derived cell line provides a useful in vitro model for investigating sex steroid−responsive molecular mechanisms and neuroendocrine-related processes in Japanese eel. Full article
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40 pages, 1680 KB  
Article
Teleoperated Quadruped Robot-Based SLAM LiDAR for Standardized Tree Stem Diameter Derivation in Spanish Mediterranean Savanna-like Woodlands: A Field Feasibility Study
by Leon Vehlken, Jan Wolf, Diana López Serrano, Marie Gröbner, Victor Rolo, Gerardo Moreno, M. Pilar Martín, Arnaud Carrara, Sung-Ching Lee, Georg Bareth and Alexander Jenal
Robotics 2026, 15(9), 176; https://doi.org/10.3390/robotics15090176 (registering DOI) - 19 Sep 2026
Abstract
Mediterranean Dehesa woodlands require monitoring of tree structural change, but conventional stem-diameter inventories are labor-intensive. Mobile simultaneous localization and mapping (SLAM) LiDAR can acquire three-dimensional tree structure, although inconsistent trajectories and uneven stem coverage may reduce measurement repeatability. This study evaluated whether a [...] Read more.
Mediterranean Dehesa woodlands require monitoring of tree structural change, but conventional stem-diameter inventories are labor-intensive. Mobile simultaneous localization and mapping (SLAM) LiDAR can acquire three-dimensional tree structure, although inconsistent trajectories and uneven stem coverage may reduce measurement repeatability. This study evaluated whether a teleoperated quadruped carrying a Hovermap ST sensor along standardized circular trajectories could acquire stem-level point clouds suitable for diameter-at-breast-height (DBH) estimation in open Dehesa terrain. Backpack-mounted surveys were conducted in 2025 at Majadas de Tiétar, followed by quadruped-mounted surveys in 2026 at two Spanish sites. DBH was derived from horizontal slices at 1.3 m using Circle Fitting, Convex Hull Peeling, and Kalman-filter reconstruction, then compared with height-matched manual references. Backpack estimates for 186 caliper-referenced observations achieved R² values of 0.853–0.869, Lin’s concordance coefficients of 0.787–0.833, and root-mean-square errors of 5.22–6.11 cm. After excluding one bifurcation-affected observation, 29 robot estimates achieved R² values of 0.930–0.943, concordance coefficients of 0.941–0.956, and root-mean-square errors of 3.17–3.84 cm. Robot acquisitions showed more balanced radial point distributions and fewer reconstruction errors. However, dataset differences preclude controlled platform comparison. Quadruped-mounted SLAM LiDAR therefore shows promise for standardized one-time DBH inventories, while paired same-tree repeatability studies remain necessary before application to multitemporal growth monitoring. Full article
(This article belongs to the Special Issue Multi-Robot Systems for Environmental Monitoring and Intervention)
23 pages, 3387 KB  
Article
Rapid and Interpretable Wheat Seed Variety Identification Using Morphology-Guided Feature Engineering and Ensemble Learning
by Li Wang, Jingyuan Yun, Chunmei Wang, Xueqiang Gao, Jianbo Liu, Limiao Deng and Tianyu Zhu
AgriEngineering 2026, 8(9), 397; https://doi.org/10.3390/agriengineering8090397 (registering DOI) - 19 Sep 2026
Abstract
Confirming seed variety identity is important in certification, breeding-material management and grain trade, but visually similar cultivars remain difficult to distinguish consistently. We present GAFE-Stack, a morphology-guided classifier that expands seven kernel measurements into twenty-one interpretable descriptors and combines five complementary learners. The [...] Read more.
Confirming seed variety identity is important in certification, breeding-material management and grain trade, but visually similar cultivars remain difficult to distinguish consistently. We present GAFE-Stack, a morphology-guided classifier that expands seven kernel measurements into twenty-one interpretable descriptors and combines five complementary learners. The method is evaluated on the small, balanced public UCI Seeds benchmark (blackN=210; black70 kernels per variety), whose measurements were extracted from soft X-ray images. Across ten repeats of stratified five-fold cross-validation, GAFE-Stack achieved 96.33black±2.29% accuracy and 96.67% under leave-one-out validation. Its observed mean differences from seven re-implemented references ranged from black0.48 to black4.29 percentage points; after accounting for dependence among repeated folds and applying Holm adjustment, none of the comparisons was significant at blackα=0.05. The strongest individual member, LightGBM, achieved a slightly higher mean accuracy (black96.76%), whereas GAFE-Stack had lower fold-level dispersion (black2.29% versus black2.60%) and fewer pooled Kama–Canadian confusions than the RBF-SVM baseline. The complete pipeline achieved black93.33% and black94.52% accuracy with black30 and black60 labelled training kernels, respectively, compared with black96.33% using the full training folds. From stored morphometric inputs, single-thread CPU training required black0.85 s and batch prediction processed approximately black52,600 kernels/s without a GPU. Applying the same dimension-typed construction rules to Raisin, Rice and Dry Bean datasets produced small positive mean changes of black0.09black0.37 points, with corrected intervals including zero. Lot-purity results are reported only as an exploratory resampling analysis of UCI observations. The present evidence therefore supports GAFE-Stack as an interpretable proof-of-concept approach to seed screening under benchmark conditions; validation on independently acquired kernels, measurement systems and physical seed lots remains future work. Full article
25 pages, 12662 KB  
Article
Toughening PLA via Diisocyanate-Induced Dynamic Vulcanization of Carboxylated Oleic Acid: Role of Crosslinked Network Topology
by Dongmei Xie, Xiao Li, Jiaqi Cai, Xiaodi Mao, Hongzhi Liu and Ping Zhang
Polymers 2026, 18(18), 2297; https://doi.org/10.3390/polym18182297 (registering DOI) - 19 Sep 2026
Abstract
Diisocyanate-induced dynamic vulcanization of difunctional fatty acids has emerged as a universal strategy to efficiently improve the impact resistance of polylactic acid (PLA). However, how the crosslinked network topology of the in situ formed polyamide elastomer (COPA) affects its toughening efficacy on PLA [...] Read more.
Diisocyanate-induced dynamic vulcanization of difunctional fatty acids has emerged as a universal strategy to efficiently improve the impact resistance of polylactic acid (PLA). However, how the crosslinked network topology of the in situ formed polyamide elastomer (COPA) affects its toughening efficacy on PLA remains unknown. Here, we synthesized two carboxylated fatty acids with distinct molecular architectures (TCOA and NCOA) from technical-grade and high-purity oleic acids via UV-initiated thiol-ene click chemistry. These two diacids, along with tetradecanedioic acid (TA) without a dangling chain, were dynamically vulcanized with hexamethylene diisocyanate (HDI) to toughen PLA. By varying NCO/COOH molar ratios between HDI and TCOA, their effects on gel content, crosslinking density, phase morphology, and mechanical properties of resulting blends were systematically investigated. With increasing the ratio, both gel content and interfacial adhesion with PLA in the blends were enhanced, accompanied by the transformation of phase structure from “sea-island” morphology to a partially or fully co-continuous one. At a molar ratio of 1.8:1, the notched impact strength value of the blend reached 86.5 kJ/m2. By substituting TCOA with high-purity NCOA or TA, comparable gel content and interfacial compatibilization level, and co-continuous morphologies were achieved. Notably, NCOA yielded a PLA blend with a remarkably higher impact toughness (131.0 kJ/m2). The linear chain structure of TA led to a higher crosslinking density of the formed TAPA domains, which unfavorably suppressed their cavitation during impact fracture and thus resulted in an inferior impact strength (7.0 kJ/m2). These findings provide valuable insights into the toughening mechanism of PLA via dynamic vulcanization of monomers. Full article
30 pages, 3786 KB  
Review
The Role of Diet in Functional Dyspepsia
by Luisa Bertin, Cedric Van de Bruaene, Elena Formisano, Marcella Pesce, Stefania Piccirelli, Daniele Salvi, Karen Routhiaux, Andrea Pasta, Francesco Calabrese, Federico Caldart, Salvatore Crucillà, Giovanni Sarnelli, Elisa Marabotto, Javier Chahuan, Tom van Gils and Edoardo Vincenzo Savarino
Nutrients 2026, 18(18), 3064; https://doi.org/10.3390/nu18183064 (registering DOI) - 19 Sep 2026
Abstract
Functional dyspepsia (FD) is a disorder of gut–brain interaction affecting approximately 7% of the global population under Rome IV criteria, characterised by postprandial fullness, early satiation, epigastric pain, and/or epigastric burning in the absence of identifiable organic disease. Meal ingestion is the dominant [...] Read more.
Functional dyspepsia (FD) is a disorder of gut–brain interaction affecting approximately 7% of the global population under Rome IV criteria, characterised by postprandial fullness, early satiation, epigastric pain, and/or epigastric burning in the absence of identifiable organic disease. Meal ingestion is the dominant symptom trigger in the majority of patients, yet the relationship between diet and FD is bidirectional: dietary exposures drive symptoms through established pathophysiological mechanisms, while FD itself can promote maladaptive eating behaviours and progressive dietary restriction with nutritional and psychological consequences. The pathophysiological substrate linking diet to symptom generation encompasses impaired gastric accommodation and emptying, visceral hypersensitivity to mechanical and chemical stimuli, low-grade duodenal mucosal immune activation and epithelial barrier impairment, alterations of the small intestinal microbiota, and gut–brain axis dysregulation. Dietary fat is the macronutrient most consistently implicated, acting through cholecystokinin-mediated chemosensory and mechanosensory pathways. Cross-sectional population data suggest that a high protein intake may also be associated with epigastric pain, although this association did not persist after multivariable adjustment, while the evidence linking total carbohydrate intake to symptoms remains sparse and inconsistent, so that no recommendation on total carbohydrate intake can currently be made. Cognitive factors, including learned food beliefs, expectancy, and nocebo effects, further modulate symptom perception independently of actual nutrient content. Among dietary patterns, Mediterranean-style eating offers the most coherent mechanistic rationale and the strongest observational support, while a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP) shows selective benefit in patients with postprandial distress syndrome features or prominent bloating. Common triggers including spicy foods, coffee, and alcohol show heterogeneous evidence driven by individual sensitivity rather than dose-related effects. Nutritional concerns are substantial: clinically significant weight loss affects approximately 35% of tertiary-care patients, and nearly 40% screen positive for avoidant/restrictive food intake behaviour using validated screening tools. Dietary management should therefore be individualised, nutritionally adequate, and integrated within a broader biopsychosocial framework, with screening for disordered eating before any elimination strategy is initiated. Full article
(This article belongs to the Special Issue Nutrition in Neurogastroenterology)
25 pages, 508 KB  
Article
Hybrid Graph Retrieval-Augmented Language Agents for Collaborative Recommendation
by Ivan Bulychev and Andrey Savchenko
AI 2026, 7(9), 380; https://doi.org/10.3390/ai7090380 (registering DOI) - 19 Sep 2026
Abstract
Recent advances in large language model (LLM) agents have shown promise for autonomous decision-making in recommender systems. However, existing approaches suffer from two fundamental limitations: flat agent memories that conflate different information modalities and prohibitive computational costs that prevent scaling beyond a few [...] Read more.
Recent advances in large language model (LLM) agents have shown promise for autonomous decision-making in recommender systems. However, existing approaches suffer from two fundamental limitations: flat agent memories that conflate different information modalities and prohibitive computational costs that prevent scaling beyond a few hundred users. We propose Hybrid-GraphRAG, a recommender system that integrates hierarchical agent memory structures, graph-based retrieval-augmented generation (Graph RAG), and knowledge distillation for scalable deployment. Our approach extends agent-based collaborative filtering by structuring agent memories into intrinsic, collaborative, and interaction tiers that disentangle different information types; performing multi-hop retrieval over a dynamically constructed heterogeneous interaction graph to enable relational reasoning; and distilling LLM-generated memory dynamics into efficient graph neural encoders with adaptive gating between full and efficient inference paths. Experiments on Amazon review datasets (CDs and Vinyl, Office Products) demonstrate that Hybrid-GraphRAG achieves recommendation quality comparable to full LLM-based agents while reducing computational cost by 85% and improving NDCG@10 by 12.7% over flat-memory agent baselines. Our results establish a principled bridge between semantic agent reasoning and scalable graph-based recommendation. Full article
(This article belongs to the Topic AI Agents: Progress, Architecture, and Applications)
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19 pages, 8106 KB  
Article
Effects of Wood Vinegar Application and Planting Density on Cadmium Uptake and Accumulation in a Maize–Wheat Rotation System Under Straw Removal Management
by Jing Duo, Ying Zhao, Yuhao Liu, Jiarun Ye, Zhenzhu Cao, Haitao Liu, Shiliang Liu, Guiying Jiang and Fang Liu
Agronomy 2026, 16(18), 1851; https://doi.org/10.3390/agronomy16181851 (registering DOI) - 19 Sep 2026
Abstract
Cadmium (Cd) contamination in agricultural soils threatens food safety and human health. To address this issue, wood vinegar (WV) and high-density planting were combined in our field study. The results revealed that the combined application significantly increased maize (Zea mays) grain [...] Read more.
Cadmium (Cd) contamination in agricultural soils threatens food safety and human health. To address this issue, wood vinegar (WV) and high-density planting were combined in our field study. The results revealed that the combined application significantly increased maize (Zea mays) grain and straw biomass while enhancing Cd accumulation in aboveground tissues. Notably, grain Cd concentrations remained relatively low, ranging from 0.032 to 0.047 mg·kg−1, whereas the Cd enrichment coefficients in stems and leaves rose by 70.03% and 67.42%, respectively. Additionally, WV application altered soil Cd speciation and reduced the exchangeable Cd fraction. The ZT2 treatment reduced the exchangeable Cd fraction by 24.56%, while the ZT3 treatment reduced total soil Cd by 6.91%, indicating its potential to enhance Cd removal. In the subsequent wheat (Triticum aestivum) season, maize straw removal combined with high-density planting considerably decreased Cd concentrations across wheat tissues, with maximum reductions of 25.59% in grain and 45.63% in straw. However, wheat grain Cd concentrations remained above the applicable food safety limit. These management practices were also associated with shifts in the soil microbial community, including changes in the relative abundance of Actinobacteria and Acidobacteria. Partial least squares path modeling (PLS-PM) indicated that Cd accumulation in maize straw showed the strongest negative association with Cd concentrations in wheat grain. Overall, integrating WV application with high-density planting represents a promising agronomic strategy for enhancing Cd phytoextraction by maize and mitigating Cd accumulation in subsequent wheat. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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