Saved Queries

by Sebastián Chapela, Jaen Cagua-Ordoñez, Juan Marcos Parise-Vasco, Daniel Tettamanti Miranda, Claudia Kecskes, Natalia Llobera, Jesica Asparch, Mariana Rella, María Victoria Peroni, Martha Montalvan, María Jimena Reberendo, Facundo Gutierrez, Mario O. Pozo, Ludwig Álvarez-Córdova and Daniel Simancas-Racines

J. Clin. Med. 2026, 15(5), 1677; https://doi.org/10.3390/jcm15051677 (registering DOI) - 24 Feb 2026

Abstract

Background: Accurately estimating resting energy expenditure (REE) in critically ill obese patients remains a significant clinical challenge, as predictive equations are consistently inadequate. Metabolic heterogeneity across obesity classes and the role of substrate utilization are insufficiently characterized. Objective: To evaluate the impact of different weight-normalization methods on the interpretation of REE and to identify independent metabolic determinants of weight-adjusted energy expenditure in critically ill patients with obesity. Methods: Bicentric cross-sectional study of 148 critically ill adults with obesity undergoing indirect calorimetry. REE normalized by actual body weight (REE/kg), ideal body weight (REE/IBW), and adjusted body weight (REE/AdjBW) was calculated. Multivariable models with robust standard errors (HC3), stratified analyses by obesity class (I–III) with a Chow test, and internal validation were performed using 10-fold cross-validation and bootstrap resampling (1000 iterations). Results: Absolute REE did not differ significantly between BMI categories (p = 0.679), while REE/kg progressively decreased from normal weight (27.8 kcal/kg/day) to class III obesity (16.9 kcal/kg/day; p < 0.001). The respiratory quotient (RQ) emerged as the most robust independent correlate of adjusted REE (β = −13 to −15 kcal·kg⁻¹·day⁻¹; p < 0.001), whereas clinical severity scores (SOFA, APACHE II) and comorbidity (Charlson) did not show significant associations. Stratified analyses revealed significant structural heterogeneity between obesity classes (F = 4.545, p = 0.0001), with no significant predictors identified in class III obesity, likely reflecting limited statistical power in this subgroup. Conclusions: Normalizing REE using different weight indices fundamentally alters its metabolic interpretation. RQ surpasses traditional clinical scores as a correlate of adjusted REE, consistent with a phenotype of metabolic inflexibility. The heterogeneity between obesity classes underscores the need for individualized indirect calorimetry rather than reliance on predictive equations. Full article

(This article belongs to the Special Issue Clinical Advances in Critical Care Medicine)

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13 pages, 856 KB

Open AccessArticle

Expert Perspectives on Managing Iron Deficiency in People with CKD and/or HF

by Sunil Bhandari, John G. F. Cleland, Fozia Z. Ahmed, Fraser J. Graham, Matt Hall, Paul R. Kalra, Philip A. Kalra, Kate I. Stevens, David C. Wheeler, Simon G. Williams, Dora. I. A. Pereira, Marco Soscia, Harry Lewis and Imogen Taylor

J. Clin. Med. 2026, 15(4), 1676; https://doi.org/10.3390/jcm15041676 - 23 Feb 2026

Abstract

Background: Iron deficiency (ID) is common among people with chronic kidney disease (CKD) and/or heart failure (HF). Despite the additional burden ID causes among people with CKD and HF, there is considerable uncertainty surrounding the best way to diagnose it and, subsequently, identify who is most likely to benefit from receiving iron therapy. Methods: This manuscript reports the markers and thresholds used in ID diagnosis, treatment, and management in the UK by nephrologists and cardiologists who manage people with chronic kidney disease or heart failure, as well as investigating future challenges and questions that remain unanswered. The research involved three stages: an online questionnaire, individual interviews, and a panel meeting, which discussed the findings from the first two stages. Results: The panel concluded that there is no robust definition of iron deficiency that can be applied to chronic kidney disease and heart failure. Existing methods of diagnosing iron deficiency come with various problems; a transferrin saturation of <20% is the most popular, but it is not regarded as a perfect solution. Transferrin saturation is also the most popular way of assessing the success of iron deficiency treatment. Clinicians generally do not vary treatment regimens based on severity or subgroups. There are large variations in monitoring and the ability to administer iron therapy in secondary care. Conclusions: There is a clear need to consolidate current approaches to diagnosing and treating iron deficiency in people with chronic kidney disease and/or heart failure. Simple markers and thresholds, and simple strategies to implement them are required. Full article

(This article belongs to the Section Nephrology & Urology)

27 pages, 3067 KB

Open AccessArticle

An Integrated Assessment of Battery and Hydrogen Electric Vehicles for Urban and Interurban Service Operations

by Giuseppe Napoli, Salvatore Micari, Antonio Comi, Ippolita Idone, Antonio Polimeni, Valerio Gatta and Edoardo Marcucci

Energies 2026, 19(4), 1113; https://doi.org/10.3390/en19041113 - 23 Feb 2026

Abstract

Urban freight and service operations represent a critical challenge for cities, contributing to greenhouse gas emissions, congestion, and competition for curb space. In addition to parcel deliveries, many service trips combine transport with installation, maintenance, or packaging recovery, generating long vehicle dwell times and inefficient use of public space. This paper investigates alternative operational scenarios for such activities, evaluating technological and organizational options that can reduce their environmental and spatial impacts. The study compares a diesel LCV baseline with four zero-emission configurations: battery electric LCVs; battery electric LCVs integrated with micro-hubs and cargo e-bikes; hydrogen fuel cell LCVs for long-range operations, and hydrogen fuel cell LCVs combined with cargo e-bikes via micro-hubs. The methodological framework is based on a vehicle routing problem (VRP) formulation supported by empirical data from Rome. It integrates indicators of energy use, carbon emissions, and curb-side occupation, and it includes the spatial representation of routes on urban and inter-urban maps to highlight operational differences across the five scenarios. Results indicate that zero-emission vehicles can eliminate tailpipe emissions, while logistics reorganization through decoupling improves the use of public space and enables the recovery of packaging materials. Battery solutions appear best suited to short and medium distances, whereas hydrogen is advantageous for longer routes. Overall, the study shows that combining technological and organizational measures provides a robust pathway toward sustainable logistics and more efficient service operations in metropolitan contexts. Full article

(This article belongs to the Special Issue Smart and Sustainable Mobility Systems: Advances in Electric and Hybrid Vehicles)

32 pages, 2169 KB

Open AccessArticle

Cross-View Localization Based on Few-Shot Learning for Mars Rover via MarsCVFP Guidance

by Yuke Kou, Wenhui Wan, Kaichang Di, Zhaoqin Liu, Man Peng, Yexin Wang, Bin Xie, Biao Wang and Waichung Liu

Remote Sens. 2026, 18(4), 668; https://doi.org/10.3390/rs18040668 - 23 Feb 2026

Abstract

High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40m×40m. Full article

(This article belongs to the Special Issue Planetary Geologic Mapping and Remote Sensing (Third Edition))

24 pages, 6258 KB

Open AccessArticle

Psoralen Promotes Direct Chemical Reprogramming of Mouse Embryonic Fibroblasts into Osteoblast-like Cells

by Wenjie Li, Haixia Liu, Xinyu Wan, Ding Cheng, Ruyuan Zhu and Zhiguo Zhang

Pharmaceutics 2026, 18(2), 279; https://doi.org/10.3390/pharmaceutics18020279 - 23 Feb 2026

Abstract

Background/Objectives: Cells derived from direct chemical reprogramming into osteoblasts represent a promising source for bone regeneration, but the efficiency needs improvement. Here, we systematically evaluated whether the natural compound psoralen (Psr) could enhance this process and explored its therapeutic potential and mechanism of action. Methods: Mouse embryonic fibroblasts (MEFs) were treated with a cocktail of forskolin and phenamil (FP), supplemented with Psr. In vitro differentiation was assessed by alkaline phosphatase and Alizarin Red S staining, reverse transcription quantitative PCR, immunofluorescence and Western blot. The bone-regenerative potential of the derived chemically induced osteoblast-like cells (ciOBs) was evaluated in critical-sized calvarial defects, femoral cortical defects and a subcutaneous ectopic implantation model, using micro-computed tomography and histology. Mechanistic insights of Psr were gained by analyzing the adenylyl cyclase 9 (ADCY9)/cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/cAMP response element-binding protein (CREB) axis using inhibitor SQ22536. Results: Psr acted synergistically with the FP cocktail to drive efficient osteogenic reprogramming of MEFs. At an optimal concentration of 25 μM, Psr enabled the most robust induction of early osteogenic markers and generation of mature, mineralizing ciOBs in vitro. In vivo, FP + Psr-induced ciOBs repaired critical-sized calvarial and femoral cortical defects and generated substantial, vascularized bone tissue in ectopic sites. Mechanistically, Psr co-treatment potently activated the ADCY9/cAMP/PKA/CREB pathway, and pharmacological inhibition of this pathway completely abolished the pro-osteogenic effects of Psr. Conclusions: Psr acts as a potent synergistic enhancer of direct chemical reprogramming, generating functional osteoblast-like cells with robust bone-regenerative capacity via activation of the ADCY9/cAMP/PKA/CREB pathway. Full article

(This article belongs to the Section Biopharmaceutics)

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39 pages, 1877 KB

Open AccessArticle

Rare Earth Elements and Technology-Related Trace Metals in Paediatric Scalp Hair: A 2001 Urban Baseline from Spain

by Antonio Peña-Fernández, Manuel Higueras, Roberto Valiente Borox and M. Carmen Lobo-Bedmar

J. Xenobiot. 2026, 16(1), 38; https://doi.org/10.3390/jox16010038 - 23 Feb 2026

Abstract

Rare earth elements (REEs) and technology-related trace elements are increasingly used in modern products and processes, but biomonitoring data in healthy children and adolescents remain scarce; scalp hair provides a practical, integrative matrix for assessing multi-element patterns over time. Scalp hair collected in April–May 2001 from children (6–9 years; n = 120) and adolescents (13–16 years; n = 97) living in Alcalá de Henares (Spain) was retrieved from archival storage and analysed in 2025 using a single QA/QC-controlled ICP–MS workflow. Seven REEs (Ce, La, Pr, Nd, Gd, Er, and Y) and nine technology-related trace elements (Bi, Sb, Th, U, Pd, Pt, Rh, Ir, and Rb) were quantified after rigorous decontamination; left-censored data were treated using Kaplan–Meier, regression on order statistics, and maximum-likelihood approaches, and population reference values were derived as percentile-based upper limits (P95, 95% CI). In children, REEs were frequently detected and showed strong within-suite covariation, with medians in the low ng g⁻¹ range (e.g., Ce ≈ 0.011 µg g⁻¹; La ≈ 0.007 µg g⁻¹), whereas in adolescents, most REEs were near reporting limits. Sb and U were ubiquitous in both age groups, while platinum-group elements were largely undetected. Shale-normalised REE patterns were subparallel across normalisers, La/Ce anomalies were centred below unity, and weak soil–hair correlations suggested multiple microenvironmental exposure pathways. These data provide a robust pre-diffusion baseline for REE metals in European youth, offering a benchmark for future urban exposome assessments. Full article

(This article belongs to the Section Emerging Chemicals)

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38 pages, 2511 KB

Open AccessArticle

Privacy-by-Design in AI-Assisted Systems for Caregivers of Children with Autism: A Secure Multi-Agent Architecture

by Ionuț Croitoru, Cristina Elena Turcu and Corneliu Octavian Turcu

Appl. Sci. 2026, 16(4), 2157; https://doi.org/10.3390/app16042157 - 23 Feb 2026

Abstract

Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits their adoption in regulated healthcare environments. To address these challenges, this paper proposes a Privacy-by-Design (PbD) multi-agent architecture that enables consent-aware, auditable, and privacy-preserving AI-assisted support for caregivers of children with ASD. The effectiveness of the proposed architecture was evaluated using two datasets: one focusing on clinically grounded autism-related knowledge and another reflecting naturalistic caregiver observation language. System performance was assessed using a Retrieval-Augmented Generation Assessment (RAGAs)-based framework with a Large Language Model (LLM)-as-a-Judge approach implemented via a locally deployed Llama 3 8B model. The system achieved answer relevancy scores of 0.767 for the clinical dataset and 0.750 for the observational dataset, with corresponding Recall@K values of 0.400 and 0.742, respectively. Context precision ranged from 0.599 to 0.631, and no harmful content was detected. Overall, the proposed architecture demonstrates secure caregiver–specialist collaboration through consent-aware routing, anonymised data storage, and controlled data reconstruction, providing a regulation-aligned design option for privacy-preserving AI integration in assisted care platforms. Full article

(This article belongs to the Special Issue Intelligent Data Processing and Management: Technologies and Applications)

30 pages, 1890 KB

Open AccessArticle

Economic Analysis of Nuclear Power Peak Shaving Based on AEL Hydrogen Production

by Jiaoshen Xu, Ge Qin, Chengcheng Zhang, Bo Dong, Dongyuan Li, Jinling Lu and Hui Ren

Processes 2026, 14(4), 725; https://doi.org/10.3390/pr14040725 - 23 Feb 2026

Abstract

Under high renewable energy penetration, nuclear power units face significant challenges in peak regulation and market clearing due to constraints on minimum technical output and ramping capability. To address this issue, a long-term power system of Guangdong Province in 2035 is taken as the study case, and an energy–reserve co-clearing simulation framework based on Security-Constrained Unit Commitment (SCUC) and Security-Constrained Economic Dispatch (SCED) is established to systematically evaluate the clearing performance of nuclear power and the formation mechanism of residual electricity under multiple market scenarios. On this basis, a nuclear power-coupled Alkaline Electrolysis (AEL) hydrogen production pathway is proposed as a peak-shaving utilization option, and an economic assessment model for nuclear-based hydrogen production is developed to quantify the investment performance under different hydrogen production capacities and operating modes. The results indicate that the integration of an AEL hydrogen production system can effectively alleviate the rigidity of nuclear power output. Under the “12-3-48-3” flexible peak-shaving mode, the residual electricity available for hydrogen production increases by approximately 30% compared with a typical peak-shaving strategy. Under scenarios with low electricity prices and green hydrogen prices, when the hydrogen production capacity is configured at 50–100 MW, the investment payback period is approximately six years, and the project exhibits strong economic robustness against variations in the discount rate. These findings demonstrate that nuclear-based hydrogen production is economically feasible in future power systems with high renewable penetration, providing quantitative support for nuclear flexibility enhancement and the coordinated development of low-carbon energy systems. Full article

(This article belongs to the Special Issue Optimal Design, Control and Simulation of Energy Management Systems)

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28 pages, 1468 KB

Open AccessArticle

Transcriptomic Analysis of Adult Mouse Cardiac Stromal Cells Using Single-Cell qRT-PCR

by Rita Alonaizan, Patricia Chaves-Guerrero, Sara Samari, Michela Noseda, Nicola Smart and Carolyn Carr

Cells 2026, 15(4), 384; https://doi.org/10.3390/cells15040384 - 23 Feb 2026

Abstract

Fate-mapping studies have challenged the longstanding view of the adult mammalian heart as a post-mitotic organ, suggesting limited cardiomyocyte renewal. This has spurred efforts to determine whether selected cardiac stromal cells have regenerative potential; however, their contribution to cardiac regeneration has been found to be minimal compared with that of cardiomyocyte proliferation. Despite this, transplantation of some cardiac stromal cell populations has shown therapeutic potential through paracrine signalling. The identity of the paracrine-active stromal cell populations remains unclear due to overlapping characteristics with other cardiac stromal cell populations, such as fibroblasts, mesenchymal cells, and pericytes. This study sought to clarify the transcriptional identity and heterogeneity of adult mouse cardiac stromal cells by developing a cardiac collagenase–trypsin protocol and comparing it to the established method for isolating cardiosphere-derived cells (CDCs). This novel protocol resulted in a higher cell yield and shorter expansion time, and the resulting cells showed superior survival under serum starvation compared to commercially acquired cardiac fibroblasts (CFs). Single-cell qRT-PCR analysis revealed that collagenase–trypsin cells (CTs) and CDCs share similar gene expression profiles, distinct from those of CFs. Notably, CTs exhibited higher expression of Tcf21 and lower expression of Tbx5, suggesting an epicardial-derived fibroblast phenotype, whereas Tbx5 was enriched in CDCs and CFs, reflecting heterogeneity within the cardiac fibroblast compartment. This study offers insights into the complex identity of cardiac stromal cells and concludes that CTs closely resemble CDCs but can be generated more rapidly, making them a robust and efficient source of paracrine-active cardiac stromal cells. Full article

(This article belongs to the Special Issue Advances in Cardiomyocyte and Stem Cell Biology in Heart Disease)

20 pages, 10209 KB

Open AccessArticle

Physics-Guided Adaptive Graph Transformer for Multi-Modal Bearing Fault Diagnosis Under Variable Working Conditions

by Gongwen Li, Na Xia, Xu Liu, Jinhua Wu and Haoyu Ping

Machines 2026, 14(2), 251; https://doi.org/10.3390/machines14020251 - 23 Feb 2026

Abstract

Multi-sensor fusion provides richer information for bearing fault diagnosis. However, under variable working conditions, the coupling relationships among signals from different sensors exhibit significant non-stationarity and directionality, posing challenges for modeling and practical deployment. Existing methods often rely on fixed or symmetric graph structures or construct correlation relationships entirely based on data-driven approaches; this makes balancing physical consistency, robustness, and computational efficiency difficult. To address these issues, we propose a Physics-guided Adaptive Graph Transformer Network (AGTN) for multi-modal bearing fault diagnosis under variable working conditions. More specifically, we offer innovative improvements across three aspects. Firstly, we introduce domain knowledge priors into the graph structure learning process to adaptively construct sparse and asymmetric dynamic graph structures that capture physically meaningful directional dependencies among different sensor signals. Secondly, we combine a graph-aware transformer to jointly model the temporal features and structural correlations of multi-source signals. Finally, we further introduce a hierarchical subgraph training strategy that significantly reduces memory usage and training time while ensuring diagnostic performance. Experimental results on a self-built multi-condition bearing dataset show that AGTN achieves an average diagnostic accuracy of 99.42% under the same distribution conditions and demonstrates good generalization and robustness, e.g., variable speed and load and sensor failure. In particular, when using only 25% of the nodes for training, the model can still maintain a diagnostic accuracy of 97.9%, while reducing the peak memory usage to about 19% of that of full-graph training. The above results validate the effectiveness of the proposed method under complex industrial conditions, as well as its practical application potential in resource-constrained scenarios. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Perception and Diagnosis of Mechanical Equipment)

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26 pages, 8135 KB

Open AccessArticle

DADD-PINN: Dual Adaptive Domain Decomposition Physics-Informed Neural Networks

by Yunkang Xiong, Hongyu Wei, Zhiying Ma, Zhihong Ding and Yaxin Peng

Mathematics 2026, 14(4), 744; https://doi.org/10.3390/math14040744 - 23 Feb 2026

Abstract

When solving partial differential equations (PDEs), traditional Physics-Informed Neural Networks (PINNs) often encounter difficulties in capturing critical physical features and addressing information bias between subdomains. To overcome these limitations, this paper proposes a Dual Adaptive Domain Decomposition Physics-Informed Neural Network (DADD-PINN). The core of this method lies in the construction of a dual-driven architecture that facilitates intra-subdomain feature extraction and inter-subdomain feature coordination. Within each subdomain, the solver’s precision is significantly enhanced by integrating a multi-criterion adaptive sampling strategy with a dynamic weighting mechanism. Experimental results demonstrate that DADD-PINN reduces the optimal

L^{2}

error by 1–2 orders of magnitude compared to existing baselines. The model exhibits superior generalization and robustness across various physical fields, offering a new route toward accurate and efficient solutions for complex PDEs. Full article

(This article belongs to the Special Issue Computational Intelligence and Data Analysis)

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25 pages, 4545 KB

Open AccessArticle

Symmetry-Guided Analysis of Market Characteristics and Electricity Prices Anomaly: A Comparative Framework of Influencing Factors

by Siting Dai, Wenyang Deng and Mengke Zhang

Symmetry 2026, 18(2), 390; https://doi.org/10.3390/sym18020390 - 23 Feb 2026

Abstract

Electricity spot prices jointly encode network physics and strategic bidding outcomes. In a well-functioning market, nodal and temporal price patterns tend to remain approximately invariant under mild perturbations-exhibiting symmetry-preserving regularities in distribution shape, spatial gradients, and temporal variation. Conversely, congestion binding, net-load stress, and abnormal bidding can induce symmetry breaking, manifested as heavy tails, mean shifts, and localized price discontinuities. This study develops a symmetry-guided and explainable diagnostic framework to identify price anomalies and attribute their dominant drivers. First, representative anomaly types (spike and mean shift) are defined using statistically and operationally motivated criteria, together with robustness checks across alternative thresholds. Second, principal component analysis is applied to construct compact, anomaly-specific feature sets, filtering weakly related variables while retaining system stress, congestion proxies, and renewable-induced variability indicators. Third, leveraging the optimization structure of market clearing and the associated KKT conditions, we characterize the price–feature linkage as a piecewise mapping and quantify each feature’s contribution via a sampling-based influence scoring procedure, yielding a ranked causal attribution. Case studies on a regional day-ahead spot market dataset demonstrate that the proposed framework achieves high consistency with expert assessments, with traceability accuracy exceeding 85% overall and particularly strong performance for spike-type anomalies. The method reduces reliance on purely manual diagnosis and black-box learning, and provides symmetry-oriented, actionable evidence for market surveillance and renewable-friendly flexibility and congestion management design. The proposed framework enables transparent identification of dominant structural drivers underlying different types of electricity price anomalies, linking observed price signals to market-clearing mechanisms. The results provide actionable diagnostic insights for market monitoring and regulatory assessment in electricity markets with high renewable penetration. Full article

(This article belongs to the Special Issue Navigating New Horizons: Symmetry and Advances in the Integration and Active Support of Large-Scale Renewable Energy)

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16 pages, 1105 KB

Open AccessSystematic Review

Comparison of Reconstructive Materials in Paediatric Orbital Fractures: A Systematic Review

by Jane Chen, Anton Sklavos, Mustafa Mian and Ricky Kumar

Craniomaxillofac. Trauma Reconstr. 2026, 19(1), 12; https://doi.org/10.3390/cmtr19010012 - 23 Feb 2026

Abstract

Paediatric orbital fractures require careful reconstruction to prevent long-term functional and aesthetic sequelae. Material selection is critical due to the anatomical and developmental considerations unique to children. Comparative data to guide decision making remain sparse and inconclusive. A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase (through February 2025), following Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. Studies reporting outcomes and/or complications associated with implant materials used in the reconstruction of paediatric orbital fractures were included. Outcomes included postoperative diplopia, enophthalmos, restriction of eye movements, removal of material, and return to theatre (RTT). In total, 54 studies encompassing a total of 562 patients and 563 implants were included. Polymers (n = 169), alloplasts (n = 167) and autologous (n = 166) implants were the most commonly used reconstructive material. Late postoperative diplopia occurred in 7% of polymers (12/169), 6% of alloplasts (10/167), 29% of allografts (6/21), 24% of xenografts (6/25) and 33% of metals (2/6). Reported enophthalmos was highest in the autologous group (8%) but was only reported in 34 of the 54 studies. Infection, removal of implant material and RTT were low across all groups (1–4%). No donor site morbidity was reported. Robust studies with standardised outcomes and adequate follow-up are needed to inform evidence-based material selection in paediatric orbital reconstruction. Full article

(This article belongs to the Special Issue Advances in Facial Trauma Surgery)

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14 pages, 449 KB

Open AccessArticle

Graph Contrastive Learning via Noisy Training for Cold-Start Recommendation

by Tingting Fang, Guicheng Shen and Qiurui Sun

Electronics 2026, 15(4), 902; https://doi.org/10.3390/electronics15040902 - 23 Feb 2026

Abstract

This paper studies the problem of cold-start recommendation with graph contrastive learning. Graph contrastive learning has achieved state-of-the-art performance for the recommendation. However, it lacks robustness in cold-start scenarios due to noisy user–item interactions. Recent works have been proposed to improve the performance of noisy user-item interactions; however, they can achieve effective performance only on existing user–item interactions, which are not cold-start interactions. The question of how to find an optimal graph contrastive learning method that is suitable for cold-start cases still remains to be explored. We propose a novel method, graph contrastive learning via noisy training (GCLNT), to alleviate the cold-start recommendation problem. Specifically, GCLNT identifies user–item interactions with different preferences, and assigns them to different preference environments. With such different preference environments, noisy training is used to enhance the model’s robustness. We evaluate GCLNT on three datasets, and the results demonstrate the effectiveness of GCLNT in handling the cold-start in recommender systems. Full article

(This article belongs to the Special Issue AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond)

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19 pages, 1901 KB

Open AccessArticle

Beyond Utility: Language Intervention, Identity Dynamics, and Political Attitude Change Among Palestinian High School Students and Pre-Service Teachers in Post-7 October Israel

by Rakefet Erlich Ron

Educ. Sci. 2026, 16(2), 353; https://doi.org/10.3390/educsci16020353 - 23 Feb 2026

Abstract

This quasi-experimental study employed Generalized Estimating Equations (GEE) to examine the longitudinal changes in intergroup perceptions following a targeted Hebrew language intervention on intergroup perceptions among 119 Arab citizens of Israel, segmented into high school students (HSS; adolescents) and pre-service Teachers (PSTs; young adults). Focusing on instrumental language acquisition as a form of positive intergroup contact, the research measured changes in self-efficacy in Hebrew, endorsement of democratic influence strategies, hope for peace, and common ancestry categorization (Semitic/Abrahamic) across two time points (pre/post intervention). Results indicated a robust positive association between the time of intervention and four of the five tested outcome variables, supporting the instrumental pathway hypothesis. Complex interactions revealed that participants identifying as Palestinian, who exhibited lower baseline hopes and categorization scores, demonstrated the sharpest increase in both hope for peace and Abrahamic categorization. Conversely, sensitivity to inequality diverged by group, dropping significantly among PSTs but increasing among HSSs. These findings highlight that while language intervention bridges ideological divides, its impact is shaped by the professional socialization inherent in teacher training. For PSTs, the combination of linguistic proficiency and emerging professional identity appears to mitigate feelings of marginalization, offering a constructive pathway for negotiating identity, status, and belonging. Full article

(This article belongs to the Special Issue Teacher Preparation in Multicultural Contexts)

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