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Xanthoceras sorbifolium (Yellowhorn) is an underutilized, multipurpose, climate-resilient oilseed with emerging food and industrial potential. This review consolidates current knowledge on its botany, agronomy, kernel composition, extraction technologies, protein and bioactive functionality, food uses, regulatory considerations, and sustainability challenges. Yellowhorn offers high-quality oil with ≈94% unsaturated fatty acids (notably 3.5–4% nervonic acid), while defatted kernel meal contains 31–37% protein (w/w). The matrix also carries bioactives such as tocopherols in the oil (70–530 mg/kg), phytosterols (1420–2970 mg/kg), and saponins (up to 11.62%), alongside flavonoid extracts that show promising antioxidant activity (DPPH EC₅₀ ≈ 10.7 µg/mL). Extraction methods, including cold pressing, solvent systems, and supercritical CO₂, present trade-offs in yield (≈87.8%, ≈60.4–98.04%, and ≈56.5–89.63% respectively), bioactive retention, and scalability, while co-product valorization can improve economic and environmental performance. Regulatory acceptance in the U.S. will likely depend on a refined-oil, specification-driven Generally Recognized as Safe (GRAS) pathway supported by compositional and toxicological evidence. Sustainability priorities include breeding improvements and supply-chain development on marginal lands, valorization of co-products, and integration of life cycle assessment (LCA), both of which are currently under-reported for Yellowhorn. Future directions emphasize process optimization for simultaneous oil-protein recovery, selective purification of functional lipids, encapsulation for stability, and human studies to substantiate claims. Collectively, Yellowhorn represents a promising climate-ready ingredient system requiring targeted research to enable safe, scalable, and sustainable adoption. Full article

(This article belongs to the Section Resources and Sustainable Utilization)

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24 pages, 1881 KB

Open AccessArticle

Tolerance Based Thermo-Optical Risk Framework for Parabolic Trough Collectors Under Receiver Misalignment

by Fatih Ünal, Nesrin İlgin Beyazit and Merve Sentürk Acar

Appl. Sci. 2026, 16(7), 3168; https://doi.org/10.3390/app16073168 (registering DOI) - 25 Mar 2026

Abstract

Parabolic trough collectors (PTCs) are highly sensitive to receiver positioning accuracy; however, most existing studies report optical efficiency degradation without formally defining alignment tolerance limits. This study proposes a tolerance-based thermo-optical risk framework to quantify allowable receiver misalignment envelopes for reliable PTC operation. A Monte Carlo Ray Tracing (MCRT) methodology is employed to evaluate the impact of angular receiver misalignment on optical efficiency and circumferential heat flux redistribution. Beyond conventional efficiency metrics, normalized flux-based thermal non-uniformity indicators are introduced to assess thermo-mechanical risk without requiring full thermo-fluid modeling. The results reveal a nonlinear decoupling between optical acceptability and thermal safety. While optical efficiency remains above 0.80 up to approximately ±6°, pronounced flux localization and rapid growth of thermal stress indicators occur beyond ±4°, marking the onset of thermally critical behavior. The identified ±4° threshold corresponds to approximately twice the collector half-acceptance angle (θ_₍crit₎/δ ≈ 2), demonstrating geometry-dependent scaling characteristics. The proposed framework formalizes the optical–thermal decoupling phenomenon and transforms conventional efficiency-based evaluation into a reliability-informed alignment tolerance assessment tool applicable to manufacturing precision, installation control, and operational quality management in CSP systems. Full article

(This article belongs to the Section Mechanical Engineering)

22 pages, 808 KB

Open AccessArticle

Environment-Dependent Downlink Pinching-Antenna Systems: Spectral–Energy Efficiency Tradeoffs and Design

by Xiangyu Zha, Yongji Chen and Qi Wang

Sensors 2026, 26(7), 2051; https://doi.org/10.3390/s26072051 (registering DOI) - 25 Mar 2026

Abstract

Pinching-antenna systems (PASSs) offer a low-complexity and reconfigurable solution for near-field downlink communications by deploying multiple radiating elements along a single waveguide. Existing studies mainly assume simplified propagation conditions or focus on spectral efficiency, while the impact of environment-dependent interference patterns arising from user-specific blockage conditions on energy-efficient design remains unclear. An energy-efficient downlink design for single-waveguide PASS based on environment-division multiple access (EDMA) is investigated. Under a given propagation environment, EDMA exploits user-dependent blockage and visibility differences through proper pinching-antenna placement, thereby inducing different multi-user interference patterns without increasing radio-frequency hardware complexity. We examine how such blockage-dependent interference influences the relationship between spectral efficiency and energy efficiency, and develop an energy-aware EDMA framework that jointly considers pinching-antenna locations and transmit power allocation under quality-of-service constraints. The resulting coupled design problem is solved through an alternating optimization procedure. EDMA is compared with conventional time-division multiple access (TDMA) using a unified hardware and power-consumption model. Numerical results reveal clear energy-efficiency threshold behaviors with respect to blockage intensity, user population, and service requirements. The results further show that EDMA can significantly outperform TDMA in specific operating regimes. Full article

(This article belongs to the Special Issue 6G Communication and Edge Intelligence in Wireless Sensor Networks)

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22 pages, 896 KB

Open AccessArticle

Autoencoder-Enhanced Hierarchical Mondrian Anonymization via Latent Representations

by Junpeng Hu, Tao Hu, Zhenwu Xu, Jinan Shen and Minghui Zheng

Entropy 2026, 28(4), 372; https://doi.org/10.3390/e28040372 (registering DOI) - 25 Mar 2026

Abstract

Releasing structured microdata requires balancing utility and privacy under group-based disclosure risks. We propose AE-LRHMA, a hybrid anonymization framework that performs Mondrian-style hierarchical partitioning in an autoencoder-learned latent space and integrates local (k,e)-microaggregation. To explicitly control sensitive-value concentration and diversity within each equivalence class, we introduce a tunable constraint set consisting of k, a maximum sensitive proportion threshold, and an optional sensitive-entropy threshold (used as a hard gate when enabled and otherwise as a soft term in split scoring). The anonymized output is generated via standard interval/set generalization in the original space. Experiments on Adult and Bank Marketing demonstrate that AE-LRHMA yields lower information loss and more stable group structures than representative baselines under comparable settings. We further report linkage-attack-oriented risk metrics to empirically characterize relative disclosure trends without claiming formal guarantees, such as differential privacy. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

12 pages, 568 KB

Open AccessReview

Cutaneous Malignancy Risk in Facial and Hand Vascularized Composite Allotransplantation Recipients: A Review of Immunosuppressive Regimens and Their Oncologic Impact

by Beatrice Corsini, Ferruccio Paganini, Sara Matarazzo and Luigi Valdatta

Life 2026, 16(4), 544; https://doi.org/10.3390/life16040544 (registering DOI) - 25 Mar 2026

Abstract

Facial vascularized composite allotransplantation (fVCA) is one of the most complex forms of vascularized composite allotransplantation and requires lifelong immunosuppression to ensure graft survival. Despite significant advances in surgical techniques and postoperative care over the past two decades, the true incidence of cutaneous malignancies in fVCA recipients remains poorly characterized due to the limited number of procedures, heterogeneous immunosuppressive protocols, and relatively short follow-up. This narrative review summarizes current evidence on oncologic risk in facial VCA, focusing on the effects of different immunosuppressive regimens and the challenges posed by the high immunogenicity of skin and mucosa. Available data indicate that malignancies, including cutaneous and other neoplasms, occur in approximately 10% of recipients, based on heterogeneous case-series data with immunosuppressive therapies largely extrapolated from solid organ transplantation. Calcineurin inhibitors, corticosteroids, and azathioprine are associated with increased oncologic risk, whereas mycophenolate mofetil and mTOR inhibitors may confer a more favorable profile. Overall, fVCA, unlike solid organ transplantation, is a life-enhancing procedure, highlighting the need for tailored immunosuppressive strategies, rigorous dermatologic surveillance, and further research supported by dedicated registries to better define long-term malignancy risk. Full article

(This article belongs to the Special Issue Advances in Skin-Related Disease: Pathogenesis and Novel Therapeutic Strategies)

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26 pages, 16104 KB

Open AccessArticle

Multi-Slot Attention with State Guidance for Egocentric Robotic Manipulation

by Sofanit Wubeshet Beyene and Ji-Hyeong Han

Electronics 2026, 15(7), 1365; https://doi.org/10.3390/electronics15071365 (registering DOI) - 25 Mar 2026

Abstract

Visual perception is fundamental to robotic manipulation for recognizing objects, goals, and contextual details. Third-person cameras provide global views but can miss contact-rich interactions and require calibration. Wrist-mounted egocentric cameras reduce these limitations but introduce occlusion, motion blur, and partial observability, which complicate visuomotor learning. Furthermore, existing perception modules that rely solely on pixels or fuse imagery with proprioception as flat vectors do not explicitly model structured scene representations in dynamic egocentric views. To address these challenges, a multi-slot attention fusion encoder for egocentric manipulation is introduced. Learnable slot queries extract localized visual features from image tokens, and Feature-wise Linear Modulation (FiLM) conditions each slot on the robot’s joint states, producing a structured slot-based latent representation that adapts to viewpoint and configuration changes without requiring object labels or external camera priors. The resulting structured slot-based latent representation is used as input to a Soft Actor–Critic (SAC) agent, which achieves a higher mean cumulative return than pixel-only CNN/DrQ and state-only baselines on a ManiSkill3 egocentric manipulation task. Probing experiments and real-camera evaluation further show that the learned representation remains stable under egocentric viewpoint shifts and partial occlusions, indicating robustness in practical manipulation settings. Full article

(This article belongs to the Section Artificial Intelligence)

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21 pages, 3589 KB

Open AccessArticle

An MCDE-YOLOv11-Based Online Detection Method for Broken and Impurity Rates in Potato Combine Harvesting

by Yongfei Pan, Wenwen Guo, Jian Zhang, Minsheng Wu, Ang Zhao, Zhixi Deng and Ranbing Yang

Agronomy 2026, 16(7), 693; https://doi.org/10.3390/agronomy16070693 (registering DOI) - 25 Mar 2026

Abstract

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance. Full article

(This article belongs to the Special Issue Agricultural Imagery and Machine Vision)

16 pages, 348 KB

Open AccessArticle

Challenges in Diagnosis and Management of Coffin–Lowry Syndrome—Single-Center Experience

by Ana Maria Chirilas, Alexandru Cărămizaru, Anca-Lelia Riza, Andreea Mitut-Veliscu, Andrei Costache, Rebecca-Cristiana Șerban, Aritina Morosanu, Carmen Niculescu, Alexandru-Cătălin Pâslaru, Florin Burada and Ioana Streata

Diagnostics 2026, 16(7), 990; https://doi.org/10.3390/diagnostics16070990 - 25 Mar 2026

Abstract

Background/Objectives: Coffin–Lowry syndrome (CLS) is a rare X-linked disease caused by pathogenic variants in the RPS6KA3 gene. It is generally characterized by syndromic intellectual disability and distinctive facial features, skeletal abnormalities, stimulus-induced drop attacks in males, and variable manifestations in females. Methods: We report clinical and genetic findings in a series of 10 cases, eight males and two females, evaluated at the Regional Centre of Medical Genetics Dolj—Emergency Clinical County Hospital Craiova. Results: Genetic testing identified 10 de novo variants in the RPS6KA3 gene consisting of six missense mutations, one nonsense variant, one frameshift, and two variants in non-coding or intronic regions. Case management requires multidisciplinary coordination and is limited to resources mostly available in reference centers. Conclusions: CLS highlights the importance of molecular diagnosis in rare genetic disorders, particularly when clinical features are subtle or atypical. These findings have practical implications for clinical management, suggesting the need for comprehensive genetic screening and individualized care approaches. Full article

(This article belongs to the Special Issue Genetic Diagnosis of Rare Neurological Disorders: Advances, Challenges, and Clinical Impact)

16 pages, 1139 KB

Open AccessArticle

Rapid Detection and Quantification of DMNB Vapors Using a Handheld Ion Mobility Spectrometer Operated near Ambient Temperature

by Victor Bocoș-Bințințan, Tomáš Rozsypal, Alin-Gabriel Moraru, Maria-Paula Bocoș-Bințințan, Adrian Pătruț and Petrișor Pătrașcu

Sensors 2026, 26(7), 2047; https://doi.org/10.3390/s26072047 - 25 Mar 2026

Abstract

The detection of plastic explosives in vapor form is extremely challenging due to the very low volatility of their primary components, such as RDX and PETN. To overcome this limitation, volatile chemical markers like 2,3-dimethyl-2,3-dinitrobutane (DMNB) are added to explosive formulations to enable indirect vapor detection. This study presents a rapid method for detecting and quantifying DMNB vapors using a handheld ion mobility spectrometer (IMS) operating near ambient temperature, ammonia-doped and equipped with a non-radioactive corona discharge ionization source. The instrument, model LCD-3.2E (Smiths Detection Ltd.), is based on a twin drift–cell time-of-flight configuration and simultaneously records ion mobility spectra in both positive and negative modes. DMNB generated distinct product ion peaks in both modes, with reduced mobility values (K₀) of 1.42 cm²·V⁻¹·s⁻¹ (positive) and 1.37 cm²·V⁻¹·s⁻¹ (negative). The method demonstrated high sensitivity, with limits of detection calculated at 1.4 ppb_v (10.2 × 10⁻³ mg·m⁻³) in positive mode and 3.1 ppb_v (22.7 × 10⁻³ mg·m⁻³) in negative mode. The IMS system provided rapid responses within seconds and covered a quantifiable concentration range of 5–3000 ppb_v, with saturation estimated to appear above approximately 5 ppm_v (36.6 mg·m⁻³). The simultaneous dual-polarity response of the DT IMS enhances both the selectivity and reliability of identification. These findings confirm the capability of portable IMSs for fast trace vapor detection in DMNB, supporting its application in field-based screening scenarios such as luggage inspection or container interrogation, where indirect detection of plastic explosives is required. Full article

(This article belongs to the Section Chemical Sensors)

30 pages, 3710 KB

Open AccessArticle

An LLM–BERT and Complex Network Framework for Construction Accident Causation Analysis

by Ruyu Deng, Ruoxue Zhang and Yihua Mao

Buildings 2026, 16(7), 1298; https://doi.org/10.3390/buildings16071298 - 25 Mar 2026

Abstract

Construction accident reports contain rich causal evidence; however, their unstructured narratives make systematic analysis difficult. Recent advances in large language models (LLMs) have created new opportunities to leverage such information at scale. This study develops an integrated LLM–BERT–network framework for analyzing construction accident causation. Based on 347 official construction accident investigation reports, a DeepSeek-based pipeline with human-in-the-loop quality control was used to extract causal keywords describing direct and indirect causes, yielding 2572 keywords. A BERT-based semantic normalization procedure then consolidated synonymous expressions, reducing 811 deduplicated keywords to 104 normalized terms (an 87.2% reduction in vocabulary size). A manual sample-based evaluation further supported the reliability of the LLM-based extraction and BERT-based normalization procedures. The normalized keywords were further organized into a hierarchical taxonomy and used to construct a directed keyword-association network linking indirect and direct causes for structured relational analysis. To strengthen methodological rigor, additional validation and analytical experiments were conducted, including manual sample-based evaluation of keyword extraction, sensitivity analysis of normalization settings, and examination of representative failure cases. The results support the reliability and robustness of the proposed framework. The analysis indicates that behavior-related factors and management deficiencies occupy structurally important positions in the directed network. Overall, the findings suggest that construction accidents arise from the interaction of human, managerial, environmental, material, and technical factors rather than isolated single causes. Effective prevention therefore requires system-oriented interventions that strengthen worker competence, supervision, training, accountability, and hazard identification. Full article

(This article belongs to the Section Construction Management, and Computers & Digitization)

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22 pages, 2106 KB

Open AccessArticle

Rigid-Chain Following and Kinematic Response Analysis on Piecewise Non-Smooth Paths: A DGPS-Based Solution Method

by Yaxuan Zhao, Ziheng Li and Hualu Liu

Algorithms 2026, 19(4), 252; https://doi.org/10.3390/a19040252 - 25 Mar 2026

Abstract

Rigid-body chain following on piecewise analytic paths is a fundamental subroutine in motion planning and multibody simulation. The problem is nontrivial when only the leader trajectory of the first node is available: enforcing fixed inter-node distances reduces to circle–curve intersection, which is generally multi-valued and becomes particularly challenging near non-smooth junctions. We present a Dichotomy Geometric Path Search (DGPS) framework that converts each constraint into a one-dimensional root-finding task and resolves the branch selection through no-backtracking ordering: at every time step, the admissible solution for the current node is the nearest feasible root in the past relative to its immediately preceding node. DGPS combines backward bracketing with bisection, achieving robust convergence. Compared with the inverse Jacobian method, which maps end-effector velocities to joint velocities via explicit inversion, the proposed approach avoids Jacobian inversion and globally coupled nonlinear solves. We further characterize the local structure of the zero set and establish monotonicity/uniqueness conditions that justify stable root selection across piecewise junctions. Extensive tests on representative piecewise trajectories (line–arc–line, polylines with corners, piecewise sinusoids, and time reparameterization) show that DGPS enforces distance constraints to near machine precision, produces interpretable speed/acceleration transients around non-smooth events, and exhibits computational costs consistent with iteration difficulty. The results support DGPS as a general, efficient solver requiring only the prescribed leader trajectory. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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10 pages, 475 KB

Open AccessArticle

Quality of ChatGPT-Generated Responses to Common Patient Questions About Peripheral Nerve Stimulation: A Cross-Sectional Study

by Charles A. Odonkor, Muhammad Uzair Siddique, Sarvesh Palaniappan, Jacob Locklear, Sreekrishna Pokuri, Alexandra Adler, Peju Adekoya, Annie W. Hsu, Jonathan Paek, Hari Prabhakar, Yuri Chaves Martins, Christina Smith, Uzondu Osuagwu, Frederick K. Comrie and Alaa Abd El Sayed

Clin. Pract. 2026, 16(4), 66; https://doi.org/10.3390/clinpract16040066 (registering DOI) - 25 Mar 2026

Abstract

Background: Peripheral nerve stimulation (PNS) is increasingly used in selected patients with neuropathic pain, and many individuals seek supplemental online information to clarify procedural expectations and postoperative care. Large language models such as ChatGPT may provide scalable patient education; however, their performance for PNS-related questions has not been evaluated. This study assessed the reliability, accuracy, and comprehensibility of ChatGPT-5.0 responses to common PNS patient questions. Methods: We conducted a cross-sectional evaluation of ChatGPT-5.0 responses to 21 standardized questions derived through expert consensus, spanning pre-implantation, implantation, and post-implantation domains. Sixteen board-certified interventional pain specialists and a nurse educator independently rated each response using validated scales for reliability (1–6), accuracy (1–3), and comprehensibility (1–3). Descriptive statistics were calculated, and domain-level patterns were examined. Results: Clinician ratings demonstrated generally strong performance across all domains. Mean reliability was 4.7 ± 1.4, mean accuracy 2.6 ± 0.6, and mean comprehensibility 2.8 ± 0.5. Foundational questions addressing mechanisms, expectations, and postoperative care received the highest ratings. Lower ratings were observed for implantation-focused items requiring procedural nuance. No response fell below predefined acceptability thresholds, and sensitivity analyses confirmed that including one partial evaluator did not alter the observed trends. Conclusions: ChatGPT-5.0 generated responses to PNS-related patient questions that clinicians rated as generally reliable, accurate, and understandable, particularly for foundational and postoperative topics. Performance was more variable for procedural questions, underscoring the need for clinician oversight and verification. These findings provide a benchmark of current LLM capabilities and highlight the importance of ongoing evaluation as models evolve and as patients access versions with differing functionalities. Full article

22 pages, 12862 KB

Open AccessArticle

On-Premise Multimodal AI Assistance for Operator-in-the-Loop Diagnosis in Machine Tool Mechatronic Systems

by Seongwoo Cho, Jongsu Park and Jumyung Um

Appl. Sci. 2026, 16(7), 3166; https://doi.org/10.3390/app16073166 - 25 Mar 2026

Abstract

Modern machine tools are safety-critical mechatronic systems, yet shop floor maintenance from abnormal events still relies heavily on scarce expert know-how and time-consuming manual searches across heterogeneous controller documentation. This paper presents an on-premise multimodal AI assistant. It integrates large language models with retrieval augmented generation and real-time machine signals to support operator-in-the-loop fault diagnosis. The proposed system provides three tightly coupled functions: (1) alarm-grounded guidance, which answers controller alarms and recommends corrective actions by grounding generation on manuals, maintenance procedures, and historical alarm cases; (2) parameter-aware reasoning, which injects live process and health indicators (e.g., spindle temperature, vibration, and axis states) into the reasoning context through an industrial data pipeline, enabling context specific troubleshooting; and (3) vision enabled support, which retrieves similar visual cases and generates concise visual instructions when text alone is insufficient. The assistant is deployed within an intranet environment to satisfy industrial security and privacy requirements and is orchestrated via lightweight tool calling for seamless integration with existing shop floor systems. Experiments on real machine tool alarm scenarios demonstrate that the proposed system achieves 82% answer correctness for alarm Q&A and improves response consistency and time-to-resolution compared with baseline keyword search and template-based guidance. The results suggest that grounded, multimodal chatbot assistants can act as practical AI-based feedback and decision support mechanisms for mechatronic production equipment, bridging human skill gaps while enhancing reliability and maintainability. Full article

(This article belongs to the Special Issue Integrating AI into Mechatronics and Robotics: Innovations and Applications)

12 pages, 7795 KB

Open AccessArticle

AI-Based Modeling of Post-Fire Evapotranspiration Using Vegetation Recovery Indicators: Application to the 2022 Chongqing Burned Areas

by Ziyan Zhao and Rongfei Zhang

Forests 2026, 17(4), 410; https://doi.org/10.3390/f17040410 - 25 Mar 2026

Abstract

The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day⁻¹, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article

(This article belongs to the Special Issue Hydrological Modeling with AI in Forests)

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26 pages, 963 KB

Open AccessReview

Toxicity and Appeal of Flavoured E-Cigarettes and Flavour Ban Outcomes: A Narrative Review

by Stijn Everaert, Filip Lardon, Eric Deconinck, Sophia Barhdadi, Dirk Adang, Nicolas Van Larebeke, Greet Schoeters, Adrien Meunier, Veerle Maes, Suzanne Gabriels, Eline Remue, Katrien Eger, Pieter Goeminne and Frieda Matthys

Int. J. Environ. Res. Public Health 2026, 23(4), 416; https://doi.org/10.3390/ijerph23040416 - 25 Mar 2026

Abstract

Background: E-cigarette use has risen sharply among young never-smokers, largely driven by the availability of several thousand appealing flavours. This narrative review synthesises evidence on the health effects of vaping, flavour toxicology and attractiveness, designs and outcomes of flavour bans, and complementary measures. Methods: Peer-reviewed publications and institutional reports (up to January 2026) were retrieved from PubMed, Web of Science, Google Scholar, and reference lists of included articles. Evidence from about 200 references was synthesised by a multidisciplinary working group. Results: Although flavouring substances are generally considered safe for ingestion, their inhalation toxicity remains uncertain. In vitro and in vivo studies have reported oxidative stress, inflammation, cytotoxicity, impaired ciliary function, transcriptomic changes, genotoxicity, and DNA damage. These findings—along with the strong youth appeal of fruit/sweet flavours, the inconclusive effects of flavours on smoking cessation, and persisting uncertainties—support banning non-tobacco e-cigarette flavours under the precautionary principle. Flavour bans can reduce e-cigarette use and initiation, especially among young adults, although partial substitution towards combustible cigarettes has been reported in some U.S. states. Policy success requires effective enforcement, prevention of industry circumvention, curbing cross-border sales, and closing regulatory loopholes—ideally at the international level (e.g., EU-wide). Conclusions: E-cigarette flavours may increase vaping toxicity and strongly appeal to youth, justifying flavour bans to prioritise youth protection. To maximise effectiveness, accompanying measures and sustained investment in tobacco prevention, youth education, and accessible evidence-based smoking cessation support are essential. Full article

(This article belongs to the Special Issue Regulating the Use of Electronic Devices for Vaping and Vaping to Promote Youth and Young Adult Health)

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