Saved Queries

Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper presents an architectural approach and implementation of a semantic search module integrated into an existing cloud-based modeling and simulation system. The proposed method employs a strategy for serializing graph structures into textual descriptions, followed by the generation of vector embeddings via local ONNX inference and indexing within a vector database (Qdrant). Experimental validation performed on a diverse corpus of complex dynamic models, compares the proposed approach against traditional information retrieval methods (Full-Text Search, Keyword Search in PostgreSQL, and Apache Lucene with Standard and BM25 scoring). The results demonstrate the distinct advantage of semantic search, achieving high precision (over 90%) within the scope of the evaluated corpus and effectively eliminating information noise. In comparison, keyword search exhibited only 24.8% precision with a significant rate of false positives, while standard full-text analysis failed to identify relevant models for complex conceptual queries (0 results). Despite a recorded increase in latency (~2 s), the study proves that the vector-based approach is a significantly more robust solution for detecting hidden semantic connections in mathematical model databases, providing a foundation for future developments toward multi-vector indexing strategies. Full article

(This article belongs to the Special Issue Intelligent Agents and Their Application)

30 pages, 4371 KB

Open AccessSystematic Review

Standardizing TEER Measurements in Blood-Brain Barrier-on-Chip Systems: A Systematic Review of Electrode Designs and Configurations

by Nazanin Ghane, Reza Jafari and Naser Valipour Motlagh

Biomimetics 2026, 11(2), 119; https://doi.org/10.3390/biomimetics11020119 - 5 Feb 2026

Abstract

The blood-brain barrier (BBB) is one of the most selective physiological interfaces in the human body. Transendothelial electrical resistance (TEER) has become a widely adopted quantitative metric for assessing its in vitro structural and functional integrity. Although TEER measurements are routinely incorporated into BBB-on-chips, the absence of harmonized electrode architectures, measurement settings, and reporting standards continues to undermine reproducibility and translational reliability among laboratories. This systematic review provides the first comprehensive classification and critical comparison of electrode configurations used for TEER assessment, specifically within BBB-on-chip systems. Eligible studies were analyzed and categorized according to electrode design, fabrication method, integration strategy, and operational constraints. We critically evaluated six principal electrode architectures, highlighting their performance trade-offs in terms of uniformity of current distribution, long-term stability, scalability, and compatibility with dynamic shear conditions. Furthermore, we propose a bioinspired TEER reporting framework that consolidates essential metadata, including electrode specification, temperature control, viscosity effects, and blank resistance correction. Our analysis proposes screen-printed and hybrid silver-indium tin oxide (ITO) electrodes as promising candidates for next-generation BBB platforms. Moreover, our review provides a structured roadmap for standardizing TEER electrode design and reporting practices to facilitate interlaboratory consistency and accelerate the adoption of BBB-on-chip systems as truly biomimetic platforms for predictive neuropharmacological workflows. Full article

(This article belongs to the Section Biomimetic Design, Constructions and Devices)

18 pages, 4740 KB

Open AccessArticle

Functionalized Siloxane Coating as Protection of the Surface of Cement Composites Against Phototropic Colonization

by Joanna Karasiewicz, Marta Thomas, Paulina Nowicka-Krawczyk, Rafał M. Olszyński, Piotr K. Zakrzewski and Agnieszka Ślosarczyk

Int. J. Mol. Sci. 2026, 27(3), 1586; https://doi.org/10.3390/ijms27031586 - 5 Feb 2026

Abstract

This article presents the concept of using a functionalised siloxane compound HOL9 with amphiphilic properties as a coating for cement composites to enhance their antifouling properties against algae. The biological properties of the compound were assessed based on its ability to inhibit chlorophyll fluorescence intensity, which is used as an indicator of photosynthetic activity and biofilm development. The greatest decrease in algal photosynthetic activity was observed for a 10% aqueous solution of HOL9 applied by painting. In these conditions, the maximum ^chlFI value decreased by 97.6%. In addition, the impact of the protective coating containing HOL9 on the fundamental physical and mechanical characteristics of the cement composite, along with its resilience to frost cycling, was thoroughly investigated. The coating applied by immersion demonstrated a 50.7% strength loss after 150 freeze–thaw cycles, while the coating applied by painting exhibited a 43.8% loss. In comparison, the control samples experienced a 42.8% strength reduction. It has been demonstrated that the method of application, the modifier concentration, and the type of solvent can have a substantial impact on the protective properties of concrete. The most marked inhibition of algae photosynthetic activity was observed with a 10% aqueous solution applied by painting. Full article

(This article belongs to the Special Issue Molecular Advancements in Functional Materials)

28 pages, 1384 KB

Open AccessReview

Artificial Intelligence for Exosomal Biomarker Discovery for Cardiovascular Diseases: Multi-omics Integration, Reproducibility, and Translational Prospects

by Rasit Dinc and Nurittin Ardic

Cells 2026, 15(3), 304; https://doi.org/10.3390/cells15030304 - 5 Feb 2026

Abstract

Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and network-based approaches, can support EV biomarker development by integrating multi-omics profiles with clinical metadata. These approaches enable feature selection, disease subtyping, and interpretable model development. Among the AI approaches evaluated, ensemble methods (Random Forest, gradient boosting) demonstrate the most consistent performance for EV biomarker classification (AUC 0.80–0.92), while graph neural networks (GNNs) are particularly promising for path integration but require larger validation cohorts. Evolutionary neural networks applied to EV morphological features yield comparable discrimination but face interpretability challenges for clinical use. Current studies report promising discrimination performance for selected EV-derived panels in acute myocardial infarction and heart failure. However, most evidence remains exploratory, based on small cohorts (n < 50) and limited external validation. For clinical implementation, EV biomarkers need direct comparison against established standards (high-sensitivity troponin and natriuretic peptides), supported by locked-in assay plans, and validation in multicenter cohorts using MISEV-aligned protocols and transparent AI reporting practices. Through a comprehensive, integrative, and comparative analysis of AI methodologies for EV biomarker discovery, together with explicit criteria for reproducibility and translational readiness, this review establishes a practical framework to advance exosomal diagnostics from exploratory research toward clinical implementation. Full article

16 pages, 3059 KB

Open AccessArticle

Comparative Evaluation of YOLO- and Transformer-Based Models for Photovoltaic Fault Detection Using Thermal Imagery

by Mahdi Shamisavi, Isaac Segovia Ramirez and Carlos Quiterio Gómez Muñoz

Energies 2026, 19(3), 845; https://doi.org/10.3390/en19030845 - 5 Feb 2026

Abstract

Photovoltaic systems represent one of the most reliable and widely used technologies for electricity generation from renewable energy sources, although their performance is affected by the occurrence of faults and defects that lead to energy losses and efficiency reduction. Therefore, detecting and localizing defects in photovoltaic panels is essential. A wide variety of image analysis techniques based on aerial thermal imagery acquired by drones have been widely implemented for proper maintenance operations, requiring a comprehensive comparison among these approaches to assess their relative performance and suitability for different scenarios. This study presents a comparative evaluation of several vision-based approaches using artificial intelligence for photovoltaic defect detection. YOLO- and Transformer-based models are analyzed and benchmarked in terms of accuracy, inference time, per-class performance, and sensitivity to object size. Experimental results demonstrate that both YOLO- and Transformer-based models are computationally lightweight and suitable for real-time implementation. However, Transformer-based architectures exhibit higher detection accuracy and stronger generalization capabilities, while YOLOv5 achieves superior inference speed. The RF-DETR-Small model provides the best balance between accuracy, computational efficiency, and robustness across different defect types and object scales. These findings highlight the potential of Transformer-based vision models as a highly effective alternative for real-time, on-site photovoltaic fault detection and predictive maintenance applications. Full article

(This article belongs to the Special Issue Renewable Energy System Forecasting and Maintenance Management)

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25 pages, 1023 KB

Open AccessReview

A Green Energy Closed-Loop System Based on Aluminum

by Hong-Wen Wang and Liang-Ying Huang

Energies 2026, 19(3), 853; https://doi.org/10.3390/en19030853 - 5 Feb 2026

Abstract

This paper presents a focused review of a closed-loop system for sustainable hydrogen production utilizing the reaction between metallic aluminum powders and water, coupled with renewable energy-driven recycling of aluminum hydroxide (or alumina) byproducts back to metallic aluminum powders. A green energy closed-loop system based on aluminum could be achieved if the converting process is accomplished by a green Hall–Héroult process, where a cermet inert anode was used. Meanwhile, the byproduct alumina is converted back to the liquid form of aluminum at high temperature (up to 960 °C), producing pure oxygen. A high-pressure atomization process is then used to break the aluminum droplets into powder using argon gas. The technical feasibility, thermodynamic efficiency, economic viability, environmental sustainability, and comparison of this green aluminum cycle with existing hydrogen production and energy storage technologies are discussed. The aluminum–water reaction offers exceptional energy density (29.7 kJ/g of Al), ambient temperature operation, and zero direct carbon emissions. However, commercial implementation faces substantial challenges including overall round-trip energy efficiency (estimated 34.5–46.6%), technological maturity of the recycling process, passivation layer management, and economic competitiveness with conventional water electrolysis. Despite these challenges, the system demonstrates advantages for seasonal energy storage, off-grid applications, and integration with intermittent renewable energy sources. This analysis provides a framework for researchers, engineers, and policymakers to assess the potential role of aluminum-based energy cycles in the global energy transition toward carbon neutrality. Full article

(This article belongs to the Special Issue Simulation and Optimisation for Operational Decision-Making in Renewable Energy Supply Chains and Systems)

25 pages, 2356 KB

Open AccessArticle

Application and Comparison of FPGA-Based Carry Chain TDC and DDMTD Schemes in High-Precision Time Synchronization

by Yuzhen Huang, Jiajie Yu, Wenlong Xia, Qinggong Guo and Linyu Huang

Sensors 2026, 26(3), 1052; https://doi.org/10.3390/s26031052 - 5 Feb 2026

Abstract

High-precision phase difference measurement based on field-programmable gate arrays (FPGA) has important application requirements in fields such as high-stability time-frequency transmission, signal synchronization, and precision testing. Addressing the limitations of traditional methods in terms of temperature stability and measurement accuracy, this paper proposes two high-precision phase difference measurement schemes based on the FPGA platform. An eight-parallel-multi-carry chain time-to-digital converter (TDC) and digital dual-mixer time difference (DDMTD) measurement modules are constructed to perform high-precision phase difference measurements on the phase-shifted output signal of the MMCM dynamic phase-shifted module. Results show that at room temperature (25 °C), the single-carry chain TDC exhibits better measurement accuracy than the DDMTD, and the single-carry chain TDC’s measurement error range of 4.7–6.0 ps is superior to the DDMTD’s 20–75 ps error range. Under different temperature conditions, the eight-parallel-multi-carry chain TDC consistently demonstrates superior measurement accuracy, resolution, and temperature stability compared to the single-carry chain TDC. In terms of measurement accuracy, under room temperature conditions, in three sets of phase difference tests (178.5714 ps, 357.1428 ps, and 535.7142 ps), the measurement error of the eight-parallel-multi-carry chain TDC was controlled within 4.6 ps, which is better than the 4.7–6.0 ps error range of the single-carry chain TDC. Average resolution: The average resolution of the single-carry chain TDC was 6.329 ps, while the average resolution of the eight-parallel-multi-carry chain TDC improved to 0.833 ps. Temperature stability: Within the temperature range of 10 °C to 100 °C, the temperature coefficient of the single-carry chain TDC was 0.002127 ps/°C, while the temperature coefficient of the eight-parallel-multi-carry chain TDC decreased to 0.000564 ps/°C. This paper also summarizes the advantages and limitations of the above methods in terms of implementation complexity and robustness, providing a reference for the optimized design of high-precision phase difference measurement technology for FPGA platforms. Full article

(This article belongs to the Section Electronic Sensors)

11 pages, 503 KB

Open AccessArticle

Perioperative Inflammatory Cytokines in Parkinson’s Disease

by Jong-Woan Kim, Seung-ah Yoo, Yemi Choi, Gi Heon Jeong, Jaeseung Lee and Jin Joo

Biomolecules 2026, 16(2), 261; https://doi.org/10.3390/biom16020261 - 5 Feb 2026

Abstract

Background: Neuroinflammation is increasingly recognized as an important contributor to Parkinson’s disease (PD), yet perioperative immune responses in this population remain incompletely characterized. This study investigated perioperative cytokine dynamics in patients with PD compared with healthy controls (HCs) undergoing orthopedic surgery under general anesthesia. Methods: In this prospective pilot observational study, 50 patients scheduled for lower limb orthopedic surgery were enrolled (25 PD patients, 25 HCs). Serum cytokines (IL-6, IL-8, VEGF, MCP-1, HMGB1, S100B, and PARK7) were measured immediately after anesthesia induction (PRE) and 24 h postoperatively (POST). Between-group comparisons were performed using independent t-tests, and within-group perioperative changes were assessed using paired t-tests. Absolute (Δ = POST − PRE) and relative perioperative changes were analyzed. Results: IL-6 increased significantly after surgery in both groups, with no significant differences in absolute or relative perioperative changes between the PD and HC group. IL-8 concentrations were numerically higher in PD patients at both time points, but perioperative changes did not differ significantly between groups. VEGF decreased modestly within the PD group, whereas no significant change was observed in HCs; however, between-group differences in perioperative VEGF changes were not significant. S100B and PARK7 increased postoperatively in HCs but not in PD patients, while MCP-1 and HMGB1 showed no significant perioperative changes. Conclusions: In this pilot study, perioperative cytokine responses in patients with PD were largely comparable to those in HCs. Despite evidence of chronic low-grade inflammation in the PD group, no disease-specific amplification of acute perioperative inflammatory responses was observed. These findings suggest that perioperative immune activation in PD may be selective rather than global. Full article

29 pages, 890 KB

Open AccessArticle

Enhancing Cross-Regional Generalization in UAV Forest Segmentation Across Plantation and Natural Forests with Attention-Refined PP-LiteSeg Networks

by Xinyu Ma, Shuang Zhang, Kaibo Li, Xiaorui Wang, Hong Lin and Zhenping Qiang

Remote Sens. 2026, 18(3), 523; https://doi.org/10.3390/rs18030523 - 5 Feb 2026

Abstract

Accurate fine-scale forest mapping is fundamental for ecological monitoring and resource management. While deep learning semantic segmentation methods have advanced the interpretation of high-resolution UAV imagery, their generalization across diverse forest regions remains challenging due to high spatial heterogeneity. To address this, we propose two enhanced versions based on the PP-LiteSeg architecture for robust cross-regional forest segmentation. Version 01 (V01) integrates a multi-branch attention fusion module composed of parallel channel, spatial, and pixel attention branches. This design enables fine-grained feature enhancement and precise boundary delineation in structurally regular artificial forests, such as the Huayuan Forest Farm. As a result, V01 achieves a mIoU of 92.64% and an F1-score of 96.10%, representing an approximately 18 percentage-point mIoU improvement over PSPNet and DeepLabv3+. Building on this, Version 02 (V02) introduces a lightweight residual connection that directly shortcuts the fused features, thereby improving feature stability and robustness under complex textures and illumination, and demonstrates stronger performance in naturally heterogeneous forests (Longhai Township), attaining an mIoU of 91.87% and an F1-score of 95.77% (5.72 percentage-point mIoU gain over DeepLabv3+). We further conduct comprehensive comparisons against conventional CNN baselines as well as representative lightweight and transformer-based models (BiSeNetV2 and SegFormer-B0). In bidirectional cross-region transfer (train on one region and directly test on the other), V02 exhibits the most stable performance with minimal degradation, highlighting its robustness under domain shift. On a combined cross-regional dataset, V02 achieves a leading mIoU of 91.50%, outperforming U-Net, DeepLabv3+, and PSPNet. In summary, V01 excels in boundary delineation for regular plantation forests, whereas V02 shows more stable generalization across highly varied natural forest landscapes, providing practical solutions for region-adaptive UAV forest segmentation. Full article

(This article belongs to the Special Issue Remote Sensing-Assisted Forest Inventory Planning)

19 pages, 966 KB

Open AccessArticle

Exploring Castanea sativa Shells (CSSs) as a Source of AKR1B1 and AKR1B10 Inhibitors: From Extraction to Bioactivity Testing

by Lucia Piazza, Lorena Tedeschi, Francesca Felice, Antonella Cecchettini, Elisa Ceccherini, Martina Avanatti, Adrian Florentin Suman, Francesco Balestri, Silvia Rocchiccioli and Giovanni Signore

Molecules 2026, 31(3), 563; https://doi.org/10.3390/molecules31030563 - 5 Feb 2026

Abstract

Chestnut shells are widely recognized as a source of bioactive compounds, including polyphenols and other antioxidant molecules. The industrial chestnut food chain generates large amounts of this by-product, which represents both a waste disposal challenge and a potential source of promising biomolecules. Thermal treatments occurring during industrial processing, however, may affect both chemical composition and bioactivity. Characterization of the chemical composition and biological activity of chestnut shells can contribute to the valorisation of this industrial by-product. Understanding which molecular alterations are caused by the processing is essential to assess the real potential of chestnut shell biomass. This study provides a comparative analysis of Castanea sativa shells, both raw and industrially processed. Evaluation was performed at different levels, exploiting mass spectrometry–based metabolite profiling, Total Phenolic Index analysis, antioxidant capacity, and inhibitory activity against AKR1B and AKR1B10, two reductases involved in key physiopathologic pathways. A comparison between extraction solvents (water and ethanol) and processing status (raw versus industrially processed) was performed. Overall, our results support the view that chestnut shell residues represent a valuable source of bioactive extracts. In a circular economy framework, such extracts could be developed to act on AKR1B1/AKR1B10 activity and oxidative stress, thereby contributing to the valorisation of chestnut processing by-products. Full article

(This article belongs to the Section Cross-Field Chemistry)

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18 pages, 1758 KB

Open AccessArticle

A Comprehensive Analysis of Influencing Factors in Highway Route Selection and Application of an Integrated Optimization Model

by Zhigang Zeng, Sende Wang, Jian Zhang and Haikuo Liu

Symmetry 2026, 18(2), 296; https://doi.org/10.3390/sym18020296 - 5 Feb 2026

Abstract

To address the complex influencing factors, divergent stakeholder demands, and the challenge of quantitative comparison in alignment selection for highway expansion and reconstruction, we systematically reviewed the relevant factors. These factors were classified into four categories—economy, technology, safety, and environment—and comprise 16 subfactors in total. The symmetry of the route selection process is disrupted by the varying priorities of different stakeholders, leading to asymmetric evaluations of the alternatives. Using the G30 Lianhuo Expressway Jingqing section expansion and reconstruction project as a case study, we applied the Analytic Hierarchy Process (AHP) combined with expert judgment to derive weights for each factor. The results indicate that environmental factors carry substantial weight, reflecting increased awareness of environmental protection in contemporary projects. We then developed a comparative model based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Applying this model to alignment alternatives between the Jingjiadian and Huachacun sections indicates that Option 4 is the preferred alignment. Overall, the AHP–TOPSIS composite evaluation framework effectively integrates expert knowledge with objective quantitative analysis. It enables the scientific ranking of alternatives and provides decision support for alignment selection in mountainous highways and other linear engineering projects. Full article

(This article belongs to the Section Engineering and Materials)

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23 pages, 20925 KB

Open AccessArticle

Monitoring Heterogeneous Deformation of Transportation Infrastructure in Beijing Using Sentinel-1 InSAR Time Series

by Weizhen Lin, Xi Guo, Yidi Wang, Changyang Hu and Zhang Yunjun

Remote Sens. 2026, 18(3), 520; https://doi.org/10.3390/rs18030520 - 5 Feb 2026

Abstract

Transportation infrastructure is vulnerable to heterogeneous deformation, yet such deformation remains insufficiently monitored and characterized in metropolitan regions due to the lack of high-resolution deformation gradient products and comparison with industrial standards. Here, we generated a 45 m resolution interferometric synthetic aperture radar (InSAR) surface displacement time series across the Beijing Plain using Sentinel-1 SAR imagery acquired between 2014 and 2024, and calculated deformation gradients along all ring roads, major expressways, and airport runways. These deformation gradients are compared with national standards to evaluate their structural risks. Our analysis shows that (1) subsidence in the Beijing Plain is concentrated in the northern, eastern, and southern regions, where the northeastern region has been uplifting since 2018 due to the groundwater recovery in Beijing; (2) all ring roads, expressways, and airport runways are relatively stable during our observation period of 2015–2021, except for the central runway of Beijing Capital International Airport, which has accumulated a deformation gradient of 1.9‰ during 2015–2021, exceeding the safety limit of 1.5‰, indicating structural risks. These results demonstrate the effectiveness of high-resolution InSAR time series for monitoring deformation and pinpointing potential structural risks. Full article

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18 pages, 954 KB

Open AccessArticle

Comparison of the Effectiveness of Vojta Therapy and the NDT Bobath Concept in the Treatment of Congenital Muscular Torticollis in Infants—A Retrospective Cohort Pilot Study

by Marcin Machnia, Adam Płusajski, Ewelina Leśniak, Karolina Urazińska and Wojciech Kałużyński

J. Clin. Med. 2026, 15(3), 1286; https://doi.org/10.3390/jcm15031286 - 5 Feb 2026

Abstract

Background/Objectives: Congenital muscular torticollis (CMT) affects 0.3–3.9% of infants, requiring early physiotherapy to prevent deformities. Vojta and NDT Bobath therapies are widely used, yet comparative evidence remains limited. To compare Vojta versus NDT Bobath efficacy in improving head tilt and cervical rotation in infants with CMT. Methods: Retrospective cohort study (2016–2024) at Polish Mother’s Memorial Hospital included 53 infants under 5 months with ultrasound-confirmed CMT. Non-random allocation based on therapist availability introduced selection bias. Participants received Vojta (n = 29) or NDT Bobath (n = 24) two 30 min sessions weekly for 20 weeks plus home exercises. Blinded physicians measured outcomes. Results: Vojta showed greater angular improvements versus NDT Bobath: head tilt MD = −5.69° (p < 0.001, Hedges’ g = 1.29) and neck rotation MD = −5.89° (p < 0.001, Hedges’ g = 1.21). Early intervention (1–2 months) demonstrated 5-fold (RR = 5.46) and 8-fold (RR = 8.19) higher likelihood of achieving optimal thresholds (70°/90°) versus later intervention (3–4 months) both p < 0.001. No therapy × age interaction was found, indicating consistent between-group differences across age strata. Large effect sizes suggest clinically meaningful angular improvements. Conclusions: Vojta therapy was associated with superior angular outcomes versus NDT Bobath, with early initiation showing better results. However, the retrospective non-randomized design, small sample (n = 53), and absence of functional outcome assessment limit causal inference. Only biomechanical outcomes were measured; functional motor development, complications, and quality of life were not evaluated. Prospective randomized trials with functional assessments and larger samples are essential to confirm these associations and determine clinical significance. Full article

(This article belongs to the Section Clinical Rehabilitation)

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18 pages, 5522 KB

Open AccessArticle

A Study on the Hydrogen and Oxygen Stable Isotope Characteristics of Water in Small Watersheds on the Southern Slope of the Qilian Mountains

by Qixin He, Guangchao Cao, Guangzhao Han, Meiliang Zhao, Jiaqi Bai and Wenqian Ye

Water 2026, 18(3), 423; https://doi.org/10.3390/w18030423 - 5 Feb 2026

Abstract

This study, based on stable hydrogen and oxygen isotope observations of multiple water bodies (precipitation, river water, soil water, and groundwater) in the Ami Dongsou alpine arid watershed on the southern slope of the Qilian Mountains during 2023–2024, reveals significant seasonal fluctuations in water isotope characteristics and water source renewal mechanisms. The results show that precipitation and soil water exhibit notable enrichment during the dry season, primarily due to enhanced evaporation causing light isotopes to evaporate and heavy isotopes to accumulate. River water, influenced by both precipitation recharge and evaporation, shows smaller seasonal fluctuations. Groundwater isotopes remain stable, reflecting a slower water source renewal process with minimal seasonal influence. Through quantitative comparisons of the evaporation line’s slope and intercept, this study finds that precipitation is most significantly affected by evaporation, while groundwater is least influenced, showing more stable isotope characteristics. Climate and topography in high-altitude areas significantly regulate water isotope characteristics, especially during the dry season, where evaporation plays a dominant role in the enrichment of precipitation and river water isotopes. This study innovatively establishes an evidence framework for the linkage of multiple water body isotopes, revealing the “seasonal strong fluctuations + differential water body responses + high-altitude regulation” mechanism of water isotopes in alpine arid regions. It provides new data support for water resource management, particularly in aspects such as water source allocation during the dry season, groundwater protection, and evaporation enrichment effect prediction. Future research could expand the sample size and integrate multi-source data and hydrological models to further improve the accuracy of hydrological process predictions, offering more precise support for watershed water resource management and ecological protection. Full article

(This article belongs to the Special Issue Applications of Stable Isotopes in Understanding Hydrological and Hydrochemical Processes)

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22 pages, 1655 KB

Open AccessArticle

MuRaF-LULC: A Systematic Multivariate Random Forest Framework for Annual Land-Use and Land-Cover Mapping and Long-Term Change Detection

by Yunuen Reygadas

Land 2026, 15(2), 268; https://doi.org/10.3390/land15020268 - 5 Feb 2026

Abstract

Land-use and land-cover (LULC) change is one of the most pervasive drivers of socioenvironmental transformation worldwide. Given its impacts on ecosystems and climate, the systematic analysis of LULC dynamics remains a central objective of land-change science. Despite major advances in Earth observation capabilities, robust, flexible, and scalable algorithms for long-term monitoring remain unevenly adopted, particularly in remote, forested tropical regions. This study introduces the Multivariate Random Forest Land-Use and Land-Cover (MuRaF-LULC) framework, a supervised and generalizable framework that produces annual, multi-class LULC maps from Landsat time series, with interannual change derived through year-to-year comparisons. A key methodological component of the framework is its predictor-selection strategy, in which variable-importance rankings are used to identify an optimized subset of predictors prior to final model training. MuRaF-LULC was implemented in Google Earth Engine (GEE) and evaluated in Guatemala’s Maya Biosphere Reserve (MBR) for the 2018–2024 period using probability-based sampling and uncertainty-aware accuracy assessment and area estimation. Results show that MuRaF-LULC generates robust annual LULC classifications across multiple years (overall accuracy = 0.90–0.92) and reliable estimates of agropecuario expansion (the dominant transition in the study area) when change is assessed over longer temporal windows where transitions signals stabilize and for which the framework is best suited (producer’s accuracy = 0.97 ± 0.03; user’s accuracy = 0.69 ± 0.05). By prioritizing consistent annual, multiclass LULC trajectories, MuRaF-LULC complements breakpoint- and disturbance-oriented approaches commonly used in land-change studies. Implemented in publicly available, well-documented GEE scripts, MuRaF-LULC facilitates policy-relevant LULC assessment by remote sensing practitioners in governmental and private organizations, where reproducibility, clarity, and ease of deployment are as important as methodological sophistication. Full article

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