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Lou Gehrig’s disease, also known as ALS, is a progressive neurodegenerative condition that weakens muscles and can lead to paralysis as it progresses. For patients with severe paralysis, eye-tracking devices such as eye mouse enable communication. However, the equipment is expensive, and the calibration process is very difficult and frustrating for patients to use. To alleviate this problem, we propose a simple and efficient method to type texts intuitively with graphical guidance on the screen. Specifically, the method detects patients’ eye blinks in video frames to navigate through three sequential steps, narrowing down the choices from 9 letters, to 3 letters, and finally to a single letter (from a 26-letter alphabet). In this way, a patient is able to rapidly type a letter of the alphabet by blinking a minimum of three times and a maximum of nine times. The proposed method integrates an API of large language model (LLM) to further accelerate text input and correct sentences in terms of typographical errors, spacing, and upper/lower case. Experiments on ten participants demonstrate that the proposed method significantly outperforms three state-of-the-art methods in both typing speed and typing accuracy, without requiring any calibration process. Full article

(This article belongs to the Section Biomedical Sensors)

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22 pages, 8351 KiB

Open AccessReview

Recent Progress in DNA Biosensors: Target-Specific and Structure-Guided Signal Amplification

by Jae Eon Lee and Seung Pil Pack

Biosensors 2025, 15(8), 476; https://doi.org/10.3390/bios15080476 (registering DOI) - 23 Jul 2025

Abstract

Deoxyribonucleic acid (DNA) is not only a fundamental biological molecule but also a versatile material for constructing sensitive and specific biosensing platforms. Its ability to undergo sequence-specific hybridization via Watson–Crick base pairing enables both precise target recognition and the programmable construction of nanoscale structures. The demand for ultrasensitive detection increases in fields such as disease diagnostics, therapeutics, and other areas, and the inherent characteristics of DNA have driven the development of a wide range of signal amplification strategies. Among these, polymerase chain reaction (PCR), rolling circle amplification (RCA), and loop-mediated isothermal amplification (LAMP) represent powerful target-based methods that enzymatically increase the concentration of nucleic acid targets, thereby boosting detection sensitivity. In parallel, structure-based strategies leverage the nanoscale spatial programmability of DNA to construct functional architectures with high precision. DNA can be used as a scaffold, such as DNA nanostructures, to organize sensing elements and facilitate signal transduction. It can also function as a probe, like aptamers, to recognize targets with high affinity. These versatilities enable the creation of highly sophisticated sensing platforms that integrate molecular recognition and signal amplification. Driven by DNA nano-assembly capability, both target-based and structure-based approaches are driving the advancement of highly sensitive, selective, and adaptable diagnostic technologies. This review highlights recent developments in DNA nano-assembly-driven amplification strategies. Full article

(This article belongs to the Special Issue Aptamer-Based Sensing: Designs and Applications)

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27 pages, 705 KiB

Open AccessArticle

A Novel Wavelet Transform and Deep Learning-Based Algorithm for Low-Latency Internet Traffic Classification

by Ramazan Enisoglu and Veselin Rakocevic

Algorithms 2025, 18(8), 457; https://doi.org/10.3390/a18080457 (registering DOI) - 23 Jul 2025

Abstract

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

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

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20 pages, 870 KiB

Open AccessArticle

Purchasing Decisions with Reference Pointsand Prospect Theoryin the Metaverse

by Theodore Tarnanidis, Nana Owusu-Frimpong, Bruno Barbosa Sousa, Vijaya Kittu Manda and Maro Vlachopoulou

Adm. Sci. 2025, 15(8), 287; https://doi.org/10.3390/admsci15080287 (registering DOI) - 23 Jul 2025

Abstract

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality. Full article

(This article belongs to the Section Strategic Management)

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12 pages, 1031 KiB

Open AccessArticle

Ultrasound Pattern of Indeterminate Thyroid Nodules with Prevalence of Oncocytes

by Sium Wolde Sellasie, Stefano Amendola, Leo Guidobaldi, Francesco Pedicini, Isabella Nardone, Tommaso Piticchio, Simona Zaccaria, Luigi Uccioli and Pierpaolo Trimboli

J. Clin. Med. 2025, 14(15), 5206; https://doi.org/10.3390/jcm14155206 (registering DOI) - 23 Jul 2025

Abstract

Objectives: Oncocyte-rich indeterminate thyroid nodules (O-ITNs) present diagnostic and management challenges due to overlapping features between benign and malignant lesions and differing cytological classifications. This study aimed primarily to assess the ultrasound (US) characteristics and US-based risk of O-ITNs using the American College of Radiology Thyroid Imaging Reporting And Data Systems (ACR TI-RADS). A secondary objective was to compare the Bethesda System for Reporting Thyroid Cytopathology (BSRTC) and Italian Consensus for the Classification and Reporting of Thyroid Cytology (ICCRTC) cytological systems regarding classification and clinical management implications for O-ITNs. Methods: A retrospective study was conducted on 177 ITNs (TIR3A and TIR3B) evaluated between June 2023 and December 2024 at CTO-Alesini, Rome (Italy). Nodules were assessed with US, cytology, and histology. Oncocyte predominance was defined as >70% oncocytes on fine-needle aspiration (FNA). US features were analyzed according to ACR TI-RADS. Nodules were reclassified by BSRTC, and potential differences in clinical case management (CCM) were analyzed. Results: O-ITNs comprised 47.5% of the sample. Compared to non-O-ITNs, O-ITNs were larger and more frequently showed low-risk US features, including a higher prevalence of ACR TI-RADS 3 nodules. However, no progressive increase in the risk of malignancy (ROM) was observed across ACR TI-RADS classes within O-ITNs. Histological malignancy was identified in 47.1% of O-ITNs, a lower proportion compared to non-O-ITNs, though the difference was not statistically significant. Classification discordance with potential management impact was lower in O-ITNs (20.2%) than in non-O-ITNs (38.7%). Conclusions: O-ITNs typically exhibit benign-appearing US features and lower classification discordance between BSRTC and ICCRTC, yet US risk stratification fails to differentiate malignancy risk within O-ITNs. A tailored approach integrating cytology and cautious US interpretation is essential for optimal O-ITN management. Full article

(This article belongs to the Section Endocrinology & Metabolism)

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20 pages, 1776 KiB

Open AccessReview

Bridging Theory and Practice: A Review of AI-Driven Techniques for Ground Penetrating Radar Interpretation

by Lilong Zou, Ying Li, Kevin Munisami and Amir M. Alani

Appl. Sci. 2025, 15(15), 8177; https://doi.org/10.3390/app15158177 (registering DOI) - 23 Jul 2025

Abstract

Artificial intelligence (AI) has emerged as a powerful tool for advancing the interpretation of ground penetrating radar (GPR) data, offering solutions to long-standing challenges in manual analysis, such as subjectivity, inefficiency, and limited scalability. This review investigates recent developments in AI-driven techniques for GPR interpretation, with a focus on machine learning, deep learning, and hybrid approaches that incorporate physical modeling or multimodal data fusion. We systematically analyze the application of these techniques across various domains, including utility detection, infrastructure monitoring, archeology, and environmental studies. Key findings highlight the success of convolutional neural networks in hyperbola detection, the use of segmentation models for stratigraphic analysis, and the integration of AI with robotic and real-time systems. However, challenges remain with generalization, data scarcity, model interpretability, and operational deployment. We identify promising directions, such as domain adaptation, explainable AI, and edge-compatible solutions for practical implementation. By synthesizing current progress and limitations, this review aims to bridge the gap between theoretical advancements in AI and the practical needs of GPR practitioners, guiding future research towards more reliable, transparent, and field-ready systems. Full article

(This article belongs to the Special Issue Machine Learning and Data Analysis: Bridging Theory and Real-World Solutions)

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23 pages, 1127 KiB

Open AccessArticle

NOVA: A Retrieval-Augmented Generation Assistant in Spanish for Parallel Computing Education with Large Language Models

by Gabriel A. León-Paredes, Luis A. Alba-Narváez and Kelly D. Paltin-Guzmán

Appl. Sci. 2025, 15(15), 8175; https://doi.org/10.3390/app15158175 (registering DOI) - 23 Jul 2025

Abstract

This work presents the development of NOVA, an educational virtual assistant designed for the Parallel Computing course, built using a Retrieval-Augmented Generation (RAG) architecture combined with Large Language Models (LLMs). The assistant operates entirely in Spanish, supporting native-language learning and increasing accessibility for students in Latin American academic settings. It integrates vector and relational databases to provide an interactive, personalized learning experience that supports the understanding of complex technical concepts. Its core functionalities include the automatic generation of questions and answers, quizzes, and practical guides, all tailored to promote autonomous learning. NOVA was deployed in an academic setting at Universidad Politécnica Salesiana. Its modular architecture includes five components: a relational database for logging, a vector database for semantic retrieval, a FastAPI backend for managing logic, a Next.js frontend for user interaction, and an integration server for workflow automation. The system uses the GPT-4o mini model to generate context-aware, pedagogically aligned responses. To evaluate its effectiveness, a test suite of 100 academic tasks was executed—55 question-and-answer prompts, 25 practical guides, and 20 quizzes. NOVA achieved a 92% excellence rating, a 21-second average response time, and 72% retrieval coverage, confirming its potential as a reliable AI-driven tool for enhancing technical education. Full article

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21 pages, 3672 KiB

Open AccessArticle

Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision

by Ganglong Duan, Shaoyang Zhang, Yanying Shang, Yongcheng Shao and Yuqi Han

Appl. Sci. 2025, 15(15), 8176; https://doi.org/10.3390/app15158176 (registering DOI) - 23 Jul 2025

Abstract

Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection. Full article

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11 pages, 1006 KiB

Open AccessArticle

Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy

by Takeshi Tsuda, Amy Walczak and Karen O’Neil

J. Cardiovasc. Dev. Dis. 2025, 12(8), 280; https://doi.org/10.3390/jcdd12080280 (registering DOI) - 23 Jul 2025

Abstract

Background: The onset of cardiomyopathy in Duchenne muscular dystrophy (DMD) is insidious and poorly defined. We proposed integrated wall stress (iWS) as a marker of total left ventricular (LV) workload and tested whether the increased iWS represents early DMD cardiomyopathy. Methods: Peak systolic wall stress (PS-WS) was calculated in M-mode echocardiography with simultaneous blood pressure measurement. iWS was defined as a product of PS-WS and heart rate (HR) divided by 60 (=PS-WS/RR interval). We measured iWS in normal controls (CTRL), DMD with normal LV shortening fraction (%LVSF ≥ 30%) (DMD-A), and DMD with decreased %LVSF (<30%) (DMD-B). Results: 40 CTRL and 79 DMD patients were studied. Despite comparable %LVSF, both HR and iWS were significantly higher in DMD-A (n = 50) than in CTRL (p < 0.0001). iWS was significantly higher in DMD-B (n = 29) than in DMD-A (p < 0.0001) despite comparable HR. PS-WS was significantly higher in DMD-A than in CTRL and higher in DMD-B than in DMD-A, suggesting high HR is not a sole determinant of increased iWS in DMD-A compared with CTRL. In a longitudinal study in 35 DMD patients over 4.0 ± 2.0 years, iWS showed significant increase (p = 0.0062) alongside a significant decline in %LVSF (p < 0.0001). Conclusions: iWS significantly increased in DMD before %LVSF declined. The progressive increase of iWS in DMD is initially associated with increased HR and then with increased PS-WS. iWS may serve as a useful echocardiographic marker in identifying preclinical DMD cardiomyopathy. Full article

(This article belongs to the Section Pediatric Cardiology and Congenital Heart Disease)

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23 pages, 2274 KiB

Open AccessReview

Nature-Based Solutions for Water Management in Europe: What Works, What Does Not, and What’s Next?

by Eleonora Santos

Water 2025, 17(15), 2193; https://doi.org/10.3390/w17152193 (registering DOI) - 23 Jul 2025

Abstract

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

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23 pages, 2363 KiB

Open AccessReview

Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey

by Akzhibek Amirova, Ibraheem Shayea, Didar Yedilkhan, Laura Aldasheva and Alma Zakirova

Technologies 2025, 13(8), 313; https://doi.org/10.3390/technologies13080313 (registering DOI) - 23 Jul 2025

Abstract

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

(This article belongs to the Section Information and Communication Technologies)

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23 pages, 964 KiB

Open AccessArticle

Cultural Ecosystem Services of Grassland Communities: A Case Study of Lubelskie Province

by Teresa Wyłupek, Halina Lipińska, Agnieszka Kępkowicz, Kamila Adamczyk-Mucha, Wojciech Lipiński, Stanisław Franczak and Agnieszka Duniewicz

Sustainability 2025, 17(15), 6697; https://doi.org/10.3390/su17156697 (registering DOI) - 23 Jul 2025

Abstract

Grassland communities consist primarily of perennial herbaceous species, with grasses forming a dominant or significant component. These ecosystems have been utilised for economic purposes since the earliest periods of human history. In the natural environment, they fulfil numerous critical functions that, despite increasing awareness of climate change, often remain undervalued. Grasslands contribute directly to climate regulation, air purification, soil conservation, flood mitigation, and public health—all of which positively affect the well-being of nearby populations. Moreover, they satisfy higher-order human needs known as “cultural” services, providing aesthetic enjoyment and recreational opportunities. These services, in tangible terms, support the development of rural tourism. The objective of this study was to examine the perception of cultural ecosystem services provided by different types of grassland communities—meadows, pastures, and lawns. The study employed a structured questionnaire to evaluate the perceived significance and functions of these communities. Respondents assessed their aesthetic and recreational value based on land-use type. To quantify these dimensions, the study applies the Recreational and Leisure Attractiveness Index (RLAI), the Aesthetic Attractiveness Index (AAI), ranking methods, and contingent valuation techniques. Based on the respondents’ declared WTP (willingness to pay) and WTA (willingness to accept) values, statistically significant differences in the perceived value of land-use types were identified. Lawns were rated highest in terms of recreational attractiveness, meadows in terms of aesthetics, while pastures achieved the highest economic values. Significant differences were also observed depending on respondents’ place of residence and academic background. The results indicate that the valuation of cultural services encompasses both functional and psychological aspects and should be integrated into local land-use and landscape planning policies. Full article

(This article belongs to the Section Health, Well-Being and Sustainability)

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18 pages, 1154 KiB

Open AccessArticle

Predicting Major Adverse Cardiovascular Events After Cardiac Surgery Using Combined Clinical, Laboratory, and Echocardiographic Parameters: A Machine Learning Approach

by Mladjan Golubovic, Velimir Peric, Marija Stosic, Vladimir Stojiljkovic, Sasa Zivic, Aleksandar Kamenov, Dragan Milic, Vesna Dinic, Dalibor Stojanovic and Milan Lazarevic

Medicina 2025, 61(8), 1323; https://doi.org/10.3390/medicina61081323 (registering DOI) - 23 Jul 2025

Abstract

Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential yet often limited by models that overlook atrial mechanics and underutilized biomarkers. Materials and Methods: This study aimed to develop an interpretable machine learning model for predicting perioperative MACE by integrating clinical, biochemical, and echocardiographic features, with a particular focus on novel physiological markers. A retrospective cohort of 131 patients was analyzed. An Extreme Gradient Boosting (XGBoost) classifier was trained on a comprehensive feature set, and SHapley Additive exPlanations (SHAPs) were used to quantify each variable’s contribution to model predictions. Results: In a stratified 80:20 train–test split, the model initially achieved an AUC of 1.00. Acknowledging the potential for overfitting in small datasets, additional validation was performed using 10 independent random splits and 5-fold cross-validation. These analyses yielded an average AUC of 0.846 ± 0.092 and an F1-score of 0.807 ± 0.096, supporting the model’s stability and generalizability. The most influential predictors included total atrial conduction time, mitral and tricuspid annular orifice areas, and high-density lipoprotein (HDL) cholesterol. These variables, spanning electrophysiological, structural, and metabolic domains, significantly enhanced discriminative performance, even in patients with preserved left ventricular function. The model’s transparency provides clinically intuitive insights into individual risk profiles, emphasizing the significance of non-traditional parameters in perioperative assessments. Conclusions: This study demonstrates the feasibility and potential clinical value of combining advanced echocardiographic, biochemical, and machine learning tools for individualized cardiovascular risk prediction. While promising, these findings require prospective validation in larger, multicenter cohorts before being integrated into routine clinical decision-making. Full article

(This article belongs to the Section Intensive Care/ Anesthesiology)

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17 pages, 4216 KiB

Open AccessArticle

Sugarcane Phenology Retrieval in Heterogeneous Agricultural Landscapes Based on Spatiotemporal Fusion Remote Sensing Data

by Yingpin Yang, Zhifeng Wu, Dakang Wang, Cong Wang, Xiankun Yang, Yibo Wang, Jinnian Wang, Qiting Huang, Lu Hou, Zongbin Wang and Xu Chang

Agriculture 2025, 15(15), 1578; https://doi.org/10.3390/agriculture15151578 (registering DOI) - 23 Jul 2025

Abstract

Accurate phenological information on sugarcane is crucial for guiding precise cultivation management and enhancing sugar production. Remote sensing offers an efficient approach for large-scale phenology retrieval, but most studies have primarily focused on staple crops. The methods for retrieving the sugarcane phenology—the germination, tillering, elongation, and maturity stages—remain underexplored. This study addresses the challenge of accurately monitoring the sugarcane phenology in complex terrains by proposing an optimized strategy integrating spatiotemporal fusion data. Ground-based validation showed that the change detection method based on the Double-Logistic curve significantly outperformed the threshold-based approach, with the highest accuracy for the elongation and maturity stages achieved at the maximum slope points of the ascending and descending phases, respectively. For the germination and tillering stages with low canopy cover, a novel time-windowed change detection method was introduced, using the first local maximum of the third derivative curve (denoted as Point A) to establish a temporal buffer. The optimal retrieval models were identified as 25 days before and 20 days after Point A for germination and tillering, respectively. Among the six commonly used vegetation indices, the NDVI (normalized difference vegetation index) performed the best across all the phenological stages. Spatiotemporal fusion using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) significantly improved the monitoring accuracy in heterogeneous agricultural landscapes, reducing the RMSE (root-mean-squared error) by 21–46%, with retrieval errors decreasing from 18.25 to 12.97 days for germination, from 8.19 to 4.41 days for tillering, from 19.17 to 10.78 days for elongation, and from 19.02 to 15.04 days for maturity, highlighting its superior accuracy. The findings provide a reliable technical solution for precision sugarcane management in heterogeneous landscapes. Full article

(This article belongs to the Topic Transport, Transformation and Cycling of Elements in Water and Soil and Their Response to Human Activities)

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15 pages, 1493 KiB

Open AccessReview

Research Progress on the Effect of Thesium chinense Turcz. on Neurodegenerative Diseases

by Ziyi Li, Yanfang Zhao, Rong Wang, Ruoxuan Zhou, Xuehua Chen, Jingchen Jiang, Yilan Dai and Huaiqing Luo

Int. J. Mol. Sci. 2025, 26(15), 7079; https://doi.org/10.3390/ijms26157079 (registering DOI) - 23 Jul 2025

Abstract

Thesium chinense Turcz., a traditional Chinese medicinal herb, is enriched with bioactive constituents such as flavonoids and polysaccharides, demonstrating multifaceted therapeutic properties including anti-inflammatory, antioxidant, and neuroprotective effects. This review systematically elucidates the regulatory mechanisms by which active components of Thesium chinense Turcz. modulate pathological processes in NDDs, such as neuroinflammation and oxidative stress. Furthermore, it synthesizes evidence of its neuroprotective efficacy across experimental models and evaluates its translational potential for clinical applications. By integrating preclinical findings and mechanistic insights, this work provides a robust theoretical foundation for advancing natural product-based therapeutics in the management of NDDs. Full article

(This article belongs to the Section Molecular Neurobiology)

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