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Non-syndromic cleft lip with or without cleft palate (ns-CL/P) is one of the most common craniofacial anomalies with a multifactorial etiology. To investigate the contribution of rare variants to disease risk, we performed whole-exome sequencing (WES) in 58 patients with ns-CL/P from a homogeneous Polish population, excluding from analysis 423 previously investigated cleft candidate genes. After stringent filtering, prioritization, and segregation analysis, we identified 31 likely pathogenic (LP) variants across 30 genes, significantly enriched in categories related to developmental processes. Notably, 29% of variants occurred in genes not previously linked to clefting, including AGO1, ARID1A, ATP1A1, FOXA2, GDF7, HOXB3, LRP5, MAML1, and ZNF319. Three were de novo: FOXA2_p.Arg260Pro, MAML1_p.Gln65Ter, and ZNF319_p.Gln64Ter. Most of the remaining variants were inherited from unaffected parents, suggesting incomplete penetrance and possible modifier effects consistent with the heterogeneous etiology of ns-CL/P. Additionally, analysis of common variants in the 30 loci harboring rare LP variants revealed nominal associations with ns-CL/P for NXN, EXT1, MAML1, and TP53BP2 loci. These results support the candidacy of these genes and suggest contributions from both rare and common variants. In conclusion, we report novel LP variants expanding the spectrum of candidate genes and providing new insights into the genetic landscape of orofacial clefts. Full article

(This article belongs to the Special Issue State-of-the-Art Molecular Genetics and Genomics in Poland: 3rd Edition)

25 pages, 1907 KB

Open AccessArticle

Collapse Risk Assessment for Tunnel Entrance Construction in Weak Surrounding Rock Based on the WOA–XGBOOST Method and a Game Theory-Informed Combined Cloud Model

by Weiqiang Zheng, Bo Wu, Shixiang Xu, Ximao Chen, Yongping Ye, Yongming Liu, Zhongsi Dou, Cong Liu, Yuxuan Zhu and Zhiping Li

Appl. Sci. 2025, 15(24), 13194; https://doi.org/10.3390/app152413194 - 16 Dec 2025

Abstract

In order to reduce the risk of collapse disasters during tunnel construction in mountainous areas and to make full use of the available data, a collapse risk assessment model for highway tunnel construction was established based on the WOA–XGBOOST algorithm. Three major categories of tunnel construction risk, namely engineering geological factors, survey and design factors, and construction management factors, were selected as the first-level indicators, and 14 secondary indicators were further specified as the input variables of the collapse risk assessment model for tunnel construction. The confusion matrix and accuracy metrics were employed to evaluate the training and prediction performance of the risk assessment model on both the training set and the test set. The results show that subjective weights derived from the G1 method were integrated with objective weights generated by the WOA–XGBOOST algorithm. A game-theory-based weight integration strategy was then applied to optimize the combined weights, effectively mitigating the biases inherent in single-method weighting approaches. Risk quantification was systematically conducted using a cloud model, while spatial risk distribution patterns were visualized through graphical cloud-mapping techniques. After completion of model training, the proposed model achieved a high accuracy of over 99% on the training set and around 95% on the held-out test set based on an available dataset of 100 collapse-prone tunnel construction sections. Case-based verification further suggests that, in the studied collapse scenarios, the predicted risk levels are generally consistent with the actual engineering risks, indicating that the model is a promising tool for assisting tunnel construction risk assessment under similar conditions. The research outcomes provide an efficient and reliable approach for assessing risks in tunnel construction, thereby offering a scientific basis for engineering decision-making processes. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Geoenvironmental Engineering)

32 pages, 2966 KB

Open AccessArticle

CSPC-BRS: An Enhanced Real-Time Multi-Target Detection and Tracking Algorithm for Complex Open Channels

by Wei Li, Xianpeng Zhu, Aghaous Hayat, Hu Yuan and Xiaojiang Yang

Electronics 2025, 14(24), 4942; https://doi.org/10.3390/electronics14244942 - 16 Dec 2025

Abstract

Ensuring worker safety compliance and secure cargo transportation in complex port environments is critical for modern logistics hubs. However, conventional supervision methods, including manual inspection and passive video monitoring, suffer from limited coverage, poor real-time responsiveness, and low robustness under frequent occlusion, scale variation, and cross-camera transitions, leading to unstable target association and missed risk events. To address these challenges, this paper proposes CSPC-BRS, a real-time multi-object detection and tracking framework for open-channel port scenarios. CSPC (Coordinated Spatial Perception Cascade) enhances the YOLOv8 backbone by integrating CASAM, SPPELAN-DW, and CACC modules to improve feature representation under cluttered backgrounds and degraded visual conditions. Meanwhile, BRS (Bounding Box Reduction Strategy) mitigates scale distortion during tracking, and a Multi-Dimensional Re-identification Scoring (MDRS) mechanism fuses six perceptual features—color, texture, shape, motion, size, and time—to achieve stable cross-camera identity consistency. Experimental results demonstrate that CSPC-BRS outperforms the YOLOv8-n baseline by improving the mAP@0.5:0.95 by 9.6% while achieving a real-time speed of 132.63 FPS. Furthermore, in practical deployment, it reduces the false capture rate by an average of 59.7% compared to the YOLOv8 + Bot-SORT tracker. These results confirm that CSPC-BRS effectively balances detection accuracy and computational efficiency, providing a practical and deployable solution for intelligent safety monitoring in complex industrial logistics environments. Full article

10 pages, 1143 KB

Open AccessArticle

APACHE II and NUTRIC Scores for Mortality Prediction in Chronic Critical Illness: A “Right-Side” Prognostic Modeling Approach

by Dmitrij V. Zhidilyaev, Levan B. Berikashvili, Mikhail Ya. Yadgarov, Petr A. Polyakov, Alexey A. Yakovlev, Artem N. Kuzovlev and Valery V. Likhvantsev

Diagnostics 2025, 15(24), 3218; https://doi.org/10.3390/diagnostics15243218 - 16 Dec 2025

Abstract

Background/Objectives: Accurate prognostication for patients with chronic critical illness (CCI) following brain injury remains challenging. Conventional scoring systems like the Acute Physiology and Chronic Health Evaluation II (APACHE II) and the Nutrition Risk in the Critically Ill (NUTRIC) score are validated as “left-side” models for risk stratification at intensive care unit (ICU) admission but may not capture the evolving trajectory of prolonged illness. This study aimed to evaluate the prognostic performance of APACHE II and NUTRIC as “right-side” models—assessed at intervals closer to the outcome—by testing the hypothesis that their predictive accuracy for in-hospital mortality improves when measured nearer to the time of death. Methods: In this real-world data analysis study, data were extracted from the electronic health records (Russian Intensive Care Dataset [RICD] v. 2.0) of 328 adult patients with CCI following brain injury. The discriminative ability of repeatedly assessed APACHE II and NUTRIC scores for predicting mortality was analyzed by calculating the area under the receiver operating characteristic curve (AUROC) for three predefined intervals before death: within ≤7 days, 8–14 days, and ≥15 days. Results: Among the 328 patients (median age 64 years; 18.3% in-hospital mortality), a total of 380 paired score assessments were analyzed. The predictive performance for both scores was highest within 7 days of death (APACHE II AUROC: 0.883; NUTRIC AUROC: 0.839). Discriminatory ability declined at 8–14 days (APACHE II AUROC: 0.807; NUTRIC AUROC: 0.778) and was poorest at ≥15 days before death (APACHE II AUROC: 0.671; NUTRIC AUROC: 0.681). The NUTRIC score consistently demonstrated higher AUROC values than APACHE II across all intervals, though the differences were not statistically significant. Conclusions: In patients with CCI following brain injury, the prognostic accuracy of APACHE II and NUTRIC scores is time-dependent, peaking immediately before death and offering poor long-term prediction from admission. These findings underscore the limitation of static, admission-based models and highlight the necessity for developing dynamic, personalized and time-sensitive prognostic tools tailored to the evolving course of chronic critical illness. Full article

(This article belongs to the Special Issue Predictive and Prognostic Markers in Critically Ill Patients, 2nd Edition)

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33 pages, 1246 KB

Open AccessReview

Predicting Coastal Flooding and Overtopping with Machine Learning: Review and Future Prospects

by Moeketsi L. Duiker, Victor Ramos, Francisco Taveira-Pinto and Paulo Rosa-Santos

J. Mar. Sci. Eng. 2025, 13(12), 2384; https://doi.org/10.3390/jmse13122384 - 16 Dec 2025

Abstract

Flooding and overtopping are major concerns in coastal areas due to their potential to cause severe damage to infrastructure, economic activities, and human lives. Traditional methods for predicting these phenomena include numerical and physical models, as well as empirical formulations. However, these methods have limitations, such as the high computational costs, reliance on extensive field data, and reduced accuracy under complex conditions. Recent advances in machine learning (ML) offer new opportunities to improve predictive capabilities in coastal engineering. This paper reviews ML applications for coastal flooding and overtopping prediction, analyzing commonly used models, data sources, and preprocessing techniques. Several studies report that ML models can match or exceed the performance of traditional approaches, such as empirical EurOtop formulas or high-fidelity numerical models, particularly in controlled laboratory datasets where numerical models are computationally intensive and empirical methods show larger estimation errors. However, their advantages remain task- and data-dependent, and their generalization and interpretability may lag behind physics-based methods. This review also examines recent developments, such as hybrid approaches, real-time monitoring, and explainable artificial intelligence, which show promise in addressing these limitations and advancing the operational use of ML in coastal flooding and overtopping prediction. Full article

(This article belongs to the Special Issue Coastal Disaster Assessment and Response—2nd Edition)

19 pages, 358 KB

Open AccessArticle

The Role of Social Media in Shaping Knowledge, Attitudes, and Purchase Intention Toward Genetically Modified Foods in Saudi Arabia

by Mohammad Hatim Abuljadail

Sustainability 2025, 17(24), 11279; https://doi.org/10.3390/su172411279 - 16 Dec 2025

Abstract

Genetically Modified Foods (GMFs) have become one of the most controversial innovations in food production and biotechnology. Public concerns regarding the safety, ethical considerations, and health impacts of GMFs have fueled widespread debate. This study explores the impact of social media exposure on individuals’ knowledge of and attitudes toward Genetically Modified Foods and how these factors influence their purchase intention. The findings from an online survey of 467 participants in Saudi Arabia show that higher levels of social media exposure are associated with increased knowledge and stronger perceptions of both benefits and risks of GMFs. Purchase intentions, however, are driven primarily by perceived benefits (positively) and perceived risks (negatively), while knowledge exerts an indirect effect through these attitudinal components. Full article

(This article belongs to the Section Sustainable Food)

26 pages, 1000 KB

Open AccessReview

Neurological Sequelae of Long COVID: Mechanisms, Clinical Impact and Emerging Therapeutic Insights

by Muhammad Danial Che Ramli, Beevenna Kaur Darmindar Singh, Zakirah Zainal Abidin, Athirah Azlan, Amanina Nurjannah, Zaw Myo Hein, Che Mohd Nasril Che Mohd Nassir, Rajesh Thangarajan, Noor Aishah Bt. Mohammed Izham and Suresh Kumar

COVID 2025, 5(12), 207; https://doi.org/10.3390/covid5120207 - 16 Dec 2025

Abstract

The COVID-19 pandemic has demonstrated that its effects go far beyond the initial respiratory illness, with many survivors experiencing lasting neurological problems. Some patients develop a condition known as Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), which includes current issues such as reduced cognitive function, chronic headaches, depression, neuropathic pain, and sensory disturbances. These symptoms can severely disrupt daily life and overall well-being. In this narrative review, we provide an overview of current understanding regarding the neurological effects of COVID-19, with a focus on Long COVID. We discuss possible underlying mechanisms, including direct viral invasion of the nervous system, immune-related damage, and vascular complications. We also summarize findings from cohort studies and meta-analyses that explore the causes, symptom patterns, and frequency of these neurological issues. Approximately one-third of people who have had COVID-19 report neurological symptoms, especially those who experienced severe illness or were infected with pre-Omicron variants. Emerging research has identified potential biomarkers such as neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) that may help in diagnosis. Treatment approaches under investigation include antiviral medications, nutraceuticals, and comprehensive rehabilitation programs. Factors like older age, existing health conditions, and genetic differences in ACE2 and TMPRSS2 genes may affect an individual’s risk. To effectively address these challenges, current research is essential to improve diagnostic methods, develop targeted treatments, and enhance rehabilitation strategies. Ultimately, a coordinated, multidisciplinary effort is crucial to reduce the neurological impact of Long COVID and support better recovery for patients. Full article

(This article belongs to the Special Issue Exploring Neuropathology in the Post-COVID-19 Era)

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21 pages, 1307 KB

Open AccessArticle

Probabilistic Prediction of Local Scour at Bridge Piers with Interpretable Machine Learning

by Jaemyeong Choi, Jongyeong Kim, Soonchul Kwon and Taeyoon Kim

Water 2025, 17(24), 3574; https://doi.org/10.3390/w17243574 - 16 Dec 2025

Abstract

Local pier scour remains one of the leading causes of bridge failure, calling for predictions that are both accurate and uncertainty-aware. This study develops an interpretable data-driven framework that couples CatBoost (Categorial Gradient Boosting) for deterministic point prediction with NGBoost (Natural Gradient Boosting) for probabilistic prediction. Both models are trained on a laboratory dataset of 552 measurements of local scour at bridge piers using non-dimensional inputs (y/b, V/V_c, b/d₅₀, Fr). Model performance was quantitatively evaluated using standard regression metrics, and interpretability was provided through SHAP (Shapley Additive Explanations) analysis. Monte Carlo–based reliability analysis linked the predicted scour depths to a reliability index β and exceedance probability through a simple multiplicative correction factor. On the held-out test set, CatBoost offers slightly higher point-prediction accuracy, while NGBoost yields well-calibrated prediction intervals with empirical coverages close to the nominal 68% and 95% levels. This framework delivers accurate, interpretable, and uncertainty-aware scour estimates for target-reliability, risk-informed bridge design. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

49 pages, 2062 KB

Open AccessArticle

Timing Circular Regeneration with Adaptive Reuse Potential: A Century of Transformations at the Renoma Department Store, Wroclaw

by Elżbieta Komarzyńska-Świeściak, Krystyna Kirschke and Paweł Kirschke

Sustainability 2025, 17(24), 11276; https://doi.org/10.3390/su172411276 - 16 Dec 2025

Abstract

Historic department stores are an underexamined lever for circular, low-carbon urban transition. This study tests whether Langston’s Adaptive Reuse Potential (ARP) can be applied retrospectively and how contextual readiness shapes the timing of interventions. Using the Renoma Department Store in Wroclaw, Poland (1930–2025), we reconstruct five adaptive phases and combine expert scoring of seven obsolescence dimensions (O1–O7) with a Readiness index covering finance, governance/approvals, use commitment, delivery/supply chain, and policy priority. Decision windows are interpreted via a WAIT–PREPARE–GO lens. Results show that peaks in ARP and Readiness aligned with major reinvestments—post-war reconstruction, socialist modernisation, and post-EU-accession renewal—while the original steel frame retained high structural reserves, indicating that timing was driven more by institutional and economic conditions than by technical decay. We propose ARP as an interpretive lens for circular regeneration and show that the Readiness index clarifies feasibility and risk. The combined ARP × Readiness approach yields a replicable, phase-sensitive diagnosis of adaptive capacity and intervention timing, contributing evidence to circular city practice and aligning with New European Bauhaus principles of sustainability, inclusion, and quality of place. Full article

(This article belongs to the Special Issue Circular Economy and Circular City for Sustainable Development)

27 pages, 558 KB

Open AccessSystematic Review

Bridging Regulation and Innovation: A Systematic Review of Cryptocurrency Taxation and Fiscal Policy (2020–2025)

by Rosario Violeta Grijalva-Salazar, Jose Antonio Caicedo-Mendoza, Arturo Jaime Zúñiga-Castillo, Erikson Olivas-Valencia and Víctor Hugo Fernández-Bedoya

J. Risk Financial Manag. 2025, 18(12), 720; https://doi.org/10.3390/jrfm18120720 - 16 Dec 2025

Abstract

Taxation on cryptocurrency is becoming critical in global fiscal governance as digital assets adapt to the modern reality of existing outside of traditional regulatory constructs. Theoretical and practical understanding of cryptocurrency taxation is quite new, and so a systematic review was designed to present the most recent empirical research evidence on the legal, fiscal and behavioral aspects of cryptocurrency taxation from across the globe. Using the PRISMA-2020 guidelines, a structured search was applied to the Scopus database on 21 May 2025, with the search terms “crypto-currency”, “cryptoasset” and “taxation.” The inclusion criteria consisted of original research articles published between the years of 2020 and 2025 in English or Spanish, that could be accessed via institutional library support, and that were related to taxation, legal regulation and/or compliance. Out of the original identified 224 records, 36 met the eligibility criteria after screening and verification through seven different stages of review. Socially, five themes were produced by the findings: legal ambiguity surrounding fiscal treatment, limited tax literacy and compliance issues, macroeconomic and monetary issues, application of digital technologies for fiscal tracking, and environmental repercussions from crypto mining. Many countries do not have any coherent tax frameworks to govern the risk that emerges from cryptocurrency taxation, creating uncertainty for both regulators and investors. The findings outlined in this systematic review point to the urgent need for creating a coherent approach to cryptocurrency taxation based on definitions, digital approaches to traceability, and tax literacy compliance strategies. In order to create effective cryptocurrency taxation, there must be a base balance between ensuring innovation, fiscal responsibility, transparency, equity and sustainability in the developing digital economy. Full article

(This article belongs to the Special Issue Commercial Banking and FinTech in Emerging Economies, 2nd Edition)

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25 pages, 31455 KB

Open AccessArticle

Temporal and Spatial Changes in Soil Drought and Identification of Remote Correlation Effects

by Weiran Luo, Jianzhong Guo, Ziwei Li, Ning Li, Fei Wang, Hexin Lai, Ruyi Men, Rong Li, Mengting Du, Kai Feng, Yanbin Li, Shengzhi Huang and Qingqing Tian

Agriculture 2025, 15(24), 2603; https://doi.org/10.3390/agriculture15242603 - 16 Dec 2025

Abstract

Under the extensive influence of the monsoon climate, droughts in the Yangtze River Basin (YRB) occur frequently and pose a serious threat to grain security. To better understand the evolution and drivers of soil drought, this study employed remote sensing-based soil moisture and atmospheric circulation data from 2000 to 2022. It assessed the spatiotemporal characteristics of soil drought across the YRB and its sub-basins, identified the main mutation points and types, and quantified the relative contributions of climatic and circulation factors. The results show that: (1) the most severe soil drought month occurred in August 2022 (Standardized Soil Moisture Index SSMI = –1.69), with two major mutation points in May 2011 (“decrease to increase”) and June 2019 (“increase to decrease”); (2) drought mutations were mainly categorized as “interrupted decrease” (9 sub-basins) and “increase to decrease” (1 sub-basin), most occurring after 2010; (3) the year 2022 experienced the most severe annual drought (SSMI = –0.94), with extreme drought covering 39.36% of the basin in August; (4) precipitation (PC) was the dominant climatic factor influencing drought (percentage area of significant coherence PASC = 15.48%), while the Interannual Pacific Oscillation (IPO), Pacific Decadal Oscillation (PDO), and Dipole Mode Index (DMI) all showed significant remote-correlation effects, with mean Shapley additive explanations (SHAP) values of 0.138, 0.111, and 0.090, respectively. This study clarifies the spatiotemporal patterns and drivers of soil drought in the YRB, providing a scientific basis for improved drought monitoring and agricultural risk management. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

21 pages, 5190 KB

Open AccessArticle

Monitoring and Prediction of Differential Settlement of Ultra-High Voltage Transmission Towers in Goaf Areas

by Yi Zhou, Ying Jing, Yuesong Zheng, Laizhong Ding, Zhiyao Mai, Yaxing Guo, Dongya Wu and Zhengxi Wang

GeoHazards 2025, 6(4), 83; https://doi.org/10.3390/geohazards6040083 - 16 Dec 2025

Abstract

Critical transmission lines frequently traverse geologically complex mountainous regions, where harsh environments and variable climatic conditions pose significant geohazard risks. Utilizing 163 Sentinel-1A scenes (January 2018 to October 2023), we employed Multi-Temporal InSAR (MT-InSAR) to derive the deformation field along the transmission corridor. Time-series analysis of the Lingshao (LS) line towers, interpreted through the principles of mining subsidence, revealed the mechanisms behind their differential tilt. Simultaneously, time-series deformation at the tower footings was input to a deep learning model for 365-day prediction; the accuracy and practical applicability of which were rigorously assessed. The results demonstrate that (1) a unidirectional subsidence funnel within the transmission corridor deformation field, in the absence of zonal settlement features, strongly indicates the presence of a goaf beneath the line; (2) the integrated approach combining time-series InSAR with the settlement trough method proves feasible for monitoring transmission tower tilt, as validated through field verification; (3) the magnitude and direction of tower tilt correlate directly with their position in the mining-induced subsidence basin, showing convergent tilt in tensile zones, divergent tilt in compressive zones, and uniform settlement in neutral zones; (4) for the eight selected typical tower footings, predicted deformation values ranged from −284.6 mm to −186.3 mm, showing excellent agreement with measurements through correlation coefficients of 0.989–0.999 and Root Mean Square Error (RMSE) values of 0.54–2.17 mm. The framework enables proactive hazard avoidance during line routing and provides early warning for tower defects, significantly enhancing power infrastructure resilience in mining-affected regions. Full article

17 pages, 2147 KB

Open AccessArticle

Microplastics in the Canary Islands: A Case Study on Transport and Tourist Pressure

by Ludovit Schreiber, Zoraida Sosa-Ferrera and José Juan Santana-Rodríguez

Environments 2025, 12(12), 494; https://doi.org/10.3390/environments12120494 - 16 Dec 2025

Abstract

Microplastics (MPs) are a global concern due to their persistence and capacity to adsorb and transport pollutants. The Canary Islands, influenced by the Canary Current, are particularly vulnerable to MPs accumulation from remote sources. The European Union’s Watch List includes emerging contaminants that require monitoring to assess potential ecological risks, though limited data hinder definitive evaluations. This study conducted a monitoring campaign between December 2023 and September 2024 across eleven beaches on four eastern islands of the archipelago. The aim was to assess MPs pollution (particles between 1 and 5 mm) and the presence of 26 organic contaminants from the EU Watch List adsorbed onto MPs, evaluating seasonal variation and tourism influence. Results show that beaches facing north and east had significantly higher MPs levels—up to an order of magnitude greater (ranged from <10 to >500 items/m²)—due to strong wind exposure, confirming the role of the Canary Current in MPs transport. White/transparent fragments dominated (>50%) among MPs types. Eight Watch List compounds were identified, with UV filters—commonly found in sunscreens—being the most frequently detected, present at nearly all sampling sites. Octocrylene reached concentrations up to 17,811 ng/g in highly touristic beaches. These findings highlight the environmental pressure on insular coastal zones and the relevance of combining MPs monitoring with targeted contaminant analysis in regions affected by oceanic currents and tourism. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Plastic Contamination)

17 pages, 1979 KB

Open AccessArticle

Groundwater Storage Changes Derived from GRACE-FO Using In Situ Data for Practical Management

by Hongbo Liu, Jianchong Sun, Litang Hu, Shinan Tang, Fei Chen, Junchao Zhang and Zhenyuan Zhu

Water 2025, 17(24), 3572; https://doi.org/10.3390/w17243572 - 16 Dec 2025

Abstract

The ongoing global decline in groundwater levels poses significant challenges for sustainable water management. Satellite gravity missions, such as the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO), provide valuable estimates of groundwater storage changes at regional scales. However, the relatively coarse spatial resolution of these satellite data limits their direct applicability to local groundwater management. In this study, we address this limitation for China by analyzing groundwater monitoring data from 108 cities with shallow groundwater use and 37 cities with deep groundwater use from the period 2019–2022, integrating in situ groundwater level records, official monitoring reports, monthly dynamic data, and GRACE-FO-derived groundwater storage estimates. Our findings reveal rapid groundwater depletion in northern China, especially in Xinjiang and Hebei Provinces. Fluctuations in shallow groundwater levels in Beijing and Jiangsu are closely related to precipitation variability. For deep aquifer regions, GRACE-FO-derived groundwater storage changes show a moderate Pearson correlation coefficient of 0.45 and groundwater level variations. Regional analysis for 2019–2021 in the Northeast Plain and the Huang–Huai–Hai Basin indicates better agreement between satellite-derived storage and groundwater levels, with a Pearson correlation coefficient of 0.58 in the Huang–Huai–Hai Basin. Groundwater level dynamics are strongly influenced by both precipitation and pumping, with an approximate three-month lag between precipitation events and groundwater storage responses. Overall, satellite gravity data are suitable for use in regional groundwater assessment and could serve as valuable indicators in areas with intensive deep groundwater exploitation. To enable fine-scale groundwater management, future work should focus on improving the spatial resolution through downscaling and other advanced techniques. Full article

(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)

26 pages, 21352 KB

Open AccessArticle

Study on the Spatial Association Complexity and Formation Mechanism of Green Innovation Efficiency Network for Sustainable Urban Development: Taking the Yangtze River Delta Urban Agglomeration as an Example

by Binghui Zhang, Ling Xu, Shaojun Zhong, Kailin Zeng and Wenxing Zhu

Sustainability 2025, 17(24), 11273; https://doi.org/10.3390/su172411273 - 16 Dec 2025

Abstract

Against the backdrop of China’s “dual carbon” strategy and regional integration, enhancing green innovation efficiency (GIE) has become a core issue for the Yangtze River Delta Urban Agglomeration (YRDUA) in achieving sustainable and high-quality development. This study employs the Super EBM model to measure the GIE of 41 cities in the YRDUA from 2012 to 2022 and further integrates a modified gravity model with social network analysis to uncover the structural complexity and spatial directionality of its spatial association network. In addition, the Exponential Random Graph Model (ERGM) is applied to explore the formation mechanisms of the green innovation efficiency network. Results show the following: (1) GIE presents a fluctuating upward trend, with the mean rising from 0.747 in 2012 to 0.906 in 2022 and disparities gradually narrowing, but provincial gradients persist, implying potential “Matthew effect” risks. (2) Network density continues to increase, with S-density rising from 0.0061 in 2012 to 0.0335 in 2022; supporting and basic connections serve as key drivers of network complexity, whereas the significant decline of edge connections may weaken the network’s extensibility. (3) Node connections display preference and attachment, causing polarization; transitivity and triadic cooperation rise markedly, increasing by 41.89% and 40.86%, respectively, reflecting strong self-organization. (4) Reciprocity and agglomeration drive network formation, and economic and technological differences promote it, while disparities in innovation input and government roles vary across periods. Geographic distance hinders formation, though its effect is weakening. These findings enhance the methodological approaches to sustainability research and provide insights for optimizing regional cooperation and advancing green integration in the YRDUA. Full article

(This article belongs to the Section Sustainable Urban and Rural Development)

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