Search Results (1,574)

Plastic pollution is a problem for many municipalities, water authorities, and industries, including transportation, energy, agriculture, fisheries, real estate, tourism, hospitality, insurance, and healthcare. Efforts to understand and mitigate plastic pollution would benefit from a dedicated map satisfying basic criteria including traceability, scalability, spatio-temporal resolution, and data flexibility. This article details and demonstrates how several existing pollution maps satisfy these criteria and makes recommendations on their use for specific activities, including temporal monitoring, root-cause analysis (RCA), cleanups, and tourism guides. Advantages of using plastic density rather than piecewise logs as the primary data format are highlighted, in particular feasible memory requirements and access to cloud data. Environmental plastic mapping by passive optical sensors, which offer the potential of comprehensive qualified data, is also surveyed, including demonstration of an original shortwave infrared (SWIR) polarization imager, and dynamic plastic pollution monitoring is demonstrated through the application-programming interface (API) of the Google Maps platform utilizing both sensor and published survey data. Full article

Background: Olfactory dysfunction is a pathology associated with viral infections, toxic damage, aging, and neurodegenerative diseases. Damage to the olfactory epithelium impairs olfactory function and related neurological behaviors. This study evaluated the restorative effects of Scutellaria baicalensis Georgi (SBG) extract and baicalein in a methimazole-induced olfactory dysfunction model. Methods: Olfactory epithelial damage was induced in mice with methimazole, followed by treatment with SBG extract or baicalein. Olfactory and neurobehavioral functions were assessed using odor-finding, novel object recognition (NOR), Morris water maze (MWM), open field (OFT), and elevated plus maze tests (EPM). Histological, immunohistochemical, and in vitro analyses were performed to evaluate epithelial regeneration, mature olfactory sensory neurons (OSNs) expressing olfactory marker protein (OMP), and proliferative activity. Results: Methimazole induced severe olfactory epithelial damage, impairing olfactory behavior and reducing learning and memory. Treatment with SBG extract and baicalein significantly improved olfactory and cognitive functions. Histological and immunohistochemical analyses confirmed restoration of epithelial structure and olfactory neurons. In vitro, SBG extract increased epithelial cell density and modulated proliferative activity. Conclusions: SBG extract and baicalein promote recovery of olfactory function and improve neurobehavioral outcomes, indicating their potential as therapies for olfactory dysfunction. Full article

Bio-silica particles derived from rice husks were coated with MgAl layered double hydroxides (LDHs) and thermally converted into layered double oxides (LDOs) to evaluate fluoride capture and release capability. The deposition of an MgAl LDH layer on the silica particle makes the LDH more accessible for interaction. Fluoride loading was tested in aqueous and ethanol–water media, with mixed solvents consistently enhancing uptake. Release studies in demineralized water showed relatively rapid desorption (~1500 min), whereas embedding particles in an acrylic matrix reduced the release rate by nearly two orders of magnitude, enabling sustained release levels suitable for dental applications. Ethanol promoted both ion exchange and memory effect mechanisms, providing tunable control over fluoride incorporation and release. These functional composites demonstrate potential for controlled delivery in dental restorative materials, highlighting their potential as adaptive fillers that can enhance the mechanical properties while also serving a functional base for low fluoride release. Full article

Abiotic stresses, including drought, salinity, alkalinity, temperature extremes, flooding, heavy metals, and emerging pollutants, challenge plant growth and productivity by disturbing water relations, ion balance, redox homeostasis, membrane stability, energy metabolism, and developmental progression. Although substantial progress has been made in the identification of stress-responsive hormones, second messengers, kinases, transcription factors, transporters, and metabolic regulators, plant stress adaptation cannot be fully explained by linear signaling cascades or single tolerance genes. A major unresolved question is how early molecular events are reorganized into coordinated physiological and developmental outputs that support survival, recovery, and productivity. In this review, we propose an intermolecular interaction-driven adaptive remodeling framework for plant abiotic stress responses. This framework emphasizes that stress tolerance emerges from dynamic changes in receptor–ligand recognition, protein–protein interactions, calcium decoding, redox-sensitive modification, phosphorylation networks, transcriptional regulation, chromatin-associated control, and metabolite-mediated feedback. We further emphasize ROS as integrative redox switches that connect stress sensing, defense activation, senescence-related transitions, and recovery, and chromatin-associated mechanisms as regulators that may stabilize primed or memory-like adaptive states. We discuss how these interaction networks converge on core signaling hubs, including abscisic acid, reactive oxygen species, Ca²⁺, and kinase/phosphatase systems, and how they remodel stomatal behavior, root architecture, ion and pH homeostasis, redox buffering, metabolism, development, and reproductive resilience. We further highlight how natural variation, multi-omics, genome editing, high-throughput phenotyping, and field validation can translate interaction-centered stress biology into crop resilience. This perspective provides a conceptual bridge between molecular stress perception, network behavior, physiological adaptation, and climate-resilient agriculture. Full article

Water and sanitation governance sits at the intersection of global development ambitions and highly localized service realities. While SDG6 sets universal targets for clean water and sanitation, the institutional and fiscal arrangements that translate those targets into actual service outcomes operate primarily at the subnational level. The discrepancy between globally defined objectives and locally executed delivery creates a structural research gap: how do the fiscal architectures of local governments influence progress towards SDG6? This study addresses this question for a panel of OECD countries by developing a deep learning-based estimation framework that combines bidirectional long short-term memory (BiLSTM) networks with Tianji’s horse racing optimization (THRO) algorithm. Three distinct operationalizations of fiscal decentralization are tested against SDG6 outcomes: subnational expenditure share (EFDM), subnational revenue share (RFDM), and a composite index balancing both dimensions (CFDM). Model adequacy is assessed using a layered diagnostic protocol involving regression fit, country-level residual patterns, error density profiles, Bland–Altman limits of agreement and inter-annual error trajectories. Among the three configurations, CFDM consistently records superior performance (; ; ), while even the weakest specification clears , attesting to the overall robustness of the proposed architecture. The margin by which CFDM outperforms its alternatives highlights a key finding: neither spending authority nor revenue capacity alone accurately reflects the fiscal reality of local water and sanitation governance; it is their combined effect that is important. The expenditure dimension is further proven to be the more influential of the two unidimensional proxies, consistent with the capital-intensive and maintenance-heavy nature of water infrastructure. On the other hand, coefficient findings show that fiscal decentralization is positively associated with SDG6 achievement for all models. Beyond its empirical contributions, the study introduces a methodological template for applying hybrid AI optimization to policy-relevant sustainability panels. It also connects two largely parallel bodies of scholarship, fiscal federalism and SDG research, that have rarely been examined together. Full article

River water quality monitoring data are often non-stationary and nonlinear and may contain occasional abnormal values. To support anomaly early warning, this study proposes an LSTM–SOA–DA framework. Water quality monitoring data for six indicators, including pH, DO, COD_Mn, NH₃-N, TP, and TN, were collected from the Bahekou section in Xi’an at 4 h intervals from 2021 to 2023 and chronologically divided into training and testing sets at an 8:2 ratio. The Seagull Optimization Algorithm (SOA) was used to optimize the L2 regularization coefficient, initial learning rate, and number of hidden units of the Long Short-Term Memory (LSTM) network, establishing an LSTM-SOA forecasting model. Compared with traditional LSTM, BP neural network, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other optimization-based LSTM models, the proposed model achieved better RMSE and R² performance, indicating improved prediction accuracy. Based on the residuals between observed and predicted values, the DA method was then used to determine indicator-specific anomaly thresholds from the residual distributions. The model identified 193 abnormal points in the test set. After manual rechecking, the Precision, Recall, and F1-score reached 87.6%, 93.9%, and 90.64%, respectively. These results suggest that the LSTM–SOA–DA framework can effectively identify abnormal fluctuations in river water quality data and support timely water environment management. Full article

Under the national context of energy transition and energy conservation, accurate prediction of thermal load in oil and gas gathering and transportation stations is crucial for ensuring operational safety and reducing energy consumption. To address the limitations of traditional forecasting methods in handling the nonlinear, non-stationary, and long-term temporal dependencies of thermal load data, this paper proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and an attention mechanism, namely the CNN–LSTM–Attention model. First, key influencing factors such as ambient temperature, return water temperature, and the previous hour’s thermal load were selected as model inputs through correlation analysis. Subsequently, a CNN was employed to extract spatial features from multi-source data, LSTM to capture temporal dependencies, and an attention mechanism to dynamically focus on critical operational nodes, thereby enhancing the model’s ability to perceive important features. The experimental results show that the proposed model performs excellently in heat load prediction, achieving a root mean square error of 5.98, a mean absolute error of 4.66, and a mean absolute percentage error of 9.66%, with an R-squared (R²) value of 0.9568. Its prediction accuracy and stability are significantly superior to those of the standalone CNN and standalone LSTM models. This study provides an effective algorithmic solution for precise thermal load forecasting in oil and gas gathering and transportation stations and offers insights for optimizing the applicability of deep learning models in industrial scenarios. Full article

To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R² followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R² values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and lake area status of Qinghai Lake to provide basic background for future prediction. Reliable forecasting of such climate sensitive lake systems remains difficult because conventional statistical models often fail to capture non-linear fluctuations, whereas standalone deep learning models may overlook long-term deterministic evolution. To address this challenge, we developed a serial decomposition GeoAI framework that integrates autoregressive integrated moving average (ARIMA), one-dimensional convolutional neural networks (1D-CNNs), and long short-term memory (LSTM) networks for non-stationary water level forecasting. Using annual water level observations from 1960 to 2025, the ARIMA component was first used to extract the low-frequency deterministic trend, after which the CNN-LSTM module reconstructed the nonlinear residual variability. The model was trained on the 1960–2012 period and validated over 2013–2025, which represents the most dynamic expansion stage of Qinghai Lake. The hybrid framework outperformed the benchmark models, achieving a Root Mean Square Error (RMSE) of 0.2033 m, Mean Absolute Error (MAE) of 0.1727 m, and Mean Squared Error (MSE) of 0.0413 m² during validation. The decomposition strategy effectively reduced phase lag and amplitude attenuation, improving both predictive accuracy and process interpretability. Multi-step forecasting for 2026–2056 suggests that Qinghai Lake will continue to rise, reaching approximately 3204.08 m by 2056, although the growth rate is projected to slow as negative hydrological feedback strengthen. By explicitly separating deterministic climate scale signals from nonlinear short-term variability, the proposed framework provides a robust and transferable geoinformation based tool for forecasting water level dynamics and supporting adaptive management in climate sensitive, data scarce lake basins. Full article

This study examines whether conventional and Islamic real estate indices are associated with different patterns of financial connectedness and long-memory behavior in selected eco-resource sectors. The analysis focuses on four resource-related financial markets—water, food, agriculture and livestock, and reduced-energy sector exposure—and evaluates how the inclusion of different real estate indices changes the connectedness structure of this system. Bayesian Time-Varying Parameter Vector Autoregression (TVP-VAR) is used to estimate time-varying connectedness and spillover dynamics, while Long Short-Term Memory Neural Networks (LSTM-NN) are applied as a complementary tool to assess long-memory and forecasting-related patterns in the connectedness series. Compared with using either method alone, this design captures both the evolving network structure of market-based risk transmission and the persistence of connectedness patterns over time. Using market data from 20 September 2016 to 9 January 2026, the results show that conventional real estate indices are generally associated with stronger connectedness in the eco-resource financial network, suggesting greater potential for market-based risk transmission. In contrast, Islamic real estate indices exhibit comparatively lower connectedness and more persistent long-memory behavior in the examined sample. These findings indicate that real estate asset heterogeneity matters for understanding financial connectedness among selected sustainability-related sectors. The study contributes to sustainable finance by showing how conventional and Islamic real estate assets may play different roles in the financial connectedness of resource-related markets. Full article

Soil moisture (SM) is a critical variable in land–atmosphere water and energy exchange, and synthetic aperture radar (SAR) observations offer an effective means for large-scale and fine-resolution SM monitoring. Sentinel-1A, with its all-time and all-weather capability, has become an indispensable data source for SM retrieval, while comprehensive comparisons of machine learning and deep learning methods for regional and global scale SM retrieval remain insufficient. In this study, four widely used machine learning (ML) algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), convolutional neural network (CNN), and long short-term memory (LSTM), are evaluated for SM retrieval from Sentinel-1A observations across the International Soil Moisture Network (ISMN) at global and regional scales. Multiple-source dynamic parameters, including Sentinel-1A observations, MODIS vegetation parameters, ERA5-Land meteorological and soil variables, are used as inputs, as well as static geospatial parameters. Validation results demonstrate that tree-based ensemble methods (RF and XGBoost) consistently outperform deep learning methods across all scales. Specifically, XGBoost achieves the best performance with satisfactory SM retrieval results. Moreover, XGBoost is insensitive to Sentinel-1A viewing geometry, allowing fusion of multi-orbit observations to improve temporal resolution without accuracy loss. These findings demonstrate the effectiveness of tree-based ML for global/regional SM retrieval from Sentinel-1A. In addition, this study performs a comprehensive evaluation of spatial generalization ability and orbit robustness of different retrieval models under global heterogeneous environments, and proposes a reliable scheme for generating high-spatiotemporal-resolution SM products. Full article

Chondrichthyans are among the most threatened vertebrate groups worldwide, yet their ecology and long-term trajectories remain poorly understood in data-limited regions such as the Mediterranean Sea. This study used Local Ecological Knowledge (LEK) provided by fishers to investigate perception-based temporal changes in the reported composition of chondrichthyan taxa across Italian waters. A total of 57 semi-structured interviews were conducted across multiple Geographical Sub-Areas, collecting information on reported taxon occurrence, perceived abundance trends, and temporal contrasts between earlier and more recent phases of fishers’ careers. Overall, 35 taxa were reported. The number of taxa reported was significantly higher for the present than for the past. Rather than indicating a real increase in biodiversity, this pattern is more plausibly interpreted as a shift in ecological perception, potentially influenced by shifting baselines, changes in detectability, evolving fishing practices, and improved taxonomic awareness. Taxon-level analyses showed contrasting patterns, with some taxa increasing in reporting frequency, whereas others, such as Squatina squatina, declined markedly in contemporary reports. Anecdotal recollections of large catches and large individuals were consistent with fishers’ perceptions of historically more frequent encounters and body sizes, although these accounts should be interpreted as qualitative evidence. Several frequently reported taxa are currently classified as threatened, highlighting a mismatch between perceived commonness and conservation status. These findings show that LEK primarily reflects ecological perception and memory and should therefore be integrated with conventional data sources to support chondrichthyan conservation in the Mediterranean Sea. Full article

The present investigation was designed to assess how perinatal contact with the pyrethroid insecticide cyfluthrin (CY) influences cognitive performance in developing rat progeny and to clarify the contributing cellular events through examination of neuroinflammatory processes alongside pyroptotic and apoptotic pathways. An experimental framework involving CY administration during gestation was implemented using Sprague–Dawley (SD) dams, with subsequent monitoring of placental parameters and neonatal outcomes. Once offspring reached postnatal day twenty-one, their behavior was characterized via a battery consisting of the open field paradigm, novel object recognition task, and the Morris water navigation test. Hippocampal tissue architecture and fine structural details were visualized by employing hematoxylin–eosin (HE) staining and Nissl substance labeling. Protein and transcript abundances for pro-inflammatory mediators (TNF-α, IL-6), synaptic constituents (postsynaptic density protein-95, PSD-95; synaptophysin, SYP), and pyroptotic machinery components (NLRP3, GSDMD, Caspase-1) within hippocampal homogenates were quantified through immunoblotting and real-time quantitative PCR procedures, and the spatial distribution of these molecules was validated via immunohistochemical detection. Neuronal apoptosis was assessed by TUNEL staining. The results demonstrated that gestational CY exposure led to reduced placental weight and diameter, decreased blood sinus area in the labyrinth zone, lower offspring birth weight, and impaired catch-up growth. Behavioral tests revealed that CY-exposed offspring exhibited diminished spontaneous locomotor activity, impaired novel object recognition memory, and significant deficits in spatial learning and memory. Pathological analysis showed disorganized neuronal arrangement and reduced Nissl bodies in the hippocampal CA1 region. Compared to the control group, CY exposure markedly upregulated the protein expression of TNF-α and IL-6, downregulated PSD-95 and SYP, activated the NLRP3/GSDMD/Caspase-1-mediated pyroptotic pathway, and increased the expression of the apoptotic protein Caspase-3, culminating in a significant increase in hippocampal neuronal apoptosis. In conclusion, early-life exposure to cyfluthrin impairs cognitive function in offspring, an effect closely associated with the induction of hippocampal neuroinflammation and the activation of pyroptotic and apoptotic pathways. These findings provide novel toxicological evidence for a more comprehensive assessment of the potential health risks posed by CY exposure in human populations. Full article

Efficient operation of rainwater pumping stations is essential for mitigating urban flooding under climate change. This study focuses on the Samcheok Osipcheon watershed, located in Gangwon-do, South Korea, and proposes a deep learning-based inflow prediction framework for the Samcheok-si drainage system using SWMM-simulated datasets. A total of 900 rainfall scenarios were generated and used to train three models: ANN, CNN, and LSTM. All models reproduced inflow hydrographs with high accuracy, but the CNN model showed overfitting with oscillations in the recession limb. The LSTM model demonstrated the best performance, achieving an NSE of 0.97 and a PPE of 3.45%. Based on the predicted inflow, two pump operation strategies were evaluated. The proactive operation considering upstream surcharge conditions, combined with second-level control, reduced peak water levels from 2.585 m to 2.439 m (approximately 5.6%) compared to the conventional operation. In addition, second-level pump operation reduced excessive discharge and stabilized detention basin water levels. The results indicate that the proposed framework can support real-time pump operation, enhance the resilience and sustainability of urban drainage systems, and contribute to sustainable urban flood mitigation. Full article

Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity, is associated with monoaminergic dysfunction, neuronal damage, and gut microbiota disorders. Low molecular weight fucoidan (LMWF) is a sulfated polysaccharide extracted from Saccharina japonica (Phaeophyta), processes antioxidant, anti-inflammatory, and neuroprotective properties, suggesting its potential relevance for ADHD-related pathophysiology. This study investigated the therapeutic effects of LMWF on ADHD-like symptoms in spontaneously hypertensive rats (SHR). Behavioral tests revealed that LMWF reduced hyperactivity and anxiety-related behavior in the open field test, and improved spatial memory in the Morris water maze test. LMWF treatment significantly increased dopamine (DA), norepinephrine (NE), and 5-hydroxyindoleacetic acid (5-HIAA) levels in the prefrontal cortex (PFC). The transcript levels of tyrosine hydroxylase (Th) and synaptosome-associated protein-25 (Snap25) were upregulated, while dopamine transport (Dat) was downregulated in the PFC. TH protein expression was elevated in the striatum (STR), and neuronal integrity was preserved in the STR and cerebellum. LMWF also reshaped gut microbiota composition and enhanced microbial diversity, contributing to improved gut-brain axis homeostasis. These findings suggest that LMWF may serve as a promising dietary intervention for ADHD through neurochemical restoration and microbiota modulation. Full article