Search Results (6,475)

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. Full article

This study investigates the long-term trends (1992–2022) of nitrogen and phosphorus loadings exported by the River Po to the Adriatic Sea, to better analyse how changes in hydrology are affecting the timing and magnitude of river nutrient loads. We used 30 years of monitoring data in order to (a) identify the main temporal patterns and their interactions at a decadal, annual and seasonal scale, (b) estimate precipitation effects on load formation and evaluate whether and to which extent the hydrological regime affects nutrient export across the years and (c) analyse the nutrient export regime at a monthly scale and the main transport dynamic of N and P chemical species (hydrological vs. biogeochemical control). The long-term analysis shows a general decrease of both P and N loadings, but the trends are different between the elements and their chemical species, as well as undergoing different seasonal variations. We found a statistically significant relationships between precipitation and loads, which demonstrates that precipitation patterns drive the exported load at the intra- and interannual time scales considered in this study. Precipitation-induced load trends trigger seasonal changes in nutrient deliveries to the sea, peaking in spring and autumn. The nitrogen decrease is mainly concentrated in the summer dry period, while total phosphorus diminishes mainly in spring and autumn. This mismatch of N and P results in variable molar N:P ratios within the year. The effects of extreme drought and flood events, along with the progressive decrease in the snowmelt contribution to water fluxes, are expected to exacerbate the variability in the N and P loadings, which in turn is expected to perturbate the biodiversity, food webs and trophic state of the Northern Adriatic Sea. Full article

Enhancing regional ecosystem stability and managing land resources effectively requires identifying ecological function zones and understanding the factors that influence them. However, most current studies have primarily focused on ecosystem service bundles, paying less attention to the trade-offs, synergies, and ecological sensitivity, leading to a more uniform approach to functional zoning. This study aimed to analyze and describe the spatial and temporal patterns of four essential ecosystem services, including water yield (WY), net primary productivity (NPP), soil conservation (SC), and habitat quality (HQ), in the Sichuan-Yunnan ecological buffer area over the period from 2005 to 2019. Spatial overlay analysis was used to assess ecological sensitivity, trade-offs, synergies, and ecosystem service bundles to define ecological functional zones. Geographic detectors were then applied to identify the primary drivers of spatial variation in these zones. The findings showed a progressive improvement in ecosystem service functions within the Sichuan-Yunnan ecological buffer zone. Between 2005 and 2019, NPP, soil conservation, and water yield all demonstrated positive trends, while HQ displayed a declining trend. There was significant spatial heterogeneity and distinct regional patterns in ecosystem service functions, with a general decrease from southwest to northeast, particularly in NPP and HQ. Trade-offs were evident in most ecosystem services, with the most significant between WY and HQ and most in the northeast and east regions. Ecological sensitivity decreased from southwest to northeast. Regions with a higher ecological sensitivity were primarily situated in the southwestern region, and their spatial distribution pattern was comparable to that of high habitat quality. The spatial overlay analysis categorized areas into various types, including human production and settlement zones, ecologically vulnerable zones, ecological transition zones, and ecological conservation zones, accounting for 17.28%, 22.30%, 7.41%, and 53.01% of the total area, respectively. The primary environmental factor affecting ecological function zoning was identified as precipitation, while the main social variables were human activity and population density. This study enhances the understanding of ecological functions and supports sustainable development in the Sichuan-Yunnan ecological buffer area, offering important guidance for ecological zoning. Full article

Specimen data play a crucial role in geographical distribution research. In this study, the collection information of liverwort specimens in China was compiled and analyzed to investigate the history, current status, and limitations of liverwort research in China. By utilizing the latest systematic research findings and corresponding environmental data, a niche model was developed to offer theoretical support for exploring the potential geographical distribution and diversity of liverwort resources. A total of 55,427 liverwort specimens were collected in China, resulting in the recording of 1212 species belonging to 169 genera and 63 families. However, there are imbalances in the distributions of liverwort data among different groups, collection units, and geographical areas, with families such as Lejeuneaceae, Porellaceae, and Plagiochilaceae having the highest number of specimens. Similarly, genera such as Porella, Frullania, and Horikawaella were well represented. Remarkably, 125 species had specimen counts exceeding 100. Unfortunately, approximately 51.77% of the species had fewer than 10 recorded specimens. There were four obvious peaks in the collection years of the bryophyte specimens in China, among which the largest collection occurred from 2010 to 2023. Notably, the number of specimens collected at different stages closely aligned with the history of taxonomic research on liverworts in China. The results of the integrity of the liverwort collection indicate that there is insufficient representation of some families and genera, with a concentration of common and widely distributed large families and genera. Tropical and subtropical humid areas are key regions for liverwort diversity, with water and temperature being the primary environmental factors influencing their geographical distribution. The specific temporal and spatial data of species recorded from plant specimens will enhance the study of species diversity, comprehensive protection, and sustainable utilization. Additionally, these data will contribute to the investigation of large-scale biodiversity distribution patterns and the impact of global change on diversity. Full article

Abstract: Background: An increase in plant-sourced (PS) nutrient intake is promoted in support of a sustainable diet. PS dietary minerals and proteins have bioactive properties that can affect bone health and the risk of fracture. Methods: In a group randomised, cross-over design, this study evaluated the post-ingestion temporal pattern of change in arterialised ionised calcium (iCa), parathyroid hormone (PTH), C-terminal crosslinked telopeptide of type I collagen (CTX) and procollagen type 1 amino-terminal propeptide (P1NP) for 4 h following ingestion of a novel supplement (SUPP) containing a PS marine multi-mineral + PS protein isolate. A diurnally matched intake of mineral water was used as a control (CON). Results: Compared to baseline, the change in iCa concentration was 0.022 (95%CI, 0.006 to 0.038, p = 0.011) mmol/l greater in SUPP than CON, resulting in a −4.214 (95%CI, −8.244 to −0.183, p = 0.042) pg/mL mean reduction in PTH, a −0.64 (95%CI_, −0.199 to −0.008, p = 0.029) ng/mL decrease in the biomarker of bone resorption, CTX, and no change in the biomarker of bone formation, P1NP. Conclusions: When used as a dietary supplement, or incorporated into a food matrix, the promotion of PS marine multi-mineral and PS protein isolates may contribute to a more sustainable diet and overall bone health. Full article

Background: Basal cell carcinoma is the most common nonmelanoma skin cancer, followed by cutaneous squamous cell carcinoma. The objective of the current study was to retrospectively evaluate the epidemiology, characteristic variations, histological aspects, and prognosis of basal cell carcinoma of the facial region based on a single-centre experience. Methods: Data from 125 patients admitted to the Department of Oral and Maxillofacial Surgery, University Medical Center Schleswig-Holstein (UKSH), Kiel, for surgical treatment of basal cell carcinomas of the face between January 2015 and April 2021 were evaluated. Results: The mean patient age was 79.58 years, 60.5% were male and 39.5% were female. Six patients (4.8%) had tumour recurrence with no regional metastasis. Seventy-nine patients (63%) were classified as T1. The nose and the temporal region were the most common areas. The mean tumour thickness was 3.20 mm. Conclusions: Micronodular, sclerosing/morphoeic, nodular, and superficial growth patterns of basal cell carcinoma are highly correlated to recurrence, so an excision safety margin is recommended. There is a strong correlation between tumour thickness and recurrence among basal cell carcinoma cases. When completely excised, the recurrence rate for basal cell carcinoma is relatively low. Full article

Accurate wind speed forecasting is crucial for the efficient operation of renewable energy platforms, such as wind turbines, as it facilitates more effective management of power output and maintains grid reliability and stability. However, the inherent variability and intermittency of wind speed present significant challenges for achieving precise forecasts. To address these challenges, this study proposes a novel method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep learning-based Long Short-Term Memory (LSTM) network for wind speed forecasting. In the proposed method, CEEMDAN is utilized to decompose the original wind speed signal into different modes to capture the multiscale temporal properties and patterns of wind speeds. Subsequently, LSTM is employed to predict each subseries derived from the CEEMDAN process. These individual subseries predictions are then combined to generate the overall final forecast. The proposed method is validated using real-world wind speed data from Austria and Almeria. Experimental results indicate that the proposed method achieves minimal mean absolute percentage errors of 0.3285 and 0.1455, outperforming other popular models across multiple performance criteria. Full article

The study of the spatiotemporal evolution characteristics of ecosystem service values (ESVs) is an important basis for the coordinated development of the regional nature, economy, and society and the optimization of the ecological environment. The ecological zone is an important component of the Beijing–Hangzhou Grand Canal cultural belt. Ecosystem services are a concrete manifestation of land use structure and function. A thorough study of the value of ecosystem services in areas along the Beijing–Hangzhou Grand Canal is important for promoting the long-term and stable sustainable development of the regional economy. Based on a revised equivalent factor table, this study selected land use data from 1991, 2006, and 2021 to analyze the temporal and spatial evolution characteristics of ESVs along the Beijing–Hangzhou Grand Canal. The results show that (1) the ESVs along the Grand Canal first increased and then decreased from 1991 to 2021. The reason for this is the change in land use along the Beijing–Hangzhou Grand Canal. Specifically, the conversion of land use types from farmland to water areas contributed to the increase in the value of ecosystem services, while the conversion of farmland and grassland into construction land led to a decrease in the service value of the region. (2) the value of individual ecosystem services along the Beijing–Hangzhou Grand Canal from 1991 to 2021 varied greatly. The ESV provided by hydrological regulation was the largest and the ESV provided by maintenance nutrients was the smallest. (3) the areas along the Beijing–Hangzhou Grand Canal exhibited a specific pattern in terms of the value of ecosystem services, with the regions centered in Beijing and Tianjin showing relatively low values, while the middle section of the Grand Canal demonstrated relatively high ESV. According to the spatial and temporal distribution characteristics and the leading factor for the changes in ESVs, appropriate policies can be formulated in respective regions to implement ecological protection and land use planning, thereby providing a reference for the adaptation and restoration strategies of the ecosystem along the Grand Canal. Full article

Miniaturization promotes the efficiency and exploration domain in scientific fields such as computer science, engineering, medicine, and biotechnology. In particular, the field of microfluidics is a flourishing technology, which deals with the manipulation of small volumes of liquid. Dispersed droplets or bubbles in a second immiscible liquid are of great interest for screening applications or chemical and biochemical reactions. However, since very small dimensions are characterized by phenomena that differ from those at macroscopic scales, a deep understanding of physics is crucial for effective device design. Due to small volumes in miniaturized systems, common measurement techniques are not applicable as they exceed the dimensions of the device by a multitude. Hence, image analysis is commonly chosen as a method to understand ongoing phenomena. Artificial Intelligence is now the state of the art for recognizing patterns in images or analyzing datasets that are too large for humans to handle. X-ray-based Computer Tomography adds a third dimension to images, which results in more information, but ultimately, also in more complex image analysis. In this work, we present the application of the U-Net neural network to extract certain states during droplet formation in a capillary, which forms a constantly repeated process that is captured on tens of thousands of CT images. The experimental setup features a co-flow setup that is based on 3D-printed capillaries with two different cross-sections with an inner diameter, respectively edge length of 1.6 mm. For droplet formation, water was dispersed in silicon oil. The classification into different droplet states allows for 3D reconstruction and a time-resolved 3D analysis of the present phenomena. The original U-Net was modified to process input images of a size of 688 × 432 pixels while the structure of the encoder and decoder path feature 23 convolutional layers. The U-Net consists of four max pooling layers and four upsampling layers. The training was performed on 90% and validated on 10% of a dataset containing 492 images showing different states of droplet formation. A mean Intersection over Union of 0.732 was achieved for a training of 50 epochs, which is considered a good performance. The presented U-Net needs 120 ms per image to process 60,000 images to categorize emerging droplets into 24 states at 905 angles. Once the model is trained sufficiently, it provides accurate segmentation for various flow conditions. The selected images are used for 3D reconstruction enabling the 2D and 3D quantification of emerging droplets in capillaries that feature circular and square cross-sections. By applying this method, a temporal resolution of 25–40 ms was achieved. Droplets that are emerging in capillaries with a square cross-section become bigger under the same flow conditions in comparison to capillaries with a circular cross section. The presented methodology is promising for other periodic phenomena in different scientific disciplines that focus on imaging techniques. Full article

The Chukchi Sea is an open estuary in the southwestern Arctic. Its near-surface salinities are higher than those of the surrounding open Arctic waters due to the key inflow of saltier and warmer Pacific waters through the Bering Strait. This salinity distribution may suggest that interannual changes in the Bering Strait mass transport are the sole and dominant factor shaping the salinity distribution in the downstream Chukchi Sea. Using satellite sea surface salinity (SSS) retrievals and altimetry-based estimates of the Bering Strait transport, the relationship between the Strait transport and Chukchi Sea SSS distributions is analyzed from 2010 onward, focusing on the ice-free summer to fall period. A comparison of five different satellite SSS products shows that anomalous SSS spatially averaged over the Chukchi Sea during the ice-free period is consistent among them. Observed interannual temporal change in satellite SSS is confirmed by comparison with collocated ship-based thermosalinograph transect datasets. Bering Strait transport variability is known to be driven by the local meridional wind stress and by the Pacific-to-Arctic sea level gradient (pressure head). This pressure head, in turn, is related to an Arctic Oscillation-like atmospheric mean sea level pattern over the high-latitude Arctic, which governs anomalous zonal winds over the Chukchi Sea and affects its sea level through Ekman dynamics. Satellite SSS anomalies averaged over the Chukchi Sea show a positive correlation with preceding months’ Strait transport anomalies. This correlation is confirmed using two longer (>40-year), separate ocean data assimilation models, with either higher- (0.1°) or lower-resolution (0.25°) spatial resolution. The relationship between the Strait transport and Chukchi Sea SSS anomalies is generally stronger in the low-resolution model. The area of SSS response correlated with the Strait transport is located along the northern coast of the Chukotka Peninsula in the Siberian Coastal Current and adjacent zones. The correlation between wind patterns governing Bering Strait variability and Siberian Coastal Current variability is driven by coastal sea level adjustments to changing winds, in turn driving the Strait transport. Due to the Chukotka coastline configuration, both zonal and meridional wind components contribute. Full article

This study focuses on a complex Brazilian Neoproterozoic karst (hydro)geological and geomorphological area, consisting of metapelitic–carbonate sedimentary rocks of ~740–590 Ma, forming the largest carbonate sequence in the country. At the center of the area lies the Lagoa Santa Karst Environmental Protection Area (LSKEPA), located near the Minas Gerais’ state capital, Belo Horizonte, and presents a series of lakes associated with the large fluvial system of the Velhas river under the influence, locally, of carbonate rocks. The hydrodynamics of carbonate lakes remain enigmatic, and various factors can influence the behavior of these water bodies. This work analyzed the hydrological behavior of 129 lakes within the LSKEPA to understand potential connections with the main karst aquifer, karst-fissure aquifer, and porous aquifer, as well as their evolution patterns in the physical environment. Pluviometric surveys and satellite image analysis were conducted from 1984 to 2020 to observe how the lakes’ shorelines behaved in response to meteorological variations. The temporal assessment for understanding landscape evolution proves to be an effective tool and provides important information about the interaction between groundwater and surface water. The 129 lakes were grouped into eight classes representing the hydrological connection patterns with the aquifers in the region, with classes defined for perennial lakes: (1) constantly connected, (2) seasonally disconnected, and (3) disconnected; for intermittent lakes: (4) disconnected during the analyzed time interval, (5) seasonally connected, (6) disconnected, (7) extremely disconnected, and (8) intermittent lakes that connected and stopped drying up. The patterns observed in the variation of lakes’ shorelines under the influence of different pluviometric moments showed a positive correlation, especially in dry periods, where these water bodies may be functioning as recharge or discharge zones of the karst aquifer. These inputs and outputs are conditioned to the well-developed karst tertiary porosity, where water flow in the epikarst moves according to the direction of enlarged karstified fractures, rock foliation planes, and lithological contacts. Other factors may condition the hydrological behavior of the lakes, such as rates of evapotranspiration, intensity of rainfall during rainy periods, and excessive exploitation of water. Full article

Circadian rhythms are found in a wide range of organisms and have garnered significant research interest in the field of chronobiology. Under normal circadian function, metabolic regulation is temporally coordinated across tissues and behaviors within a 24 h period. Metabolites, as the closest molecular regulation to physiological phenotype, have dynamic patterns and their relationship with circadian regulation remains to be fully elucidated. In this study, untargeted brain metabolomics was employed to investigate the daily rhythms of metabolites at four time points corresponding to four typical physiological states in Vespertilio sinensis. Key brain metabolites and associated physiological processes active at different time points were detected, with 154 metabolites identified as rhythmic. Analyses of both metabolomics and transcriptomics revealed that several important physiological processes, including the pentose phosphate pathway and oxidative phosphorylation, play key roles in regulating rhythmic physiology, particularly in hunting and flying behaviors. This study represents the first exploration of daily metabolic dynamics in the bat brain, providing insights into the complex regulatory network of circadian rhythms in mammals at a metabolic level. These findings serve as a valuable reference for future studies on circadian rhythms in nocturnal mammals. Full article

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry. Full article

Vegetation quality is crucial for maintaining ecological health, and remote sensing techniques offer precise assessments of vegetation’s environmental quality. Although existing indicators and remote sensing approaches provide extensive spatial coverage, challenges remain in effectively integrating diverse indicators for a comprehensive evaluation. This study introduces a comprehensive ecological quality index (EQI) to assess vegetation quality on the Mongolian Plateau from 2001 to 2020 and to identify the determinants of EQI variations over space and time. We developed the EQI using remotely sensed normalized difference vegetation index (NDVI) data and the net primary productivity (NPP). Our analysis revealed distinct spatial patterns, with high ecological quality concentrated in northern Mongolia and eastern Inner Mongolia. Temporal fluctuations, indicative of ecological shifts, were primarily observed in eastern Mongolia and specific zones of Inner Mongolia. We employed a Geographically Optimal Zones-based Heterogeneity (GOZH) model to analyze the spatial scales and interactions influencing EQI patterns. This study found that precipitation, with an Omega value of 0.770, was the dominant factor affecting the EQI, particularly at spatial scales of 40–50 km. The GOZH model provided deeper insights into the spatial determinants of the EQI compared with previous models, highlighting the importance of climatic variables and their interactions in driving ecological quality. This research enhanced our understanding of vegetation quality dynamics and established a foundation for ecosystem conservation and informed management strategies, emphasizing the critical role of climate, especially precipitation, in shaping ecological landscapes. Full article

This study aims to create a reliable water-demand forecasting system using Long Short-Term Memory networks. The model integrates hourly water demands from 10 District Metered Areas of a Water Distribution Network in northeast Italy and weather data, handling missing values with LSTM-based data imputation. It considers temporal aspects like time, weekdays, holidays, and weekend patterns, employing sine and cosine transformations to capture daily cycles. To ensure the model’s robustness, the testing was conducted during the last week of the dataset, specifically week 81, with iterative adjustments to the LSTM’s hyperparameters to optimize prediction accuracy. These tuning efforts focused on learning rate, number of layers, and batch size, tailored to maximize the system’s performance. This method is essential for smart decision-making in water utility management and demonstrates significant potential for improving operational efficiencies. Full article