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Why is a spring like a simple machine? What forces do you need for a spring to change shape? How do springs store energy? Look at everything from historical examples of springs, such as a ballista, to the role of levers in complex machines, such as racing cars.

Out of 5 million Indian spring water systems, a few were characterised for hydrochemistry and freshwater potential. The present study focuses on analysing the hydrochemistry, discharge, and drinking/irrigation water quality of both cold and thermal spring clusters namely Southern Kerala Springs (SKS) and Dakshina Kannada Springs (DKS) of Southern Western Ghats, India. Currently, eleven springs from SKS and ten from DKS including one thermal spring (TS) with temperature ranges from 34 to 37 °C were considered. The study revealed that cold springs (CS) of SKS are Na-Cl type, while the thermal and cold-water springs in DKS are Na-HCO 3 and mixing water type, respectively. Two distinct mechanisms predominantly define the hydro-chemical composition of the springs—SKS are influenced by precipitation, whereas DKS is likely by chemical weathering processes. While comparing the major ions and saturation indices of thermal springs (TS), it is evident that silicate minerals predominantly affect the chemical composition of water. CaCO 3 − is oversaturated in TS water and tends to precipitate as a scale layer. PCA showed that both geogenic and anthropogenic factors influence water chemistry. WQI categorized the CS in both the clusters are in the “Excellent” rank as compared to TS. Irrigation water quality signifies that the cold springs are only suitable for irrigation. Moreover, it is evident from the discharge that both SKS and DKS were rainfed in nature. Discharge monitoring designated that the CS could augment drinking water supplies in the nearby regions indicating the necessity of conservation and sustainable use considering future freshwater scarcity.

Cyanobacteria are an ancient group of prokaryotes capable of oxygenic photosynthesis. Recently, thermal crises symptoms in hot springs have been associated with acute cyanopeptides poisoning. The aim of this work is to develop a fast, easy and reliable method to monitor the presence of toxic cyanopeptides in geothermal waters. The analytical method based on capillary zone electrophoresis coupled with tandem mass spectrometry (CZE-MS/MS) was developed for the simultaneous determination of 14 cyanopeptides in less than 7.5 min. A basic 50 mM ammonium acetate buffer at pH 10.2 was selected as the background electrolyte, positive electrospray ionization (ESI+) was employed for all compounds, and a salting-out assisted liquid–liquid extraction (SALLE) protocol with acetonitrile as an extraction solvent and MgSO4 as an auxiliary salting-out agent was optimized as sample treatment. Six natural hot springs in the province of Granada (Andalucía, Spain) were sampled at the beginning of the summer season (June) and at the end (September). Biomass collected at two sample points (Santa Fe and Zújar) contained cyanobacteria cells from the genera Phormidium, Leptolyngbya, and Spirulina. Nevertheless, cyanotoxins covered by this work were not found in any of the water samples analyzed. The greenness and transferability of the method was evaluated highlighting its sustainability and applicability.

\"Immerse yourself in hot springs from around the globe with this stunning visual oasis that features over 200 photos and fascinating insights showcasing their unique topography, uses, cultural meanings, and more\"-- Provided by publisher.

Karst springs are essential drinking water resources, however, modeling them poses challenges due to complex subsurface flow processes. Deep learning models can capture complex relationships due to their ability to learn non‐linear patterns. This study evaluates the performance of the Transformer in forecasting spring discharges for up to 4 days. We compare it to the Long Short‐Term Memory (LSTM) Neural Network and a common baseline model on a well‐studied Austrian karst spring (LKAS2) with an extensive hourly database. We evaluated the models for two further karst springs with diverse discharge characteristics for comparing the performances based on four metrics. In the discharge‐based scenario, the Transformer performed significantly better than the LSTM for the spring with the longest response times (9% mean difference across metrics), while it performed poorer for the spring with the shortest response time (4% difference). Moreover, the Transformer better predicted the shape of the discharge during snowmelt. Both models performed well across all lead times and springs with 0.64–0.92 for the Nash–Sutcliffe efficiency and 10.8%–28.7% for the symmetric mean absolute percentage error for the LKAS2 spring. The temporal information, rainfall and electrical conductivity were the controlling input variables for the non‐discharge based scenario. The uncertainty analysis revealed that the prediction intervals are smallest in winter and autumn and highest during snowmelt. Our results thus suggest that the Transformer is a promising model to support the drinking water ion management, and can have advantages due to its attention mechanism particularly for longer response times. Key Points The Transformer architecture was applied in karst hydrology for the first time, showing high performance for discharge forecasting Monte Carlo dropout revealed that the prediction intervals are smallest and cover the measured discharges best in winter and autumn The high temporal resolution of the input data sets improved the forecasting performance

Submarine springs near Izola, in the Northern Adriatic Sea, appear in funnel-shaped depressions and smell strongly of sulfur. Along the Mediterranean coast there are many submarine karst springs containing brackish or fresh water, but submarine sulfur springs are not particularly common. Three submarine sulfur springs and one terrestrial sulfur spring were investigated to better understand the water properties, water–rock interaction within the aquifer, and to explore the origin of the spring water. Groundwater and seawater samples were also collected for comparison. Based on the geological setting, physicochemical parameters, hydrogeochemical data, and stable isotope data ( δ 18 O, δ 2 H, δ 13 C DIC , δ 34 S SO4 , δ 18 O SO4 ), we can affirm that (1) the large concentration of seawater in the submarine springs samples is due to sampling challenges; (2) springs recharge from precipitation where confined karst aquifers outcrop; (3) deep water circulation is indicated; (4) redox conditions can provide a suitable environment for bacterial reduction of the marine or organic sulfate to the odorous H 2 S; (5) geological data suggests that the coals beneath the alveolinic-nummulitic limestones are the source of sulfur. A multi-parameter and interdisciplinary approach has proven important in assessing submarine sulfur springs affected by seawater input.

\"The modern meaning of the term \"spa\" describes health resorts that offer beauty treatments, massages, and therapies. The Victorian spa was a sanitarium that provided medical treatments based on the use of water, supplemented by massage, vibration, electricity, and radioactivity. The Victorian spa was to provide gentler alternative treatments that emphasized the healing power of nature\"-- Provided by publisher.

Sparse precipitation data in karst catchments challenge hydrologic models to accurately capture the spatial and temporal relationships between precipitation and karst spring discharge, hindering robust predictions. This study addresses this issue by employing a coupled deep learning model that integrates a variation autoencoder (VAE) for augmenting precipitation and a long short‐term memory (LSTM) network for karst spring discharge prediction. The VAE contributes by generating synthetic precipitation data through an encoding‐decoding process. This process generalizes the observed precipitation data by deriving joint latent distributions with improved preservation of temporal and spatial correlations of the data. The combined VAE‐generated precipitation and observation data are used to train and test the LSTM to predict spring discharge. Applied to the Niangziguan spring catchment in northern China, the average performance of NSE, root mean square error, mean absolute error, mean absolute percentage error, and log NSE of our coupled VAE/LSTM model reached 0.93, 0.26, 0.15, 1.8, and 0.92, respectively, yielding 145%, 52%, 63%, 70% and 149% higher than an LSTM model using only observations. We also explored temporal and spatial correlations in the observed data and the impact of different ratios of VAE‐generated precipitation data to actual data on model performances. This study also evaluated the effectiveness of VAE‐augmented data on various deep‐learning models and compared VAE with other data augmentation techniques. We demonstrate that the VAE offers a novel approach to address data scarcity and uncertainty, improving learning generalization and predictive capability of various hydrological models. However, we recognize that innovations to address hydrologic problems at different scales remain to be explored. Plain Language Summary Millions of people around the world use springs as their water sources. To protect these precious springs, water resources managers must understand how spring discharges change in the future due to climate change and human activities. A widely used tool to improve this understanding is a computer model. A trustworthy computer model requires plenty of quality data. However, they are generally unavailable for many springs. To address this data scarcity issue, we applied a computer‐based learning technique, VAE, that learned the patterns of real‐world data and generated data that complied with the learned pattern. We then combined the generated and real‐world data to train a computer‐based learning model, a LSTM network, to simulate spring discharges. We tested our method using Niangziguan Spring in northern China, demonstrating that adding VAE‐generated data improved the LSTM model significantly. In addition, we investigated the effectiveness of the VAE in improving other commonly used models. Finally, we show that our model can accurately predict spring discharges, and VAE improves our model. Nevertheless, we recognize that developing new technologies that allow the cost‐effective collection of high‐resolution spatiotemporal necessary data is the future. Key Points A generative variational autoencoder is applied to augment precipitation data to improve an long short‐term memorynetwork for spring discharge prediction Augmenting precipitation data improves various deep learning models' learning generalization and predictive capability The generative variation autoencoder offers a novel solution to address data scarcity issues across diverse research domains