Inflow Volume Research Articles

Numerical Assessment of the Coastal Reservoir’s Water Reliability and Flushing in a Shallow Estuary

Freshwater shortages in coastal regions are intensifying due to rapid urbanisation, economic growth, and climate variability, particularly in deltaic areas where rivers meet the sea. This study evaluates the feasibility of implementing a Coastal Reservoir (CR) as an innovative solution to increase freshwater availability without relying on desalination. Using the Brisbane River Estuary (BRE), Australia, as a case study, the research examines critical factors such as freshwater inflow, seawater intrusion, and reservoir volume requirements. A three-dimensional hydrodynamic model (MIKE 3) was calibrated and validated using observed data from the 2008 and 2011 flow events. Simulation results indicate that a freshwater discharge of 150 m³/s during a spring-neap tidal cycle effectively pushes saline water out of the estuary. The CR can store 300 GL/year of freshwater with 92% reliability, meeting Southeast Queensland’s (SEQ) annual water demand of 440 GL during drought conditions combined with existing infrastructure. During its initial filling phase, the CR can flush 95% of saltwater within 240 days, using a steady inflow of 150 m3/s. The findings demonstrate the technical feasibility of CRs as a sustainable and practical water management strategy for mitigating freshwater shortages in BRE and other similar coastal regions.

Study of the impact of drought on the runoff system and the dam on Oued Za using romote sensing techniques (Eastern Morocco)

The irregularity of precipitation that most Moroccan territories receive is mainly due to the association of precipitation with the atmospheric system in general. This phenomenon has an obvious impact, particularly in semi-arid areas, which suffer from scarcity of water resources and low precipitation rates. In turn, the Oued El Hay basin (northern margins of the highlands) is characterized by aridity resulting from its difficult climatic conditions, since climatic elements have an important and decisive role in the periodic and more severe occurrence of droughts, due to their seasonal variations throughout the year. This is directly reflected in the general water supply, whether surface runoff, which remains low in the region, or groundwater, which is directly affected by prevailing climatic conditions. The fluctuation and lack of precipitation is reflected in the water inputs that feed the groundwater, given the excessive exploitation to which the water is exposed to meet the growing needs of the region. Given the relationship between surface runoff and the volume of water supplies mobilized, the frequency of droughts has a significant impact on the volume and supply of water mobilized by dams. At the basin level, a significant drop in the volume of inflows was recorded at the dam on Oued Za. The maximum recorded in 2004/2005 did not exceed 46 million m³. This article aims to highlight the impact of drought on the surface runoff system of Oued El Hay, to study the evolution of the volume of water from the dam on Oued Za based on data recorded between 1978/79 and 2020/21, and to monitor the volume of water mobilized using remote sensing techniques.

Application of the CMIP6 Approach for Determination of Climate Change Impact on Inflow to Malampuzha Reservoir, India

Optimal operation of a reservoir is important for the efficient planning and management of water resources. Inflow estimation into the reservoir is the basic information needed by a policy maker for the optimal planning of the reservoir operation. Climate change has become a major cause for the changes in the hydrological phenomena. An attempt was made to analyse the impact of climate change on Malampuzha reservoir inflow for the future using recent Shared Socioeconomic Pathways (SSP) scenarios. Two SSP scenarios, SSP 245 and SSP 585 were considered for the evaluation of changes in the meteorological variables for the future period. Rainfall-runoff modelling of the catchment area of the Malampuzha reservoir was done with IHACRES model for the period 2003-2022. The model performance was accepted in calibration (2003-2016) and validation (2017-2022) periods with R2 of 0.90 and 0.91 and RMSE 2.63 and 4.53 m3/s respectively. The outputs of SSP245 and SSP585 scenarios were given to the developed runoff model to estimate the future inflow volume into Malampuzha reservoir for the period of 2023 to 2042. From the results, the change in monthly temperature varies from -0.88oC to 3.3oC and change in monthly precipitation varies from -0.64% to 1.73% which indicates a decrease in the inflow of water into the reservoir for future period by 2.6% and 9.3% than the base period (2003– 2022) for SSP245 and SSP585 scenarios, respectively. The results of the study implies that better planning of the reservoir operation is needed for meeting the future demands.

Exploring the Variability in Triage Nursing Workload: Insights From a Multicentre Observational Study.

Although there has been a significant amount of research on nurse workloads in hospital environments, there has been a lack of comprehensive investigation of the workload of triage nurses. This study assesses the workload fluctuations of triage nurses in various emergency departments in the same region and examines how workload varies based on emergency department visit volumes and seasonal fluctuations. The study is a retrospective, multicentre, observational study from January 1, 2019, to December 31, 2023, in seven Italian emergency departments, gathering information on all patients who went through the triage process. We documented the number of nurses present throughout each shift and adjusted the data to account for the number of patients attended to by each triage nurse per shift. The EDs were classified into high-inflow, medium-inflow and low-inflow categories according to the annual number of patients they served. The study included 1,223,331 emergency department triage assessments. During daytime shifts, triage nurses in high-inflow emergency departments assessed a median of 74 patients, while those in medium-inflow and low-inflow emergency departments assessed 63 and 32 patients, respectively. During nighttime shifts, triage nurses in high-inflow emergency departments assessed 26 patients, compared to 14 in medium-inflow and 5 in low-inflow emergency departments. Significant seasonal fluctuations in the workload of triage nurses were observed. This study reveals substantial disparities in the number of patients triage nurses assess, which vary according to emergency department patient inflow volumes and seasonal factors. The findings emphasise the need for more standardised and equitable workload distribution among triage nurses. Future research should aim to establish a systematic nurse staffing model for triage to ensure manageable workloads and maintain patient safety. The Strengthening the Reporting of Observational Studies in Epidemiology was used. No patient or public contribution.

Systematic evaluation of swale length, shape, and longitudinal slope with simulated highway runoff for better swale design.

Swales are a low-cost, conveyance and treatment system to manage roadway runoff, but available design guidance is limited. Eight grass swales were constructed in Raleigh, North Carolina, USA, to systematically evaluate the effects of design factors: length, shape, and longitudinal slope under two different storm sizes. Water from an onsite reservoir was used to generate synthetic runoff and simulate flow through the swales. Inflow volume, total suspended sediment (TSS), nitrogen, phosphorus, and four total metals (copper, lead, zinc, and cadmium) were tested with simulated levels representing highway runoff. Efficiency ratios were used to estimate the reductions in inflow volume, pollutant concentrations, and mass loads. Swale length, shape, longitudinal slope, and storm size significantly influenced runoff volume reduction. The longer (30m) trapezoidal swale constructed on the flatter (1%) longitudinal slope provided maximum reductions in sediment and heavy metal concentrations during small-medium storms. Larger storms had modestly reduced pollutant and volume mitigation. Effluent nutrient concentrations generally exceeded the influent exporting nitrogen and phosphorus from all swale configurations. Significantly better pollutant load reductions were provided by the longer swales for all pollutants, except dissolved phosphorus. Therefore, to optimize swale function, designers could maximize the swale length to the greatest extent practicable, particularly when swales receive inflow from end-of-pipe systems draining roadway surfaces. The trapezoidal cross-section was superior to the triangular cross-section for stormwater treatment. Proper vegetation establishment and maintaining optimal grass height are key to proper swale functioning.

Good agreement between cardiac magnetic imaging protocols using free-breathing and breath-holding techniques for flow quantification in valvular heart disease

Abstract Background Cardiac magnetic resonance (CMR) evaluation of valvular heart disease is an important diagnostic tool when echocardiography is inconclusive. Flow quantification is usually performed during breath hold, which can be challenging in valvular heart disease patients often suffering from dyspnea and heart failure. Purpose The purpose of the present study is to evaluate a free-breathing protocol of flow measurement in the aortic, pulmonary and tricuspid valves in healthy subjects (HS) and patients with tricuspid regurgitation (TR). Methods Phase contrast flow volume measurements were performed in 20 HS and 25 patients with TR using standard breath-holding (BH) and a free-breathing (FB) technique. Two venc-adjusted volume acquisition were acquired in the aortic, pulmonary and tricuspid valve during BH and one during FB. Mean, standard deviation (SD) and limits of agreement (LoA) for aortic forward flow volume (AoFF), pulmonary forward flow volume (PuFF) and tricuspid inflow volume (TrIF) were evaluated. Results Figure 1 presents flow volume (ml) of AoFF, PuFF and TrIF during BH (mean of 2 measurements) and FB. Conclusions The present study demonstrated good agreements between phase contrast flow measurements using FB and BH techniques. These parameters are used in the evaluation of aortic, mitral, pulmonary and tricuspid regurgitations using CMR imaging. Thus, free-breathing CMR acquisition can be an important complement in the assessment of valvular heart disease and may be applied in patients with difficulty to hold their breath.

Effects of low impact development on runoff pollution and water quality resilience in an urbanized estuary area

In urbanized estuary areas, water quality often deteriorates due to the impact of surface runoff or overflow during heavy rainfall events. Low impact development (LID) can mitigate this impact and improve water quality resilience. However, there were few studies on quantifying this effect of LID, particularly in estuary areas. In this study, water quality resilience is expressed as the abnormal rate of water quality during rainfall events and the time required for water quality to recover from abnormality to normal. In this study, a coupled Storm Water Management Model (SWMM) and Environmental Fluid Dynamic Code (EFDC) model is used to examine the effects of LID on runoff pollution and water quality resilience in the Shenzhen River estuary (including Shenzhen River and Deep Bay) in China. The results indicate that (1) The inflow volume into the estuary is not only affected by the drainage area but also by the service area and capacity of the sewer interception system; LID can reduce not only the stormwater runoff but also the overflow from the interception systems along rivers. (2) The marginal benefit of the LID proportion increase in pollution load reduction diminishes as the proportion of LID implementation increases. (3) LID can reduce the high value of pollution concentration caused by rainfall runoff; and the reduction effect on the rivers is greater than that on the bay. (4) The rivers and the estuary have a much higher abnormal rate than the inner bay and the outer bay; however, the estuary and the inner bay have longer recovery time than the rivers and the outer bay. The maximum abnormal rate, average abnormal rate, and recovery time of different indicators in the entire estuary area are decreased by 10%-189%, 6%-71%, and 0.8–1.1 cycles when the runoff from 80% of built-up area is treated by LID facilities; LID can significantly reduce abnormal rate of the rivers and recovery time of the inner bay. Therefore, the proposed metric and model can fully reflect the spatial variations in water quality resilience, and LID can enhance the water quality resilience of the urbanized estuary areas under rainfall.

Management of vascular access inflow-outflow imbalance: A bimodal approach.

A more accurate descriptive and clinically useful diagnosis based upon pathophysiology for what is commonly referred to as venous outflow stenosis is inflow-outflow imbalance. In these cases, the total outflow capacity of the AV access is inadequate to handle the inflow volume (Qa) without an increase in pressure. The relative inadequacy of the access outflow capacity in comparison to Qa results in increased outflow resistance and a proportional increase in intraluminal pressure. The clinical indicators associated with venous stenosis are the resulting manifestations of this imbalance. The point at which this occurs is dependent upon variations in these two parameters-Qa and outflow resistance. The variations in these two parameters are considerable and reciprocal. Excessive Qa results in or can lead to an entire list of serious problems that adversely affect patient morbidity and mortality. Most studies dealing with AV access Qa reduction have been for the treatment of an existing condition rather than its prevention; however, prevention of disease rather than waiting for its development is an important tenet of medical practice. The resulting clinical picture of inflow-outflow imbalance is taken as an indication for corrective treatment. In the past, in most cases this has meant angioplasty to open the outflow if it is reduced; however, this clinical picture may be associated with an excessive Qa and angioplasty in these cases creates the risk for a further increase in Qa. It is the authors' opinion that access flow measurements should be a part of the evaluation of these cases prior to planning treatment. Using this information, a bimodal approach to primary treatment should be adopted involving either angioplasty for cases with a low or normal Qa or flow reduction in cases with an elevated Qa.

Investigation of an Ensemble Inflow-Prediction System for Upstream Reservoirs in Sai River, Japan

In this study, an ensemble inflow-prediction system was developed for a hydropower-generation dam in the upper Sai River basin, and the accuracy of ensemble inflow prediction, which is important for efficient dam operation, was investigated. First, the Water and Energy Based Distributed Hydrological Model for Snow (WEB-DHM-S), a hydrological model developed for the Sai River basin, can represent the hydrological process from warm to cold seasons. Next, a system was developed on the Data Integration and Analysis System (DIAS) to predict inflows into the dam by inputting real-time meteorological data and ensemble rainfall forecast data into WEB-DHM-S. The WEB-DHM-S was calibrated and validated over a 3-year period from August 2015 to July 2018, and showed good agreement with observed inflows from base flow to peak flow and snowmelt runoff in each year. The results of inflow forecasting during frontal rainfall in August 2021 by inputting ensemble rainfall forecasts up to 39 h ahead showed that at the Inekoki Dam site, the total inflow (volume) to the peak was predicted with an accuracy of within 20% at 30 h, 24 h, 18 h, 12 h, and 6 h before the peak. These ensemble inflow forecasts can help optimize dam operations.

Method for determining reagent optimum volume for acid treatments of carbonate reservoirs based on a pressure build-up curve method

Relevance. The need to increase the efficiency of acid treatments of carbonate reservoirs. Often, the design of treatments does not take into account the actual operating conditions of production wells, which leads to an overestimation or underestimation of the required volume of the reagent. Aim. To propose a method for determining the optimal amount of reagent for carrying out acid treatments of carbonate reservoirs in order to restore the permeability of the rock in the near-well zone, taking into account the actual operating conditions of the well, which allows rationalizing the use of subsoil user resources. Objects. Producing oil wells exploiting oil deposits in carbonate deposits of the Perm region, as well as the results of hydrodynamic studies carried out on these wells. Methods. Theoretical substantiation of the possibility of determining the zone of deteriorated permeability around the well using the pressure recovery curve graph, graphical processing of diagnostic graphs of the pressure recovery curve, mathematical calculation of technological indicators based on hydrodynamic testing data of wells, integration of inflow volumes, analysis of technological data and conditions of applicability of the methodology. Results. The authors have developed the methodology for determining the optimal volume of reagent for acid treatments of carbonate reservoirs based on the pressure recovery curve method. The optimal volume of reagent for 21 production wells was calculated. Using the example of 13 wells, the efficiency of the injected volume of reagent was determined and an overestimated volume of reagent injected into the formation was demonstrated. The authors assessed the subsoil user's costs taking into account the excess consumption of the reagent. The factors influencing the success of the method application depending on the operating conditions of the wells were identified. Criteria inappropriateness of acid treatment were identified.

Risk assessment of karst water inrush in tunnel engineering based on improved game theory and uncertainty measure theory

The sudden inrush of water poses a serious threat to the safety of workers during tunnel construction in the karst region of southwest China. To mitigate this risk, a model is proposed to assess the risk of water surge through a tunnel by combining improved game theory with uncertainty measure theory. Eight indicators of risk were extracted based on the solubility of rock, its geological structure, capacity for surface catchment, and hydrogeological factors, and were incorporated into the proposed model. The subjective weights of these indicators were obtained using the analytic hierarchy process, while their objective weights were calculated through the entropy weighting method and the criteria importance through intercriteria correlation method. An improved game theory-based method of combinatorial weighting was then used to construct the corresponding weight vectors. Single-indicator measurement functions and multi-indicator measurement matrices were utilized to classify and evaluate the indicators of the risk of a surge in water level based on a confidence criterion. The proposed method was applied to five typical karst sections of the Yanjin Tunnel of the Chongqing–Kunming High-speed Railway Project, and the method was validated by comparing the recorded and estimated inflow volume ranges during the project’s construction, showing consistency with the actual evaluation results. This proposed model thus offers a practical tool for assessing the risk of water inrush in karst tunnels.

The curse of COVID-19 on foreign direct investment: assessing the role of property rights amidst the pandemic

ABSTRACT How did the COVID-19 pandemic impact foreign direct investment (FDI)? Following the outbreak of COVID-19, the overall volume of FDI inflows plummeted. However, COVID-19’s adverse effects on FDI in host countries exhibited discernible variability contingent upon the characteristics of domestic institutions, particularly the robustness of property rights within these countries. While host countries with strong property rights experienced a mitigated negative impact on FDI, contrary to general expectations, our analysis suggests that host countries with weak property rights may have also evaded a significant reduction in FDI inflows during the pandemic. This was primarily because both strong and weak property rights institutions in host countries conveyed positive signals to investors, which may have incentivized them to continue their investments even amidst the pandemic-induced crisis. Analyzing a time-series cross-sectional dataset of 126 developing countries covering the period 2009–2021, our findings reveal a U-shaped relationship between FDI and property rights. This implies that COVID-19’s impact manifested as a weak negative force on FDI inflows in host countries with weak property rights as well as those with strong property rights. Conversely, host countries with moderately strong property rights tended to suffer a substantial reduction in FDI.

Characterizing hydro‐geotechnical processes of slopes implemented with bioretention cells

AbstractLow impact development (LID) practices are rarely implemented on slopes due to concerns about their poor hydrological performance and the potential impact on slope stability. Implementing LIDs on slopes, involving alterations to surface topography and subsurface hydrology (specifically, the formation of a groundwater mound), can pose challenges in maintaining slope stability. However, sloping areas often require LIDs for sustainable development. It is imperative to supplement the conventional design standard to address these challenges. Before proposing specific solutions, the impact of LIDs on slope stability should be understood first. This includes quantifying the extent of changes in slope stability before and after LID implementation, as well as identifying the key factors that influence stability. To address these, we developed a numerical model in HYDRUS‐2D to simulate the hydrological and geotechnical processes of slopes implemented with a two‐stepped bioretention cell (BC) system. The numerical model was calibrated and validated using monitoring data from a stepped BC in Cincinnati, Ohio, USA. Simulation scenarios encompassed three generic slope angles (15°, 20°, and 25°) with and without BCs, two initial groundwater table positions and two inflow volumes. The hydrological process was characterized by the evolution of the groundwater mound, and the geotechnical process was quantified using the factor of safety (FoS). Our findings indicated slope cutting and filling was likely to enhance stability with an increase in the FoS of 0.1. However, the formation of groundwater mounds, resulting from exfiltration, led to an approximate 0.1–0.2 reduction in the FoS, depending on the inflow volume. The subsequent groundwater mound evolution had no further impact on stability. Ultimately, the FoS was slightly reduced by around 0–0.1 due to the combined effect of cutting and filling operations and groundwater mounds. It was generally safe to implement LIDs on 15° slopes. For steeper slopes, special design considerations are necessary, such as reducing the drainage area to strike a balance between hydrological performance and slope safety.

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.

МНОГОЛЕТНЯЯ ДИНАМИКА ТРАНСГРАНИЧНОГО ПРИТОКА БИОГЕННЫХ СОЕДИНЕНИЙ ПО РЕКЕ ИЛЕ И ЕГО ТРАНСФОРМАЦИЯ ПО ТЕЧЕНИЮ РЕКИ

The paper presents the results of studies devoted to the assessment of the long-term dynamics of the volume of transboundary inflow of biogenic compounds to the territory of Kazakhstan along the Ile River. Based on long-term data from the State Monitoring of RSE «Kazhydromet,» the peculiarities of the transformation of the flow of biogenic compounds (nitrogen and phosphorus) along the river course, considering the influence of natural and anthropogenic factors, are analyzed. Information about the importance of studying biogenic substances in the formation of surface water quality, as well as river runoff’s role in the biological productivity and sanitary regime of final water bodies, is provided. The role of the transboundary flow of the Ile River as the main factor in forming the biogenic substances regime on the territory of Kazakhstan is highlighted. A quantitative assessment of the volume of average annual inflow of these compounds for the period from 2003 to 2020 is given, along with the main factors determining the transformation of nutrient runoff along the river course.

Simulation and Risk Assessment of Flood Disaster at the Entrance to a Rail Transit Station under Extreme Weather Conditions—A Case Study of Wanqingsha Station of Guangzhou Line 18

With the rapid development of urbanization and underground transportation, as well as the frequent occurrence of extreme weather conditions such as extreme rainfall, flooding disasters for rail transit are becoming severe, and need to be urgently clarified in terms of the mechanism causing them. In this study, a comprehensive model for water damage at the entrance to a rail transit station is proposed, emphasizing the entire process of extreme weather–surface ponding–underground intrusion. The model is validated by the inundation process of Line 5 of the Zhengzhou Metro during the “7.20” event and further applied to Wanqingsha Station of Guangzhou Metro Line 18 in China to determine the surrounding water depth, distribution, total water inflow volume, and water damage time under different rainfall intensities, rain patterns and protection scenarios. It was found that when rainfall reaches the level of a 1-in-2000-years event, the surface water begins to invade the internal rail transit system through the rail transit entrances. When facing extreme rainfall akin to the “7.20” event in Zhengzhou, the rail transit system in Wanqingsha Station meets a heightened risk of water damage, resulting in significantly deeper water levels compared to 1-in-5000-year rainfall event in Guangzhou and exceeds the height of the subway entrances. Analysis of the water intrusion process reveals that, as rainfall intensity escalates, the total inflow water volume into the rail transit system increases while escape time diminishes. Moreover, under identical rainfall intensity, pre-type rainfall yields the highest total water inflow, whereas mid-type rainfall exhibits the shortest escape time. Enhancing the protection conditions can markedly attenuate surface water intrusion into the subterranean rail transit system, thereby enhancing the evacuation time for individuals within the system.

Designing Effective Low-Impact Developments for a Changing Climate: A HYDRUS-Based Vadose Zone Modeling Approach

Low-Impact Developments (LIDs), like green roofs and bioretention cells, are vital for managing stormwater and reducing pollution. Amidst climate change, assessing both current and future LID systems is crucial. This study utilizes variably saturated flow modeling with the HYDRUS software (version 4.17) to analyze ten locations in Ontario, Canada, focusing on Toronto. Historical and projected climate data are used in flow modeling to assess long-term impacts. Future predicted storms, representing extreme precipitation events, derived from a regional climate model, were also used in the flow modeling. This enabled a comprehensive evaluation of LID performance under an evolving climate. A robust methodology is developed to analyze LID designs, exploring parameters like water inflow volumes, peak intensity, time delays, runoff dynamics, and ponding patterns. The findings indicate potential declines in LID performance attributed to rising water volumes, resulting in notable changes in infiltration for green roofs (100%) and bioretention facilities (50%) compared to historical conditions. Future climate predicted storms indicate reduced peak reductions and shorter time delays for green roofs, posing risks of flooding and erosion. Anticipated extreme precipitation is projected to increase ponding depths in bioretention facilities, resulting in untreated stormwater overflow and prolonged ponding times exceeding baseline conditions by up to 13 h at numerous Ontario locations.

A multidimensional pre-operative planning method of unruptured vertebral artery dissecting aneurysms using three-dimensional AWE mapping and hemodynamic simulation

ObjectiveHigh-resolution magnetic resonance imaging (HR-MRI) can provide valuable insights into the evaluation of vascular pathological conditions, and 3D digital subtraction angiography (3D-DSA) offers clear visualization of the vascular morphology and hemodynamics. This study aimed to investigate the potential of a multimodal method to treat unruptured vertebral artery dissection aneurysms (u-VADAs) by fusing image data from HR-MRI and 3D-DSA. MethodsThis observational study enrolled 5 patients diagnosed with u-VADAs, who were scheduled for interventional treatment. The image data of HR-MRI and 3D-DSA were merged by geometry software, resulting in a multimodal model. Quantified values of aneurysm wall enhancement (AWE), wall shear stress (WSS), neck velocity, inflow volume, intra-stent flow velocity (ISvelocity), and intra-aneurysmal velocity (IAvelocity) were calculated from the multimodal method. ResultsWe found the actual lengths of u-VADAs in the multimodal model were longer than the 3D-DSA model. We formulated surgical plannings based on the WSS, IA velocity, and neck velocity. The post-operative value of IAvelocity, neck velocity, and follow-up quantified values of AWE were decreased compared with the pre-operative condition. After that, u-VADAs were complete occlusion in four patients and near-complete occlusion in one patient during the 6th-month follow-up after surgery. ConclusionThe multidimensional method combining HR-MRI with 3D-DSA may provide more valuable information for treating VADAs, with the potential to develop effective surgical planning.

Interacting impacts of hydrological changes and air temperature warming on lake temperatures highlight the potential for adaptive management

Globally, climate warming is increasing air temperatures and changing river flows, but few studies have explicitly considered the consequences for lake temperatures of these dual effects, or the potential to manage lake inflows to mitigate climate warming impacts. Using a one-dimensional model, we tested the sensitivity of lake temperatures to the separate and interacting effects of changes in air temperature and inflow on a small, short-residence time (annual average ≈ 20 days), temperate lake. Reducing inflow by 70% increased summer lake surface temperatures 1.0–1.2 °C and water column stability by 11–19%, equivalent to the effect of 1.2 °C air temperature warming. Conversely, similar increases in inflow could result in lake summer cooling, sufficient to mitigate 0.75 °C air temperature rise, increasing to more than 1.1 °C if inflow temperature does not rise. We discuss how altering lake inflow volume and temperature could be added to the suite of adaptation measures for lakes.

An attempt to augment performance of machine learning models in a pilot-scale urban wastewater treatment system

The challenge we face is striking a balance between ensuring the standards for wastewater treatment processes and minimizing energy consumption. As such, accurate predictive models become critical. With machine learning algorithms having the ability to discern relationships between inputs and outputs, broad interest has been shown in their applications in wastewater treatment. A key research focus is on effectively adjusting these generic machine learning algorithms to adapt to frequent fluctuations of water quality, inflow volume, and environmental variations. This study developed a Nonlinear Autoregressive neural network (NARX) model having an adaptive real-time updating module to predict the ammonium concentration in the treated effluent. The model has exhibited high predictive accuracy on the testing data, achieving an R2 value of 0.997. By incorporating an adaptive real-time updating module, the model's capability to predict effluent ammonium concentration on novel data sets has been significantly improved, with the R2 value for these predictions increasing from an initial 0.918 to a more refined range of 0.966 to 0.984. Our findings confirm that integrating real-time updating capabilities with machine learning algorithms substantially improves the stability and predictive performance of the models. These outcomes will prompt a broader integration of artificial intelligence in the domain of environmental engineering, which shall foster the implementation of more accurate and eco-friendly wastewater treatment strategies.