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Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy. HIGHLIGHTS Identifying pollution sources can be applied for both instantaneous and continuous discharge scenarios.; To characterize a single pollution source, data from two monitoring sites along the pollutant's path are necessary and sufficient.; The strategic placement of monitoring sites and improved sampling enhance the effectiveness and precision of the Bayesian-SWMM approach for identifying multiple unauthorized discharge sources.;

Ventilation is paramount in sanitary and stormwater sewer systems to mitigate odor problems and avert pressure surges. Existing numerical models have constraints in practical applications in actual sewer systems due to insufficient airflow modeling or suitability only for steady-state conditions. This research endeavors to formulate a mathematical model capable of accurately simulating various operational conditions of sewer systems under the natural ventilation condition. The dynamic water flow is modeled using a shock-capturing MacCormack scheme. The dynamic airflow model amalgamates energy and momentum equations, circumventing laborious pressure iteration computations. This model utilizes friction coefficients at interfaces to enhance the description of the momentum exchange in the airflow and provide a logical explanation for air pressure. A systematic analysis indicates that this model can be easily adapted to include complex boundary conditions, facilitating its use for modeling airflow in real sewer networks. Furthermore, this research uncovers a direct correlation between the air-to-water flow rate ratio and the filling ratio under natural ventilation conditions, and an empirical formula encapsulating this relationship is derived. This finding offers insights for practical engineering applications. HIGHLIGHTS The non-steady conditions were considered.; Existing models were improved to address the instability issue in coupled water and air calculations.; The relationship between the airflow and the water level under non-steady conditions was investigated.; The validation of the established model was conducted, demonstrating its effectiveness and feasibility.; Factors influencing the airflow were analyzed.;

BackgroundGlioblastoma multiforme (GBM) with synchronous metastasis(SM) is a rare occurrence. We extracted the data of GBM patients from the SEER database to look into the incidence of SM in GBM, determine the prognostic significance of SM in GBM, and assess therapeutic options for patients presenting with SM.MethodsFrom 2004 to 2015, information on GBM patients was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score matching (PSM) method was employed to mitigate confounding factors between SM and non-SM groups, subsequently investigating the prognostic significance of SM in patients with GBM. Multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic variables for GBM patients with SM. A forest plot was used to visualize the results.ResultsA cohort of 19,708 patients was obtained from the database, among which 272 (1.4%) had SM at the time of diagnosis. Following PSM at a 3:1 ratio, in both univariate and multivariate cox regression analysis, SM (HR = 1.27, 95% CI: 1.09–1.46) was found to be an independent predictive predictor for GBM patients. Furthermore, the Cox proportional hazard forest plot demonstrated that independent risk variables for GBM patients with SM included age (Old vs. Young, HR = 1.44, 95% CI: 1.11–1.88), surgery (biopsy vs. no surgery, HR = 0.67, 95% CI: 0.46–0.96;Subtotal resection vs. no surgery, HR = 0.47, 95% CI: 0.32–0.68;Gross total resection vs. no surgery, HR = 0.44, 95% CI: 0.31–0.62), radiotherapy (HR = 0.58, 95% CI: 0.41–0.83), and chemotherapy (HR = 0.51, 95% CI: 0.36–0.72).ConclusionThe predictive value of SM in GBM was determined by this propensity-matched analysis using data from the SEER database. Radiotherapy, chemotherapy, and surgery constitute an effective treatment regimen for patients with SM. A more positive approach toward the use of aggressive treatment for GBM patients with SM may be warranted.

As an important component of the deep tunnel drainage system for dealing with urban waterlogging, the rotating stepped dropshaft has been proposed due to its small air entrainment. However, the hydraulic characteristics inside the shaft still need to be fully studied. In this study, the flow patterns, water velocity, and pressure in the rotating stepped dropshaft under different flow rates and geometric parameters were studied using a three-dimensional numerical model. The results show that increasing the central angle of the step and reducing the step height can both reduce the terminal velocity. A theoretical formula for predicting the terminal velocity was established and well validated. The connection between the shaft and the outlet pipe poses a severe threat to the structural safety due to alternating positive and negative pressures. Wall-attached swirling flow generates a circular high-pressure zone at the bottom of the dropshaft and the larger the flow rate, the greater the pressure gradient at the center of the bottom. By using the momentum theorem and considering the impact pressure range of the swirling flow, the shaft bottom pressure can be predicted reasonably well. HIGHLIGHTS The hydraulic characteristics of rotating stepped dropshafts are analyzed and the terminal velocity is evaluated theoretically and well validated.; The pressure distribution inside rotating stepped dropshafts and the impact effect of the wall-attached swirling flow on the shaft bottom are determined.; A simplified theoretical model is established to predict the impact pressure of the shaft.;

Plunge dropshafts are widely used in urban drainage systems, but they can carry large amounts of air into the system, endangering the safety of the system. In this paper, a 3D numerical model is established to investigate the water flow configuration and air characteristics in a helicoidal ramp dropshaft. The simulations under different outlet pressures and water flow rates were simulated, and the results show that the air demand decreases with the increase of outlet pressure, especially in the case of low water flow. In the case of higher outlet pressure, the air demand first increases then decreases with the increase of the water flow rate. The dragging force of water has little effect on the pressure gradient in the dropshaft. The increase of ramp turns leads to the increase of terminal velocity at the same flow rate, and the air demand first increases and then decreases with the increase of ramp turns. HIGHLIGHTS The water flow configuration and air entrainment in a helicoidal ramp type dropshaft was investigated.; The air demand decreases with the increase of outlet pressure, especially in the case of low water flow.; The increase of spiral turns leads to the increase of maximum velocity at the same flow rate, and the air demand first increases and then decreases with the increase of spiral turns.;

Mineral precipitation or scale formation in oil and gas wellbore is recognized as an obstacle to hydrocarbon recovery. A method using computational fluid dynamics for structure optimization of downhole descaling tools is presented in this paper. The effects of key parameters, such as the nozzle structure style, cone angle, and incidence angle on the descaling performance, are systemically investigated. Numerical results indicate that the cone nozzle has higher jet efficiency because of its smoother velocity and pressure changes, as well as less energy loss. Under the conditions considered, key parameters of the downhole descaling tool are optimized as an incidence angle of 10° and a cone angle of 14°. Furthermore, the numerical results are validated with experimental results, in which the average error between the experimental and numerical results is ∼7.1%. The proposed numerical approaches can accurately predict the jetting status, which consequently has technical guiding significance for the design of downhole descaling tools.

Augmented reality (AR) can represent a contextualised scientific inquiry environment in which students may explore the real world and develop science process skills via interacting with rich information from virtual systems. However, it remains a challenge for most students to complete scientific inquiry tasks without proper support. Research evidence has suggested the potential of reflective scaffolding when applying scientific inquiry. Accordingly, we designed a contextualised reflective mechanism-based AR learning model to assist students in completing scientific inquiry tasks. Guided by the proposed model, we designed four stages of scientific inquiry learning: conceptual understanding, reflective cognition, in-depth inquiry, and knowledge building. A quasi-experiment and lag sequential analysis were conducted by recruiting 81 sixth-grade students to examine the effects of the proposed model on their scientific inquiry learning performances, higher order thinking, and behavioural patterns. The experimental results reveal that the proposed approach improved students' inquiry learning performances and higher order thinking tendency (problem-solving tendency and metacognitive awareness). Moreover, the evidence from this study also suggests that the students who learned with the proposed approach exhibited more observation, comparison, exploration, and reflection behavioural patterns in the field trip than those who learned without the contextualised reflective mechanism. Implications are discussed.