Search Results (66)

Entrained gas in hydraulic oil undermines system stability. A rapid engineering method for predicting the separation efficiency of gas–liquid cyclone separators is still lacking. This study proposes an engineering-oriented predictive framework by combining the split ratio, the characteristic scale of the locus of zero vertical velocity envelope, and the axial residence time. A relative migration index, derived from maximum tangential velocity and axial residence time, is coupled with a relative overflow-pipe insertion indicator to characterize the interaction between swirl intensity and effective separation space. The separation-capability transition is described using a coupled logistic mapping. Model coefficients are identified via Eulerian–Eulerian simulations on a calibration set. The model was evaluated on isolated simulation validation sets with varying geometries and inlet gas volume fractions, yielding an R² of 0.762 and a root mean square error (RMSE) of 0.07. Particle Image Velocimetry validation tests on one representative prototype geometry gave RMSE values of 0.061 for simulation versus test and 0.108 for prediction versus test. The framework captures the macroscopic trend of separation efficiency within the investigated range, with the caveat that part of the model coefficients and intermediate inputs remain conditioned by simulation-derived quantities. Full article

Siltation in irrigation canals adversely affects overflow capacity and accessibility, making its identification crucial for dredging, prevention, and maintenance, among other purposes. In this study, the siltation risks of Golmud irrigation canals were assessed from three perspectives: hydrodynamic impact, anthropogenic impact, and greening impact. The assessment factors included sediment deposition risk, bed erosion risk, proximity to public administration and services, proximity to residential areas, proximity to commercial services, and proximity to green spaces. The entropy weight method and TOPSIS method were employed to calculate the comprehensive siltation risk level, with model validation confirming a high overall accuracy of 94%. The results showed that among the six factors, proximity to public administration and services had the greatest influence on siltation, with a weight of 0.29. Additionally, the most vulnerable siltation locations were primarily in the city center, reflecting the susceptibility of urban areas to anthropogenic activities. This study develops a rapid and objective risk-scanning tool that couples hydrodynamics with land-use factors, providing a standardized technical pathway for the checking of large-scale urban infrastructure. Full article

To improve fine particle retention in cyclone clarifiers for mine water treatment, we developed three baffle-structured cyclone clarifiers based on the traditional design: flat-baffle cyclone clarifier, convex-baffle cyclone clarifier, and concave-baffle cyclone clarifier. Using numerical simulation, a comparative analysis was conducted on the differences in flow field characteristics and particle separation performance between the traditional cyclone clarifier and the three types of baffle-structured cyclone clarifiers. The convex-baffle cyclone clarifier showed the highest pressure drop. At Section II-II, low tangential velocity minimized internal swirl, while Section I-I exhibited high axial velocity near the wall. The low upward axial velocity in the central region of Section II-II enhanced fine particle settling. The convex baffle also promoted uniform streamlines and efficient space utilization. The concave-baffle cyclone clarifier exhibited a larger flow angle relative to the baffle than the flat-baffle cyclone clarifier, causing stronger impingement and turbulence that transported particles to the overflow outlet. In contrast, the convex-baffle cyclone clarifier’s smaller flow angle yielded weaker impingement and more stable flow, reducing particle escape. Simulations confirmed that baffle-structured cyclone clarifiers improve particle removal. The removal efficiency of the convex-baffle cyclone clarifier reaches 78.19%, representing a 5.22% improvement compared to the traditional cyclone clarifier. Furthermore, the convex-baffle cyclone clarifier demonstrated the most effective removal of 5 μm particles compared with both the flat-baffle and concave-baffle cyclone clarifier. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

This study addresses the problem of anomaly detection in water treatment systems by proposing a hybrid VAE–LSTM model with a combined loss function that integrates reconstruction and prediction errors. Following the signal flow of wastewater treatment systems, data acquisition, transmission, and cyberattack scenarios were simulated, and a dual-dimensional learning framework of “feature space—temporal space” was designed: the VAE learns latent data distributions and computes reconstruction errors, while the LSTM models temporal dependencies and computes prediction errors. Anomaly decisions are made through feature extraction and weighted scoring. Experimental comparisons show that the proposed fusion model achieves an accuracy of approximately 0.99 and an F1-Score of about 0.75, significantly outperforming single models such as Isolation Forest and One-Class SVM. It can accurately identify attack anomalies in devices such as the LIT101 sensor and MV101 actuator, e.g., water tank overflow and state transitions, with reconstruction errors primarily beneath 0.08 ensuring detection reliability. In terms of time efficiency, Isolation Forest is suitable for real-time preliminary screening, while VAE-LSTM adapts to high-precision detection scenarios with an “offline training (423 s) + online detection (1.39 s)” mode. This model provides a practical solution for intelligent monitoring of industrial water treatment systems. Future research will focus on model lightweighting, enhanced data generalization, and integration with edge computing to improve system applicability and robustness. The proposed approach breaks through the limitations of traditional single models, demonstrating superior performance in detection accuracy and scenario adaptability. It offers technical support for improving the operational efficiency and security of water treatment systems and serves as a paradigm reference for anomaly detection in similar industrial systems. Full article

Over the past 30 years, the City of Portland, Oregon, USA, has emerged as a national leader in green stormwater infrastructure (GSI). The initial impetus for implementing sustainable stormwater infrastructure in Portland stemmed from concerns about flooding and water quality in the city’s two major rivers, the Columbia and the Willamette. Heavy rainfall often led to combined sewer overflows, significantly polluting these waterways. A partial solution was the construction of “The Big Pipe” project, a large-scale stormwater containment system designed to filter and regulate overflow. However, Portland has taken a more comprehensive and long-term approach by integrating sustainable stormwater management into urban planning. Over the past three decades, the city has successfully implemented GSI to mitigate these challenges. Low-impact development strategies, such as bioswales, green streets, and permeable surfaces, have been widely adopted in streetscapes, pathways, and parking areas, enhancing both environmental resilience and urban livability. This perspective highlights the history of the implementation of Portland’s GSI programs, current design and performance standards, and challenges and lessons learned throughout Portland’s recent history. Innovative approaches to managing runoff have not only improved stormwater control but also enhanced green spaces and contributed to the city’s overall climate resilience while addressing economic well-being and social equity. Portland’s success is a result of strong policy support, effective integration of green and gray infrastructure, and active community involvement. As climate change intensifies, cities need holistic, adaptive, and community-centered approaches to urban stormwater management. Portland’s experience offers valuable insights for cities seeking to expand their GSI amid growing concerns about climate resilience, equity, and aging infrastructure. Full article

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner. Full article

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions. Full article

Existing databases supporting Online Transaction Processing (OLTP) workloads based on non-volatile memory (NVM) almost all use Multi-Version Concurrency Control (MVCC) protocol to ensure data consistency. MVCC allows multiple transactions to execute concurrently without lock conflicts, reducing the wait time between read and write operations, and thereby significantly increasing the throughput of NVM OLTP engines. However, it requires garbage collection (GC) to clean up the obsolete tuple versions to prevent storage overflow, which consumes additional system resources. Furthermore, existing GC approaches in NVM OLTP engines are inefficient because they are based on methods designed for dynamic random access memory (DRAM) OLTP engines, without considering the significant differences in read/write bandwidth and cache line size between NVM and DRAM. These approaches either involve excessive random NVM access (traversing tuple versions) or lead to too many additional NVM write operations, both of which degrade the performance and durability of NVM. In this paper, we propose TB-Collect, a high-performance GC approach specifically designed for NVM OLTP engines. On the one hand, TB-Collect separates tuple headers and contents, storing data in an append-only manner, which greatly reduces NVM writes. On the other hand, TB-Collect performs GC at the block level, eliminating the need to traverse tuple versions and improving the utilization of reclaimed space. We have implemented TB-Collect on DBx1000 and MySQL. Experimental results show that TB-Collect achieves 1.15 to 1.58 times the throughput of existing methods when running TPCC and YCSB workloads. Full article

The sustainable protection of cultural heritage is essential for the intergenerational transmission of cultural diversity and represents a central theme in the United Nations Sustainable Development Goals (SDGs) on “heritage resilience governance”. To address the policy sustainability challenges of large-scale linear heritage governance, this study examines the characteristics and shortcomings of Great Wall Cultural Preservation (GWCP) policies during its steady implementation. To analyze how policy instruments are distributed, whether policy objectives are synergistic, and whether stakeholders’ participation is reasonable, this study uses GWCP policy texts issued by China from 2006 to 2024 as research objects and establishes a three-dimensional analytical framework (“instrument–objective–stakeholder”). With the help of the NVivo 20 tool, the study analyzes the policy texts in one dimension and multiple dimensions, and finds that China’s GWCP policy has shortcomings in sustainability governance, such as the imbalance in the use of policy instruments, the overflow of contextual policy instruments, the government’s over-exertion of force, the need to release the functional space of stakeholders, and the lack of attention to the synergy between the goals of conserving architectural heritage and safeguarding the Great Wall ethos. Based on these findings, the study proposes three targeted optimization recommendations. This GWCP case study offers developing nations insights into balancing heritage protection objectives under SDG 11.4 with local development needs. Full article

Water quality in urban streams is critical for the health of aquatic and human life, as it impacts both the environment and water availability. The strong impacts of changing climate and land use on water quality necessitate a better understanding of how stream water quality changes over space and time. To this end, four key water-quality parameters—Escherichia coli (E. coli), nitrate (NO₃⁻), sulfate (SO₄²⁻), and chloride (Cl⁻)—were collected at 12 sites along Fall Creek and Pleasant Run streams in Indianapolis, Indiana USA from 2003 to 2021 on a seasonal basis: March, July, and October each year. Two-way ANOVA tests were used to determine the impacts of seasonality and location on these parameters. Correlation and RDA (redundancy analysis) were used to determine the importance of climatic drivers. Linear regressions were used to quantify the impacts of land-use types on water quality integrating buffer zone size and sub-watershed analysis. Strong seasonal variations of the water-quality parameters were found. March had higher levels of NO₃⁻, SO₄²⁻, and Cl⁻ than other months. July had the highest E. coli concentrations compared to March and October. Seven-days antecedent snow and precipitation were found to be significantly related to Cl⁻ and log₁₀(E. coli) and can explain up to 53% and 31% of their variations, respectively. Spatially, urban built-up land in a 1000 m buffer around the sampling sites was positively correlated with the log₁₀(E. coli) variation, while lawn cover was positively related to NO₃⁻ concentrations within 500 m buffers. Conversely, NDVI (Normalized Difference Vegetation Index) values were negatively related to all variables. In conclusion, E. coli is more impacted by higher precipitation and urban land coverage, which could be related to more combined sewer overflow events in July. Cl⁻ peaking in March and its relationship with snow indicate salt runoff during snow melting events. NO₃⁻ and SO₄²⁻ increases are likely due to fertilizer input from residential lawns near streams. This suggests that Indianapolis stream water-quality changes are influenced by both changing climate and land-cover/-muse types. Full article

Developed countries with animal husbandry are confronted with the pressing issues of ensuring stable livestock product supplies while maintaining ecological sustainability. Additional research is required to ascertain whether environmental regulation can effectively facilitate the green transformation of animal husbandry and establish a harmonious equilibrium between environmental protection and economic growth. It is essential for the empirical development of environmental policies in animal husbandry, as it evaluates the impact of regulatory measures on this sector’s ecological efficiency and precisely investigates the underlying mechanisms of these effects. This paper evaluates the nonlinear impact of environmental regulation policies on the ecological efficiency of animal husbandry using the super-efficiency EBM model, spatial Durbin model, and panel threshold model, which are based on panel data from 31 Chinese provinces (2010–2022). The findings indicated that: (1) The ecological efficiency and environmental regulation intensity of animal husbandry in China exhibited a fluctuating upward trend. The environmental regulation is ranked from high to low in the following order: Northeast, West, Central, and Eastern regions. Conversely, the regions with high ecological efficiency are concentrated in the Northeast and Western regions. (2) The impacts of environmental regulation on the ecological efficiency of animal husbandry were N-type nonlinear, with the extreme points being 6.322 and 9.456. Environmental regulation also produced an “inverted N” type spatial spillover effect on the ecological efficiency of animal husbandry in adjacent areas, with extreme values of 5.330 and 7.670. (3) Environmental regulation considerably enhanced the ecological efficiency of animal husbandry in the Eastern and Central regions in terms of location characteristics. The influence on the Western and Northeastern regions exhibited N-type nonlinear characteristics. (4) From 2017 to 2022, ER had an N-type nonlinear effect on animal husbandry ecological efficiency in terms of temporal heterogeneity. However, the effect was not significant from 2010 to 2016. Full article

With the frequent occurrence of extreme weather events worldwide, the compound frequency of drought and flood events has significantly increased, imposing multidimensional pressures on agricultural water resource management. Agricultural water consumption accounts for approximately 70%, with severe waste, as a large amount of water is lost during transmission and distribution. Faced with increasingly severe and frequent extreme weather, traditional drainage systems may become unsustainable. Identifying the factors influencing drainage time is crucial for efficient drainage. The MIKE URBAN model has significant potential in farmland waterlogging simulation and drainage network optimization. This study validated the model’s accuracy based on infiltration well overflow capacity experiments, with Average Relative Error (ARE) values of 2.29%, 6.52%, 4.41%, 3.17%, 4.37%, and 5.69%, demonstrating good simulation accuracy. The MIKE URBAN model was used to simulate drainage networks, explore factors affecting drainage time, establish an annual cost system for the drainage network, and optimize the network using a genetic algorithm with the objective of minimizing annual costs. Research findings: There is a clear negative correlation between the maximum inflow of infiltration wells and drainage time. As inflow increases, drainage becomes faster, but beyond 0.0075 m³/s (27 m³/h), the efficiency gains level off. This indicates that selecting infiltration wells with at least a 20% opening ratio is essential. Similarly, increasing the collector’s diameter enhances drainage efficiency significantly, though the effect follows a diminishing return pattern. While smaller lateral spacing improves local water collection, it may lead to flow congestion if the collector is undersized; conversely, larger spacing increases drainage paths and delays, even if the collector is large. An optimal spacing range of 100–150 m is suggested alongside the collector diameter. Lateral diameter also affects performance: increasing it reduces drainage time, but the benefit plateaus around 200 mm, making further enlargement cost-ineffective. The genetic algorithm helped to optimize the drainage network design. Utilizing the genetic algorithm, the drainage network was optimized in just 15 iterations. The fitness function value rapidly decreased from 351,000 CNY to 55,000 CNY and then stabilized, reducing the annual cost from 59,640.67 CNY to 45,337.86 CNY—a 24% savings—highlighting the approach’s effectiveness in designing efficient and economical farmland drainage systems. This study has shown that the simulation-based optimization of drainage networks provides a more rational and cost-effective approach to planning drainage infrastructure. Full article

The shaft hydropower plant (SHPP) is a novel hydraulic concept for low-head hydropower sites with several environmental and operational advantages over conventional layouts. However, the first two projects implementing this concept have shown comparatively high construction costs and project risks. Therefore, further optimization is required to increase economic attractiveness and enable broader market adoption. Initial model tests recommend a square-shaped shaft inlet with a three-sided approach flow for low-loss and fish-friendly inflow conditions. Yet, this design requires significant space for structural implementation and may be unsuitable for use with multiple shafts or as an extension of non-powered dams and weirs. This research paper presents the application of a computational fluid dynamics simulation setup to evaluate the hydraulic performance of various design configurations, especially alternative design layouts with a one-sided approach flow without further physical model tests. The simulation setup is calibrated against observations including head loss and velocity measurements from the physical model tests, and its satisfactory performance enables the analysis of alternative design layouts. This study aims to derive the most significant design parameters for achieving the desired hydraulic conditions at the intake. Increasing the flow depth before the intake and enlarging the inlet area have the most significant impact, while increasing the overflow of the front gate has the least significant effect. The chosen CFD application is deemed suitable for hydraulic design optimization and provides guidance on the key parameters to focus on for tailored site-specific design development. Full article

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain’s shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status. Full article