Search Results (29)

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract the built-up urban footprint via the constructed VMNUI. This method achieved an overall accuracy >0.95 and a Kappa coefficient of 0.80 when the results were compared against land use samples. Utilizing Centroid Migration Analysis, clustering, Geographical Detector, and GTWR, we quantitatively analyzed Fujian’s urban spatial form and its driving mechanisms. The results indicate that the calibration and integration of NTL data effectively resolved saturation and overflow issues in the DMSP data, revealing an urban expansion rate of 3.79%, which centered on coastal areas. Geographical Detector analysis identified fixed-asset investment (q = 0.83), population (0.80), precipitation (0.78), and highway density (0.76) as dominant factors; GDP ∩ fixed-asset investment yielded the strongest interaction (0.873). GTWR further identified that slope aspect, GDP, and secondary industry share accelerated expansion in eastern Fujian, whereas population, urbanization rate, and mean temperature were key drivers of expansion in the west. This study analyzed the spatiotemporal evolution patterns and driving mechanisms of urban spatial form development in Fujian Province over a long period, and based on the results, actionable, science-based optimization strategies with practical implications are proposed for sustainable development in the region. Full article

Most existing ultra-deep gas wells are characterized by high temperature, high pressure, and high sulfur content. During development, they face serious challenges such as unclear mechanisms of acid gas-induced blowouts and difficulties in wellbore pressure inversion, posing significant challenges to well control operations. To reveal the reasons behind the tendency of acidic gases to trigger blowouts and to clarify the impact of different concentrations of acidic gases on the flow behavior of annular fluids, this study considers the effects of solubility and phase changes on the physical properties of acidic gases. A method replacing critical parameters with pseudo-critical parameters is used to analyze the variation trends of gas density, solubility, and other properties along the well depth. A mathematical model for the annular flow of acidic gas overflow incorporating solubility phase change effects is established. The model is numerically solved using a four-point difference scheme, exploring the essential characteristics of gas flow in the annulus after overflow, and discussing the distribution patterns of physical properties of acidic gases, as well as dynamic parameters such as wellbore pressure and temperature along the well depth. Numerical simulations show that the physical properties of acidic gases change significantly with well depth: the more acidic gas present in the wellbore, the smaller the deviation factor, and the greater the density and viscosity, with parameter changes exceeding 40% near the pseudo-critical point for binary mixtures with 40% H₂S. Compared to pure methane, mixed fluids containing acidic gas experience more than 20% volume expansion near the wellhead for ternary mixtures with 20% CO₂ and 20% H₂S, and the flow velocity increases by more than 10% for mixtures with ≥30% acidic gas content, leading to a higher risk of a sudden pressure drop during well control. This study clarifies the migration patterns of acidic gas overflow in HPHT (high pressure, high temperature) gas wells, providing valuable guidance for optimizing well control design, improving well control emergency plans, and developing well-killing measures. Full article

To address the current limitations of spaceborne solid-state storage systems that cannot effectively support the parallel storage of multiple high-speed data streams, the throughput bottleneck of NAND FLASH-based solid-state storage systems was analyzed in relation to the high-speed data input requirements of payloads. A four-stage pipeline operation and bus parallel expansion scheme was proposed to enhance the throughput. Additionally, to support the parallel storage of multichannel data and continuity of pipeline loading, the shortcomings of existing caching schemes were analyzed, leading to the design of a storage system based on Synchronous Dynamic Random Access Memory (SDRAM). Model simulations indicate that, under extreme conditions, the proposed scheme could continuously receive and cache multiple high-speed file data streams into the SDRAM. File data were dynamically written into FLASH based on the priority and status of each partition cache autonomously, without overflow during caching. The system eventually entered a regular dynamic balance scheduling state to achieve parallel reception, caching, and autonomous scheduling of storage for multiple high-speed payload data streams. The data throughput rate of the storage system can reach 4 Gbps, thus satisfying future requirements for multichannel high-speed payload data storage in spaceborne solid-state storage systems. Full article

Climate change has a significant impact on river flows, leading to overflows and floods that affect populations, especially in Andean regions. This study examines flood scenarios in the Cunas River Basin (Junín, Peru) through hydrological and hydraulic simulations under various climate projections. A Reliability Ensemble Averaging (REA) approach was employed using CMIP6 climate models. In this analysis, precipitation data were processed, basin parameters were calculated, and peak flows and the extent of flood-prone areas were estimated. HEC-HMS software was used to simulate peak flows corresponding to return periods of 25, 50, 100, 139, and 200 years, while HEC-RAS was employed to determine flood zones. Model calibration and validation relied on historical precipitation data from nearby stations. The results indicate a considerable increase in peak flows and flood-prone areas due to climate change. A 3.32% increase in peak flow, a 55.35% expansion in flood-prone areas, and a 34.12% rise in flood depth are observed. These findings highlight the importance of implementing riverine protection structures. This study provides key information for flood risk management in the Peruvian highlands, using widely accepted tools to understand the hydrological response to climate change. Full article

Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) in high-density urban areas has surged exponentially. To address these computational and memory challenges in high-density urban PSInSAR processing, this paper proposes an efficient method for integrating regular grid partitioning and connected component constraints. First, adaptive dynamic regular grid partitioning was employed to divide monitoring areas into sub-blocks, balancing memory usage and computational efficiency. Second, a weighted least squares adjustment model using common PS points in overlapping regions eliminated systematic inter-sub-block biases, ensuring global consistency. A graph-based connected component constraint mechanism was introduced to resolve multi-component segmentation issues within sub-blocks to preserve discontinuous PS information. Experiments on TerraSAR-X data covering Fuzhou, China (590 km²), demonstrated that the method processed 1.4 × 10⁷ PS points under 32 GB memory constraints, where it achieved a 25-fold efficiency improvement over traditional global PSInSAR. The deformation rates and elevation residuals exhibited high consistency with conventional methods (correlation coefficient ≥ 0.98). This method effectively addresses the issues of memory overflow, connectivity loss between sub-blocks, and cumulative merging errors in large-scale PS networks. It provides an efficient solution for wide-area millimeter-scale deformation monitoring in high-density urban areas, supporting applications such as geohazard early warning and urban infrastructure safety assessment. Full article

Volume kinetics is a pharmacokinetic method for analysis of the distribution and elimination of infusion fluids. The approach has primarily been used to improve the planning of fluid therapy during surgery but is also useful for answering physiological questions. The kinetics is based on 15–35 serial measurements of the blood hemoglobin concentration during and after the fluid is administered intravenously. Crystalloid fluid, such as isotonic saline and Ringer’s lactate, distributes between three compartments that are filled in succession depending on how much fluid is administered. The equilibration of fluid between these three compartments is governed by five rate constants. The compartments are the plasma (V_c), and a fast-exchange (V_t1) and a slow-exchange interstitial compartment (V_t2). The last compartment operates like an overflow reservoir and, if filled, markedly, prolongs the half-life of the fluid. By contrast, the volume of a colloid fluid distributes in a single compartment (V_c) from where the expansion is reduced by capillary leakage and urinary excretion. This review gives 15 examples of physiological or medical questions where volume kinetics has provided answers. These include why urine flow is low during general anesthesia, the inhibitory effects of anesthetics on lymphatic pumping, the influence of dopamine and phenylephrine on urine output, fluid maldistribution in pre-eclampsia, plasma volume oscillations, and issues related to the endothelial glycocalyx layer. Full article

Reversible data hiding typically relies on two main techniques: prediction-error expansion and histogram shifting. These techniques complement each other to facilitate effective data embedding by defining non-positive and non-negative thresholds, thereby reducing distortion. The goal is to minimize overflow and underflow pixels by constraining thresholds appropriately. Managing these pixels remains challenging as they must be mapped within the payload. While double modification testing can eliminate the location map for some images, it is highly complex and struggles with images near intensity limits. In this paper, we show that the non-positive and non-negative thresholds for each predicted value are bounded by their infimum and supremum. By restricting the thresholds to these bounds, we maximize the number of embeddable pixels while minimizing the location map size. Moreover, our approach enables the rapid determination of the first operating thresholds and the development of encoding and decoding formulas for RDH without modification. Performance comparisons with established algorithms demonstrate the advantages of our proposed method. Full article

The reasonable and efficient prediction of dam failure events is of great significance to the emergency rescue operations and the reduction in dam failure losses. This work presents a model that is based on the physical mechanism. It is coupled with a multi-architecture (multi-CPU and GPU) open-source two-dimensional flood model, which is based on high-precision terrain and land use data. The aim is to enhance the accuracy of dam break flood process simulations. The model uses DEM data as a computational grid and updates it at each time step to reflect breach evolution. Simultaneously, the breach evolution model incorporates an analysis of stress on sediment particles, establishing the initial erosion state and lateral expansion model while accounting for seepage. The determination of the overflow of the breach is resolved through the application of a two-dimensional hydrodynamic model. This approach achieves a robust connection between the upstream reservoir, the dam structure, and the downstream inundation area. The coupled model is utilized to calculate the failure of earth-rock dams and landslide dams, and a sensitivity analysis is conducted. Taum Sauk Dam and Tangjiashan landslide dam were selected to represent earth dam break and barrier lake break, respectively, which are the main types of dam breaks. The obtained results demonstrate strong concurrence with the measured data, the relative errors of the four important parameters of the application case, the peak discharge of the breach, the top width of the final breach, the depth of the breach and the arrival time of the maximum peak discharge are all within ±10%. Although the relative error of the completion time of the final breach is greater than 10%, it is about 30% less than the relative error of the physical model. Full article

With urban expansion, traditional drainage systems in densely populated cities face significant challenges, leading to frequent flooding and pollution issues. Deep tunnel drainage systems emerge as an innovative approach, offering underground storage for excess precipitation and alleviating urban inundation. This research investigates the deployment of a deep tunnel system in Guangzhou’s densely populated urban core. By integrating with existing networks, this system aims to curtail over-flow contamination and boost sewage-handling capacity. Successful implementation hinges on the thorough evaluation and synchronization with broader urban development objectives. In Guangzhou, where traditional methods fall short, deep tunnels present a viable option. This study explores techniques for identifying drainage deficiencies, devising enhancements, and refining citywide strategies. Economic analysis indicates that deep tunnels are more cost-effective than conventional drainage upgrades, offering long-term benefits for land conservation and drainage efficiency. Following implementation, these systems markedly enhance sewage management, diminish overflow incidents, and improve pollution mitigation. Although initial investments are substantial, the enduring advantages in land preservation and drainage efficiency are significant. Thus, deep tunnel systems emerge as a practical flood control solution for high-density urban areas like Guangzhou, fostering sustainable metropolitan growth. Full article

Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM), a widely used pipe convergence module in the storm water management model (SWMM), with an inundation module that describes the overflow expansion process associated with terrain and land-cover. The IUFM was used in a case study of the Anhua Bridge (a typical concave-down overpass) study area in Beijing, China. The spatial-temporal variations in flood depth modeled by the IUFM were verified to be reliable by comparison with actual measurements and other simulations. The validated IUFM was used to obtain temporal variations in flood range, depth, and volume under four rainstorm scenarios (return periods of 3-year, 10-year, 50-year, and 100-year). The results showed that the surface runoff process, overflow from drainage networks, and overflow expansion process could affect the flooding status by changing the composition and spatial configuration of pervious or impervious patches, drainage capacity, and underlying surface characteristics (such as terrain and land-cover). Overall, although the simulation results from the IUFM contain uncertainties from the model structures and inputs, the IUFM is an effective tool that can provide accurate and timely information to prevent and control urban flood disasters and provide decision-making support for long-term storm water management and sponge city construction. Full article

This paper aims to explore an alternative reversible digital watermarking solution to guarantee the integrity of and detect tampering with data of probative importance. Since the payload for verification is embedded in the contents, algorithms for reversible embedding and extraction, imperceptibility, payload capacity, and computational time are issues to evaluate. Thus, we propose a reversible and imperceptible audio information-hiding algorithm based on modified integer discrete cosine transform (intDCT) coefficient expansion. In this work, the original signal is segmented into fixed-length frames, and then intDCT is applied to each frame to transform signals from the time domain into integer DCT coefficients. Expansion is applied to DCT coefficients at a higher frequency to reserve hiding capacity. Objective evaluation of speech quality is conducted using listening quality objective mean opinion (MOS-LQO) and the segmental signal-to-noise ratio (segSNR). The audio quality of different frame lengths and capacities is evaluated. Averages of 4.41 for MOS-LQO and 23.314 [dB] for segSNR for 112 ITU-T test signals were obtained with a capacity of 8000 bps, which assured imperceptibility with the sufficient capacity of the proposed method. This shows comparable audio quality to conventional work based on Linear Predictive Coding (LPC) regarding MOS-LQO. However, all segSNR scores of the proposed method have comparable or better performance in the time domain. Additionally, comparing histograms of the normalized maximum absolute value of stego data shows a lower possibility of overflow than the LPC method. A computational cost, including hiding and transforming, is an average of 4.884 s to process a 10 s audio clip. Blind tampering detection without the original data is achieved by the proposed embedding and extraction method. Full article

The impedance change in an induction coil surrounding a metal tube adapter is investigated using the truncated region eigenfunction expansion (TREE) method. The conventional TREE method is inapplicable to this problem as a consequence of the numerical overflow of the eigenfunctions of the air–metal multi-subdomain regions. The difficulty is surmounted by a normalization procedure for the numerical eigenfunctions obtained from the 1D finite element method (FEM). An efficient algorithm is devised by the Clenshaw–Curtis quadrature rule for integrals involving the numerical eigenfunctions. The numerical results of the TREE and FEM simulation coincide very well in all cases, and the efficiency of the proposed method is also confirmed. Full article

A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system. Full article

The current centrifugal concentrators do not continuously concentrate heavy minerals of large mass weight—for example, in the processing of iron oxides. A cyclone centrifugal separator is a new type of beneficiation equipment that has been developed on the basis of the principles of centrifugal separation and hydrocyclones. In this study, a cyclone centrifugal separator was used for the beneficiation of a hematite–quartz mixture, and the separation effect under different operating conditions was analyzed. The results showed that various factors, such as the feeding concentration, the feeding pressure, and the number of fluidization holes in the separation cone, had a significant influence on the separation performance of the cyclone centrifugal separator. The single-factor separation test results showed that the appropriate operating conditions were a feeding concentration of 10%, a feeding pressure of 120 kPa, a 40-hole separation cone, a settling port pressure of 70 kPa, and an overflow port pressure of 50 kPa. The results of the open circuit separation test that was designed on the basis of the single-factor experiment showed that the concentrate grade of Fe in the hematite–quartz mixture, with 44.12% Fe, was 54.42% after beneficiation, indicating better beneficiation separation effects. This study provides a new approach for the beneficiation of a large number of difficult-to-select hematite ores in China, and can provide a reference for the further expansion and application of the cyclone centrifugal separator in the field of mineral processing. Full article