Search Results (17,219)

Compared with conventional gas reservoirs, deep shale gas reservoirs are characterized by developed faults and fractures, strong heterogeneity, high stress sensitivity, and complex in situ stress distribution. To address traditional 3D static models’ inability to predict in situ stress changes in strongly heterogeneous reservoirs during fracturing, this study takes the deep shale gas in the Zigong block of the Sichuan Basin as an example. By comprehensively considering the heterogeneity and anisotropy of geomechanical parameters and natural fractures in shale gas reservoirs, a 4D in situ stress multi-physics coupling model for shale gas reservoirs based on geology–engineering integration is established. Through coupling geomechanical parameters with fracturing operation data, the dynamic evolution laws of multi-scale stress fields from single-stage to platform-scale during large-scale fracturing of horizontal wells in deep shale gas reservoirs are systematically studied. The research results show the following: (1) The fracturing process has a significant impact on the magnitude and direction of the stress field. With the injection of fracturing fluid, both the minimum and maximum horizontal principal stresses increase, with the minimum horizontal principal stress rising by 1.8–6.4 MPa and the maximum horizontal principal stress by 1.1–3.2 MPa; near the wellbore, there is an obvious deflection in the direction of in situ stress. (2) As the number of fracturing stages increases, the minimum horizontal principal stress shows an obvious cumulative growth trend, with a more significant increase in the later stages, and there is a phenomenon of stress accumulation along the wellbore, with the stress difference decreasing from 15 MPa to 11 MPa. (3) The on-site adoption of the fracturing operation method featuring overall flush advancement and inter-well staggered fracture placement has achieved good stress balance; comparative analysis shows that the stress communication degree of the 400 m well spacing is weaker than that of the 300 m well spacing. This study provides a more reasonable simulation method for large-scale fracturing development of deep shale gas, which can more accurately predict and evaluate the dynamic stress field changes during fracturing, thereby guiding fracturing operations in actual production. Full article

Improving sowing quality is crucial for ensuring wheat emergence and healthy growth. To address issues of poor wheat sowing quality, such as uneven sowing depth and inadequate soil coverage, in the Yangtze River Delta region of China, this study systematically analyzed the effects of the implement’s structural and operational parameters on sowing quality. Based on this analysis, a double-shaft rotary tillage and double-press seeder was designed. Protrusions on the grooving press roller are used to form seed furrows, rotary tiller blades cover the seeds with soil, and the rear press roller compacts the soil. DEM-MBD (discrete element method–multibody dynamics) coupled simulations, combined with single-factor and central composite design (CCD) experiments, were conducted with seeding depth as the evaluation index and four experimental factors: the protrusion height on the press grooving roller, forward speed, seed mass in the seed box, and straw mulching amount. The optimal protrusion height was 29 mm. The effects of rotary tiller blade working depth, rotational speed, and forward speed on soil-covering mass and its coefficient of variation were evaluated through discrete element method (DEM) simulations. The optimal working depth and rotational speed were found to be 55 mm and 350 r·min⁻¹, respectively, based on single-factor and Box–Behnken Design experiments. Field experiments based on optimized parameters showed results consistent with the simulations. The qualified rate of seeding depth decreased as forward speed increased. The optimal forward speed was 4.5 km·h⁻¹, at which the average seeding depth was 25.7 mm, the qualified seeding depth rate was 90%, the soil-covering mass within a 50 cm² area was 143.2 g, and the coefficient of variation was 13.21%, meeting the requirements for wheat sowing operations. Full article

The application of tuned mass dampers (TMDs) to high-rise buildings or slender bridges can significantly decrease the dynamical vibrations due to external excitation, such as wind or earthquake loads. However, the individual properties of a TMD such as mass, stiffness and damping have to be designed carefully with respect to the dynamical properties of the investigated structure. In real-world structures, the influence of uncertain system properties might be critical for the performance of a TMD and thus the whole structure. Therefore, the design under uncertainty of such systems is an important issue, which is addressed in the current paper. For our investigations, we consider linear single-degree-of-freedom (SDOF) systems, where analytical formulas for the deterministic design already exist, and linear multi-degree-of-freedom (MDOF) systems, where a time integration and numerical optimization algorithms are usually applied to obtain the optimal TMD parameters. If the numerical optimization should be coupled with a sampling-based uncertainty quantification method, such as Monte Carlo sampling, the design procedure would require the evaluation of a coupled double-loop approach, which is very demanding from the computation point of view. Therefore, we focus the following paper on an efficient analytical uncertainty quantification approach, which estimates the mean and scatter from a Taylor series expansion. Additionally, we introduce an efficient mode decomposition approach for MDOF systems with multiple TMDs, which estimates the maximum displacements using a modal analysis instead of a demanding time integration. Different optimal design problems are formulated as single- or multi-objective optimization tasks, where the statistical properties of the maximum displacements are considered as safety margins in the optimization goal functions. The application of numerical optimization algorithms is straightforward and not limited to specific algorithms. As numerical examples, we investigate an SDOF system with single TMD and a multi-story frame with multiple TMDs. The presented procedure might be interesting for the design process of structures, where the dynamical vibrations reach a critical threshold. Full article

This study investigates the spatial coupling between geohazard susceptibility and soil conservation capacity in Guangdong Province, China, using integrated spatial analysis and machine learning approaches. Through kernel density estimation, hotspot analysis, principal component analysis (PCA), and t-SNE clustering applied to 11,252 geohazard records and nine soil conservation factors, we identify three critical mechanisms: (1) Topographic steepness (LS factor) constitutes the primary control on geohazard distribution (r = 0.162, p < 0.001), with high-risk clusters concentrated in northeastern mountainous regions (Meizhou-Huizhou-Heyuan); (2) Vegetation coverage (C_mean) mediates rainfall impacts, exhibiting significant risk reduction (r = −0.099, p < 0.001) despite counterintuitive negative correlations with mean rainfall erosivity; (3) Soil conservation effectiveness depends on topographic context, reducing geohazard density in moderate slopes (Cluster 0: 527.04) but proving insufficient in extreme terrain (Cluster 2: LS = 20.587). The emerging role of rainfall variability (R_slope, r = 0.183) highlights climate change impacts. Full article

The highly complex memristive chaotic map provides an excellent alternative for engineering applications. To design a memristive chaotic map with high complexity, this paper proposes a new three-dimensional memristive chaotic map (named MLM) by cascading and coupling a non-locally active memristor with a locally active memristor. The dynamical behaviors of MLM are revealed through phase diagrams, Lyapunov exponent spectra, bifurcation diagrams, and dynamic distribution diagrams. Notably, the internal frequency of MLM exhibits unique LE-controlled behavior and shows an extension of the chaotic parameter range. The high complexity of MLM is validated through the use of Spectral entropy (SE) and C0, and Permutation Entropy (PE) complexity algorithms. Subsequently, a pseudorandom number generator (PRNG) based on MLM is designed. NIST test results validate the high randomness of the PRNG. Finally, the STM32 hardware platform is used to implement MLM, and attractors under different parameters are measured by an oscilloscope, verifying the numerical analysis results. Full article

The rapid evolution of ventilators and their circuits, coupled with varying maximum usage durations set by different hospitals globally, poses a significant risk for the proliferation and transmission of nosocomial infections in intensive care settings. This study investigated temporal changes in bacterial community structure and predicted metabolic functions in ventilator circuits over a three-week period, with a specific focus on ESKAPE pathogens. The results of full-length 16S rRNA sequencing revealed dynamic shifts in bacterial communities, with an increased bacterial diversity and unique species prevalence in week-2 compared to week-1 and week-3. However, a marked emergence of pathogenic bacteria, including Serratia marcescens and Chryseobacterium indologenes, was observed in week-3 compared to week-1 and week-2. Additionally, the abundance of ESKAPE pathogens, including Klebsiella pneumoniae and Acinetobacter baumannii, was higher in week-3 compared to week-1 and week-2. Furthermore, the PCR analysis revealed a higher detection rate of Pseudomonas aeruginosa and K. pneumoniae in week-3 than in the previous weeks. FAPROTAX analysis further revealed a high abundance of specific functions associated with the pathogens of pneumonia, nosocomial, and septicemia in week-3 compared to the other two weeks, suggesting a shift toward more virulent or opportunistic pathogens with increased utilization of ventilator circuits. These findings highlight the microbial risks associated with prolonged use of ventilator circuits, underscoring the need for continuous microbial surveillance throughout their usage, and provide a foundation for optimizing infection control strategies in intensive care settings. Full article

This study employs molecular orbital (MO) analysis, density of states (DOS) analysis, and advanced techniques such as charge density difference (CDD), transition density matrix (TDM), transition electric dipole moment density (TEDM), and transition magnetic dipole moment density (TMDM) to systematically investigate the electronic structure characteristics of Fc-[8]CPP and Fc-[11]CPP. Using density functional theory (DFT) and time-dependent DFT (TD-DFT), the π-electron delocalization properties and optical behaviors of these molecules were analyzed. Furthermore, their responses to external electromagnetic fields were explored through electronic circular dichroism (ECD) and Raman spectroscopy, comparing chiral optical responses and electron–vibration coupling effects to elucidate their photophysical properties. The results reveal that the HOMO-LUMO energy gaps of Fc-[8]CPP and Fc-[11]CPP are 5.81 eV and 5.95 eV, respectively, with a slight increase as ring size grows; Fc-[8]CPP exhibits a stronger chiral response, while Fc-[11]CPP shows reduced chirality due to enhanced symmetry. Finally, TD-DFT calculations demonstrate that their optical absorption is dominated by localized excitations with partial charge transfer contributions. These findings provide a theoretical foundation for designing conjugated macrocyclic materials with superior optoelectronic performance. Full article

The architecture, engineering, and construction (AEC) industry is highly fragmented, yet decisions made during the design phase critically shape downstream sustainability performance. Unlike prior research that primarily weighted interactions by frequency, this study introduces an Interaction-Quality Index that evaluates the quality of design team interactions. This represents a novel approach, as it combines Social Network Analysis with Monte Carlo simulation to quantify how collaboration, coordination, and trust influence sustainable outcomes in construction projects. Through a structured literature review, three systemic interaction drivers; collaboration, coordination, and trust were identified. An interaction-quality index was then formulated, weighting each driver according to its relative impact on sustainable outcomes. Social Network Analysis coupled with Monte Carlo simulation validated the index in a real-world building project, demonstrating its usefulness in identifying critical interaction nodes and highlighting how improvements in collaboration, coordination, and trust can strengthen network cohesion and enhance sustainability-oriented decision-making. The proposed index offers construction managers a quantitative tool to integrate social dynamics into holistic sustainability strategies, advancing practice in line with systems-thinking approaches to sustainable construction management. Full article

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse process operation conditions, embodied carbon of supplying H₂ and the associated carbon emission factor of MW treatment (CEF). Four machine learning techniques, including support vector machine, artificial neural network, Gaussian process regression, and XGBoost, are trained, each achieving test R² close to 0.90 and RMSE of ~0.26. These models are integrated with heuristic algorithms to optimize operating parameters under various green hydrogen mixes (20–80%). Our results show that machine learning models outperform the detailed process model (DPM), achieving a minimum CEF of ~1.3 to ~1.1 kg CO₂-eq/kg MW with higher computational stabilities. Importantly, the optimization times dropped from hours (DPM) to seconds (machine learning models) and the combination of Gaussian process regression and particle swarm optimization is highlighted, with an optimization time under one second. The optimized process holds promise in carbon reduction compared to traditional MW disposal methods. These findings show machine learning can achieve high predictive accuracy while dramatically enhancing optimization speed and stability, providing a scalable framework for extensive scenario analysis during waste-to-energy process design and further real-time optimization application. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

Ecological zoning is a key approach to promoting regional ecological protection and sustainable development. At present, landscape ecological risk (LER), driven by both natural and anthropogenic factors, continues to intensify, thereby disrupting ecosystem functions and weakening their service capacity. Although ecosystem services (ESs) and LER have been increasingly integrated into ecological management and policy-making in recent years, the interactive relationship between them remains insufficiently explored, particularly in the context of ecological zoning based on their coupled characteristics. Therefore, this study focuses on the Hengduan Mountain region from 2000 to 2020, analyzing the relationship between ES trade-offs and LER, constructing ecological zones, and proposing targeted management strategies. The results show that: (1) ESs in the region are primarily characterized by concave trade-offs, with decreasing trade-off intensity over time. The overall LER level has decreased, exhibiting a spatial pattern of higher risk in the south and lower risk in the north. (2) Bivariate spatial autocorrelation analysis reveals that LER is positively correlated with the trade-offs of carbon storage and soil conservation, shifts from a negative to a positive correlation with carbon storage and water yield, and shifts from a positive to a negative correlation with soil conservation and water yield. (3) Based on overlay zoning, the region is divided into protection, warning, and restoration zones, each with corresponding management measures. This study takes ecological zoning as a starting point to deeply analyze the relationship between ES trade-offs and LER, providing a scientific basis for sustainable development of mountain ecosystems. Full article

This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area and employs a multi-model coupling method of InVEST-Bathtub-GeoSOS-FLUS to predict and analyze the impacts of sea level rise and rapid urbanization on ecological source areas by the year 2100. The InVEST model is used to delineate areas with higher habitat quality scores as ecological source areas. The Bathtub inundation model predicts the impact ranges under three different sea level rise scenarios by 2100. The FLUS model simulates the land-use pattern of the Greater Bay Area in 2100. Finally, the raster calculator is used to conduct overlay analysis and accurately calculate the impact on ecological source areas under the combined effects of sea level rise and urban expansion. The results show that by 2100, the proportion of cultivated land in the Greater Bay Area is expected to decrease from 24.95% to 10.55%, while the proportion of urban land will increase from 7.69% to 26.84%. Under the dual impacts of the three sea level rise scenarios and urbanization, the affected areas of ecological source areas will reach 109.88 km², 125.05 km², and 255.10 km², respectively. This study provides an important basis and decision-making support for the sustainable planning and scientific management of ecological source areas in the Greater Bay Area. Full article

Negative air ions (NAI) represent an important ecological value indicator for green tree species. Flow of sap is a crucial indicator for water utilization and physiological state of trees. Although there have been some advancements in studies on the correlation between the release of NAI by plants and sap flow in recent years, it is still unclear how the release of NAI by plants changes during drought stress and recovery processes, as well as the coupling effect between the release of NAI by plants and sap flow under drought stress. In this context, four typical green tree species, Robinia pseudoacacia, Quercus variabilis, Pinus tabulaeformis, and Platycladus orientalis, were selected as experimental materials. A drought stress and recovery control experiment was conducted based on OTC. The dynamic data of negative air ion concentration (NAIC) and sap flow rate during the process of drought stress and recovery were monitored to clarify the characteristics and correlations of NAI and sap flow changes in the experimental tree species under drought stress and recovery. The main research results are as follows: (1) At the end of the drought period, the NAI and sap flow in the drought treatment group significantly decreased (p < 0.01), compared with the control group (CK), and the reduction rate of sap flow (77.73 ± 4.96%) for each tree species was higher than that of NAI (47.78% ± 4.96%). (2) At 1 day after rehydration, the recovery amplitudes of NAI and sap flow for all tree species were the greatest; at 7 days after rehydration, the NAI and sap flow of the drought treatment group recovered to the levels of the control group (p > 0.05). (3) During different stages of drought rehydration, the response degree of NAI to sap flow varied. The study found that in the drought-rehydration stage, the correlation between the NAI released by each tree species and sap flow was the lowest at the drought endpoint. In conclusion, this research clarifies the changing patterns of plant NAI release and sap flow during drought-rehydration, as well as the response changes of NAI to sap flow. It provides a theoretical basis for selecting drought-tolerant tree species in arid regions. Full article

Slope stability monitoring in open-pit mining remains a critical challenge for geological hazard prevention, where conventional qualitative methods often fail to address dynamic risks. This study proposes an integrated framework combining empirical modeling (slope classification, hazard assessment, and safety ratings) with multi-source real-time monitoring (synthetic aperture radar, machine vision, and Global Navigation Satellite System) to achieve quantitative stability analysis. The method establishes an initial stability baseline through mechanical modeling (Bishop/Morgenstern–Price methods, safety factors: 1.35–1.75 across five mine zones) and dynamically refines it via 3D terrain displacement tracking (0.02 m to 0.16 m average cumulative displacement, 1 h sampling). Key innovations include the following: (1) a convex hull-displacement dual-criterion algorithm for automated sensitive zone identification, reducing computational costs by ~40%; (2) Ku-band synthetic aperture radar subsurface imaging coupled with a Global Navigation Satellite System and vision for centimeter-scale 3D modeling; and (3) a closed-loop feedback mechanism between empirical and real-time data. Field validation at a 140 m high phosphate mine slope demonstrated robust performance under extreme conditions. The framework advances slope risk management by enabling proactive, data-driven decision-making while maintaining compliance with safety standards. Full article

Accurate path-loss characterization in the upper mid-band is critical for 5G/6G outdoor planning, yet classical deterministic expressions lose fidelity at 18 GHz, and purely data-driven regressors offer limited physical insight. We present a physics-guided residual learner that couples a calibrated two-ray model with an XGBoost regressor trained on the deterministic residuals. To enlarge the feature space without promoting overfitting, synthetic samples obtained by perturbing antenna height and ground permittivity within realistic bounds are introduced with a weight of $w=0.3$. The methodology is validated with narrowband measurements collected along two straight 25 m corridors. Under cross-corridor transfer, the hybrid predictor attains $0.59$–$0.62$ dB RMSE and $R^2\ge 0.996$, reducing the error of a pure-ML baseline by half and surpassing deterministic formulas by a factor of four. Small-scale analysis yields decorrelation lengths of 0.23 m and 0.41 m; a cross-correlation peak of unity at $\Delta =0.10$ m confirms the physical coherence of both corridors. We achieve <1 dB error using a small set of field measurements plus simple synthetic data. The method keeps a clear mathematical core and can be extended to other priors, NLOS cases, and semi-open hotspots. Full article