Search Results (7,235)

The 2022 Chongqing wildfires, occurring during an unprecedented heatwave, severely degraded subtropical forest ecosystems and disrupted hydrological cycling. We developed an integrated artificial intelligence framework combining Long Short-Term Memory and Transformer architectures to simulate post-fire evapotranspiration (ET) dynamics using 37 months of field observations (2022–2025) across 24 plots with four burn severities. The Penman–Monteith–Leuning model provided physically based benchmarks. Results revealed three distinct recovery phases: destruction/stagnation (0–7 months, ET at 6%–10% of pre-fire levels), rapid recovery (8–19 months), and stabilization (20–37 months, reaching 100% ET recovery). The coupled LSTM–Transformer ensemble achieved superior performance (RMSE = 0.10 mm·day⁻¹, NSE = 0.98), outperforming single models by 31% in uncertainty reduction. SHAP analysis identified phase-dependent factor shifts: soil water content dominated Stage I (42.5%), while leaf area index (LAI) controlled Stages II–III (>48%). A bimodal LAI time-lag effect emerged: 4–7 days (leaf water potential equilibrium, 27.7% contribution) and 8–14 days (root uptake compensation, 21.7%). Burn severity significantly extended time-lags (severe burns: 12/21 days vs. unburned: 5/12 days), indicating hydraulic system reconstruction requirements. Despite equivalent LAI recovery, severe burns maintained 12%–15% ET reduction, suggesting lasting hydraulic limitations. This study demonstrates that physics-constrained AI models effectively capture complex post-fire ecohydrological dynamics while providing mechanistic interpretability, advancing understanding of vegetation–water coupling reconstruction under increasing fire frequency. Full article

Yucca decipiens is a native species from arid and semi-arid regions with emerging nutritional and biotechnological potential. This study evaluated its proximate composition, elemental profile determined by inductively coupled plasma mass spectrometry (ICP-MS), and growth performance under nursery conditions. Proximate analysis revealed a high dietary fiber content in leaves (58.93%) and higher carbohydrate levels in stems (28.83%). Free amino acid content was significantly higher in stems (2.75 g histidine equivalents kg⁻¹) than in leaves (1.76 g kg⁻¹). Multi-elemental profiling (63 elements) showed organ-specific accumulation patterns, with essential macro- and micronutrients predominantly concentrated in leaves, including potassium (28,334 ppm) and calcium (15,345 ppm), while iron was the most abundant trace element in stems (1253 ppm). Principal component analysis (PCA) revealed clear organ-specific mineral partitioning between leaves and stems, indicating differentiated physiological roles and potential selective biomass utilization. Growth assessment conducted over a two-year period demonstrated steady biomass accumulation and good adaptive performance under nursery conditions. Overall, the results highlight the emerging nutritional and agroindustrial relevance of Yucca decipiens for applications in semi-arid environments. Full article

This study presents an analytical investigation of the dynamic behavior of concrete beams reinforced with different types of nano-clay (NC) particles and resting on a Winkler–Pasternak elastic foundation. The equivalent elastic properties of the nanocomposite were determined using an Eshelby-based homogenization model. An improved quasi-three-dimensional beam theory was applied to formulate the governing equations of motion, which were subsequently then analytically solved using Navier’s method. The analysis shows that NC reinforcement systematically elevates the natural frequencies of the beam, with the magnitude of improvement varying by particle type and concentration. Increasing the NC volume fraction to 30% leads to a significant rise in the fundamental frequency, reaching about 30% for hectorite (SHca-1) compared with the unreinforced beam, whereas montmorillonite (SWy-1) produces a more moderate increase of approximately 13%. This reinforcing effect remains consistent across different span-to-depth ratios, indicating that the influence of nano-clay content on the dynamic response is largely independent of beam slenderness. Furthermore, increasing the Winkler foundation stiffness results in an almost linear rise in frequency of approximately 18–22%, whereas the Pasternak shear parameter produces a stronger effect, reaching around 25% enhancement depending on the reinforcement type. These results indicate that incorporating nano-clay platelets can be an effective strategy for enhancing the vibrational stiffness of concrete beams and improving their dynamic performance when interacting with supporting soil media. Full article

Natural fibre hybrid composites have gained attention as cleaner alternatives to synthetic glass fibre systems due to their renewable feedstocks and inherent density advantage. However, moisture ingress degrades fibre–matrix integrity and mechanical performance, making durability a critical design constraint. This study systematically investigates the water absorption kinetics and post-immersion mechanical property retention in ramie–flax/epoxy hybrid composites across four fibre loadings (10–40 wt.%), with the ramie-to-flax weight ratio fixed at 1:1 in all formulations. Tensile, flexural, and impact properties were evaluated under dry and saturated conditions; Fickian diffusion kinetics were analysed to quantify moisture transport parameters; and fracture surfaces were examined by SEM. A density-based material efficiency analysis quantified the lightweighting benefit relative to equivalent synthetic glass/epoxy composites. Water absorption increased monotonically with fibre content; all formulations reached equilibrium after approximately 120 h. The 30 wt.% composite achieved dry-state tensile, flexural, and impact strengths of ca.49 MPa, ca.58 MPa, and 2.82 kJ/m² respectively, retaining ca.78%, ca.69%, and ca.82% after full saturation, superior to all other loadings. These results establish 30 wt.% as the optimal fibre loading for moisture-exposed semi-structural applications, supporting the adoption of ramie–flax composites within a cleaner manufacturing framework. Full article

The continuous emergence of SARS-CoV-2 variants highlights the need for novel antiviral agents with favorable safety profiles. Marine microalgae constitute a valuable source of bioactive compounds, including antiviral peptides. Building on previous in silico identification of peptides derived from the marine microalga Phaeodactylum tricornutum with predicted activity against SARS-CoV-2, this study evaluated the antiviral capacity of peptide fractions generated by enzymatic hydrolysis and separated by molecular weight (10–30, 5–10, 3–5, and <3 kDa) in human alveolar epithelial A549 cells infected with the SARS-CoV-2. Cytotoxicity analyses, assessed using MTT and resazurin assays, revealed a moderate, concentration-dependent reduction in metabolic activity while maintaining overall cell viability within an acceptable range for antiviral evaluation, with higher-molecular-weight fractions (10–30 and 5–10 kDa) displaying the most stable profiles. Antiviral activity was assessed by flow cytometry following post-infection treatment. Lower-molecular-weight fractions (3–5 and <3 kDa) showed early reductions in infection at low concentrations but exhibited variable responses. In contrast, the 10–30 and 5–10 kDa fractions showed more robust, dose-dependent inhibition at medium and high concentrations, reducing infection levels to levels close to those observed in uninfected controls. Comparative analysis with the reference antiviral drug lopinavir demonstrated that peptide fractions exhibit lower cytotoxicity while retaining antiviral activity under equivalent experimental conditions. Overall, these results indicate that antiviral efficacy is strongly influenced by peptide molecular weight and consistency of response. This work provides experimental in vitro validation of P. tricornutum–derived peptide fractions as marine antiviral candidates and supports the integration of in silico and functional approaches for marine drug discovery. Full article

Vessel vibrations have serious safety risks and must be effectively mitigated. This study investigates the reduction in ship pitch–roll vibrations modeled as a two degrees of freedom of nonlinear spring–pendulum system subjected to multi-parametric excitation, using proportional–derivative controller. The main objective is to develop a rapid and efficient analytical approach to nonlinear vibration analysis. A non-perturbative approach is employed to transform weakly nonlinear oscillators of ordinary differential equations into equivalent linear ones without using Taylor expansions. He’s frequency formula plays a central role in this transformation. The resulting parametric solutions are validated using Mathematica Software (v13) and show a strong agreement with the original nonlinear model. The effects of various parameters on stability are examined. Theoretical analysis is conducted using the multiple time scales method to identify worst resonance conditions and derive frequency response equations. Stability near simultaneous sub-harmonic resonance is assessed using Routh–Hurwitz criterion. Numerical simulations based on the fourth-order Runge–Kutta method confirm the effectiveness of proportional–derivative control. Excellent agreement between analytical and numerical results demonstrates the accuracy, efficiency, and practical applicability of the proposed method. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article

As urbanization intensifies, improving street thermal comfort has become a critical issue in urban renewal. While existing studies generally assume that increasing the Green View Index (GVI) linearly improves pedestrian thermal comfort, this study identifies a significant “Decoupling Effect” in high-density commercial areas through field measurements and numerical simulations of three typical street types (commercial–service, ecological–recreational, and historical–cultural) in Shenyang. Integrating DeepLab V3 semantic segmentation with ENVI-met version 5.1.1 microclimate simulation, the results demonstrate a robust monotonic negative correlation between GVI and Physiological Equivalent Temperature (PET) in ecological streets (Spearman’s ρ = −0.692, p < 0.001), confirming the consistent cooling benefit of greenery in nature-dominated environments. However, a distinct “Threshold Effect” was identified in commercial streets using Piecewise Linear Regression (PLR). A critical breakpoint was detected at GVI = 22.08%. Below this threshold, visual greenery effectively contributes to cooling (slope = −0.454); yet, once GVI exceeds 22.08%, the cooling efficacy diminishes significantly (slope = −0.109), marking the onset of a “decoupling” phase. Specifically, despite Wenhua Road achieving a GVI of ~24.5% with a complex “three-board, four-belt” structure, its PET peak reaches 46.15 °C, approximately 5.5 °C higher than ecological streets. Mechanism analysis reveals that under peak thermal stress (Traffic Heat ≈ 75 W/m²), the high-intensity anthropogenic heat and hardscape radiation exceed the evaporative cooling threshold of vegetation. This study reveals the non-linear relationship between visual greenery and the physical thermal environment, suggesting that simply pursuing visual green quantity is ineffective in commercial canyon renewal; instead, a threshold-based synergistic optimization of canopy shading and pavement thermal performance is required. These findings provide a quantitative basis for sustainable street landscape planning and urban climate adaptation strategies in high-density cities. Full article

Heavy and overloaded freight traffic strongly affects pavement performance, yet short-term weigh-in-motion (WIM) measurements are not easily converted into design-oriented traffic inputs. Using the Nairobi Southern Bypass in Kenya as a case study, this study develops axle load spectrum (ALS) and equivalent single axle load (ESAL) indicators from more than 1.5 million axle group records collected between June and December 2025 and proposes an XGBoost-based conditional axle load spectrum (CA-ALS) framework. The data revealed strongly right-skewed load distributions, with a limited number of heavily loaded axle groups dominating pavement damage. Compared with the static ALS by axle group type baseline, the CA-ALS reduced log loss from 2.7563 to 2.6709 in conditional spectrum prediction. In the December 2025 tandem axle benchmark, the CA-ALS increased the ESAL-based verification input by 6.0% at b = 4 and 11.1% at b = 5 relative to the stronger static reference. A legal-load-capped counterfactual analysis further showed that, for all heavy vehicles, observed overloading increased ESAL by 161.0% at b = 4 and 239.4% at b = 5. These results indicate that the CA-ALS provides condition-sensitive traffic inputs for design traffic verification, scenario-based pavement checks, and overload-sensitive evaluation based on short-term WIM observations. Full article

This study systematically investigated the bearing capacity and failure mechanisms of ultra-high-performance concrete (UHPC) pipe jacking structures using three-edge bearing tests and numerical simulations. Full-scale double-layer reinforced pipes had an inner diameter of 2.5 m and wall thicknesses of 180 mm (P1) and 200 mm (P2). The tests showed that the failure process can be divided into four stages: elastic deformation, crack propagation, reinforcement yielding, and ultimate failure. Increasing the wall thickness significantly improved performance: P2 had a cracking load 52.73% higher and an ultimate bearing capacity 5.7% higher than P1, with better deformation resistance and crack control. A theoretical model considering the plastic hinge mechanism at the pipe crown was developed, treating the three-edge load as an equivalent distributed plate load. The calculated results agreed well with experimental measurements. An ABAQUS finite element model successfully reproduced the full mechanical response from initial loading to failure. Parametric analysis indicated optimal performance at a hoop reinforcement ratio of approximately 1.4%. Even at 0.6%, the ultimate bearing capacity reached 367 kN/m, meeting current design code requirements. This study is novel in conducting full-scale UHPC pipe jacking tests, proposing a theoretical model accounting for crown plastic hinges, and establishing a finite element method that reproduces the entire failure process. Optimizing wall thickness and hoop reinforcement can enhance structural safety and durability, providing guidance for the design and engineering of pipe jacking structures. Full article

Three-dimensional spatial effects in deep excavations critically govern the mechanical response of retaining structures and adjacent soils, yet their quantitative characterization remains a challenge. This study systematically investigates the spatial behavior of row-pile-supported foundation pits through an integrated approach combining model tests, theoretical analysis, and numerical simulations. A novel formulation for the spatial effect influence coefficient K is derived from limit equilibrium principles and subsequently validated via ABAQUS-based finite element simulations. Model test results reveal pronounced spatial heterogeneity in earth pressure and bending moment distributions along the pit perimeter: lateral earth pressure at corner regions exceeds that at mid-side locations at equivalent depths, whereas bending moments in mid-side piles are substantially larger than those at corners. Displacement field measurements further demonstrate that corner zones, constrained bidirectionally, undergo minimal deformation, while maximum displacement occurs at the midpoints of the long sides. These observations collectively confirm the existence of a marked corner effect and a subdued side-midpoint effect under three-dimensional confinement. Complementary numerical analyses indicate that the coefficient K decreases monotonically with increasing half-angle corners and distance from the corner, thereby quantitatively capturing the decay of spatial constraint intensity. Together, these findings establish a theoretical framework for assessing excavation-induced spatial effects and provide actionable guidance for the rational design of deep foundation pit support systems. Full article

Many countries rely on coal for energy security during renewable transitions. This study conducts a technical, economic, and environmental analysis of hybridizing a supercritical coal-fired power unit with photovoltaics (PV) to create a sustainable hybrid system at a plant in Silesian Voivodeship, Poland. The goal is to assess costs and optimal operating conditions for a coal–PV hybrid under varying scenarios, using a decision-support model that integrates fuel prices, CO₂ emission charges (EUA), and technical parameters. Two main scenarios are modeled. In auxiliary-only PV (112 MW system), real-time power supplies pumps and fans, cutting coal consumption without storage; LCOE decreases with annual hours (2800–7000), outperforming conventional coal across EUA prices (20–50 EUR/t). In PV surplus export, excess generation (1300 h/year) is grid-fed for revenue, amplifying LCOE reductions—hybrid superiority emerges above 34 EUR/t EUA, per equivalence thresholds. Results show coal electricity exceeds low-emission costs above 34 EUR/t CO₂, with maximum disparity at 50 EUR/Mg. The hybrid leverages existing infrastructure, mitigates solar intermittency via auxiliary supply, ensures baseload continuity, boosts flexibility, and prolongs asset life—reducing >123,000 EUA/year at 145,000 MWh PV output. This sustainable hybrid promotes energy transition, reduces fossil fuel dependence, and aligns with global sustainability goals. Full article

Energy Management Strategies (EMSs) are crucial for enhancing fuel economy and reducing emissions in light commercial vehicles (LCVs). This paper presented three EMS approaches for LCVs with hybrid powertrains: Rule-Based Control (RBC) and two optimization-based strategies, the Equivalent Consumption Minimization Strategy (ECMS) and Model Predictive Control (MPC). To enhance robustness under varying operating conditions, optimization algorithms were designed and tuned using the WLTC City driving cycle, and adaptive components were included. For a fair assessment of overall efficiency, all strategies were compared under identical constraints on hydrogen and electrical energy consumption. The results showed that, under these constraints, MPC achieved the longest driving distance, highlighting its superior energy utilization capability. In a broader comparative analysis, both the ECMS and MPC outperformed the benchmark RBC, with MPC demonstrating the most consistent performance, enhanced stability, and strong adaptability in dynamic scenarios. The findings indicate that MPC offers notable advantages for LCV energy management, combining efficiency, robustness, and interpretability, positioning it as a promising candidate for practical implementation in future hybrid powertrain systems. Full article

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

This research demonstrates the principle and optimization methodology to create economic and miniaturized high-resolution micro-moisture sensors. The interdigitated fringe electric field-based moisture measurement principle is firstly investigated to sketch the key parameters of printed circuit board (PCB)-based sensors for further performance optimization. Then, a comprehensive study is conducted to analyze parameter variations with conclusions of suggested design rules to achieve higher measurement sensitivity. Two prototypes are designed and manufactured to validate the proposed theoretical contributions. Water droplets are employed to control the ambient relative humidity, which is adopted as the actual moisture variable in this work. A double-correlated sampling circuit is used for capacitance sensing. Both of them demonstrate a linearity of 1% and sensitivity of 0.1 pF/mg levels, but prototype 2 gains a better batch consistency, which is beneficial for commercialization. Further data analysis suggests that the equivalent input–output sensitivity reaches a level of 1.2403 pF/%RH (relative humidity), which is significantly higher than other types of published interdigitated fringe electric field-type moisture sensors. The optimized prototypes also show advantages of miniaturized size, low cost and high consistency, which can potentially impact the industry applications. Full article