Search Results (1,750)

To enhance the utilization efficiency of coal gangue aggregate, coarse aggregates are chemically modified with 5% sodium silicate solution. The effects of this modification on the compressive strength and microstructural characteristics of concrete are systematically investigated through integrated macro-testing and micro-characterization. By evaluating the compressive performance of modified coal gangue concrete blocks, the optimal mix ratio of each strength grade of blocks is determined. Experimental results indicate that the apparent density, water absorption, and crushing index of the modified coal gangue coarse aggregate exhibit better mechanical properties than the control group. The modified coal gangue coarse aggregate demonstrates improved mechanical performance, with the compressive strength of 28-day concrete showing a 15.3% increase relative to the control group. Furthermore, using a sodium silicate solution effectively enhances the interface transition zone’s performance between coal gangue coarse aggregate and cement mortar, improving the compactness of this interface. The modified coal gangue concrete blocks exhibit higher compressive strength than the original material. When the substitution rate remains constant, the compressive strength of modified coal gangue concrete decreases with increasing water–cement ratio. Similarly, at a constant water–binder ratio, compressive strength decreases with higher modified gangue aggregate replacement. Finally, compressive tests are conducted on masonry constructed with hollow blocks of strength grades MU7.5, MU10, and MU15. Then, a calculation model for the average compressive strength of modified coal gangue concrete hollow block masonry is proposed, providing theoretical support for its engineering application. Full article

Meso-scale combustors face persistent challenges in sustaining stable combustion and efficient heat transfer due to high surface-to-volume ratios and attendant heat losses. In contrast, larger outlet diameters exhibit weaker recirculation and more diffused temperature zones, resulting in reduced combustion efficiency and thermal confinement. The behavior of non-premixed flames in meso-scale combustor has been investigated through a comprehensive numerical study, utilizing computational fluid dynamics (CFD) under stoichiometric natural gas (methane)–air conditions; three outlet configurations (6 mm, 8 mm, and 10 mm) were analysed to evaluate their impact on temperature behaviour, vortex flow, swirl intensity, and central recirculation zone (CRZ) formation. Among the tested geometries, the 6 mm outlet produced the most robust central recirculation, intensifying reactant entrainment and mixing and yielding a sharply localised high-temperature core approaching 1880 K. The study highlights the critical role of geometric parameters in governing heat release distribution, with the 6 mm configuration achieving the highest exhaust temperature (920 K) and peak wall temperature (1020 K), making it particularly suitable for thermoelectric generator (TEG) integration. These findings underscore the interplay between combustor geometry, flow dynamics, and heat transfer mechanisms in meso-scale systems, providing valuable insights for optimizing portable power generation devices. Full article

Deep reinforcement learning (DRL)-based HVAC control has shown clear advantages over rule-based and model predictive methods. However, most prior studies remain limited to HVAC-only optimization or simple coordination with operable windows. Such approaches do not adequately address buildings with fixed glazing systems—a common feature in high-rise offices—where the lack of operable windows restricts adaptive envelope interaction. To address this gap, this study proposes a multi-zone control framework that integrates HVAC systems with electrochromic windows (ECWs). The framework leverages the Q-value Mixing (QMIX) algorithm to dynamically coordinate ECW transmittance with HVAC setpoints, aiming to enhance energy efficiency and thermal comfort, particularly in high-consumption buildings such as offices. Its performance is evaluated using EnergyPlus simulations. The results show that the proposed approach reduces HVAC energy use by 19.8% compared to the DQN-based HVAC-only control and by 40.28% relative to conventional rule-based control (RBC). In comparison with leading multi-agent deep reinforcement learning (MADRL) algorithms, including MADQN, VDN, and MAPPO, the framework reduces HVAC energy consumption by 1–5% and maintains a thermal comfort violation rate (TCVR) of less than 1% with an average temperature variation of 0.35 ${}^{\circ }\mathrm{C}$ Moreover, the model demonstrates strong generalizability, achieving 16.58–58.12% energy savings across six distinct climatic regions—ranging from tropical (Singapore) to temperate (Beijing)—with up to 48.2% savings observed in Chengdu. Our framework indicates the potential of coordinating HVAC systems with ECWs in simulation, while also identifying limitations that need to be addressed for real-world deployment. Full article

The changing structure of modern cities intensifies anthropopressure, resulting in the need to create plans for the protection of biodiversity in cities. This can be achieved by establishing lawns and flower strips along the streets and maintaining parks and squares in cities, creating green infrastructure and contributing to sustainable urban development. However, this vegetation also requires protection that is safe for the environment and city residents. Entomopathogenic fungi (EPF) are among the most well-known and effective microorganisms that infect plant pests and conduct the disease process leading to their death. The aim of the study was to conduct a comparative analysis of the generic composition of EPF and determine the density of their colony-forming units (CFUs) in soils from flower strips and lawns located along the main communication routes of the city of Siedlce (Poland). Soil samples collected from two sites and two habitats (a flower strip and a lawn directly adjacent to it)—Site No. 1, Wyszyńskiego Street; Site No. 2, Jagiełły Street—in the spring and autumn of 2021/2022 and 2024. At each site within the habitat, three zones (repeats) were designated, spaced approximately 10–15 m apart. Approximately six samples were collected from each replication, and then a mixed sample was prepared. Four genera of EPF were found in the soil samples: Beauveria, Metarhizium, Cordyceps, and Akanthomyces. The location, habitat type, and season had a significant effect on the diversity of individual genera of fungi and the density of colony-forming units (CFUs) in the studied soils. The dominant types of EPF, forming the most CFUs in the soils from the studied flower strips and the adjacent lawns, were Metarhizium spp. and Beauveria spp. It was found that EPF occurred in higher densities in the soil from the studied habitats (flower strips and lawns) in autumn than in spring. Both of these semi-natural habitats constitute forms of urban greenery that increase biodiversity and provide valuable ecosystem services that support sustainable urban development. Full article

This study investigates the fracture behavior of recycled aggregate concrete by integrating fractal theory and empirical modeling to quantify how recycled coarse aggregates (RCAs) and recycled fine aggregates (RFAs) influence crack complexity and maximum crack width under varying content and loads. The results reveal distinct scale-dependent behaviors between RCA and RFA. For RCA, moderate dosages enhance fractal complexity (a measure of surface roughness) by promoting micro-crack proliferation, while excessive RCA reduces complexity due to matrix homogenization. In contrast, RFA significantly increases both fractal complexity and crack width under equivalent loads, reflecting its susceptibility to micro-scale interfacial transition zone (ITZ) degradation. Non-linear thresholds are identified: RCA’s fractal complexity plateaus at high loads as cracks coalesce into fewer dominant paths, while RFA’s crack width growth decelerates at extreme dosages due to balancing effects like particle packing. Empirical models link aggregate dosage and load to fractal dimension and crack width with high predictive accuracy (R² > 0.85), capturing interaction effects such as RCA’s load-induced complexity reduction and RFA’s load-driven crack width amplification. Secondary analyses further demonstrate that fractal dimension correlates with crack width through non-linear relationships, emphasizing the coupled nature of micro- and macro-scale damage. These findings challenge conventional design assumptions by differentiating the impacts of RCA (macro-crack coalescence) and RFA (micro-crack proliferation), providing actionable thresholds for optimizing mix designs. The study also advances sustainable material design by offering a scientific basis for updating standards to accommodate higher recycled aggregate percentages, supporting circular economy goals through reduced carbon emissions and waste diversion, and laying the groundwork for resilient, low-carbon infrastructure. Full article

To address the coarse and mixed grain phenomena in ultra-large martensitic stainless steel forgings, this study investigated the hot deformation behavior of 04Cr13Ni5Mo martensitic stainless steel under deformation conditions of 950–1200 °C and strain rates of 0.001–0.1 s⁻¹ using Gleeble-1500D thermomechanical simulation tests. Based on the experimental data, the flow stress curves of the steel were obtained, and a dynamic recrystallization (DRX) kinetic model was established. The model was then integrated into finite element software for simulation to verify its reliability, providing theoretical guidance for optimizing high-temperature forging processes. The results demonstrate that dynamic recrystallization in 04Cr13Ni5Mo steel occurs more readily at temperatures above 1050 °C and strain rates below 0.1 s⁻¹. Under the selected hot compression test condition (1100 °C/0.01 s⁻¹), the simulated grain size in the central deformation zone was 48.98 μm, closely matching the experimentally measured value of 48.18 μm. This agreement confirms the reliability of finite element-based prediction and control of grain size in martensitic stainless steel forgings. Full article

This study presents the diagnostics and microclimate analysis of four case studies located in the Alps region in Valtellina and Valposchiavo. The primary focus is on evaluating and comparing microclimatic conditions, encompassing temperature (T°C), relative humidity (RH%), mixing ratio (MR), and dew point depression (DPD). The choice of the variables and statistic metrics depends substantially on the aim to identify the risk factor for the preservation of the historical materials of historical buildings, and the procedures for identifying the anomalies in the trends useful to study how to prevent these anomalies in the future. The paper has the target to support the activities of restorers and building managers for improving the restoration process. While various moisture detection methodologies have been studied, no single approach is preferred for analyzing moisture via microclimate monitoring in built heritage. Therefore, this research delves into the influence of various factors, including altitude, location, building type, structure, materials, orientation, and use, on the microclimatic parameters. Altitude and building use significantly influence indoor microclimates: unoccupied structures exhibit greater stability, whereas seasonal use increases condensation risks. Key risks included high RH% and critical T-RH zones (T > 25 °C + RH > 65%), exacerbating material stress. Probability density function (PDF) analysis reveals temperature and RH% distributions, highlighting bimodal T°C patterns and prolonged RH% in high-elevation exposed sites. The findings underscore the need for tailored conservation strategies and targeted interventions to mitigate microclimate-induced deterioration in Alpine heritage. Full article

Understanding mixing behavior at river confluences is essential for effective watershed management in response to increasing environmental issues such as algal blooms and chemical pollution. This study focused on the confluence of the Nakdong and Geumho Rivers, employing high-resolution field measurements using an ADCP (M9) and YSI EXO sensors. Water temperature (°C) and electrical conductivity (μS/cm) data were collected under three representative conditions, including flow ratios of 0.91, 0.45, and 0.29, as well as 0.05, with a maximum temperature difference of up to 6 °C. Mixing behavior was three-dimensionally analyzed by integrating cross-sectional and longitudinal data, and the accuracy of visualization was evaluated using IDW and Kriging spatial interpolation techniques. The analysis revealed that under low flow ratio conditions, vertical mixing was delayed; the thermal stratification persisted up to approximately 3 km downstream from the confluence (Line 3), and complete mixing was not achieved until about 7 km downstream (Line 5) due to density currents. Quantitative comparison indicated that IDW (R² = 0.901, RMSE = 31.522) outperformed Kriging (R² = 0.79, RMSE = 35.458). This study provides a quantitative criterion for identifying the mixing completion zone, thereby addressing the limitations of previous studies that relied on numerical models or limited field data, and offering practical evidence for water quality monitoring and sustainable river management. Full article

The deep-sea convergence zone (CZ) is a critical phenomenon for long-range underwater acoustic propagation. Accurate prediction of its distance, width, and gain is essential for enhancing sonar detection performance. However, conventional ray-tracing models, which assume vertically stratified sound speed profiles (SSPs), fail to account for horizontal sound speed gradients in the mixed layer, leading to significant prediction errors. To address this, we propose a novel ray-tracing model that incorporates horizontally inhomogeneous SSPs in the mixed layer. Our approach combines empirical orthogonal function (EOF) decomposition with the Del Grosso sound speed formula to construct a continuous 3D sound speed field. We further derive a modified ray equation including horizontal gradient terms and solve it using a fourth-order Runge–Kutta method. Simulation and experimental validation in the South China Sea demonstrate that our model reduces the prediction error for the first CZ distance by 2.26%, width by 2.66%, and gain deviation by 5.85% compared to the Bellhop model. These results confirm the effectiveness of our method in improving CZ parameter prediction accuracy. Full article

The European energy transition, marked by the increasing share of renewable sources in the production mix, brings to the fore the issue of maintaining power quality under conditions of high variability. This study proposes an adaptive monitoring model based on a zonal classification of electrical networks according to the volatility of net renewable production (wind and photovoltaic). The approach relies on a proprietary Renewable Variability Index (RVI), developed using publicly available European datasets, to assess the mismatch between electricity consumption and renewable generation in six representative countries: Germany, Denmark, Spain, Poland, Romania, and Sweden. Based on this index, the model defines three zonal risk levels and recommends differentiated power quality monitoring strategies: continuous high-resolution observation in critical areas, adaptive monitoring in medium-risk zones, and conditional event-based activation in stable regions. The results demonstrate a significant reduction in data acquisition requirements, without compromising the capacity to detect disruptive events. By incorporating adaptability, risk sensitivity, and selective allocation of monitoring resources, the proposed framework enhances operational efficiency in smart grid environments. It aligns with current trends in smart grid digitalization, enabling scalable, context-aware control and protection mechanisms that support Europe’s sustainability and energy security objectives while contributing to the broader goals of sustainable energy transition and long-term grid resilience. Full article

Although numerous observational and theoretical studies have examined the mean and turbulent structure of the tropical cyclone boundary layer (TCBL) over the open ocean, there have been comparatively fewer studies that have examined the kinematic and thermal structure of the TCBL across the land–ocean interface. This study examines the impact of different continental environments on the thermodynamic evolution of the TCBL during the landfall transition using high-resolution, full-physics numerical simulations. During landfall, the changes in the wind field within the TCBL due to the development of the internal boundary layer (IBL), combined with the formation of a surface cold pool, generates a pronounced thermal asymmetry in the boundary layer. As a result, the maximum thermodynamic boundary layer height occurs in the rear-right quadrant of the storm relative to its motion. In addition, azimuthal and vertical advection by the mean flow lead to enhanced turbulent kinetic energy (TKE) in front of the vortex (enhancing dissipative heating immediately onshore) and onshore precipitation to the left of the storm track (stabilizing the environment). The strength and depth of thermal asymmetry in the boundary layer depend on the contrast in temperature and moisture between the continental and storm environments. Dry air intrusion enhances cold pool formation and stabilizes the onshore boundary layer, reducing mechanical mixing and accelerating the decay of the vortex. The temperature contrast between the continental and storm environments establishes a coastal baroclinic zone, producing stronger baroclinicity and inflow on the left of the track and weaker baroclinicity on the right. The resulting gradient imbalance in the front-right quadrant triggers radial outflow through a gradient adjustment process that redistributes momentum and mass to restore dynamical balance. Therefore, the surface thermodynamic conditions over land play a critical role in shaping the evolution of the TCBL during landfall, with the strongest asymmetries in thermodynamic boundary layer height emerging when there are large thermal contrasts between the hurricane and the continental environment. Full article

Background/Objectives: Epilepsy is a neurological disorder that severely impacts patients’ quality of life. In clinical practice, specific pharmacological and surgical interventions are tailored to distinct seizure types. The identification of the epileptogenic zone enables the implementation of surgical procedures and neuromodulation therapies. Consequently, accurate classification of seizure types and precise determination of focal epileptic signals are critical to provide clinicians with essential diagnostic insights for optimizing therapeutic strategies. Traditional machine learning approaches are constrained in their efficacy due to limited capability in autonomously extracting features. Methods: This study proposes a novel deep learning framework integrating temporal and spatial information extraction to address this limitation. Multivariate variational mode decomposition (MVMD) is employed to maintain inter-channel mode alignment during the decomposition of multi-channel epileptic signals, ensuring the synchronization of time–frequency characteristics across channels and effectively mitigating mode mixing and mode mismatch issues. Results: The Bern–Barcelona database is employed to classify focal epileptic signals, with the proposed framework achieving an accuracy of 98.85%, a sensitivity of 98.75%, and a specificity of 98.95%. For multi-class seizure type classification, the TUSZ database is utilized. Subject-dependent experiments yield an accuracy of 96.17% with a weighted F1-score of 0.962. Meanwhile, subject-independent experiments attain an accuracy of 87.97% and a weighted F1-score of 0.884. Conclusions: The proposed framework effectively integrates temporal and spatial domain information derived from multi-channel epileptic signals, thereby significantly enhancing the algorithm’s classification performance. The performance on unseen patients demonstrates robust generalization capability, indicating the potential clinical applicability in assisting neurologists with epileptic signal classification. Full article

Territorial transformations in Indigenous regions are shaped by intersecting ecological, political, and cultural dynamics. In San Juan Bautista Valle Nacional, Oaxaca, the construction of the Cerro de Oro dam disrupted river flows, displaced livelihoods, and triggered the decline of irrigated agriculture. This study examines the long-term impacts of these changes on land use, demographics, and cultural practices, emphasizing women’s contributions to community resilience. Using a mixed-methods approach, the study integrates geospatial analysis (1992–2021), census data (2000–2020), documentary review, and ethnographic fieldwork, including participatory mapping. Results show a shift toward seasonal rainfed agriculture, fluctuating forest cover, and a rise in female-headed households. Women have emerged as central actors in adapting to change through practices such as seed saving, agroforestry, and backstrap-loom weaving. These spatially grounded practices, enacted across varied socio-ecological zones, sustain food systems, preserve biodiversity, and reinforce biocultural memory. Although often overlooked in formal governance, women’s territorial agency plays a vital role in shaping land use and community adaptation. This research highlights the need to recognize Indigenous women’s roles in managing change and sustaining territorial heritage. Acknowledging these contributions is essential for building inclusive, culturally grounded, and sustainable development pathways in regions facing structural and environmental pressures. Full article

This study presents a novel approach to the parametrization of 3D PETRO FACIES and SEISMO FACIES using supervised and unsupervised learning, supported by a coherent structural and stratigraphic framework, to enhance understanding of the presence of hydrocarbons in the Dzików–Uszkowce region. The prediction relies on selected seismic attributes and well logging data, which are essential in hydrocarbon exploration. Three-dimensional seismic data, a crucial source of information, reflect the propagation velocity of elastic waves influenced by lithological formations and reservoir fluids. However, seismic response similarities complicate accurate seismic image interpretation. Three-dimensional seismic data were also used to build a structural–stratigraphic model that partitions the study area into coeval strata, enabling spatial analysis of the machine learning results. In the 3D seismic model, PETRO FACIES classification achieved an overall accuracy of 80% (SD = 0.01), effectively distinguishing sandstone- and mudstone-dominated facies (RT1–RT4) with F1 scores between 0.65 and 0.85. RESERVOIR FACIES prediction, covering seven hydrocarbon system classes, reached an accuracy of 70% (SD = 0.01). However, class-level performance varied substantially. Non-productive zones such as HNF (No Flow) were identified with high precision (0.82) and recall (0.84, F1 = 0.83), while mixed-saturation facies (HWGS, BSWGS) showed moderate performance (F1 = 0.74–0.81). In contrast, gas-saturated classes (BSGS and HGS) suffered from extremely low F1 scores (0.08 and 0.12, respectively), with recalls as low as 5–7%, highlighting the model’s difficulty in discriminating these units from water-saturated or mixed facies due to overlapping seismic responses and limited training data for gas-rich intervals. To enhance reservoir characterization, SEISMO FACIES analysis identified 12 distinct seismic facies using key attributes. An additional facies (facies 13) was defined to characterize gas-saturated sandstones with high reservoir quality and accumulation potential. Refinements were performed using borehole data on hydrocarbon-bearing zones and clay volume (VCL), applying a 0.3 VCL cutoff and filtering specific facies to isolate zones with confirmed gas presence. The same approach was applied to PETRO FACIES and a new RT facie was extracted. This integrated approach improved mapping of lithological variability and hydrocarbon saturation in complex geological settings. The results were validated against two blind wells that were excluded from the machine learning process. Knowledge of the presence of gas in well N-1 and its absence in well D-24 guided verification of the models within the structural–stratigraphic framework. Full article

This scoping review paper provides an overview of the evolution, the current stage, and the future prospects of fracture studies on composite laminates. A fundamental understanding of composite materials is presented by highlighting the roles of the fiber and matrix, outlining the applications of various synthetic fibers used in current structural sectors. Challenges posed by interlaminar delamination, one of the critical failure modes, are highlighted. This paper systematically discusses the fracture behavior of these laminates under mixed-mode and complex loading conditions. Standardized fracture toughness testing methods, including Mode I Double Cantilever Beam (DCB), Mode II End-Notched Flexure (ENF) and Mixed-Mode Bending (MMB), are initially discussed, which is followed by a decade-wide chronological analysis of fracture mechanics approaches. Key advancements, including toughening mechanisms, Cohesive Zone Modeling (CZM), Virtual Crack Closure Technique (VCCT), Extended Finite Element Method (XFEM) and Digital Image Correlation (DIC), are analyzed. The review also addresses recent trends in fracture studies, such as bio-inspired architecture, self-healing systems, and artificial intelligence in fracture predictions. By mapping the trajectory of past innovations and identifying unresolved challenges, such as scale integration, dataset standardization for AI, and manufacturability of advanced architectures, this review proposes a strategic research roadmap. The major goal is to enable unified multi-scale modeling frameworks that merge physical insights with data learning, paving the way for next-generation composite laminates optimized for resilience, adaptability, and environmental responsibility. Full article