Search Results (338)

In the background of sustainable development in the construction industry, recycled cement mortar (RCM) has emerged as a research hotspot due to its eco-friendly features, where mechanical properties serve as critical indicators for evaluating its engineering applicability. This study proposes an artificial neural network (ANN) model optimized by intelligent algorithms, including the GWO (grey wolf optimizer), PSO (particle swarm optimization), and a GA (genetic algorithm), to predict the compressive strength of recycled mortar. By integrating experimental and prediction data, we establish a comprehensive database with eight input variables, including the water–cement ratio (W/C), cement–sand ratio (C/S), fly ash content (FA), aggregate replacement rate (ARR), and curing age. The predictive performance of neural network models with different database sizes (database 1: experimental data of RCM; database 2: experimental data of RCM and ordinary mortar; database 3: model prediction data of RCM, experimental data of RCM, and ordinary mortar) is analyzed. The results show that the intelligent optimization algorithms significantly enhance the predictive performance of the ANN model. Among them, the PSO-ANN model demonstrates optimal performance, with R² = 0.92, MSE = 0.007, and MAE = 0.0632, followed by the GA-ANN model and the GWO-ANN model. SHAP analysis reveals that the W/C, C/S, and curing age are the key variables influencing the compression strength. Furthermore, the size of the dataset does not significantly influence the computation time for the above models but is primarily governed by the complexity of the optimization algorithms. This study provides an efficient data-driven method for the mix design of RCM and a theoretical support for its engineering applications. Full article

Ground-penetrating radar (GPR) is widely used for thickness or compaction degree detection of asphalt pavement layers, where the dielectric properties of asphalt mixtures serve as a key parameter influencing detection accuracy. These properties are closely related to the composition of the mixture and are susceptible to environmental factors such as water or ice. To clarify the influence of various factors on the dielectric behavior of asphalt mixtures, an experimental study was conducted under controlled environmental conditions. Asphalt mixture specimens with different air void contents (5.49~10.29%) were prepared, and variables such as void fraction, moisture, and ice presence were systematically controlled. Air-coupled GPR was employed to measure the specimens, and the relative permittivity was calculated using both the reflection coefficient method (RCM) and the thickness inversion algorithm (TIA). Discrepancies between the two methods were compared and analyzed. Results indicate that the RCM is significantly influenced by surface water or ice and is only suitable for dielectric characterization under dry pavement conditions. In contrast, the TIA yields more reliable results across varying surface environments. A unified model (the optimized shape factor u = −4.5 and interaction coefficient v = 5.1) was established to describe the relationship between the dielectric properties of asphalt mixtures and their volumetric parameters (bulk specific density, air void content, voids in mineral aggregate, and voids filled with asphalt). This study enables quantitative analysis of the effects of water, ice, and mixture composition on the dielectric properties of asphalt mixtures, providing a scientific basis for non-destructive and accurate GPR-based evaluation of asphalt pavements. Full article

Understanding suspended sediment transport in macrotidal embayments is crucial for assessing water quality, ecosystem function, and long-term morphological stability. This study provides a high-resolution, localized estimate of suspended sediment flux and examines the empirical relationship between turbidity (NTU, nephelometric turbidity unit) and total suspended matter (TSM, mg·L⁻¹) in the main tidal channel of Gomso Bay, a UNESCO-designated tidal flat on the west coast of Korea. A 13 h high-resolution fixed-point observation was conducted during a semi-diurnal tidal cycle using a multi-instrument platform, including an RCM, CTD profiler, tide gauge, and water sampling for gravimetric TSM analysis. Vertical measurements at the surface, mid, and bottom layers, taken every 15–30 min, revealed a strong linear correlation (R² = 0.94) between turbidity and TSM, empirically validating the use of optical sensors for real-time sediment monitoring under the highly dynamic conditions of Korean west-coast tidal channels. The net suspended sediment transport load was estimated at approximately 5503 kg·m⁻¹, with ebb-dominant residual currents indicating a net seaward sediment flux at the observation site. Residual flows over macrotidal channels are known to vary laterally, with landward fluxes often occurring over shoals. Importantly, the results from this single-station, short-duration observation indicate a predominantly seaward suspended sediment transport during the study period, which should be interpreted as a localized and time-specific estimate rather than a bay-wide characteristic. Nevertheless, these findings provide a baseline for assessing sediment flux and contribute to future applications in digital twin modeling and coastal management. Gomso Bay is part of the UNESCO-designated ‘Getbol, Korean Tidal Flats’, underscoring the global significance of preserving and monitoring this dynamic coastal system. Full article

Restrictive cardiomyopathy (RCM) poses a significant challenge in diagnosis, is frequently identified in advanced stages, and has limited therapeutic options, which may lead to adverse cardiovascular outcomes. This narrative review examines the application of artificial intelligence (AI) across key diagnostic modalities and delineates priorities for translational advancement. The discussed diagnostic tools include echocardiography, cardiac magnetic resonance (CMR), electrocardiography (ECG), and electronic health records (EHR). A targeted, non-systematic search of PubMed and Scopus was performed to identify studies focused on model development, validation, or diagnostic accuracy concerning RCM and related infiltrative disorders. The findings suggest that AI can enable earlier detection, standardize imaging protocols, and enhance phenotype-driven management of RCM. Nonetheless, several challenges exist, including limited data access, the absence of external validation, variability across imaging devices and locations, and the imperative for transparent, explainable systems. Key priorities for successful implementation encompass establishing multi-center collaborations, detecting and correcting bias, clinician involvement in deployment, and integrating multimodal data, including imaging, signal data, and -omics. If effectively integrated into clinical practice, AI has the potential to redefine the management of RCM from a condition recognized primarily in its later stages to one characterized by early detection, dynamic risk assessment, and personalized treatment strategies. Full article

We present the spatial regime conversion method (SRCM), a novel hybrid modelling framework for simulating reaction–diffusion systems that adaptively combines stochastic discrete and deterministic continuum representations. Extending the regime conversion method (RCM) to spatial settings, the SRCM employs a discrete reaction–diffusion master equation (RDME) representation in regions of low concentration and continuum partial differential equations (PDEs) where concentrations are high, dynamically switching based on local thresholds. This is an advancement over the existing methods in the literature, requiring no fixed spatial interfaces, enabling efficient and accurate simulation of systems in which stochasticity plays a key role but is not required uniformly across the domain. We specify the full mathematical formulation of the SRCM, including conversion reactions, hybrid kinetic rules, and consistent numerical updates. The method is validated across several one-dimensional test systems, including simple diffusion from a region of high concentration, the formation of a morphogen gradient, and the propagation of FKPP travelling waves. The results show that the SRCM captures key stochastic features while offering substantial gains in computational efficiency over fully stochastic models. Full article

To address the challenge of feature extraction and reconstruction in weak-texture environments, and to provide data support for environmental perception in mobile robots operating in such environments, a Feature Extraction and Reconstruction in Weak-Texture Environments solution is proposed. The solution enhances environmental features through laser-assisted marking and employs a two-step feature extraction strategy in conjunction with binocular vision. First, an improved SURF algorithm for feature point fast localization method (FLM) based on multi-constraints is proposed to quickly locate the initial positions of feature points. Then, the robust correction method (RCM) for feature points based on light strip grayscale consistency is proposed to calibrate and obtain the precise positions of the feature points. Finally, a sparse 3D (three-dimensional) point cloud is generated through feature matching and reconstruction. At a working distance of 1 m, the spatial modeling achieves an accuracy of ±0.5 mm, a relative error of 2‰, and an effective extraction rate exceeding 97%. While ensuring both efficiency and accuracy, the solution demonstrates strong robustness against interference. It effectively supports robots in performing tasks such as precise positioning, object grasping, and posture adjustment in dynamic, weak-texture environments. Full article

Normal concrete exhibits poor resistance to chloride penetration, often leading to reinforcement corrosion and premature structural failure. In contrast, ultra-high-performance concrete (UHPC) demonstrates superior resistance to corrosion caused by chloride salts. The chloride diffusion behaviour of UHPC was investigated via long-term immersion (LTI) and rapid chloride migration (RCM) tests. Additionally, this study presents the first development of a time-dependent diffusion model for UHPC under chloride corrosion, as well as the proposal of a performance-based design method for calculating the protective layer thickness. Results show that the incorporation of steel fibers reduced the chloride diffusion coefficient ($D$) by 37.9%. The free chloride content (FCC) in UHPC increased by 92.0% at 2 mm after 300 d of the action of LTI. $D$ decreased by up to 91.0%, whereas the surface chloride concentration ($C_{\mathrm{s}}$) increased by up to 92.5% under the action of LTI. The time-dependent models of $D$ and $C_{\mathrm{s}}$ followed power and logarithmic functions, respectively. An increase in UHPC surface temperature, relative humidity, and tensile stress ratio significantly diminishes the chloride resistance of UHPC. The minimum UHPC protective layer thicknesses required for UHPC-HPC composite beams with design service lives of 100 years, 150 years, and 200 years are 30 mm, 37 mm, and 43 mm, respectively. Full article

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in integrating uncertainty modelling into both criticality assessment and maintenance planning. Existing approaches often neglect combining expert-driven assessments with optimization models, limiting their applicability in real-world scenarios where cost-effective and risk-informed decision-making is crucial. Maintenance inefficiencies due to suboptimal asset selection result in substantial financial and safety-related consequences in asset-intensive industries. This study presents a framework integrating Reliability-Centered Maintenance (RCM) principles with fuzzy logic and decision-support methodologies to optimise maintenance portfolios for offshore O&G assets, particularly focusing on corrosion management. The framework evaluates asset criticality through comprehensive FMEA, employing MCDM and fuzzy logic to enhance maintenance planning and extend asset lifespan. A case study on offshore asset corrosion management demonstrates the framework’s effectiveness, selecting 60% of highly critical assets for maintenance, compared to 10% by current industry practices. This highlights the potential risk reduction and prevention of critical failures that might otherwise go unnoticed, providing actionable insights for asset integrity managers in the O&G sector. Full article

The Athabasca River Basin (ARB) is the location of the Canadian oil sands industry and 70.8% of global estimated bitumen deposits. The Athabasca River is the water source for highly water-intensive bitumen processing. Our objective is to project ARB temperature, precipitation, total runoff, climate moisture index (CMI), and standardized precipitation evapotranspiration index (SPEI) for 2011–2100 using the superior modelling skill of seven regional climate models (RCMs) from Coordinated Regional Climate Downscaling Experiment (CORDEX). These projections show an average 6 °C annual temperature increase for 2071–2100 under RCP 8.5 relative to 1971–2000. Resulting increases in evapotranspiration may be partially offset by an average 0.3 mm/day annual precipitation increase. The projected precipitation increases are in the winter, spring, and autumn, with declines in summer. CORDEX RCMs project a slight increase (0.04 mm/day) in annual averaged runoff, with a shift to an earlier springtime melt pulse. However, these are countered by projected declines in summer and early autumn runoff. There will be significant decreases in annual and summertime CMI and annual SPEI. We conclude that there will be increasingly stressed ARB water availability, particularly in summer, doubtless resulting in repercussions on ARB industrial activities with their extensive water allocations and withdrawals. Full article

This study presents a comprehensive spatiotemporal assessment of climatic and bioclimatic conditions across Türkiye for both a historical reference period (1995–2014) and future projections (2020–2099) under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP3-7.0) scenarios using the regional climate model (RCM) COSMO-CLM to downscale large-scale signals to a regional scale at high resolution (0.11). A comparison of the model with ERA5-Land reanalysis data revealed annual biases of +1.41 °C (warm) and −0.28 mm/day (dry), emphasizing the importance of bias correction in regional climate assessments. Bias-corrected future projections indicate a marked warming trend and significant decline in precipitation, especially after the 2060s, with pronounced spatial variability across regions. The most intense warming period of the century is the 2060–2079 period, with an anticipated increase of 0.109 °C/year under the SSP3-7.0 scenario, while, under the SSP2-4.5, it is the 2040–2059 period with an increase of 0.068 °C/year. Bioclimatic variables further illustrate shifts in temperature extremes, seasonal variability, and precipitation patterns. Coastal regions are expected to experience a delay in the onset of wet seasons of 1–2 months, while high-altitude zones show earlier shifts of up to 4 months. Four distinct clusters were identified by using k-means clustering method, each with unique temporal and spatial evolution under both SSP scenarios. Clusters 1 and 2, which predominantly represent continental and interior regions, exhibit a strong association with earlier precipitation onset. Notably, arid and semi-arid conditions expand northward, replacing temperate zones in Central Anatolia. Overall, findings suggest that Türkiye is undergoing a substantial climatic transition toward hotter and drier conditions, regardless of the emission scenario. This study has critical implications for ecological resilience, agricultural sustainability, and water resource management, and offers valuable information for targeted climate adaptation strategies and land-use planning in vulnerable regions of Türkiye. Full article

Here, we assess the projected changes in photovoltaic power generation potential (PVpot) in Greece for the 21st century. Our analysis is based on an ensemble of high-resolution Regional Climate Model (RCM) simulations from the EURO-CORDEX initiative following three different Representative Concentration Pathways (RCPs), namely, RCP2.6 (strong mitigation), RCP4.5 (moderate mitigation), and RCP8.5 (no further mitigation). The spatial patterns of the PVpot changes in the near future (2021–2050) and at the end of the century (2071–2100) relative to the 1971–2000 baseline period are presented along with the corresponding statistical robustness. In addition, we analyze time series of the projected PVpot changes. Finally, we isolate the effects of specific climatic variables on the projected PVpot changes and discuss the importance of PV energy production in Greece. Full article

Heat stress severely impacts pig welfare and farm productivity. However, existing methods lack the capability to detect subtle physiological cues (e.g., skin erythema) in complex farm environments while maintaining real-time efficiency. This paper proposes PigStressNet, a novel lightweight detector designed for accurate and efficient heat stress recognition. Our approach integrates four key innovations: (1) a Normalization-based Attention Module (NAM) integrated into the backbone network enhances sensitivity to localized features critical for heat stress, such as posture and skin erythema; (2) a Rectangular Self-Calibration Module (RCM) in the neck network improves spatial feature reconstruction, particularly for occluded pigs; (3) an MBConv-optimized detection head (MBHead) reduces computational cost in the head by 72.3%; (4) the MPDIoU loss function enhances bounding box regression accuracy in scenarios with overlapping pigs. We constructed the first fine-grained dataset specifically annotated for pig heat stress (comprising 710 images across 5 classes: standing, eating, sitting, lying, and stress), uniquely fusing posture (lying) and physiological traits (skin erythema). Experiments demonstrate state-of-the-art performance: PigStressNet achieves 0.979 mAP for heat stress detection while requiring 15.9% lower computation (5.3 GFLOPs) and 11.7% fewer parameters compared to the baseline YOLOv12-n model. The system achieves real-time inference on embedded devices, offering a viable solution for intelligent livestock management. Full article

Cotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. To lower the computational complexity, the SPPF module is substituted with SimSPPF. The C3k2_RCM module is appended for long–range context modeling, and the ARelu activation function is employed to alleviate the vanishing gradient problem. A database comprising 3000 images covering six types of cotton leaf diseases was constructed, and data augmentation techniques were applied. The experimental results show that ACURS-YOLO attains impressive performance indicators, encompassing a mAP_0.5 value of 94.6%, a mAP_0.5:0.95 value of 83.4%, 95.5% accuracy, 89.3% recall, an F1 score of 92.3%, and a frame rate of 148 frames per second. It outperforms YOLOv11 and other conventional models with regard to both detection precision and overall functionality. Ablation tests additionally validate the efficacy of each component, affirming the framework’s advantage in addressing complex detection environments. This framework provides an efficient solution for the automated monitoring of cotton leaf diseases, advancing the development of smart sensors through improved detection accuracy and practical applicability. Full article

This study quantifies the uncertainty in monthly streamflow predictions under future climate scenarios in two periods (near and far future) for the Riu Mannu di Narcao basin in Sardinia, Italy. The sources of uncertainty include the hydrological model structure, model parameters, and variability in climatic inputs derived from global and regional climate models (GCM-RCM coupling) and representative concentration pathways (RCPs). Three conceptual and lumped hydrological models (GR3M, ABCD, and IHACRES) were combined with four climate models and two RCPs (RCP 4.5 and RCP 8.5) to assess future streamflow. Monte Carlo simulations were performed to evaluate parameter uncertainty, and the analysis of variance (ANOVA) method was applied to quantify the different sources of uncertainty. The results reveal that, as a single source, GCM-RCM coupling is the largest contributor, accounting for 47.32% (54.64%) of total near (far) future monthly streamflow projection uncertainties, followed by the hydrological model structure at 16.02% (21.09%), RCP scenarios at 15.35% (8.54%), and parameter uncertainty at 0.79% (1.39%). A consistent decline in median monthly streamflow is projected, especially during winter months (December to February), raising a concern about water availability in the region. Our study quantified different sources of uncertainty in monthly streamflow predictions under climate change, disentangling the roles of the hydrological model, model parameters, climate model, and climate scenario for reliable future streamflow projections. Full article

The main goal of this study is to present future changes in various precipitation indices at a kilometer-scale resolution for Bulgaria on an annual and seasonal basis. Numerical simulations were conducted using the Non-Hydrostatic Regional Climate Model version 4 (RegCM4-NH) following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol for three 10-year periods (1995–2004, 2041–2050, and 2090–2099), with horizontal grid resolutions of 15 km and 3 km, on the petascale supercomputer HPC Discoverer at Sofia Tech Park. Data from the Hadley Centre Global Environment Model version 2 (HadGEM2-ES), based on the Representative Concentration Pathway 8.5 (RCP8.5) scenario, were used as boundary conditions for the regional climate model (RCM) simulations, which were subsequently downscaled to the kilometer-scale (3 km) simulations using a one-way nesting approach. High-resolution model data were compared with high-resolution observational datasets as well as lower-resolution (15 km) data. Future changes in precipitation indices were analyzed on both annual and seasonal scales, including mean daily and hourly precipitation, the frequency and intensity of wet days (>1 mm/day) and wet hours (>0.1 mm/hour), extreme daily precipitation (99th percentile, p99), and extreme hourly precipitation (99.9th percentile, p99.9) for both future periods. Additionally, changes in near-surface (2 m) temperature and surface snow amount were also presented. There is no substantial difference in projected temperature change between the resolutions. A positive trend in annual mean precipitation is expected in the near future. Extreme precipitation (p99 and p99.9) is projected to increase in spring and winter, accompanied by a rise in daily and hourly precipitation intensity across both future periods. An increase in surface snow amount is observed in the central Danubian Plain, Thracian Lowland, and parts of the Rila and Pirin mountains for the near-future period. However, surface snow amount is expected to decrease by the end of the century. Full article