Search Results (19,927)

The polyglycerol esters (PGEs) of fatty acids have a wide range of HLB values and applications in diverse industries, such as pharmaceuticals and cosmetics. While the physicochemical properties of oil-soluble PGEs dissolved in oil phases are well studied in the literature, there is no information on their structuring in aqueous phases and emulsifying abilities. We combined rheological and differential scanning calorimetry (DSC) measurements and microscopy observations to characterize the dependence of oil-soluble PGE structuring in aqueous phases on the PGE concentration, the temperature of solution homogenization, and the PGE molecular structure. Excellent correlations between the considerable changes in solution viscosity and the temperatures of the two endo- and exothermic peaks in the DSC thermograms are observed. Single-tail PGE molecules, which have a higher number of polyglycerol units, are better organized in networks, and the viscosity of their aqueous solutions is higher compared to that of the respective double-tail PGE molecules. PGEs exhibit good emulsifying ability and the viscosity of the produced emulsions at room temperature can differ by orders of magnitudes depending on the temperature of emulsification. The reported properties of oil-soluble PGEs could be of interest for increasing the range of their applicability in practice. Full article

To support the 6G vision of seamless “space–air–ground-integrated” global coverage, optical satellite networks must enable high-speed, low-latency, and intelligent data transmission. However, conventional inter-satellite laser link-based optical transport networks suffer from inefficient bandwidth utilization and nonlinear latency accumulation caused by multi-hop routing, which severely limits their ability to support ultra-low-latency and real-time applications. To address the critical challenges of high topological complexity and stringent real-time requirements in satellite elastic optical networks, we propose an asynchronous advantage actor–critic-based quality of service-aware routing optimization mechanism for the optical inter-satellite link (OISL-AQROM). By establishing a quantitative model that correlates the optical service unit (OSU) C value with node hop count, the algorithm enhances the performance of latency-sensitive services in dynamic satellite environments. Simulation results conducted on a Walker-type low Earth orbit (LEO) constellation comprising 1152 satellites demonstrate that OISL-AQROM reduces end-to-end latency by 76.3% to 37.6% compared to the traditional heuristic multi-constrained shortest path first (MCSPF) algorithm, while supporting fine-grained dynamic bandwidth adjustment down to a minimum granularity of 2.6 Mbps. Furthermore, OISL-AQROM exhibits strong convergence and robust stability across diverse traffic loads, consistently outperforming MCSPF and deep deterministic policy gradient (DDPG) algorithm in overall efficiency, load adaptability, and operational reliability. The proposed algorithm significantly improves service quality and transmission efficiency in commercial mega-constellation optical satellite networks, demonstrating engineering applicability and potential for practical deployment in future 6G infrastructure. Full article

This study proposes an enhanced deep learning framework for accurate detection of P-peaks in noisy photoplethysmographic (PPG) signals, utilizing a hybrid architecture that integrates wavelet-based analysis with neural network components. The P-peak detection task is formulated as a binary classification problem, where the model learns to identify the presence of a peak at each time step within fixed-length input windows. A temporal attention mechanism is incorporated to dynamically focus on the most informative regions of the signal, improving both localization and robustness. The proposed architecture combines Discrete Wavelet Transform (DWT) for multiscale signal decomposition, Convolutional Neural Networks (CNNs) for morphological feature extraction, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. A temporal attention layer is introduced after the recurrent layers to enhance focus on time steps with the highest predictive value. An evaluation was conducted on 30 model variants, exploring different combinations of input types, decomposition levels, and activation functions. The best-performing model—Type30, which includes DWT (3 levels), CNN, LSTM, and attention—achieves an accuracy of 0.918, precision of 0.932, recall of 0.957, and F1-score of 0.923. The findings demonstrate that attention-enhanced hybrid architectures are particularly effective in handling signal variability and noise, making them highly suitable for real-world applications in wearable PPG monitoring, digital twins for Heart Rate Variability (HRV), and intelligent health systems. Full article

In this paper, an artificial neural network (ANN) model of the MLP 5-17-1 type was developed to predict the gross calorific value (HHV) of various waste types based on ultimate analysis. The dataset comprised heterogeneous samples, including biomass, municipal and industrial waste, sludges, and derived fuels, ensuring the model’s diversity and universality. The model achieved high accuracy (R² = 0.92; RMSE = 2.36; MAE = 1.68; MAPE = 10.99%), comparable to previous research results. The heterogeneity of the samples confirmed wide variations in composition and energy properties, which are crucial for developing a universal predictive model. The results confirm that ANN is a reliable tool for assessing the energy potential of waste and highlight the importance of expanding databases and optimizing parameters in future research. Full article

The increasing burial depth of deep shale formations in the southern Sichuan leads to more complex in situ stresses and geological structures, which in turn raises the challenges of hydraulic fracturing. Although enlarging the treatment scale and injection rate can enhance reservoir stimulation, the intensive development of faults and fractures in deep shale formations aggravates stress instability, inducing casing deformation, fracture communication, and other engineering risks that constrain efficient shale gas production. In this study, a cross-scale geomechanical model linking the regional to near-wellbore domains was constructed. A dynamic evolution equation was established based on flow–stress coupling, and a numerical conversion from the geological model to the finite element model was implemented through self-programming, thereby developing a simulation method for dynamic geomechanical evolution during hydraulic fracturing. Results indicate that dynamic variations in pore pressure dominate stress redistribution, while near-wellbore heterogeneity and mechanical property distribution significantly affect prediction accuracy. The injection of fracturing fluid generates a high-pressure gradient that drives pore pressure diffusion along fracture networks and faults, exhibiting a strong near-wellbore but weak far-field non-steady spatial attenuation. As the pore pressure increases, the peak value reaches 1.4 times the original pressure. The triaxial stress shows a negative correlation and decreases. The horizontal minimum principal stress shows the most significant drop, with a reduction of 15.79% to 20.68%, while the vertical stress changes the least, with a reduction of 5.7% to 7.14%. Compared with the initial stress state, the horizontal stress difference increases during fracturing. Rapid pore-pressure surges and fault distributions further trigger stress reorientation, with the magnitude of rotation positively correlated with the intensity of pore-pressure variation. The high porosity and permeability characteristics of the initial fracture network lead to a rapid attenuation of the stress around the wellbore. In the middle and later stages, as the pressure balance is achieved through fracture filling, the pore pressure rises and the stress decline tends to stabilize. The findings provide significant insights into the dynamic stress evolution of deep shale reservoirs during fracturing and offer theoretical support for optimizing fracturing design and mitigating engineering risks. Full article

Sarcopenia is a common age-related skeletal muscle disorder that lacks diagnostic and therapeutic options. Emerging evidence suggests that cuproptosis, a copper-dependent form of regulated cell death, contributes to muscle atrophy, yet the underlying associations remain poorly understood. To address this gap, we integrated two GEO datasets (GSE1428 and GSE25941) for differential expression analysis and applied weighted gene co-expression network analysis (WGCNA) to identify disease-related modules. Cuproptosis-related genes (CRGs) from GeneCards database were intersected with DEGs and WGCNA gene modules to obtain sarcopenia-associated cuproptosis DEGs (SAR-CUP DEGs). Functional enrichment was performed using GO, KEGG, GSEA and GSVA. Hub genes were further identified through three machine learning algorithms (LASSO, RF, and SVM). Regulatory networks were constructed via NetworkAnalyst and GeneMANIA database. A diagnostic model was also developed and later validated in an independent dataset (GSE136344). Experimental validation was performed in a D-galactose-induced sarcopenia cell model. We identified 367 DEGs and 7 co-expression modules, among which 14 SAR-CUP DEGs were mainly enriched in mitochondrial energy metabolism pathways. Machine learning methods highlighted SLC25A12 and PABPC4 as hub genes. Regulatory network analysis revealed key modulators, such as FOXC1, miR-16-5p, GOT2, and GOT1. Diagnostic performance analysis demonstrated strong predictive value for SLC25A12 (AUC = 0.879) and PABPC4 (AUC = 0.858), and RT-qPCR confirmed their downregulation in the sarcopenia cell model (p < 0.01). In conclusion, SLC25A12 and PABPC4 are promising biomarkers linking copper metabolism dysregulation with sarcopenia, offering potential targets for diagnosis and therapy. Full article

Effective pavement repair planning is vital for sustaining performance and minimizing lifecycle costs. At the network level, most agencies still rely on deterministic repair-effect assumptions, where repair outcomes are defined by fixed restoration values derived from experience or experimental averages. However, such assumptions often deviate from actual field performance, leading to overestimated repair efficiency and suboptimal investment decisions. This study develops a framework that integrates stochastic repair effects estimated from historical repair data using a probabilistic model for estimating repair effects. The effects of different repairs are represented as probability distributions derived from the latent-variable projection of stochastic deterioration hazard functions, which define the repair transition probabilities. These stochastic transitions are embedded within a Markov Decision Process to optimize the selection of repair types according to condition state, repair effect, cost, and serviceability thresholds, all within a constrained budget. The framework’s application to Addis Ababa’s 150 km urban road network resulted in a five-year optimal strategy that prioritized cost-effective treatments, such as patching, leading to an improvement in network serviceability from 65.7% to 81.2% at a total cost of USD 11.12 million. A comparative analysis of the deterministic restoration approach, commonly used by the agency, overestimated network-level performance by approximately 19%, as it ignored the variability of recovery captured by the stochastic model. Hence, the proposed stochastic framework enables agencies to achieve realistic, data-driven, and sustainable repair optimization, avoiding overestimation of repair benefits while maintaining serviceability within budget constraints. Full article

The development of new tools to assist the system operator has been crucial in modern power systems due to the system complexity and operational challenges. Among these tools, the system’s load margin, which indicates the maximum load level allowed without instability occurring, stands out. The physical characteristics of the modern power system in the stability threshold condition and the abundant data from Phasor Measurement Units (PMUs) can be used by machine learning techniques to predict the load margins of power systems. This paper proposes a new Physics-Informed Neural Network for computing the precise value of the load margin of power systems equipped with PMUs adopting experimental and physical knowledge in the training process through three loss functions. A PMU allocation procedure is applied to reduce the number of PINN entries. Case studies applying the proposed PINN are performed on the IEEE 68-bus system, and comparative analyses are conducted with traditional Artificial Neural Networks (ANNs), Graph Neural Networks (GNNs) and Physics-Guided Neural Networks (PGNNs). Results show better Root Mean Square Error values for the proposed PINN compared to the ANN, GNN and PGNN for different numbers of PMUs allocated in the test system. Full article

The prediction of concrete compressive strength (CSMPa) is fundamental in experimental civil engineering as it enables the optimization of mix design and complements laboratory testing through predictive tools. This study presents a systematic and reproducible methodology for comparing eight regression algorithms—including linear models, neural networks, and boosting methods—applied to three experimental datasets that represent different types of concrete: high-performance concrete (HPC), conventional concrete, and recycled-aggregate concrete (RAC). In order to make such comparison, some performance metrics were calculated (RMSE, MAE, MAPE, $R^2$, and nRMSE) through hyperparameter optimization using RandomizedSearchCV and homogeneous cross-validation. The boosting methods achieved the best performance, with CatBoost standing out by reaching $R^2$ values between 0.92 and 0.95 and RMSE between 3.4 and 4.4 MPa, confirming its inter-dataset stability and generalization capability. These results indicate consistent predictive accuracy across concretes of different compositions and production contexts. As an applied contribution, three interactive inference systems were developed in Google Colab to estimate CS from mix parameters, promoting reproducibility, open access, and practical use in quality-control processes. Full article

Grouting power serves as a critical parameter reflecting real-time energy input during grouting operations, and its accurate prediction is essential for intelligent control and engineering safety. Existing prediction methods often struggle to handle the strong nonlinearity, noise interference, adaptability to varying conditions in grouting power data. To address these challenges, an intelligent grouting system that integrates real-time data collection and core control modules has been developed. Subsequently, a grouting power prediction model is then proposed, which combines Complete Ensemble Empirical Mode Decomposition and Adaptive Noise (CEEMDAN) with a Convolutional Neural Net-work-Bidirectional Long Short-Term Memory Neural Network (CNN-BiLSTM) is proposed. The approach employs CEEMDAN to decompose the nonlinear and non-stationary power sequence into multiple intrinsic mode functions (IMFs). Each IMF is then separated into linear and nonlinear components using a moving average method. The linear components are predicted using an Autoregressive Integrated Moving Average (ARIMA) model, while the nonlinear components are predicted using a CNN-BiLSTM model. The final prediction is obtained by reconstructing the results from both components. Experimental comparisons under both normal and heaving grouting conditions demonstrate that the proposed model significantly outperforms LSTM, CNN-LSTM, and CNN-BiLSTM models. With 80% of the dataset used for training, the RMSE for normal conditions is reduced by 95.69%, 85.11%, and 80.55%, respectively, and for heaving conditions by 94.91%, 90.71%, and 84.62%, respectively. This research provides high-precision predictive support for grouting regulation under complex working conditions, offering substantial engineering application value. Full article

At present, the digital construction of museums has created a novel cultural ecosystem that integrates digital preservation of cultural heritage, intelligent management, immersive experiences, and cloud-based services. However, insufficient synergistic integration of technological applications constrains the comprehensive release of the digital construction’s efficacy, while the absence of cultural assessment dimensions hinders the effective articulation of mechanisms whereby digital technology empowers cultural innovation. These concerns collectively constitute the primary impediments hindering museums from attaining sustainable development. The effectiveness of museum digital construction is fully clarified by combining grounded theory qualitative research methods with cultural cycle theory in this study. The Analytic Network Process (ANP) is used to manage interdependent relationships between factors, and cloud models are used to clarify indicator ambiguity, which allows for accurate assessment of digital construction results, consequently bolstering the sustainability of museum digitalization initiatives. The developed ‘qualitative–quantitative’ collaborative evaluation methodology for museum digital construction includes three sub-objectives: technology embedding, value co-creation, and institutional adaptation, as well as five primary indicators and ten secondary indicators. An empirical analysis of the ‘Smart Jiangxi Museum’ digital construction initiative at the Jiangxi Provincial Museum in China indicates that the project has achieved an ‘excellent’ standard. The findings of a previous qualitative study are effectively supported by this conclusion. This study presents a systematic approach for museum evaluation and gives decision-making guidance for museums to attain sustainable use of cultural resources, promote social knowledge transmission, and facilitate green, low-carbon transformation of operational models in the digital era. Full article

Macadamia (Macadamia spp.), as a high-value cash crop, relies on varietal adaptability screening and quality optimization for enhanced industrial benefits. However, existing research has predominantly focused on the mature tree stage. Systematic studies on the physiological characteristics during the seedling stage and comprehensive multi-indicator evaluations remain insufficient, limiting improved variety selection and industrial development. This study investigated three macadamia varieties (A4, A16, A203). We systematically measured leaf morphology, photosynthetic parameters, antioxidant enzyme activities, and free amino acid content at the seedling stage, combined with a comprehensive analysis of mature fruit morphology, mineral elements, amino acid composition, and pericarp phenolic compounds. The results indicated that at the seedling stage: A4 exhibited the highest SPAD value and CAT activity, significantly exceeding A16 and A203 by 137.14% and 139.82%, respectively, alongside the lowest MDA content, highlighting its superior stress resistance; A16 showed the highest Pn, Cleaf, and WUE, with total amino acid content being 38.09% and 18.79% higher than A4 and A203, respectively; A203 demonstrated the highest light energy utilization efficiency, significantly higher SOD activity compared to A16 and A203, and the lowest O²⁻ content. Regarding fruit quality: A16 kernels contained the highest total amino acids and umami amino acids, with sweet and aromatic amino acids also being significantly higher than in other varieties; A203 performed notably well in K, Mg, and Mn content, with medicinal amino acids accounting for over 70% of the total; A4 pericarp contained significantly higher levels of phenolic compounds, such as p-hydroxybenzoic acid, compared to A16 and A203, some exceeding 80%. Correlation analysis revealed a complex regulatory network among fruit traits, mineral elements, amino acids, and phenolics. In summary, A4, A16, and A203 possess respective advantages in high stress resistance, superior flavor quality, and high nutritional functionality. This study establishes a comprehensive “morphology–photosynthesis–antioxidant activity–amino acids–quality” evaluation system, providing a scientific basis for targeted breeding and whole-industry-chain development of macadamia. Full article

Correcting atmospheric effects on hyperspectral optical satellite scenes is paramount to ensuring the accuracy of derived bio-geophysical products. The open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was first initiated in 2016 and has now been extended to provide a comprehensive assessment of atmospheric processors of space-borne imaging spectroscopy missions (EnMAP and PRISMA) over land surfaces. The exercise contains 90 scenes, covering stations of the Aerosol Robotic Network (AERONET) for assessing aerosol optical depth (AOD) and water vapour (WV) retrievals, as well as stationary networks (RadCalNet and HYPERNETS) and ad hoc campaigns for surface reflectance (SR) validation. AOD, WV, and SR retrievals were assessed using accuracy, precision, and uncertainty metrics. For AOD retrieval, processors showed a range of uncertainties, with half showing overall uncertainties of <0.1 but going up to uncertainties of almost 0.4. WV retrievals showed consistent offsets for almost all processors, with uncertainty values between 0.171 and 0.875 g/cm². Average uncertainties for SR retrievals depend on wavelength, processor, and sensor (uncertainties are slightly higher for PRISMA), showing average values between 0.02 and 0.04. Although results are biased towards a limited selection of ground measurements over arid regions with low AOD, this study shows a detailed analysis of similarities and differences of seven processors. This work provides critical insights for understanding the current capabilities and limitations of atmospheric correction algorithms for imaging spectroscopy, offering both a foundation for future improvements and a first practical guide to support users in selecting the most suitable processor for their application needs. Full article

To effectively combat wildfires, ground teams must reach the fire site via road network within critical response time. However, low-standard forest roads can reduce firetruck speeds and delay fire response times. This study aimed to investigate how improving road standards affects firefighting access within critical response time and contributes to reducing timber losses. This study was conducted in Antalya, the city most affected by wildfires in Türkiye. In the study, highly fire-prone forests were first identified based on a fire probability map of Antalya, developed through a GIS-based MCDA model incorporating the Fuzzy-AHP method. Then, the highly fire-prone forests and their corresponding timber volume were determined. Finally, the economic value of timber saved from fire and the present net value of total road costs were determined. As a result of improving forest roads, the forest areas that could be reached in time increased by 11.04%, making an additional 81,867.53 hectare of highly fire-prone forests accessible. The amount and economic value of timber products saved in this area were 971,195.55 m³ and €37,689,301, respectively. The cost of improved roads was €37,386,622 while the resulting positive net economic value of €302,679 indicates that investing in forest roads improvements is a viable option. Full article

Heavy oil reservoirs often enter a high-water-cut and low-production stage after multiple cycles of steam stimulation. Converting to steam flooding can enhance recovery, yet the reliable prediction of incremental production potential and optimal design of injection–production parameters remain limited. In this study, a real heavy oil reservoir block was selected to develop a hybrid modeling framework integrating numerical simulation and machine learning for predicting steam flooding performance. A conceptual model was established on a numerical simulation platform to reproduce the transition from cyclic stimulation to continuous steam flooding, analyzing temperature, oil saturation, and recovery evolution under different geological, operational, and process conditions. Sensitive parameters were identified through single- and multi-factor analyses, and mathematical models for multiple injection–production schemes—continuous, cyclic, and asynchronous—were constructed for optimization. A comprehensive multi-scenario dataset combining simulation and field data was used to train and validate several machine learning models, including artificial neural networks, gradient boosting decision trees, XGBoost, and LightGBM. Among them, the LightGBM model achieved the highest predictive accuracy (R² = 0.99) and computational efficiency. The proposed framework enables the rapid and reliable prediction of incremental oil potential and provides a robust tool for optimizing steam flooding parameters, offering significant value for field-scale heavy oil development. Full article