Search Results (48,088)

This study presents a cutting-edge imaging technique for special unmanned vehicles (UAVs) designed to enhance tunnel inspection capabilities. This technique integrates ghost imaging inspired by the human visual system with lateral inhibition and variable resolution to improve environmental perception in challenging conditions, such as poor lighting and dust. By emulating the high-resolution foveal vision of the human eye, this method significantly enhances the efficiency and quality of image reconstruction for fine targets within the region of interest (ROI). This method utilizes non-uniform speckle patterns coupled with lateral inhibition to augment optical nonlinearity, leading to superior image quality and contrast. Lateral inhibition effectively suppresses background noise, thereby improving the imaging efficiency and substantially increasing the signal-to-noise ratio (SNR) in noisy environments. Extensive indoor experiments and field tests in actual tunnel settings validated the performance of this method. Variable-resolution sampling reduced the number of samples required by 50%, enhancing the reconstruction efficiency without compromising image quality. Field tests demonstrated the system’s ability to successfully image fine targets, such as cables, under dim and dusty conditions, achieving SNRs from 13.5 dB at 10% sampling to 27.7 dB at full sampling. The results underscore the potential of this technique for enhancing environmental perception in special unmanned vehicles, especially in GPS-denied environments with poor lighting and dust. Full article

Rail corrugation intensifies wheel–rail vibrations, often leading to damage in vehicle–track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner–Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies. PE is used to evaluate the randomness of each IMF component, discarding those with high permutation entropy values. Subsequently, correlation analysis is performed on the retained IMFs to identify the component most strongly correlated with the original signal. The selected component is subjected to SPWVD time–frequency analysis to identify the location and wavelength of the corrugation occurrence. Filtering is applied to the IMF based on the frequency concentration observed in the time–frequency analysis results. Then, frequency–domain integration is performed to estimate the rail’s corrugation depth. Finally, the algorithm is validated and analyzed using both simulated data and measured data. Validation results show that this approach reliably identifies the wavelength and depth characteristics of rail corrugation. Additionally, the time–frequency analysis results reveal variations in the severity of corrugation damage at different locations. Full article

The classified water pricing system is an effective measure for promoting the rational utilization of water resources under market mechanisms. Studying the water prices of three different types of water sources, including reservoir water, river water, and wastewater treatment plant effluent, is the basis for promoting the use of reclaimed water. However, there remains a spectrum of viewpoints on how to establish a pricing mechanism for reclaimed water at present. This study primarily focuses on the multi-level quality-separated water supply system in Yiwu City, China. It analyzes the limitations of the current water pricing formation mechanism and the externality of integrating reclaimed water into the unified allocation of multiple water sources. Based on the principles of full-cost water pricing and externality theory, a supply-side classified water pricing permit cost and pricing calculation model, covering the entire process of the social water cycle, is proposed. It focuses on the analysis of the impact of equity costs and environmental costs on supply-side classified water pricing. The Shapley value method is used for the technology of cost allocation among stakeholders to reasonably distribute the calculated water pricing of reclaimed water. The price of reclaimed water varies depending on the user type, with residential users paying 2.93 CNY/t, industrial users 4.00 CNY/t, and government allocations at 8.52 CNY/t. Compared with the classified water prices of various stakeholders on the user side of reservoir water, reclaimed water has a significant price advantage. This research demonstrates that the supply-side classified water pricing model, which includes a permit cost and pricing calculation framework, can encourage the supply of higher-quality water at corresponding higher prices, while also providing the internalization of external costs. Furthermore, the Shapley value method of cost allocation can realize the fair burden of stakeholders on the calculated water price and maintain the competitive advantage of reclaimed water prices. Full article

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems. Full article

The optimization of the energy system serving users’ aggregations at urban level, such as Energy Communities, is commonly addressed by optimizing separately the set of energy conversion and storage systems from the scheduling of energy demand. Conversely, this paper proposes an integrated approach to include the demand side in the design and operation optimization of the energy system of an Energy Community. The goal is to evaluate the economic, energetic, and environmental benefits when users with different demands are aggregated, and different degrees of flexibility of their electricity demand are considered. The optimization is based on a Mixed-Integer Linear Programming approach and is solved multiple times by varying (i) the share of each type of user (residential, commercial, and office), (ii) the allowed variation of the hourly electricity demand, and (iii) the maximum permitted CO₂ emissions. Results show that an hourly flexibility of up to 50% in electricity demand reduces the overall system cost and the amount of energy withdrawn from the grid by up to 25% and 31%, respectively, compared to a non-flexible system. Moreover, the aggregation of users whose demands match well with electricity generation from renewable sources can reduce CO₂ emissions by up to 30%. Full article

Satellite-based precipitation products (SPPs) are essential for climate monitoring, especially in regions with sparse observational data. This study compares the performance of the latest version (V07B) and its predecessor (V06B) of the Integrated Multi-satellitE Retrievals for GPM (IMERG) across South America and the adjacent oceans. It focuses on evaluating their accuracy under different precipitation regimes in Brazil using 22 years of IMERG Final data (2000–2021), aggregated into seasonal totals (summer, autumn, winter, and spring). The observations used for the evaluation were organized into 0.1° × 0.1° grid points to match IMERG’s spatial resolution. The analysis was restricted to grid points containing at least one rain gauge, and in cases where multiple gauges were present within a grid point the average value was used. The evaluation metrics included the Root Mean Square Error (RMSE) and categorical indices. The results reveal that while both versions effectively capture major precipitation systems such as the mesoscale convective system (MCS), South Atlantic Convergence Zone (SACZ), and Intertropical Convergence Zone (ITCZ), significant discrepancies emerge in high-rainfall areas, particularly over oceans and tropical zones. Over the continent, however, these discrepancies are reduced due to the correction of observations in the final version of IMERG. A comprehensive analysis of the RMSE across Brazil, both as a whole and within the five analyzed regions, without differentiating precipitation classes, demonstrates that version V07B effectively reduces errors compared to version V06B. The analysis of statistical indices across Brazil’s five regions highlights distinct performance patterns between IMERG versions V06B and V07B, driven by regional and seasonal precipitation characteristics. V07B demonstrates a superior performance, particularly in regions with intense rainfall (R1, R2, and R5), showing a reduced RMSE and improved categorical indices. These advancements are linked to V07B’s reduced overestimation in cold-top cloud regions, although both versions consistently overestimate at rain/no-rain thresholds and for light rainfall. However, in regions prone to underestimation, such as the interior of the Northeastern region (R3) during winter, and the northeastern coast (R4) during winter and spring, V07B exacerbates these issues, highlighting challenges in accurately estimating precipitation from warm-top cloud systems. This study concludes that while V07B exhibits notable advancements, further enhancements are needed to improve accuracy in underperforming regions, specifically those influenced by warm-cloud precipitation systems. Full article

As future infrastructures increasingly rely on digital systems, their exposure to cyber threats has grown significantly. The complex and hyper-connected nature of these systems presents challenges for enhancing cyber resilience against adverse conditions, stresses, attacks, or compromises on cybersecurity resources. Integrating risk assessment with cyber resilience allows for adaptive approaches that can effectively safeguard critical infrastructures (CIs) against evolving cyber risks. However, the wide range of methods, frameworks, and standards—some overlapping and others inadequately addressed in the literature—complicates the selection of an appropriate approach to cyber risk assessment for cyber resilience. To investigate this integration, this study conducts a systematic literature review (SLR) of relevant methodologies, standards, and regulations. After conducting the initial screening of 173 publications on risk assessment and cyber resilience, 40 papers were included for thorough review. The findings highlight risk assessment methods, standards, and guidelines used for cyber resilience and provide an overview of relevant regulations that strengthen cyber resilience through risk assessment practices. The results of this paper will offer cybersecurity researchers and decision-makers an illuminated understanding of how risk assessment enhances cyber resilience by extracting risk assessment best practices in the literature supported by relevant standards and regulations. Full article

Wnt (wingless-type MMTV integration site family) signaling is an evolutionary conserved system highly active during embryogenesis, but in adult hearts has low activities under normal conditions. It is essential for a variety of physiological processes including stem cell regeneration, proliferation, migration, cell polarity, and morphogenesis, thereby ensuring homeostasis and regeneration of cardiac tissue. Its dysregulation and excessive activation during pathological conditions leads to morphological and functional changes in the heart resulting in impaired myocardial regeneration under pathological conditions such as myocardial infarction, heart failure, and coronary artery disease. Several groups of Wnt inhibitors have demonstrated the ability to modulate the Wnt pathway and thereby significantly reduce fibrosis and improve cardiac function after myocardial ischemia. Their inhibitory effect can be realized at multiple levels, which include the inhibition of Wnt ligands, the inhibition of Frizzled receptors, the stabilization of the β-catenin destruction complex, and the disruption of nuclear β-catenin interactions. In this review, we overview the function of Wnt signaling in responses of cardiac cells to pathological conditions, especially ischemic heart disease, with an emphasis on the use of inhibitors of this signaling as a therapeutic approach. Finally, we summarize the current knowledge about the potential of the targeting of Wnt signaling in therapeutic applications. Full article

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor’s lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies. A dual fiber Bragg grating (FBG) sensor system is employed for simultaneous temperature and strain monitoring, while curing kinetics tests confirm the self-catalytic nature of the process and yield the corresponding kinetic equations. The multi-physics simulation model demonstrates strong agreement with the experimental data. The results show that optimizing the curing process reduces the maximum stress from 45.1 MPa to 38.6 MPa, effectively alleviating the stress concentration. These findings highlight the significant influence of the post-curing temperature phase on residual stress. The proposed model offers a reliable tool for stress prediction and process optimization in various insulating materials, providing valuable insights for motor insulation system design. Full article

Blueberry plants are among the most important fruit-bearing shrubs, but they have shallow, hairless roots that are not conducive to water and nutrient uptake, especially under drought conditions. Therefore, the mechanism underlying blueberry root drought tolerance should be clarified. Hence, we established a yeast expression library comprising blueberry genes associated with root responses to drought stress. High-throughput sequencing technology enabled the identification of 1475 genes potentially related to drought tolerance. A subsequent KEGG enrichment analysis revealed 77 key genes associated with six pathways: carbon and energy metabolism, biosynthesis of secondary metabolites, nucleotide and amino acid metabolism, genetic information processing, signal transduction, and material transport and catabolism. Metabolomic profiling of drought-tolerant yeast strains under drought conditions detected 1749 differentially abundant metabolites (DAMs), including several up-regulated metabolites (organic acids, amino acids and derivatives, alkaloids, and phenylpropanoids). An integrative analysis indicated that genes encoding several enzymes, including GALM, PK, PGLS, and PIP5K, modulate key carbon metabolism-related metabolites, including D-glucose 6-phosphate and β-D-fructose 6-phosphate. Additionally, genes encoding FDPS and CCR were implicated in terpenoid and phenylalanine biosynthesis, which affected metabolite contents (e.g., farnesylcysteine and tyrosine). Furthermore, genes for GST and GLT1, along with eight DAMs, including L-γ-glutamylcysteine and L-ornithine, contributed to amino acid metabolism, while genes encoding NDPK and APRT were linked to purine metabolism, thereby affecting certain metabolites (e.g., inosine and 3′,5′-cyclic GMP). Overall, the yeast functional screening system used in this study effectively identified genes and metabolites influencing blueberry root drought tolerance, offering new insights into the associated molecular mechanisms. Full article

To simultaneously acquire the spectral and polarization information of the target and achieve the monitoring and identification of the target object, a polarization spectral integrated imaging system is proposed in this paper. Firstly, the structural principle of the polarization spectral integrated imaging system is introduced. The relationship between the spatial resolution, spectral resolution, and the system’s structural parameters is analyzed. The design of the optical part of the polarization spectral integrated imaging system is completed, along with the tolerance analysis. Secondly, the mechanical structure of the polarization spectral integrated imaging system is designed. Finally, by using a drone to carry the polarization spectral integrated imaging system, a simulation experiment for sea surface oil spill monitoring is conducted, and the hyperspectral and polarization information of the ocean, crude oil, fuel oil, palm oil, diesel, and gasoline are obtained. The polarization and spectral information were integrated. The integration of hyperspectral and polarization data yields remarkable enhancement outcomes, allowing for the clear delineation of previously challenging-to-identify crude oil contamination areas against the marine background in the fused images, characterized by sharper boundaries and improved discriminability. This accomplishment underscores the feasibility of our system for the rapid identification of large-scale oil spill events. Full article

In response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with model predictive control (MPC), leveraging the operational characteristics of multi-loop systems. The proposed strategy adeptly marries the swift response of PID controllers with the foresight and optimization capabilities of MPC. A dynamic model of a supercritical unit is constructed using the subspace identification method. The model’s high precision is confirmed by its alignment with field data. Load change simulations demonstrate that the PID–MPC hybrid controller shows faster response times and more precise tracking capabilities compared to the feedforward-PID strategy. It achieves substantial improvements in the IAE index for three loops, with increases of 29.2%, 54.1%, and 57.3% over the feedforward-PID controller. An energy-saving performance analysis indicates that the proactive control actions of both the PID–MPC and MPC strategies lead to dynamic exergy efficiency and coal consumption rates with a broader range of dynamic process changes. The disturbance scenario simulation regarding the proposed controller achieves faster settling times and minimizes control deviation compared to the traditional controller. Full article

In a previous study, we developed an integrated reaction system combining NH₃ decomposition and CO₂ methanation within a membrane reactor, significantly enhancing reactor performance through efficient H₂ separation. Ru/Ba/γ-Al₂O₃ and Ru/ZrO₂ were employed as catalysts for each reaction. To ensure the accuracy and reliability of our results, they were validated through 1D models using FlexPDE Professional Version 7.21/W64 software. Key parameters such as reactor arrangement, catalyst bed positioning, overall heat transfer coefficient, rate constants, and H₂ permeance were investigated to optimize system efficiency. The study revealed that positioning the NH₃ decomposition on the shell side and CO₂ methanation on the tube side resulted in a better performance. Additionally, shifting the methanation catalyst bed downward by approximately one-eighth (10 mm from 80 mm) achieves the highest CO₂ conversion. A sensitivity analysis identified the rate constant of the NH₃ decomposition catalyst and the H₂ permeance of the membrane as the most influential factors in enhancing CO₂ conversion. This highlights the priority of improving membrane H₂ permeance and catalytic activity for NH₃ decomposition to maximize system efficiency. Full article

The interplay between nutrition and skin health provides a crucial lens for understanding, diagnosing, and managing eating disorders (EDs) such as anorexia nervosa (AN), bulimia nervosa (BN), and binge-eating disorder (BED). This review explores the dermatological manifestations resulting from the nutritional deficiencies commonly associated with EDs, including conditions like hair loss, xerosis, and brittle nails. These changes in the skin and its appendages often reflect deeper systemic dysfunctions, such as deficiencies in essential micronutrients (zinc, iron, and vitamins A and C), hormonal imbalances, and electrolyte disturbances. Recognizing these dermatological signs as diagnostic tools is vital for the early identification and intervention of EDs. By integrating dermatological observations with psychiatric and nutritional care, a holistic, multidisciplinary approach can be developed to address both the physical and psychological complexities of EDs. This review highlights the critical role of these skin-related markers in promoting timely diagnosis and effective treatment. To examine the relationship between specific nutrients and dermatological manifestations in EDs, a systematic review of three electronic databases—PubMed, Google Scholar, and ResearchGate—was conducted. The findings underline the importance of early recognition of these skin symptoms for effective management. Collaborative care involving dermatologists, psychiatrists, and nutritionists is essential for diagnosing and treating EDs. Such integrated efforts ensure a comprehensive approach to these multifaceted conditions, ultimately improving patient outcomes and enhancing overall care. Full article

Turbomachinery engines face significant failure risks due to the combination of thermal loads and high-amplitude vibrations in turbine and compressor blades. Accurate stress distribution measurements are critical for enhancing the performance and safety of these systems. Blade tip timing (BTT) has emerged as an advanced alternative to traditional measurement methods, capturing blade dynamics by detecting deviations in blade tip arrival times through sensors mounted on the stator casing. This research focuses on developing an analytical model to quantify the uncertainty budget involved in designing a calibration setup for BTT systems, ensuring targeted performance levels. Unlike existing approaches, the proposed model integrates both operational variability and sensor performance characteristics, providing a comprehensive framework for uncertainty quantification. The model incorporates various operating and measurement scenarios to create an accurate and reliable calibration tool for BTT systems. In the broader context, this advancement supports the use of BTT for qualification processes, ultimately extending the lifespan of turbomachinery through condition-based maintenance. This approach enhances performance validation and monitoring in power plants and aircraft engines, contributing to safer and more efficient operations. Full article