Search Results (52)

The transition toward a renewable-based energy structure has significantly accelerated the advancement of energy storage technologies. Compressed air energy storage (CAES) is regarded as a highly promising long-duration energy storage solution due to the advantages of its large scale and long service life. However, the efficiency of conventional compressed air energy storage (CAES) systems remains limited due to the inadequate utilization of thermal energy. Isothermal compressed CAES (ICAES) technology, based on liquid pistons, can overcome the efficiency bottleneck by enabling temperature control during air compression. However, the operation of liquid pistons under high-pressure storage conditions remains a challenge because of the high compression ratio. To enhance the utilization rate of the two-stage liquid piston unit by using the synchronous operations of compression and discharge processes, this paper proposes a coordinated operation scheme. Then, a multi-stage ICAES system under constant-pressure air storage is proposed. Mathematical models and energy efficiency analysis methods of the multi-stage ICAES system are also established. Finally, the operational characteristics are analyzed in combination with the ICAES at 200 kWh. The results show that the proposed system can achieve an overall efficiency of 68.0%, under 85% and 90% efficiencies for low-pressure and linear equipment, respectively. The coordinated operation of the two-stage liquid piston unit can be further extended to multi-stage operations, demonstrating broad application prospects in ICAES systems. Full article

Compressed air energy storage (CAES) systems represent a critical technological solution for addressing power grid load fluctuations by generating electrical power during peak load periods and storing energy during low load periods. As a prominent branch of CAES, isothermal compressed air energy storage (ICAES) systems have attracted significant research attention due to their elimination of requirements for high-temperature storage chambers and high-temperature compressors. Implementing constant-pressure operation in air storage reservoirs not only enhances energy storage density but also improves system safety. However, existing constant-pressure air storage methodologies necessitate supplementary infrastructure, such as high-pressure water reservoirs or elevated hydraulic columns, thereby escalating capital expenditures. This study introduces a novel constant-pressure air storage strategy for ICAES systems utilizing a linear-driven liquid piston mechanism. The proposed approach achieves constant-pressure air storage through the dual-mode operation strategies of buffer tanks (CBA and CBP modes) and hydraulic cylinders (CPP and CPW modes), eliminating the requirement for an auxiliary high-pressure apparatus or extensive civil engineering modifications. A prototype two-stage constant-pressure ICAES architecture was proposed, integrating low-pressure equipment with liquid pistons and providing detailed operational processes for preconditioning, energy storage, and power generation. A comprehensive mathematical model of the system is developed and validated through process simulation and performance characterization of a 100 kWh capacity system. It demonstrates that under operational conditions of 1 MPa of low pressure and 5 MPa of storage pressure, the system achieves an efficiency of 74.0% when the low-pressure equipment and liquid piston exhibit efficiencies of 85% and 90%, respectively. Furthermore, parametric analysis reveals a negative correlation between system efficiency and low-pressure parameters. Full article

This paper presents the identification of the functional requirements and development of a preliminary concept of the AgriRover, a low-cost, modular autonomous vehicle intended to support sustainable practices in traditional vineyards in developing countries, focusing on the Ica region of Peru. Viticulture in this region faces acute challenges such as soil salinity, climate variability, labour shortages, and low technological readiness. Rather than offering a ready-made technological integration, this study adopts a step-by-step design approach grounded in the realities of smallholder farmers. The authors mapped the phenological stages of grapevines using the BBCH scale and systematically reviewed available sensing and monitoring technologies to determine the most context-appropriate solutions. Virtual modelling and preliminary analysis validate AgriRover’s geometric configuration and path-following capabilities within narrow vineyard rows. The proposed platform is meant to be adaptable, scalable, and maintainable using locally available material and human resources. AgriRover offers a practical and affordable foundation for precision agriculture in resource-constrained settings by aligning viticultural challenges with sensor deployment strategies and sustainability criteria. The sustainability analysis of the initial AgriRover concept was evaluated using the CML methodology, accounting for local waste processing rates and energy mixes to reflect environmental realities in Peru. Full article

With the gradual maturation of optically pumped magnetometer (OPM) technology, the use of OPMs to acquire weak magnetocardiography (MCG) signals has started to gain widespread application. Due to the complexity of magnetic environments, MCG signals are often subject to interference from various unknown sources. Independent component analysis (ICA) is one of the most widely used methods for blind source separation. However, in practical applications, the numbers of retained components and filtering components are often selected manually, relying on subjective experience. This study proposes an adaptive ICA method that estimates the signal-to-noise ratio (SNR) before processing to determine the number of components and selects heartbeat-related components based on their characteristic indicators. The method was validated using phantom experiments and MCG data in a 128-channel OPM-MCG system. In the human subject experiment, the array output SNR reached 31.8 dB, and the processing time was significantly reduced to 1/38 of the original. The proposed method outperformed traditional techniques in terms of its ability to identify artifacts and efficiency in this regard, providing strong support for the broader clinical application of OPM-MCG. Full article

Compared with traditional invasive coronary angiography (ICA), coronary CT angiography (CCTA) has the advantages of being rapid, economical, and minimally invasive. The wide-detector CT, with its superior temporal resolution and robust three-dimensional reconstruction technology, thus enables CCTA in patients with high heart rates and arrhythmias, leading to a high potential for clinical application. This paper systematically summarizes wide-detector CT hardware configurations of various vendors routinely used for CCTA examinations and reviews the effects of patient heart rate and heart rate variability, scanning modality, reconstruction algorithms, tube voltage, and scanning field of view on image quality and radiation dose. In addition, novel technologies in the field of CT applied to CCTA examinations are also presented. Since this examination has a diagnostic accuracy that is highly consistent with ICA, it can be further used as a routine examination tool for coronary artery disease in clinical practice. Full article

In recent decades, with the expansion of distributed energy generation technologies and the increasing need for more flexibility and efficiency in energy distribution systems, microgrids have been considered a promising innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject of extensive research in the field of microgrid operational optimization. The energy demand is considered a crucial element for energy management due to its fluctuating nature over the day. The use of demand response strategies for energy management is one of the most important factors in dealing with renewables. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) have gained great attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper, both ICA and GA, as well as their hybrid application, are used to significantly enhance the voltage regulation in microgrids. The integration of optimization techniques with demand response strategies improves the overall system efficiency and stability. The results proved that the hybrid method provides valuable insights for optimizing energy management systems. Full article

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert–Huang transform (HHT) was applied to each source signal to obtain a joint time–frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%. Full article

New clinical strategies for treating severe bone and cartilage injuries are required, especially for use in combination with implant procedures. For this purpose, p(VCL-co-HEMA) thermosensitive hydrogels have been activated with icariin-loaded nanoparticles to be used as bone-cell-harvesting platforms. Supercritical CO₂-SAS technology has been applied to encapsulate icariin, a small molecule that is involved in osteoblastic differentiation. Thus, physical-chemical analysis, including swelling and transmittance, showed the impact of HEMA groups in hydrogel composition. Moreover, icariin (ICA) release from p(VCL-co-HEMA) platforms, including pVCL@ICA nanoparticles, has been studied to evaluate their efficacy in relevant conditions. Finally, the thermosensitive hydrogels’ cell compatibility, transplant efficiency, and bone differentiation capacity were tested. This study identifies the optimal formulations for icariin-activated hydrogels for both control and HEMA formulations. Using this technique, osteoblastic sheets that were rich in collagen type I were successfully transplanted and recultivated, maintaining an optimal extracellular matrix (ECM) composition. These findings suggest a new cell-sheet-based therapy for bone regeneration purposes using customized and NP-activated pVCL-based cell platforms. Full article

Accurate identification of lithology in petroleum engineering is very important for oil and gas reservoir evaluation, drilling decisions, and petroleum geological exploration. Using a cross-plot to identify lithology only considers two logging parameters, causing the accuracy of lithology identification to be insufficient. With the continuous development of artificial intelligence technology, machine learning has become an important means to identify lithology. In this study, the cutting logging data of the Junggar Basin were collected as lithologic samples, and the identification of argillaceous siltstone, mudstone, gravel mudstone, silty mudstone, and siltstone was established by logging and logging parameters at corresponding depths. Aiming at the non-equilibrium problem of lithologic data, this paper proposes using equilibrium accuracy to evaluate the model. In this study, manifold learning is used to reduce logging and logging parameters to three dimensions. Based on balance accuracy, four dimensionality reductions including isometric feature mapping (ISOMAP), principal component (PCA), independent component (ICA), and non-negative matrix factorization (NMF) are compared. It is found that ISOMAP improves the balance accuracy of the LightGBM model to 0.829, which can effectively deal with unbalanced lithologic data. In addition, the particle swarm optimization (PSO) algorithm is used to automatically optimize the super-parameters of the lightweight gradient hoist (LightGBM) model, which effectively improves the balance accuracy and generalization ability of the lithology identification model and provides strong support for fast and accurate lithology identification. Full article

The accurate identification of corn leaf diseases is crucial for preventing disease spread and improving corn yield. Plant leaf images are often affected by factors such as complex backgrounds, climate, light, and sample data imbalance. To address these issues, we propose a lightweight convolutional neural network, ICS-ResNet, based on ResNet50. This network incorporates improved spatial and channel attention modules as well as a deep separable residual structure to enhance recognition accuracy. (1) The residual connections in the ResNet network prevent gradient loss during deep network training. (2) The improved channel attention (ICA) and spatial attention (ISA) modules fully utilize semantic information from different feature layers to accurately localize key features of the network. (3) To reduce the number of parameters and lower computational costs, we replace traditional convolutional computation with a depth-separable residual structure. (4) We also employ cosine annealing to dynamically adjust the learning rate, enhancing the network’s training stability, improving model convergence, and preventing local optima. Experiments on the corn dataset in Plant Village compare the proposed ICS-ResNet with eight popular networks: CSPNet, InceptionNet_v3, EfficientNet, ShuffleNet, MobileNet, ResNet50, ResNet101 and ResNet152. The results show that the ICS-ResNet achieves an accuracy of 98.87%, which is 5.03%, 3.18%, 1.13%, 1.81%, 1.13%, 0.68%, 0.44% and 0.60% higher than the other networks, respectively. Furthermore, the number of parameters and computations are reduced by 69.21% and 54.88%, respectively, compared to the original ResNet50 network, significantly improving the efficiency of corn leaf disease classification. The study provides strong technical support for sustainable agriculture and the promotion of agricultural science and technology innovation. Full article

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R² of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies. Full article

The potential energy of compressed air represents a multi-application source of power. Historically employed to drive certain manufacturing or transportation systems, it became a source of vehicle propulsion in the late 19th century. During the second half of the 20th century, significant efforts were directed towards harnessing pressurized air for the storage of electrical energy. Today’s systems, which are based on storing the air at a high pressure, are usually recognized as compressed air energy storage (CAES) installations. This paper aims to provide an overview of different technologies that take advantage of the energy accumulated in the compressed air. Particular attention is paid to the CAES installations that are working as electrical energy storage systems (EESs). These systems, developed originally as large capacity (>100 MW_e) and fuel-based installations, may soon become fully scalable, highly efficient, and fuel-free electrical energy storage systems. To present this opportunity, a thorough review encompassing previous and up-to-date advancements in their development was carried out. In particular, CAES concepts, such as diabatic (D-CAES), adiabatic (A-CAES), and isothermal (I-CAES), are described in detail. This review also provides the detailed characteristics of the crucial elements of these configurations, including compressors, expanders, air storage chambers, and thermal storage tanks. Knowledge of these components and their role allows us to understand the main challenges behind the further development of the mentioned CAES setups. Apart from the CAES systems that are designed as EES systems, this paper describes other prospective technologies that utilize the energy of pressurized air. Accordingly, compressed air cars and their key elements are explained in detail. Moreover, the technology renowned as wave-driven compressed air energy storage (W-CAES) is described as well, indicating that the utilization of pressurized air represents a viable option for converting ocean energy into electrical power. Full article

Zanthoxylum nitidum (Roxb.) DC. (Z. nitidum) is a traditional Chinese medicinal plant that is indigenous to the southern regions of China. Previous research has provided evidence of the significant anti-inflammatory, antibacterial, and anticancer properties exhibited by Z. nitidum. The potential therapeutic effects and cardiac toxicity of Z. nitidum remain uncertain. The aim of this research was to investigate the potential therapeutic properties of the four main compounds of Z. nitidum in cardiovascular diseases, their impact on the electrical activity of cardiomyocytes, and the underlying mechanism of their anti-inflammatory effects. We selected the four compounds from Z. nitidum with a high concentration and specific biological activity: nitidine chloride (NC), chelerythrine chloride (CHE), magnoflorine chloride (MAG), and hesperidin (HE). A proteomic analysis was conducted on the myocardial tissues of beagle dogs following the administration of NC to investigate the role of NC in vivo and the associated biological processes. A bioinformatic analysis was used to predict the in vivo biological processes that MAG, CHE, and HE were involved in. Molecular docking was used to simulate the binding between compounds and their targets. The effect of the compounds on ion channels in cardiomyocytes was evaluated through a patch clamp experiment. Organ-on-a-chip (OOC) technology was developed to mimic the physiological conditions of the heart in vivo. Proteomic and bioinformatic analyses demonstrated that the four compounds of Z. nitidum are extensively involved in various cardiovascular-related biological pathways. The findings from the patch clamp experiments indicate that NC, CHE, MAG, and HE elicit a distinct activation or inhibition of the I_K1 and I_Ca-L in cardiomyocytes. Finally, the anti-inflammatory effects of the compounds on cardiomyocytes were verified using OOC technology. NC, CHE, MAG, and HE demonstrate anti-inflammatory effects through their specific interactions with prostaglandin-endoperoxide synthase 2 (PTGS2) and significantly influence ion channels in cardiomyocytes. Our study provides a foundation for utilizing NC, CHE, MAG, and HE in the treatment of cardiovascular diseases. Full article

In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies. Full article

The purpose of this study is to explore the pharmacological mechanism of icariin (ICA) in the treatment of Alzheimer’s disease (AD) based on network pharmacology and network molecular docking technology. In order to investigate the regulatory effect of ICA on the expression level of AD pathological phosphorylation regulatory proteins, this study further explored the possible molecular mechanism of ICA regulating AD autophagy through network pharmacology. Macromolecular docking network was verified by Autodock Vina 1.1.2 software. The main active ingredients of ICA, the physicochemical properties, and pharmacokinetic information of ICA were predicted using online databases and relevant information. The results showed that the targets of MAPK3, AKT1, HSP90AA1, ESR1, and HSP90AA1 were more critical in the treatment of AD. Autophagy, apoptosis, senescence factors, phosphatidylinositide 3-kinase/protein kinase B (P13K/AKT) signaling pathway, MAKP, mTOR, and other pathways were significantly associated with AD. Docking of ICA with HIF-1, BNIP3, PINK1, and Parkin pathway molecules showed that the key targets of the signaling pathway were more stably bound to ICA, which may provide a better pathway for ICA to regulate autophagy by providing a better pathway. ICA can improve AD, and its mechanism may be related to the P13K/AKT, MAKP, and mTOR signaling pathways, thereby regulating autophagy-related proteins. Full article