Search Results (426)

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected ($T_C$) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the $T_C$ and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the $T_C$ image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the $T_C$ and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics. Full article

Background: Platelets, a type of anucleated cell, play a crucial role in cardiovascular diseases (CVDs). Therefore, targeting platelet activation is essential for mitigating CVDs. Endogenous agonists, such as collagen, activate platelets by initiating signal transduction through specific platelet receptors, leading to platelet aggregation. Eugenol, primarily sourced from clove oil, is known for its antibacterial, anticancer, and anti-inflammatory properties, making it a valuable medicinal agent. In our previous study, eugenol was shown to inhibit platelet aggregation induced by collagen and arachidonic acid. We concluded that eugenol exerts a potent inhibitory effect on platelet activation by targeting the PLCγ2–PKC and cPLA2-TxA2 pathways, thereby suppressing platelet aggregation. In our current study, we found that eugenol significantly inhibits NF-κB activation. This led us to investigate the relationship between the NF-κB and cPLA2 pathways to elucidate how eugenol suppresses platelet activation. Methods: In this study, we prepared platelet suspensions from the blood of healthy human donors to evaluate the inhibitory mechanisms of eugenol on platelet activation. We utilized immunoblotting and confocal microscopy to analyze these mechanisms in detail. Additionally, we assessed the anti-thrombotic effect of eugenol by observing fluorescein-induced platelet plug formation in the mesenteric microvessels of mice. Results: For immunoblotting and confocal microscopy studies, eugenol significantly inhibited NF-κB-mediated signaling events stimulated by collagen in human platelets. Specifically, it reduced the phosphorylation of IKK and p65 and prevented the degradation of IκBα. Additionally, CAY10502, a cPLA2 inhibitor, significantly reduced NF-κB-mediated signaling events. In contrast, BAY11-7082, an IKK inhibitor, did not affect collagen-stimulated cPLA2 phosphorylation. These findings suggest that cPLA2 acts as an upstream regulator of NF-κB activation during platelet activation. Furthermore, both BAY11-7082 and CAY10502 significantly reduced the collagen-induced rise in intracellular calcium levels. In the animal study, eugenol demonstrated potential as an anti-thrombotic agent by significantly reducing platelet plug formation in fluorescein-irradiated mouse mesenteric microvessels. Conclusion: Our study uncovered a novel pathway in platelet activation involving the cPLA2-NF-κB axis, which plays a key role in the antiplatelet effects of eugenol. These findings suggest that eugenol could serve as a valuable and potent prophylactic or therapeutic option for arterial thrombosis. Full article

In recent years, small-scale microgrids have become popular in the power system industry because they provide an efficient electrical power generation platform to guarantee autonomy and independence from the power grid, which is a critical feature in cases of catastrophic events or remote areas. On the other hand, due to the short distances among multiple distribution generation systems in small-scale microgrids, the interconnection couplings among them increase significantly, which jeopardizes the stability of the entire system. Therefore, this work proposes a novel method to design decentralized robust controllers based on a retrofit model predictive control scheme to tackle the issue of instability due to the short distances among generation systems. In this approach, the retrofit model predictive controller receives the measured feedback signal from the interconnection current and generates a control command signal to limit the interconnection current to prevent instability. To design a retrofit controller, only the model of a robust closed-loop system, as well as an interconnection line, is required. The model predictive control signal is added in parallel to the control signal from the existing robust voltage source inverter controller. Simulation results demonstrate the superior performance of the proposed technique as compared with the virtual impedance and retrofit linear quadratic regulator techniques (benchmarks) with respect to peak-load demand, plug-and-play capability, nonlinear load, and inverter efficiency. Full article

Topology optimization has become a popular tool for designing optimal shapes while meeting specific objectives and restrictions. However, the resulting shape from the optimization process may not be easy to manufacture using typical methods like machining and may require interpretation and validation. Additionally, the final shape depends on chosen parameters. In this study, we conduct a sensitivity analysis of the main parameters involved in 3D topology optimization—penalization and filter radius—focusing on the density-based method. We analyze the features and characteristics of the results, concluding that a machinable and low interpretable part is not an attainable result in by-default topology optimization. Therefore, we propose a new method for obtaining more manufacturable and easily interpretable parts. The main goal is to assist designers in choosing appropriate parameters and understanding what to consider when seeking optimized shapes, giving them a new plug-and-play tool for manufacturable designs. We chose the density-based topology optimization method due to its popularity in commercial packages, and the conclusions may directly influence designers’ work. Finally, we verify the study results through different cases to ensure the validity of the conclusions. Full article

The existing image-restoration methods are only effective for specific degradation tasks, but the type of image degradation in practical applications is unknown, and mismatch between the model and the actual degradation will lead to performance decline. Attention mechanisms play an important role in image-restoration tasks; however, it is difficult for existing attention mechanisms to effectively utilize the continuous correlation information of image noise. In order to solve these problems, we propose a Progressive and Efficient All-in-one Image Restoration Lightweight Network (PerNet). The network consists of a Plug-and-Play Efficient Local Attention Module (PPELAM). The PPELAM is composed of multiple Efficient Local Attention Units (ELAUs) and PPELAM can effectively use the global information and horizontal and vertical correlation of image degradation features in space, so as to reduce information loss and have a small number of parameters. PerNet is able to learn the degradation properties of images very well, which allows us to reach an advanced level in image-restoration tasks. Experiments show that PerNet has excellent results for typical restoration tasks (image deraining, image dehazing, image desnowing and underwater image enhancement), and the excellent performance of ELAU combined with Transformer in the ablation experiment chapter further proves the high efficiency of ELAU. Full article

Local indoor farming plays a significant role in the sustainable food production sector. The operation and energy costs, however, have led to bankruptcy and difficulties in cost management of indoor farming operations. To control the volatility and reduce the electricity costs for indoor farming, the agrivoltaics agrotunnel introduced here uses: (1) high insulation for a building dedicated to vertical growing, (2) high-efficiency light emitting diode (LED) lighting, (3) heat pumps (HPs), and (4) solar photovoltaics (PVs) to provide known electric costs for 25 years. In order to size the PV array, this study develops a thermal model for agrotunnel load calculations and validates it using the Hourly Analysis Program and measured data so the effect of plant evapotranspiration can be included. HPs are sized and plug loads (i.e., water pump energy needed to provide for the hybrid aeroponics/hydroponics system, DC power running the LEDs hung on grow walls, and dehumidifier assisting in moisture condensation in summer) are measured/modeled. Ultimately, all models are combined to establish an annual load profile for an agrotunnel that is then used to model the necessary PV to power the system throughout the year. The results find that agrivoltaics to power an agrotunnel range from 40 to 50 kW and make up an area from 3.2 to 10.48 m²/m² of an agrotunnel footprint. Net zero agrotunnels are technically viable although future work is needed to deeply explore the economics of localized vertical food growing systems. Full article

Platelets play a significant role in hemostasis, forming plugs at sites of vascular injury to limit blood loss. However, if platelet activation is not controlled, it can lead to thrombotic events, such as myocardial infarction and stroke. To prevent this, antiplatelet agents are used in clinical settings to limit platelet activation in patients at risk of arterial thrombotic events. However, their use can be associated with a significant risk of bleeding. An enhanced comprehension of platelet signaling mechanisms should facilitate the identification of safer targets for antiplatelet therapy. Over the past decade, our comprehension of the breadth and intricacy of signaling pathways that orchestrate platelet activation has expanded exponentially. Several recent studies have provided further insight into the regulation of platelet signaling events and identified novel targets against which to develop novel antiplatelet agents. Antiplatelet drugs are essential in managing atherothrombotic vascular disease. The current antiplatelet therapy in clinical practice is limited in terms of safety and efficacy. Novel compounds have been developed in response to patient variability and resistance to aspirin and/or clopidogrel. Recent studies based on randomized controlled trials and systematic reviews have definitively demonstrated the role of antiplatelet therapy in reducing the risk of cardiovascular events. Antiplatelet therapy is the recommended course of action for patients with established atherosclerosis. These studies compared monotherapy with a P2Y12 inhibitor versus aspirin for secondary prevention. However, in patients undergoing percutaneous coronary intervention, it is still unclear whether the efficacy of P2Y12 inhibitor monotherapy after a short course of dual antiplatelet therapy depends on the type of P2Y12 inhibitor. This paper focuses on the advanced-stage evaluation of several promising antiplatelet drugs. Full article

Geothermal energy plays a crucial role in the large-scale deep decarbonisation process and the transition of energy structure in our country. Due to the complex reservoir environment of geothermal energy, characterised by low porosity and permeability, conventional fracturing methods struggle to create a complex network of fractures. Temporary plugging and diverting fracturing technology (TPDF) is a key technology to improve the efficiency of geothermal reservoir extraction. However, there is still a lack of knowledge about the migration and sealing law of temporary plugging agents in complex fractures. Therefore, in this study, two multiphase flow models of temporary plugging particle transport at the fracture slit and inside the complex fracture were established by using a Computational Fluid Dynamics (CFD)-Discrete Element Method (DEM) algorithm. The influence of fracturing fluid concentration, temperature, the concentration of temporary plugging particles, and particle size combinations on migration blocking in fractures was investigated. The simulation results indicate the following: High-viscosity fracturing fluid may cause plugging particles to adhere to each other to form clusters of plugging particles, reducing dispersion during transport and slowing down the velocity of the plugging particles. A particle concentration that is too high does not have a better temporary plugging effect. The use of different combinations of particle sizes is significantly better than using a single particle size, which is a key factor for the success of fracture plugging. The research findings are of great theoretical and practical significance for scaled-up, vibration-controlled fracturing technology in geothermal reservoirs. Full article

The complex and critical global issues of the 21st century resulting from the unsustainable growth of tourism and hospitality, like air, land, and water pollution, have exacerbated concerns over whether educational institutions equip future managers and employees with adequate skills to meet the new demands of the current era. These ever-growing global sustainability issues stemming from the sophisticated interactions between people and the planet have no simple answers. They require well-skilled critical thinkers disposed of analyticity and systematicity to consider them and make positive contributions through their sustainable decisions. Despite this recognition, there are limited studies of the tourism and hospitality industry focusing on this crucial topic, and it is unclear how sustainability knowledge may result in more sustainable decision-making abilities. Accordingly, the current study proposed a model that links tourism and hospitality students’ sustainability knowledge to their sustainable tourism decision-making, testing the mediating role of critical thinking. Applying a quantitative research design, the researchers used a self-administered online survey to collect data from 146 full-time tourism students in Northern Cyprus. The PROCESS plug-in for the statistical package for social sciences was used to test the hypotheses of this study. The result of this study revealed that sustainability knowledge and critical thinking play significant roles in students’ sense of sustainable tourism competency and their sustainable tourism decision-making power. This study discusses how critical thinking serves as a mediating factor between knowledge of environmental sustainability, perceived competency in sustainable tourism, and sustainable decision-making ability. This study offers a more nuanced view of critical thinking’s function in terms of converting knowledge into sustainable tourism practices. Full article

In microfluidic systems, it is important to maintain flow stability to execute various functions, such as chemical reactions, cell transportation, and liquid injection. However, traditional flow sources, often bulky and prone to unpredictable fluctuations, limit the portability and broader application of these systems. Existing fluidic stabilizers, typically designed for specific flow sources, lack reconfigurability and adaptability in terms of the stabilization ratios. To address these limitations, a modular and standardized stabilizer system with tunable stabilization ratios is required. In this work, we present a Lego-like modular microfluidic stabilizer system, which is fabricated using 3D printing and offers multi-level stabilization combinations and customizable stabilization ratios through the control of fluidic RC constants, making it adaptable to various microfluidic systems. A simplified three-element circuit model is used to characterize the system by straightforwardly extracting the RC constant without intricate calculations of the fluidic resistance and capacitance. By utilizing a simplified three-element model, the stabilizer yields two well-fitted operational curves, demonstrating an R-square of 0.95, and provides an optimal stabilization ratio below 1%. To evaluate the system’s effectiveness, unstable input flow at different working frequencies is stabilized, and droplet generation experiments are conducted and discussed. The results show that the microfluidic stabilizer system significantly reduces flow fluctuations and enhances droplet uniformity. This system provides a new avenue for microfluidic stabilization with a tunable stabilization ratio, and its plug-and-play design can be effectively applied across diverse applications to finely tune fluid flow behaviors in microfluidic devices. Full article

Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention ($O_2$SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. $O_2$SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method’s effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach. Full article

The integration of Synthetic Aperture Radar (SAR) imaging technology with deep neural networks has experienced significant advancements in recent years. Yet, the scarcity of high-quality samples and the difficulty of extracting prior information from SAR data have experienced limited progress in this domain. This study introduces an innovative sparse SAR imaging approach using a self-supervised non-local asymmetric pixel-shuffle blind spot network. This strategy enables the network to be trained without labeled samples, thus solving the problem of the scarcity of high-quality samples. Through asymmetric pixel-shuffle downsampling (AP) operation, the spatial correlation between pixels is broken so that the blind spot network can adapt to the actual scene. The network also incorporates a non-local module (NLM) into its blind spot architecture, enhancing its capability to analyze a broader range of information and extract more comprehensive prior knowledge from SAR data. Subsequently, Plug and Play (PnP) technology is used to integrate the trained network into the sparse SAR imaging model to solve the regularization term problem. The optimization of the inverse problem is achieved through the Alternating Direction Method of Multipliers (ADMM) algorithm. The experimental results of the unlabeled samples demonstrate that our method significantly outperforms traditional techniques in reconstructing images across various regions. Full article

Portal hypertension from chronic liver disease leads to the formation of collateral blood vessels called spontaneous portosystemic shunts (SPSS). These shunts may form from existing vessels or through neo-angiogenesis. Their location affects clinical outcomes due to varying risks and complications. This review summarizes current knowledge on SPSS, covering their clinical impact and management strategies. Recent data suggest that SPSS increases the risk of variceal bleeding, regardless of shunt size. The size of the shunt is crucial in the rising incidence of hepatic encephalopathy (HE) linked to SPSS. It also increases the risk of portopulmonary hypertension and portal vein thrombosis. Detecting and assessing SPSS rely on computed tomography (CT) and magnetic resonance imaging. CT enables precise measurements and the prediction of cirrhosis progression. Management focuses on liver disease progression and SPSS-related complications, like HE, variceal bleeding, and portopulmonary hypertension. Interventional radiology techniques such as balloon-occluded, plug-assisted, and coil-assisted retrograde transvenous obliteration play a pivotal role. Surgical options are rare but are considered when other methods fail. Liver transplantation (LT) often resolves SPSS. Intraoperative SPSS ligation is still recommended in patients at high risk for developing HE or graft hypoperfusion. Full article

Detail preservation is a major challenge for single image super-resolution (SISR). Many deep learning-based SISR methods focus on lightweight network design, but these may fall short in real-world scenarios where performance is prioritized over network size. To address these problems, we propose a novel plug-and-play attention module, rich elastic mixed attention (REMA), for SISR. REMA comprises the rich spatial attention module (RSAM) and the rich channel attention module (RCAM), both built on Rich Structure. Based on the results of our research on the module’s structure, size, performance, and compatibility, Rich Structure is proposed to enhance REMA’s adaptability to varying input complexities and task requirements. RSAM learns the mutual dependencies of multiple LR-HR pairs and multi-scale features, while RCAM accentuates key features through interactive learning, effectively addressing detail loss. Extensive experiments demonstrate that REMA significantly improves performance and compatibility in SR networks compared to other attention modules. The REMA-based SR network (REMA-SRNet) outperforms comparative algorithms in both visual effects and objective evaluation quality. Additionally, we find that module compatibility correlates with cardinality and in-branch feature bandwidth, and that networks with high effective parameter counts exhibit enhanced robustness across various datasets and scale factors in SISR. Full article

The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images. Full article