Search Results (500)

Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative adversarial networks (GANs) have demonstrated considerable promise. However, such approaches are frequently constrained by their reliance on homogeneous discriminators possessing identical architectures, a limitation that can precipitate the emergence of undesirable artifacts in the resultant fused images. To surmount this challenge, this paper introduces HCSPNet, a novel GAN-based framework. HCSPNet distinctively incorporates heterogeneous dual discriminators, meticulously engineered for the fusion of disparate source images inherent in the IVIF task. This architectural design ensures the steadfast preservation of critical information from the source inputs, even when faced with scenarios of image degradation. Specifically, the two structurally distinct discriminators within HCSPNet are augmented with adaptive salient information distillation (ASID) modules, each uniquely structured to align with the intrinsic properties of infrared and visible images. This mechanism impels the discriminators to concentrate on pivotal components during their assessment of whether the fused image has proficiently inherited significant information from the source modalities—namely, the salient thermal signatures from infrared imagery and the detailed textural content from visible imagery—thereby markedly diminishing the occurrence of unwanted artifacts. Comprehensive experimentation conducted across multiple publicly available datasets substantiates the preeminence and generalization capabilities of HCSPNet, underscoring its significant potential for practical deployment. Additionally, we also prove that our proposed heterogeneous dual discriminators can serve as a plug-and-play structure to improve the performance of existing GAN-based methods. Full article

Esterification is a key transformation in the production of lubricants, pharmaceuticals, and fine chemicals. Conventional processes employing homogeneous acid catalysts suffer from limitations such as corrosive byproducts, energy-intensive separation, and poor catalyst reusability. This review provides a comprehensive overview of heterogeneous catalytic systems, including ion exchange resins, zeolites, metal oxides, mesoporous materials, and others, for improved ester synthesis. Recent advances in membrane-integrated reactors, such as pervaporation and nanofiltration, which enable continuous water removal, shifting equilibrium and increasing conversion under milder conditions, are reviewed. Dual-functional membranes that combine catalytic activity with selective separation further enhance process efficiency and reduce energy consumption. Enzymatic systems using immobilized lipases present additional opportunities for mild and selective reactions. Future directions emphasize the integration of pervaporation membranes, hybrid catalyst systems combining biocatalysts and metals, and real-time optimization through artificial intelligence. Modular plug-and-play reactor designs are identified as a promising approach to flexible, scalable, and sustainable esterification. Overall, the interaction of catalyst development, membrane technology, and digital process control offers a transformative platform for next-generation ester synthesis aligned with green chemistry and industrial scalability. Full article

Spina Bifida (SB) and Congenital Diaphragmatic Hernia (CDH) are complex congenital anomalies that pose significant challenges in pediatric healthcare. This review synthesizes recent advancements in understanding the genetic, metabolic, and environmental factors contributing to these conditions, with the aim of integrating mechanistic insights into therapeutic innovations. In SB, key findings highlight the roles of KCND3, a critical regulator of spinal cord development, and VANGL2, essential for planar cell polarity and neural tube closure. MicroRNAs such as miR-765 and miR-142-3p are identified as key regulators of these genes, influencing neural development. Additionally, telomere shortening—a marker of cellular senescence—alongside disruptions in folate metabolism and maternal nutritional deficiencies, significantly increases the risk of SB. These findings underscore the crucial role of telomere integrity in maintaining neural tissue homeostasis during embryonic development. For CDH, genetic deletions, including those on chromosome 15q26, and chromosomal abnormalities have been shown to disrupt lung and vascular development, profoundly impacting neonatal outcomes. MicroRNAs miR-379-5p and miR-889-3p are implicated in targeting essential genes such as IGF1 and FGFR2, which play pivotal roles in pulmonary function. Promising emerging therapies, including degradable tracheal plugs and fibroblast growth factor-based treatments, offer potential strategies for mitigating pulmonary hypoplasia and improving clinical outcomes. This review underscores the intricate interplay of genetic, metabolic, and environmental pathways in SB and CDH, identifying critical molecular targets for diagnostics and therapeutic intervention. By integrating findings from genetic profiling, in vitro models, and clinical studies, it aims to inform future research directions and optimize patient outcomes through collaborative, multidisciplinary approaches. Full article

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. The primary control employs droop-based regulation, and a distributed secondary layer enables proportional power sharing via peer-to-peer communication. A modified IEEE 14-bus test system is implemented in PSCAD to validate the proposed strategy under scenarios including load disturbances, reactive demand variations, and plug-and-play operations. Compared to conventional droop-based control, the proposed framework reduces frequency nadir by up to 0.3 Hz and voltage deviation by 1.1%, achieving optimised sharing indices. Results demonstrate that consensus-based coordination enhances dynamic stability and power-sharing fairness and supports the flexible integration of heterogeneous assets without requiring centralised control. Full article

This paper presents an approach for integrating data between a production system and its digital twin, focusing on achieving proactivity in production control. Recognizing the unique nature of each production system, this research highlights that a universal, plug-and-play solution is only partially feasible, primarily through general guidelines. The study successfully applied and automated proposed data acquisition methods, resulting in a functional, simulation-based digital twin that adheres to the latest ISO standards. The developed solution incorporates multiple data acquisition strategies, including files containing comma-separated values, a permanent connection to the production control system database, open platform communications unified architecture, and external command files for scenario alteration. The main motivation behind the presented implementation is its application on the shop-floors of small and medium enterprises, where it could provide useful tools for keeping up with the ever-rising competition in the manufacturing sector. This integrated approach allows for affordable and accurate system representation within the proactive simulation concept. The methodology was empirically validated across two distinct production systems: a lab-scale food and beverage line focusing on product tracking, and a sub-assembly line with automated guided vehicle optimization. Despite system variability, the core data acquisition methods demonstrated remarkable adaptability. Full article

Tracking unmanned aerial vehicles (UAVs) in infrared video is an essential technology for the anti-UAV task. Given frequent UAV target disappearances caused by occlusion or moving out of view, global trackers, which have the unique ability to recapture targets, are widely used in infrared UAV tracking. However, global trackers perform poorly when dealing with large target scale variation because they cannot maintain approximate consistency between target sizes in the template and the search region. To enhance the scale adaptation of global trackers, we propose a plug-and-play scale adaptation enhancement module (SAEM). This can generate a scale adaptation enhancement kernel according to the target size in the previous frame, and then perform implicit scale adaptation enhancement on the extracted target template features. To optimize training, we introduce an auxiliary branch to supervise the learning of SAEM and add Gaussian noise to the input size to improve its robustness. In addition, we propose a one-stage anchor-free global tracker (OSGT), which has a more concise structure than other global trackers to meet the real-time requirement. Extensive experiments on three Anti-UAV Challenge datasets and the Anti-UAV410 dataset demonstrate the superior performance of our method and verify that our proposed SAEM can effectively enhance the scale adaptation of existing global trackers. Full article

Pulmonary dynamic contrast-enhanced (DCE) MRI is clinically useful for assessing pulmonary perfusion, but its signal-to-noise ratio (SNR) is limited. A self-supervised learning network-based plug-and-play (PnP) denoising model was developed to improve the image quality of pulmonary perfusion MRI. A dataset of patients with suspected pulmonary diseases was used. Asymmetric pixel-shuffle downsampling blind-spot network (AP-BSN) training inputs were two-dimensional background-subtracted perfusion images without clean ground truth. The AP-BSN is incorporated into a PnP model (PnP-BSN) for balancing noise control and image fidelity. Model performance was evaluated by SNR, sharpness, and overall image quality from two radiologists. The fractal dimension and k-means segmentation of the pulmonary perfusion images were calculated for comparing denoising performance. The model was trained on 29 patients and tested on 8 patients. The performance of PnP-BSN was compared to denoising convolutional neural network (DnCNN) and a Gaussian filter. PnP-BSN showed the highest reader scores in terms of SNR, sharpness, and overall image quality as scored by two radiologists. The expert scoring results for DnCNN, Gaussian, and PnP-BSN were 2.25 ± 0.65, 2.44 ± 0.73, and 3.56 ± 0.73 for SNR; 2.62 ± 0.52, 2.62 ± 0.52, and 3.38 ± 0.64 for sharpness; and 2.16 ± 0.33, 2.34 ± 0.42, and 3.53 ± 0.51 for overall image quality (p < 0.05 for all). PnP-BSN outperformed DnCNN and a Gaussian filter for denoising pulmonary perfusion MRI, which led to improved quantitative fractal analysis. Full article

The increasing demand for IoT solutions in smart cities, coupled with the increasing use of sensors and actuators and automation in these environments, has highlighted the need for efficient communication between Internet of Things (IoT) devices. The success of such systems relies on interactions between devices that are governed by communication protocols which define how information is exchanged. However, the heterogeneity of sensor networks (wired and wireless) often leads to incompatibility issues, hindering the seamless integration of diverse devices. To address these challenges, standardisation is essential to promote scalability and interoperability across IoT systems. The IEEE 1451 standard provides a solution by defining a common interface that enables plug-and-play integration and enhances flexibility across diverse IoT devices. This standard enables seamless communication between devices from different manufacturers, irrespective of their characteristics, and ensures compatibility via the Transducer Electronic Data Sheet (TEDS) and the Network Capable Application Processor (NCAP). By reducing system costs and promoting adaptability, the standard mitigates the complexities posed by heterogeneity in IoT systems, fostering scalable, interoperable, and cost-effective solutions for IoT systems. The IEEE 1451 standard addresses key barriers to system integration, enabling the full potential of IoT technologies. This paper aims to provide a comprehensive review of the challenges transducer networks face around IoT applications, focused on the context of smart cities. This review underscores the significance and potential of the IEEE 1451 standard in establishing a framework that enables the harmonisation of IoT applications. The primary contribution of this work lies in emphasising the importance of adopting the standards for the development of harmonised and flexible systems. Full article

The electrical grid is undergoing increasing integration of decentralized power sources connected to the low-voltage network. In this context, the concept of a microgrid has emerged as a system comprising small-scale energy sources, loads, and storage devices, coordinated to operate as a single controllable entity capable of functioning in either grid-connected or islanded mode. The microgrid may be organized in a centralized configuration, such as a master-slave scheme, wherein the centralized converter, i.e., the grid-forming converter (GFC), plays a pivotal role in ensuring system stability and control. This paper introduces a plug-in repetitive controller (RC) strategy tuned to even harmonic orders for application in a three-phase GFC, diverging from the conventional approach that focuses on odd harmonics. The proposed control is designed within a synchronous reference frame and is targeted at centralized AC microgrids, particularly during islanded operation. Simulation results are presented to assess the microgrid’s power flow and power quality, thereby evaluating the performance of the GFC. Additionally, the proposed control was implemented on a Texas Instruments TMS320F28335 digital signal processor and validated through hardware-in-the-loop (HIL) simulation using the Typhoon HIL 600 platform, considering multiple scenarios with both linear and nonlinear loads. The main results highlight that the RC improves voltage regulation, mitigates harmonic distortion, and increases power delivery capability, thus validating its effectiveness for GFC operation. Full article

Although cooperative perception enhances situational awareness by enabling vehicles to share intermediate features, real-world deployment faces challenges due to heterogeneity in sensor modalities, architectures, and encoder parameters across agents. These domain gaps often result in semantic inconsistencies among the shared features, thereby degrading the quality of feature fusion. Existing approaches either necessitate the retraining of private models or fail to adapt to newly introduced agents. To address these limitations, we propose PnPDA⁺, a unified and modular domain adaptation framework designed for heterogeneous multi-vehicle cooperative perception. PnPDA⁺ consists of two key components: a Meta Feature Extraction Network (MFEN) and a Plug-and-Play Domain Adapter (PnPDA). MFEN extracts domain-aware and frame-aware meta features from received heterogeneous features, encoding domain-specific knowledge and spatial-temporal cues to serve as high-level semantic priors. Guided by these meta features, the PnPDA module performs adaptive semantic conversion to enhance cross-agent feature alignment without modifying existing perception models. This design ensures the scalable integration of emerging vehicles with minimal fine-tuning, significantly improving both semantic consistency and generalization. Experiments on OPV2V show that PnPDA⁺ outperforms state-of-the-art methods by 4.08% in perception accuracy while preserving model integrity and scalability. Full article

Despite the impressive performance of existing image compression algorithms, they struggle to balance perceptual quality and high image fidelity. To address this issue, we propose a novel invertible neural network-based remote sensing image compression (INN-RSIC) method. Our approach captures the compression distortion from an existing image compression algorithm and encodes it as Gaussian-distributed latent variables using an INN, ensuring that the distortion in the decoded image remains independent of the ground truth. By using the inverse mapping of the INN, we input the decoded image with randomly resampled Gaussian variables, generating enhanced images with improved perceptual quality. We incorporate channel expansion, Haar transformation, and invertible blocks into the INN to accurately represent compression distortion. Additionally, a quantization module (QM) is introduced to mitigate format conversion impact, enhancing generalization and perceptual quality. Extensive experiments show that INN-RSIC achieves superior perceptual quality and fidelity compared to existing algorithms. As a lightweight plug-and-play (PnP) method, the proposed INN-based enhancer can be easily integrated into existing high-fidelity compression algorithms, enabling flexible and simultaneous decoding of images with enhanced perceptual quality. Full article

A self-centering prefabricated concrete frame structure has good seismic performance, and its seismic capacity is mainly provided by the recovery force of the unbonded prestressing tendons and the energy-dissipation deformation capacity of embedded steel reinforcement. Relocating embedded reinforcement to external positions enables replaceability of energy dissipation components. And the configuration of external energy dissipation components is the primary factor influencing their energy dissipation capacity. Based on the existing external “Plug & Play” configuration, the internal steel bar size and material properties such as those of steel bar and filling material were varied in this study, and then, cyclic tension-compression experimental studies and numerical simulations were conducted to investigate the energy dissipation performance index and key influencing factors of this type of external composite steel bar. The research results showed that the composite steel bars designed in the experiments exhibited superior overall energy dissipation performance. Specimens utilizing Q345B steel as the core material outperformed those with Grade 30 steel. Moreover, the slenderness ratio of the composite steel bars and the diameter ratio between the end region and weakened segment of the internal steel bars were identified as critical parameters governing energy dissipation performance, and recommendations for optimal parameter ranges were discussed. This study provides a theoretical foundation for implementing external composite steel bars in self-centering structural systems. Full article

This paper investigates the distributed time-varying (TV) resource management problem (RMP) for microgrids (MGs) within a multi-agent system (MAS) framework. A novel fixed-time (FXT) distributed optimization algorithm is proposed, capable of operating over switching communication graphs and handling both local inequality and global equality constraints. By incorporating a time-decaying penalty function, the algorithm achieves an FXT consensus on marginal costs and ensures asymptotic convergence to the optimal TV solution of the original RMP. Unlike the prior methods with centralized coordination, the proposed algorithm is fully distributed, scalable, and privacy-preserving, making it suitable for real-time deployment in dynamic MG environments. Rigorous theoretical analysis establishes FXT convergence under both identical and nonidentical Hessian conditions. Simulations on the IEEE 14-bus system validate the algorithm’s superior performance in convergence speed, plug-and-play adaptability, and robustness to switching topologies. Full article

In this paper, four kinds of flooding systems, high-molecular-weight polymer (HMP), low-molecular-weight polymer (LMP), hydrophobic association polymer (HAP), and LMP/petroleum sulfonate (PS), are preferred. By comparing the static performance, their good basic characteristics as an oil displacement system are clarified. The application concentration range of the polymer solution is optimized and designed in combination with core injectivity experiments and mobility control theory. The oil displacement system and its injection volume have been optimized via three parallel core flooding experiments. The results show that the increase of the polymer molecular weight and the association will enhance the viscosity-increasing performance, viscosity stability, viscoelasticity, and hydrodynamic characteristic size of the solution. According to whether the injection pressure curve reaches equilibrium and the time required for equilibrium, the matching relationship between the polymer and the reservoir can be divided into plugging, flow difficulty and flow smoothly. Based on the mobility control theory, the minimum mobility of the target core occurs when the water saturation is 30–40%. Therefore, the polymer formulation for the application of combined cores with viscosities of 50 mD, 210 mD, and 350 mD is set at 1500 mg/L for LMP and 800 mg/L for MAP. HAP has the best profile improvement effect, but its lowest EOR is 9.68%, which mainly acts on high-permeability layers; LMP can produce more remaining oil in middle-permeability layers, and its EOR can reach 12.01%; LMP/PS can give full play to the oil displacement performance of the polymer and the oil washing ability of the surfactant, and its highest EOR is 21.32%. Meanwhile, the emulsification effect also makes the profile improvement last longer. According to the EOR efficiency and final oil recovery, the optimal injection volume of LMP/PS can be designed to be 0.6–0.7 PV. Full article

Over the past several decades, viral vector-based vaccines have emerged as some of the most versatile and potent platforms in modern vaccinology. Their capacity to deliver genetic material encoding target antigens directly into host cells enables strong cellular and humoral immune responses, often superior to what traditional inactivated or subunit vaccines can achieve. This has accelerated their application to a wide array of pathogens and disease targets, from well-established threats like HIV and malaria to emerging infections such as Ebola, Zika, and SARS-CoV-2. The COVID-19 pandemic further highlighted the agility of viral vector platforms, with several adenovirus-based vaccines quickly authorized and deployed on a global scale. Despite these advances, significant challenges remain. One major hurdle is pre-existing immunity against commonly used vector backbones, which can blunt vaccine immunogenicity. Rare but serious adverse events, including vector-associated inflammatory responses and conditions like vaccine-induced immune thrombotic thrombocytopenia (VITT), have raised important safety considerations. Additionally, scaling up manufacturing, ensuring consistency in large-scale production, meeting rigorous regulatory standards, and maintaining equitable global access to these vaccines present profound logistical and ethical dilemmas. In response to these challenges, the field is evolving rapidly. Sophisticated engineering strategies, such as integrase-defective lentiviral vectors, insect-specific flaviviruses, chimeric capsids to evade neutralizing antibodies, and plug-and-play self-amplifying RNA approaches, seek to bolster safety, enhance immunogenicity, circumvent pre-existing immunity, and streamline production. Lessons learned from the COVID-19 pandemic and prior outbreaks are guiding the development of platform-based approaches designed for rapid deployment during future public health emergencies. This review provides an exhaustive, in-depth examination of the historical evolution, immunobiological principles, current platforms, manufacturing complexities, regulatory frameworks, known safety issues, and future directions for viral vector-based vaccines. Full article