Search Results (30)

Inflammatory skin diseases are highly prevalent conditions characterized by complex immune responses that result in skin tissue damage and pain, significantly impacting patients’ physical health. Traditional therapeutic approaches, including oral administration and injections, continue to exhibit inherent limitations. Consequently, there is growing interest in exploring alternative drug delivery systems that offer more effective, targeted, and patient-friendly therapeutic options. Transdermal administration emerges as a promising solution for managing inflammatory skin diseases, facilitating sustained drug release, and reducing the frequency of dosing. This review provides a comprehensive overview of the skin barrier and critically summarizes clinically adopted transdermal drug delivery systems (TDDSs), including sonophoresis, iontophoresis, chemical penetration enhancers, and electroporation. Particular emphasis is placed on emerging advances in microneedle- and nanocarrier-facilitated transdermal delivery strategies. Moreover, the article synthesizes recent fundamental evidence regarding the application of TDDSs in the treatment of atopic dermatitis, psoriasis, and acne. This review examines fundamental research evaluating various transdermal drug delivery systems for the treatment of major inflammatory skin diseases, with an emphasis on their mechanisms of action, advantages, challenges, and future directions. Transdermal drug delivery systems hold the potential to deliver more efficient and safer treatment and management strategies for patients afflicted with inflammatory skin diseases. Full article

This article presents a novel tightly coupled dipole array (TCDA) with a bandwidth of 26.2:1 (VSWR < 3) across 0.20–5.23 GHz. By adding a new dual-stopband resistive frequency selective surface (RFSS) between the dipole and the floor, the short-circuit points formed by the floor at the frequency points corresponding to λ = 2 h and h are both eliminated (h is the height from the antenna to the floor). A specially integrated feed network is also applied to significantly reduce the complexity and profile height to 0.05 λ_low. The simulation and experimental results show that the designed TCDA has extremely wide bandwidth, good directivity and beam scanning potential. Compared with previous designs, it greatly extends the bandwidth, improves the gain, reduces the profile height, and simplifies the feeding method. Full article

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with maintained detection accuracy. Full article

Intestinal inflammation in piglets leads to diarrhea, decreased immune function, and dysbiosis of the gut microbiota. Probiotics are widely recognized and used in the prevention and control of enteritis in piglets. Parabacteroides goldsteinii (PG) is a probiotic, and there are few reports on the anti-inflammatory properties of this in piglets. Therefore, this study selected 10 Duroc × Landrace × Yorkshire (DLY) piglets aged 50 days and randomly divided them into a control group (CT group) and an experimental group (PG group) for a 14-day experiment. During days 1–7, the PG group established an inflammation model by gavage with 4% dextran sulfate sodium (DSS). The results showed that DSS increased the content of IL-6 and IL-8 (p < 0.05), while it decreased the content of IL-10 in the serum (p < 0.05), For days 8–14, the DSS-treated piglets were administered a 7.9 × 10⁸ CFU/mL PG suspension via gavage. The content of IL-6, IL-8, and IL-10 in the piglets did not differ between the CT and PG groups (p > 0.05). Some beneficial bacteria, such as Lactobacillales and Butyricimonas, were significantly enriched in the PG group (p < 0.05). The PG group showed lower alpha diversity (p < 0.05), and the metabolic pathways exhibited the highest abundance in the KEGG functional prediction analysis. The above studies confirm that PG alleviates intestinal inflammation and changes the gut microbial composition. Full article

Chitin synthase is an essential enzyme of the chitin synthesis pathway during molting. In this study, we identified and characterized a chitin synthase (EsCHS) gene in the Chinese mitten crab, Eriocheir sinensis. The spatio-temporal expression and functional role of EsCHS were investigated. The open reading frame of EsCHS was 4725 bp long and encoded 1574 amino acid residues that contained the typical domain structure of the glycosyltransferase family 2. Phylogenetic analysis revealed that EsCHS belongs to the group I chitin synthase family. The expression of EsCHS was found in regenerative limbs, the cuticle and the intestines. During the molting cycle, EsCHS began to increase in the pre-molt stage and reached a significant peak in the post-molt stage. The knockdown of EsCHS resulted in the significant downregulation of chitin biosynthesis pathway genes, including TRE, HK, G6PI, PAGM and UAP. Moreover, the long-term RNAi of EsCHS resulted in thinning procuticles, abnormal molting and high mortality, suggesting that EsCHS is indispensable for the formation of chitin in the cuticle during molting. In conclusion, EsCHS is involved in the chitin biosynthesis pathway and plays an important role in molting in E. sinensis. These findings highlight the potential of incorporating EsCHS into selective breeding programs to optimize molting regulation and improve growth performance in crustacean aquaculture. Full article

All eight pangolin species, especially captive Manis pentadactyla, are critically endangered and susceptible to various pathogenic microorganisms, causing mass mortality. They are involved in the complement system, iron transport system, and inflammatory factors. M. pentadactyla exhibited a higher abundance of opportunistic pathogens, Moraxella, which potentially evaded complement-mediated immune response by reducing C5 levels and counteracting detrimental effects through transferrin neutralization. In addition, we found that the major structure of C5a, an important inflammatory factor, was lacking in M. javanica. In brief, this study revealed the differences in immune factors and microbiome between M. javanica and M. pentadactyla, thus providing a theoretical basis for subsequent immunotherapy. Full article

This paper proposes a novel voltage estimation scheme for the modular multilevel converter (MMC) based on model predictive control (MPC). The developed strategy is presented by combining a disturbance observer (DOB) with an adaptive neural network (ANN) for voltage estimation in the MMC. Firstly, the ac-side and dc bus voltages are estimated as the disturbance items of the DOB which acts as the cost function during each control cycle and ensures the minimal computational cost. Then, the submodule (SM) capacitor voltage estimation is achieved based on the ANN with the estimated ac-side and dc bus voltages. The proposed method requires only one current sensor per arm and has a simple structure with three weights to be adjusted. Comprehensive simulation studies and experiments are presented to demonstrate its effectiveness and feasibility. The results indicate that the proposed method has a high accuracy, a fast dynamic response, and no effects on the original MPC performance. Full article

Corrosion is known as a breakdown effect that causes the deterioration of substances in enriched petroleum/gas conditions. This reaction occurs in all materials, which is highlighted in alloys. In the present study, the morphological properties, as well as the corrosion resistance behavior of the AISI1045 steel substrate coated with 321 austenitic stainless steel metal particulate fillers, were investigated. The electro-explosive spraying technique was employed to achieve a homogenous coating on the substrate surface. According to the results, the grain size of the 321 austenitic stainless steel coating layer was shrunk and reduced to 1–3 μm after the coating procedure. The coated layer also showed a homogenous and uniform thickness with an average value of 137 μm. Also, the average adhesion strength of 49.21 MPa was obtained between the sprayed coating and the substrate. The analytical analysis found the presence of Fe-Cr and Fe-Ni phases in the coating layer. The hardness of the original metal wire is 186 HV, and the microhardness of the coating after spraying is 232 HV. After subjecting the specimen to the corrosion examination, a 0.1961 mm/a corrosion rate was obtained for up to 120 h. Moreover, the corrosion products of CaCO₃, Fe₃O₄, and MgFe₂O₄ were determined by XRD analysis. Furthermore, the observed results were further confirmed by the data obtained from EPMA and EDS evaluations. Hence, this study implies the beneficial role of electro-explosive sprayed alloy 321 austenitic stainless steel in creating a protective layer against corrosion on 45 steel substrate in an enriched oil/water environment. Full article

SAR-ATR (synthetic aperture radar-automatic target recognition) is a hot topic in remote sensing. This work suggests a few-shot target recognition approach (FTL) based on the concept of transfer learning to accomplish accurate target recognition of SAR images in a few-shot scenario since the classic SAR ATR method has significant data reliance. At the same time, the strategy introduces a model distillation method to improve the model’s performance further. This method is composed of three parts. First, the data engine, which uses the style conversion model and optical image data to generate image data similar to SAR style and realize cross-domain conversion, can effectively solve the problem of insufficient training data of the SAR image classification model. Second is model training, which uses SAR image data sets to pre-train the model. Here, we introduce the deep Brownian distance covariance (Deep BDC) pooling layer to optimize the image feature representation so that the model can learn the image representation by measuring the difference between the joint feature function of the embedded feature and the edge product. Third, model fine-tuning, which freezes the model structure, except the classifier, and fine-tunes it by using a small amount of novel data. The knowledge distillation approach is also introduced simultaneously to train the model repeatedly, sharpen the knowledge, and enhance model performance. According to experimental results on the MSTAR benchmark dataset, the proposed method is demonstrably better than the SOTA method in the few-shot SAR ATR issue. The recognition accuracy is about 80% in the case of 10-way 10-shot. Full article

Wastewater is an abundant source of nutrients and energy. Under the circumstances of circular economy and carbon neutrality, resource recovery from wastewater has recently been motivated. The microalgal process is a promising alternative for resource recovery from wastewater, and it possesses potential for the cost reduction of wastewater treatment and fertilizer production, energy generation, and greenhouse gas emissions reduction. This paper reviews and discusses state-of-the-art microalgal process development, including microalgal species screening, configuration, biotic consortia construction, infection avoidance, nutrients balancing, operational parameter optimization, and biomass harvesting enhancement. Due to the lack of literature on practical applications, the microalgal process lacks economic and environmental feasibility assessments. Life cycle assessments from the perspective of circular economy and carbon neutrality on upscaled microalgal processes are required for various wastewater scenarios. To promote the upscaled application and successful implementation, efforts are also suggested to establish utilization guidelines, advanced recommendations, reliable standards, and proper safety evaluation criteria. This work could provide a reference and direct the follow-up research, development, and application of microalgae (MA)-based processes for resource recovery from wastewater. Full article

This paper proposes a feature fusion algorithm for solving the path planning problem of multiple unmanned aerial vehicles (UAVs) using GPS and communication denial conditions. Due to the blockage of GPS and communication, UAVs cannot obtain the precise position of a target, which leads to the failure of path planning algorithms. This paper proposes a feature fusion proximal policy optimization (FF-PPO) algorithm based on deep reinforcement learning (DRL); the algorithm can fuse image recognition information with the original image, realizing the multi-UAV path planning algorithm without an accurate target location. In addition, the FF-PPO algorithm adopts an independent policy for multi-UAV communication denial environments, which enables the distributed control of UAVs such that multi-UAVs can realize the cooperative path planning task without communication. The success rate of our proposed algorithm can reach more than 90% in the multi-UAV cooperative path planning task. Finally, the feasibility of the algorithm is verified by simulations and hardware. Full article

This paper develops a framework for geolocating a ground moving target with images taken from an unmanned aerial vehicle (UAV). Unlike the usual moving target geolocation approaches that rely heavily on a laser rangefinder, multiple UAVs, prior information of the target or motion assumptions, the proposed framework performs the geolocation of a moving target with monocular vision and does not have any of the above restrictions. The proposed framework transforms the problem of moving target geolocation to the problem of stationary target geolocation by matching corresponding points. In the process of corresponding point matching, we first propose a Siamese-network-based model as the base model to match corresponding points between the current frame and the past frame. Besides the introduction of a base model, we further designed an enhanced model with two outputs, where a row-ness loss and a column-ness loss are defined for achieving a better performance. For the precision of corresponding point matching, we propose a compensation value, which is calculated from the outputs of the enhanced model and improves the accuracy of corresponding point matching. To facilitate the research on corresponding point matching, we constructed a dataset containing various aerial images with corresponding point annotations. The proposed method is shown to be valid and practical via the experiments in simulated and real environments. Full article

Many visual SLAM systems are generally solved using natural landmarks or optical flow. However, due to textureless areas, illumination change or motion blur, they often acquire poor camera poses or even fail to track. Additionally, they cannot obtain camera poses with a metric scale in the monocular case. In some cases (such as when calibrating the extrinsic parameters of camera-IMU), we prefer to sacrifice the flexibility of such methods to improve accuracy and robustness by using artificial landmarks. This paper proposes enhancements to the traditional SPM-SLAM, which is a system that aims to build a map of markers and simultaneously localize the camera pose. By placing the markers in the surrounding environment, the system can run stably and obtain accurate camera poses. To improve robustness and accuracy in the case of rotational movements, we improve the initialization, keyframes insertion and relocalization. Additionally, we propose a novel method to estimate marker poses from a set of images to solve the problem of planar-marker pose ambiguity. Compared with the state-of-art, the experiments show that our system achieves better accuracy in most public sequences and is more robust than SPM-SLAM under rotational movements. Finally, the open-source code is publicly available and can be found at GitHub. Full article

In this paper, we propose a C51-Duel-IP (C51 Dueling DQN with Independent Policy) dynamic destination path-planning algorithm to solve the problem of autonomous navigation and avoidance of multiple Unmanned Aerial Vehicles (UAVs) in the communication denial environment. Our proposed algorithm expresses the Q function output by the Dueling network as a Q distribution, which improves the fitting ability of the Q value. We also extend the single-step temporal differential (TD) to the N-step timing differential, which solves the problem of inflexible updates of the single-step temporal differential. More importantly, we use an independent policy to achieve autonomous avoidance and navigation of multiple UAVs without any communication with each other. In the case of communication rejection, the independent policy can achieve the consistency of multiple UAVs and avoid the greedy behavior of UAVs. In multiple-UAV dynamic destination scenarios, our work includes path planning, taking off from different initial positions, and dynamic path planning, taking off from the same initial position. The hardware-in-the-loop (HITL) experiment results show that our C51-Duel-IP algorithm is much more robust and effective than the original Dueling-IP and DQN-IP algorithms in an urban simulation environment. Our independent policy algorithm has similar effects as the shared policy but with the significant advantage of running in a communication denial environment. Full article

This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target’s altitude, the proposed framework does not have the same limitations and performs geolocation of moving targets utilizing dual UAVs equipped with the low-quality navigation state sensors. Considering the Gaussian measurement errors and yaw-angle measurement bias provided by low-quality sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables the historical measurement data to be used to estimate the current position of the moving target; after that, we propose a target altitude estimation method based on multiview geometry, which utilizes multiple images, including historical images, to estimate the altitude of the moving target; finally, considering the negative influence of yaw-angle measurement bias on the processes of target altitude estimation and parameter regression, we take advantage of multiple iterations among the two processes to accurately estimate the moving target’s two-dimensional position and the yaw-angle measurement biases of two UAVs. The effectiveness and practicability of the framework proposed in this paper are proved by simulation experiments and actual flight experiments. Full article