Traditional ionic liquids are sensitive to humidity but with long response time and nonlinear response. Pure liquid-state ionic liquids are usually hard for dehydration which have ultralong response ...time for humidity sensing. The immobilization of ionic liquids provide a possible way for high performance humidity sensing. Hydrophobic materials and structures also promised faster response in humidity sensing, because of easier desorption of water. In this work, we prepared flexible humidity sensitive composites based on hydrophobic ionic liquid and polymer. The combination of hydrophobic ionic liquid with hydrophobic polymer realized linear response, high sensitivity with low hysteresis to humidity. By adjusting the ratio of ionic liquid, not only the impedance but also the hydrophobicity of composite could be modulated, which had a significant influence on the humidity sensing performance. The morphology and microstructure of the material also affected its interaction with water molecules. Due to the diverse processing methods of polymer, highly transparent film fabricated by spinning-coating and nanofibrous membrane fabricated by electrospinning could be prepared and exhibited different response time, which could be used for different application scenarios. Especially, the fibrous membrane made with electrospinning method showed an ultrafast response and could distinguish up to 120 Hz humidity change, due to its fibrous structure with high specific surface area. The humidity sensors with ultrafast, linear response and high sensitivity showed potential applications in human respiratory monitoring and flexible non-contact switch. To better show the multifunction of ionic liquid-polymer composite, as a proof of concept, we fabricated an integrated humidity sensitive color change device by utilizing lower ionic liquid content composite for sensing in the humidity sensing module and higher ionic liquid content composite as the electrolyte in the electrochromic module.
A poly(toluidine blue) modified glassy carbon electrode (PTB/GCE) was prepared by two-step electropolymerization, and was used for the stripping voltammetric analysis of trace uranium for the first ...time. The results showed that the PTB modified electrode had higher sensitivity of uranium detection compared with the glassy carbon electrode, and the standard addition method was satisfactory for seawater, river water and tap water. The PTB/GCE can be used as a mercury-free electrode for the electrochemical detection of uranyl ions in the environmental water, which provides a new way for the rapid detection of trace uranium.
The development of Neural Radiance Fields (NeRFs) has provided a potent representation for encapsulating the geometric and appearance characteristics of 3D scenes. Enhancing the capabilities of NeRFs ...in open-vocabulary 3D semantic perception tasks has been a recent focus. However, current methods that extract semantics directly from Contrastive Language-Image Pretraining (CLIP) for semantic field learning encounter difficulties due to noisy and view-inconsistent semantics provided by CLIP. To tackle these limitations, we propose OV-NeRF, which exploits the potential of pre-trained vision and language foundation models to enhance semantic field learning through proposed single-view and cross-view strategies. First, from the single-view perspective, we introduce Region Semantic Ranking (RSR) regularization by leveraging 2D mask proposals derived from Segment Anything (SAM) to rectify the noisy semantics of each training view, facilitating accurate semantic field learning. Second, from the cross-view perspective, we propose a Cross-view Self-enhancement (CSE) strategy to address the challenge raised by view-inconsistent semantics. Rather than invariably utilizing the 2D inconsistent semantics from CLIP, CSE leverages the 3D consistent semantics generated from the well-trained semantic field itself for semantic field training, aiming to reduce ambiguity and enhance overall semantic consistency across different views. Extensive experiments validate our OV-NeRF outperforms current state-of-the-art methods, achieving a significant improvement of 20.31% and 18.42% in mIoU metric on Replica and ScanNet, respectively. Furthermore, our approach exhibits consistent superior results across various CLIP configurations, further verifying its robustness. Codes are available at: https://github.com/pcl3dv/OV-NeRF.
In this study, the thermal model of the creep feed deep profile (CFDP) grinding process of a small-module gear was established regarding the spatial interaction between the workpiece and grinding ...wheel, as well as the thermal boundary and the energy partition. A 3D dynamic finite element simulation of small-module gear CFDP grinding was carried out by utilizing the element birth and death technique. The validation experiments aided the specially designed thermocouples in that the grinding temperature on both sides of the tooth groove could be measured simultaneously, which demonstrated a satisfactory agreement with the experimental findings. The average difference was approximately 10%, which proved the feasibility and accuracy of the simulation. The effects of three possible geometric structures for both sides of the tooth groove being processed on the grinding temperature distribution were discussed based on the validated finite element model. This study is expected to enhance comprehension of the temperature distribution in the CFDP grinding of a small-module gear and offer recommendations for optimizing the process parameters in the precision machining of a small-module gear.
Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, ...eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.
•A brightness-adaptive kernel prediction network is proposed for iTM task.•Restoration of details in underexposed regions is important but difficult.•Region loss forces the model pay attention to ...overexposed and underexposed regions.•Our method achieves the best performance compared with recent competitors.
The inverse tone mapping (iTM) technique that produces a high dynamic range (HDR) image from one single standard dynamic range (SDR) image has received much attention in industry and academia recently. However, existing methods to recover HDR images mainly focus on overexposed regions but ignore underexposed regions. The underexposed regions in an image are susceptible to noise and artifacts, which will reduce the quality of the image and the user’s visual experience. Therefore, in this paper, we propose a brightness-adaptive iTM model based on deep learning to focus on the content restoration of both the overexposed and underexposed regions in an SDR image simultaneously. In this model, instead of directly predicting the HDR output, we adopt an encoder-decoder network to predict spatially adaptive kernels, which further convolute the input SDR image to produce the HDR result. With the spatially adaptive kernels, the input regions with different exposures can be adaptively mapped by making full use of neighborhood information. Importantly, brightness-adaptive skip connections in the encoder-decoder network, as well as a region loss, are designed to force the proposed model to attach importance to overexposed and underexposed regions. Besides, a global branch is employed in our encoder to exploit both the global and local brightness. Extensive qualitative and quantitative experiments demonstrate that the proposed approach outperforms recent methods on multiple metrics.
Background
Pyroptosis and polarization are significant contributors to the onset and development of many diseases. At present, the relationship between pyroptosis and polarization in acute lung ...injury (ALI) caused by sepsis remains unclear.
Methods
The ALI model for sepsis was created in mice and categorized into the blank control, lipopolysaccharide (LPS) group, LPS + low‐dose Belnacasan group, LPS + high‐dose Belnacasan group, LPS + low‐dose Wedelolactone group, LPS + high‐dose Wedelolactone group, and positive control group. The wet‐dry specific gravity was evaluated to compare pulmonary edema. Hematoxylin–eosin, Masson, and terminal deoxynucleotidyl transferase dUTP nick end labeling staining techniques were conducted to observe and contrast the pathological changes in lung tissue. ELISA was utilized to identify M1 and M2 macrophages and correlated inflammatory factors. Immunohistochemical staining and flow cytometry were employed to identify markers of M1 and M2 macrophages in lung tissue. Propidium iodide staining, together with flow cytometry, was utilized to observe the degree and positive rate of pyroptosis of alveolar macrophages. Western blot analysis was conducted to detect the expression levels of Caspase 1, Caspase 11, GSDMD, and IL‐18 in the lung tissues of each group. The real‐time quantitative polymerase chain reaction method was used to ascertain relative expression levels of NLRP3, Caspase 1, Caspase 11, GSDMD, IL‐18, iNOS, and Arg‐1 in lung tissues of all groups.
Results
In mice with sepsis‐induced ALI, both classical and nonclassical pathways of pyroptosis are observed. Inhibiting pyroptosis has been found to ameliorate lung injury, pulmonary edema, and inflammation induced by LPS. Notably, the expression of NLRP3, Caspase 1, Caspase 11, GSDMD, IL‐1β, IL‐18, TGF‐β, CD86, CD206, iNOS, and Arg‐1 were all altered in this process. Additionally, alveolar macrophages were polarized along with pyroptosis in mice with ALI caused by sepsis.
Conclusion
Pyroptosis of alveolar macrophages in the context of ALI in mice infected with sepsis has been linked to the polarization of alveolar macrophages toward type M1.
The worldwide emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis threatens to make this disease incurable. Drug resistance mechanisms are only partially ...understood, and whether the current understanding of the genetic basis of drug resistance in M. tuberculosis is sufficiently comprehensive remains unclear. Here we sequenced and analyzed 161 isolates with a range of drug resistance profiles, discovering 72 new genes, 28 intergenic regions (IGRs), 11 nonsynonymous SNPs and 10 IGR SNPs with strong, consistent associations with drug resistance. On the basis of our examination of the dN/dS ratios of nonsynonymous to synonymous SNPs among the isolates, we suggest that the drug resistance-associated genes identified here likely contain essentially all the nonsynonymous SNPs that have arisen as a result of drug pressure in these isolates and should thus represent a near-complete set of drug resistance-associated genes for these isolates and antibiotics. Our work indicates that the genetic basis of drug resistance is more complex than previously anticipated and provides a strong foundation for elucidating unknown drug resistance mechanisms.
Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised ...attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, we have developed a StyleGAN termed STGAN-WO which performs weight decomposition through utilizing the style vector to construct a fully controllable weight matrix to regulate image synthesis, and employs orthogonal regularization to ensure each entry of the style vector only controls one independent feature matrix. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way. Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods.
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