RESUMO
To address the current difficulties in fire detection algorithms, including inadequate feature extraction, excessive computational complexity, limited deployment on devices with limited resources, missed detections, inaccurate detections, and low accuracy, we developed a highly accurate algorithm named YOLOFM. We utilized LabelImg software to manually label a dataset containing 18644 images, named FM-VOC Dataset18644. In addition, we constructed a FocalNext network, which utilized the FocalNextBlock module from the CFnet network. This improves the integration of multi-scale information and reduces model parameters. We also proposed QAHARep-FPN, an FPN network that integrates the structure of quantization awareness and hardware awareness. This design effectively reduces redundant calculations of the model. A brand-new compression decoupled head, named NADH, was also created to enhance the correlation between the decoupling head structure and the calculation logic of the loss function. Instead of using the CIoU loss for bounding box regression, we proposed a Focal-SIoU loss. This promotes the swift convergence of the network and enhances the precision of the regression. The experimental results showed that YOLOFM improved the baseline network's accuracy, recall, F1, mAP50, and mAP50-95 by 3.1%, 3.9%, 3.0%, 2.2%, and 7.9%, respectively. It achieves an equilibrium that combines performance and speed, resulting in a more dependable and accurate solution for detection jobs.
RESUMO
Polycarbonate (PC) is a durable and transparent optical plastic material commonly used as shatter-resistant alternative to traditional optical glass. Broadband antireflective (AR) coatings with excellent mechanical strength and environmental stability are essential for PC to achieve high light transmission and visual quality. In this work, chloroform vapor treatment was employed to partially embed the silica coating into the PC substrate for adhesion enhancement, which also divided the silica coating layer into bottom and middle layers with different refractive indices. The contact between the silica nanoparticles and the substrate was transformed from "point-contact" to "area-contact", which enhanced the adhesion between coating and PC substrate. After the deposition of a top layer coating consisted of silica nanoparticles with smaller diameter, a triple-layer refractive index graded AR structure was constructed. Hexamethyldisilazane vapor surface modification was performed to decrease the surface free energy of top coating layer. The triple-layer coating coated PC exhibits superior antireflection property with an average reflectance of only 0.43% over a wide wavelength range of 400-1000 nm. After 100 times of friction or 5 months of exposure to a contaminated environment, the reflectance of coated PC shows barely noticeable difference, indicating its excellent mechanical strength and environmental stability.
RESUMO
Mechanical and contamination-resistant properties are the most crucial and challenging issues that impede the practical applications of sol-gel antireflective (AR) coating. In this paper, we report a low-temperature vapor surface treatment strategy for the partial embedding and surface functionalization of silica nanoparticles (SNPs) on flexible polymeric glass substrates. SNPs, which were synthesized via the Stöber method, were partially embedded into the polymeric glass substrates by vapor-phase surface treatment using volatile chloroform. Further vapor-phase surface treatments by water and hexamethyldisilazane (HMDS) were applied successively to achieve high trimethylsilyl coverage of the SNPs. The HMDS modification could convert the polar surface of SNPs to a nonpolar surface for contamination resistance, while ammonia, as a byproduct generated, could help to cross-link the SNPs via self-condensation of silanol groups, thus hardening the coating. The SNP-CWH coated polymethylmethacrylate (PMMA) substrate shows an average transmittance of 98.62% in the wavelength region of 400-800 nm, which is 6.32% higher than that of the uncoated bare PMMA. The AR performance of SNP-CWH coated PMMA shows almost no degradation after 100 times of rubbing or bending, indicating the greatly enhanced abrasion resistance and flexibility. Furthermore, the SNP-CWH coating exhibits superior contamination-resistant property, where the transmittance curve of the coated substrate displays a barely noticeable change after exposure to a "dirty" environment with water and organic contaminants for 6 months. This work paves a new way for developing mechanically robust and contamination-resistant AR coating for polymeric substrates.
RESUMO
Environmental stability is of great interest for sol-gel porous antireflective (AR) coatings. In this work, sol-gel silica AR coatings with excellent environmental stability were prepared via ammonia vapor treatment (AVT) combined with organosilane (hexamethyldisilazane (HMDS) or hexadecyltrimethoxysilane (HTMS)) vapor treatment. The surface free energy (SFE) of the coatings treated with different approaches was estimated through Owens-Wendt method combined with Wenzel equation from the contact angles of water, glycerol and diiodomethane. Scanning electron microscope and Fourier transform infrared spectroscopy were employed to study the surface morphology and chemical composition of the silica coatings treated with different methods. The silica coatings treated by combined vapor phase method possess the SFE of 24.11â¯mJ·m-2 for N-HD-SiO2 and 34.18â¯mJ·m-2 for N-HT-SiO2. After being placed in a 90%RH humid environment for 2â¯months, the peak transmittance of BK7 glasses coated with N-HD-SiO2 and N-HT-SiO2 only decreases by 0.58% and 0.95%, respectively. Meanwhile, N-HD-SiO2 and N-HT-SiO2 coated BK7 glasses also show quite stable optical transmittance after exposure to a vacuum oil environment for 2â¯months. The mechanism of the combined vapor phase surface treatment is discussed based on the combination analysis of surface morphology, chemical composition and SFE of the coatings.
RESUMO
A common pathological finding in autism is a localized deficit in Purkinje cells (PCs). Cerebellar abnormalities have also been reported in schizophrenia. Using a mouse model that exploits a known risk factor for these disorders, maternal infection, we asked if the offspring of pregnant mice given a mid-gestation respiratory infection have cerebellar pathology resembling that seen in these disorders. We also tested the effects of maternal immune activation in the absence of virus by injection of the synthetic dsRNA, poly(I:C). We infected pregnant mice with influenza on embryonic day 9.5 (E9.5), or injected poly(I:C) i.p. on E12.5, and assessed the linear density of PCs in the cerebellum of adult or postnatal day 11 (P11) offspring. To study granule cell migration, we also injected BrdU on P11. Adult offspring of influenza- or poly(I:C)-exposed mice display a localized deficit in PCs in lobule VII of the cerebellum, as do P11 offspring. Coincident with this are heterotopic PCs, as well as delayed migration of granule cells in lobules VI and VII. The cerebellar pathology observed in the offspring of influenza- or poly(I:C)-exposed mice is strikingly similar to that observed in autism. The poly(I:C) findings indicate that deficits are likely caused by the activation of the maternal immune system. Finally, our data suggest that cerebellar abnormalities occur during embryonic development, and may be an early deficit in autism and schizophrenia.
