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Degradation of the mechanical properties of heat-treated wood is a significant problem that needs to be addressed. This study aimed to stabilize the mechanical strength of heat-treated spruce wood by adding gaseous ammonia during the heat treatment. Gaseous ammonia penetrates rapidly into wood and is expected to form ammonium hydroxide when combined with water in the wood. This modification strategy neutralizes the acids produced by the degradation of hemicelluloses and reduces the degradation of the wood polymer composition and cell-wall structure. The preservation of wood polymer composition and cell-wall structure increases the indentation modulus of the wood cell walls. This increases the strength of the wood cell walls, resulting in an improvement in the mechanical properties of the heat-treated wood. The heat-treated wood’s dimensional stability and equilibrium moisture content are only slightly affected by the weak alkalinity modification.

Seismic data interpolation is an essential procedure in seismic data processing. However, conventional interpolation methods may generate inaccurate results due to the simplicity of assumptions, such as linear events or sparsity. In contrast, deep learning trains a deep neural network with a large data set without relying on predefined assumptions. However, the lack of physical priors in the traditional pure data-driven deep learning frameworks may cause low generalization for different sampling patterns. Inspired by the framework of projection onto convex sets (POCS), a new neural network is proposed for seismic interpolation, called POCS-Net. The forward Fourier transform, the inverse Fourier transform, and the threshold parameter in POCS are replaced by neural networks that are independent in different iterations. The threshold is trainable in POCS-Net rather than manually set. A nonnegative constraint is imposed on the threshold to make it consistent with traditional POCS. POCS-Net is essentially an end-to-end neural network with priors of a sampling pattern and a predefined iterative framework. Numerical results on 3D synthetic and field seismic data sets demonstrate the superiority of the reconstruction accuracy of the proposed method compared with the traditional and natural image-learned POCS methods.

3D-printed artefacts are becoming more common, and the effect of printing parameters on their properties is key to their performance in applications. Although parameters like build orientation and raster direction are well-studied the effect of layer thickness is less well-known. This study determines the influence of layer thickness on the mechanical properties of polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) 3D printed specimens made with fused filament fabrication (FFF). Samples were printed with differing layer thicknesses and tensile tested according to ASTM D638. The study also found that when increasing the layer thickness the mechanical properties of the specimens for both ABS and PLA decreased. When it came to ultimate tensile strength, the effect of layer thickness on PLA was more significant than on ABS. Considering the mechanical properties as well as aspects such as printing time and simplicity of printing, an optimum print setting could be determined. The study found that PLA was more significantly affected by the change in layer thickness compared to ABS.

PurposeIn gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.Design/methodology/approachTo diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.FindingsExperiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.Originality/valueVibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

Bo Li,1,2 Li Jiang,1,2 Ni Wu,1,2 Yao Chen,1,2 Zhi Xu,3 Fengcheng Xu,2,4 Heping Chen,1 Tianhu Liu2,4 1Clinical Pharmacy Department, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China; 2Drug Clinical Trial Management Center, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China; 3Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China; 4Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of ChinaCorrespondence: Bo Li, Clinical Pharmacy Department, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China, Email lbydcg@163.com Tianhu Liu, Department of Cardiovascular Medicine, The Third Affiliated Hospital of Chengdu Medical College, Pidu District People’s Hospital, Chengdu, Sichuan, People’s Republic of China, Email lthzgl@163.comBackground: In 2021, National Health Commission of the People’s Republic of Chinese issued a document that no longer recommended the routine skin test for cephalosporin (RSTC). However, there is still resistance to the cancellation of RSTC in a primary hospital. The study aimed to explore the potential factors for hindering the abolition of the RSTC in a county-level hospital based on the PRECEDE model.Methods: The cross-sectional study was conducted on healthcare workers in the Pidu District People’s Hospital, Chengdu, by online questionnaire from September 10 to September 25 in the 2021.The PRECEDE model was used to divide the potential factors of healthcare professionals in hindering the abolition of the RSTC into predisposing factors, enabling factors and reinforcing factors. Data were analyzed by ANOVA, Chi-square test, multiple linear and multiple logistic regression analysis.Results: We collected 605 respondents’ valid questionnaires. 254 healthcare professionals were against cancellation of the RSTC, accounting for 41.98%. Multiple linear regression analysis showed that working for 6~10 years (β = 1.953, P = 0.024), medium (β = 1.995, P = 0.030) or senior (β = 4.003, P = 0.007) professional qualification, pharmacists (β = 3.830, P = 0.013) and working in surgical department (β= 4.462, P < 0.001) were significantly associated with higher score of predisposing factors, enabling factors, and reinforcing factors on abolition of RSTC. Furthermore, multiple logistic regression analysis showed that pharmacists (OR=3.113, 95% CI: 1.341– 7.223, P=0.030), medium professional qualification (OR=1.272, 95% CI: 0.702– 2.302, P=0.008), scores of predisposing factors (OR=1.335, 95% CI: 1.033– 1.726, P=0.009), and scores of enabling factors (OR=1.208, 95% CI: 1.109– 1.315, P< 0.001) were independently associated with the positive anticipated behavior on the abolition of RSTC. While nurses (OR=0.516, 95% CI: 0.284– 0.938, P< 0.001) were independently associated with anticipated negative behavior.Conclusion: Pharmacists, medium professional qualification, and healthcare professionals with higher scores of predisposing and enabling factors were more likely to have a positive anticipated behavior on the abolition of RSTC, while nurses did not.Keywords: cephalosporin, skin test, potential factors, PRECEDE model, cross-sectional study

This study proposes a novel modified sign coherence factor (SCF) weighting adapted to the frequency-domain (FD) beamforming for ultrasound plane-wave imaging to achieve a high frame rate and better image quality. First, before beamforming, the sign components were extracted from the radiofrequency signals of aperture data. Second, the modified SCF was established using the FD beamformed sign components. Finally, the FD beamformed image was weighted by the modified SCF. To assess the performance of the proposed modified SCF for FD beamforming, the resolution, contrast, computation complexity and execution time of the generated images were evaluated. The results revealed that the FD-SCF could significantly improve the computational load compared with the classic delay-and-sum SCF on the premise of equal image quality improvement. Therefore, high image quality and low computational load have been successfully combined under the proposed weighting method.

Image retargeting aims to resize a photograph without distorting its content. Recent solutions leverage generative adversarial networks (GANs) to learn an image’s internal distribution. Patches are then replicated and generated to fill the target aspect ratio to preserve the internal distribution. Although these approaches are somewhat successful, they do not have a sense of image semantics or the image’s contents, causing semantic errors during generation (e.g., a person with two heads). Our model addresses this by allowing user intervention. The users can preserve the objects they desire through user-defined masks. Our model enforces the masked object to appear at the user-defined location. It also utilizes a de-association regularizer to loosen the association of the selected object with its surroundings. Our method prevents object distortion and enables the user to remove or relocate the object from the input image while allowing the user to set its target size.

The purpose of this study was to explore the therapeutic effect of silver nanoparticles in the treatment of central serous chorioretinopathy (CSC). After the establishment of CSC in New Zealand rabbits (n = 128), the animals were treated with iodized lecithin tablets by gavage (ILT only, n = 64) or ILT gavage followed by injection of silver nanoparticles into the vitreous cavity (SN, n = 64). The sum retinal layer thickness of SN group before gavage was lower than that of ILT group, and the total retinal thickness of SN group after gavage was also lower than that of ILT group. There were interaction effects between groups and time (Fgroup effect = 42.570, Ftime effect = 19.250, Finteraction effect = 2.885, all P < 0.05). Electroretinograms showed lower peak latency in oscillatory potential (OP1, OP2, and OP3) in the SN group than in the ILT group before and after gavage (intergroup effect: F1 = 40.280, F2 = 52.760, F3 = 133.700; time effect: F1 = 10.330, F2 = 9.082, F3 = 93.760; interaction effects: F1 = 4.204, F2 = 3.059, F3 = 12.260; all P < 0.05). The b-wave amplitudes and total amplitudes were higher in the SN group than in the ILT group before and after gavage (intergroup effect: F1 = 10.540, F2 = 13.270; time effect: F1 = 25.910, F2 = 35.120; interaction effects: F1 = 2.817, F2 = 5.264; all P < 0.05). No significant differences in intraocular pressure were observed, and there were no interaction effects (Fintergroup effect = 0.046, Ftime effect = 0.001, Finteraction effect = 0.027, all P > 0.05). We conclude that silver nanoparticles provide a significant therapeutic effect on CSC in rabbits, ensuring the integrity of retinal morphology, structure, function, and limiting damage to visual function.

Organic electrochemical transistors (OECTs) and OECT-based circuitry offer great potential in bioelectronics, wearable electronics and artificial neuromorphic electronics because of their exceptionally low driving voltages (10 mS) and biocompatibility1–5. However, the successful realization of critical complementary logic OECTs is currently limited by temporal and/or operational instability, slow redox processes and/or switching, incompatibility with high-density monolithic integration and inferior n-type OECT performance6–8. Here we demonstrate p- and n-type vertical OECTs with balanced and ultra-high performance by blending redox-active semiconducting polymers with a redox-inactive photocurable and/or photopatternable polymer to form an ion-permeable semiconducting channel, implemented in a simple, scalable vertical architecture that has a dense, impermeable top contact. Footprint current densities exceeding 1 kA cm−2 at less than ±0.7 V, transconductances of 0.2–0.4 S, short transient times of less than 1 ms and ultra-stable switching (>50,000 cycles) are achieved in, to our knowledge, the first vertically stacked complementary vertical OECT logic circuits. This architecture opens many possibilities for fundamental studies of organic semiconductor redox chemistry and physics in nanoscopically confined spaces, without macroscopic electrolyte contact, as well as wearable and implantable device applications.

Ionic liquid-mediated hexagonally porous ZnO nanocrystal-supported Au catalysts: highly efficient and stable materials for oxidative esterification of methylacrolein.

Spinel Li4Ti5O12 hollow nanospheres self-assembled with lamellar nanocrystallites, prepared by employing colloid carbon spheres as templates, exhibit excellent capacity retention.

The rGO/NC interface, as a protective barrier, can effectively prevent the aggregation of Ru atoms during the repreparation process and promote the NRR process.