System-level modelling and validation of a strain energy harvesting system by directly coupling finite element and electrical circuits

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.— There is a lack of system-level finite element (FE) model which can directly predict the performance of a piezoelectric energy harvester connected with interface circuits and electric load. This work developed a system-level model of piezoelectric strain energy harvesting system by directly coupling the finite element and electrical circuits. The strain energy harvester (SEH) is a macro fibber composite adhesively bonded to a composite beam. Simulations were performed with the SEH connected with three circuits individually (i) a load resistor, (ii) a rectifier terminated with a load resistor and (iii) a rectifier terminated with a smoothing capacitor and a load resistor. Experimental tests were carried out to validate the simulation results. Good agreements were observed between the simulated and measured results. The developed model is able to predict the performance of the energy harvesting system when different circuit was connected. The validated system-level model can be used for the design and optimization of piezoelectric energy harvesting system by investigating the interactions between energy harvester and electrical circuits

The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices

Fairness in Face Presentation Attack Detection

Face presentation attack detection (PAD) is critical to secure face recognition (FR) applications from presentation attacks. FR performance has been shown to be unfair to certain demographic and non-demographic groups. However, the fairness of face PAD is an understudied issue, mainly due to the lack of appropriately annotated data. To address this issue, this work first presents a Combined Attribute Annotated PAD Dataset (CAAD-PAD) by combining several well-known PAD datasets where we provide seven human-annotated attribute labels. This work then comprehensively analyses the fairness of a set of face PADs and its relation to the nature of training data and the Operational Decision Threshold Assignment (ODTA) on different data groups by studying four face PAD approaches on our CAAD-PAD. To simultaneously represent both the PAD fairness and the absolute PAD performance, we introduce a novel metric, namely the Accuracy Balanced Fairness (ABF). Extensive experiments on CAAD-PAD show that the training data and ODTA induce unfairness on gender, occlusion, and other attribute groups. Based on these analyses, we propose a data augmentation method, FairSWAP, which aims to disrupt the identity/semantic information and guide models to mine attack cues rather than attribute-related information. Detailed experimental results demonstrate that FairSWAP generally enhances both the PAD performance and the fairness of face PAD

Tea and Pleurotus ostreatus Intercropping Modulates Structure of Soil and Root Microbial Communities

Intercropping with Pleurotus ostreatus has been demonstrated to increase the tea yield and alleviate soil acidification in tea gardens. However, the underlying mechanisms remain elusive. Here, high-throughput sequencing and Biolog Eco analysis were performed to identify changes in the community structure and abundance of soil microorganisms in the P. ostreatus intercropped tea garden at different seasons (April and September). The results showed that the soil microbial diversity of rhizosphere decreased in April, while rhizosphere and non-rhizosphere soil microbial diversity increased in September in the P. ostreatus intercropped tea garden. The diversity of tea tree root microorganisms increased in both periods. In addition, the number of fungi associated with organic matter decomposition and nutrient cycling, such as Penicillium, Trichoderma, and Trechispora, was significantly higher in the intercropped group than in the control group. Intercropping with P. ostreatus increased the levels of total nitrogen (TN), total phosphorus (TP), and available phosphorus (AP) in the soil. It also improved the content of secondary metabolites, such as tea catechins, and polysaccharides in tea buds. Microbial network analysis showed that Unclassified_o__Helotiales, and Devosia were positively correlated with soil TN and pH, while Lactobacillus, Acidothermus, and Monascus were positively correlated with flavone, AE, and catechins in tea trees. In conclusion, intercropping with P. ostreatus can improve the physical and chemical properties of soil and the composition and structure of microbial communities in tea gardens, which has significant potential for application in monoculture tea gardens with acidic soils

Synthesis and Immobilization of Pt Nanoparticles on Amino-Functionalized Halloysite Nanotubes toward Highly Active Catalysts

A simple and effective method for the preparation of platinum nanoparticles (Pt NPs) grown on amino-functionalized halloysite nanotubes (HNTs) was developed. The nanostructures were synthesized through the functionalization of the HNTs, followed by an in situ approach to generate Pt NPs with diameter of approximately 1.5 nm within the entire HNTs. The synthesis process, composition and morphology of the nanostructures were characterized. The results suggest PtNPs/NH2-HNTs nanostructures with ultrafine PtNPs were successfully synthesized by green chemically-reducing H2PtCl6 without the use of surfactant. The nanostructures exhibit promising catalytic properties for reducing potassium hexacyanoferrate(III) to potassium hexacyanoferrate(II). The presented experiment for novel PtNPs/NH2-HNTs nanostructures is quite simple and environmentally benign, permitting it as a potential application in the future field of catalysts

PointGPT: Auto-regressively Generative Pre-training from Point Clouds

Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, addressing the challenges associated with disorder properties, low information density, and task gaps. Specifically, a point cloud auto-regressive generation task is proposed to pre-train transformer models. Our method partitions the input point cloud into multiple point patches and arranges them in an ordered sequence based on their spatial proximity. Then, an extractor-generator based transformer decoder, with a dual masking strategy, learns latent representations conditioned on the preceding point patches, aiming to predict the next one in an auto-regressive manner. Our scalable approach allows for learning high-capacity models that generalize well, achieving state-of-the-art performance on various downstream tasks. In particular, our approach achieves classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the ScanObjectNN dataset, outperforming all other transformer models. Furthermore, our method also attains new state-of-the-art accuracies on all four few-shot learning benchmarks.Comment: 9 pages, 2 figure

High affinity binding of H3K14ac through collaboration of bromodomains 2, 4 and 5 is critical for the molecular and tumor suppressor functions of PBRM1.

Polybromo-1 (PBRM1) is an important tumor suppressor in kidney cancer. It contains six tandem bromodomains (BDs), which are specialized structures that recognize acetyl-lysine residues. While BD2 has been found to bind acetylated histone H3 lysine 14 (H3K14ac), it is not known whether other BDs collaborate with BD2 to generate strong binding to H3K14ac, and the importance of H3K14ac recognition for the molecular and tumor suppressor function of PBRM1 is also unknown. We discovered that full-length PBRM1, but not its individual BDs, strongly binds H3K14ac. BDs 2, 4, and 5 were found to collaborate to facilitate strong binding to H3K14ac. Quantitative measurement of the interactions between purified BD proteins and H3K14ac or nonacetylated peptides confirmed the tight and specific association of the former. Interestingly, while the structural integrity of BD4 was found to be required for H3K14ac recognition, the conserved acetyl-lysine binding site of BD4 was not. Furthermore, simultaneous point mutations in BDs 2, 4, and 5 prevented recognition of H3K14ac, altered promoter binding and gene expression, and caused PBRM1 to relocalize to the cytoplasm. In contrast, tumor-derived point mutations in BD2 alone lowered PBRM1\u27s affinity to H3K14ac and also disrupted promoter binding and gene expression without altering cellular localization. Finally, overexpression of PBRM1 variants containing point mutations in BDs 2, 4, and 5 or BD2 alone failed to suppress tumor growth in a xenograft model. Taken together, our study demonstrates that BDs 2, 4, and 5 of PBRM1 collaborate to generate high affinity to H3K14ac and tether PBRM1 to chromatin. Mutations in BD2 alone weaken these interactions, and this is sufficient to abolish its molecular and tumor suppressor functions