Study of the Effect of Retarder and Expander on the Strength and Cracking Performance of Rubber Concrete Based on Back Propagation Neural Network.

The advantages of rubber concrete (RC) are good ductility, fatigue resistance, and impact resistance, but few studies have been conducted on the effects of different rubber admixtures on the strength of RC and the cracking performance of rubber mortar. In this study, the compressive and flexural tests of rubber concrete and the crack resistance test of rubber mortar were carried out by changing the rubber content and adding expansion agent and retarder in this test. The test results show that the strength of RC decreases with the increase in rubber admixture. At 15% of rubber admixture, the expansion agent and retarder increase the compressive strength and flexural strength of RC the most; the compressive strength increased to 116% (22.6 MPa) and 109% (21.2 MPa), and the flexural strength increased to 111% (4.02 MPa) and 116%. (4.21 MPa). At the same rubber admixture, the expander improves the cracking time of the rubber mortar by about 3 times, and the retarder improves the cracking time of the rubber mortar by about 1.6 times. The BP neural network (BPNN) was established to simulate and predict the compressive and flexural strengths of RC with different admixtures and the cracking time of rubber mortar. The simulation results show that the predicted 7-day compressive strength of RC fits best with the actual value, with a value of 0.994, and the predicted 28-day flexural strength was closest to the measured value, with an average relative error of 1.7%. It was shown that the calculation results of the artificial intelligence prediction model are more accurate. The simulation results and test results show that the expander and retarder significantly improve the strength of RC as well as the cracking performance of rubber mortar.

High-Performance Complementary Circuits from Two-Dimensional MoTe2.

Two-dimensional (2D) materials hold great promise for future complementary metal-oxide semiconductor (CMOS) technology. However, the lack of effective methods to tune the Schottky barrier poses a challenge in constructing high-performance complementary circuits from the same material. Here, we reveal that the polarity of pristine MoTe2 field-effect transistors (FETs) with minimized air exposure is n-type, irrespective of the metal contact type. The fabricated n-FETs with palladium contact can reach electron currents up to 275 µA/µm at VDS = 2 V. For p-FETs, we introduce a novel nitric oxide doping strategy, allowing a controlled transition of MoTe2 FETs from n-type to unipolar p-type. By doping only in the contact region, we demonstrate hole currents up to 170 µA/µm at VDS= -2 V with preserved Ion/Ioff ratios of 105. Finally, we present a complementary inverter circuit comprising the high-performance n- and p-type FETs based on MoTe2, promoting the application of 2D materials in future electronic systems.

Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter.

PURPOSE: To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network. METHODS: We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data. RESULTS: The proposed method increases the true positive rate compared to the rule-based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model. CONCLUSIONS: Combing the filter-based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter-based image processing algorithm, the data driven PVS segmentation model using quality-controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate.

Metagenomic Views of Microbial Communities in Sand Sediments Associated with Coral Reefs.

Reef sediments, the home for microbes with high abundances, provide an important source of carbonates and nutrients for the growth and maintenance of coral reefs. However, there is a lack of systematic research on the composition of microbial community in sediments of different geographic sites and their potential effect on nutrient recycling and health of the coral reef ecosystem. In combination of biogeochemical measurements with gene- and genome-centric metagenomics, we assessed microbial community compositions and functional diversity, as well as profiles of antibiotic resistance genes in surface sediments of 16 coral reef sites at different depths from the Xisha islands in the South China Sea. Reef sediment microbiomes are diverse and novel at lower taxonomic ranks, dominated by Proteobacteria and Planctomycetota. Most reef sediment bacteria potentially participate in biogeochemical cycling via oxidizing various organic and inorganic compounds as energy sources. High abundances of Proteobacteria (mostly Rhizobiales and Woeseiales) are metabolically flexible and contain rhodopsin genes. Various classes of antibiotic resistance genes, hosted by diverse bacterial lineages, were identified to confer resistance to multidrug, aminoglycoside, and other antibiotics. Overall, our findings expanded the understanding of reef sediment microbial ecology and provided insights for their link to the coral reef ecosystem health.

Imaging perivascular space structure and function using brain MRI.

In this article, we provide an overview of current neuroimaging methods for studying perivascular spaces (PVS) in humans using brain MRI. In recent years, an increasing number of studies highlighted the role of PVS in cerebrospinal/interstial fluid circulation and clearance of cerebral waste products and their association with neurological diseases. Novel strategies and techniques have been introduced to improve the quantification of PVS and to investigate their function and morphological features in physiological and pathological conditions. After a brief introduction on the anatomy and physiology of PVS, we examine the latest technological developments to quantitatively analyze the structure and function of PVS in humans with MRI. We describe the applications, advantages, and limitations of these methods, providing guidance and suggestions on the acquisition protocols and analysis techniques that can be applied to study PVS in vivo. Finally, we review the human neuroimaging studies on PVS across the normative lifespan and in the context of neurological disorders.

Three-dimensional self-attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis.

PURPOSE: To develop a new 3D generative adversarial network that is designed and optimized for the application of multimodal 3D neuroimaging synthesis. METHODS: We present a 3D conditional generative adversarial network (GAN) that uses spectral normalization and feature matching to stabilize the training process and ensure optimization convergence (called SC-GAN). A self-attention module was also added to model the relationships between widely separated image voxels. The performance of the network was evaluated on the data set from ADNI-3, in which the proposed network was used to predict PET images, fractional anisotropy, and mean diffusivity maps from multimodal MRI. Then, SC-GAN was applied on a multidimensional diffusion MRI experiment for superresolution application. Experiment results were evaluated by normalized RMS error, peak SNR, and structural similarity. RESULTS: In general, SC-GAN outperformed other state-of-the-art GAN networks including 3D conditional GAN in all three tasks across all evaluation metrics. Prediction error of the SC-GAN was 18%, 24% and 29% lower compared to 2D conditional GAN for fractional anisotropy, PET and mean diffusivity tasks, respectively. The ablation experiment showed that the major contributors to the improved performance of SC-GAN are the adversarial learning and the self-attention module, followed by the spectral normalization module. In the superresolution multidimensional diffusion experiment, SC-GAN provided superior predication in comparison to 3D Unet and 3D conditional GAN. CONCLUSION: In this work, an efficient end-to-end framework for multimodal 3D medical image synthesis (SC-GAN) is presented. The source code is also made available at https://github.com/Haoyulance/SC-GAN.

Gate-Tunable Plasmon-Enhanced Photodetection in a Monolayer MoS2 Phototransistor with Ultrahigh Photoresponsivity.

Monolayer transition metal dichalcogenides (TMDs), direct bandgap materials with an atomically thin nature, are promising materials for electronics and photonics, especially at highly scaled lateral dimensions. However, the characteristically low total absorption of photons in the monolayer TMD has become a challenge in the access to and realization of monolayer TMD-based high-performance optoelectronic functionalities and devices. Here, we demonstrate gate-tunable plasmonic phototransistors (photoFETs) that consist of monolayer molybdenum disulfide (MoS2) photoFETs integrated with the two-dimensional plasmonic crystals. The plasmonic photoFET has an ultrahigh photoresponsivity of 2.7 × 104 AW-1, achieving a 7.2-fold enhancement in the photocurrent compared to pristine photoFETs. This benefits predominately from the combination of the enhancement of the photon-absorption-rate via the strongly localized-electromagnetic-field and the gate-tunable plasmon-induced photocarrier-generation-rate in the monolayer MoS2. These results demonstrate a systematic methodology for designing ultrathin plasmon-enhanced photodetectors based on monolayer TMDs for next-generation ultracompact optoelectronic devices in the trans-Moore era.

Volumetric distribution of perivascular space in relation to mild cognitive impairment.

Vascular contributions to early cognitive decline are increasingly recognized, prompting further investigation into the nature of related changes in perivascular spaces (PVS). Using magnetic resonance imaging, we show that, compared to a cognitively normal sample, individuals with early cognitive dysfunction have altered PVS presence and distribution, irrespective of Amyloid-ß. Surprisingly, we noted lower PVS presence in the anterosuperior medial temporal lobe (asMTL) (1.29 times lower PVS volume fraction in cognitively impaired individuals, p < 0.0001), which was associated with entorhinal neurofibrillary tau tangle deposition (beta (standard error) = -0.98 (0.4); p = 0.014), one of the hallmarks of early Alzheimer's disease pathology. We also observed higher PVS volume fraction in centrum semi-ovale of the white matter, but only in female participants (1.47 times higher PVS volume fraction in cognitively impaired individuals, p = 0.0011). We also observed PVS changes in participants with history of hypertension (higher in the white matter and lower in the asMTL). Our results suggest that anatomically specific alteration of the PVS is an early neuroimaging feature of cognitive impairment in aging adults, which is differentially manifested in female.

High-speed integrated micro-LED array for visible light communication.

In this Letter, we report high-speed integrated 14 µm in diameter micro-light-emitting diode (µLED) arrays with the parallel configuration, including ${2} \times {2}$2×2, ${2} \times {3}$2×3, ${2} \times {4}$2×4, and ${2} \times {5}$2×5 arrays. The small junction area of µLED (${\sim}{191}\;\unicode{x00B5}{\rm m}^2$∼191µm2) in each element facilitates the operation of higher injection current density up to ${13}\;{{\rm kA/cm}^2}$13kA/cm2, leading to the highest modulation bandwidth of 615 MHz. The optical power of ${2} \times {5}$2×5 array monotonically increases (${\sim}{10}$∼10 times higher) as the number of arrays increases (1 to 10), while retaining the fast modulation bandwidth. A clear eye diagram up to 1 Gbps without any equalizer further shows the capability of this high-speed transmitter for VLC. These results mean that tailoring the optical power of µLEDs in a parallel-biased integrated array can further enhance the data transmission rate without degradation of the modulation bandwidth.