Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures

Multiple mechanisms have been proposed to explain how greenery enhances their mental wellbeing. Mediation studies, however, focus on a limited number of mechanisms and rely on remotely sensed greenery measures, which do not accurately capture how neighborhood greenery is perceived on the ground. To examine: 1) how streetscape and remote sensing-based greenery affect people's mental wellbeing in Guangzhou, China; 2) whether and, if so, to what extent the associations are mediated by physical activity, stress, air quality and noise, and social cohesion; and 3) whether differences in the mediation across the streetscape greenery and NDVI exposure metrics occurred. Mental wellbeing was quantified by the WHO-5 wellbeing index. Greenery measures were extracted at the neighborhood level: 1) streetscape greenery from street view data via a convolutional neural network, and 2) the NDVI remote sensing images. Single and multiple mediation analyses with multilevel regressions were conducted. Streetscape and NDVI greenery were weakly and positively, but not significantly, correlated. Our regression results revealed that streetscape greenery and NDVI were, individually and jointly, positively associated with mental wellbeing. Significant partial mediators for the streetscape greenery were physical activity, stress, air quality and noise, and social cohesion; together, they explained 62% of the association. For NDVI, only physical activity and social cohesion were significant partial mediators, accounting for 22% of the association. Mental health and wellbeing and both streetscape and satellite-derived greenery seem to be both directly correlated and indirectly mediated. Our findings signify that both greenery measures capture different aspects of natural environments and may contribute to people's wellbeing by means of different mechanisms

An improved higher-order analytical energy operator with adaptive local iterative filtering for early fault diagnosis of bearings

Early fault diagnosis in rolling bearings is crucial to maintenance and safety in industry. To highlight the weak fault features from complex signals combined with multiple interferences and heavy background noise, a novel approach for bearing fault diagnosis based on higher-order analytic energy operator (HO-AEO) and adaptive local iterative filtering (ALIF) is put forward. HO-AEO has better effect in dealing with heavy noise. However, it is subjected to the limitation of mono-components. To solve this limitation, ALIF is adopted firstly to decompose the nonlinear, non-stationary signals into multiple mono-components adaptively. In the next, the resonance frequency band as the optimal intrinsic mode function (IMF) is selected according to the maximum kurtosis. In the following, HO-AEO is utilized to highlight weak fault characteristics of the selected IMF. Finally, the early bearing fault is diagnosed by the energy operator spectrum based on fast Fourier transform (FFT). Comparisons in the simulation indicate that the fourth order HO-AEO shows the best performance in fault diagnosis compared with Teager energy operator (TEO), analytic energy operator (AEO), the second and the third order HO-AEO. The simulated test and experimental results demonstrate that the proposed approach could effectively extract weak fault characteristics from contaminated vibration signals

Real3D-AD: A Dataset of Point Cloud Anomaly Detection

High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset, addressing the limitations in the field. With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection to date. Real3D-AD surpasses existing 3D anomaly detection datasets available regarding point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect prototype. Additionally, we present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection. To address this, we propose Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations. Extensive experiments on the Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility and accessibility, we provide the Real3D-AD dataset, benchmark source code, and Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD

Ferroelectric and non-linear dielectric characteristics of Bi₀.₅Na₀.₅TiO₃ thin films deposited via a metallorganic decomposition process

Polycrystalline Bi₀.₅Na₀.₅TiO₃ (NBT) thin films have been successfully fabricated via a metal organic decomposition process on Pt/Ti/SiO₂/Si substrates. The structural evolution of the as-prepared thin filmsannealed over the moderate temperature range 500–700 °C is studied. NBT thin filmsannealed at 700 °C are of single phase NBT perovskite type. They exhibit a well-defined P-E hysteresis loop at room temperature. The measured dielectric constant is 465–410 over the frequency range of 1 kHz to 1 MHz. The corresponding dielectric loss is ∼10⁻². The measured capacitance-voltage curve shows strong non-linear dielectric behavior leading to a high tunability of the dielectric constant, up to 14% at 1 MHz.J.X. acknowledges financial support from the Department of Education, Science and Training DEST in the form of an Endeavor Australian Research Fellowship Award. J.X., Y.L., and R.L.W. acknowledge financial support from the Australian Research Council ARC in the form of ARC Discovery Grants

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but their performance deviates greatly. We realize that the lack of actual IM settings most probably hinders the development and usage of these methods in real-world applications. As far as we know, IAD methods are not evaluated systematically. As a result, this makes it difficult for researchers to analyze them because they are designed for different or special cases. To solve this problem, we first propose a uniform IM setting to assess how well these algorithms perform, which includes several aspects, i.e., various levels of supervision (unsupervised vs. semi-supervised), few-shot learning, continual learning, noisy labels, memory usage, and inference speed. Moreover, we skillfully build a comprehensive image anomaly detection benchmark (IM-IAD) that includes 16 algorithms on 7 mainstream datasets with uniform settings. Our extensive experiments (17,017 in total) provide in-depth insights for IAD algorithm redesign or selection under the IM setting. Next, the proposed benchmark IM-IAD gives challenges as well as directions for the future. To foster reproducibility and accessibility, the source code of IM-IAD is uploaded on the website, https://github.com/M-3LAB/IM-IAD

EasyNet: An Easy Network for 3D Industrial Anomaly Detection

3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pre-trained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pre-trained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pre-trained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU

Two-Stage Differential Hydrocarbon Enrichment Mode of Maokou Formation in Southeastern Sichuan Basin, Southwestern China

AbstractSichuan Basin is one of the most potential areas for natural gas exploration and development in China. The Maokou Formation in the basin is one of the important gas-bearing layers in southeastern Sichuan. In recent years, several exploration wells have obtained industrial gas flow in the first member of the Middle Permian Maokou Formation (hereinafter referred to as the Permian Mao-1 member of Maokou Formation), revealing that it may become a new field of oil and gas exploration in Sichuan Basin. Drilling and field survey results show that the shale of Maokou Formation in southeastern Sichuan contains eyeball-shaped limestone. Early studies suggest that the Permian Mao-1 member of Maokou Formation in Sichuan Basin is a set of high-quality carbonate source rocks, but ignoring its oil and gas exploration potential as an unconventional shale reservoir similar to the shale. The enrichment regularity of unconventional natural gas has not been studied from the perspective of source-internal accumulation. And there is a lack of analysis of oil and gas enrichment mode. In this study, we took the Permian Mao-1 member of Maokou Formation in southeastern Sichuan as the target layer. Through macroscopic outcrop observation and geochemical analysis and based on unconventional oil and gas enrichment theory, we carried out a study on natural gas enrichment mode of eyeball-shaped limestone of the Permian Mao-1 member of Maokou Formation in Sichuan Basin. The results show that the hydrocarbon enrichment pattern of the Maokou Formation in southeastern Sichuan is different from the accumulation and occurrence process of common unconventional shale gas reservoirs and conventional carbonate reservoirs. It is a special new hydrocarbon accumulation mode between the above two. According to the difference in the charging time of the hydrocarbon, the background of the reservoiring dynamics, and the occurrence state of oil and gas, we divide the two-stage differential enrichment mode of oil and gas, that is, “early intralayer near-source enrichment” and “late interlayer pressure relief adjustment.

Foam materials with controllable pore structure prepared from nanofibrillated cellulose with addition of alcohols

Low-density foams based on nanofibrillated cellulose (NFC) made from Pinus massonianesoftwood pulp were prepared from NFC aqueous suspensions containing one of four C2–C4alcohols followed by freeze-drying, with the goal of controlling their pore structure and reducing the shrink rate. The foams prepared from NFC suspensions containing ethanol, isopropanol and n-butanol exhibited highly porous structures with a honeycomb-like cellular texture featuring well-defined “cell walls” between the layers. By contrast, the tert-butanol/NFC foam featured a higher number of smaller size pores with irregular shape. The foams prepared by freezing at −196 °C with ethanol also revealed small size pores, with no layered pore structure. The results obtained suggested that freeze-drying could be used to control the key foam parameters by adding different alcohols into an NFC suspension and adjusting the freezing temperature. Combining the obtained information, a possible formation mechanism was proposed. The microstructure, density, porosity, shrinkage, mechanical properties and thermal properties of NFC foams were determined. The obtained NFC foams feature low shrinkage upon formation and thermal conductivity. Smaller Young’s modulus and energy absorption yet similar yield stress values compared to the blank indicate that the freeze-drying in the presence of alcohols tends to generate “soft” foams