Cohesive Soil Stabilized Using Sewage Sludge Ash/Cement and Nano Aluminum Oxide

ABSTRACTIn order to improve soft soil strength, a mixture of incinerated sewage sludge ash (SSA) and cement was applied as a soil stabilizer. The intended mix ratio for SSA and cement was 3:1. A-6 clay was selected as the untreated soil. In this study, 15% of clay soil was replaced by SSA/cement to produce the treated soil specimens. Then, four different volumes, namely 0, 1, 2, and 3%, of nano-Al2O3 were mixed with the treated soil as an additive. Tests such as compaction, pH values, Atterberg limits, unconfined compressive strength (UCS), swell potential, California bearing ratio (CBR), and permeability were performed. The results indicate that both UCSs and CBR values of untreated soil were greatly improved by the use of 15% SSA/cement. Moreover, a 1% addition of nano-Al2O3 enhanced the treated soil in terms of both UCS and CBR values. Furthermore, the swell potential was effectively reduced by the use of 15% SSA/cement as compared with untreated soil and the 1% nano-Al2O3 additive fraction offered the best performance. From this study, we conclude that 15% of SSA/cement replacement could effectively stabilize A-6 clay soil, and 1% of nano-Al2O3 additive may be the optimum amount to add to the soil

Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation

Accurate segmentation of lesion regions is crucial for clinical diagnosis and treatment across various diseases. While deep convolutional networks have achieved satisfactory results in medical image segmentation, they face challenges such as loss of lesion shape information due to continuous convolution and downsampling, as well as the high cost of manually labeling lesions with varying shapes and sizes. To address these issues, we propose a novel medical visual prompting (MVP) framework that leverages pre-training and prompting concepts from natural language processing (NLP). The framework utilizes three key components: Super-Pixel Guided Prompting (SPGP) for superpixelating the input image, Image Embedding Guided Prompting (IEGP) for freezing patch embedding and merging with superpixels to provide visual prompts, and Adaptive Attention Mechanism Guided Prompting (AAGP) for pinpointing prompt content and efficiently adapting all layers. By integrating SPGP, IEGP, and AAGP, the MVP enables the segmentation network to better learn shape prompting information and facilitates mutual learning across different tasks. Extensive experiments conducted on five datasets demonstrate superior performance of this method in various challenging medical image tasks, while simplifying single-task medical segmentation models. This novel framework offers improved performance with fewer parameters and holds significant potential for accurate segmentation of lesion regions in various medical tasks, making it clinically valuable

Morphological and molecular identification for four new wood-inhabiting species of Trechispora (Basidiomycota) from China

Four new wood-inhabiting fungi, Trechispora albofarinosa, T. bisterigmata, T. pileata and T. wenshanensis spp. nov., are proposed based on a combination of morphological features and molecular evidence. Trechispora albofarinosa is characterized by the farinose basidiomata with flocculence hymenial surface, a monomitic hyphal system with clamped generative hyphae, and ellipsoid, warted basidiospores. Trechispora bisterigmata is characterized by the membranous basidiomata with odontioid hymenial surface, rhizomorphic sterile margin, barrelled basidia and subglobose to broad ellipsoid, smooth basidiospores. Trechispora pileata is characterized by the laterally contracted base, solitary or imbricate basidiomata, fan shaped pileus, radially striate-covered surface with appressed scales, odontioid hymenophore surface, and subglobose to broad ellipsoid, thin-walled, smooth basidiospores. Trechispora wenshanensis is characterized by a cottony basidiomata with a smooth hymenial surface, and ellipsoid, thin-walled, warted basidiospores. Sequences of ITS and LSU marker of the studied samples were generated, and phylogenetic analyses were performed with the maximum likelihood, maximum parsimony, and Bayesian inference methods. The phylogenetic tree inferred from the ITS+nLSU sequences highlighted that four new species were grouped into the genus Trechispora

Mobile phone addiction and mental health: the roles of sleep quality and perceived social support

As a global phenomenon, mobile phone addiction has become an increasingly common issue among Chinese university students. Although previous research explored the link between mobile phone addiction and mental health, the possible mechanism underlying the above association is unclear. We administered a cross-sectional survey to 585 participants from two universities in Kunming, southwest China, from October 2021 to January 2022. Our results suggested that mobile phone addiction was negatively associated with mental health, and sleep quality partially mediated the relationship between mobile phone addiction and mental health. Furthermore, perceived social support positively moderated the direct effect of sleep quality on mental health, as well as the indirect effect of mobile phone addiction on mental health. These findings provide a new insight into the underlying mechanism by which mobile phone addiction affects university students’ mental health. The results emphasize a necessary task for administrators, health workers, and family members to attach importance to the overuse of mobile phones among university students

Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning

Stable imaging in adverse environments (e.g., total darkness) makes thermal infrared (TIR) cameras a prevalent option for night scene perception. However, the low contrast and lack of chromaticity of TIR images are detrimental to human interpretation and subsequent deployment of RGB-based vision algorithms. Therefore, it makes sense to colorize the nighttime TIR images by translating them into the corresponding daytime color images (NTIR2DC). Despite the impressive progress made in the NTIR2DC task, how to improve the translation performance of small object classes is under-explored. To address this problem, we propose a generative adversarial network incorporating feedback-based object appearance learning (FoalGAN). Specifically, an occlusion-aware mixup module and corresponding appearance consistency loss are proposed to reduce the context dependence of object translation. As a representative example of small objects in nighttime street scenes, we illustrate how to enhance the realism of traffic light by designing a traffic light appearance loss. To further improve the appearance learning of small objects, we devise a dual feedback learning strategy to selectively adjust the learning frequency of different samples. In addition, we provide pixel-level annotation for a subset of the Brno dataset, which can facilitate the research of NTIR image understanding under multiple weather conditions. Extensive experiments illustrate that the proposed FoalGAN is not only effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency for the NTIR2DC task.Comment: 14 pages, 14 figures. arXiv admin note: text overlap with arXiv:2208.0296

(2S,NS)-N-Allyl-N-benzyl-1-hydr­oxy-3-(4-hydroxy­phen­yl)-N-methyl­propan-2-aminium bromide

The title compound, C20H26NO2 +·Br−, is an N-chiral quaternary ammonium salt synthesized from (2S*)-N-benzyl-N-methyl­tyrosine methyl ester. The dihedral angle between the phenyl ring and the benzene ring is 11.61 (19)°. In the crystal structure, the allyl group is disordered over two positions with site occupancy factors of ca 0.8 and 0.2. The bromide anion links to the quaternary ammonium cations via O—H⋯Br hydrogen bonding. An intramolecular O—H⋯Br hydrogen bond is also observed