Recycling drinking water treatment sludge in construction and building materials: A review.

Drinking water treatment sludge (DWTS) is a by-product of water treatment, and it is difficult to recycle to high value and poses potential environmental risks. Recycling DWTS into cement-based materials is an effective measure to achieve its high-volume utilization and reduce its environmental load. DWTS is rich in silica-alumina phases and has potential pozzolanic activity after drying, grinding and calcination, giving it similar properties to traditional supplementary cementitious materials.

Upcycling textile sludge into magnesium oxychloride cement: Physical properties, microstructure, and leaching behavior.

Textile sludge is a by-product produced during the wastewater treatment process in the textile printing and dyeing industry. Textile sludge is rich in heavy metal elements, which makes it a potential risk to the surrounding environment. This study designs a magnesium oxychloride cement (MOC) components to solidify harmful substances in textile sludge and studies the influence of textile sludge ash (TSA) on the mechanical properties and microstructure of MOC samples.

Twist-Induced Modification in the Electronic Structure of Bilayer WSe.

The recent discovery of strongly correlated phases in twisted transition-metal dichalcogenides (TMDs) highlights the significant impact of twist-induced modifications on electronic structures. In this study, we employed angle-resolved photoemission spectroscopy with submicrometer spatial resolution (μ-ARPES) to investigate these modifications by comparing valence band structures of twisted (5.3°) and nontwisted (AB-stacked) bilayer regions within the same WSe device.

Contrastive Bayesian Analysis for Deep Metric Learning.

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting.

Coinfection of Two Species in a Single Tick Species Provides New Insight into - and -Vector Interactions.

Rickettsiae are obligate intracellular bacteria that can cause life-threatening illnesses. There is an ongoing debate as to whether established infections by one species preclude the maintenance of the second species in ticks. Here, we identified two species in inoculum from Haemaphysalis montgomeryi ticks and subsequently obtained pure isolates of each species by plaque selection.

Fabrication of Loose Nanofiltration Membranes with High Rejection Selectivity between Natural Organic Matter and Salts for Drinking Water Treatment.

Loose nanofiltration (LNF) membranes with a molecular weight cut-off (MWCO) of about 1000 Da and high surface negative charge density have great application potential for drinking water treatment pursuing high rejection selectivity between natural organic matter (NOM) and mineral salts. This study was conducted to exploit the novel method coupling non-solvent induced phase separation (NIPS) and interfacial polymerization (IP) for the preparation of high-performance LNF membranes. A number of LNF membranes were synthesized by varying the polyethersulfone (PES) and piperazine (PIP) concentrations in the cast solution for the PES support layer preparation.

Mechanical, water resistance and environmental benefits of magnesium oxychloride cement incorporating rice husk ash.

Magnesium oxychloride cement (MOC) has received extensive attention as an eco-friendly cement, but its poor water resistance limits its engineering applications. In this study, MOC mixture (MOCM) was modified with 10-50 % rice husk ash (RHA) (wt% of MgO), and the development of their fresh properties, mechanical strength and microstructure was investigated. The results show that the incorporation of RHA to MOCM increases the setting time of the mixture and reduces its flowability.

Recycling coral waste into eco-friendly UHPC: Mechanical strength, microstructure, and environmental benefits.

On islands far away from the mainland, the raw materials for concrete production are often more difficult to obtain. Converting the coral waste generated during the island construction process into a marine ultra-high performance concrete (UHPC) mixture is an eco-friendly strategy. Coral powder (CP) is used to partially replace cement and silica fume (SF), and its mechanical strength, microstructure and environmental benefits are evaluated.

Local Semantic Correlation Modeling Over Graph Neural Networks for Deep Feature Embedding and Image Retrieval.

Deep feature embedding aims to learn discriminative features or feature embeddings for image samples which can minimize their intra-class distance while maximizing their inter-class distance. Recent state-of-the-art methods have been focusing on learning deep neural networks with carefully designed loss functions. In this work, we propose to explore a new approach to deep feature embedding.

Bio-cement-modified construction materials and their performances.

The microbial induced mineral precipitation can be used to modify and improve the performance of construction materials and can partially replace ordinary Portland cement. Microbially induced carbonate precipitation (MICP) mainly uses the urease secreted during the growth of urease-producing bacteria (UPB) to hydrolyze urea produce CO and reacts with Ca to form CaCO. Microbially induced struvite precipitation (MISP) mainly uses the urease to decompose urea to produce NH.

Removing Adversarial Noise via Low-Rank Completion of High-Sensitivity Points.

Deep neural networks are fragile under adversarial attacks. In this work, we propose to develop a new defense method based on image restoration to remove adversarial attack noise. Using the gradient information back-propagated over the network to the input image, we identify high-sensitivity keypoints which have significant contributions to the image classification performance.

Deformable adversarial registration network with multiple loss constraints.

Deformable medical image registration has the necessary value of theoretical research and clinical application. Traditional methods cannot meet clinical application standards in terms of registration accuracy and efficiency. This article proposes a deformable generate adversarial registration framework, which avoids the dependence on ground-truth deformation.

Experimental study on full-volume slag alkali-activated mortars: Air-cooled blast furnace slag versus machine-made sand as fine aggregates.

As the industrial waste from blast furnace ironmaking, air-cooled blast furnace slag (ACBFS) puts a lot of pressure on the environment. It is becoming more and more urgent to deal with the increasing ACBFS. In this study, the concept of "full-volume slag alkali-activated mortars (FSAM)" is proposed using ground granulated water-cooled blast furnace slag (GGBS) as aluminosilicate material and ACBFS to replace machine-made sand, aiming to solve the current situation of increasing scarcity of natural resources.

Zero-Shot Learning to Index on Semantic Trees for Scalable Image Retrieval.

In this study, we develop a new approach, called zero-shot learning to index on semantic trees (LTI-ST), for efficient image indexing and scalable image retrieval. Our method learns to model the inherent correlation structure between visual representations using a binary semantic tree from training images which can be effectively transferred to new test images from unknown classes. Based on predicted correlation structure, we construct an efficient indexing scheme for the whole test image set.

Complete genome sequence of a novel bovine hepacivirus from Yunnan, China.

We detected a novel bovine hepacivirus N (HNV) subtype, IME_BovHep_01, in the serum of cattle in Tengchong, Yunnan, China, by high-throughput sequencing. The complete genome of IME_BovHep_01, was sequenced using an Illumina MiSeq sequencer and found to be 8850 nt in length, encoding one hypothetical protein. BLASTn analysis showed that the genome sequence shared similarity with the bovine hepacivirus isolate BovHepV_209/Ger/2014, with 88% query coverage and 70.

Intercalated Iridium Diselenide Electrocatalysts for Efficient pH-Universal Water Splitting.

Developing bifunctional catalysts for both hydrogen and oxygen evolution reactions is a promising approach to the practical implementation of electrocatalytic water splitting. However, most of the reported bifunctional catalysts are only applicable to alkaline electrolyzer, although a few are effective in acidic or neutral media that appeals more to industrial applications. Here, a lithium-intercalated iridium diselenide (Li-IrSe ) is developed that outperformed other reported catalysts toward overall water splitting in both acidic and neutral environments.

Animal Scanner: Software for classifying humans, animals, and empty frames in camera trap images.

Camera traps are a popular tool to sample animal populations because they are noninvasive, detect a variety of species, and can record many thousands of animal detections per deployment. Cameras are typically set to take bursts of multiple photographs for each detection and are deployed in arrays of dozens or hundreds of sites, often resulting in millions of photographs per study. The task of converting photographs to animal detection records from such large image collections is daunting, and made worse by situations that generate copious empty pictures from false triggers (e.

Supervised Deep Feature Embedding With Handcrafted Feature.

Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and handcrafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a handcrafted feature model.

Pothole-rich Ultrathin WO Nanosheets that Trigger N≡N Bond Activation of Nitrogen for Direct Nitrate Photosynthesis.

Nitrate is a raw ingredient for the production of fertilizer, gunpowder, and explosives. Developing an alternative approach to activate the N≡N bond of naturally abundant nitrogen to form nitrate under ambient conditions will be of importance. Herein, pothole-rich WO was used to catalyse the activation of N≡N covalent triple bonds for the direct nitrate synthesis at room temperature.

Microstructure of ultra high performance concrete containing lithium slag.

Lithium slag (LS) is discharged as a byproduct in the process of the lithium carbonate, and it is very urgent to explore an efficient way to recycle LS in order to protect the environments and save resources. Many available supplementary cementitious materials for partial replacement of cement and/or silica fume (SF) can be used to prepare ultra high performance concrete (UHPC). The effect of LS to replace SF partially by weight used as a supplementary cementitious material (0%, 5%, 10% and 15% of binder) on the compressive strengths and microstructure evolution of UHPC has experimentally been studied by multi-techniques including mercury intrusion porosimetry, scanning electron microscope and nanoindentation technique.

Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising.

In this paper, we develop a new low-rank matrix recovery algorithm for image denoising. We incorporate the total variation (TV) norm and the pixel range constraint into the existing reweighted low-rank matrix analysis to achieve structural smoothness and to significantly improve quality in the recovered image. Our proposed mathematical formulation of the low-rank matrix recovery problem combines the nuclear norm, TV norm, and norm, thereby allowing us to exploit the low-rank property of natural images, enhance the structural smoothness, and detect and remove large sparse noise.