A Study on Hydraulic Conductivity of Fine Oil Sand Tailings

In oil sand waste tailings pond, the gravity segregation takes place, where coarse particles settle relatively more quickly than fine particles, and a stable suspension, known as the mature fine tailings (MFT), is formed. Compression of MFT appears to be very slow, and MFT remains suspended in tailings pond for decades due to the low permeability. Large volumes of MFT continually accumulate in tailings ponds, and therefore MFT storage requires a large containment pond, which generates environmental concerns and leads to MFT management challenges. Hydraulic conductivity is one of the most important properties of MFT because it controls consolidation behaviors. Clear understandings of hydraulic conductivity and its relationship with void ratio are essential to MFT management and treatment. This study establishes the relationship between hydraulic conductivity and a relatively wide range of void ratios for MFT through three laboratory tests, i.e. the standard oedometer test, the falling head test and the Rowe cell test. Based on the hydraulic conductivity data of this study together with the data reported in the literature, data regression models are developed to correlate the hydraulic conductivity with a wide range of void ratios (k-e relationship) for fine oil sand tailings. Empirical equations, which were proposed to predict the hydraulic conductivity for plastic soils, are evaluated their suitability and performances in terms of predicting the hydraulic conductivity for fine oil sand tailings

Exploring FemTech Affordances: A Computational Analysis of Fertility and Pregnancy Apps

FemTech applications are mobile applications designed to promote women\u27s health and wellness. They have gained increasing attention with a growing market share in the digital health industry. However, most of the existing products seem to be digital health apps with pink-themed design, but not oriented to female users or female-specific illnesses. To improve the understanding of FemTech apps, this study aims to explore the different types of affordances appearing in FemTech apps, through an analysis of user reviews of fertility and pregnancy apps. We applied topic modelling analysis on the data collected and extracted three types of affordances: instrumental, experiential, and empowerment. Our findings suggest that FemTech designers can consider these affordances to meet female users\u27 expectations better and improve their experience. Furthermore, our study sheds light on the potential of FemTech in promoting female empowerment, which could inspire future research in this field

Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention

Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990-2000, and in Zhejiang Province during the periods 2000-2010 and 2010-2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management

Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

Recommender systems are essential for online applications, and sequential recommendation has enjoyed significant prevalence due to its expressive ability to capture dynamic user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason for this issue is that language models often lack an understanding of domain-specific knowledge and item-related textual content. To address this issue, we adopt a new sequential recommendation paradigm and propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations. We demonstrate the effectiveness of our approach through experiments on several benchmark datasets, showing promising results and providing valuable insights into the influence of our model on sequential recommendation tasks. Furthermore, our experimental codes are publicly available

Distributed Semi-Supervised Sparse Statistical Inference

This paper is devoted to studying the semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for $M$-estimation and generalized linear model according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data.Comment: 41 pages, 4 figure

Evolutionary Expansion of Nematode-Specific Glycine-Rich Secreted Peptides

A genome‐wide survey across 10 species from algae Guillardia theta to mammals revealed that Caenorhabditis elegans and Caenorhabditis briggsae acquired a large number of glycine‐rich secreted peptides (GRSPs, 110 GRSPs in C. elegans and 93 in C. briggsae) during evolution in this study. Chromosomal mapping indicated that most GRSPs were clustered on their genomes [103 (93.64%) in C. elegans and 82 (88.17%) in C. briggsae]. Totally, there are 18 GRSPs cluster units in C. elegans and 13 in C. briggsae. Except for four C. elegans where GRSP clusters lacking matching clusters in C. briggsae, all other GRSP clusters had its corresponding orthologous clusters between the two nematodes. Using eight transcriptomic datasets of Affmyetrix microarray, genome‐wide association studies identified many co‐expressed GRSPs clusters after C. elegans infections. Highly homologous coding sequences and conserved exon‐intron organizations indicated that GRSP tight clusters might have originated from local DNA duplications. The conserved synteny blocks of GRSP clusters between their genomes, the co‐expressed GRSPs clusters after C. elegans infections, and a strong purifying selection of protein‐coding sequences suggested evolutionary constraint acting on C. elegans to ensure that C. elegans could rapidly launch and fulfill systematic responses against infections by co‐expression, co‐regulation, and co‐functionality of GRSP clusters

Investigation of HIV-1 Gag binding with RNAs and Lipids using Atomic Force Microscopy

Atomic Force Microscopy was utilized to study the morphology of Gag, {\Psi}RNA, and their binding complexes with lipids in a solution environment with 0.1{\AA} vertical and 1nm lateral resolution. TARpolyA RNA was used as a RNA control. The lipid used was phospha-tidylinositol-(4,5)-bisphosphate (PI(4,5)P2). The morphology of specific complexes Gag-{\Psi}RNA, Gag-TARpolyA RNA, Gag-PI(4,5)P2 and PI(4,5)P2-{\Psi}RNA-Gag were studied. They were imaged on either positively or negatively charged mica substrates depending on the net charges carried. Gag and its complexes consist of monomers, dimers and tetramers, which was confirmed by gel electrophoresis. The addition of specific {\Psi}RNA to Gag is found to increase Gag multimerization. Non-specific TARpolyA RNA was found not to lead to an increase in Gag multimerization. The addition PI(4,5)P2 to Gag increases Gag multimerization, but to a lesser extent than {\Psi}RNA. When both {\Psi}RNA and PI(4,5)P2 are present Gag undergoes comformational changes and an even higher degree of multimerization

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme; (2) increasing the information density of basic semantic units; (3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively...Comment: Submitted to IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING(TKDE), under revie