Intensive agriculture and urbanization have led to the excessive and repeated input of nitrogen (N) into soil and further increased the amount of nitrate (NO3−) leaching into groundwater, which has ...become an environmental problem of widespread concern. This review critically examines both the recent advances and remaining knowledge gaps with respect to the N cycle in the vadose zone-groundwater system. The key aspects regarding the N distribution, transformation, and budget in this system are summarized. Three major missing N pieces (N in dissolved organic form, N in the deep vadose zone, and N in the nonagricultural system), which are crucial for closing the N cycle yet has been previously assumed to be insignificant, are put forward and discussed. More work is anticipated to obtain accurate information on the chemical composition, transformation mechanism, and leaching flux of these missing N pieces in the vadose zone-groundwater system. These are essential to support the assessment of global N stocks and management of N contamination risks.
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•The review focuses on N cycle in the vadose zone-groundwater system.•N distribution levels, transformation activity and leaching flux are collected.•Methodologies of N analysis, N transformation and N budget analysis are summarized.•Three missing N pieces and future research needs are highlighted.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In many real-world networks of interest in the field of remote sensing (e.g., public transport networks), nodes are associated with multiple labels, and node classes are imbalanced; that is, some ...classes have significantly fewer samples than others. However, the research problem of imbalanced multi-label graph node classification remains unexplored. This non-trivial task challenges the existing graph neural networks (GNNs) because the majority class can dominate the loss functions of GNNs and result in the overfitting of the majority class features and label correlations. On non-graph data, minority over-sampling methods (such as the synthetic minority over-sampling technique and its variants) have been demonstrated to be effective for the imbalanced data classification problem. This study proposes and validates a new hypothesis with unlabeled data over-sampling, which is meaningless for imbalanced non-graph data; however, feature propagation and topological interplay mechanisms between graph nodes can facilitate the representation learning of imbalanced graphs. Furthermore, we determine empirically that ensemble data synthesis through the creation of virtual minority samples in the central region of a minority and generation of virtual unlabeled samples in the boundary region between a minority and majority is the best practice for the imbalanced multi-label graph node classification task. Our proposed novel data over-sampling framework is evaluated using multiple real-world network datasets, and it outperforms diverse, strong benchmark models by a large margin.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep learning techniques to graph data in remote sensing ...(e.g., public transport networks) have been conducted. In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassifications have an equal loss and thus seek to maximize the posterior probability of the sample nodes under labeled classes. The graph data used in realistic scenarios tend to follow unbalanced long-tailed class distributions, where a few majority classes contain most of the vertices and the minority classes contain only a small number of nodes, making it difficult for the GNN to accurately predict the minority class samples owing to the classification tendency of the majority classes. In this paper, we propose a dual cost-sensitive graph convolutional network (DCSGCN) model. The DCSGCN is a two-tower model containing two subnetworks that compute the posterior probability and the misclassification cost. The model uses the cost as ”complementary information” in a prediction to correct the posterior probability under the perspective of minimal risk. Furthermore, we propose a new method for computing the node cost labels based on topological graph information and the node class distribution. The results of extensive experiments demonstrate that DCSGCN outperformed other competitive baselines on different real-world imbalanced long-tailed graphs.
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Knowledge of entity histories is often necessary for comprehensive understanding and characterization of entities. Yet, the analysis of an entity’s history is often most meaningful when carried out ...in comparison with the histories of other entities. In this paper, we describe a novel task of
history-based entity categorization
and
comparison
. Based on a set of entity-related documents which are assumed as an input, we determine latent entity categories whose members share similar histories; hence, we are effectively grouping entities based on the correspondences in their historical developments. Next, we generate comparative timelines for each determined group allowing users to elucidate similarities and differences in the histories of entities. We evaluate our approach on several datasets of different entity types demonstrating its effectiveness against competitive baselines.
Actionable knowledge graph (AKG), a specialized version of knowledge graph, was proposed recently to represent, analyze, and predict human action, thus facilitating deeper understanding of human ...action by robots. However, the automatic construction of AKGs from action-related corpora is still an unexplored problem. In this study, we first propose three unsupervised matrix factorization-based frameworks for AKG generation from three different perspectives: subject, context and functionality of action, respectively. Further, we propose a hybrid model based on neural network matrix factorization (NNMF) that considers multi-source signals simultaneously. It not only learns the latent action representations, but also learns the optimal learning objective rather than assuming it to be fixed. To quantitatively verify the utility of the constructed AKGs, we introduce a novel application, that is, predicting the most likely missing action records in Wikipedia biographies. Experimental results on a large-scale Wikipedia biography dataset show that the proposed model brings significant improvement over the baselines, which demonstrates the strong expressiveness of our generated AKGs.
We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs ...given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.
The coronavirus disease (COVID-19) pandemic has impacted HIV prevention strategies globally. However, changes in pre-exposure prophylaxis (PrEP) adherence and HIV-related behaviors, and their ...associations with medication adherence among men who have sex with men (MSM) PrEP users remain unclear since the onset of the COVID-19 pandemic.
A Retrospective Cohort Study of HIV-negative MSM PrEP users was conducted in four Chinese metropolises from December 2018 to March 2020, assessing the changes in PrEP adherence and HIV-related behaviors before and during the COVID-19. The primary outcome was poor PrEP adherence determined from self-reported missing at least one PrEP dose in the previous month. We used multivariable logistic regression to determine factors correlated with poor adherence during COVID-19.
We enrolled 791 eligible participants (418 52.8% in daily PrEP and 373 47.2% in event-driven PrEP). Compared with the data conducted before the COVID-19, the proportion of PrEP users decreased from 97.9 to 64.3%, and the proportion of poor PrEP adherence increased from 23.6 to 50.1% during the COVID-19 odds ratio (
) 3.24, 95% confidence interval (
) 2.62-4.02. While the percentage of condomless anal intercourse (CAI) with regular partners (11.8 vs. 25.7%) and with casual partners (4.4 vs. 9.0%) both significantly increased. The proportion of those who were tested for HIV decreased from 50.1 to 25.9%. Factors correlated with poor PrEP adherence during the COVID-19 included not being tested for HIV (adjusted odds ratio a
= 1.38 95%
: 1.00, 1.91), using condoms consistently with regular partners (vs. never, a
= 2.19 95%
: 1.16, 4.13), and being married or cohabitating with a woman (vs. not married, a
= 3.08 95%
: 1.60, 5.95).
Increased poor PrEP adherence and CAI along with the decrease in HIV testing can lead to an increase in HIV acquisition and drug resistance to PrEP. Targeted interventions are needed to improve PrEP adherence and HIV prevention strategies.
•The porous structure of mZVI/GCS effectively mitigated the passivation of commercial mZVI, and increased the longevity of mZVI.•Cr(VI) maximum adsorption capacity of mZVI/GCS reached 243.63 ...mg/g.•mZVI/GCS exhibited great adaptation to the common hydrogeological environment (pH, temperature, and coexisting ions) of groundwater.•Column capacity for Cr(VI) removal of mZVI/GCS packed column was 6.4 times higher than that of mZVI packed column in 50 days.•Cr(VI) removal mechanism of mZVI/GCS included reduction and electrostatic attraction.
Microscale zero-valent iron (mZVI) has shown great potential for groundwater Cr(VI) remediation. However, low Cr(VI) removal capacity caused by passivation restricted the wide use of mZVI. We prepared mZVI/GCS by encapsulating mZVI in a porous glutaraldehyde-crosslinked chitosan matrix, and the formation of the passivation layer was alleviated by reducing the contact between zero-valent iron particles. The average pore diameter of mZVI/GCS was 8.775 nm, which confirmed the mesoporous characteristic of this material. Results of batch experiments demonstrated that mZVI/GCS exhibited high Cr(VI) removal efficiency in a wide range of pH (2-10) and temperature (5-35°C). Common groundwater coexisting ions slightly affected mZVI/GCS. The material showed great reusability, and the average Cr(VI) removal efficiency was 90.41% during eight cycles. In this study, we also conducted kinetics and isotherms analysis. Pseudo-second-order model was the most matched kinetics model. The Cr(VI) adsorption process was fitted by both Langmuir and Freundlich isotherms models, and the maximum Langmuir adsorption capacity of mZVI/GCS reached 243.63 mg/g, which is higher than the adsorption capacities of materials reported in most of the previous studies. Notably, the column capacity for Cr(VI) removal of a mZVI/GCS-packed column was 6.4 times higher than that of a mZVI-packed column in a 50-day experiment. Therefore, mZVI/GCS with a porous structure effectively relieved passivation problems of mZVI and showed practical application prospects as groundwater Cr(VI) remediation material with practical application prospects.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Low-cost and efficient electrocatalysts for water splitting hold a significant position in future renewable energy system. Herein, we first fabricated a self-standing bimetallic FeCo Prussian blue ...analogue nanosheet array (FeCo PBA) on Ni foam through ion exchange between K3Fe(CN)6 and hydrothermal synthesized Co3(PO4)2·8H2O nanosheet arrays. Then the obtained FeCo PBA were facilely transformed into FeCo/C and FeCoP/C nanosheet arrays through hydrogenation and phosphidation, respectively. Benefiting from the enhanced mass transfer in porous structure, the intimate contact with Ni framework and the synergistic effect of bimetal sites, the resultant FeCo/C NS and FeCoP/C NS demonstrate superior oxygen and hydrogen evolution activity in alkaline media, respectively. Impressively, a low cell voltage of 1.55 V is sufficient to afford a current density of 10 mA cm−2 by coupling FeCo/C NS and FeCoP/C NS in a two-electrode water splitting electrolyzer, surpassing the performance of PtC based couple (Vcell,10 = 1.60 V). This work provides a new approach to construct highly efficient and cost-effective water splitting electrodes.
FeCo/C and FeCoP/C nanosheet arrays for highly efficient water splitting are prepared by facile transformation of self-standing FeCo Prussian Blue Analogous. Display omitted
•Self-standing FeCo Prussian blue analogue nanosheet array (FeCo PBA) on Ni foam is fabricated by ion exchange reaction.•Facile conversion of FeCo PBA into efficient water splitting electrodes is achieved by hydrogenation and phosphorization.•A water splitting electrolysor with low cell voltage (1.55 V) is constructed with the as-obtained FeCo/C NS and FeCoP/C NS.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Reducing Hg contamination in soil using eco-friendly approaches has attracted increasing attention in recent years. In this study, a novel multi-metal-resistant Hg-volatilizing fungus belonging to ...Lecythophora sp., DC-F1, was isolated from multi-metal-polluted mining-area soil, and its performance in reducing Hg bioavailability in soil when used in combination with biochar was investigated. The isolate displayed a minimum inhibitory concentration of 84.5mg·L−1 for Hg(II) and volatilized >86% of Hg(II) from LB liquid medium with an initial concentration of 7.0mg·L−1 within 16h. Hg(II) contents in soils and grown lettuce shoots decreased by 13.3–26.1% and 49.5–67.7%, respectively, with DC-F1 and/or biochar addition compared with a control over 56days of incubation. Moreover, treatment with both bioagents achieved the lowest Hg content in lettuce shoots. Hg presence and DC-F1 addition significantly decreased the number of fungal ITS gene copies in soils. High-throughput sequencing showed that the soil fungal community compositions were more largely influenced by DC-F1 addition than by biochar addition, with the proportion of Mortierella increasing and those of Penicillium and Thielavia decreasing with DC-F1 addition. Developing the coupling of Lecythophora sp. DC-F1 with biochar into a feasible approach for the recovery of Hg-contaminated soils is promising.
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•A novel metal-resistant Hg(II)-volatilizing fungus, Lecythophora sp. DC-F1, was isolated.•DC-F1 and biochar both effectively reduced Hg(II) contents in soil and plants.•The soil with both bioagents exhibited the lowest Hg uptake in lettuce shoots.•Soil fungal abundance and community structure were influenced to a greater degree by DC-F1 addition than by biochar addition.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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