240 research outputs found
Peer effects of income in consumption
This article provides a new perspective of peer effects that coexist
in different consumer activities and investigates how consumption
of a household is affected by the level of incomes of its peers.
Using unique panel data on Chinese households between 2011
and 2019, we explore the causal relationship between peers’
income and household consumption and then analyze plausible
mechanisms behind it. We find that the peer effect of income in
consumption is significantly positive. Higher level of average
income in a reference group is associated with the household’s
greater expenditure on consumption and the improvement of consumption
structure. There is also evidence that peer household
income helps to encourage the household consumption through
its impact on household income and peer household consumption.
By identifying peers’ income as the average income of other households
living in the same region, in the same age group and with
the same level of education, our research contributes to the literature
on peer effects in consumption, mapping relationships
between intragroup income and individual consumptio
Landscape Pattern Analysis and Quality Evaluation in Beijing Hanshiqiao Wetland Nature Reserve
AbstractTaking the Landsat TM and ASTER images of Hanshiqiao wetland nature reserve in 1988, 1996 and 2004 as data source, based on the landscape types from imagery classification, the reserve landscape pattern and its changes were analyzed, meanwhile, the landscape quality and its changes were evaluated and discussed. Several landscape pattern indices were analyzed, the results indicated that from 1988 to 2004, as the result of natural factors and human disturbances, the landscape structure has been changed, landscape fragmentation has become more and more serious, patches have been tended to regular shape, and connectivity of the natural wetland has been weakened. In addition, the landscape quality was evaluated based on the indicators of pressure, state and response. The results showed that during 1996-2004 periods, the landscape quality for Hanshiqiao wetland nature reserve has degraded obviously, which was mainly influenced by human activities breaking into wetland landscape. Effective wetland management and control is therefore needed to solve the issues of the wetland loss and degradation in Hanshiqiao wetland nature reserve
Sequential Recommendation with Diffusion Models
Generative models, such as Variational Auto-Encoder (VAE) and Generative
Adversarial Network (GAN), have been successfully applied in sequential
recommendation. These methods require sampling from probability distributions
and adopt auxiliary loss functions to optimize the model, which can capture the
uncertainty of user behaviors and alleviate exposure bias. However, existing
generative models still suffer from the posterior collapse problem or the model
collapse problem, thus limiting their applications in sequential
recommendation. To tackle the challenges mentioned above, we leverage a new
paradigm of the generative models, i.e., diffusion models, and present
sequential recommendation with diffusion models (DiffRec), which can avoid the
issues of VAE- and GAN-based models and show better performance. While
diffusion models are originally proposed to process continuous image data, we
design an additional transition in the forward process together with a
transition in the reverse process to enable the processing of the discrete
recommendation data. We also design a different noising strategy that only
noises the target item instead of the whole sequence, which is more suitable
for sequential recommendation. Based on the modified diffusion process, we
derive the objective function of our framework using a simplification technique
and design a denoise sequential recommender to fulfill the objective function.
As the lengthened diffusion steps substantially increase the time complexity,
we propose an efficient training strategy and an efficient inference strategy
to reduce training and inference cost and improve recommendation diversity.
Extensive experiment results on three public benchmark datasets verify the
effectiveness of our approach and show that DiffRec outperforms the
state-of-the-art sequential recommendation models
Influential factors associated with consecutive crash severity: A two-level logistic modeling approach
A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers
Nanolamellar Tantalum Interfaces in the Osteoblast Adhesion
The design of topographically patterned surfaces is considered to be a preferable approach for influencing cellular behavior in a controllable manner, in particular to improve the osteogenic ability of bone regeneration. In this study, we fabricated nanolamellar tantalum (Ta) surfaces with lamellar wall thicknesses of 40 and 70 nm. The cells attached to nanolamellar Ta surfaces exhibited higher protein adsorption and expression of β1 integrin, as compared to the nonstructured bulk Ta, which facilitated the initial cell attachment and spreading. We thus, as expected, observed significantly enhanced osteoblast adhesion, growth, and alkaline phosphatase activity on nanolamellar Ta surfaces. However, the beneficial effects of nanolamellar structures on osteogenesis became weaker as the lamellar wall thickness increased. The interaction between cells and Ta surfaces was examined through adhesion forces using atomic force microscopy. Our findings indicated that the Ta surface with a lamellar wall thickness of 40 nm exhibited the strongest stimulatory effect. The observed strongest adhesion force between the cell-attached tip and the Ta surface with a 40 nm thick lamellar wall encouraged the much stronger binding of cells with the surface and thus well-attached, -stretched, and -grown cells. We attributed this to the increase in the available contact area of cells with the thinner nanolamellar Ta surface. The increased contact area allowed the enhancement of the cell surface interaction strength and, thus, improved osteoblast adhesion. This study suggests that the thin nanolamellar topography shows immense potential in improving the clinical performance of dental and orthopedic implants
Polystyrene nanoplastics mediated the toxicity of silver nanoparticles in zebrafish embryos
The widespread distribution of nanoplastics and nanomaterials in aquatic environments is of great concern. Nanoplastics have been found to modulate the toxicity of other environmental pollutants in organisms, while few studies have focused on their influences on nanomaterials. Thus, this study evaluated the influences of polystyrene (PS) nanoplastics on the toxicity of silver nanoparticles (AgNPs) to zebrafish (Danio rerio) embryos, including acute toxicity, oxidative stress, apoptosis, immunotoxicity, and metabolic capability. The results showed that the presence of PS nanoplastics could act as a carrier of the co-existing AgNPs in waters. The release ratio of Ag+ from AgNPs was up to 4.23%. The lethal effects of AgNPs on zebrafish embryos were not significantly changed by the co-added PS nanoplastics. Whereas, the alterations in gene expression related to antioxidant and metabolic capability in zebrafish (sod1, cat, mt2, mtf-1, and cox1) caused by AgNPs were significantly enhanced by the presence of PS nanoplastics, which simultaneously lowered the apoptosis and immunotoxicity (caspase9, nfkβ, cebp, and il-1β) induced by AgNPs. It suggests the presence of PS nanoplastics suppressed the AgNPs-induced genotoxicity in zebrafish. The released Ag+ from AgNPs may be responsible for the toxicity of AgNPs in zebrafish, while the subsequent absorption and agglomeration of AgNPs and the released Ag+ on PS nanoplastics may alleviate the toxicity
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender
systems for its representation learning capability on user and item embeddings.
However, GCN is vulnerable to noisy and incomplete graphs, which are common in
real world, due to its recursive message propagation mechanism. In the
literature, some work propose to remove the feature transformation during
message propagation, but making it unable to effectively capture the graph
structural features. Moreover, they model users and items in the Euclidean
space, which has been demonstrated to have high distortion when modeling
complex graphs, further degrading the capability to capture the graph
structural features and leading to sub-optimal performance. To this end, in
this paper, we propose a simple yet effective Quaternion-based Graph
Convolution Network (QGCN) recommendation model. In the proposed model, we
utilize the hyper-complex Quaternion space to learn user and item
representations and feature transformation to improve both performance and
robustness. Specifically, we first embed all users and items into the
Quaternion space. Then, we introduce the quaternion embedding propagation
layers with quaternion feature transformation to perform message propagation.
Finally, we combine the embeddings generated at each layer with the mean
pooling strategy to obtain the final embeddings for recommendation. Extensive
experiments on three public benchmark datasets demonstrate that our proposed
QGCN model outperforms baseline methods by a large margin.Comment: 13 pages, 7 figures, 6 tables. Submitted to ICDE 202
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Sequential Recommendation (SR) has received increasing attention due to its
ability to capture user dynamic preferences. Recently, Contrastive Learning
(CL) provides an effective approach for sequential recommendation by learning
invariance from different views of an input. However, most existing data or
model augmentation methods may destroy semantic sequential interaction
characteristics and often rely on the hand-crafted property of their
contrastive view-generation strategies. In this paper, we propose a
Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for
sequential recommendation, which applies the meta-optimized two-step training
strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first
introduces a simple yet effective augmentation method called
Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational
AutoEncoders (VAE) as the view generator and can constitute contrastive views
while preserving the original sequence's semantics. Next, the model employs a
meta-optimized two-step training strategy, which aims to adaptively generate
contrastive views without relying on manually designed view-generation
techniques. Finally, we evaluate our proposed method Meta-SGCL using three
public real-world datasets. Compared with the state-of-the-art methods, our
experimental results demonstrate the effectiveness of our model and the code is
available
- …