53 research outputs found
Evaluation of Effectiveness of Speed Reduction Markings on Driving Speed in Highway Tunnel Entrance and Exit Areas
Tunnels are critical areas for highway safety because the severity of crashes in tunnels tends to be more serious. Controlling vehicle speed is regarded as a feasible measure to reduce the accident rate in the tunnel entrance and exit areas. This paper aims to evaluate the effectiveness of three types of speed reduction markings (SRMs) in tunnel entrance and exit zones by conducting a driving simulation experiment. For this study, 25 drivers completed the driving tasks in the day and night scenarios. The vehicle speed and acceleration data were collected for analysing and the relative speed contrast, time mean speed and acceleration were adopted as indices to evaluate the effectiveness of SRMs. The repeated ANOVA test results revealed that SRMs have a significant effect in reducing vehicle speed, especially in the exit zone. Colour Anti-skid Markings (CASMs) produced a more obvious deceleration in the entrance zone. In the entrance zone, a similar downward trend was performed in the situation of NSRMs and SRMs, but a lower speed occurred in case of SRMs. Besides, CASMs work better and cause an obvious gap of 10 km/h in daytime and 5 km/h at night compared to the speed without SRMs. In the exit zone, the present study supports the conclusion that the drivers are prone to accelerate. Our results showed that the drivers accelerated in case of NSRMs, while they slowed down in case of SRMs. Thus, SRMs are necessarily implemented in the highway tunnel entrance and exit zones. Our study also indicates that though CASMs result in lower speed at night, the Transverse Speed Reduction Markings(TSRMs) have a better performance than CASMs in daytime. The investigation provides essential information for developing a new marking design criterion and intelligent driver support systems in the highway tunnel zones.</p
PathMLP: Smooth Path Towards High-order Homophily
Real-world graphs exhibit increasing heterophily, where nodes no longer tend
to be connected to nodes with the same label, challenging the homophily
assumption of classical graph neural networks (GNNs) and impeding their
performance. Intriguingly, we observe that certain high-order information on
heterophilous data exhibits high homophily, which motivates us to involve
high-order information in node representation learning. However, common
practices in GNNs to acquire high-order information mainly through increasing
model depth and altering message-passing mechanisms, which, albeit effective to
a certain extent, suffer from three shortcomings: 1) over-smoothing due to
excessive model depth and propagation times; 2) high-order information is not
fully utilized; 3) low computational efficiency. In this regard, we design a
similarity-based path sampling strategy to capture smooth paths containing
high-order homophily. Then we propose a lightweight model based on multi-layer
perceptrons (MLP), named PathMLP, which can encode messages carried by paths
via simple transformation and concatenation operations, and effectively learn
node representations in heterophilous graphs through adaptive path aggregation.
Extensive experiments demonstrate that our method outperforms baselines on 16
out of 20 datasets, underlining its effectiveness and superiority in
alleviating the heterophily problem. In addition, our method is immune to
over-smoothing and has high computational efficiency
Stackelberg game-based three-stage optimal pricing and planning strategy for hybrid shared energy storage
Inspired from sharing economy and advanced energy storage technologies, hybrid shared energy storage (HSES), as an innovative business model, can provide flexible storage leasing services to new energy stations (NESs) and bring additional profits to the energy storage owner. Under this business model, pricing and planning issues are the main focus of the HSES operator to increase revenues but are rarely considered in current studies. Therefore, a Stackelberg game-based three-stage optimal pricing and planning strategy of HSES is formulated for the operator. First, an HSES model considering two leasing options is developed to provide two kinds of short-term use rights of energy storage resources for NESs. Then, the interactions between selfish NESs and the HSES operator are characterized as a Stackelberg game, and a bi-level pricing and planning strategy optimization model is developed to help the HSES operator make optimal decisions. Finally, considering different characteristics in each stage of the Stackelberg game, a three-stage solution method based on the genetic algorithm (GA) and mixed-integer linear programming (MILP) models is proposed to solve the optimization problem. Case studies on six NESs in a certain region are taken to verify the effectiveness of the proposed strategy. Simulation results show that the HSES operator can obtain maximum profit under the proposed pricing and planning strategy. In addition, the proposed HSES leasing model can provide additional benefits to both the operator and NESs
Metasurface-based Spectral Convolutional Neural Network for Matter Meta-imaging
Convolutional neural networks (CNNs) are representative models of artificial
neural networks (ANNs), that form the backbone of modern computer vision.
However, the considerable power consumption and limited computing speed of
electrical computing platforms restrict further development of CNNs. Optical
neural networks are considered the next-generation physical implementations of
ANNs to break the bottleneck. This study proposes a spectral convolutional
neural network (SCNN) with the function of matter meta-imaging, namely
identifying the composition of matter and mapping its distribution in space.
This SCNN includes an optical convolutional layer (OCL) and a reconfigurable
electrical backend. The OCL is implemented by integrating very large-scale,
pixel-aligned metasurfaces on a CMOS image sensor, which accepts 3D raw
datacubes of natural images, containing two-spatial and one-spectral
dimensions, at megapixels directly as input to realize the matter meta-imaging.
This unique optoelectronic framework empowers in-sensor optical analog
computing at extremely high energy efficiency eliminating the need for coherent
light sources and greatly reducing the computing load of the electrical
backend. We employed the SCNN framework on several real-world complex tasks. It
achieved accuracies of 96.4% and 100% for pathological diagnosis and real-time
face anti-spoofing at video rate, respectively. The SCNN framework, with an
unprecedented new function of substance identification, provides a feasible
optoelectronic and integrated optical CNN implementation for edge devices or
cellphones with limited computing capabilities, facilitating diverse
applications, such as intelligent robotics, industrial automation, medical
diagnosis, and astronomy
An exploration of the correlations between seven psychiatric disorders and the risks of breast cancer, breast benign tumors and breast inflammatory diseases: Mendelian randomization analyses
BackgroundPrevious observational studies have showed that certain psychiatric disorders may be linked to breast cancer risk, there is, however, little understanding of relationships between mental disorders and a variety of breast diseases. This study aims to investigate if mental disorders influence the risks of overall breast cancer, the two subtypes of breast cancer (ER+ and ER-), breast benign tumors and breast inflammatory diseases.MethodsDuring our research, genome-wide association study (GWAS) data for seven psychiatric disorders (schizophrenia, major depressive disorder, bipolar disorder, post-traumatic stress disorder, panic disorder, obsessive-compulsive disorder and anorexia nervosa) from the Psychiatric Genomics Consortium (PGC) and the UK Biobank were selected, and single-nucleotide polymorphisms (SNPs) significantly linked to these mental disorders were identified as instrumental variables. GWAS data for breast diseases came from the Breast Cancer Association Consortium (BCAC) as well as the FinnGen consortium. We performed two-sample Mendelian randomization (MR) analyses and multivariable MR analyses to assess these SNPs’ effects on various breast diseases. Both heterogeneity and pleiotropy were evaluated by sensitivity analyses.ResultsWhen the GWAS data of psychiatric disorders were derived from the PGC, our research found that schizophrenia significantly increased the risks of overall breast cancer (two-sample MR: OR 1.05, 95%CI [1.03-1.07], p = 3.84 × 10−6; multivariable MR: OR 1.06, 95%CI [1.04-1.09], p = 2.34 × 10−6), ER+ (OR 1.05, 95%CI [1.02-1.07], p = 5.94 × 10−5) and ER- (two-sample MR: OR 1.04, 95%CI [1.01-1.07], p = 0.006; multivariable MR: OR 1.06, 95%CI [1.02-1.10], p = 0.001) breast cancer. Nevertheless, major depressive disorder only showed significant positive association with overall breast cancer (OR 1.12, 95%CI [1.04-1.20], p = 0.003) according to the two-sample MR analysis, but not in the multivariable MR analysis. In regards to the remainder of the mental illnesses and breast diseases, there were no significant correlations. While as for the data from the UK Biobank, schizophrenia did not significantly increase the risk of breast cancer.ConclusionsThe correlation between schizophrenia and breast cancer found in this study may be false positive results caused by underlying horizontal pleiotropy, rather than a true cause-and-effect relationship. More prospective studies are still needed to be carried out to determine the definitive links between mental illnesses and breast diseases
Cationic nanoparticles directly bind angiotensin-converting enzyme 2 and induce acute lung injury in mice
Ainsliaea daheishanensis (Asteraceae): a new species from China
In this work, we describe a new species, Ainsliaea daheishanensis Y.L.Peng, C.X.Yang & Y.Luo, based on morphological traits. The new species was discovered in the mountains of Yunnan, near the border between Myanmar and China. The new species differs from the phenotypically closely-related Ainsliaea foliosa Handel-Mazzetti in the morphology of the leaf veins and phyllaries, those having a protruding abaxial reticulate pattern in the lower and median part of stem with white hairs and narrow inner phyllaries. A key to the three closed Ainsliaea species occurring in China is provided
Blumea htamanthii (Asteraceae), a new species from Myanmar
A new species, Blumea htamanthii Y.L. Peng, C.X. Yang & Y. Luo from Myanmar is described. The new species is distinguished from B. bifoliata by its leaves with short petioles, abaxially purple, leaf blade with papillary hair and sparse multicellular villous, capitula with 1–4 heads, glabrous florets and usually unbranched stems. A key to Blumea species in Myanmar is provided
Ainsliaea daheishanensis (Asteraceae): a new species from China
In this work, we describe a new species, Ainsliaea daheishanensis Y.L.Peng, C.X.Yang & Y.Luo, based on morphological traits. The new species was discovered in the mountains of Yunnan, near the border between Myanmar and China. The new species differs from the phenotypically closely-related Ainsliaea foliosa Handel-Mazzetti in the morphology of the leaf veins and phyllaries, those having a protruding abaxial reticulate pattern in the lower and median part of stem with white hairs and narrow inner phyllaries. A key to the three closed Ainsliaea species occurring in China is provided
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