Modeling the impact of road grade on vehicle operation, vehicle energy consumption, and emissions

Motor vehicle emissions and their impacts on local air pollutant concentrations are a primary concern in cities. Properly quantifying energy and emissions is the key step in identifying the major sources of air pollution, evaluating whether transportation activities are consistent with air quality goals, and providing decision makers with reference for implementation of new policies for sustainable development. Mathematical models are commonly used to predict vehicle energy consumption and emissions. Vehicle-specific power (VSP) is widely used in such models to evaluate engine load, and it is represented as a function of vehicle mass, vehicle dynamic parameters (rolling/drag coefficient), driving behavior (speed and acceleration) and road conditions (gravitational acceleration and road gradient). In the U.S. Environmental Protection Agency’s (USEPA’s) MOVES (MOtor Vehicle Emission Simulator) model, speed and VSP levels are tied to vehicle energy consumption and emission rates. Detailed and accurate speed-acceleration joint distributions (SAJDs, also known as Watson plots) can be used to reflect onroad activity required for calculating the distribution of activities in MOVES VSP and speed bins, and thus for estimating vehicle energy consumption and emissions. Road grade is also a critical variable that affects engine operations, as uphill grades require that the engine perform additional work against gravity in the direction of vehicle motion (while downhill grades obtain an energy benefit). Real-world vehicle speed and acceleration can be easily collected using low-cost global positioning system (GPS) data loggers, on-board diagnostics (OBD) system data loggers, and smartphones apps. But, The effect of road grade is usually ignored in emission modeling. On the other hand, very little attention has been paid to the interaction between real-world road grade and onroad activity patterns and the resulting impact on energy use and emissions. However, road grade is expected to impact vehicle operations due to drivers’ response to uphill and downhill driving, or due to vehicle mechanical performance. It is currently unclear that how speed and accelerations vary across different road grade levels, and how the interaction of driver behavior and road grade affect engine power, energy consumption, and emissions modeling. This study is directed at answering two issues: 1): how road grade impacts vehicle speed and acceleration distributions, and how such distributions vary across vehicle types, roadway types, traffic conditions, etc., and 2): how significant the impact of integrating grade interactions is with respect to energy, emissions, and air quality modeling.Ph.D

Comparative mapping of quantitative trait loci associated with waterlogging tolerance in barley (Hordeum vulgare L.)

<p>Abstract</p> <p>Background</p> <p>Resistance to soil waterlogging stress is an important plant breeding objective in high rainfall or poorly drained areas across many countries in the world. The present study was conducted to identify quantitative trait loci (QTLs) associated with waterlogging tolerance (e.g. leaf chlorosis, plant survival and biomass reduction) in barley and compare the QTLs identified across two seasons and in two different populations using a composite map constructed with SSRs, RFLP and Diversity Array Technology (DArT) markers.</p> <p>Results</p> <p>Twenty QTLs for waterlogging tolerance related traits were found in the two barley double haploid (DH) populations. Several of these QTLs were validated through replication of experiments across seasons or by co-location across populations. Some of these QTLs affected multiple waterlogging tolerance related traits, for example, QTL Q_wt4-1 contributed not only to reducing barley leaf chlorosis, but also increasing plant biomass under waterlogging stress, whereas other QTLs controlled both leaf chlorosis and plant survival.</p> <p>Conclusion</p> <p>Improving waterlogging tolerance in barley is still at an early stage compared with other traits. QTLs identified in this study have made it possible to use marker assisted selection (MAS) in combination with traditional field selection to significantly enhance barley breeding for waterlogging tolerance. There may be some degree of homoeologous relationship between QTLs controlling barley waterlogging tolerance and that in other crops as discussed in this study.</p

Opposing gradients of ribbon size and AMPA receptor expression underlie sensitivity differences among cochlear-nerve/hair-cell synapses

The auditory system transduces sound-evoked vibrations over a range of input sound pressure levels spanning six orders of magnitude. An important component of the system mediating this impressive dynamic range is established in the cochlear sensory epithelium, where functional subtypes of cochlear nerve fibers differ in threshold sensitivity, and spontaneous discharge rate (SR), by more than a factor of 1000 (Liberman, 1978), even though, regardless of type, each fiber contacts only a single hair cell via a single ribbon synapse. To study the mechanisms underlying this remarkable heterogeneity in threshold sensitivity among the 5–30 primary sensory fibers innervating a single inner hair cell, we quantified the sizes of presynaptic ribbons and postsynaptic AMPA receptor patches in >1200 synapses, using high-power confocal imaging of mouse cochleas immunostained for CtBP2 (C-terminal binding protein 2, a major ribbon protein) and GluR2/3 (glutamate receptors 2 and 3). We document complementary gradients, most striking in mid-cochlear regions, whereby synapses from the modiolar face and/or basal pole of the inner hair cell have larger ribbons and smaller receptor patches than synapses located in opposite regions of the cell. The AMPA receptor expression gradient likely contributes to the differences in cochlear nerve threshold and SR seen on the two sides of the hair cell in vivo (Liberman, 1982a); the differences in ribbon size may contribute to the heterogeneity of EPSC waveforms seen in vitro (Grant et al., 2010).National Institute on Deafness and Other Communication Disorders (U.S.) (Grants RO1 DC0188)National Institute on Deafness and Other Communication Disorders (U.S.) (P30 DC5029

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field

Extracellular vesicles secreted by Echinococcus multilocularis:Important players inangiogenesis promotion

The involvement of Echinococcus multilocularis, and other parasitic helminths, in regulating host physiology is well recognized, but molecular mechanisms remain unclear. Extracellular vesicles (EVs) released by helminths play important roles in regulating parasite-host interactions by transferring materials to the host. Analysis of protein cargo of EVs from E. multilocularis protoscoleces in the present study revealed a unique composition exclusively associated with vesicle biogenesis. Common proteins in various Echinococcus species were identified, including the classical EVs markers tetraspanins, TSG101 and Alix. Further, unique tegumental antigens were identified which could be exploited as Echinococcus EV markers. Parasite- and host-derived proteins within these EVs are predicted to support important roles in parasite-parasite and parasite-host communication. In addition, the enriched host-derived protein pay loads identified in parasite EVs in the present study suggested that they can beinvolved in focal adhesion and potentially promote angiogenesis. Further, increase dangiogenesis was observed in livers of mice infected with E. multilocularis and the expression of several angiogenesis-regulated molecules, including VEGF, MMP9,MCP-1, SDF-1 and serpin E1 were increased. Significantly, EVs released by the E.multilocularis protoscolex promoted proliferation and tube formation by humanumbilical vein endothelial cells (HUVECs) in vitro. Taken together, we present the first evidence that tapeworm-secreted EVs may promote angiogenesis in Echinococcus-infections, identifying central mechanisms of Echinococcus-host interaction

MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images

In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique.Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly.Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies.By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications. However, transformer-based architectures lack global semantic information interaction and require a large-scale training dataset, making it challenging to train with small data samples. In order to solve these challenges, we present a hierarchical contextattention transformer network (MHITNet) that combines the multi-scale, transformer, and hierarchical context extraction modules in skip-connections. The multi-scale module captures deeper CT semantic information, enabling transformers to encode feature maps of tokenized picture patches from various CNN stages as input attention sequences more effectively. The hierarchical context attention module augments global data and reweights pixels to capture semantic context.Extensive trials on three datasets show that the proposed MHITNet beats current best practise

Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis

Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral data which contain multiple types of behaviors become more and more common. In this paper, we aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. To this end, this paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context information while modeling a behavior sequence, a projection mechanism which can model multi-faceted relationships among different types of behaviors, and a multi-faceted attention mechanism which can dynamically find out informative periods from different facets. Many kinds of behavioral data belong to spatio-temporal data. An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed. Moreover, a residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation and other types of behavior representations. Qualitative and quantitative experiments on real-world datasets have demonstrated the effectiveness of this model

Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model

Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model