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

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

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

Demethylating therapy increases cytotoxicity of CD44v6 CAR-T cells against acute myeloid leukemia

BackgroundCD44v6 chimeric antigen receptor T (CD44v6 CAR-T) cells demonstrate strong anti-tumor ability and safety in acute myeloid leukemia (AML). However, the expression of CD44v6 on T cells leads to transient fratricide and exhaustion of CD44v6 CAR-T cells, which affect the application of CD44v6 CAR-T. The exhaustion and function of T cells and CD44v6 expression of AML cells are associated with DNA methylation. Hypomethylating agents (HAMs) decitabine (Dec) and azacitidine (Aza) have been widely used to treat AML. Therefore, there may be synergy between CD44v6 CAR-T cells and HAMs in the treatment of AML.MethodsCD44v6 CAR-T cells pretreated with Dec or Aza were co-cultured with CD44v6+ AML cells. Dec or aza pretreated AML cells were co-cultured with CD44v6 CAR-T cells. The cytotoxicity, exhaustion, differentiation and transduction efficiency of CAR-T cells, and CD44v6 expression and apoptosis in AML cells were detected by flow cytometry. The subcutaneous tumor models were used to evaluate the anti-tumor effect of CD44v6 CAR-T cells combined with Dec in vivo. The effects of Dec or Aza on gene expression profile of CD44v6 CAR-T cells were analyzed by RNA-seq.ResultsOur results revealed that Dec and Aza improved the function of CD44v6 CAR-T cells through increasing the absolute output of CAR+ cells and persistence, promoting activation and memory phenotype of CD44v6 CAR-T cells, and Dec had a more pronounced effect. Dec and Aza promoted the apoptosis of AML cells, particularly with DNA methyltransferase 3A (DNMT3A) mutation. Dec and Aza also enhanced the CD44v6 CAR-T response to AML by upregulating CD44v6 expression of AML cells regardless of FMS-like tyrosine kinase 3 (FLT3) or DNMT3A mutations. The combination of Dec or Aza pretreated CD44v6 CAR-T with pretreated AML cells demonstrated the most potent anti-tumor ability against AML.ConclusionDec or Aza in combination with CD44v6 CAR-T cells is a promising combination therapy for AML patients

On the origin and magnitude of surface stresses due to metal nanofilms

Metallisation is a vital process for micro- and nanofabrication, allowing the controlled preparation of material surfaces with thin films of a variety of metals. The films are often subjected to further processing, including etching, patterning, chemical modification, and additional lamination. The extensive applications of metallised substrates include chemical sensors and nanoelectronics. Here, we report an experimental study of the metallization of silicon cantilevers with nano-films of chromium and titanium. Analysis of the stress distribution throughout the cantilever showed that metallisation causes a constant stress along the length of the beam, which can be calculated from interferometric quantification of the beam curvature. The structure of the metal/silicon interface was imaged using electron microscopy in an attempt to ascertain the physical origin of the stress. A theoretical model is constructed for the stressed beam system, and it is shown that there is no single parameter that can describe the change in stress. The resultant structure after deposition varies significantly for each metal, which gives rise to a variety of stress directions and magnitudes

Regional Emission Analysis Using Travel Demand Models and MOVES-Matrix

DTRT13-G-UTC29Citation: Xiaodan Xu, Haobing Liu, Yanzhi \u201cAnn\u201d Xu, Michael O. Rodgers, and Randall L. Guensler (2018) Regional Emission Analysis using Travel Demand Models and MOVES-Matrix, 97th Annual Meeting of the Transportation Research Board, Washington, D.C., January 7-11, 2018.Travel demand models (TDM) are developed by Metropolitan Planning Organizations (MPOs) for analyzing regional travel patterns but are often used to prepare activity inputs for use with the U.S. Environmental Protection Agency\u2019s (EPA) MOtor Vehicle Emission Simulator (MOVES) emissions model for air quality conformity analysis. This latter application requires modelers to either prepare multiple MOVES runs for various scenarios, or to develop their own pre- and post-processors for emission modeling. Both approaches involve a cumbersome and time consuming process