CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Establishment and application of a VP3 antigenic domain-based peptide ELISA for the detection of antibody against goose plague virus infection

The detection of antibody against goose plague virus (GPV) infection has never had a commercialized test kit, which has posed challenges to the prevention and control of this disease. In this study, bioinformatics software was used to analyze and predict the dominant antigenic regions of the main protective antigen VP3 of GPV. Three segments of bovine serum albumin (BSA) vector-coupled peptides were synthesized as ELISA coating antigens. Experimental results showed that the VP3-1 (358-392aa) peptide had the best reactivity and specificity. By using the BSA-VP3-1 peptide, a detection method for antibody against GPV infection was established, demonstrating excellent specificity with no cross-reactivity with common infectious goose pathogen antibodies. The intra-batch coefficient of variation and inter-batch coefficient of variation were both less than 7%, indicating good stability and repeatability. The dynamic antibody detection results of gosling vaccines and the testing of 120 clinical immune goose serum samples collectively demonstrate that BSA-VP3-1 peptide ELISA can be used to detect antibody against GPV in the immunized goose population and has higher sensitivity than traditional agar gel precipitation methods. Taken together, the developed peptide-ELISA based on VP3 358-392aa could be useful in laboratory viral diagnosis, routine surveillance in goose farms. The main application of the peptide-ELISA is to monitor the antibody level and vaccine efficacy for GPV, which will help the prevention and control of gosling plague

Adaptive probabilistic load forecasting for individual buildings

Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters. It acts as a basis for arranging distributed energy resources, implementing demand response, etc. Compared to aggregated-level load, the electric load of an individual building is more stochastic and thus spawns many probabilistic forecasting methods. Many of them resort to artificial neural networks (ANN) to build forecasting models. However, a well-designed forecasting model for one building may not be suitable for others, and manually designing and tuning optimal forecasting models for various buildings are tedious and time-consuming. This paper proposes an adaptive probabilistic load forecasting model to automatically generate high-performance NN structures for different buildings and produce quantile forecasts for future loads. Specifically, we cascade the long short term memory (LSTM) layer with the adjusted Differential ArchiTecture Search (DARTS) cell and use the pinball loss function to guide the model during the improved model fitting process. A case study on an open dataset shows that our proposed model has superior performance and adaptivity over the state-of-the-art static neural network model. Besides, the improved fitting process of DARTS is proved to be more time-efficient than the original one

Enhanced stability of M1 protein mediated by a phospho-resistant mutation promotes the replication of prevailing avian influenza virus in mammals

Avian influenza virus (AIV) can evolve multiple strategies to combat host antiviral defenses and establish efficient infectivity in mammals, including humans. H9N2 AIV and its reassortants (such as H5N6 and H7N9 viruses) pose an increasing threat to human health; however, the mechanisms involved in their increased virulence remain poorly understood. We previously reported that the M1 mutation T37A has become predominant among chicken H9N2 isolates in China. Here, we report that, since 2010, this mutation has also been found in the majority of human isolates of H9N2 AIV and its emerging reassortants. The T37A mutation of M1 protein enhances the replication of H9N2 AIVs in mice and in human cells. Interestingly, having A37 instead of T37 increases the M1 protein stability and resistance to proteasomal degradation. Moreover, T37 of the H9N2 M1 protein is phosphorylated by protein kinase G (PKG), and this phosphorylation induces the rapid degradation of M1 and reduces viral replication. Similar effects are also observed in the novel H5N6 virus. Additionally, ubiquitination at K187 contributes to M1-37T degradation and decreased replication of the virus harboring T37 in the M1 protein. The prevailing AIVs thereby evolve a phosphoresistant mutation in the M1 protein to avoid viral protein degradation by host factors, which is advantageous in terms of replication in mammalian hosts

Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.Peer reviewe

Pulmonary Toxicity and Adjuvant Effect of Di-(2-exylhexyl) Phthalate in Ovalbumin-Immunized BALB/c Mice

BACKGROUND: Asthma is a complex pulmonary inflammatory disease, which is characterized by airway hyperresponsiveness, variable airflow obstruction and inflammation in the airways. The majority of asthma is allergic asthma, which is a disease caused by type I hypersensitivity mediated by IgE. Exposures to a number of environmental chemicals are suspected to lead to asthma, one such pollutant is di-(2-ethylheyl) phthalate (DEHP). DEHP is a manufactured chemical that is commonly added in plastic products to make them flexible. Epidemiological studies have revealed a positive association between DEHP exposure and asthma prevalence. METHODOLOGY/PRINCIPAL FINDINGS: The present study was aimed to determine the underlying role of DEHP exposure in airway reactivity, especially when combined with allergen exposure. The biomarkers include pulmonary histopathology, airway hyperresponsiveness (lung function), IgE, IL-4, IFN-γ and eosinophils. Healthy balb/c mice were randomly divided into eight exposure groups (n = 8 each): (1) saline control, (2) 30 µg/(kg•d) DEHP, (3) 300 µg/(kg•d) DEHP, (4) 3000 µg/(kg•d) DEHP, and (5) ovalbumin (OVA)-sensitized group, (6) OVA-combined with 30 µg/(kg•d) DEHP, (7) OVA-combined with 300 µg/(kg•d) DEHP, and (8) OVA-combined with 3000 µg/(kg•d) DEHP. Experimental tests were conducted after 52-day DEHP exposure and subsequently one week of challenge with aerosolized OVA. The principal findings include: (1) Strong postive associations exist between OVA-combined DEHP exposure and serum total IgE (T-IgE), as well as histological findings. These positive associations show a dose-dependent low dose sensitive effect of DEHP. (2) IL-4, eosinophil recruitment and lung function are also indicators for adjuvant effect of DEHP. CONCLUSIONS/SIGNIFICANCE: Our results suggest that except the significant changes of immunological and inflammatory biomarkers (T-IgE, IL-4, IFN-γ and eosinophils), the pulmonary histological (histopathological examination) and physiological (lung function) data also support that DEHP may promote and aggravate allergic asthma by adjuvant effect

LHCb upgrade software and computing : technical design report

This document reports the Research and Development activities that are carried out in the software and computing domains in view of the upgrade of the LHCb experiment. The implementation of a full software trigger implies major changes in the core software framework, in the event data model, and in the reconstruction algorithms. The increase of the data volumes for both real and simulated datasets requires a corresponding scaling of the distributed computing infrastructure. An implementation plan in both domains is presented, together with a risk assessment analysis

Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHCb spectrometer. The LHCb Upgrade I will begin operation in 2020. Consolidation will occur, and modest enhancements of the Upgrade I detector will be installed, in Long Shutdown 3 of the LHC (2025) and these are discussed here. The main Upgrade II detector will be installed in long shutdown 4 of the LHC (2030) and will build on the strengths of the current LHCb experiment and the Upgrade I. It will operate at a luminosity up to 2×1034 cm−2s−1, ten times that of the Upgrade I detector. New detector components will improve the intrinsic performance of the experiment in certain key areas. An Expression Of Interest proposing Upgrade II was submitted in February 2017. The physics case for the Upgrade II is presented here in more depth. CP-violating phases will be measured with precisions unattainable at any other envisaged facility. The experiment will probe b → sl+l−and b → dl+l− transitions in both muon and electron decays in modes not accessible at Upgrade I. Minimal flavour violation will be tested with a precision measurement of the ratio of B(B0 → μ+μ−)/B(Bs → μ+μ−). Probing charm CP violation at the 10−5 level may result in its long sought discovery. Major advances in hadron spectroscopy will be possible, which will be powerful probes of low energy QCD. Upgrade II potentially will have the highest sensitivity of all the LHC experiments on the Higgs to charm-quark couplings. Generically, the new physics mass scale probed, for fixed couplings, will almost double compared with the pre-HL-LHC era; this extended reach for flavour physics is similar to that which would be achieved by the HE-LHC proposal for the energy frontier