Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

A high-throughput pipeline for scalable kit-free RNA extraction

An overreliance on commercial, kit-based RNA extraction in the molecular diagnoses of infectious disease presents a challenge in the event of supply chain disruptions and can potentially hinder testing capacity in times of need. In this study, we adapted a well-established, robust TRIzol-based RNA extraction protocol into a high-throughput format through miniaturization and automation. The workflow was validated by RT-qPCR assay for SARS-CoV-2 detection to illustrate its scalability without interference to downstream diagnostic sensitivity and accuracy. This semi-automated, kit-free approach offers a versatile alternative to prevailing integrated solid-phase RNA extraction proprietary systems, with the added advantage of improved cost-effectiveness for high volume acquisition of quality RNA whether for use in clinical diagnoses or for diverse molecular applications

Multi-agent modeling and analysis of EV users’ travel willingness based on an integrated causal/statistical/behavioral model

An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EE-based simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users

CAR T cells targeting BAFF-R can overcome CD19 antigen loss in B cell malignancies

CAR T cells targeting CD19 provide promising options for treatment of B cell malignancies. However, tumor relapse from antigen loss can limit efficacy. We developed humanized, second-generation CAR T cells against another B cell–specific marker, B cell activating factor receptor (BAFF-R), which demonstrated cytotoxicity against human lymphoma and acute lymphoblastic leukemia (ALL) lines. Adoptively transferred BAFF-R-CAR T cells eradicated 10-day preestablished tumor xenografts after a single treatment and retained efficacy against xenografts deficient in CD19 expression, including CD19-negative variants within a background of CD19-positive lymphoma cells. Four relapsed, primary ALLs with CD19 antigen loss obtained after CD19-directed therapy retained BAFF-R expression and activated BAFF-R-CAR, but not CD19-CAR, T cells. BAFF-R-CAR, but not CD19-CAR, T cells also demonstrated antitumor effects against an additional CD19 antigen loss primary patient–derived xenograft (PDX) in vivo. BAFF-R is amenable to CAR T cell therapy, and its targeting may prevent emergence of CD19 antigen loss variants

Highly stretchable polymer semiconductor films through the nanoconfinement effect

Soft and conformable wearable electronics require stretchable semiconductors, but existing ones typically sacrifice charge transport mobility to achieve stretchability. We explore a concept based on the nanoconfinement of polymers to substantially improve the stretchability of polymer semiconductors, without affecting charge transport mobility. The increased polymer chain dynamics under nanoconfinement significantly reduces the modulus of the conjugated polymer and largely delays the onset of crack formation under strain. As a result, our fabricated semiconducting film can be stretched up to 100% strain without affecting mobility, retaining values comparable to that of amorphous silicon. The fully stretchable transistors exhibit high biaxial stretchability with minimal change in on current even when poked with a sharp object. We demonstrate a skinlike finger-wearable driver for a light-emitting di

Observation of CR Anisotropy with ARGO-YBJ

The measurement of the anisotropies of cosmic ray arrival direction provides important informations on the propagation mechanisms and on the identification of their sources. In this paper we report the observation of anisotropy regions at different angular scales. In particular, the observation of a possible anisotropy on scales between $\sim$ 10 $^{\circ}$ and $\sim$ 30 $^{\circ}$ suggests the presence of unknown features of the magnetic fields the charged cosmic rays propagate through, as well as potential contributions of nearby sources to the total flux of cosmic rays. Evidence of new weaker few-degree excesses throughout the sky region $195^{\circ}\leq$ R.A. $\leq 315^{\circ}$ is reported for the first time.Comment: Talk given at 12th TAUP Conference 2011, 5-9 September 2011, Munich, German

Balancing hydrogen adsorption/desorption by orbital modulation for efficient hydrogen evolution catalysis

Hydrogen adsorption/desorption behavior plays a key role in hydrogen evolution reaction (HER) catalysis. The HER reaction rate is a trade-off between hydrogen adsorption and desorption on the catalyst surface. Herein, we report the rational balancing of hydrogen adsorption/desorption by orbital modulation using introduced environmental electronegative carbon/nitrogen (C/N) atoms. Theoretical calculations reveal that the empty d orbitals of iridium (Ir) sites can be reduced by interactions between the environmental electronegative C/N and Ir atoms. This balances the hydrogen adsorption/ desorption around the Ir sites, accelerating the related HER process. Remarkably, by anchoring a small amount of Ir nanoparticles (7.16 wt%) in nitrogenated carbon matrixes, the resulting catalyst exhibits significantly enhanced HER performance. This includs the smallest reported overpotential at 10 mA cm(-2) (4.5 mV), the highest mass activity at 10 mV (1.12 A mg(Ir)(-1)) and turnover frequency at 25 mV (4.21 H2 s(-1)) by far, outperforming Ir nanoparticles and commercial Pt/C