Short-term Prediction of Freeway Travel Times Using Data from Bluetooth Detectors

There is increasing recognition among travelers, transportation professionals, and decision makers of the importance of the reliability of transportation facilities. An important step towards improving system reliability is developing methods that can be used in practice to predict freeway travel times for the near future (e.g. 5 – 15 minutes). Reliable and accurate predictions of future travel times can be used by travelers to make better decisions and by system operators to engage in pre-active rather than reactive system management. Recent advances in wireless communications and the proliferation of personal devices that communicate wirelessly using the Bluetooth protocol have resulted in the development of a Bluetooth traffic monitoring system. This system is becoming increasingly popular for collecting vehicle travel time data in real-time, mainly because it has the following advantages over other technologies: (1) measuring travel time directly; (2) anonymous detection; (3) weatherproof; and (4) cost-effectiveness. The data collected from Bluetooth detectors are similar to data collected from Automatic Vehicle Identification (AVI) systems using dedicated transponders (e.g. such as electronic toll tags), and therefore using these data for travel time prediction faces some of the same challenges as using AVI measurements, namely: (1) determining the optimal spacing between detectors; (2) dynamic outlier detection and travel time estimation must be able to respond quickly to rapid travel time changes; and (3) a time lag exists between the time when vehicles enter the segment and the time that their travel time can be measured (i.e. when the vehicle exits the monitored segment). In this thesis, a generalized model was proposed to determine the optimal average spacing of Bluetooth detector deployments on urban freeways as a function of the length of the route for which travel times are to be estimated; a traffic flow filtering model was proposed to be applied as an enhancement to existing data-driven outlier detection algorithms as a mechanism to improve outlier detection performance; a short-term prediction model combining outlier filtering algorithm with Kalman filter was proposed for predicting near future freeway travel times using Bluetooth data with special attention to the time lag problem. The results of this thesis indicate that the optimal detector spacing ranges from 2km for routes of 4km in length to 5km for routes of 20km in length; the proposed filtering model is able to solve the problem of tracking sudden changes in travel times and enhance the performance of the data-driven outlier detection algorithms; the proposed short-term prediction model significantly improves the accuracy of travel time prediction for 5, 10 and 15 minutes prediction horizon under both free flow and non-free flow traffic states. The mean absolute relative errors (MARE) are improved by 8.8% to 30.6% under free flow traffic conditions, and 7.5% to 49.9% under non-free flow traffic conditions. The 90th percentile errors and standard deviation of the prediction errors are also improved

UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis

This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system.Comment: 9 pages, accepted by SemEval@ACL 202

Metamaterial absorber integrated microfluidic terahertz sensors

Spatial overlap between the electromagnetic fields and the analytes is a key factor for strong light-matter interaction leading to high sensitivity for label-free refractive index sensing. Usually, the overlap and therefore the sensitivity are limited by either the localized near field of plasmonic antennas or the decayed resonant mode outside the cavity applied to monitor the refractive index variation. In this paper, by constructing a metal microstructure array-dielectric-metal (MDM) structure, a novel metamaterial absorber integrated microfluidic (MAIM) sensor is proposed and demonstrated in terahertz (THz) range, where the dielectric layer of the MDM structure is hollow and acts as the microfluidic channel. Tuning the electromagnetic parameters of metamaterial absorber, greatly confined electromagnetic fields can be obtained in the channel resulting in significantly enhanced interaction between the analytes and the THz wave. A high sensitivity of 3.5 THz/RIU is predicted. The experimental results of devices working around 1 THz agree with the simulation ones well. The proposed idea to integrate metamaterial and microfluid with a large light-matter interaction can be extended to other frequency regions and has promising applications in matter detection and biosensing

A comment on "Ab initio calculations of pressure-dependence of high-order elastic constants using finite deformations approach" by I. Mosyagin, A.V. Lugovskoy, O.M. Krasilnikov, Yu.Kh. Vekilov, S.I. Simak and I.A. Abrikosov

Recently, I. Mosyagin, A.V. Lugovskoy, O.M. Krasilnikov, Yu.Kh. Vekilov, S.I. Simak and I.A. Abrikosov in the paper: "Ab initio calculations of pressure-dependence of high-order elastic constants using finite deformations approach"[Computer Physics Communications 220 (2017) 2030] presented a description of a technique for ab initio calculations of the pressure dependence of second- and third-order elastic constants. Unfortunately, the work contains serious and fundamental flaws in the field of finite-deformation solid mechanics.Comment: 3 pages, 0 figure

Identification of thioredoxin-1 as a biomarker of lung cancer and evaluation of its prognostic value based on bioinformatics analysis

BackgroundThioredoxin-1 (TXN), a redox balance factor, plays an essential role in oxidative stress and has been shown to act as a potential contributor to various cancers. This study evaluated the role of TXN in lung cancer by bioinformatics analyses.Materials and methodsGenes differentially expressed in lung cancer and oxidative stress related genes were obtained from The Cancer Genome Atlas, Gene Expression Omnibus and GeneCards databases. Following identification of TXN as an optimal differentially expressed gene by bioinformatics, the prognostic value of TXN in lung cancer was evaluated by univariate/multivariate Cox regression and Kaplan–Meier survival analyses, with validation by receiver operation characteristic curve analysis. The association between TXN expression and lung cancer was verified by immunohistochemical analysis of the Human Protein Atlas database, as well as by western blotting and qPCR. Cell proliferation was determined by cell counting kit-8 after changing TXN expression using lentiviral transfection.ResultsTwenty differentially expressed oxidative stress genes were identified. Differential expression analysis identified five genes (CASP3, CAT, TXN, GSR, and HSPA4) and Kaplan–Meier survival analysis identified four genes (IL-6, CYCS, TXN, and BCL2) that differed significantly in lung cancer and normal lung tissue, indicating that TXN was an optimal differentially expressed gene. Multivariate Cox regression analysis showed that T stage (T3/T4), N stage (N2/N3), curative effect (progressive diseases) and high TXN expression were associated with poor survival, although high TXN expression was poorly predictive of overall survival. TXN was highly expressed in lung cancer tissues and cells. Knockdown of TXN suppressed cell proliferation, while overexpression of TXN enhanced cell proliferation.ConclusionHigh expression of TXN plays an important role in lung cancer development and prognosis. Because it is a prospective prognostic factor, targeting TXN may have clinical benefits in the treatment of lung cancer

Effects of meteorological factors on the incidence of meningococcal meningitis

Background and Objectives: Substantial climate changes have led to the emergence and re-emergence of various infectious diseases worldwide, presenting an imperative need to explore the effects of meteorological factors on serious contagious disease incidences such as that of meningococcal meningitis (MCM).Methods: The incidences of MCM and meteorology data between 1981 and 2010 were obtained from Chaoyang city. Structure Equation Modeling was used to analyze the relationships between meteorological factors and the incidence of MCM, using the LISREL software.Results: The SEM results showed that Adjusted Goodness of Fit Index (AGFI) = 0.30, Goodness of Fit Index (GFI) = 0.63, and Root Mean Square Error of Approximation (RMSEA) = 0.31. Humidity and temperature both had negative correlations with MCM incidence, with factor loads of -0.32 and -0.43, while sunshine was positively correlated with a factor load of 0.42. For specific observable variables, average air pressure, average evaporation, average air temperature, and average ground temperature exerted stronger influence, with item loads between observable variables and MCM incidence being -0.42, 0.34, -0.32, and -0.32 respectively.Conclusion: Public health institutions should pay more attention to the meteorological variables of humidity, sunshine, and temperature in prospective MCM control and prevention.Keywords: Meningococcal meningitis, Neisseria meningitidis, epidemiology, humidity, temperature, sunshine, meteorological variables, structure equation mode

Experimental study on friction pressure drop and circumferential heat transfer characteristics in helical tubes

Helical tubes are widely used in nuclear plants, heat recovery process, and refrigeration technology. The fluid is influenced by centrifugal force flow through the helical tube, accompanied by secondary flow which is conducive to the enhancement of heat transfer. However, the uneven circumferential heat transfer caused by the secondary flow was seldom reported, while the pressure drops and heat transfer characteristics of helical tubes under single-phase and two-phase flow conditions need to be supplemented. This paper investigated the friction pressure drop and circumferential heat transfer characteristics based on the experiments on helical tubes with the coil diameter to the tube diameter varying from 28.5 to 128.5 and lift angle varying from 3° to 10°. The results showed that the coil diameter was the key parameter affecting the pressure drop and non-uniform circumferential heat transfer, compared with the lift angle. At the same cross section, the heat transfer coefficient at the outside tube wall was the highest, which was more obvious under small coil diameter conditions. Correlations of flow resistance and heat transfer were proposed for the single-phase and saturated boiling two-phase flow, respectively, and the predicted values were improved compared with the prediction results of correlations in the existing literature