OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes.

To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost.

Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26-45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.

An electrochemical aptamer biosensor based on "gate-controlled" effect using ß-cyclodextrin for ultra-sensitive detection of trace mercury.

A new strategy for construction an electrochemical biosensor for Hg(2+) analysis with extremely high sensitivity was proposed based on "T-Hg(2+)-T" coordination by aptamers and "gate-controlled" amplification by switch of the channels of the probe. SH-ß-cyclodextrin was self-assembled on the gold electrode to form an orderly arranged molecularly layer with interspaces among ß-CDs; thionine labeled aptamer was then linked on the assembled ß-CD. When Hg(2+) was added, the aptamer combined with Hg(2+) and formed "T-Hg(2+)-T" structure, which caused the aptamer folded and the labeled thionine covered the interspaces to block off the channel for probe entrance. The oxidative current of probe decreased, which provide the basis for the determination of Hg(2+). With the gate-controlled amplification, the changes of trace amounts of Hg(2+) will produce great changes of the probe current. The sensor exhibited significantly higher sensitivity with a detection limit of 5.0×10(-15) mol/L, which is lower than other reported methods.