A freeway vehicle early warning method based on risk map: Enhancing traffic safety through global perspective characterization of driving risk.

In the era of rapid advancements in intelligent transportation, utilizing vehicle operating data to evaluate the risk of freeway vehicles and study on vehicle early warning methods not only lays a theoretical foundation for improving the active safety of vehicles, but also provides the technical support for reducing accident rate. This paper proposes a freeway vehicle early warning method based on risk map to enhance vehicle safety. Firstly, Modified Time-to-Collision (MTTC), a two-dimensional indicator that describes the risk of inter-vehicle travel, is used as an indicator of road traffic risk. This paper designs a transformation function to probabilistically transform MTTC into Risk Indicators (RI). The single-vehicle risk map is generated based on the mapping relationship between the risk values and the corresponding roadway segments. These single-vehicle risk maps of all vehicles on the road are superimposed to construct the risk map, which is used to describe the risk distribution in the freeway. Then, a vehicle early warning framework is built based on the risk map. The risk values in the risk map are compared with predefined early warning thresholds to alert the vehicle when it enters a high-risk state. Finally, VISSIM is used to carry out simulation experiments. The experiment simulates a freeway accident stopping situation. This scenario includes sub-scenarios such as unplanned stopping and lane-changing, continuous lane-changing, and adjacent lane overtaking. We analyze the risk map and vehicle warning results in different sub-scenarios, evaluate the risk changes of the vehicles before and after receiving the warning, and compare the warning results of the method in this paper with other alternative methods. The method is applied to 17 vehicles in the simulation to adjust their motion states. The results show that the total warning time is reduced by 29.6% and 73.3% of vehicles change lanes away from the accident vehicle. The overall results validate the effectiveness of the vehicle early warning method based on risk map proposed in this paper.

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Enhancing predictions of remedial reagent transport via a vertical groundwater circulation well with high-resolution aquifer characterization.

The vertical groundwater circulation well (GCW) is a commonly used technique in contaminated sites to remove secondary contaminants from low permeable zones. Early GCW studies often used simple subsurface hydraulic properties, such as anisotropic homogeneous aquifers or low conductivity lens/blocks, to mimic the complex subsurface heterogeneity. Although studies based on simplified representations of aquifer heterogeneity provide straightforward flow and transport information for engineering design of a GCW, they may over- or under-estimate contaminant fate and transport in the field. The objective of this study is to identify key heterogeneity factors that control the capture zone extension and to examine the extent to which the accuracy of estimated heterogeneity spatial distributions influences the prediction of remedial reagent transport. To achieve these objectives, we utilized Monte Carlo simulation to investigate the extension of the circulation zone in heterogeneous aquifers and to identify the key factors that contribute most to the variability of the circulation zone. Three commonly used geostatistical approaches (equivalent homogeneous, kriging, and highly parameterized methods) were employed to estimate the spatial distributions of key factors. The reliabilities of these estimated fields were evaluated through their remedial reagent transport predictability. The key factor analysis revealed that the mean porosity value, the variance of lnK, and the correlation length of lnK profoundly influence the lateral expansion of the capture zone. Neglecting the aquifer hydraulic conductivity heterogeneity underestimates the extension of the circulation zone and the spread of remedial reagent. Additionally, utilizing a highly parameterized approach to estimate the high-resolution K field can accurately reproduce the key remedial reagent distributions. The concentration arrival time and peak concentration are significantly improved compared to those predictions based on the equivalent homogeneous and kriged K fields.

High sensitivity and automatic chemiluminescence detection of glucose and lactate using a spin-disc paper-based device.

This paper reports a spin-disc paper-based device with 10 individual detection units containing electromagnetic modules controlling the sample incubation time before chemiluminescence (CL) signal detection. After the sample was added to the top paper chip and incubated with the enzyme, the electromagnet was turned off to allow contact between the top and bottom paper. The H2O2 generated by the sample flowed vertically to the bottom paper and initiated the oxidase of the luminol to generate the CL signal. After one detection the disc was automatically rotated to the next position to repeat the above detection. The advantage of using the device over the lateral flow and the in situ detection was firstly proved using the detection of H2O2 and the glucose/lactate sample with 5 minute incubation. The CL intensity was increased 300 times/1000 times as the glucose/lactate was incubated for 5 minutes compared to the non-incubated samples. Afterward, the device was employed to separately detect glucose and lactate diluted in PBS, artificial sweat, artificial saliva, and fresh cell culture media. Finally, the device was employed to detect the glucose and lactate in the media collected over the 24 hour culture of PC3 cells. The uptake and production rates of glucose and lactate were correspondingly determined as 0.328 ± 0.015 pmol h-1 per cell and 1.254 ± 0.053 pmol h-1 per cell, respectively. The reported device has wide application potential due to its capabilities in automatic detection of multiple samples with very high sensitivity and small sample volume (down to 0.5 µL).

Continuous production of bimetallic nanoparticles on carbon nanotubes based on 3D-printed microfluidics.

Nanoparticle-functionalized carbon nanotubes are promising in many research fields, especially in sensing, due to their intriguing performance in catalysis. However, these nanomaterials are mainly produced through batch processes under harsh conditions, thus encountering inherent limitations of low throughput and uncontrollable morphology of functional nanoparticles (NPs). In this work, we propose a method for high-yield and continuous production of bimetallic (Pt-Pd) NPs on multi-walled carbon nanotubes (MWCNTs) at room temperature through a custom 3D-printed microfluidic platform. A homogenous particle nucleation and growth environment could be created on the microfluidic platform that was equipped with two 3D-printed micromixers. Pt-Pd NPs loaded on MWCNTs were prepared in the microfluidic platform with high throughput and controlled size, dispersity and composition. The synthetic parameters for these nanocomposites were investigated to optimize their electrocatalytic performance. The optimized nanocomposites exhibited excellent electrocatalytic activity with exceptional sensitivity and wide detection range, superior to their counterparts prepared via conventional approaches. This method proposed here could be further adapted for manufacturing other catalyst support materials, opening more avenues for future large-scale production and catalytic investigation of functional nanomaterials.

Multiplexed detection of biomarkers using a microfluidic chip integrated with mass-producible micropillar array electrodes.

Quantifying multiple biomarkers with high sensitivity in tiny biological samples is essential to meet the growing demand for point-of-care testing. This paper reports the development of a novel microfluidic device integrated with mass-producible micropillar array electrodes (µAEs) for multiple biomarker detections. The µAE are mass-fabricated by soft lithography and hot embossing technique. Pt-Pd bimetallic nanoclusters (BNC) are modified on the surface of µAEs by constant potential (CP)/multi-potential step (MPS) electrodeposition strategies to improve the electroanalytical performance. The experimental result displays that Pt-Pd BNC/µAEs have good sensitivity enhancement compared with bare planar electrodes and bare µAEs, the enhancement being 56.5 and 9.5 times respectively, from the results of the H2O2 detection. Furthermore, glucose, uric acid and sarcosine were used as model biomarkers to show the biosensing capability with high sensitivity. The linear range and LOD of the glucose, uric acid and sarcosine detection are 0.1 mM-12 mM, 10 µM-800 µM and 2.5 µM-100 µM, 58.5, 3.4 and 0.4 µM, respectively. In particular, biosensing chips show wide linear ranges covering required detection ranges of glucose, uric acid and sarcosine in human serum, indicating the developed device has great potential in self-health management and clinical requirements.

[N2O Emission from the Processes of Wastewater Treatment: Mechanisms and Control Strategies].

As a direct carbon emission source, the amount of nitrous oxide (N2, which is actually caused by AOB denitrification. To control the N2O emission during biological N-removal, complete HND and NO2- accumulation for AOB denitrification should be avoided to a large extent. For this purpose, DO in aerobic tanks should be controlled at a normal level (approximately 2 mg·L-1), and solid retention time (SRT) should be extended, up to 20 d, which would avoid accumulating N2O for AOB denitrification. Additionally, external carbon should be supplemented in time to promote HDN approaching the end, N2. This review summarizes the mechanisms of all the mentioned N2O emission pathways and discusses the control strategies of N2O emission according to the associated mechanisms.

Development of a novel microfluidic biosensing platform integrating micropillar array electrode and acoustic microstreaming techniques.

Quantifying biomarkers at the early stage of the disease is challenging due to the low abundance of biomarkers in the sample and the lack of sensitive techniques. This article reports the development of a novel microfluidic electrochemical biosensing platform to address this challenge. The electrochemical sensing is achieved by utilizing a micropillar array electrode (µAE) coated with 3D bimetallic Pt-Pd nanotrees to enhance the sensitivity. A bubble-based acoustic microstreaming technique is integrated with the device to increase the contact of analyte molecules with the surface of electrodes to further enhance the electrochemical performance. The current density of Pt-Pd NTs/µAE with acoustic microstreaming is nearly 22 times that of the bare planar electrode in potassium ferrocyanide solution. The developed biosensor has demonstrated excellent sensing performance. For hydrogen peroxide detection, both the Pt-Pd NTs/µAE and acoustic microstreaming contribute to the sensitivity enhancement. The current density of the Pt-Pd NTs/µAE is approximatively 28 times that of the bare µAE. With acoustic microstreaming, this enhancement is further increased by nearly 1.6 times. The platform has a linear detection range of 5-1000 µM with a LOD of 1.8 µM toward hydrogen peroxide detection, while for sarcosine detection, the linear range is between 5 and 100 µM and LOD is 2.2 µM, respectively. Furthermore, the sarcosine biosensing shows a high sensitivity of 667 µA mM-1∙cm-2. Such a sensing platform has the potential as a portable device for high sensitivity detection of biomarkers.

High-Throughput Microfluidic Production of Bimetallic Nanoparticles on MXene Nanosheets and Application in Hydrogen Peroxide Detection.

Nanoparticle-functionalized transition-metal carbides and nitrides (MXenes) have attracted extensive attention in electrochemical detection owing to their excellent catalytic performance. However, the mainstream synthetic routes rely on the batch method requiring strict experimental conditions, generally leading to low yield and poor size tunability of particles. Herein, we report a high-throughput and continuous microfluidic platform for preparing a functional MXene (Ti3C2Tx) with bimetallic nanoparticles (Pt-Pd NPs) at room temperature. Two 3D micromixers with helical elements were integrated into the microfluidic platform to enhance the secondary flow for promoting transport and reaction in the synthesis process. The rapid mixing and strong vortices in these 3D micromixers prevent aggregation of NPs in the synthesis process, enabling a homogeneous distribution of Pt-Pd NPs. In this study, Pt-Pd NPs loaded on the MXene nanosheets were synthesized under various hydrodynamic conditions of 1-15 mL min-1 with controlled sizes, densities, and compositions. The mean size of Pt-Pd NPs could be readily controlled within the range 2.4-9.3 nm with high production rates up to 13 mg min-1. In addition, synthetic and electrochemical parameters were separately optimized to improve the electrochemical performance of Ti3C2Tx/Pt-Pd. Finally, the optimized Ti3C2Tx/Pt-Pd was used for hydrogen peroxide (H2O2) detection and shows excellent electrocatalytic activity. The electrode modified with Ti3C2Tx/Pt-Pd here presents a wide detection range for H2O2 from 1 to 12 000 µM with a limit of detection down to 0.3 µM and a sensitivity up to 300 µA mM-1 cm-2, superior to those prepared in the traditional batch method. The proposed microfluidic approach could greatly enhance the electrochemical performance of Ti3C2Tx/Pt-Pd, and sheds new light on the large-scale production and catalytic application of the functional nanocomposites.

Numerical Study on a Bio-Inspired Micropillar Array Electrode in a Microfluidic Device.

The micropillar array electrode (µAE) has been widely applied in microchip-based electrochemical detection systems due to a large current response. However, it was found that amplifying the current through further adjusting geometrical parameters is generally hindered by the shielding effect. To solve this problem, a bio-inspired micropillar array electrode (bµAE) based on the microfluidic device has been proposed in this study. The inspiration is drawn from the structure of leatherback sea turtles' mouths. By deforming a µAE to rearrange the micropillars on bilateral sides of the microchannel, the contact area between micropillars and analytes increases, and thus the current is substantially improved. A numerical simulation was then used to characterize the electrochemical performance of bµAEs. The effects of geometrical and hydrodynamic parameters on the current of bµAEs were investigated. Moreover, a prototypical microchip integrated with bµAE was fabricated for detailed electrochemical measurement. The chronoamperometry measurements were conducted to verify the theoretical performance of bµAEs, and the results suggest that the experimental data are in good agreement with those of the simulation model. This work presents a novel bµAE with great potential for highly sensitive electrochemical detection and provides a new perspective on the efficient configuration of the µAE.

Microscopic Simulating the Impact of Cruising for Parking on Traffic Efficiency and Emission with Parking-and-Visit Test Data.

Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed with normal cars. In order to mitigate the influence of cruising for parking on normal cars, the simulation framework based on VISSIM was proposed for reproducing the cruising vehicles and normal traffic flows. The car-following model of cruising vehicles was calibrated by the GPS and video data. The scenarios under different cruising ratios were analyzed to evaluate the influence of cruising for parking on traffic efficiency and emissions. Finally, the layout optimization changing the parking locations and positions of entrance-exit gates were discussed to mitigate the negative effect. The results indicated that cruising for parking deteriorates the traffic congestion and emissions on the road sections, intersections and network. The closer distances the intersections and sections are to the parking lot, the greater the negative impact is. But the negative effect after the 30% proportion on traffic performance only illustrates the slight deterioration, because the carrying capacity of the network is reached. The research results provide a quantitative method for the hidden contribution of cruising for parking on traffic congestion and emissions.

A Ti3 C2 TX /Pt-Pd based amperometric biosensor for sensitive cancer biomarker detection.

The detection of cancer biomarkers is of great significance for the early screening of cancer. Detecting the content of sarcosine in blood or urine has been considered to provide a basis for the diagnosis of prostate cancer. However, it still lacks simple, high-precision and wide-ranging sarcosine detection methods. In this work, a Ti3 C2 TX /Pt-Pd nanocomposite with high stability and excellent electrochemical performance has been synthesized by a facile one-step alcohol reduction and then used on a glassy carbon electrode (GCE) with sarcosine oxidase (SOx ) to form a sarcosine biosensor (GCE/Ti3 C2 TX /Pt-Pd/SOx ). The prominent electrocatalytic activity and biocompatibility of Ti3 C2 TX /Pt-Pd enable the SOx to be highly active and sensitive to sarcosine. Under the optimized conditions, the prepared biosensor has a wide linear detection range to sarcosine from 1 to 1000 µM with a low limit of detection of 0.16 µM (S/N = 3) and a sensitivity of 84.1 µA/mM cm2 . Besides, the reliable response in serum samples shows its potential in the early diagnosis of prostate cancer. More importantly, the successful construction and application of the amperometric biosensor based on Ti3 C2 TX /Pt-Pd will provide a meaningful reference for detecting other cancer biomarkers.

Key Factors Analysis of Severity of Automobile to Two-Wheeler Traffic Accidents Based on Bayesian Network.

The purpose of this paper is to analyze the complex coupling relationships among accident factors contributing to the automobile and two-wheeler traffic accidents by establishing the Bayesian network (BN) model of the severity of traffic accidents, so as to minimize the negative impact of automobile to two-wheeler traffic accidents. According to the attribution of primary responsibility, traffic accidents were divided to two categories: the automobile and two-wheeler traffic as the primary responsible party. Two BN accident severity analysis models for different primary responsible parties were proposed by innovatively combining the Kendall correlation analysis method with the BN model. A database of 1560 accidents involving an automobile and two-wheeler in Guilin, Guangxi province, were applied to calibrate the model parameters and validate the effectiveness of the models. The result shows that the BN models could reflect the real relationships among the influential factors of the two types of traffic accidents. For traffic accidents of automobiles and two-wheelers as the primary responsible party, respectively, the biggest influential factors leading to fatality were weather and visibility, and the corresponding fluctuations in the probability of occurrence were 32.20% and 27.23%, respectively. Moreover, based on multi-factor cross-over analysis, the most influential factors leading to fatality were: {Off-Peak Period → Driver of Two-Wheeler: The elderly → Driving Behavior of Two-Wheeler: Parking} and {Drunk Driving Two-Wheeler → Having a License of Automobiles → Visibility: 50 m~100 m}, respectively. The results provide a theoretical basis for reducing the severity of automobile to two-wheeler traffic accidents.

Level of Service Model of the Non-Motorized Vehicle Crossing the Signalized Intersection Based on Riders' Perception Data.

This article aims to analyze the factors affecting the LOS (level of service) of non-motorized vehicles crossing the signalized intersection and to construct an appropriate method to evaluate the LOS. Aiming at the mixed non-motorized traffic flow of electric vehicles and bicycles in the Chinese metropolis, the delay model in the highway capacity manual (HCM) was modified by taking two new factors into account: the pedestrian traffic rule compliance rate and the fuzzy perception of arrival rate in reality. The results show that the data obtained by the modified model are more consistent with the actual one. Next, a comparison was established between the linear regression method and cumulative logistic regression to determine the variables that affect the LOS, and finally, a LOS evaluation index system based on threshold schemes was defined. The recommended LOS model can provide corresponding references for traffic engineers who seek to improve the level of service in urban intersections.

Nano-hydroxyapatite-evoked immune response synchronized with controllable immune adjuvant release for strengthening melanoma-specific growth inhibition.

Concerns about the potential systematic toxicity limit the extensive application of traditional therapeutic drugs for melanoma therapy, nano-hydroxyapatite (nHA) with good biocompatibility and anti-tumor ability could be an alternative choice. In this study, nHA was employed as an anti-tumor biomaterial due to its tumor-specific toxicity. Meanwhile, granulocyte-macrophage colony-stimulating factor (GM-CSF) served as the immune adjuvant to activate the immune response. The delivery platform was fabricated by co-encapsulation of both nHA and GM-CSF into a biocompatible thermosensitive PLGA-PEG-PLGA hydrogel. The results showed that the bio-activities of nHA and GM-CSF could be well-maintained within the hydrogel. Interestingly, the addition of nHA could attenuate the burst release of GM-CSF due to possible protein absorption capacity of nHA, which is beneficial for GM-CSF sustainable release at the tumor site, achieving boosted and prolonged anti-tumor immunity. The in vitro and in vivo data demonstrated that nHA/GM-CSF hydrogel exhibited greater potency to inhibit tumor growth via enhanced CD8+ T-cell response compared with hydrogel and nHA hydrogel groups, contributed by the synergistic effects of nHA and GM-CSF. Overall, the strategy combining nHA and immune adjuvant shows great promise, which largely broadens the choice of combinational therapies for melanoma. STATEMENT OF SIGNIFICANCE: Nano-hydroxyapatite (nHA) has been confirmed to specifically inhibit melanoma tumor growth and induce immune response. However, its antitumor efficiency and immunity-evoking capacity are limited. In this study, granulocyte-macrophage colony-stimulating factor (GM-CSF) was introduced to serve as the immune adjuvant. Both of them were encapsulated into a biocompatible thermosensitive PLGA-PEG-PLGA hydrogel. The addition of nHA could attenuate the burst release of GM-CSF due to the interaction with nHA, which is beneficial for GM-CSF sustainable release at tumor site, achieving boosted and prolonged anti-tumor immunity. Anti-tumor immune response could be activated due to the release of tumor-associated antigen and tumor debris induced by the specifically tumor inhibition effect of nHA and GM-CSF. The combination of nHA and GM-CSF could play synergistic inhibiting effect on tumor growth via boosting and prolonging anti-tumor immunity.

Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis.

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither ß1 or ß2 is the biggest the best for the traffic.

Incorporating multiple congestion levels into spatiotemporal analysis for the impact of a traffic incident.

Traffic incidents occurring on the road interrupt the smooth mobility of traffic flow and lead to traffic congestion. Although there has been a proliferation of studies that attempt to estimate the spatiotemporal impact of a traffic incident, most, if not all, of them focus exclusively on the differentiation of bi-level traffic status. In this research, we propose to incorporate multiple congestion levels in the spatiotemporal analysis for the impact of a traffic incident, which is new to the literature. The input to our model includes the historical speed on a given road and the occurrence time and location of the incident. The model then outputs the spatiotemporal impact region with multiple congestion levels. We first use a discriminant indicator to initially indicate the traffic status according to the travelling speed of probe vehicles. We then develop an integer programming model with a set of novel constraints to estimate the spatiotemporal region impacted by the incident. Unlike existing studies that only distinguish between uncongested and congested status, our model allows us to determine the impact region with diverse congestion levels. We validate our model using both simulation and real data. Results demonstrate that our approach can not only ensure the consistency of the propagation of shockwaves even when multiple congestion levels are incorporated, but also produce more accurate estimation of the delay caused by the incident when compared to the current state-of-the-art approach.

Risk perception and the warning strategy based on safety potential field theory.

Benefiting from the rapid development of communication and intelligent vehicle technology in recent years, most traffic information is capable of being collected, processed, and transmitted to each vehicle through a connected and automated vehicles (CAVs) system. To meet the higher requirements of driving safety in CAVs environment, it is necessary to develop more effective safety evaluation indicators that combine all the traffic information received by the vehicle. To this end, this study proposes a novel methodology for risk perception and warning strategy based on safety potential field model to minimize driving risk in the CAVs environment. A dynamic safety potential field model was constructed to describe the spatial distribution of driving risk encountered by vehicles. This safety potential field model can comprehensively consider the impact of various types of traffic information on driving risk. And then, a novel driving risk indicator, named potential field indicator (PFI), was established to evaluate the level of driving risk. Finally, an early warning strategy was proposed to prevent accidents, whose performance was evaluated by several simulations carried out through SUMO simulator. The comparison with some classic risk indicators indicate that our proposed PFI can more accurately reflect the actual driving risk faced by vehicles under different vehicle motion states and thus is more suitable for driving risk assessment in the CAVs environment. It is expected that the findings in this study could be valuable in improving the performance of strategic decision-making in driver assistance systems in the CAVs environment.

Electrochemical Performance of Micropillar Array Electrodes in Microflows.

The microchip-based electrochemical detection system (µEDS) has attracted plenty of research attention due to its merits including the capability in high-density integration, high sensitivity, fast analysis time, and reduced reagent consumption. The miniaturized working electrode is usually regarded as the core component of the µEDS, since its characteristic directly determines the performance of the whole system. Compared with the microelectrodes with conventional shapes such as the band, ring and disk, the three-dimensional (3D) micropillar array electrode (µAE) has demonstrated significant potential in improving the current response and decreasing the limits of detection due to its much larger reaction area. In this study, the numerical simulation method was used to investigate the performance of the µEDS, and both the geometrical and hydrodynamic parameters, including the micropillars shape, height, arrangement form and the flow rate of the reactant solution, were taken into consideration. The tail effect in µAEs was also quantitatively analyzed based on a pre-defined parameter of the current density ratio. In addition, a PDMS-based 3D µAE was fabricated and integrated into the microchannel for the electrochemical detection. The experiments of cyclic voltammetry (CV) and chronoamperometry (CA) were conducted, and a good agreement was found between the experimental and simulation results. This study would be instructive for the configuration and parameters design of the µEDS, and the presented method can be adopted to analyze and optimize the performance of nanochip-based electrochemical detection system (nEDS).

Automated traffic incident detection with a smaller dataset based on generative adversarial networks.

An imbalanced and small training sample can cause an incident detection model to have a low detection rate and a high false alarm rate. To solve the scarcity of incident samples, a novel incident detection framework is proposed based on generative adversarial networks (GANs). First, spatial and temporal rules are presented to extract variables from traffic data, which is followed by the random forest algorithm to rank the importance of variables. Then, some new incident samples are generated using GANs. Finally, the support vector machine algorithm is applied as the incident detection model. Real traffic data, which were collected from a 69.5-mile section of the I-80 highway, are used to validate the proposed approach. A total of 140 detectors are installed on the section enabling traffic flow to be measured every 30s. During 14 days, 139 incident samples and 946 nonincident samples were extracted from the raw data. Five categories of experiments are designed to evaluate whether the proposed framework can solve the small sample size problem, imbalanced sample problem, and timeliness problem in the current incident detection system. The experimental results show that our proposed framework can considerably improve the detection rate and reduce the false alarm rate of traffic incident detection. The balance of the dataset can improve the detection rate from 87.48% to 90.68% and reduce the false alarm rate from 12.76% to 7.11%. This paper lends support to further studies on combining GANs with the machine learning model to address the imbalance and small sample size problems related to intelligent transportation systems.