Penetration Rate Research Articles

Voltage quality improvement method based on adaptive power control of photovoltaic inverter

Abstract With the increasing penetration rate of distributed photovoltaics in the distribution network in China, the energy pressure has been effectively alleviated. However, the voltage over-limit problem of its parallel outlets cannot be ignored. Therefore, this paper put forward a power control method with photovoltaic inverters which is adaptive. First, the principle of photovoltaic voltage overstepping the limit is analyzed, and second, an adaptive control tactic is brought up for the disturbance of the volatile load of photovoltaic output. Finally, combined with the analysis and verification of simulation examples, the results show that the proposed strategy can effectively improve the voltage quality of the grid and prove the effectiveness of the proposed strategy.

Humic acid enhances the co-transport of colloids and phosphorus in saturated porous media

Phosphorus (P) has been widely recognized as a substance that is difficult to transport due to its tendency to become easily fixed in the soil. However, many reports demonstrate that groundwater P pollution is rising in humus-rich areas. Research is urgently needed to confirm (or reject) the hypothesis that increased P pollution is related to humus, as there is currently limited quantitative research on this topic. In this study, we conducted a series of batch equilibrium adsorption-desorption experiments and column experiments to quantify the effects of montmorillonite colloids (MCs) and humic acids (HCs, the main components of humus) on the P transport behavior. The results indicate that P's adsorption and desorption behavior on MCs can be well simulated using the Langmuir and Temkin models (R2 > 0.91). Compared to the non-HC treatments, HCs significantly increased MCs' P adsorption and desorption capacity 5.18 and 7.21 times, respectively. Moreover, HCs facilitated the transport ability of the MC-P mixture through the saturated quartz sand column. In a 0.1 M NaCl solution, the MC-P mixture is nearly completely adsorbed on the surface of quartz sand, with a penetration rate of only 0.5%. In contrast, the HC-MC-P mixture can evidently penetrate further at a rate of 26.1%. The transport parameters fitted using HYDRUS-1D further indicated that the presence of humic acids significantly decreased the deposition coefficients of colloids, thereby enhancing the co-transport of colloids and P through the quartz sand porous medium. The potential mechanism of P pollution in humus-rich areas is likely enhanced by the formation of an HC-colloid-P mixture, which greatly increases the adsorption amount of P on colloids and enhances the electrostatic and spatial repulsion between colloids as well as between colloids and quartz sand. It reduces the aggregation and adsorption of colloids, ultimately transferring P into groundwater through colloid-facilitated co-transport. The findings of this study clarified the relationship between the transport of P, colloids, and HCs, which provides a theoretical basis for explaining the P pollution mechanism in humus-rich areas.

Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility. Doi: 10.28991/CEJ-2024-010-09-019 Full Text: PDF

Research on the Impact of China's Internet Financial Development on the Traditional Financial Industry

With the development of Internet technology, Internet finance was born. Internet finance has appeared in people's sight in a new business form, which has brought significant changes to people's lives. People no longer need to go to the banks or other financial institutions' operations to handle business in a tedious way. However, at the same time, the high efficiency and convenience brought by Internet finance has also had a certain impact on the traditional financial industry. This paper will explore this issue in depth with previous studies, especially in the Chinese market where the Internet penetration rate is high. The paper defines Internet finance and its characteristics, emphasizes its convenience, low operating costs and easy accessibility, and deeply analyzes the connection between Internet finance and traditional finance, and at the end of the paper, it puts forward some policies to deal with the problems faced by traditional finance. This paper points out that Internet finance and traditional finance are interdependent, and the growth of Internet finance is realized based on traditional finance. The emergence of Internet finance has challenged and transformed traditional financial services, prompting them to undergo a digital transformation to meet the evolving needs and expectations of consumers. This study comprehensively examines these dynamics and the continued evolution of financial services in the era of Internet finance.

Enhancing Tunnel Boring Machine Penetration Rate Predictions through Particle Swarm Optimization and Elman Neural Networks

Enhancing Tunnel Boring Machine Penetration Rate Predictions through Particle Swarm Optimization and Elman Neural Networks

Decentralized human-like control strategy of mixed-flow multi-vehicle interactions at uncontrolled intersections: A game-theoretic approach

A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers’ actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.

Application of DFN approach for the selection of maximized penetration rate model of TBM in hard rock

Application of DFN approach for the selection of maximized penetration rate model of TBM in hard rock

Study on susceptibility differences of Tetranychus cinnabarinus (Boisduval) males and females to acaricides

The carmine spider mite, Tetranychus cinnabarinus (Boisduval), is an important agricultural arthropod pest. This study investigated acaricide susceptibility differences between sexes to determine the biochemical mechanisms potentially involved in these differences. The susceptibility of females and males to seven acaricides (pyridaben, cyflumetofen, abamectin, chlorpyrifos, propargite, profenofos and fenpropathrin) was compared with laboratory bioassays. Males were more susceptible than females for each acaricide. Differences in the lethal concentration 50 % (LC50) values between the sexes were observed to range from 2.04-fold to 6.05-fold. The surface area was markedly greater for females than males (by 1.87-fold) and the weight was significantly greater for females compared to males (by 4.67-fold). The specific surface area of male was 2.61-fold higher than that of female. There were no differences in the cuticle structure, whereas the thickness of females was 1.63-fold than that of males. The penetration rate of males was statistically significantly higher than that of females, both in pyridaben and cyflumetofen. Synergism experiments and biochemical assays suggested the involvement of the three detoxification enzyme systems in the sexual susceptibility of T. cinnabarinus. The activities of cytochrome P450 monooxygenases (P450s) (p < 0.01), glutathione-S-transferase (GSTs) (p < 0.05) and carboxylesterase (CarEs) (p < 0.05) in females were significantly higher than that in males. The results reveal that the individual size (specific surface area), cuticle thickness, and detoxification enzyme activity were involved in the sexual susceptibility to acaricides of T. cinnabarinus.

Assessing the impact of driver compliance on traffic flow and safety in work zones amid varied mixed autonomy scenarios

The safety of work zones is a critical issue for drivers, transportation agencies, and governing authorities. In particular, the vehicles that perform lane changes in the proximity of the work zones involving lane closure, pose a significant threat to the safety of the public and the work zone workers, as they need to complete a forced merging. Yet, there is no comprehensive simulation framework to examine the work zone traffic safety under different compliance distributions of the drivers to the warning delivery for the work zone in a mix-autonomy operation of autonomous and human-driven vehicles. To fill this void, we present an integrated microsimulation framework to assess the correlation between the number of vehicles that perform late merge at the taper (LMT) and traffic mobility and safety under different empirical compliance distributions of the drivers to the warning delivery for the downstream work zone.We employ different work zone configurations to illustrate the relationship between late merges at the taper and performance indicators for traffic mobility and safety of the work zone under a variety of work zone configurations. Simulation results show that compliance distribution significantly impacts the number of late merges at the taper (LMTs) and thereby traffic safety and efficiency. Our findings demonstrate that when human-driven vehicles exhibit high compliance behavior to the merging warning signs, it can offset the impact of the lower percentage of market penetration rate (MPR) levels for autonomous operation to achieve comparable traffic safety and efficiency. We further employ the conflation of microsimulation observations and data-driven models to design a regression model to predict LMTs as an indicator for traffic conditions using the work zone configuration as input variables. In particular, we address the heterogeneity induced by the compliance distribution of drivers by sampling the data points from the distribution to capture the diversity in compliance behaviors of the drivers. Our findings can provide insights for practitioners and researchers regarding the optimal compliance distribution using the performance measurements demonstrated in this work.

Improved sequential impedance modeling and stability analysis of virtual synchronous generators considering frequency coupling effects

With the increasing penetration rate of distributed power supply, the interaction between grid-connected inverters and power grid is prone to harmonic oscillation, which will seriously threaten the stable operation of power grid. At present, impedance analysis has been proved to be an effective tool to study the stability of grid-connected systems. However, few studies have considered the influence of frequency coupling effect on the output impedance characteristics of grid-connected inverters. To solve this problem, the sequence impedance model of a three-phase grid-connected inverter controlled by a virtual synchronous generator is established by harmonic linearization method based on the frequency coupling effect. In addition, an improved method of sequence impedance modeling based on grid impedance is proposed to effectively eliminate the influence of coupling terms. Finally, the correctness and practicability of the proposed simplified sequence impedance model are verified by simulation and experiment.

Estimating traffic demand of different transportation modes using floating smartphone data

Traffic demand is crucial for traffic planning, helping to understand traffic volume, identify congestion points, and develop strategies for efficient and sustainable transportation. This paper introduces a method to estimate origin-destination (OD) matrices for drivers, cyclists, and pedestrians using floating smartphone data (FSD) and the information minimisation (IM) model. The FSD is used in the gravity model (GM) to provide more accurate inputs for the IM model by estimating an a priori matrix and traffic demand at intersections. Additionally, FSD allowed a direct route choice proportions deriviation without needing a traffic assignment model. The proposed estimation model was tested with real data and analyzed in a simulation environment, examining the impact of increasing the penetration rate (PR) of FSD. Results indicate that traffic demand in the study area was accurately estimated with an average Rxy value of 92%, aligning well with simulation results.

The Influence of Perceived Benefits, Financial Literacy, and Demographics on Health Insurance Purchase Intention by Gen Z Which is Mediated by Attitude

The insurance penetration rate in Indonesia remains low and has even been declining, despite the significant role the insurance industry plays in supporting national development. Meanwhile, Generation Z is beginning to dominate Indonesia's demographic structure, particularly as the demographic bonus period approaches. Low financial literacy is often cited as a cause of the low attitude and purchase intention towards life insurance. Additionally, low perceived benefit of insurance is also known to influence Generation Z's attitude. Generation Z is also associated with behaviors that differ from previous generations. This study aims to identify the factors influencing Generation Z's attitude and purchase intention towards life insurance, focusing on financial literacy, perceived benefit, and demographics. The research method is quantitative, with samples taken from Generation Z residents in Jabodetabek who have income but do not yet have life insurance. The research instrument used is a questionnaire, with a total of 297 valid questionnaires collected and analyzed using variance-based structural equation modeling (SEM). The results show that financial literacy, perceived benefit, and demographics have a significant and positive influence on Generation Z's attitude and purchase intention. Among these factors, perceived benefit emerges as the strongest predictor of attitudes towards life insurance. The financial literacy level of Generation Z in Jabodetabek is found to be adequate. This indicates that financial literacy does not directly translate into insurance literacy, suggesting that financial literacy is not the main cause of the low attitude and purchase intention towards life insurance among Generation Z in Indonesia.

An internal stochastic car-following model: Stochasticity analysis of mixed traffic environment

This paper investigates the impact of adaptive cruise control(ACC) vehicles on the stochasticity of human driving behavior by constructing a stochastic car-following model of human-driven vehicles (HDVs). Utilizing NGSIM dataset, the relationship between acceleration variance and space headway is analyzed, and a novel stochastic car-following model with headway is proposed to capture the internal stochasticity of drivers. Furthermore, the interaction between HDVs and AVs is explored by discussing stochasticity and stability in mixed traffic flow, using the proposed HDV model. The model parameters are calibrated based on NGSIM dataset and the simulation results indicate that the proposed stochastic car-following model can effectively reproduce the generation and propagation of traffic shocks without lane changes. Additionally, the simulations reveal that as the penetration rate of AVs increases in a lower range (0%–50%), the stochasticity of HDVs and stability in mixed traffic flow is substantially reduced. However, at higher penetration rates, increases in the AV penetration rate have a limited effect on the stochasticity of human driving behavior and the stability of mixed traffic flow. Concurrently, under conditions of low penetration rates, a smaller AV platoon size contributes more effectively to enhancing the stability of traffic flow and suppressing the stochastic behavior of HDVs. This research provides new insights for optimizing traffic flow control with automated vehicles.

The Impact of National Culture on Technological Service Innovation

The increasing use of blockchain technology in various industries has generated a significant amount of interest among researchers and practitioners. This study aims to determine a predictive classification model of technological service innovation adoption for blockchain implementation across cultures. Questions of the research are: 1) does national culture impact the prediction of blockchain adoption across countries? and 2) if so, what cultural dimensions have the highest impact on service innovation adoption rates, across and between countries? and finally, 3) are penetration rates of blockchain adoption significantly different across countries? Industry, country, and managerial level variables along with adoption rates are examined. Model formulation and analysis is supported using a neural network to predictively classify firm and country innovation adoption. This study helps organizations understand the factors that impact the adoption of blockchain technology and will also inform policymakers about the potential benefits and cultural challenges of blockchain implementation.

Systematic Study of Battery Electric Vehicle Ownership in England

Battery electric vehicles (BEVs) form a big part of the UK’s agenda for decarbonizing road transport. Although there have been various policies such as purchase grants and vehicle tax exemptions, the BEV penetration rate in the UK was only 2.5% in September 2023. Most research analyzing electric vehicle (EV) ownership in the UK is either based on stated preference survey data or includes plug-in hybrid vehicles in the analysis because of the sparsity of BEV ownership data; there is limited research based on household revealed preference (RP) data. This paper develops a BEV ownership model using RP household-level data from England to discover influential factors, to validate the findings in the literature, or both. Specifically, this paper uses the subset of the UK National Travel Survey (NTS) special license data to estimate a series of binary logit models of BEV ownership as a function of several sociodemographic, regional, and temporal factors, and discusses the related policy implications. Household income, multivehicle ownership (resulting from range anxiety), and overnight parking on street (resulting from insufficient public charging infrastructure) are influential factors found in this study that align with previous studies. On the other hand, households with a mortgage loan, geographical attributes (such as population density), and household composition (e.g., number of adults and children) are new factors identified in this study. We also present a future BEV ownership prediction model for regions of England which clearly suggests that improving public charging infrastructure, especially in the north, is required to achieve widespread growth in BEV ownership.

Distributional equity impacts of automated vehicles: A disaggregated approach

Better understanding of the equity impacts of automated vehicles (AV) is needed to design equitable deployment plans for AV technology. The goal of this study was to develop and demonstrate a modeling framework to support distributional equity assessments of AV systems prior to their wide adoption. This modeling framework takes a disaggregated approach that integrates agent-based simulation, diverse transportation outcomes, and equity analyses. The framework uses individual-level data to capture detailed disparities in transportation outcomes, applies a state-of-the-art multi-agent traffic simulation model (MATSim) to simulate privately-owned and shared AVs simultaneously, and considers distributional equity from multiple perspectives. To test and demonstrate the framework, a case study was conducted for the Tampa Bay region. Five scenarios were considered with different AV market shares and integration strategies based on scenario planning by the U.S. Federal Highway Administration. Results reveal that high AV penetration rates were required for substantial reductions in inequality. The introduction of AVs led to a more even distribution of accessibility, but slightly more uneven distribution of traditional mobility and affordability. Impacts of disparities in outcomes for disadvantaged groups were mixed. These results suggest that AVs will likely perpetuate existing inequity in the transportation system as long as its fundamental structure remains the same as today. Results highlight the importance of planning and design strategies that directly address the distributional impacts to ensure that AV technology is deployed equitably.

Experimental investigation on high voltage electric pulse rock breaking under drilling mud conditions

High voltage electric pulse rock breaking technology is a novel and promising rock breaking technique that is environmentally friendly, efficient, and nearing industrial application. However, the drilling performance of electrode bits under varied drilling muds and the mechanism of high voltage electric pulse rock breaking remain unclear. This study entails real drilling experiments conducted on red sandstone using electrical pulse drilling (EPD) under different initial voltage peaks, electrode bits, and drilling muds to investigate the impact and mechanism of EPD. A laboratory experimental system for high voltage pulse rock breaking is established based on a 0–80 kV EPD pulse generator. Three types of electrode bits are devised, namely, petal-structural, four-point-structural, and circular-ring-structural electrode bits. The EPD experiments on red sandstone are executed under seven different drilling muds at initial peak voltages ranging from 13 to 17 kV. And the efficiency of EPD rock breaking is assessed using five quantitative indexes: rate of penetration, breaking volume, specific energy of rock breaking, and the proportion of electrical fragmentation and hydraulic-electric rock fragmentation. Furthermore, the physical parameters, i.e., density, conductivity, Zeta potential, PH, and apparent viscosity, of the seven drilling muds are measured, shedding light on the rock breaking mechanism of EPD under specific drilling mud conditions. Subsequently, the designed electrode bit is evaluated, and based on experimental phenomenon, structural optimizations of the electrode bit and EPD drilling recommendations under various drilling mud conditions in real drilling scenarios are proposed. This research is poised to offer valuable insights into the design of EPD electrode bits and the selection of drilling mud for enhancing drilling efficiency.

Characterization of the in vitro penetration and first-pass metabolism of genistein and daidzein using human and pig skin explants and Phenion full-thickness skin models.

OECD test guideline compliant skin penetration studies, which also comply with the SCCS basic criteria, are lacking for genistein and daidzein. Therefore, we have measured their penetration and metabolism using ex vivo explants of fresh (i.e., metabolically viable) pig skin, fresh and frozen human skin, and Phenion full-thickness (FT) models. Preliminary studies using fresh pig skin helped to define the optimal experimental conditions. The dermal absorption of 10nmoles/cm2 genistein and daidzein in ethanol was comparable in all four models. A first-pass metabolism in skin to glucuronide and sulfate metabolites was demonstrated for both chemicals in all models except frozen human skin. The main difference between fresh skin models was the overall extent of metabolism and the relative ratio of each metabolite, for example, much lower sulfate conjugates were formed in pig skin incubations. The extent of parent chemical metabolized and the contribution of the glucuronide pathway were relatively lower in PhenionFT models than in fresh human skin, possibly due to a higher penetration rate in this model and differences in the expression of functional metabolizing enzymes. When metabolism in human skin was abolished by freezing, more radiolabelled chemical remained in the skin tissue but the overall dermal absorption was unchanged. In conclusion, this initial characterization study showed that all models tested indicated that genistein and daidzein extensively penetrated the skin when applied to skin in ethanol. All fresh skin models produced the same metabolites, with the known species difference in the sulfation pathway demonstrated in pig skin.

Market Penetration Rate Optimization for Mobility Benefits of Connected Vehicles: A Bayesian Optimization Approach

The advancements of connected vehicle (CV) technologies promise significant safety, mobility, and environmental benefits for future transportation systems. These benefits will largely rely on the market penetration rate (MPR) of CVs and connected infrastructure. However, higher MPR is not guaranteed to result in greater benefits in a transportation system in some cases even if we do not consider the deployment cost of CVs. Therefore, understanding the optimal CV MPR to achieve the best system benefits is informative and can provide some guidance for transportation agencies to use appropriate incentives or other policies to potentially affect the speed of CV adoption. Instead of using the traditional incremental method, this paper proposed a simulation-based approach combined with Bayesian optimization to determine the optimal CV MPR that achieves the highest performance benefits for a freeway segment. The proposed methodology is tested in the I-210 E (in California, U.S.) simulation freeway segment built and calibrated in Simulation of Urban Mobility software as a case study. The weighted sum of the average total travel time on the mainline and the average queue length of on-ramps is formulated as the objective function to optimize the CV MPR. Different weight combinations are tested as different scenarios. The optimization results of these scenarios show that, when the weight of total travel time is high, the optimal CV MPR tends to be high. On the contrary, when the weight of queue length increases, higher CV MPRs may not guarantee higher benefits for the traffic system. The globally optimal CV MPR can be as low as 3%. The case study also confirms the effectiveness of optimizing the CV MPR based on microsimulation and Bayesian optimization.

Bayesian inference for estimating the penetration rate of probe vehicles from interdetection times only

Data provided by floating vehicles have the same limitation as that provided by an imperfect stationary detector that undercounts vehicles: they provide data from an unknown proportion of the total flow. Since the knowledge of this proportion is critical for some applications, its value is usually estimated by complementing with data provided by other monitoring infrastructure. However, this type of infrastructure is not the norm in many cities, particularly in developing countries. This paper extends a previously proposed method to estimate this penetration solely from a series of inter-detection times. The improved method is based on Bayesian inference and supports a series of passage times coming from multilane infrastructure, albeit still requiring an uninterrupted traffic regime. The method was applied to actual RFID interdetection times from a four-lane weaving urban freeway segment in Santiago, Chile. The resulting estimated rates are consistent with those reported by the freeway operator, showing the method’s capabilities. Thus, the method is instrumental in successfully taking full advantage of technology already in place in many cities without needing new investments in monitoring infrastructure.