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The novel contribution of this paper is to develop a quadratic perturbation method to derive general analytical buckling load formulas for cylindrical shell structures with varying wall thickness under non-uniform lateral pressure loads for the first time. Arbitrary thickness and lateral pressure loads are skillfully described and governing differential equations are derived. A quadratic perturbation method is developed to derive buckling load formulas, which establishes the relation among buckling loads, thickness, and load functions. The presented formulas are adequately validated by comparing with recent results for variable wall thickness or non-uniform external pressure, and exhibit a great advantage in determining buckling loads. For the purpose of engineering application, buckling of a cylinder with linear thickness under linear liquid pressure and wind load is deeply studied by the presented formulas. This study can provide a highly efficient method for buckling load computation, and a guide to design the thickness of the shell under non-uniform lateral loads.

This paper focuses on the buckling problem of cylindrical shells under combined non-uniform axial compression and external pressure. To obtain buckling loads, a completely new method called two-parameter perturbation method is established. Using this method, governing differential equations are solved and buckling loads expressed by load functions, load parameters, and shell dimensions are derived for the first time. Comparative studies show that buckling loads by the analytical formulas coincide with those for combined uniform loads or single wind load in literatures. Buckling of a cylinder subjected to combined bending and wind loads is analyzed, and confirmed by the Galerkin method for the case of small load variations. Therefore, the presented formulas are adequately validated. The numerical calculations are carried out to analyze the effects of non-uniform load parameters on buckling load coefficient. The obtained results in this paper can be applied to evaluate buckling loads for cylinders subjected to combined non-uniform loads in actual engineering.

This paper analytically studies the buckling of a cylindrical shell having varying thickness under non-uniform axial compressive loads for the first time, which widely exists in engineering practice. A novel quadratic perturbation technique is developed to establish general buckling load formulas for the shell. This method overcomes the difficulties of traditional energy methods in solving high order determinants and deriving direct expressions for buckling loads when shell thickness and axial load are unknown. Applying presented formulas, various shell thicknesses and axial loads are analyzed, and a series of new results for buckling loads are obtained and validated. Even for classical cosine thickness variation under uniform axial compression, we also give general conclusions compared with Koiter’s results by the energy method. The effects of thickness variations and load distribution parameters on buckling loads are analyzed in detail. The presented study in this paper fills the gap and establishes a foundation of buckling analysis for non-uniformly loading cylindrical shells with variable thickness. Certainly, the established formulas are general and available for buckling resistance capacity evaluation for the shells under all circumstances involving thickness variations or/and non-uniform axial compressive loads.

As depression has been a major contributor to the global disease burden, objective and effective computer-aided diagnosis has become an urgent problem. This study aims to assess the frontal asymmetry variation of alpha electroencephalography (EEG) in different severity depression patients and to find promising biomarkers for future depression recognition. Three-channel EEG signals from 69 depression patients (divided into three groups according to illness severity) and 14 healthy subjects were collected. Except for cross-sample entropy (CSEn), two new asymmetry indexes (Asy_SEn and Asy_LZC) based on complexity measures were proposed to quantify the difference among the four groups. One-way ANOVA was used to test the difference among all four groups, followed by the group t-test to test the difference between each two groups. All indexes show significantly increased frontal alpha asymmetry in depressive groups compared with the healthy group, and the asymmetry keeps increasing as the depression deepens. The Asy_LZC value of the confirmed depression group (0.0015 ± 0.0008) is substantially higher than the other three groups (−0.0010 ± 0.0008, −0.0006 ± 0.0008, and −0.0007 ± 0.0006). And the Asy_SEn value of the healthy group (−0.0023 ± 0.0007) is significantly lower than the two depressive groups (0.0001 ± 0.0005 and 0.0007 ± 0.0007). All healthy CSEn between each two channels is considerably lower than depressive groups with p

The novelty of this paper is to propose exact three-dimensional analytical solutions to the half infinite thermo-elastic body pressed by a rigid cylindrical structure for the first time, in which uniform heat flux flowing from rigid structure into the half infinite body is considered. Using recent general solutions expressed by four harmonic functions and extending potential theory method to consider heat effect, the governing integral equations are first derived. Then, the equations are analytically solved and displacement, stress, and temperature field components expressed by elementary functions are obtained. To validate the presented solutions, the correctness of the solutions is exactly examined, and normal contact displacement and stress without heat effect are compared with those in literatures. Furthermore, a numerical example which is the stainless steel thermo-elastic body pressed by a displacement restrictor is studied. Displacement, stress, and temperature distributions are analyzed, and the influence of heat flux on the pressed depth is also discussed in detail. The analytical solutions presented in this paper are rather important in engineering practice and can provide a basis for indentation analysis, displacement restrictor design, foundation settlement calculation, and so on.

Depression is a leading cause of disability worldwide, and objective biomarkers are required for future computer-aided diagnosis. This study aims to assess the variation of frontal alpha complexity among different severity depression patients and healthy subjects, therefore to explore the depressed neuronal activity and to suggest valid biomarkers. 69 depression patients (divided into three groups according to the disease severity) and 14 healthy subjects were employed to collect 3-channel resting Electroencephalogram signals. Sample entropy and Lempel–Ziv complexity methods were employed to evaluate the Electroencephalogram complexity among different severity depression groups and healthy group. Kruskal–Wallis rank test and group t-test were performed to test the difference significance among four groups and between each two groups separately. All indexes values show that depression patients have significantly increased complexity compared to healthy subjects, and furthermore, the complexity keeps increasing as the depression deepens. Sample entropy measures exhibit superiority in distinguishing mild depression from healthy group with significant difference even between nondepressive state group and healthy group. The results confirm the altered neuronal activity influenced by depression severity and suggest sample entropy and Lempel–Ziv complexity as promising biomarkers in future depression evaluation and diagnosis.

Exploring efficient control strategies for heterogeneously congested urban networks remains a big research challenge. The theory of macroscopic fundamental diagram (MFD) provides a new perspective for network-wide congestion control decisions. This paper proposes a coordinated perimeter control strategy for multiregion heterogeneous networks based on optimized transfer flows. First, a two-layer network partitioning method is presented to capture spatial heterogeneity dynamics of urban networks. For this partition, traffic flow equilibrium model based on MFD and multiagent based hierarchical traffic management scheme are built. Then, an improved multinomial logit model is developed for deriving optimized transfer flows among multiple congested regions. A coordinated perimeter control strategy using model predictive control is further proposed, which is aimed at tracking desired accumulations of each congested region. As a case study, the proposed control strategy is applied to the downtown network of Jinan, China, using simulation analysis. The results demonstrate that it can achieve balanced network flow distribution and increased mobility.

This paper proposes an analytical buckling load formula for a cylindrical shell subjected to local axial compression for the first time, which is achieved by a carefully constructed load description and perturbation procedure. Local axial load is described by introducing an arctangent function first. Then, the analytical solutions of local buckling load coefficients and buckling modes for a locally compressed shell are derived after solving governing differential equations by the perturbation method. For validation, using the presented analytical buckling modes, the Galerkin method is applied to obtain a numerical results, which is an infinite order determinant about local buckling load coefficient. Comparative calculation results show that local buckling load coefficients by the analytical formula are in perfect agreement with numerical ones by the Galerkin method and known results in the literature. Therefore, the validity and accuracy of the presented formula are verified. Engineering application of the analytical formula is also discussed to evaluate local buckling loads of thin-walled cylindrical shell structures such as silos, pressure vessels, and large storage tanks.

Network-wide traffic control strategies (e.g. perimeter control and route guidance) in urban networks have recently been mainly studied to relieve or postpone congestion based on the theory of macroscopic fundamental diagram (MFD). Nevertheless, these studies are mostly applied to the statically partitioned networks or the dynamic networks that fail to fully consider traffic state prediction, conflicting with strongly spatiotemporal variability of traffic congestion or objective of active traffic management (ATM). This paper proposes a methodology to dynamically identify critical congestion warning areas from heterogeneous urban road networks, which aids to design efficient perimeter control approaches. In the methodology, a dynamic directed weighted network is built on the base of the link connectivity and the real-time traffic loads, and a link travel time prediction method based on Kalman filter is developed to calibrate directed weight values and undirected input values for links. With the undirected link input information, a dynamic congestion warning community detection method which consists of three consecutive steps is developed. Firstly, it could capture emergence of new congestion areas based on the definition of congestion seed intersection. Secondly, expansion and regression of each congested area could be achieved with the objectives of spatial compactness and traffic condition homogeneity. Thirdly, a two-level merging algorithm of adjacent different congestion areas is designed utilizing modularity model in community detection of complex networks. The proposed methodology is validated using ground truth data from downtown network of Jinan City in China. The results show that the proposed algorithms can efficiently track congestion evolutionary processes and effectively detect congestion warning areas from the test real network.

Reasonable traffic network model and flexible traffic control strategy play important roles in improving the urban traffic control efficiency. Introducing granular computing theory into traffic network modeling and traffic control is a useful attempt, since granular computing is closer to the human thinking in solving problems. In this paper, the traffic elements are depicted using S-rough set to achieve the granulation partition of traffic network. Four layers are partitioned in the proposed hierarchical multigranularity traffic network model, such as vehicle layer, platoon layer, segment and intersection layer, and subregion layer. Each traffic granule is represented in rough representation form, and the dynamic characteristics are described using the elementary transfer operations based on S-rough set theory. As an application on the proposed traffic network model, an extended max-pressure traffic control strategy is applied on the platoon and segment and intersection layer. Simulation results illustrate that the proposed traffic network model and traffic control strategy achieve better performance.

Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal. Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66% were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.

Objective: Depression is a mental disorder that causes emotional changes and even suicide. However, there is still a lack of objective physiological data to support current clinical depression diagnosis. Accurate computer-aided diagnosis systems are becoming more and more crucial and urgent for future depression diagnosis. The purpose of this study is to analyze the electroencephalogram (EEG) regularity of depression using fuzzy measure entropy (FMEn), and thus to explore its role in the computer-aided diagnosis of depression. Approach: Three-channel EEG signals among 35 subjects (divided into two groups according to the severity of the disease) were recorded in this study. First, the frontal delta, theta, alpha and beta frequency bands were extracted after preprocessing, and the sample entropy (SEn) and the FMEn were calculated. Then, the difference between the two groups and the correlation between the entropy values and the Hamilton Depression Rating Scale scores were analyzed using statistical analysis. Finally, the results of FMEn were compared with those of SEn. Main results: A better statistically significant difference between the two groups using FMEn was revealed, with p < 0.01 in the theta and alpha bands. In terms of SEn, only SEn_Fp2 in the delta band, SEn_Fp2 in the theta band and SEn_Fp1 in the alpha band performed better, showing significant differences with p = 0.0006, p = 0.002 and p = 0.0114. Significance: These findings suggest that frontal EEG signal complexity analysis with depression using FMEn might be more sensitive than that using SEn. FMEn could be considered as a promising biomarker for future clinical depression detection.

The car-following model is an important micro-traffic model for simulating car-following behaviour in traffic engineering and research studies. Conventional car-following models are always presented using mathematical equations reflecting ideal traffic conditions. In the big data era, data-driven models become a popular trend. In this study, a data-driven car-following model based on the rough set theory is proposed to consider information hidden in a field data set. On the basis of field data obtained from measurement devices such as the next generation simulation (NGSIM) trajectory data set, and using the methods of the rough set theory, an optimal decision rule set is established. Redundant attributes and redundant attribute values are removed for simplifying the car-following behaviour decision problem. Attribute significance and weights are computed for selecting matching rules. A car-following behaviour decision algorithm is designed to choose appropriate rules to determine the follower's velocity according to current observations. Simulations illustrate that the proposed data-driven car-following model can simulate the micro-traffic behaviour of followers well.

This paper presents an analytical study on thermal buckling of cylindrical shells with non-uniform thickness, which is common in engineering practice. Firstly, the shell thickness is assumed to be arbitrary in the axial direction. After solving the basic partial differential equations by the perturbation method, buckling temperature and modes in terms of thickness function and geometric sizes of the shell are obtained. Using the presented formulas, this paper deeply analyzes and discusses cosine distributed and stepwise thicknesses. For simple cosine distributed thickness, the classical Galerkin method is applied to derive buckling temperature factors, while stepwise thickness is verified by the finite difference method. Results from the Galerkin method and the finite difference method are in accordance with those by presented formulas in this paper. Furthermore, the influence of parameters in thickness functions and buckling modes on buckling temperature factors is discussed, and some interesting conclusions are drawn. The presented buckling temperature formulas can be applied to evaluate stability capacity of the cylinder used in thermal environment.

Recent studies have indicated that physiological parameters change with different emotion states. This study aimed to quantify the changes of vascular function at different emotion and sub-emotion states. Twenty young subjects were studied with their finger photoplethysmographic (PPG) pulses recorded at three distinct emotion states: natural (1 minute), happiness and sadness (10 minutes for each). Within the period of happiness and sadness emotion states, two sub-emotion states (calmness and outburst) were identified with the synchronously recorded videos. Reflection index (RI) and stiffness index (SI), two widely used indices of vascular function, were derived from the PPG pulses to quantify their differences between three emotion states, as well as between two sub-emotion states. The results showed that, when compared with the natural emotion, RI and SI decreased in both happiness and sadness emotions. The decreases in RI were significant for both happiness and sadness emotions (both P< 0.01), but the decreases in SI was only significant for sadness emotion (P< 0.01). Moreover, for comparing happiness and sadness emotions, there was significant difference in RI (P< 0.01), but not in SI (P= 0.9). In addition, significant larger RI values were observed with the outburst sub-emotion in comparison with the calmness one for both happiness and sadness emotions (both P< 0.01) whereas significant larger SI values were observed with the outburst sub-emotion only in sadness emotion (P< 0.05). Moreover, gender factor hardly influence the RI and SI results for all three emotion measurements. This pilot study confirmed that vascular function changes with diffenrt emotion states could be quantified by the simple PPG measurement.

This pilot study investigated the differences of heart rate variability (HRV) indices between two opposite emotion states: happiness and sadness, to reveal the differences of autonomic nervous system activity under different emotional states. Forty-eight healthy volunteers were enrolled for this study. Electrocardiography (ECG) signals were recorded under both emotion states with a random measurement order (first happiness emotion measurement then sadness or reverse). RR interval (RRI) time series were extracted from ECGs and multiple HRV indices, including time-domain (MEAN, SDNN, RMSSD and PNN50), frequency-domain (LFn, HFn and LF/HF) and nonlinear indices (SampEn and FuzzyMEn) were calculated. In addition, the effects of heart rate (HR) and mean artery pressure (MAP) on the aforementioned HRV indices were analyzed for both emotion states. The results showed that experimental order had no significant effect on all HRV indices from both happiness and sadness emotions (all P > 0.05). The key result was that among all nine HRV indices, six indices were identified having significant differences between happiness and sadness emotion states: MEAN (P = 0.028), SDNN (P = 0.002), three frequency-domain indices (all P < 0.0001) and FuzzyMEn (P = 0.047), whereas RMSSD, PNN50 and SampEn had no significant differences between the two emotion states. All indices, except for SampEn, had significant positive correlations (all P < 0.01) for the two emotion states. Four time-domain indices decreased with the increase of HR (all P < 0.01), while frequency-domain and nonlinear indices demonstrated no HR-related changes for each emotional state. In addition, all indices (time-domain, frequency-domain and nonlinear) showed no MAP-related changes. It concluded that HRV indices showed significant differences between happiness and sadness emotion states and the findings could help to better understand the inherent differences of cardiovascular time series between different emotion states in clinical practice.

AimsThis study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities.MethodsElectrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS >17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study.ResultsA consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant (p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals (R = 0.24, p < 0.05), CSEn at 4 Hz (R = 0.26, p < 0.05) or 2 Hz (R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz (R = 0.31, p < 0.01) or 2 Hz (R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition.ConclusionThe current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method.

On the foundation of the original backpressure-based traffic light control algorithm, a distributed cooperative backpressure-based traffic light control method is proposed in this paper. The urban traffic network is modeled as a smart agent-controlled queuing network, in which the intersection agents exchange the queue length information and the selected activating light phase information of neighboring intersections through communications and determine the activating light phase at each time slot according to local traffic information. The improved phase pressure computation method considers the phase state of downstream intersections instead of only the queue length of the local intersections. Light phase switching coordination among adjacent intersections is achieved using the consensus-based bundle algorithm, in which the cooperative light phase switching problem is viewed as a task assignment issue among adjacent intersections. Simulation results illustrated that the proposed cooperative backpressure-based traffic light control method obtained better performance than the original backpressure-based and fixed-time traffic control methods.

Podocytes, dynamic polarized cells wrapped around glomerular capillaries, are an essential component of the glomerular filtration barrier. BK channels consist of one of the slit diaphragm (SD) proteins in podocytes, interact with the actin cytoskeleton, and play vital roles in glomerular filtration. Mechanistic target of rapamycin (mTOR) complexes regulate expression of SD proteins, as well as cytoskeleton structure, in podocytes. However, whether mTOR complexes regulate podocyte BK channels is still unclear. Here, we investigated the mechanism of mTOR complex regulation of BK channels via real-time PCR, western blot, immunofluorescence, and patch clamping. Inhibiting mTORC1 with rapamycin or downregulating Raptor had no significant effect on BK channel mRNA and protein levels and bioactivity. However, the dual inhibitor of mTORC1 and mTORC2 AZD8055 and short hairpin RNA targeting Rictor downregulated BK channel mRNA and protein levels and bioactivity. In addition, MK2206, GF109203X, and GSK650394, which are inhibitors of Akt, PKCα, and SGK1, respectively, were employed to test the downstream signaling pathway of mTORC2. MK2206 and GF109203X had no effect on BK channel protein levels. MK2206 caused an obvious decrease in the current density of the BK channels. Moreover, GSK650394 downregulated the BK channel protein and mRNA levels. These results indicate mTORC2 not only regulates the distribution of BK channels through Akt, but also modulates BK channel protein expression via SGK1 in podocytes.

Macroscopic fundamental diagram (MFD) characterizes a unimodal and low-scatter relationship between traffic accumulation and trip completion rate for homogenous urban network, providing a new perspective for the development of regional congestion control strategies. This paper contributes to the state-of-the-art by proposing a perimeter traffic control strategy for single urban congested region with MFD model and boundary conditions. For an urban protected region, the MFD is introduced to capture network traffic dynamics and optimal capacity. When regional accumulation exceeds the optimal capacity, the restricted inflow distribution considering boundary condition constraints of real-time traffic flows and queue dynamics is used to formulate a perimeter traffic flow control strategy for adjusting traffic signal timing on the periphery of region. To proactively reduce the possibility of queue spillbacks at border links, the dynamic queue-dependent border gated points are also integrated into the proposed control method. The simulation experiment was performed using part of urban network in Jinan, China. The results demonstrate the proposed perimeter control method alleviates the issue of congestion and improves traffic performance in the protected region and at the boundaries.

Ethnopharmacological relevance Berberine is generally extracted from Rhizoma Coptidis (Coptis chinensis Franch), a traditional Chinese medicine, which can be used in the treatment of intestinal diseases, respiratory infections and cardiovascular diseases. Berberine is especially effective for the treatment of gastrointestinal disorders such as diarrhea because of the effect of heat-clearing and detoxifying in traditional Chinese medicine theory. Aim of the study This study aimed to examine the protective effect of berberine (BBR) on the damaged colonic epithelial barrier caused by peritoneal dialysis fluid (PDF). Methods The damage to intestinal epithelial barrier was examined by intraperitoneally injecting 4.25% dextrose-containing PDF in mice and establishing a long-term PD model in rats with renal failure. Then, the therapeutic potential of berberine on PD-related colonic injuries was examined. T84 colonic epithelial cells were used to test the effect of PDF and berberine in vitro. The damaging effect of PDF and the protective effect of berberine were evaluated by histology staining, histofluorescence and transmission electron microscopy. The migration of colonic epithelial cell and actin-related protein 2 (Arp2) were tested by wound healing assay and Western blot to determine the possible mechanism in vitro. Results PD administration induced intestinal epithelial barrier dysfunction in the colon, and berberine alleviated the injury by increasing the tight junction and adhesion junction protein, both in vivo and in vitro. Berberine could also improve the morphology of microvillus. In the wound healing assay, berberine exhibited the ability to promote cell migration, indicating that berberine could probably recover the function of intestinal epithelial cells when the intestinal epithelial barrier was damaged by the PDF. Conclusions The present study demonstrates that berberine can ameliorate intestinal epithelial barrier dysfunction in the colon caused by long-term PDF through improving cell migration.

This paper presents an analytical study on the buckling of cylindrical shells under arbitrarily distributed axial loads. It has three main innovation points. First, different from the previous studies, the axial loads need not be assumed to be specific forms beforehand and be simple ones such as cosine type, which are arbitrary in this paper (including continuous and discontinuous distributions). Second, combining the separation of variables and perturbation methods, it develops a novel general method to solve the fourth-order partial differential equations with unknown variable coefficients for cylindrical shells under distributed axial loads. Last, it derives the asymptotic formula of buckling loads with respect to shell geometry sizes and load functions for the first time. Using the presented formula, two continuous axial load distributions are analyzed first and the results show a good agreement with previous studies and those by the Bubnov-Galerkin method. Furthermore, one discontinuous axial load distribution is investigated. Comparative study on effects of load distribution parameters and types is also performed. The study has sufficient accuracy and significant engineering application value.

This paper presents an analytical investigation on dynamic buckling of cylindrical shells with general thickness variations under exponentially increasing external pressure over the time. Different from the previous studies in literatures, the shell thickness varies arbitrarily and is common in actual engineering, which leads to failure of the existing methods. A new analytical method is first developed to solve the fourth-order governing partial differential equations with variable coefficients for the shell subjected to varying external pressure. Then the asymptotic formulae for dynamic buckling loads considering general thickness variations are derived and expressed by geometry sizes of the shell and thickness variation functions. To validate the presented results, two specific non-axisymmetric thickness cases are discussed in detail. The critical dynamic buckling loads show a great agreement with the previous ones by other researchers for simple and axial thickness variation situation. Finally, example calculations and parametric discussion are performed, and influences of thickness variation types, speed of external pressure and the power exponent of time on the critical dynamic buckling loads are discussed.

The large-scale VANET implies the network which covers a wide range of geographic space and contains a huge number of vehicles in urban scenario. The performance of traditional data transmitting protocols will be sharply decreased in such networks. In this paper, we propose a new data transmitting scheme based on improved AODV and RSU-assisted forwarding. On the premise that a certain number of RSUs are deployed in the network, the coverage area which has the highest average connectivity of each RSU is recognized using the improved watershed algorithm. The proposed data transmitting scheme includes three parts: source vehicle to RSU (SV2R), RSU to RSU (R2R) and RSU to destination vehicle (R2DV). The SV2R and R2DV are achieved using the improved AODV protocol based on restricted broadcasting. The R2R is achieved by backbone network. Simulation results show that, compared with the AODV protocol, the proposed method can significantly improve the performance in packet delivery fraction and overhead.

In this study, the problem of car-following stability is considered. This study presents a clustered car-following strategy based on inter-vehicle communications (IVCs), which contributes to derive a more accurate feedback control for improving the stability. In order to maintain car-following stability, two conditions are considered, which are based on the control theory and a modified optimal velocity car-following model. In simulations, the results demonstrate that the strategy is effective on the stability of car-following, and the effect becomes more obvious as the market penetration rates of IVC-equipped vehicles increase. Even in low market penetration rate (5%), the proposed strategy has significant influence on the stability and the recovering time.

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.

Weakly connected generators which are normally existed in actual power systems will be greatly affected by a nearby transient disturbance. A Smart Switching Reactor (SSR) has been used to improve the transient stability of the impacted generators. Different simulation models have been developed to study the effects of the SSR on the power system. The result shows that the transient stability has been improved while the small signal stability cannot be improved significantly. An application with the SSR has been carried out and the result demonstrates the valuable effects on protecting the generator from tripping frequently.

Development of precise active traffic control strategies urgently requires real-time estimation for operational metrics in transportation systems satisfying the level of smaller spatial granularity simultaneously. This paper proposed a probability approach to estimate real-time lane-based queue length using license plate recognition (LPR) data. The method first developed a nested logit model to depict initial lane selection and then improved a lane changing model, which can obtain lane-based equivalent cumulative arrival-departure curve. Thus queue length is obtained combined with input-output model. Finally, validation results show that the proposed approach can capture the real-time variations of queue length and achieve reasonable estimation accuracy for maximum cycle queue length.

Recent studies have indicated that there are significant changes in physiological signals among multiple emotions. This work aimed to study the differences of heart rate variability (HRV) indices among multiple emotions, and thus to explore the consistency of the differences within and without individuals. Sixty healthy students were involved in this study. Electrocardiography (ECG) from each subject was recorded among six different emotions: neutral, fear, sadness, happiness, anger and disgust. Variability series (RR interval time series) were abstracted from the ECG signals. Six HRV indices of the RR interval time series were calculated, including SDNN and RMSSD in time-domain, LF, HF, and LF/HF in frequency-domain, and SampEn in nonlinear domain. The results showed that there were significant differences of the above HRV indices among different emotions. No notable consistency of these differences was acquired, as no similar reference group orders were found, and the inconsistency measure (IM) of each single index was approximately normal distribution.

Heart rate variability (HRV) has been subject to high attention in the field of emotion recognition as it can reflect inherent information objectively and accurately. Various methods of nonlinear dynamics and complexity measures have been utilized for HRV analysis, including fractal dimension, Lyapunov exponents and entropies. Permutation entropy proposed by Bandt is a simple and fast algorithm to investigate complex dynamical systems. It symbolizes the time series and focus on the relative magnitude of data. Therefore, equalities in series should be considered carefully. In this paper, two methods of dealing with equalities have been applied to RRI time series in three emotional states: ranking equal values according to their order of appearance, or eliminate equalities by adding a little random noise. Results show that PE values derived from these two methods are significantly different. The significant differences are demonstrated under all three emotional states.

Analysis of heart rate variability (HRV) holds an important potential in emotion recognition, especially when the non-linear entropy methods are involved. Permutation pattern entropy (PPEn) and ordinal pattern entropy (OPEn) are two important Shannon entropy approaches for analyzing the inherent dynamic characteristics of RR interval time series and their performances should be tested in the emotion study. In this study, ECG signals were recorded from 60 healthy subjects by RM6240B multi-channel signal acquirement system under three emotion states: natural, happiness and sadness, at a sample rate of 1000 Hz. For each emotion state, ECG signal was recorded for five minutes. QRS complexes were identified and then the RR interval time series were analyzed by the PPEn and OPEn methods. Compared with natural emotion (1.6834±0.06), PPEn increases significantly in both happiness (1.7205±0.05) and sadness (1.7065±0.05) emotions. Meanwhile, PPEn shows significant difference between happiness and sadness emotions. For OPEn, compared with natural emotion (1.8318±0.07), it also increases in both happiness (1.8545±0.07) and sadness (1.8505±0.07) emotions. However, there is no significant difference between happiness and sadness emotions. The changes of PPEn and OPEn values during the three different emotion states suggests that emotion has a non-negligible influence on the inherent dynamic characteristics in cardiovascular system, and the PPEn and OPEn methods can be used in the emotion recognition study.

The performance of traditional AODV protocol will be sharply decreased in the scenario of large-scale VANET, which covers a wide range geographic space and contains a huge number of vehicles. This paper proposes an improved AODV routing protocol based on restricted broadcasting by communication zones for data transmitting in large-scale VANET. The whole network is divided into several communication zones according to the spatial distribution characteristic of inter-vehicle communication connectivity probabilities of road sections. The connectivity performance of the communication zone is evaluated using the grey correlation analysis method, in which both the historical and forecasting connectivity probabilities of road sections are considered. Following the maximum grey correlation degree principle, a series of key communication zones is selected to construct a restricted broadcasting area for route discovery in AODV. In the paper, the simulation scheme for the proposed AODV protocol in large-scale VANET scenario is also presented. Simulation results show that, compared with traditional AODV, the proposed improved protocol has advantages in packet delivery fraction, overhead and route setup time.

Differing from the physical connectivity of the topology structure, the logical connectivity of VANET considers both the interior network configuration and the external communication environment. Hence, the traditional mathematical analysis and modeling methods which are usually used in physical connectivity research are no longer suitable for the logical connectivity prediction. Taking the AODV protocol as an example, this paper simulates the effects of different road traffic parameters on logical connectivity probability and selects three main effect factors, roadway length, vehicle number and vehicle speed. Furthermore, the inner relation between the logical connectivity and the three road traffic parameters is studied based on data mining technique and then two logical connectivity prediction models are presented, the nonlinear regression-based model and the extreme learning machinebased model. Simulation results show that the two models are both with high accuracy in predicting the network logical connectivity under different road traffic environments.