Search

Relationship between urban diversity and urban vitality is imperative for guiding better design in urban development, although existing frameworks are not able to efficiently examine the relationship at multiple scales. In this article, we propose a new framework to integrate nighttime light (NTL) imagery and multisource urban data into multiscale geographically weighted regression (MGWR) models to examine the varying relationship between diversity and vitality across space and time. NTL is used as a proxy for urban nighttime vitality. Public transport, taxi transit, and points of interest data are used to derive three aspects of urban diversity indices: ridership diversity, spatial interaction diversity, and built environment diversity. By comparing the models in holiday and nonholiday weeks in Shenzhen, China, the NTL-based vitality proxy was found to be strongly correlated with the urban diversity indices, given by the satisfactory goodness of fit (r-squared = 0.9) of the MGWR models. The spatially varying relationships between diversity indices and nighttime vitality were observed and patterns discussed. The analysis of the coefficients revealed the importance of stable public transport and fluctuating taxi trips for nighttime vitality. The new index proposed for the diversity of spatial interaction (DSI) is a strong indicator for nighttime vitality, adding to existing vitality indicators. Furthermore, this study found that DSI and density of catering have less temporal variation, indicating their robustness in measuring nighttime vitality. This study provided empirical insights into how nighttime vitality is related to urban diversity, demonstrating new applications of NTL for intracity studies.

Picking vegetables and fruits is a huge workload. Replacing manpower with robots has a great prospect to reduce the burden. However, due to the complex terrain and different planting conditions, the agricultural picking robots are still in low popularity. In this paper, a 3-DOF banana picking manipulator is designed and developed for the banana harvesting robot, which is easy to use, and can carry out large travel expansion and wide harvesting area. Firstly, the kinematics model of the mechanical arm is established by improved D-H method. The relationship between each link and coordinate system is established, and the joint variables are obtained. Then the forward and inverse kinematics equations are calculated. Secondly, the robotic toolbox in MATLAB is used to verify the correctness of kinematic model. Finally, the trajectory simulation in joint space is carried out and the relevant curves of displacement, speed and acceleration are obtained. It shows that the process of banana harvesting by 3-DOF banana picking manipulator designed in this paper is smooth to achieve the harvesting purpose.

In recent years, local governments have paid more and more attention to the construction of urban rail transit represented by subways. The subway can greatly improve the ground traffic congestion in the city and the urban traffic environment, making people's travel more convenient. The analysis and accurate prediction of subway passenger flow has always been one of the key tasks of urban rail transit management departments. Especially in the context of the rapid growth of rail transit capacity and the changing needs of passengers, the need for passenger flow analysis is even more significant. This article completes the analysis of Shenzhen's subway passenger flow through big data analysis. It is of great significance to solve urban congestion, optimize the traffic network, and protect public transportation.

The incidence of dengue fever, a mosquito-borne disease, has significantly increased throughout the world in recent decades, which urgently requires more effective dengue intervention strategies. Although both mosquitos and humans play critical roles in dengue transmission, commonly used dengue intervention strategies mainly depend on the control of the mosquito vector. Since intervention simulations by computers offer important scientific support for disease intervention policy-making, this study aims to simulate and evaluate the intervention strategies from the perspective of the human host. This study first develops a spatially-explicit, individual-based model that can simulate an intra-urban dengue outbreak by integrating the daily activities and travels of urban populations. Then, we examined two human host intervention strategies: a dengue vaccination program and early isolation of symptomatic infected people. Taking the dengue outbreak of Shenzhen in 2014 as a case study, our simulation results show that the reduction of symptomatic infected people increases from 60.21% to 94.39% as the proportion of the immunized population increases from 25% to 50%, while early isolation of symptomatic infected people can reduce total symptomatic infected people by 46.78%. Overall, this study provides an effective, spatially-explicit, individual-based model that allows dengue transmission simulations and human host intervention simulations, which can help guide policy-making to control dengue outbreaks at an urban scale.

This paper proposes a congestion detection algorithm of urban intersection surveillance video. The algorithm is divided into two parts: global vehicle speed detection algorithm and traffic state identification algorithm. The global vehicle speed detection algorithm firstly selects Region of Interest (ROI) according to the scene of intersection, using Shi-Tomasi corner detection algorithm to find the pixels to be tracked. Then take the detected corner points as inputs of Lucas-Kanade optical flow algorithm to obtain the vehicle motion vector. After that filter and obtain global vehicle speed according to calculate the direction of vector. The traffic state identification algorithm analyses global vehicle speed of one traffic light period. With multiple judgments the algorithm final discriminates traffic state. Experiments show that the proposed algorithms have strong anti-interference ability and good vehicle detection accuracy, meet the needs of practical application.

Based on the Linux system, we design and implement a shipboard terminal that integrates data acquisition, processing, sending and receiving. Furthermore, on this terminal, a video service was set up to transmit offshore operations video to the shore-based command center. Data collected by the sensor are sent to the shore-based data center regularly through Beidou short message module. Based on the analysis and calculation of the original data, the shore-based super computing center pushes the calculated results back to the ship, providing more security for the safe and stable navigation of the scientific survey ship.

Modeling individual-based urban mobility plays an important role in traffic management, urban planning, public health, public safety and many other fields. Compared with census and travel survey data, which are costly in collection and slow in update, the emergence of massively and automatically generated individual trajectory data, such as mobile phone tracking data and transit smart card data, offers new datasets to develop individual mobility models. However, these new types of human trajectory data suffer some inherent limitations for many research and application domains. First, these large-scale trajectory data often have certain types of sampling biases in the representation of entire population. For example, mobile phone data do not likely cover children and have little coverage in elder people. However, this portion of population is important in some studies, such as household-based travel demand modeling and epidemic modeling. Second, these large-scale individual trajectory data do not often come with individual sociodemographic attributes due to privacy or technical issues. Sociodemographic attributes however are critical in many studies such as household-based travel demand modeling, sociology studies, and epidemic modeling. Therefore, this study proposes a generalizable modeling framework for individual-based urban mobility with entire population and sociodemographic details, through integrating different types of data sources. To demonstrate the proposed modeling framework, we select several typical data sources, design a set of data fusion algorithms, and simulate the daily activities and trips of the entire population in Shenzhen, China. The simulation results show that the proposed data fusion approach can effectively help with sampling bias issues and reasonably fill up sociodemographic details for the large-scale trajectory data. The proposed individual-based urban mobility model can be useful in many studies that require inputting entire population with sociodemographic attributes. This study also gives an example of addressing an important topic in the “big data” era, that is to integrate the so called “big data” and traditional data in urban studies.

When we do the GPS baseline vector solution with GAMIT/GLOBK, the NRMS (normalized root-mean-square) value of the solution results might be too large or the solution might be failing during the process. The usual way to deal with both situations is to look for the causes of the error and correct them manually. Then do the baseline solution again. In view of the large amount of GPS data and the long baseline, it might take a long time if the procedure of solution repeated. In order to solve this problem, we select five parameters (observation duration, sampling interval, epoch, starting point coordinate and baseline solution type) related to the solution results of GAMIT as the discriminant factors, and establish the GAMIT solution Bayes prediction classification model. The experimental results show that the Bayes discriminant model has good prediction performance and can efficiently predict the solution is normal or not.

Restoring facial special morphology and normal functions for large bone defects is an extremely challenging issue in reconstructive surgery. The 3D modeling technology of craniofacial implants based on mechanical topology optimization method was proposed to solve this problem. According to the range of the defective region and the key structure of anatomy in the 3D craniofacial skeleton, the parametric optimization modeling was firstly carried out. Then, by analyzing actual load bearing which is mainly from masticatory stresses, the load and boundary conditions were applied to the object to optimize. And for the optimized results, the finite element analysis software Abaqus6.14 was used for mechanical analysis. In this paper, In this paper, compared with the traditional modeling approach, the implants designed by topology optimization modeling method of two typical types of patients with the craniofacial defects proved not only had advantages of good mechanical performance, but also achieved the lightweight structure of 23% of volume decrease, which provided a new idea for the development of craniofacial surgery.

Support vector machine (SVM) is of great interest to classify high dimensional data. Parameter setting in the SVM significantly influences the classification accuracy. An improved algorithm based on particle swarm optimization (PSO) (M-PSO-SVM) for SVM parameter optimization is proposed to improve the classifier classification accuracy. This method introduces mutation operation, which allows particles to reinitialize after each iteration and update to avoid falling into local optimal solution and premature convergence. The improved PSO-based SVM classifier has been validated on a real hyperspectral dataset. Classification results show that the proposed method outperforms classifiers as well as SVM and PSO-based SVM.

Isoline need to be generated through triangulated mesh, linear interpolation, search contours. Triangulated mesh is the key step, it is especially critical to improve its efficiency and reduce its time complexity. In this paper, we posed a new algorithm for quick generation delaunay triangle net, by searching boundary before the triangulation mesh, and then selected a point from the left-most two points in all point set, and the distance between this point and midpoint of convex edge was minimal, This point and convex edge constracted new delaunay triangle, and added them to delaunay triangle. This thesis improves on the common algorithm. We can reduce the complexity of computation, and improve the efficiency of the isoline generation algorithm.

With the rapid application of high speed machining, the feed rate of moving parts has an important influence on the dynamics of high speed machine tool. In this paper, a method of modal decoupling of Operational Deflection Shapes (ODS) was proposed to investigate the dynamic behavior of machine tool with respect to different worktable feed rates. Based on this method, the dominant frequencies vibration of the machine tool at different frequencies and its transformation at different worktable feed rates was identified. Finally, a series of empty running experiments were performed under different worktable feed rates. The experimental results show that (i) the change of worktable feed rate will affect the contribution of different modes vibration to the machine tool vibration; (ii) the contribution of a certain mode vibration to the machine tool vibration is different at different worktable feed rates; (iii) with the worktable feed rate increasing, the change rule of dominant frequency vibration participation is from multimode participation to single mode dominant, and then to the multimode participation. This research will provide valuable information for the selection of machine tool operation parameters and the optimization design of machine tool structure.

With the progress of technology, people develop many protection of foundation pit is also more and more, at the same time for multi-pivot of foundation pit engineering are also put forward higher requirements. In this paper, the dynamic simulation of protection of excavation structure simulation analysis, get the change of the stress and displacement of excavation structure, through dynamic simulation results can be seen that: Multi-pivot of foundation pit with the increase of excavation depth, displacement and stress of the supporting structure are changing, due to reasons such as unloading rebound, foundation pit is easy to produce certain uplift, and with the increase of the depth of the uplift degree increases, will eventually stabilise. Due to uplift can lead to loss of stability of the protection of excavation structure, so should take certain measures to control, in order to achieve the purpose of safety construction.

In a chroma keying system, the foreground objects are recorded in front of background with solid color, which is used as side information to efficiently and accurately separate the foreground from the background. Although the technique of chroma keying has been widely used for decades, there is little work on its application to stereoscopic images/videos in either industry or academia fields. We propose a new chroma key matting method to generate high quality matting results. Specifically, the colors of foreground objects to be chroma keyed are histogram analysed in CIECAM02 color space, which can provide better hue/saturation consistency compared to previous human visual system (HVS) based color spaces such as HSV, CIELab and IPT. The color of background region, which is occluded by foreground objects, is estimated by spatial affinity. With the estimated foreground/background color for each pixel, the foreground objects can be accurately extracted along with its transparency map . By doing this, we can generate depth map with accurate and sharp boundary, which is essential for post 3D processing such as depth image based rendering (DIBR).

We present Hybrid Key, an automatic tool using chromatic information as the key signal for compositing multiple video layers together. This tool is based on our recently proposed chroma-keying mechanism which simulates human perception by utilizing a hybrid color space and reaches the minimum criteria of real-time high-definition video processing [1]. In this paper, we improve the quality of our perception-based algorithm, and optimized the implementation with a hybrid GPU/CPU architecture. Our new system introduces the task prioritizing and asynchronous processing without jeopardizing the quality. It efficiently distributes computational burdens between different processing units and therefore leaving more room for final alpha and foreground estimation as well as the newly added alpha optimization. In addition, our implementation can make use of GPU specified optimizations to further improve its efficiency. These advantages not only ensure the best overall quality in comparison to other state-of-the-art chroma keyers, but also provide a cost-efficient real-time 4K high-definition chroma keying solution and create the first iPad/iPhone application for high definition chroma keying on the go.

There are lacks of integrated analysis and visual display of multiple real-time dynamic traffic information. This research proposed a deep research and application examples on this basis which is conducted in virtual geographic environment. Currently, there are many kinds of traffic passenger flow forecasting models, and the common models include regression forecasting model and time series prediction model. The coach passenger flow shows strong regularity and stability without long-term change trend, so this research adopts regression forecasting model to forecast the coach passenger flow.

This paper takes Shenzhen Fustian comprehensive transportation junction as the case, and makes use of continuous multiple real-time dynamic traffic information to carry out monitoring and analysis on spatial and temporal distribution of passenger flow under different means of transportation and service capacity of junction from multi-dimensional space-time perspectives such as different period and special period. Virtual reality geographic information system is employed to present the forecasting result.

Chronic diseases have gradually become the main factors affecting health. In order to estimate the prevalence and determine the risk factors among individuals, hospitalization survey data (n=54,536) from a county in central China in New Rural Cooperative Medical System (NCMS) were used in our study. In this paper, χ2 test and non-conditional logistic regression analysis were the key models to analyze the prevalence and risk factors of chronic disease. Results showed that the prevalence of chronic disease individuals (n=13,610; N=54,536) is 24.96%. Coronary heart disease, cerebral infarction, hypertension are the top three diseases. Meanwhile, circulatory system diseases ranked first. With using logistic regression model, age is an independent risk factor of chronic diseases. Furthermore, because of less cost, medical institutions in the county level are the first choice of local people. In conclusion, so as to guide the rural patients with chronic diseases get treatment to the greatest extent, preventive and intervening measures should be carried out, and the lifestyle and living environment of rural residents should be improved as well.

In the real-world, people would acquire or accept the same item, which can be a product, a service, or an event, multiple times. Meanwhile, an individual in a social network may influence others again and again. To address this phenomenon, we propose MIMA, a novel influence propagation model that describes the Multiple Influences and Multiple Acceptances of an individual on some item in a social network. MIMA models two important behaviors of influence and adoption: the marginal increase of a person's influence ability on others diminishes as he or she owns more that item, and the desire of a person to accept one more item will decrease as he or she already owns some. With the MIMA model, we study a new influence maximization problem in a social network where a person may accept the item multiple times: to select k initial promoters such that the final total acceptance volume of all people in the network is maximum. We prove this problem is NP-hard and propose greedy and heuristic algorithms. Experimental results show that the greedy algorithm achieves the best influence spread when compared to the existing algorithms. The influence spread of the heuristic algorithm is only slightly worse than that of the greedy algorithm but can save significant computation time.

Modeling of gene regulatory networks play an important role in the post genomic era. In this work, we propose a Bayesian inference based model to quantitatively analyze the transcriptional regulatory network when the structure of regulatory network is given. In the proposed model, the dynamics of transcription factors are treated as a Markov process. Besides, the sequence features of genes are employed to calculate the binding affinity between transcription factor and its target genes. Experimental results on the real biological datasets show that the present model can effectively identify the activity levels of transcription factors, as well as the regulatory parameters.

In this paper, given a spatial range Q and a set of query points specified by users, the goal of this paper is to return the travel routes that fulfill two requirements: 1.) travel routes should contain all those query points specified, and 2.) travel routes should be within the spatial range Q. Furthermore, we claim that each query point may have its proper visiting time. As such, the travel routes should go through these query points at their corresponding proper visiting time. To avoid some redundant information in the travel routes, we utilize the skyline concept to retrieve travel routes with more diversity. Specifically, in our paper, we consider some factors, such as the visiting time information of POIs and the set of query points, in retrieving travel routes. These factors could be mapped into dimensional spaces. Then, each travel route is viewed as a data point in the dimensional space. Thus, skyline data points (referred to as skyline travel routes) are returned as the query result. Skyline travel routes could provide more diversity in the query result of trip route recommendations. To evaluate our proposed methods, we conducted extensive experiments on real datasets. The experimental results show that skyline travel routes indeed provide more diversity in the query result. In addition, we evaluate the efficiency of retrieving skyline travel routes.

This paper presents a novel mechanism for automatically matting monochromatic background images and videos in real-time. The proposed mechanism simulates the process of human perception on isolating foreground elements in a given scene. The perceptual analysis is performed on optimized hue, saturation, lightness and chroma from CIECAM02 color appearance model, which has the best overall performance across the tested data sets. The foreground and background sample-pairs for alpha estimation are adaptively predicted based on the prior analysis rather than direct sampling. To achieve real-time performance, the entire procedures are optimized for parallel processing on the GPUs (Graphics Processing Units). The qualitative evaluation shows that our determined alpha mattes and foreground colors especially in large seemingly translucent areas are more acceptable by human eyes. And the quantitative comparison between our mechanism and other existing approaches also validates the advantage in speed and quality.

In recent years, location-based social networking services (LBSNSs) have become popular, generating a huge volume of geo-social networking data, such as check-in records and geo-tagged photos. The geo-social networking data provide a new source for discovering the real-world user behaviors. The information is useful for different applications, such as location prediction and point-of-interest (POI) recommendation. For LBSNSs, the research in POI recommendation have widely studied the user preferences over POIs and social influences between users. However, POIs are usually favored by or suitable for different kinds of groups, such as a small group, a tight group, or a close group. In this paper, we propose an approach to discovering POI signatures from geo-social networking data. For each POI, we first discover whether it has been visited by any groups of people and the features of these groups from user trajectories. We then generate the signature for each POI based on the discovered group features. We conduct experiments on the real data of the check-in records from Bright kite, and show the various kinds of POI signatures we found.

Connected dominated subsets (CDS) of nodes in wireless sensor network are always selected to form a virtual backbone. Construction of r-hop 2-connected dominating sets can reduce the size of CDS and the total amount of information to be transmitted. The 2-connected properties can provide higher fault tolerance and robustness as well. In this paper an approximation r-hop 2-connected dominating set algorithm for wireless sensor and ad-hoc networks is presented. The main strategy is partition to three phases: in the first phase, an r-hop independent dominating set is established; in the second phase, the r-hop dominating set is interconnected successively to form a connected subgraph; in the third phase, more vertices are selected and added into dominating set to make it 2-connected. Extensive simulations according to the algorithm steps have been conducted. Performance with detailed analysis has been shown at last.

Due to the flexibility and scalability in cloud computing, cloud computing nowadays plays an important role to handle a large-scale data analysis. For data processing operations, several cloud data managements (CDMs), such as HBase and Cassandra, are developed. Such CDMs usually provide key-value storages, where each key is used to access its corresponding value. Both HBase and Cassandra provide some basic operations (e.g., Get, Scan) to retrieve the values via keys specified by users. The exiting CDMs fully inherit the characteristics of cloud computing (i.e., high scalability and availability). With the aforementioned characteristics of cloud computing, CDMs are widely employed for Web data, especially for search engines. However, with the proliferation of smart phones and location-based services, data with spatial information, referring as spatial data, are dramatically increasing. Consequently, how to formulate keys for spatial data in the existing CDMs is a challenge issue. In this paper, we develop several key formulation schemes. In particular, we propose a novel Key formulation scheme based on R+-tree (abbreviated as KR+-index). With our design for keys of spatial data, the existing CDMs are able to efficiently retrieve spatial data. In light of KR+-tree, two spatial queries, k-NN query and range query, are designed. Moreover, we implement the proposed key formulation schemes on HBase and Cassandra, and import real spatial data for spatial queries. The experimental results demonstrate that KR+-tree outperforms other existing key formulations and MD-HBase.

As a major online interactive platform, microblogs have accumulated numerous data about people's interactive behaviors, which have attracted many researchers to study these data. However, the existing studies mainly focus on the community structure detection or information propagation from the conventional perspective of social network analysis. Few studies have investigated the relationships between people's online social behaviors and their geographical location information over Social Media. In this paper, we aim to analyze the relationships between people's online social activities and their geographical locations in Tencent-Microblog. We first make a statistical summary on different geographical locations and the number of users at each location. We find that the frequency distribution of the number of recorded locations from an individual follows a power law. Considering each individual's posting frequency and staying time on a certain location, we define a main location of an individual. In order to study the relations between communities and location clusters, we propose the index of location entropy to measure the degree of dispersion of the locations in each community, and the index of community entropy to measure the degree of dispersion of the communities in each location cluster. More importantly these two indexes can potentially help measure the influential power for the topic community and monitor the active degree of people's online social behavior in a location cluster.

Moving objects in the physical world usually generate many uncertain trajectories for some reasons such as the consideration of energy consumption, leaving the route passing two consecutive sampling points unknown. While such trajectories imply rich knowledge about the mobility of moving objects, they are less useful individually. This paper introduces an online trip planning system that mines collective knowledge (i.e., most possible routes between given locations) from massive uncertain trajectories following a paradigm of “uncertain+uncertainâ†'certain”. This system first builds a routable graph from uncertain trajectories, and then answers a user's online query (a sequence of point locations) by searching top-k routes on the graph. Two large-scale datasets consisting of “check-in” records from FourSquare and a trajectory dataset of taxis have been used to evaluate our system. As a result, our system provides a user with effective routes according to the user's query efficiently.

Connected dominated subsets (CDS) of nodes in wireless sensor network are always selected to form a virtual backbone. Construction of r-hop connected k-dominating sets ((k, r)-CDS) can reduce the size of CDS and the total amount of information to be transmitted. Their multiple dominating properties can provide higher fault tolerance and robustness as well. In this paper a (k, r)-CDS construction approximation algorithm for wireless sensor and ad-hoc networks is presented. The main strategy is partition to clusters, from which the r-hop k-dominating set nodes ((k, r)-DS) are selected separately. Then (k, r)-DS are connected together to form (k, r)-CDS. Extensive simulations according to the algorithm steps have been conducted. Performance with detailed analysis has been shown at last.

We have seen growing interest in validation of a business process model before it is implemented due to the complexity to model business process. In this paper, we propose a method for analyzing and validating the functional correctness of a business process model. Based on our previous work, we model a business process in UML activity diagrams with OCL constraints, then we give a formal semantics of the business process model, finally we validate the model by prototyping. We have developed a tool -- AutoPA to support our method. When applying the tool, a business process model specified by UML activity diagrams with OCL constraints is transformed into an executable prototype in Java. Both the control flow dimension and the dataflow dimension of the model are considered. With the prototype, users can validate the functional properties of the business process model in an interactive way. We use a real-world example as a case study: the business process of the first delivery of mortgage archive in a risk mitigation system of a bank.

In this paper, there are comprehensive analysis of the impact factors of fuel-gas mixing performance for gasoline direct injection engine, comparing improvements of fuel economy levels by adopting different techniques. Summarizing macroscopic parameters which impacts gasoline spray dispersion in the cylinder, such as the location of the nozzle arrangement, the number of nozzles, fuel injection pressure, nozzle hole distribution and so on. At the same time, giving deeply analysis many factors which influence evaporation rate of gasoline droplets, such as Sauter mean diameter (SMD), turbulence scale, temperature in cylinder. Such work come from conclusion of GDI engine development, which can provide theoretical basis for development of direct injection gasoline combustion system, guidance for optimizing combustion chamber and fuel injection system, and provide effective reference for engine calibration.

International audience; The Clock Constraint Specification Language (CCSL) provides expressions and relations to specify the time requirements and causal dependencies of systems. It was initially proposed, in the context of MARTE: the UML profile for Modeling and Analysis of Real-Time and Embedded Systems. In this paper, we propose a method to verify CCSL specifications. We give a formal state-based interpretation of a fundamental subset of CCSL clock constraints. Based on it, we translate a CCSL specification into a Promela model and feed the result into the model checker SPIN. Then we show some patterns for expressing the properties of the model and do the verification. A digital filter application is used as an example to illustrate the approach.

The principle of energy-saving in hydraulic hybrid vehicles was introduced. The characteristics of hybrid vehicles with electric or hydraulic were compared. Because of its higher power density and higher efficiency, hydraulic hybrid vehicle is more advantageous when working under the condition changed frequently such as city bus. Different arrangement forms of the hydraulic hybrid system were analyzed to explore their own characteristics and applications. Based on the batteries with high energy density and hydraulic accumulators with high power density, a kind of combination that include their respective advantage were proposed.

Thermally-induced errors are the major contributor to the inaccuracy of the high-precision machine tools. Limited by CNC system’s close-loop structure, the present thermal error compensations are difficult to be embedded in CNC system and only can be as an additional unit out of the CNC system. This paper designed and developed an embedded error compensation module based on the opening CNC system with field bus. The presented embedded error compensation module can exchange data with the CNC system main control module in real time, share the system resources and provide on-line temperature measurement and thermal model calculation. Then the position-dependent thermal error can be compensated by correct the coordinates command during CNC system interpolation progress. An experiment was carried out to verify the performance of the compensation system.

The study was undertaken to focus on investigating whether skin pressure caused by the cuff would affect the global autonomic nervous system (ANS) by means of heart rate variability (HRV) power spectral analysis and further objectively evaluate the physiological threshold of skin pressure on the female abdomen and lateral waist respectively. Eight healthy females were chosen as the subjects. HRV signals of each subject in a completely relaxed sitting condition were collected during 11 kinds of skin pressures (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30mmHg) caused by the cuff on the abdomen and lateral waist. Three frequency-domain parameters of HRV: normalized low frequency component (LFnorm), normalized high frequency component (HFnorm) and the ratio of low frequency component to high frequency (LF/HF) were evaluated. Results showed that for the female abdomen, HFnorm was increased and LFnorm was declined before the skin pressure was up to 15mmHg. When the skin pressure was more than 15mmHg, HFnorm went down and LFnorm went up gradually. It suggested that parasympathetic autonomic nervous system (PNS) activity was stimulated when skin pressure at the abdomen was increased by degrees. But after the skin pressure exceeded 15mmHg, PNS activity came to be weak. For the female lateral waist, both HFnorm and LFnorm were not significantly varied before the skin pressure was up to 18mmHg. But when the skin pressure exceeded 18mmHg, HFnorm was obviously declined and LFnorm was increased. This finding indicated that PNS activity was significantly inhibited when skin pressure at the lateral waist exceeded 18mmHg. Therefore, in order to reduce the negative effect of skin pressure on the female body, the skin pressure should be less than 15mmHg on the abdomen and less than 18mmHg on the lateral waist.

Mobile communication follows the development trend of internet, users can link with mobile software stores through internet, and download all kinds of applications, which has provided human beings with more diversified information application forms, and gradually changed people's living habits and operation mode. It is indicated by research reports that growth rate of global smart phone in 2010 is as high as 78.1%, showing that smart phone gradually becomes the mainstream in mobile phone market. Smart phone has the function of installing applications, provides users with more diversified mobile value-added services and will change the use habits in the future. In this study, motives of mobile application users and their degree of gratifications after use are preliminarily discussed based on the uses and gratifications theory, using the questionnaire survey method and with the main purpose of investigating the users with the experience of using mobile applications, and understanding their demographic characteristics, use motives of mobile applications, and degree of gratifications after use. Altogether 591 effective questionnaires are recovered, and data are analyzed with the statistical methods such as descriptive statistics, t-test, and oneway ANOVA. It is discovered through research that female users of mobile applications are more than male ones, most of them are students aged 20–29, have educational level of university (college), and monthly income below 625 US dollars. In respect of the use behavior, most users have use experience of more than five years, they most often use built-in applications within the mobile phone, download applications from mobile forums, usually use less than one hour in one day, and most often use game/entertainment applications in their workplaces or schools. And the main motive that mobile application users can obtain maximum gratification is “relaxing and relieving stress.”