Assessing Large-Scale Power Relations among Locations from Mobility Data

The pervasiveness of smartphones has shaped our lives, social norms, and the structure that dictates human behavior. They now directly influence how individuals demand resources or interact with network services. From this scenario, identifying key locations in cities is fundamental for the investigation of human mobility and also for the understanding of social problems. In this context, we propose the first graph-based methodology in the literature to quantify the power of Point-of-Interests (POIs) over its vicinity by means of user mobility trajectories. Different from literature, we consider the flow of people in our analysis, instead of the number of neighbor POIs or their structural locations in the city. Thus, we modeled POI’s visits using the multiflow graph model where each POI is a node and the transitions of users among POIs are a weighted direct edge. Using this multiflow graph model, we compute the attract, support, and independence powers . The attract power and support power measure how many visits a POI gathers from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of a POI to receive visitors independently from other POIs. We tested our methodology on well-known university campus mobility datasets and validated on Location-Based Social Networks (LBSNs) datasets from various cities around the world. Our findings show that in university campus: (i) buildings have low support power and attract power ; (ii) people tend to move over a few buildings and spend most of their time in the same building; and (iii) there is a slight dependence among buildings, even those with high independence power receive user visits from other buildings on campus. Globally, we reveal that (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same continent have similar independence patterns; and (iii) places with a high number of visitation and city central areas are the regions with the highest degree of independence.

The Influence of Kinematic Conditions and Variations in Component Positioning on the Severity of Edge Loading and Wear of Ceramic-on-Ceramic Hip Bearings

Dynamic separation and direct edge loading of hip replacement bearings can be caused by many factors, including implant positioning, implant design, changes in device over time, surgical variations and patient variations. Such dynamic separation and direct edge loading can lead to increased wear. Different input kinematic conditions have been used for experimental hip simulator studies to produce clinically relevant elliptical contact wear paths between the bearings during gait. The aim of this study was to investigate the influence of input kinematics (two axes of rotation simulation conditions (without abduction/adduction) and three axes of rotation simulation conditions (with abduction/adduction and different loading profiles) and variations in component positioning (different levels of medial-lateral translational mismatch at standard and steep cup inclination angles) on the occurrence, severity of edge loading, dynamic separation and wear of size 36 mm ceramic-on-ceramic hip bearings on an electromechanical hip joint simulator. The results showed that, overall, either of the two axes or three axes input profiles were equally valid in providing a suitable preclinical testing method for assessing the occurrence and severity of edge loading and wear under edge loading conditions. In terms of component positioning, as cup inclination and medial-lateral translational mismatch increased, so did dynamic separation, axial load at the rim, severity of edge loading and wear.

Edge detection Using Histogram Localization

Detection of edges under noisy environments has been gaining lot of prominence in the recent past in most of the image and video processing applications. In this work a novel approach based on the distribution of intensity values and their corresponding positions has been proposed for distinguishing the edge pixels from the grey scale images. Separate histogram has been maintained for X and Y coordinates. The first order derivative is applied over these histograms to distinguish the edge pixels. The pixel with gradient distribution below a specific threshold value is selected as an edge pixel. This method is found to work well in case of both noiseless and noisy images. Hence this method is able to perceive the underlying information in case of noisy images also. The proposed algorithm can be used for both low and high resolution images. However, the performance of the algorithm is more evident in high resolution image. A general analysis of the proposed method has been conducted for arbitrary images. The major application of the proposed work can be used for the applications that doesn’t need any preprocessing or to avoid any loss of information like in medical image analysis as it contemplate towards every intensity bin to trace the edges present in the histogram of the image rather than the overall image concerning for direct edge tracing. The results have been compared with canny algorithm which is most commonly used for edge detection.

Design and fabrication of multi-cavity injection mold using hot runners and direct edge gates

A hot runner system can provide many advantages to plastic injection mold engineers for improving product quality. In edge gate systems in particular, the gate traces can appear on the side of products rather than the top. However, it is difficult to establish hot runner systems using edge gates because of their structural differences from conventional gate systems. This article presents the entire process of preparing a 48-cavity plastic injection molding system with edge gates. This process consists of 48-cavity injection mold design, structural analysis, verification of design plans, filling analysis of multi-cavity, cooling channel design on the basis of cooling analysis, fabrication of the mold system, and test injection. All presented computer-aided engineering analyses were conducted using ANSYS and MoldFlow.