Plastic Hinging Behavior of Reinforced Concrete Bridge Columns

The location of inelastic deformations in reinforced concrete bridge columns has been examined to simulate the nonlinear response of bridge columns and estimate the ultimate displacement capacity. In bridge columns, these nonlinear deformations generally occur over a finite hinge length. A model of hinging behavior in reinforced concrete bridge columns will help guide proportioning, detailing and drift estimates for performance-based design. Data was collected during the NEESR investigation of the seismic performance of four-span large-scale bridge systems at the University of Nevada Reno that details deformations in column hinging regions during response to strong shaking events. In order to evaluate the plastic hinging regions, a photogrammetric method was used to remotely track deformations of the concrete surface in the joint regions. The surface deformations and rotations of a reinforced concrete bridge column under dynamic loading has been examined and compared with the results obtained from traditional instruments. This research utilized the experimental data from photogrammetry measurements of bridge column deformations to create a finite element model that realistically represents hinging behavior in a reinforced concrete bridge pier. The three dimensional finite element model of one column was defined with the cap beam on the top of the column and the footing system under the circular column using ABAQUS Finite Element software. The results of the FE model of the bridge column under dynamic loading were obtained and compared with the photogrammetric measurements as well as the data from the traditional instrumentations. Two plastic hinge length expressions for reinforced concrete bridge columns under static and dynamic loadings have been developed by studying the available test results in the literature. Many of the previous tests were conducted using the static loading and for small-scale components. A few of the tests focused on bridge columns and dynamic loading. Expressions that have been developed to estimate the plastic hinge lengths have either been based on the maximum drift at the top of the column, or the spread of plasticity in the hinging regions. An expression to calculate the maximum drift capacity of a bridge column in double curvature has been derived by considering the deformations due to flexure as influenced by the definition of plastic hinge length (lp), and the bond-slip effect of the longitudinal reinforcement at the connections. Drift capacity of a bridge column, which corresponds to a 20% reduction in lateral load capacity on the descending branch of the response backbone curve, has been estimated using the new expression and compared with the results that were obtained from the earlier plastic hinge length expressions. The measured drift of the bridge column from the four-span large-scale bridge system test was also compared with the calculated responses from the expressions. The proposed equations provide the best estimate of plastic hinge length for reinforced concrete bridge columns

Modeling Surface Deformations and Hinging Regions in Reinforced Concrete Bridge Columns

A high-resolution model of a bridge column was developed using the computer program ABAQUS and the accuracy of the model was evaluated for the displacement field and the rotations of a bridge system subjected to biaxial shake-table loading. The effect of simulation parameters (reinforcing bar slip within the joint and stiffness degradation of the concrete) was studied to determine the goodness-of-fit of the displacement and rotation fields recorded during the dynamic response. A Fourier Domain Error Index analyses showed that yield stress of the reinforcement and the boundary conditions of the column submodel were important parameters, and the damage and stiffness degradation parameters were not as important for the goodness-of-fit of the finite element model. The computed rotations at the plastic hinge regions near the beam caps had the best correlation

A Rare Case of Facial Steatocystoma Multiplex

Abstract Steatocystoma multiplex (SM) is a rare hereditary condition of the skin that manifests with multiple cystic lesions. Most commonly, it occurs during adolescence and early adulthood when the sebaceous activity peaks. The lesions are mostly observed on the trunk, neck, arms, axilla, groins and scalp where the pilosebaceous activity is intense. This condition may involve the face together with the body; however, predominant or isolated involvement of the face represents a rare condition. This case is presented as it was interesting, considering the age of the patient and the rare localization.  </p

Multi-object segmentation using coupled nonparametric shape and relative pose priors

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes

Prevalence of sleep disorders in the Turkish adult population epidemiology of sleep study

Sleep disorders constitute an important public health problem. Prevalence of sleep disorders in Turkish adult population was investigated in a nationwide representative sample of 5021 Turkish adults (2598 women and 2423 men, response rate: 91%) by an interviewer‐administered questionnaire. Insomnia was defined by the DSM‐IV criteria, habitual snoring and risk for sleep‐related breathing disorders (SDB) by the Berlin questionnaire, excessive daytime sleepiness (EDS) by the Epworth sleepiness scale score, and restless legs syndrome (RLS) by the complaints according to the International Restless Legs Syndrome Study Group criteria. Mean age of the participants was 40.7 ± 15.1 (range 18 to 90) years. Prevalence rates (men/women) were insomnia 15.3% (10.5%/20.2%; P &lt; 0.001), high probability of SDB 13.7% (11.1%/20.2%; P &lt; 0.001), EDS 5.4% (5.0%/5.7%; P: 0.09), RLS 5.2% (3.0%/7.3%; P &lt; 0.001). Aging and female gender were associated with higher prevalence of sleep disorders except for habitual snoring. Prevalence rates of the sleep disorders among Turkish adults based on the widely used questionnaires were close to the lower end of the previous estimates reported from different parts of the world. These findings would help for the assessment of the health burden of sleep disorders and addressing the risk groups for planning and implementation of health care

A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

Medication errors are common, fatal, costly but preventable. Location of drugs on the shelves and wrong drug names in prescriptions can cause errors during dispensing process. Therefore, a good drug-shelf arrangement system in pharmacies is crucial for preventing medication errors, increasing patient’s safety, evaluating pharmacy performance, and improving patient outcomes. The main purpose of this study to suggest a new drug-shelf arrangement for the pharmacy to prevent wrong drug selection from shelves by the pharmacist. The study proposes an integrated structure with three-stage data mining method using patient prescription records in database. In the first stage, drugs on prescriptions were clustered depending on the Anatomical Therapeutic Chemical (ATC) classification system to determine associations of drug utilizations. In the second stage association rule mining (ARM), well-known data mining technique, was applied to obtain frequent association rules between drugs which tend to be purchased together. In the third stage, the generated rules from ARM were used in multidimensional scaling (MDS) analysis to create a map displaying the relative location of drug groups on pharmacy shelves. The results of study showed that data mining is a valuable and very efficient tool which provides a basis for potential future investigation to enhance patient safety

Content Structure of University Radio Stations in Turkey As Part of Their Broadcast Policy

The aim of this study was to examine to what extent university radio stationsin Turkey are an alternative to popular, commercial radio stations by analyzingthe program content and general approaches within the framework of theirmusic and program policies. Using the qualitative research method, in-depthinterviews were carried out with the employees and representatives of 25 publicand 9 foundation university radio stations across various regions and differentcities of Turkey. Twenty university radio stations were examined on site by directobservations and face-to-face interviews, and we interviewed the representativesof 14 university radio stations via telephone or video communication. Allinterviewees were asked the same semistructured questions. Furthermore, thesupervisors of the 11 sample radio stations who archive their programs regularlyselected some of the programs and shared their archives with the researchers.These selected programs were analyzed in terms of the duration of talk andmusic, and categorized according to the production elements. It was found thatuniversity radio stations in Turkey have problems with content production forvarious reasons and are exposed to certain restrictions. Therefore, in contrast tointernational practices and the existing literature, they have content similar to thatof commercial radio stations, rather than broadcasting alternative content

Neighborhood resolved fiber orientation distributions (NRFOD) in automatic labeling of white matter fiber pathways

Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles generally involves manual selection of regions of interest by an expert, which is subject to user bias and fatigue, hence an automation is desirable. To that end, we first present a novel anatomical representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers. The resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. A fiber-to-fiber distance measure between the proposed fiber representations is then used in a density-based clustering framework to select the clusters corresponding to the major pathways of interest. In addition, neuroanatomical priors are utilized to constrain the set of candidate fibers before density-based clustering. The proposed fiber clustering approach is exemplified on automation of the reconstruction of the major fiber pathways in the brainstem: corticospinal tract (CST); medial lemniscus (ML); middle cerebellar peduncle (MCP); inferior cerebellar peduncle (ICP); superior cerebellar peduncle (SCP). Experimental results on Human Connectome Project (HCP)’s publicly available “WU-Minn 500 Subjects + MEG2 dataset” and expert evaluations demonstrate the potential of the proposed fiber clustering method in brainstem white matter structure analysis