Determination of freight transport terminal location by using the AHP method for sustainable urban development

The rapid economic growth in Samarinda encourages higher demand for logistics that required vehicles and infrastructure for freight transport. The movement of goods in Samarinda is not supported yet by strategic freight transport. This cause many of freight transport parked and unload along the roadside. Writers did some research to determine the best location for freight transport terminal development using an AHP (Analytical Hierarchy Process) method. AHP (Analytical Hierarchy Process) method combines several considerations such as accessibility, traffic performance, regional pattern of transportation to reach multi criteria problem solving. The result of the analysis points out the score for Simpang Pasir Regency which is, the evaluation quality of accessibility 64%, traffic performance 23%, and regional pattern of transportation 12%. With that result Simpang Pasir Regency is chosen as the best alternative location for freight transport terminal development.

Regional distribution of anoxic brain injury after cardiac arrest: clinical and electrographic correlates

Introduction Disorders of consciousness, EEG background suppression and epileptic seizures are associated with poor outcome after cardiac arrest. The underlying patterns of anoxic brain injury associated with each remain unknown. Our objective was to identify the distribution of anoxic brain injury after cardiac arrest, as measured with diffusion MRI, and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures. Methods We analyzed patients from a prospectively-maintained, single-center database of unresponsive patients who underwent diffusion-weighted MRI following cardiac arrest (n = 204). We classified each patient based on recovery of consciousness (command-following) before discharge, the most continuous EEG background (burst suppression versus continuous), and the presence or absence of seizures. Anoxic brain injury was measured using the apparent diffusion coefficient (ADC) signal. We identified abnormalities in ADC relative to control subjects without cardiac arrest (n = 48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each clinical and EEG variable. Finally, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures. Results Compared to control subjects, cardiac arrest patients demonstrated a reduction in the ADC signal that was most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes, but also in the basal ganglia, medial thalamus and cerebellar nuclei. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter. Discussion Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Collectively, our results suggest that the regional pattern of anoxic brain injury is relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis.

FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES

The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.