KNOWLEDGE AND ATTITUDES OF REGISTERED NURSES ON PALLIATIVE CARE

The purpose of this DNP scholarly project (SP) was to evaluate the knowledge and attitudes of registered nurses (RN) regarding palliative care (PC) before and after an educational presentation. This SP strived to answer these questions: What is the current knowledge in RNs of PC and end of life (EOL) patients before and after the education presentation? What is the anxiety level RNs feel regarding medication administration and symptom management in PC and EOL patients before and after the educational presentation? A multimethodology, cross-sectional study design was used. The participants were currently enrolled in the RN-BSN program at PSU. These nurses were licensed and working. The instrument used was the Palliative Care Knowledge Test. A pre-test was given to determine the nurses’ current knowledge regarding PC and their opinions and beliefs about EOL care. Basic demographics was collected: age, gender, area and years of nursing practice, state of current nursing practice, and if the RN has received PC education. A PowerPoint was included for participants regarding PC and the medications used for the symptoms experienced during EOL. A post-test collected quantitative data to determine if the educational presentation changed the nursing students’ opinions, beliefs, comfort level, and knowledge with PC. A resource guide on PC and the medications often ordered during this time was provided to the RNs following the post-test

Study of analgesic effect of septilin in comparison with aspirin and tramadol in experimentally induced pain in rats

Background: The task of medicine is to preserve and restore health and to relieve suffering. pain is the most common symptom that brings a patient to a physician's attention. There are many NSAIDs and opioid analgesics which have been employed for alleviating pain but none is free from side effects and drug interactions. Therefore search for a new potent, safe and non toxic drug continues.Methods: This animal study was a cross sectional, observational in nature carried out in male Wistar rats. Animals were divided into four groups with six animals in each group. Group I, II, III, IV given normal saline (control), Tab. Septilin (test), Tab. Aspirin (standard 1),Tab. Tramadol (standard2) respectively through oral feeding tube and tested for thermal reaction time on Eddys hot plate at different time intervals. Reaction times had been recorded and evaluated by using one way ANOVA followed by Tukey’s post hoc test with the help of Graph pad Prism. The ‘P’ value of <0.05 was considered significant.Results: The test group (Septilin- 500mg/kg) has shown increase in mean reaction time which is higher, when compared to control group and the increase in mean reaction time of test drug is less when compared to standard groups (Aspirin-100mg/kg and Tramadol- 2mg/kg), but is significant i.e. P value <0.05 at 30, 60, 120 min time intervals. Based on the aforementioned findings septilin has analgesic effect but less than that of the standard drugs Aspirin and Tramadol.Conclusions: The test drug Septilin has analgesic effect, but less when compared to Standard drugs Aspirin and Tramadol. To obtain the final assessment, large scale clinical trials may be necessary to prove its efficacy and further use of Septilin in a routine medical practice

Automatic extraction of faults and fractal analysis from remote sensing data

Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS)

Data Extraction as Input for the Energy analysis of an urban district with UMI.

The energy usage of an urban district has become a major subject of study and interest since we observed a significant expansion of cities caused by the movement of inhabitants. Sustainability of urban areas is mainly related to interactions between street patterns and building distances. Monitoring these connections in terms of energy flows creates possibilities of optimizing building construction and retrofitting. There are many tools used to make this possible.In this paper, we want to demonstrate how it is possible to import the 3D structure of an urban area recorded in an SHP file into the Urban Modeling Interface software. The data extraction protocol we developed to this aim principally consists in approximating clusters of XYZ coordinates into a set of boxes with minimum loss in geometry, orientation, and position of buildings. Estimations of energy consumption and CO2 are among the outcomes we were able to obtain from imported data into UMI. Using the developed data extraction strategy, we can potentially analyze the energy usage of an entire city

Urban Smart Shading Devices based on Traditional Gulf Design. Case study located in a district of a hot-arid climate city (Abu Dhabi).

In a city with hot arid weather such as Abu Dhabi, creating outdoor shading is a main concern. This not only a strategy to improve the outdoor comfort but also a very efficient solution to mitigate the urban heat island effect. In previous studies, we analyzed the extensive deployment of shading devices in 5 different districts in the main island of Abu Dhabi. In the present study, our aim is to show how innovation and tradition can be merged. Innovation is all encompassing concept but in our case the basis of innovation is its tradition. In the past, the Gulf countries built cities with narrow streets covering some of them partially. The Mashrabiya is a well-known concept used in the traditional architecture. If an innovative intervention is made in an urban context it has to be connected to the culture of the city and its roots. A system of smart shading devices based on traditional geometric design is a sustainable solution and also contributes to the education of younger generations. The proposed smart shading devices are made of tent with Photovoltaic (PV) cells integrated in it. This choice stems from the flexibility in shape that such a material can offer. We will establish a comparison between current commercially materials and our solution in terms of efficiency and life-cycle energy/cost. The market offers mainly tent made of PTFE with steel structure. A random design used in specific parking lots and small parks areas downtown Abu Dhabi. In this paper we bring alternative solutions in terms of efficiency and flexibility and also describe the different design options that we tested and how the optimal design was selected

Geographic object-based image analysis

The field of earth observation (EO) has seen tremendous development over recent time owing to the increasing quality of the sensor technology and the increasing number of operational satellites launched by several space organizations and companies around the world. Traditionally, the satellite data is analyzed by only considering the spectral characteristics measured at a pixel. The spatial relations and context were often ignored. With the advent of very high resolution satellite sensors providing a spatial resolution of ≤ 5m, the shortfalls of traditional pixel-based image processing techniques became evident. The need to identify new methods then led to focusing on the so called object-based image analysis (OBIA) methodologies. Unlike the pixel-based methods, the object-based methods which are based on segmenting the image into homogeneous regions use the shape, texture and context associated with the patterns thus providing an improved basis for image analysis. The remote sensing data normally has to be processed in a different way to that of the other types of images. In the geographic sense OBIA is referred to as Geographic Object-Based Image Analysis (GEOBIA), where the GEO pseudo prefix emphasizes the geographic components. This thesis will provide an overview of the principles of GEOBIA, describe some fundamentally new contributions to OBIA in the geographical context and, finally, summarize the current status with ideas for future developments

AI-Based Novel Camera System with Software Defined Radio Transceiver On-board

Standard RGB camera combines the number of pixels to perform RGB color, so for having each color represented in each pixel separately, three different cameras are required with different band pass filters that allow only the required color signal to pass into the sensor. The most complicated part of developing such a system is the calibration of the cameras and building a system that can read out from different sensors and integrate the result. The other challenge in the CubeSat is the limited access time and the restricted data rate speed. Also, the high speed of the CubeSat leads to have so blurring images and some other noisy images. Moreover, in some cases where it is cloudy, the captured images have cloud-only. Thus, having an intelligent system that can classify the images using machine learning algorithms as valuable images for downlink or not valuable. Valuable images mean they are clear, no cloud, and not blurring

The Role of Small Satellites in the Establishment of the Gulf Region\u27s First Graduate Level Space Studies Program

Yahsat, Northrop Grumman, and Khalifa University created the Gulf region\u27s first master\u27s level advanced studies space program. To date this program has graduated three classes of master\u27s students and received accolades from the UAE Space Agency and Abu Dhabi\u27s Mubadala Investment Company. The program\u27s primary goal is to develop the resources and work force that the UAE requires to establish itself as a space-faring nation. Integral to this program are small satellites, initially used to train and educate the students and ultimately growing to accommodate new technologies and scientific payloads developed in the UAE. The first of these small satellites, a 1U CubeSat named MYSat-1 was launched in November of 2018 and deployed from the Northrop Grumman Cygnus on 13 February, 2019. In this paper we present the role small satellites played in the establishment of this program. We discuss the challenges of establishing a satellite program at a university without a formal aerospace curriculum and how the small satellite became the anchor project for the student development. In this context, we explore the advantages of making use of the broadly established small satellite COTS component marketplace relative to the didactical benefits to be gained from having the students develop the new hardware in-house. Finally, we review the process of setting up a new small satellite lab established to be used as the primary resource for developing and testing small satellites in the country

GIS based Integration and Analysis of multiple source Information for Non-Proliferation Studies

In recent years the volume and variety of information that needs to be analysed in the context of non-proliferation have been increasing continuously Therefore, an integrated, all-source information analysis is paramount for an efficient and effective monitoring of the Non-Proliferation Treaty (NPT). The ¿Treaty Monitoring¿ workpackage of the LIMES research project addressed this issue by developing an integrated platform supporting the non-proliferation image analyst in verifying treaty compliance. The main benefits of the platform are (i) integrating information from multiple sources and time-frames, including satellite imagery, site models, open source information, reports, etc; (ii) improved information management using a GIS-based platform and (iii) enhanced methodologies for satellite image analysis. The platform components facilitate the analysis by highlighting changes and anomalies, which are potentially safeguards-relevant and by providing quantitative measurements which are not readily available from the images. It improves the efficiency and effectiveness of the information assessment by providing all-source integration capabilities, which allow to easily access supporting collateral information (e.g. Open Source information) from an image analysis task, an vice versa. The paper presents the components of the integration platform and the results of the demonstration which monitored the construction of a nuclear reactor in Olkiluoto, Finland.JRC.E.9-Nuclear security (Ispra

An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH) morphology and context) and graph-analysis algorithms (e.g., Dijkstra path). The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels