On the structural nature of cooperation in distributed network localization

We demonstrate analytically that the contribution of cooperation in improving the accuracy of distributed network localization has a fundamentally structural nature, rather then statistical as widely believed. To this end we first introduce a new approach to build Fisher Information Matrices (FIMs), in which the individual contribution of each cooperative pair of nodes is captured explicitly by a corresponding information vector. The approach offers new insight onto the structure of FIMs, enabling us to easily account for both anchor and node location uncertainties in assessing lower bounds on localization errors. Using this construction it is surprisingly found that in the presence of node location uncertainty and regardless of ranging error variances or network size, the Fisher information matrix (FIM) terms corresponding to the information added by node-to-node cooperation nearly vanish. In other words, the analysis reveals that the key contribution of cooperation in network localization is not to add statistical node-to-node information (in the Fisher sense), but rather to provide a structure over which information is better exploited

Crustal density structure of NW Iranian Plateau

We present a new 2D crustal-scale model of the northwestern Iranian plateau based on gravity–magnetic modeling along the 500 km long China–Iran Geological and Geophysical Survey in the Iranian plateau (CIGSIP) seismic profile across major tectonic provinces of Iran from the Arabian plate into the South Caspian Basin (SCB). The seismic P-wave receiver function (RF) model along the profile is used to constrain major crustal boundaries in the density model. Our 2D crustal model shows significant variation in the sedimentary thickness, Moho depth, and the depth and extent of intra-crustal interfaces. The Main Recent Fault (MRF) between the Arabian crust and the overriding central Iran crust dips at approximately 13° towards the northeast to a depth of about 40 km. The geometry of the MRF suggests about 150 km of underthrusting of the Arabian plate beneath central Iran. Our results indicate the presence of a high-density lower crustal layer beneath Zagros. We identify a new crustal-scale suture beneath the Tarom valley between the South Caspian Basin crust and Central Iran and the Alborz. This suture is associated with sharp variation in Moho depth, topography, and magnetic anomalies, and is underlain by a 20 km thick high-density crustal root at 35–55 km depth. The high-density lower crust in Alborz and Zagros may be related to partial eclogitization of crustal roots below about 40 km depth. The gravity and magnetic models indicate a highly extended continental crust for the SCB crust along the profile. Low observed magnetic susceptibility of the Kermanshah ophiolites likely indicates that the ophiolite rocks only form a thin layer that has been thrust over the sedimentary cover.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Studying the factors affecting job satisfaction of employees of Semnan health centers

Job satisfaction of employees is one of the most challenging concepts in any organization and is the basis for many of the policies and management strategies to increase productivity and efficiency. It is influenced by many factors including environmental and personal factors, income, nature and social status of the job, organizational prestige, promotion, job security, lack of role ambiguity, and physical job conditions Co-workers. This study was carried out to determine the factors affecting job satisfaction employees of Semnan health center's to meet the challenges and increase efficiency. This is a cross-sectional study carried out in 2013, over 93 employees with a employment history of over 2 years. Method and data collection was through job descriptive inventory (Job Descriptive Index); data was collected and analyzed using SPSS16. The results of questionnaire analysis in seven items regarding job satisfaction, show satisfaction supervision and leadership (77.2%), management policy (77%), job stability (63.4%), environmental conditions (59.6%), cross-relation between Co-workers (58.6%), salary and benefits (53.4%) and opportunities for growth, development and promotion (53.2%). Based on evaluated items, the overall job satisfaction in this study was moderate. Thus, in terms of job satisfaction, it seems that different factors affective in different ratios. Job satisfaction is not only very important by itself, but also important concerning its impact on other aspects of the job attitude. In fact, by increasing job satisfaction, other aspects of job attitudes such as organizational commitment, job involvement and organizational atmosphere can be improved. This point can also be seen in proposed models and research carried out by professionals in this field.

Creating Interpretable Data-Driven Approaches for Remote Health Monitoring

The impending "age wave'' underscores the urgent need for technology that supports remote health monitoring. This research focuses on developing technologies to improve clinician interpretability of remote health monitoring systems. These technologies visualize and summarize raw data and machine learning inferences. Unlike prior methods, this dissertation describes techniques developed in partnership with the target end user: clinicians. Keeping the clinician in the design loop improves technology trust, adoption, and patient outcomes.In this research, we introduce two visualization studies that illustrate and validate the human-in-the-loop approach, demonstrating that with this technique clinicians can successfully identify health events from unconventional data sources, such as smart homes and smartwatches. To complement these studies, the research also introduce methods to create machine learning models that are interpretable for humans. Here, we focus on discovering and communicating patterns that summarize complex data and justify machine learning decisions. All of the introduced algorithms yield improved objective performance on synthetic and real-world complex data. To evaluate their efficacy for remote health monitoring, the methods are assessed by clinician end users in a series of comparative surveys. In these experiments, the methods introduced in this dissertation show significantly enhanced interpretability. We discuss the implications and limitations of this work for remote health monitoring systems and offer ideas for continued work

Creating Interpretable Data-Driven Approaches for Remote Health Monitoring

We are at a turning point to address the unprecedented challenges we are facing in healthcare systems. With the aging of the population and increasing health disparities in rural areas, healthcare needs assistance from technologies to provide quality care for these populations. In collaboration with clinicians, we seek to meet this need by creating data-driven methods that provide interpretable healthcare models from ubiquitous ambient and wearable sensor data. My doctoral research goal is to introduce novel ways to help clinicians understand patients' health status by developing new visualization tools and interpretable models that analyze human health and behavior from sensor data

Road Extraction and Car Detection from Aerial Image Using Intensity and Color

In this paper a new automatic approach to road extraction from aerial images is proposed. The initialization strategies are based on the intensity, color, and Hough transform. After road elements extraction, chain codes are calculated. In the last step, using shadow, cars on the roads are detected. We implemented our method on the 25 images from "Google Earth" database. The experiments show an increase in both the completeness and the quality indexes for the extracted road

Remote Monitoring of the Performance Status and Burden of Symptoms of Patients With Gastrointestinal Cancer Via a Consumer-Based Activity Tracker: Quantitative Cohort Study

BackgroundThe number of older patients with gastrointestinal cancer is increasing due to an aging global population. Minimizing reliance on an in-clinic patient performance status test to determine a patient’s prognosis and course of treatment can improve resource utilization. Further, current performance status measurements cannot capture patients' constant changes. These measurements also rely on self-reports, which are subjective and subject to bias. Real-time monitoring of patients' activities may allow for a more accurate assessment of patients’ performance status while minimizing resource utilization. ObjectiveThis study investigates the validity of consumer-based activity trackers for monitoring the performance status of patients with gastrointestinal cancer. MethodsA total of 27 consenting patients (63% male, median age 58 years) wore a consumer-based activity tracker 7 days before chemotherapy and 14 days after receiving their first treatment. The provider assessed patients using the Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scale and Memorial Symptom Assessment Scale-Short Form (MSAS-SF) before and after chemotherapy visits. The statistical correlations between ECOG-PS and MSAS-SF scores and patients’ daily step counts were assessed. ResultsThe daily step counts yielded the highest correlation with the patients' ECOG-PS scores after chemotherapy (P<.001). The patients with higher ECOG-PS scores experienced a higher fluctuation in their step counts. The patients who walked more prechemotherapy (mean 6071 steps per day) and postchemotherapy (mean 5930 steps per day) had a lower MSAS-SF score (lower burden of symptoms) compared to patients who walked less prechemotherapy (mean 5205 steps per day) and postchemotherapy (mean 4437 steps per day). ConclusionsThis study demonstrates the feasibility of using inexpensive, consumer-based activity trackers for the remote monitoring of performance status in the gastrointestinal cancer population. The findings need to be validated in a larger population for generalizability

Surveying Information Ethics in Quran: a Quantitative Content Analysis

The purpose of this paper was to survey the quantity of information ethics. It sought to answer the question whether or not this information exists in Quranic verses. In this paper, the entire Quran was identified and analyzed with regard to information ethics from the perspective of Quranic scholars, experts and librarians. After various phases of coding and categorization, the Quranic themes were categorized on the basis of their Persian translation and then were analyzed by SPSS software. For this study, 6236 verses of the Holy Quran were studied out of which 171 verses including information ethics context were coded and classified. After that, each of them was analyzed in relation to the main research question. As the holy Quran has been revealed by God, it could be called a complete declaration of information ethics. Conclusion: Man is by nature a social creature requiring interaction. Based on this kind of life, an ethical framework is needed in order to continue his life in peace. The issue of ethics is one of the basic issues with which man is grappling throughout his daily life, which is, in itself, increasingly moving towards the production of information and knowledge. Hence, if these information are void of ethical aura, the result would be the loss of human and social relations as well as the disappearance of the human rac