Spiritual Pathology Theory of the Sound Heart Model: Socio-Cultural Factors of Spiritual Distress

Introduction: Spiritual Pathology is the study of disturbing factors in the relationship between man and God, the process of disruption in secure attachment to God, which causes spiritual distress in relation to self, people, and the world of creation.Areas covered: Balbi defines attachment as deep emotional bond with special people in life, whose interaction brings security, joy and happiness, and their presence brings peace in times of stress. Kirkpatrick generalized the style of attachment to parents to attachment to God. Expert opinion: In the Sound Heart Model, worship is natural need. The basis of religion is secure attachment to God. Secure attachment to God is belief in the presence and sufficiency of God, as a responsive safe haven. The basis of attachment to God is positive image of God and “recognizing the truth of religion” by the Prophet. The Prophet, as interpreter of Qur’an and spiritual role-model, has healthy spiritual personality. Acceptance of religion, at the time of intellectual maturity, should be done without imitation and coercion, based on knowing the truth of religion, with a free and informed choice. Spiritual pathology is the study of socio-cultural factors that cause misunderstanding of religion, negative image of God and insecure attachment to God

Decision Support Systems and Their Role in Rationalizing the Production Plans: A Case Study on a Plant in Najaf

This research focuses on how to analyze production plans based on quantitative indicators, enabling managers to produce plans that produce the results that help make full use of resources to achieve the company’s goals, maximize profits, and reduce costs to the lowest possible level. These concepts covered in this research, presented in three parts. The first part covers the scientific methodology and literature review, the second part describes the theoretical side, including presentation and analysis of DSS and the concepts of sensitivity analysis and production planning, and the third part covers the application side, applying the discussed measurements in an organization to achieve results, and recommendations

Long-term Tracking in the Wild: A Benchmark

We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this "short-term" scenario, which is poorly representative of practitioners' needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 hours of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild". The project website is http://oxuva.netComment: To appear at ECCV 201

Non-Causal Tracking by Deblatting

Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects. We propose non-causal Tracking by Deblatting which estimates continuous, complete and accurate object trajectories. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are fitted to segments, which are parts of the trajectory separated by bounces. The output is a continuous trajectory function which assigns location for every real-valued time stamp from zero to the number of frames. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity or sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall and velocity estimation.Comment: Published at GCPR 2019, oral presentation, Best Paper Honorable Mention Awar

Kinematic deprojection and mass inversion of spherical systems of known velocity anisotropy

Traditionally, the mass / velocity anisotropy degeneracy (MAD) inherent in the spherical, stationary, non-streaming Jeans equation has been handled by assuming a mass profile and fitting models to the observed kinematical data. Here, the opposite approach is considered: the equation of anisotropic kinematic projection is inverted for known arbitrary anisotropy to yield the space radial velocity dispersion profile in terms of an integral involving the radial profiles of anisotropy and isotropic dynamical pressure. Then, through the Jeans equation, the mass profile is derived in terms of double integrals of observable quantities. Single integral formulas for both deprojection and mass inversion are provided for several simple anisotropy models (isotropic, radial, circular, general constant, Osipkov-Merritt, Mamon-Lokas and Diemand-Moore-Stadel). Tests of the mass inversion on NFW models with these anisotropy models yield accurate results in the case of perfect observational data, and typically better than 70% (in 4 cases out of 5) accurate mass profiles for the sampling errors expected from current observational data on clusters of galaxies. For the NFW model with mildly increasing radial anisotropy, the mass is found to be insensitive to the adopted anisotropy profile at 7 scale radii and to the adopted anisotropy radius at 3 scale radii. This anisotropic mass inversion method is a useful complementary tool to analyze the mass and anisotropy profiles of spherical systems. It provides the practical means to lift the MAD in quasi-spherical systems such as globular clusters, round dwarf spheroidal and elliptical galaxies, as well as groups and clusters of galaxies, when the anisotropy of the tracer is expected to be linearly related to the slope of its density.Comment: Accepted in MNRAS. 19 pages. Minor changes from previous version: Table 1 of nomenclature, some math simplifications, paragraph in Discussion on alternative deprojection method by deconvolution. 19 pages. 6 figure

Online, Real-Time Tracking Using a Category-to-Individual Detector

A method for online, real-time tracking of objects is presented. Tracking is treated as a repeated detection problem where potential target objects are identified with a pre-trained category detector and object identity across frames is established by individual-specific detectors. The individual detectors are (re-)trained online from a single positive example whenever there is a coincident category detection. This ensures that the tracker is robust to drift. Real-time operation is possible since an individual-object detector is obtained through elementary manipulations of the thresholds of the category detector and therefore only minimal additional computations are required. Our tracking algorithm is benchmarked against nine state-of-the-art trackers on two large, publicly available and challenging video datasets. We find that our algorithm is 10% more accurate and nearly as fast as the fastest of the competing algorithms, and it is as accurate but 20 times faster than the most accurate of the competing algorithms

Molecular detection of Mycobacterium tuberculosis in pulmonary and extrapulmonary samples in a hospital-based study

Objective: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a deadly infectious disease. India contributes to one-third of the global TB burden. However, no studies have been carried out in the Telangana (Hyderabad) population using real-time polymerase chain reaction (RT-PCR). Therefore, the current study evaluated the role of RT-PCR as a rapid and non-invasive test to diagnose TB by testing for pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB). Materials and methods: This hospital-based study examined 1670 samples (900 EPTB; 770 PTB) comprising tissue (n = 537), peritoneal fluid (n = 420), sputum (n = 166), bronchial fluid (n = 126), cerebrospinal fluid (n = 145), ascetic fluid (n = 76), sputum pus (n = 78), urine (n = 79), and bronchoalveolar fluid (n = 43) samples. DNA from samples was separated using specific isolation kits and subjected to RT-PCR. Results: In this study, we enrolled 1670 subjects and categorized 54.4% as females and 45.6% as males. The collected samples were categorized as 48.5% of fluid samples, followed by tissue (32.2%), sputum (9.9%), urine (4.7%), and pus-swab (4.6%). RT-PCR analysis revealed that 4.7% patients were positive for Mtb. Our results revealed that 61% of the affected patients were male and 39% were female. Among the specimen types, tissue samples gave the highest proportion of positive results (36.3%). Conclusion: The results showed that RT-PCR should be implemented and given top priority in TB diagnosis to save time and facilitate a definitive diagnosis. Tissue samples are highly recommended to screen the Mtb through the technique RT-PCR. Future studies should extend the technique to the global population and exome sequencing analysis should be performed to identify TB risk markers

Multifactorial Origins of Heart and Gut Defects in nipbl-Deficient Zebrafish, a Model of Cornelia de Lange Syndrome

nipbl-deficient zebrafish provide evidence that heart and gut defects in Cornelia de Lange Syndrome are caused by combined effects of multiple gene expression changes that occur during early embryonic development

Online Learning for 3D LiDAR-based Human Detection: Experimental Analysis of Point Cloud Clustering and Classification Methods

This paper presents a system for online learning of human classifiers by mobile service robots using 3D~LiDAR sensors, and its experimental evaluation in a large indoor public space. The learning framework requires a minimal set of labelled samples (e.g. one or several samples) to initialise a classifier. The classifier is then retrained iteratively during operation of the robot. New training samples are generated automatically using multi-target tracking and a pair of "experts" to estimate false negatives and false positives. Both classification and tracking utilise an efficient real-time clustering algorithm for segmentation of 3D point cloud data. We also introduce a new feature to improve human classification in sparse, long-range point clouds. We provide an extensive evaluation of our the framework using a 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments demonstrate the influence of the system components and improved classification of humans compared to the state-of-the-art