Black holes and asymptotically safe gravity

Quantum gravitational corrections to black holes are studied in four and higher dimensions using a renormalisation group improvement of the metric. The quantum effects are worked out in detail for asymptotically safe gravity, where the short distance physics is characterized by a non-trivial fixed point of the gravitational coupling. We find that a weakening of gravity implies a decrease of the event horizon, and the existence of a Planck-size black hole remnant with vanishing temperature and vanishing heat capacity. The absence of curvature singularities is generic and discussed together with the conformal structure and the Penrose diagram of asymptotically safe black holes. The production cross section of mini-black holes in energetic particle collisions, such as those at the Large Hadron Collider, is analysed within low-scale quantum gravity models. Quantum gravity corrections imply that cross sections display a threshold, are suppressed in the Planckian, and reproduce the semi-classical result in the deep trans-Planckian region. Further implications are discussed.Comment: 22 pages, 9 figures, Sec V G added to match published versio

Logixpro Based Scada Simlations Model for Packaging System in Dry ICE Plant

Supervisory Control and Data Acquisition (SCADA) systems control and monitor industrial and critical infrastructure functions, such as electricity, gas, water, waste, railway, and traffic. The main objective of this work is to develop SCADA simulation model for packaging system in dry ice plant. Dry ice is an important refrigerant for keeping foods cold and preventing bacterial growth during shipment. Dry ice used for cooling or freezing foods must be very clean and considered food grade to ensure that food it may touch will not be contaminated. Some recent developments for its use include using the pellets in blasting or cleaning and its increasing use in transporting medical specimens, including hearts, limbs, and tissues, for reattachment and transplantation. The manufacturing process of dry ice has not changed significantly in many decades and is a relatively simple process of pressurizing and cooling gaseous carbon dioxide. But because of its growing demand, packaging becomes vital. An attempt has been made to develop and automate LOGIXPRO based SCADA simulations for dry ice plant to improve packaging and extensively reduce operating labor costs

Comment on Assessment of Aspiration-Induced lung Injuries among Acute Drug Poisoning Patients; Loghman Hakim Hospital, Poisoning center

I found one article on the website of IJMTFM, which had studied the consequences of aspiration of gastric contents in acute poisoning patients (1). It was informative. Unfortunately, there are some errors in that article, which I would like to bring to your kind attention

Entry-Level Employees’ Views of the Skills Gap in Digital Marketing

This study examines which competencies are most necessary for entry-level employees in digital marketing from the perspective of employees with 0-3 years of experience. Qualitative methods were used to conduct in-depth interviews with 12 recent graduates with 0 to 3 years of industry experience in digital marketing. Transcriptions of audio recordings were organized into individual data units. These were thematically analyzed with a grounded theory framework resulting in six main themes about the most valued competencies, as well as six subthemes. Findings suggest that in the field of digital marketing, verbal and non-verbal communication skills such as interpersonal communication, public speaking, and writing are often more valuable for new graduates than even technical or data analytical skills

Doctor of Philosophy

dissertationDifferent cell types have unique combinations of ion channels and receptors that define their physiological function. Combinations of ion channel and receptor isoforms may change in a cell-specific manner with disease progression. Thus, it is essential to design a general platform to investigate the descriptive properties of cell types and understand their role in health and disease. In this dissertation, I contributed to constellation pharmacology, a platform to assess phenotypic properties of individual cell types based on the combinations (constellations) of ion channels and receptors expressed in the plasma membrane. To validate the platform, major cell types were identified in the pacemaking circuit of mice ventral respiratory column (VRC), a region that generates and maintains respiratory rhythm. The cell-specific constellations of three major cell types were characterized and the properties of putative inspiratory neurons were examined in intact brainstem slice preparations by electrophysiological recordings. This work revealed new neuromodulators of the respiratory pacemaking circuit that were validated using intact slice preparations. To test the application of constellation pharmacology in identifying molecular changes in disease states, the molecular correlates of neuropathic pain were investigated. Concerted multivalent molecular changes were observed in the sensory neurons of rat dorsal root ganglia following chronic constriction injury and sciatic nerve ligation injury that resulted in the appearance of aberrant neuronal phenotypes, which upregulated with the progression of pain states. This work demonstrated the strength of constellation pharmacology in monitoring neuronal phenotypes with the progression of disease states. Constellation pharmacology requires the use of target selective pharmacological tools to uncover the combinations of ion channels and receptors in cell membrane. The ion channels and receptors exist in complex heteromeric isoforms for which selective pharmacological tools are not well explored. The applicability of this platform to screen for novel bioactive marine natural products with potential for targeting these ion channel isoforms was demonstrated by identifying novel neuroactive peptides from a new superfamily of snails, Crassispiridae. Thus, in this dissertation, I establish the application of constellation pharmacology to 1) describe different cellular phenotypes based on the membrane constellations, 2) investigate changes to cellular phenotypes in pathological conditions and 3) discover bioactive marine natural products

Model-driven and Data-driven Approaches for some Object Recognition Problems

Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change