Remote sensing utility in a disaster struck urban environment

Six major public health areas which might be affected by a natural disaster were identified. The functions and tasks associated with each area following a disaster, potential ways remote sensing could aid these functions, and the baseline data which would expedite problem solving associated with these functions are discussed

Classical-quantum correspondence in bosonic two-mode conversion systems: polynomial algebras and Kummer shapes

Bosonic quantum conversion systems can be modeled by many-particle single-mode Hamiltonians describing a conversion of $n$ molecules of type A into $m$ molecules of type B and vice versa. These Hamiltonians are analyzed in terms of generators of a polynomially deformed $su(2)$ algebra. In the mean-field limit of large particle numbers, these systems become classical and their Hamiltonian dynamics can again be described by polynomial deformations of a Lie algebra, where quantum commutators are replaced by Poisson brackets. The Casimir operator restricts the motion to Kummer shapes, deformed Bloch spheres with cusp singularities depending on $m$ and $n$. It is demonstrated that the many-particle eigenvalues can be recovered from the mean-field dynamics using a WKB type quantization condition. The many-particle state densities can be semiclassically approximated by the time-periods of periodic orbits, which show characteristic steps and singularities related to the fixed points, whose bifurcation properties are analyzed.Comment: 13 pages, 13 figure

Potential role of remote sensing in disaster relief management

Baseline or predisaster data which would be useful to decision making in the immediate postdisaster period were suggested for the six areas of public health concern along with guidelines for organizing these data. Potential sources of these data are identified. In order to fully assess the impact of a disaster on an area, information about its predisaster status must be known. Aerial photography is one way of acquiring and recording such data

Quasiclassical analysis of Bloch oscillations in non-Hermitian tight-binding lattices

Many features of Bloch oscillations in one-dimensional quantum lattices with a static force can be described by quasiclassical considerations for example by means of the acceleration theorem, at least for Hermitian systems. Here the quasiclassical approach is extended to non-Hermitian lattices, which are of increasing interest. The analysis is based on a generalised non-Hermitian phase space dynamics developed recently. Applications to a single-band tight-binding system demonstrate that many features of the quantum dynamics can be understood from this classical description qualitatively and even quantitatively. Two non-Hermitian and $PT$-symmetric examples are studied, a Hatano-Nelson lattice with real coupling constants and a system with purely imaginary couplings, both for initially localised states in space or in momentum. It is shown that the time-evolution of the norm of the wave packet and the expectation values of position and momentum can be described in a classical picture.Comment: 20 pages, 8 figures, typos corrected, slightly extended, accepted for publication in New Journal of Physics in Focus Issue on Parity-Time Symmetry in Optics and Photonic

A Global Hypothesis for Women in Journalism and Mass Communications: The Ratio of Recurrent and Reinforced Residuum

This paper examines the status of women in communications industries and on university faculties. It specifically tests the Ratio of Recurrent and Reinforced Residuum or R3 hypothesis, as developed by Rush in the early 1980s [Rush, Buck & Ogan,1982]. The R3 hypothesis predicts that the percentage of women in the communications industries and on university faculties will follow the ratio residing around 1/4:3/4 or 1/3:2/3 proportion females to males. This paper presents data from a nationwide U.S. survey and compares them to data from global surveys and United Nations reports. The evidence is overwhelming and shows the relevance and validity of the R3 hypothesis across different socio-economic and cultural contexts. The paper argues that the ratio is the outcome of systemic discrimination that operates at multiple levels. The obstacles to achieving equality in the academy as well as media industries are discussed and suggestions for breaking out of the R3 ratio are included.

A Global Hypothesis for Women in Journalism and Mass Communications: The Ratio of Recurrent and Reinforced Residuum

This paper examines the status of women in communications industries and on university faculties. It specifically tests the Ratio of Recurrent and Reinforced Residuum or R3 hypothesis, as developed by Rush in the early 1980s [Rush, Buck & Ogan,1982]. The R3 hypothesis predicts that the percentage of women in the communications industries and on university faculties will follow the ratio residing around 1/4:3/4 or 1/3:2/3 proportion females to males. This paper presents data from a nationwide U.S. survey and compares them to data from global surveys and United Nations reports. The evidence is overwhelming and shows the relevance and validity of the R3 hypothesis across different socio-economic and cultural contexts. The paper argues that the ratio is the outcome of systemic discrimination that operates at multiple levels. The obstacles to achieving equality in the academy as well as media industries are discussed and suggestions for breaking out of the R3 ratio are included.

The soft-x-ray spectral shape of x-ray-weak seyferts

(I) We observed eight Seyfert~2s and two X--ray--weak Seyfert~1/QSOs with the ROSAT PSPC, and one Seyfert~2 with the ROSAT HRI. These targets were selected from the Extended 12\um\ Galaxy Sample. (II) Both Seyfert~1/QSOs vary by factors of 1.5---2. The photon indices steepen in the more luminous state, consistent with the variability being mainly due to the softest X--rays, which are confined to a size of less than a parsec. (III) Both the Seyfert~2s and Seyfert~1/QSOs are best fit with a photon index of \Gamma\sim3, which is steeper than the canonical value of \Gamma\sim1.7 measured for X--ray--strong Seyferts by ROSAT and at higher energies. Several physical explanations are suggested for the steeper slopes of X--ray--weak objects. (IV) We observed one Seyfert~2, NGC~5005, with the ROSAT HRI, finding about 13\% of the soft X--rays to come from an extended component. This and other observations suggest that different components to the soft X--ray spectrum of some, if not all, X--ray--weak Seyferts may come from spatially distinct regions

Feline Hypertrophic Cardiomyopathy: A Spontaneous Large Animal Model of Human HCM.

Hypertrophic cardiomyopathy (HCM) is a common disease in pet cats, affecting 10-15% of the pet cat population. The similarity to human HCM, the rapid progression of disease, and the defined and readily determined endpoints of feline HCM make it an excellent natural model that is genotypically and phenotypically similar to human HCM. The Maine Coon and Ragdoll cats are particularly valuable models of HCM because of myosin binding protein-C mutations and even higher disease incidence compared to the overall feline population. The cat overcomes many of the limitations of rodent HCM models, and can provide enhanced translation of information from in vitro and induced small animal models to human clinical trials. Physicians and veterinarians working together in a collaborative and interdisciplinary approach can accelerate the discovery of more effective treatments for this and other cardiovascular diseases affecting human and veterinary patients

Differentiation of Cardiac from Noncardiac Pleural Effusions in Cats using Second-Generation Quantitative and Point-of-Care NT-proBNP Measurements

BACKGROUND: Pleural effusion is a common cause of dyspnea in cats. N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) measurement, using a first‐generation quantitative ELISA, in plasma and pleural fluid differentiates cardiac from noncardiac causes of pleural effusion. HYPOTHESIS/OBJECTIVES: To determine whether NT‐proBNP measurements using second‐generation quantitative ELISA and point‐of‐care (POC) tests in plasma and pleural fluid distinguish cardiac from noncardiac pleural effusions and how results compare to the first‐generation ELISA. ANIMALS: Thirty‐eight cats (US cohort) and 40 cats (UK cohort) presenting with cardiogenic or noncardiogenic pleural effusion. METHODS: Prospective cohort study. Twenty‐one and 17 cats in the US cohort, and 22 and 18 cats in the UK cohort were classified as having cardiac or noncardiac pleural effusion, respectively. NT‐proBNP concentrations in paired plasma and pleural fluid samples were measured using second‐generation ELISA and POC assays. RESULTS: The second‐generation ELISA differentiated cardiac from noncardiac pleural effusion with good diagnostic accuracy (plasma: sensitivity, 95.2%, specificity, 82.4%; pleural fluid: sensitivity, 100%, specificity, 76.5%). NT‐proBNP concentrations were greater in pleural fluid (719 pmol/L (134–1500)) than plasma (678 pmol/L (61–1500), P = 0.003), resulting in different cut‐off values depending on the sample type. The POC test had good sensitivity (95.2%) and specificity (87.5%) when using plasma samples. In pleural fluid samples, the POC test had good sensitivity (100%) but low specificity (64.7%). Diagnostic accuracy was similar between first‐ and second‐generation ELISA assays. CONCLUSIONS AND CLINICAL IMPORTANCE: Measurement of NT‐proBNP using a quantitative ELISA in plasma and pleural fluid or POC test in plasma, but not pleural fluid, distinguishes cardiac from noncardiac causes of pleural effusion in cats

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience