Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

Background. Research to date on health effects associated with incineration has found limited evidence of health risks, but many previous studies have been constrained by poor exposure assessment. This paper provides a comparative assessment of atmospheric dispersion modelling and distance from source (a commonly used proxy for exposure) as exposure assessment methods for pollutants released from incinerators. Methods. Distance from source and the atmospheric dispersion model ADMS-Urban were used to characterise ambient exposures to particulates from two municipal solid waste incinerators (MSWIs) in the UK. Additionally an exploration of the sensitivity of the dispersion model simulations to input parameters was performed. Results. The model output indicated extremely low ground level concentrations of PM10, with maximum concentrations of <0.01 μg/m3. Proximity and modelled PM10 concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spearman correlation coefficients ~ 0.7) but showed poor agreement for categorical measures (deciles or quintiles, Cohen's kappa coefficients ≤ 0.5). Conclusion. To provide the most appropriate estimate of ambient exposure from MSWIs, it is essential that incinerator characteristics, magnitude of emissions, and surrounding meteorological and topographical conditions are considered. Reducing exposure misclassification is particularly important in environmental epidemiology to aid detection of low-level risks.
An elementary construction of Anick’s fibration

spheres and Moore spaces, as well as the first author’s work on the secondary suspension, predicted the existence of a p–local fibration S2n? 1? T2n? 1? S2n+1 whose connecting map is degree pr. In a long and complex monograph, Anick constructed such a fibration for p ? 5 and r ? 1. Using new methods we give a much more conceptual construction which is also valid for p = 3 and r ? 1. We go on to establish an H space structure on T2n? 1 and use this to construct a secondary EHP sequence for the Moore space spectrum.
Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks

Hyperspectral imaging linked to subsequent neural networks based analysis has proven its suitability to unravel complex information in a number of different application areas, such as geology, defence, etc. The extension of this approach to crop plant research, plant breeding, and agriculture has started quite recently. Here, the image acquisition ranges from airborne sensing mainly for agricultural applications down to single leaf analysis in the context of precision and high-throughput plant phenotyping. All these applications have in common, that particular relevant compounds of the plant need to be determined by means of hyperspectral signatures as substitute to extensive biochemical analysis. This paper describes the quantitative assessment of a number of genetically different tobacco varieties (Nicotiana tabacum) that were grown under different environmental and nutritional conditions. The analysis of the measured hyperspectral signatures was done by artificial neural networks.
Chapter 2 The continuous latent variable modelling formalism

This chapter gives the theoretical basis for continuous latent variable models. Section 2.1 defines intuitively the concept of latent variable models and gives a brief historical introduction to them. Section 2.2 uses a simple example, inspired by the mechanics of a mobile point, to justify and explain latent variables. Section 2.3 gives a more rigorous definition, which we will use throughout this thesis. Section 2.6 describes the most important specific continuous latent variable models and section 2.7 defines mixtures of continuous latent variable models. The chapter discusses other important topics, including parameter estimation, identifiability, interpretability and marginalisation in high dimensions. Section 2.9 on dimensionality reduction will be the basis for part II of the thesis. Section 2.10 very briefly mentions some applications of continuous latent variable models for dimensionality reduction. Section 2.11 shows a worked example of a simple continuous latent variable model. Section 2.12 give some complementary mathematical results, in particular the derivation of a diagonal noise GTM model and of its EM algorithm. Latent variable models are probabilistic models that try to explain a (relatively) high-dimensional process in terms of a few degrees of freedom. Historically, the idea of latent variables arose primarily from psychometrics, beginning with the g factor of Spearman (1904) and continuing with other psychologists such as Thomson, Thurstone and Burt, who were investigating the mental ability of children as suggested by the correlation and covariance matrices from cognitive tests variables. This eventually led to the development of factor analysis. Principal component analysis, traditionally not considered a latent variable model, was first thought of by Pearson (1901) and developed as a multivariate technique by Hotelling (1933). Latent structure analysis, corresponding to models where the latent variables are categorical, originated with Lazarsfeld and Henry (1968) as a tool for sociological analysis and had a separate development from factor analysis until recently. Bartholomew (1987) gives more historical details and references. The statistics literature classifies latent variable models according to the metric (continuous) or categorical (discrete) character of the variables involved, as table 2.1 shows. Also, in a broad sense many probabilistic models commonly used in machine learning can be considered as latent variable models inasmuch as they include probability distributions for variables which are not observed: mixture models (where the variable which indexes the components is a latent variable), hidden Markov models (the state sequence is unobserved), Observed variables Metrical Categorical Metrical Factor analysis Latent trait analysis …
Mandibular ramus fractures: a rarity.

AIM To determine the incidence of mandibular ramus fractures in KLE's PK Hospital and to analyze the outcome of open reduction and internal fixation of these fractures. MATERIALS AND METHODS Using a retrospective study design, records of all trauma patients who reported to the Department of Oral and Maxillofacial Surgery, KLE's PK Hospital Belgaum, between the years January 2006 to October 2011 was obtained from the medical records office. The data variables that were analyzed were the name, age, sex, cause of injury, pretreatment occlusion, treatment given, period of MMF and post-treatment occlusion. RESULTS Total number of mandibular fracture cases was 298. Ramus fractures were 10 in number which accounted for 3.3% of fractures. The age range of these 10 patients was seen to be between 20 to 80 years with the average age being 35.6 years. Of these 10 patients, 9 were male and 1 was female and 7 patients were treated by open reduction and internal fixation and the remaining 3 by closed reduction. The average period of MMF was 3 days for the patients who underwent open reduction and internal fixation. There was improvement in occlusion in all 10 patients post-treatment and there was no complication reported in any of the cases. CONCLUSION Ramus fractures accounted for 3.3% of all mandibular fractures. Open reduction and internal fixation of ramus fractures ensures adequate functional and anatomic reduction. CLINICAL SIGNIFICANCE This study makes an attempt to throw a light on the increasing incidence of ramus fractures and a successful management of these fractures by open reduction and internal fixation. How to cite this article: Kale TP, Kotrashetti SM, Louis A, Lingaraj JB, Sarvesh BU. Mandibular Ramus Fractures: A Rarity. J Contemp Dent Pract 2013;14(1):39-42. Source of support: Nil Conflict of interest: None declared.
VEGF activates divergent intracellular signaling components to regulate retinal progenitor cell proliferation and neuronal differentiation

During vertebrate neurogenesis, multiple extracellular signals influence progenitor cell fate choices. The process by which uncommitted progenitor cells interpret and integrate signals is not well understood. We demonstrate here that in the avascular chicken retina, vascular endothelial growth factor (VEGF) secreted by postmitotic neurons acts through the FLK1 receptor present on progenitor cells to influence cell proliferation and commitment. Augmenting VEGF signals increases progenitor cell proliferation and decreases retinal ganglion cell genesis. Conversely, absorbing endogenous VEGF ligand or disrupting FLK1 activity attenuates cell proliferation and enhances retinal ganglion cell production. In addition, we provide evidence that VEGF signals transmitted by the FLK1 receptor activate divergent intracellular signaling components, which regulate different responses of progenitor cells. VEGF-induced proliferation is influenced by the MEK-ERK pathway, as well as by the basic helix-loop-helix factor HES1. By contrast, VEGF-dependent ganglion cell suppression does not require MEK-ERK activation, but instead relies on VEGF-stimulated HES1 activity, which is independent of NOTCH signaling. Moreover, elevated HES1 expression promotes progenitor cell proliferation and prevents overproduction of retinal ganglion cells owing to the loss of VEGF or sonic hedgehog (SHH), another signal that suppresses ganglion cell development. Based on previous and current findings, we propose that HES1 serves as a convergent signaling node within early retinal progenitor cells to integrate various cell-extrinsic cues, including VEGF and SHH, in order to control cell proliferation and neuronal specification.
Information Extraction and Despeckling of SAR Images with Second Generation of Wavelet Transform

Synthetic Aperture Radar (SAR) technology is mainly used to obtain high-resolution images of ground areas in resolutions even less than meter. SAR is even capable of imaging a wide area of terrain and from two and more images it is possible to reconstruct a 3D digital elevation model of ground terrain. Good thing about SAR is an all whether operation and possibility to capture images under various inclination angles. Because digital images are usually corrupted by noise that arises from an imaging device, there is always a need for a good filtering algorithm to remove all disturbances, thus enabling more information extraction. The SAR images are corrupted by a noise called speckle, which makes the interpretation of SAR images very difficult. The goal of removing speckles from the SAR image is to represent a noise-free image and preserve all important features of the SAR image, as for example edges, textures, region borders, etc.
A Microblock Density-Based Similarity Measure for Graph Clustering

Graph clustering is an important technique in data mining and network analysis, and it is widely used in chemistry, physics, biology, communication, and computer science. Similarities between vertices of a graph are the fundamental conditions for many hierarchical clustering algorithms. In the paper, we propose a new similarity measure based on microblock density, which computes the similarity between a pair of vertices by calculating the densities of their common adjacent microblock. This measure extends the scope and improves the discrimination of traditional measure, thus significantly improving the performance and stability of the similarity-based clustering algorithms. Experiments on synthetic data and real networks show that the density-based similarity approach accurately reflects the local structure of the graph and provides higher accuracy similarities for clustering and community structural detection algorithms than other state-of-the-art methods.
Analysis of 18F-FET PET for grading of recurrent gliomas: is evaluation of uptake kinetics superior to standard methods?

UNLABELLED The aim of the present study was to evaluate whether extended analyses of O-(2-18F-fluoroethyl)-L-tyrosine (FET) uptake kinetics provide results superior to those of standard tumor-to-background ratios in predicting tumor grade in patients with pretreated gliomas. METHODS Dynamic 18F-FET PET studies (0-40 min after injection of 180 MBq of 18F-FET) were performed on 45 glioma patients with suspected tumor recurrence after multimodal treatment. For the standard method, tumoral maximal standardized uptake value (SUVmax) and the ratio to the background were derived from a summed image 20-40 min after injection. Dynamic data evaluation comprised several approaches: first, SUV within a 90% isocontour threshold (SUV90) and the respective ratio to the background calculated for each time frame between 5 and 40 min after injection; second, the time to peak analysis; and third, various parameters accounting for the individual time course of 18F-FET uptake. Results were correlated with the histopathologic findings of MRI/PET-guided stereotactic biopsies and were evaluated with respect to their discriminatory power to separate low- from high-grade tumors using receiver-operating characteristic (ROC) analyses. RESULTS The parameters taking into account the individual time course of 18F-FET uptake were able to differentiate low-grade from high-grade recurrent astrocytomas with high diagnostic accuracy, reaching the best differentiation with a sensitivity and specificity of 92% and an area under the ROC curve (AUC) of 0.94. For the other parameters, the respective values were considerably lower (time to peak: 85% sensitivity and 88% specificity; SUV90-to-background ratio for single-frame evaluation of the early-uptake phase: 100% sensitivity, 62% specificity, and 0.81 AUC). The lowest performance was provided by the standard method (SUVmax: 73% sensitivity, 54% specificity, and 0.60 AUC; SUVmax-to-background ratio: 62% sensitivity, 62% specificity, and 0.59 AUC). Time-activity curves (5-40 min after injection) slightly and steadily increased in tumor-free patients and in low-grade tumors, whereas high-grade tumors showed an early peak around 10-15 min after injection followed by a decrease. CONCLUSION This study has shown differences in the dynamics of 18F-FET uptake between recurrent low- and high-grade gliomas. Therefore, parameters addressing the different kinetic behaviors allow discrimination with high diagnostic power between these 2 prognostically different groups. Thus, the techniques introduced here are clearly superior to the yet most widely used standard method.
Modular fault simulation of mixed signal circuits with fault ranking by severity

In this paper, we propose a novel approach to fault simulation of large mixed signal circuits using circuit partitioning and fault ordering. The conventional statistical fault model is divided, to separately account for global and local variations. We consider the problem of estimating the effect of a parameter deviation in a sub-module, on the system level specifications for a general nonlinear circuit. This method uses a function approximation model to estimate the system response and rank the faults according to the severity. Applications of this algorithm include estimation of fault coverage and forms a key element in test generation and diagnosis procedures.
Exploiting accelerometers to estimate displacement

Although the acceleration is physically related to the displacement of an object, i.e., to its change of position, it is demonstrated that the double integration of the acceleration does not provide accurate information about the displacement, due to the noise and measurement errors. This paper evaluates a correction technique based on the Kalman filter in order to increase the accuracy of the estimation of the displacement. Experiments were performed by acquiring the acceleration with an off-the-shelf accelerometer: the percentage error made by simply integrating the acceleration measurements may arrive to 68% in the general case of a movement in the space, but it can be dramatically reduced to 9% with the proposed approach. An even better behavior is obtained when the movement is constrained to a plane or along an axis.
Very high power PEBB technology

In the field of power electronics the power electronics building block (PEBB) is a key functional component. With regard to the applications, it is of outmost importance that the PEBB technology used is compact, cost-effective and reliable. The IGCT is at the forefront of technology in high power, medium-voltage applications. For further improvement in size and costs a new ANPC IGCT PEBB has been developed. The main new technologies to achieve higher powers are the new low-inductive gate-unit to maintain hardswitched operation up to more than 6000 A, the increased SOA of the 91 mm asymmetric IGCT (4 inch technology) and the antiparallel diode up to more than 6000 A and the active NPC technology, which allows an optimum and equal loss balancing in all power semiconductors. The maximum inverter output power has been increased by 80% with a parallel increase in the power density and reduced costs per kVA
Changes in Concerns About the Innovation Related to Adopter Characteristics, Training Workshops, and the Use of the Innovations.

ABSTRACT This paper reports on more than five years of research an teachers who were engaged in or associated with the process of working on educational change. During this period many variables that may influence individual teache decisions regarding change were studied. This paper reports findings regarding the apparent influence of these variables cn the decisions made regarding change. It also describes the implications of these findings for those responsible for guiding charge. (Author)
The War against Regulation: From Jimmy Carter to George W. Bush

Battered by our economy and disappointed by our government's role in that battering, we might be tempted to point the finger of blame at whoever's currently on the hot seat in front of us. But, as Phillip Cooper shows, we must widen our vision to take in the long history behind this dismal state of affairs. By doing so, it becomes clear that our present circumstances are in many ways the predictable outcome of a several-decades-long war against government regulation and its potential to protect and improve our lives. Cooper explains how the war against regulation has been conducted both from within and outside the government over the past thirty years. Chronicling its major battles from the Jimmy Carter years through the presidency of George W. Bush, he shows that it is a war - waged by Democrats and Republicans alike - that has been fought in high places but whose casualties include children, senior citizens, the infirm, and millions of families who have lost their homes and retirement savings. Cooper tells how Carter, praised for environmental regulation, worked to deregulate airlines, trucking, and banks; how Reagan undertook administrative rather than legislative measures against regulation - most of which weren't understood or even known by the public; and how George H.W. Bush continued the fight with the Quayle Commission. He describes Bill Clinton's commitment to fighting regulation despite having campaigned against his Republican predecessors' policies, then describes the behind-the-scenes maneuverings of George W. Bush as he sought to gut regulatory agencies entirely. He also devotes an entire chapter to parallel developments in the Supreme Court that substantially advanced the deregulation agenda during this era. Cooper contends that regulation, as one of a number of policy tools available to our leaders, is neither good nor bad in and of itself. Excessive deregulation - as opposed to regulatory reform, can present considerable peril, as current events clearly show. By considering key issues important to a more effective understanding and use of regulation in the future, "The War against Regulation" makes a vital case for restoring debate about regulation's rightful role within the republic and offers hope that a better understanding of that role can help lift us out of our current crisis.
Development of ecological indicators for the U.S. Great Lakes coastal region – A summary of applications in Lake Huron

Our overall goal was to develop indicators that both estimate ecological condition and suggest plausible causes of ecosystem degradation across the U.S. Great Lakes coastal region. Here we summarize data gathered along the U.S. Lake Huron coastline for breeding bird, diatom, fish, invertebrate, and wetland plant communities. We sampled these biotic communities on 88 sites in Lake Huron coastal wetlands, uplands, estuaries/bays, and high-energy shorelines. The sites were selected as part of a larger, stratified random design for the entire U.S. Great Lakes coastal region using gradients of anthropogenic stress that incorporated over 200 stressor variables (e.g. agriculture, land cover, human populations, and point source pollution). The U.S. Lake Huron coastal region exemplified wide variation in human-related stress relative to the entire U.S. Great Lakes coast. In general, levels of stress decreased from south to north partly reflecting the change in climate and physiography, but also due to the greater human influences in the southern region as compared with the north. The primary stressors in the southern region are due to agriculture and human development, while the northern region has substantially less agriculture and less human population. The biotic communities sampled were strongly related to the environmental stress gradients, especially agriculture and urbanization. The following indicators were developed based on responses to stress: 1) an index of biological condition for breeding bird communities corresponding to land use, 2) a diatom-inferred total phosphorus indicator corresponding to water quality, 3) exotic fish (carp [Cyprinus carpio] and goldfish [Carassius auratus]) corresponding to agriculture, and 4) a multi-taxa index for wetland plants corresponding to a cumulative stress index. These communities can all serve as useful indicators of the ecological condition of the Lake Huron coast. The ecological indicators provide a baseline on selected conditions for the U.S. Lake Huron coastal region and a means to detect change over time.
Notes on the Acquisition of Conditional Knowledge

Research in Information Extraction has been overly focused on the extraction of facts concerning individuals as compared to general knowledge pertaining to classes of entities and events. In addition, preference has been given to simple techniques in order to enable high volume throughput. In what follows we give examples of existing work in the field of knowledge acquisition, then follow with ideas on areas for exploration beyond the current state of the art, specifically with respect to the extraction of conditional knowledge, making use of deeper linguistic analysis than is currently the norm.
On the maximum efficiency of the propeller mass-ejection mechanism

Aims. We derive simple estimates of the maximum efficiency with which matter can be ejected by the propeller mechanism in diskfed, rotating magnetic neutron stars. Some binary evolution scenarios envisage that this mechanism is responsible for expelling to infinity the mass inflowing at a low rate from the companion star, therefore limiting the total amount of mass that can be accreted by the neutron star. Methods. We demonstrate that, for typical neutron star parameters, a maximum of ηpro < 5.7(P−3) 1/3 times more matter than accreted can be expelled through the propeller mechanism at the expenses of the neutron star rotational energy (P−3 is the NS spin period in unit of 10 −3 s). Approaching this value, however, would require a great deal of fine tuning in the system parameters and the properties of the interaction of matter and magnetic field at the magnetospheric boundary. Results. We conclude that some other mechanism must be invoked in order to prevent that too much mass accretes onto the neutron stars of some low mass X-ray binaries.