Latent dirichlet markov allocation for sentiment analysis

In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget

Polycrystalline materials with pores: effective properties through a boundary element homogenization scheme

In this study, the influence of porosity on the elastic effective properties of polycrystalline materials is investigated using a formulation built on a boundary integral representation of the elastic problem for the grains, which are modeled as 3D linearly elastic orthotropic domains with arbitrary spatial orientation. The artificial polycrystalline morphology is represented using 3D Voronoi tessellations. The formulation is expressed in terms of intergranular fields, namely displacements and tractions that play an important role in polycrystalline micromechanics. The continuity of the aggregate is enforced through suitable intergranular conditions. The effective material properties are obtained through material homogenization, computing the volume averages of micro-strains and stresses and taking the ensemble average over a certain number of microstructural samples. In the proposed formulation, the volume fraction of pores, their size and distribution can be varied to better simulate the response of real porous materials. The obtained results show the capability of the model to assess the macroscopic effects of porosity

Application of dual boundary element method in active sensing

In this paper, a boundary element method (BEM) for the dynamic analysis of 3D solid structures with bonded piezoelectric transducers is presented. The host structure is modelled with BEM and the piezoelectric transducers are formulated using a 3D semi-analytical finite element approach. The elastodynamic analysis of the entire structure is carried out in Laplace domain and the response in time domain is obtained by inverse Laplace transform. The BEM is validated against established finite element method (FEM)

A boundary element model for structural health monitoring using piezoelectric transducers

In this paper, for the first time, the boundary element method (BEM) is used for modelling smart structures instrumented with piezoelectric actuators and sensors. The host structure and its cracks are formulated with the 3D dual boundary element method (DBEM), and the modelling of the piezoelectric transducers implements a 3D semi-analytical finite element approach. The elastodynamic analysis of the structure is performed in the Laplace domain and the time history is obtained by inverse Laplace transform. The sensor signals obtained from BEM simulations show excellent agreement with those from finite element modelling simulations and experiments. This work provides an alternative methodology for modelling smart structures in structural health monitoring applications

Patient priorities for research: A focus group study of UK medical cannabis patients

INTRODUCTION: There has yet to be an evaluation of medical cannabis patient preferences with respect to future research. As such, prioritisation of research agendas has been largely driven by academia and industry. The primary aim of this study was to elicit priorities for research from medical cannabis patients in the United Kingdom (UK). METHODS: Patients undergoing active treatment for health conditions with medical cannabis in the UK were invited to take part in focus groups from December 2021 to February 2022. An inductive thematic analysis of responses was performed. Participants also completed a ranking exercise whereby they assigned ten counters (each equivalent to £1 million GBP) to competing research priorities. RESULTS: 30 medical cannabis patients participated across 3 focus groups. The following themes were identified as research priorities: adverse events, comparison between cannabis-based medicinal products, health conditions, pharmacology of cannabis, types of study, healthcare professionals' attitudes, social environment, agriculture and manufacturing, and the cannabis plant. Participants assigned the highest proportion of research funding to 'assessment of effect on specific symptoms' (26 counters; 8.7%). CONCLUSIONS: This study highlighted specific themes within which to focus future research on medical cannabis. Clinically, there was a directive towards ensuring that research is condition- or symptom-specific. Participants also emphasised themes on the social impact of medical cannabis, such as knowledge of medical cannabis among healthcare professionals, stigma, and effects on driving and in the workplace. These findings can guide both research funders and researchers into effectively conducting research which fits within a more patient-centric model

Pattern and degree of left ventricular remodeling following a tailored surgical approach for hypertrophic obstructive cardiomyopathy.

Background The role of a tailored surgical approach for hypertrophic cardiomyopathy (HCM) on regional ventricular remodelling remains unknown. The aims of this study were to evaluate the pattern, extent and functional impact of regional ventricular remodelling after a tailored surgical approach. Methods From 2005 to 2008, 44 patients with obstructive HCM underwent tailored surgical intervention. Of those, 14 were ineligible for cardiac magnetic resonance (CMR) studies. From the remainder, 14 unselected patients (42±12 years) underwent pre- and post-operative CMR studies at a median 12 months post-operatively (range 4-37 months). Regional changes in left ventricular (LV) thickness as well as global LV function following surgery were assessed using CMR Tools (London, UK). Results Pre-operative mean echocardiographic septal thickness was 21±4 mm and mean LV outflow gradient was 69±32 mmHg. Following surgery, there was a significant degree of regional regression of LV thickness in all segments of the LV, ranging from 16% in the antero-lateral midventricular segment to 41% in the anterior basal segment. Wall thickening was significantly increased in basal segments but showed no significant change in the midventricular or apical segments. Globally, mean indexed LV mass decreased significantly after surgery (120±29g/m2 versus 154±36g/m2; p<0.001). There was a trend for increased indexed LV end-diastolic volume (70±13 mL versus 65±11 mL; p=0.16) with a normalization of LV ejection fraction (68±7% versus 75±9%; p<0.01). Conclusion Following a tailored surgical relief of outflow obstruction for HCM, there is a marked regional reverse LV remodelling. These changes could have a significant impact on overall ventricular dynamics and function

Hartmann's Procedure or Primary Anastomosis?

Perforation following acute diverticulitis is a typical scenario during the first attack. Different classification systems exist to classify acute perforated diverticulitis. While the Hinchey classification, which is based on intraoperative findings, is internationally best known, the German Hansen-Stock classification which is based on CT scan is widely accepted within Germany. When surgery is necessary, sigmoid colectomy is the standard of care. An important question is whether patients should receive primary anastomosis or a Hartmann procedure subsequently. A priori there are several arguments for both procedures. Hartmann's operation is extremely safe and, therefore, represents the best option in severely ill patients and/or extensive peritonitis. However, this operation carries a high risk of stoma nonreversal, or, when reversal is attempted, a high risk in terms of morbidity and mortality. In contrast, primary anastomosis with or without loop ileostoma is a slightly more lengthy procedure as normally the splenic flexure needs to be mobilized and construction of the anastomosis may consume more time than the Hartmann operation. The big advantage of primary anastomosis, however, is that there is no need for the potentially risky stoma reversal operation. The most interesting question is when to do the Hartmann operation or primary anastomosis. Several comparative case series were published showing that primary anastomosis is feasible in many patients. However, no randomized trial is available to date. It is of note, that all non-randomized case series are biased, i.e. that patients in better condition received anastomosis and those with severe peritonitis underwent Hartmann's operation. This bias is undoubtedly likely to be present, even if not obvious, in the published papers! Our own data suggest that this decision should not be based on the extent of peritonitis but rather on patient condition and comorbidity. In conclusion, sigmoid colectomy and primary anastomosis is feasible and safe in many patients who need surgery for perforated diverticulitis, particularly when combined with loop ileostomy. Based on our own published analysis, however, we recommend performing Hartmann's operation in severely ill patients who carry substantial comorbidity, while the extent of peritonitis appears not to be of predominant importance. Copyright (C) 2012 S. Karger AG, Base

Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important di culties to overcome in order to obtain a good classi cation performance. In this work, a deep learning based method for accurate classi cation of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known e ectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean ac- curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech