scholarly journals Accessing Resources in Arrival Neighbourhoods: How Foci-Aided Encounters Offer Resources to Newcomers

2020 ◽  
Vol 5 (3) ◽  
pp. 78-88
Author(s):  
Nils Hans ◽  
Heike Hanhörster
Keyword(s):  
Public Spaces ◽  
Specific Information ◽  
Specific Knowledge ◽  
Daily Routines ◽  
New Immigrants ◽  
Embedded Network ◽  
Different Types ◽  
Resource Transfers ◽  
German City

<p>Numerous studies have stressed the importance of social networks for the transfer of resources. This article focuses on recently arrived immigrants with few locally embedded network contacts, analysing how they draw on arrival-specific resources in their daily routines. The qualitative research in an arrival neighbourhood in a German city illustrates that routinised and spontaneous foci-aided encounters in semi-public spaces play an important role for newcomers in providing access to arrival-specific knowledge. The article draws on the concept of ‘micro publics,’ highlighting different settings facilitating interactions and resource transfers. Based on our research we developed a classification of different types of encounter that enable resource transfer. The article specifically focuses on foci-aided encounters, as these appear to have a great impact on newcomers’ access to resources. Institutionalised to varying degrees, these settings, ranging from local mosques to football grounds, facilitate interaction between ‘old’ and ‘new’ immigrants. Interviews reveal forms of solidarity between immigrants and how arrival-specific information relevant to ‘navigating the system’ gets transferred. Interestingly, reciprocity plays a role in resource transfers also via routinised and spontaneous foci-aided encounters.</p>

scholarly journals Staging city events in public spaces: an urban design perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrew Smith ◽  
Goran Vodicka ◽  
Alba Colombo ◽  
Kristina N. Lindstrom ◽  
David McGillivray ◽  
...  
Keyword(s):  
Urban Design ◽  
Public Spaces ◽  
Content Type ◽  
Reciprocal Relationships ◽  
Limited Mobility ◽  
Different Types ◽  
Design Perspective ◽  
Conceptual Paper ◽  
Negative Impacts

PurposeThere are two main aims of this conceptual paper. The first is to explore the issues associated with staging events in public spaces, and to produce a typology of different event spaces. The second is to explore if and how events should be designed into parks, streets and squares and whether this might reduce some of the negative impacts and associated user conflicts.Design/methodology/approachThe paper analyses the history, drivers and effects of using public spaces as venues and examines the reciprocal relationships between events and the spaces that host them. To explain the range and dynamics of contemporary events, a typology of event spaces is developed. This typology highlights nine different types of event spaces which are differentiated by the level of public accessibility (free entry, sometimes free, paid entry), and the mobility of event audiences (static, limited mobility, mobile). Using this typology, the paper discusses ways that public spaces might be adapted to make them better suited to staging events. This discussion is illustrated by a range of examples.FindingsThe paper finds that it makes practical sense to adapt some urban public spaces to make them better equipped as venues, but designing in events presents new issues and does not necessarily resolve many of the problems associated with staging events. Disputes over events are inevitable and constituent features of public spaces.Originality/valueThis paper makes an original contribution by developing a new classification of event spaces and by synthesising ideas from urban design with ideas from the events literature.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Lexicographer’s Lacunas or How to Deal with Missing Representative Dictionary Forms on the Example of Czech

2019 ◽  
Author(s):  
Dominika Kováříková ◽  
Michal Škrabal ◽  
Václav Cvrček ◽  
Lucie Lukešová ◽  
Jiří Milička
Keyword(s):  
Grammatical Category ◽  
Manual Analysis ◽  
Different Types ◽  
Word Forms ◽  
Frequency Threshold

Abstract When compiling a list of headwords, every lexicographer comes across words with an unattested representative dictionary form in the data. This study focuses on how to distinguish between the cases when this form is missing due to a lack of data and when there are some systemic or linguistic reasons. We have formulated lexicographic recommendations for different types of such ‘lacunas’ based on our research carried out on Czech written corpora. As a prerequisite, we calculated a frequency threshold to find words that should have the representative form attested in the data. Based on a manual analysis of 2,700 nouns, adjectives and verbs that do not, we drew up a classification of lacunas. The reasons for a missing dictionary form are often associated with limited collocability and non-preference for the representative grammatical category. Findings on unattested word forms also have significant implications for language potentiality.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

Prospects for the classification of mental disorders of children and adolescents

2008 ◽  
Vol 23 (7) ◽  
pp. 481-485 ◽  
Author(s):  
M.H. Schmidt ◽  
J. Sinzig
Keyword(s):  
Mental Disorders ◽  
Children And Adolescents ◽  
Classification Systems ◽  
General Question ◽  
Specific Information ◽  
Specific Symptom ◽  
Trade Offs ◽  
Dimensional Classification ◽  
Dsm V

AbstractSuggestions for classification of mental disorders of children and adolescents in DSM-V and ICD-11 have been made, which differ strongly from the current descriptive approach of dimensional classification.These suggestions even comprise a dichotomized system for health care as well as for scientific purposes.Nevertheless it is obvious that we are far behind an “etiological” classification, so that trade-offs have necessarily to be made in DSM-V and ICD-11.Appropriate proposals concern the strict separation of disorders that are typical for children and adolescents as well as for adults.Furthermore a differentiation of diagnosis for infants, toddlers and preschool children is required in both classification systems. As far as it is relevant for treatment, combined diagnosis in DSM-V and subthreshold diagnosis as well as coding-possibilities for findings in molecular biology should be permitted.As personality disorders should only be diagnosed after the age of 16, it is recommended to dimensionally classify personality traits that are pathognomonic for specific symptom patterns and of prognostic relevance.DSM-V and ICD-11 should allow age-specific information on axis-IV. The article discusses the general question of how relational disorders respectively disturbances should be classified and include furthermore special recommendations concerning ICD and DSM categories.

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