scholarly journals The effects of physical restructuring on the socioeconomic status of neighbourhoods: Selective migration and upgrading

Urban Studies ◽  
2018 ◽  
Vol 56 (8) ◽  
pp. 1647-1663
Author(s):  
Merle Zwiers ◽  
Maarten van Ham ◽  
Reinout Kleinhans
Keyword(s):  
Large Scale ◽  
Spillover Effects ◽  
High Income ◽  
Direct Effects ◽  
Urban Restructuring ◽  
Deprived Neighbourhoods ◽  
New Construction ◽  
Using Data ◽  
Main Instrument ◽  
Dutch Cities

In the last few decades, many governments have implemented urban restructuring programmes with the main goal of combating a variety of socioeconomic problems in deprived neighbourhoods. The main instrument of restructuring has been housing diversification and tenure mixing. The demolition of low-quality (social) housing and the construction of owner-occupied or private rented dwellings was expected to change the population composition of deprived neighbourhoods through the in-migration of middle- and high-income households. Many studies have been critical with regard to the success of such policies in actually upgrading neighbourhoods. Using data from the 31 largest Dutch cities for the 1999 to 2013 period, this study contributes to the literature by investigating the effects of large-scale demolition and new construction on neighbourhood income developments on a low spatial scale. We use propensity score matching to isolate the direct effects of policy by comparing restructured neighbourhoods with a set of control neighbourhoods with low demolition rates, but with similar socioeconomic characteristics. The results indicate that large-scale demolition leads to socioeconomic upgrading of deprived neighbourhoods as a result of attracting and maintaining middle- and high-income households. We find no evidence of spillover effects to nearby neighbourhoods, suggesting that physical restructuring only has very local effects.

Student Perceptions of Community: A Different Perspective

NASPA Journal ◽  
1998 ◽  
Vol 35 (4) ◽  
Author(s):  
Jackie Clark ◽  
Joan Hirt
Keyword(s):  
Student Perceptions ◽  
Large Scale ◽  
Research University ◽  
Small Scale ◽  
Social Environments ◽  
Common Assumption ◽  
Small Communities ◽  
Public Research University ◽  
The Common ◽  
Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bohan Liu ◽  
Pan Liu ◽  
Lutao Dai ◽  
Yanlin Yang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Area Under The Curve ◽  
Independent Set ◽  
Reference Method ◽  
Ct Images ◽  
Loss Of Life ◽  
Case Identification ◽  
Reverse Transcription Pcr ◽  
Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

scholarly journals Integrative cBioPortal Analysis Revealed Molecular Mechanisms That Regulate EGFR-PI3K-AKT-mTOR Pathway in Diffuse Gliomas of the Brain

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3247
Author(s):  
Petar Brlek ◽  
Anja Kafka ◽  
Anja Bukovac ◽  
Nives Pećina-Šlaus
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
In Silico Analysis ◽  
Total Sample ◽  
Mtor Signaling ◽  
Biological Behavior ◽  
Mrna Transcription ◽  
Diffuse Gliomas ◽  
New Treatment ◽  
Using Data

Diffuse gliomas are a heterogeneous group of tumors with aggressive biological behavior and a lack of effective treatment methods. Despite new molecular findings, the differences between pathohistological types still require better understanding. In this in silico analysis, we investigated AKT1, AKT2, AKT3, CHUK, GSK3β, EGFR, PTEN, and PIK3AP1 as participants of EGFR-PI3K-AKT-mTOR signaling using data from the publicly available cBioPortal platform. Integrative large-scale analyses investigated changes in copy number aberrations (CNA), methylation, mRNA transcription and protein expression within 751 samples of diffuse astrocytomas, anaplastic astrocytomas and glioblastomas. The study showed a significant percentage of CNA in PTEN (76%), PIK3AP1 and CHUK (75% each), EGFR (74%), AKT2 (39%), AKT1 (32%), AKT3 (19%) and GSK3β (18%) in the total sample. Comprehensive statistical analyses show how genomics and epigenomics affect the expression of examined genes differently across various pathohistological types and grades, suggesting that genes AKT3, CHUK and PTEN behave like tumor suppressors, while AKT1, AKT2, EGFR, and PIK3AP1 show oncogenic behavior and are involved in enhanced activity of the EGFR-PI3K-AKT-mTOR signaling pathway. Our findings contribute to the knowledge of the molecular differences between pathohistological types and ultimately offer the possibility of new treatment targets and personalized therapies in patients with diffuse gliomas.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

scholarly journals Analysis of Factors Influencing Effective Utilization Coefficient of Irrigation Water in the Manas River Basin

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Lili Yang ◽  
Tong Heng ◽  
Guang Yang ◽  
Xinchen Gu ◽  
Jiaxin Wang ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Large Scale ◽  
Grey Model ◽  
Analytic Hierarchy ◽  
Effective Utilization ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Utilization Coefficient ◽  
Using Data ◽  
Effective Use

The factors influencing the effective utilization coefficient of irrigation water are not understood well. It is usually considered that this coefficient is lower in areas with large-scale irrigation. With this background, we analyzed the effective utilization coefficient of irrigation water using the analytic hierarchy process using data from 2014 to 2019 in Shihezi City, Xinjiang. The weights of the influencing factors on the effective utilization coefficient of irrigation water in different irrigation areas were analyzed. Predictions of the coefficient’s values for different years were made by understanding the trends based on the grey model. The results show that the scale of the irrigation area is not the only factor determining the effective utilization coefficient of irrigation water. Irrigation technology, organizational integrity, crop types, water price management, local economic level, and channel seepage prevention are the most critical factors affecting the effective use of irrigation water. The grey model prediction results show that the effective utilization coefficient of farmland irrigation water will continuously increase and reach 0.7204 in 2029. This research can serve as a reference for government authorities to make scientific decisions on water-saving projects in irrigation districts in terms of management, operation, and investment.

Math coaches, specialists, and student achievement: Learning from the data

2020 ◽  
Vol 102 (3) ◽  
pp. 42-45
Author(s):  
Kristin E. Harbour ◽  
Evthokia Stephanie Saclarides
Keyword(s):  
Student Achievement ◽  
Teaching And Learning ◽  
Large Scale ◽  
Elementary Mathematics ◽  
National Assessment ◽  
Mathematics Coaches ◽  
Professional Development Model ◽  
Using Data ◽  
The Relationship ◽  
Practical Implications

To support continuous professional development model in the teaching and learning of mathematics, many districts and schools have begun hiring elementary mathematics coaches and/or specialists (MCSs). However, limited large-scale empirical research exists that determines how the use of MCSs affect student learning and achievement. Kristin E. Harbour and Evthokia Stephanie Saclarides begin to fill in this gap by using data from the National Assessment of Educational Progress to explore the relationship between the presence and responsibilities of elementary MCSs and 4th-grade student achievement in mathematics. Based on their findings, they share practical implications for districts and administrators to consider.

Benchmarking driving efficiency using data science techniques applied on large - scale smartphone data

2018 ◽  
Author(s):  
Δημήτριος Τσελέντης
Keyword(s):  
Data Envelopment Analysis ◽  
Convex Hull ◽  
Large Scale ◽  
Data Science ◽  
Data Envelopment ◽  
Using Data

Ο κύριος στόχος της παρούσας διδακτορικής διατριβής είναι η ανάπτυξη μιας ολοκληρωμένης μεθοδολογικής προσέγγισης για τη συγκριτική αξιολόγηση της οδηγικής επίδοσης, όσον αφορά την οδική ασφάλεια, τόσο σε επίπεδο διαδρομής, όσο και οδηγού, με τη χρήση τεχνικών της επιστήμης δεδομένων. Η μεθοδολογική προσέγγιση στηρίζεται στον καθορισμό ενός δείκτη επίδοσης που βασίζεται στη θεωρία της Περιβάλλουσας Ανάλυσης Δεδομένων (Data Envelopment Analysis - DEA) και σχετίζεται με μακροσκοπικά συμπεριφοριστικά χαρακτηριστικά οδήγησης, όπως ο αριθμός των απότομων επιταχύνσεων/ επιβραδύνσεων, ο χρόνος χρήσης του κινητού τηλεφώνου και ο χρόνος υπέρβασης του ορίου ταχύτητας. Ακόμα, αναπτύσσονται μοντέλα μηχανικής μάθησης για τον προσδιορισμό διακριτών προφίλ οδήγησης που βασίζονται στη χρονική εξέλιξη της οδηγικής επίδοσης. Η προτεινόμενη μεθοδολογική προσέγγιση εφαρμόζεται σε πραγματικά δεδομένα οδήγησης ευρείας κλίμακας που συλλέγονται από έξυπνες συσκευές κινητών τηλεφώνων (smartphones), τα οποία αναλύονται μέσω στατιστικών μεθόδων για τον προσδιορισμό της απαιτούμενης ποσότητας δεδομένων οδήγησης που θα χρησιμοποιηθούν στην ανάλυση. Τα αποτελέσματα δείχνουν ότι ο βελτιστοποιημένος αλγόριθμος convex hull – DEA δίνει εξίσου ακριβή και ταχύτερα αποτελέσματα σε σχέση με τις κλασικές προσεγγίσεις της DEA. Ακόμα, η μεθοδολογία επιτρέπει τον προσδιορισμό των λιγότερο αποδοτικών ταξιδιών σε μια βάση δεδομένων καθώς και το αποδοτικό επίπεδο οδηγικών στοιχείων ενός ταξιδιού για να καταστεί αποδοτικότερη από την άποψη της ασφάλειας. Η περαιτέρω ομάδοποίηση των οδηγών με βάση της απόδοσή τους σε βάθος χρόνου οδηγεί στον εντοπισμό τριών ομάδων οδηγών, αυτή του μέσου οδηγού, του ασταθή οδηγού και του λιγότερο επικίνδυνου οδηγού. Τα αποτελέσματα δείχνουν ότι η εκ των προτέρων γνώση σχετικά με το ιστορικό ατυχημάτων του χρήστη φαίνεται να επηρεάζουν μόνο τη σύσταση της δεύτερης συστάδας των πιο ασταθών οδηγών, η οποία ενσωματώνει τους οδηγούς που είναι λιγότερο αποδοτικοί και ασταθής ως προς την ασφάλεια. Φαίνεται επίσης ότι η χρήση κινητών τηλεφώνων δεν αποτελεί κρίσιμο παράγοντα για τον καθορισμό της επίδοσης της ασφάλειας ενός οδηγού, καθώς διαπιστώθηκαν μικρές διαφορές σε σχέση με αυτό το χαρακτηριστική οδήγησης μεταξύ οδηγών διαφορετικών κατηγοριών επίδοσης. Επιπλέον, δείχνεται ότι απαιτείται μια διαφορετική δειγματοληψίας δεδομένων οδήγησης για κάθε α) οδικό τύπο, β) χαρακτηριστικό οδήγησης και γ) οδηγική επιθετικότητα για να συγκεντρωθούν αρκετά δεδομένα και να αποκτηθεί μια σαφής εικόνα της οδηγικής συμπεριφοράς και να εκτελεστεί ανάλυση με χρήση DEA. Τα αποτελέσματα θα μπορούσαν να αξιοποιηθούν για την παροχή εξατομικευμένης ανατροφοδότησης στους οδηγούς σχετικά με τη συνολική τους οδηγική επίδοση και την εξέλιξή της, προκειμένου να βελτιωθεί και να μειωθεί ο κίνδυνος ατυχήματος.

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