scholarly journals Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 461 ◽  
Author(s):  
Luliang Tang ◽  
Jie Gao ◽  
Chang Ren ◽  
Xia Zhang ◽  
Xue Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Big Data ◽  
Large Scale ◽  
Scientific Basis ◽  
Small Sample ◽  
Fine Grained ◽  
Urban Construction ◽  
Urban Cluster ◽  
Novel Method ◽  
Land Use Functions

The design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban construction. Traditional methods for detecting urban clusters are inaccurate since the raw data is generally collected from small sample questionnaires of resident trips rather than large-scale studies. Spatiotemporal big data provides a new lens for understanding urban clusters in a natural and fine-grained way. In this article, we propose a novel method for Detecting and Evaluating Urban Clusters (DEUC) with taxi trajectories and Sina Weibo check-in data. Firstly, DEUC applies an agglomerative hierarchical clustering method to detect urban clusters based on the similarities in the daily travel space of urban residents. Secondly, DEUC infers resident demands for land-use functions using a naïve Bayes’ theorem, and three indicators are adopted to assess the rationality of land-use functions in the detected clusters—namely, cross-regional travel index, commuting direction index, and fulfilled demand index. Thirdly, DEUC evaluates the progress of urban cluster construction by calculating a proposed conformance indicator. In the case study, we applied our method to detect and analyze urban clusters in Wuhan, China in the years 2009, 2014, and 2015. The results suggest the effectiveness of the proposed method, which can provide a scientific basis for urban construction.

scholarly journals Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

2021 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Javier Argota Sánchez-Vaquerizo
Keyword(s):  
Big Data ◽  
Empirical Data ◽  
Large Scale ◽  
Smart Cities ◽  
Digital Twin ◽  
Fine Grained ◽  
Mobility Data ◽  
Digital Twins ◽  
Alternative Scenarios ◽  
Hourly Distribution

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

scholarly journals Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data

2021 ◽  
Vol 13 (18) ◽  
pp. 3666
Author(s):  
Jiaqi Shen ◽  
Yanmin Shuai ◽  
Peixian Li ◽  
Yuxi Cao ◽  
Xianwei Ma
Keyword(s):  
Land Use ◽  
Large Scale ◽  
Driving Forces ◽  
Temporal Analysis ◽  
Scientific Basis ◽  
Physical Geography ◽  
Urban Land Use ◽  
Land Use Classification ◽  
Landsat Images ◽  
Intensity Index

It is necessary to understand the relationship between the impervious surface area (ISA) distribution, variation trends and potential driving forces over Dongying, Shandong Province. We extracted ISA information from Landsat images with 3–5 year intervals during 1995 to 2018 using Minimum Noise Fraction (MNF) transform, Pixel Purity Index (PPI), and Linear Spectral Mixture Analysis (LSMA), followed by the analysis on three driving forces of ISA expansion (physical geography, socioeconomic factors, and urban cultural features). Our results show the retrieved ISA thematic map fit the limited requirement of root mean square error (RMSE). The correct classification accuracy of ISA is greater than 83.08%. Further, the cross–comparison exhibits the general consistent with the ISA distribution of the land use classification map published by the National Basic Geographic Information Center. The gradual increasing trend can be captured on the expansion of ISA from 1995 to 2018. Despite of the central region always shown as the high ISA density, it still keeps increasing annually and radiating the surrounding region, especially in the southward which has formed into a new large–scale and high intensity of ISA in 2015–2018. Though the ISA patches scattered in the west region or along the northern and eastern part of the ocean coastline are still small, the expansion trend of ISA can be detected. The expansion intensity index (EII) of ISA measuring the situation of its expansion changes from the lowest value 0.12% between 1995 and 2000 up to the highest 0.73% between 2000 and 2005. Richly endowed by nature, the city’s natural geographical environment provides an elevated chance of further urbanization. The rapid increase of regional economy provides a fundamental driving force for expanding ISAs. The development of urban culture promotes the sustainable development of ISAs. Our results provide a scientific basis for future urban land use management, construction planning, and environmental protection in Dongying.

scholarly journals Spatial Variation and Terrain Gradient Effect of Ecosystem Services in Heihe River Basin over the Past 20 Years

2021 ◽  
Vol 13 (20) ◽  
pp. 11271
Author(s):  
Lingge Zhang ◽  
Ningke Hu
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
River Basin ◽  
Large Scale ◽  
Scientific Basis ◽  
Heihe River Basin ◽  
Heihe River ◽  
Production Volume ◽  
Gradient Effect ◽  
General Quality

With the advent of large-scale development, extreme imbalance in the ecology of the Heihe River Basin (HRB) has caused a series of ecological problems. In order to explore the spatiotemporal variation of ecosystem services (ESs) and to assess the characteristics of ESs under the terrain gradient effect (TGE), the three key ESs were quantified based on the InVEST model using five series of land-use data obtained from remote sensing images from 2000 to 2020 in this study. The terrain index was used to analyze the influence of terrain on ESs. The results show that most of the ESs were in high numbers in the south and low numbers in the north, as well as high numbers in the middle and upper reaches and low numbers at downstream locations. It was found that high-quality habitats degrade to general-quality habitats, and poor-quality habitats evolve into general-quality habitats. It was also found that the water production volume continues to decline and soil conservation becomes relatively stable with little change. This study illustrates different ESs showing obvious TGE with changes in elevation and slope. These results indicate that the effect of land-use change is remarkable and TGE is highly important to ESs in inland watersheds. This research study can provide a scientific basis for the optimization of regional ecosystem patterns. The results are of great significance in terms of rational planning land use, constructing ecological civilizations, and maintaining the physical conditions of land cover at inland river basins.

CONTRIBUTION OF DNA COPY-NUMBER VARIATION (CNV) TO CANCER SUSCEPTIBILITY AND LARGE-SCALE GENOME ALTERATIONS IN OSTEOSARCOMA (OS)

2008 ◽  
Vol 31 (4) ◽  
pp. 19
Author(s):  
I Pasic ◽  
A Shlien ◽  
A Novokmet ◽  
C Zhang ◽  
U Tabori ◽  
...  
Keyword(s):  
Genomic Instability ◽  
Large Scale ◽  
Structural Variation ◽  
Age Of Onset ◽  
Cancer Susceptibility ◽  
Small Sample Size ◽  
Snp Array ◽  
Tumour Suppressor Gene ◽  
Small Sample ◽  
International Hapmap Project

Introduction: OS, a common Li-Fraumeni syndrome (LFS)-associated neoplasm, is a common bone malignancy of children and adolescents. Sporadic OS is also characterized by young age of onset and high genomic instability, suggesting a genetic contribution to disease. This study examined the contribution of novel DNA structural variation elements, CNVs, to OS susceptibility. Given our finding of excessive constitutional DNA CNV in LFS patients, which often coincide with cancer-related genes, we hypothesized that constitutional CNV may also provide clues about the aetiology of LFS-related sporadic neoplasms like OS. Methods: CNV in blood DNA of 26 patients with sporadic OS was compared to that of 263 normal control samples from the International HapMap project, as well as 62 local controls. Analysis was performed on DNA hybridized to Affymetrix genome-wide human SNP array 6.0 by Partek Genomic Suite. Results: There was no detectable difference in average number of CNVs, CNV length, and total structural variation (product of average CNV number and length) between individuals with OS and controls. While this data is preliminary (small sample size), it argues against the presence of constitutional genomic instability in individuals with sporadic OS. Conclusion: We found that the majority of tumours from patients with sporadic OS show CN loss at chr3q13.31, raising the possibility that chr3q13.31 may represent a “driver” region in OS aetiology. In at least one OS tumour, which displays CN loss at chr3q13.31, we demonstrate decreased expression of a known tumour suppressor gene located at chr3q13.31. We are investigating the role ofchr3q13.31 in development of OS.

Enzymatic Synthesis of 15N-L-aspartic Acid Using Recombinant Aspartase from Escherichia Coli K12

2008 ◽  
Vol 59 (11) ◽  
Author(s):  
Iulia Lupan ◽  
Sergiu Chira ◽  
Maria Chiriac ◽  
Nicolae Palibroda ◽  
Octavian Popescu
Keyword(s):  
Amino Acids ◽  
Aspartic Acid ◽  
Enzymatic Synthesis ◽  
Large Scale ◽  
Recombinant Dna ◽  
Industrial Enzymes ◽  
Recombinant Dna Technology ◽  
Bacterial Fermentation ◽  
Natural Protein ◽  
Novel Method

Amino acids are obtained by bacterial fermentation, extraction from natural protein or enzymatic synthesis from specific substrates. With the introduction of recombinant DNA technology, it has become possible to apply more rational approaches to enzymatic synthesis of amino acids. Aspartase (L-aspartate ammonia-lyase) catalyzes the reversible deamination of L-aspartic acid to yield fumaric acid and ammonia. It is one of the most important industrial enzymes used to produce L-aspartic acid on a large scale. Here we described a novel method for [15N] L-aspartic synthesis from fumarate and ammonia (15NH4Cl) using a recombinant aspartase.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Strategic resources and smallholder performance at the bottom of the pyramid

2019 ◽  
Vol 22 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Matthias Olthaar ◽  
Wilfred Dolfsma ◽  
Clemens Lutz ◽  
Florian Noseleit
Keyword(s):  
Large Scale ◽  
Business Environment ◽  
Agricultural Economics ◽  
Local Market ◽  
Bottom Of The Pyramid ◽  
Fine Grained ◽  
Strategic Resources ◽  
Relative Contribution ◽  
The Relationship ◽  
Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

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