A STATISTICAL RESEARCH ON THE TYPICAL PATTERNS OF MODERN HOUSING FABRICS, CASE STUDY OF NANJING, CHINA

Proceedings of the XXV ISUF International Conference “Urban Form and Social Context: from Traditions to Newest Demands” (Krasnoyarsk, July 5–9, 2018)After nearly 20 years of massive social housing construction and another 20 years of housing real estate development, Chinese cities basically solved the citizen’s housing problem in the second decade of the 21st century. As a consequence, the major physical component of contemporary cities is modern housing fabrics, which cover over 30% urban land. It is generally believed this magnitude housing development is dominated by modernism residential building with a standard image of a slab apartment. However, as revealed in this research, the real situation is far more diversified and complicated, with various building types, like villas, slabs, towers, and different spatial arrangements, like parallel, zigzag, enclosure. How to classify these diversified realities and what are the typical patterns of different housing fabrics? To answer these questions, this research collected more than 200 housing fabric samples across the city of Nanjing. The latter is the Capital of Jiangsu Province, and a typical modern mega-city in Yangzi River Delta area. To get the reasonable categories of fabric types, a comprehensive classification system is applied. Different from the too simplified classification based on single parameter, building height, adopted in the national housing standard, this classification system is based on the matrix of various parameters, including building height, arrangement, and a building type. The various parameters and their intricate combinations guarantee the classification to be capable to seize and distinguish the formal features of different fabrics. Spacemate, a charting tool developed by B.M. Pont and et al. in TU Delft, is used to testify the classification. After the classification, the samples are divided into 21 categories. For each category, data samples, like spacing, dimension of building footprint, height, density, land coverage, and et al. are collected and a statistical analysis are conducted. Based on this qualitative sample studies, the typical patterns and their statistical models are built up. In the application part, a bioclimatic performance study of these typical patterns is presented. Due to the typicality and statistical precision, the complicated co-relation between urban fabric and bioclimatic performance could be discovered, efficiently and convincingly

Exploration of Contributing Factors of Different Distracted Driving Behaviors

The motivation of this research is to explore the contributing factors of driving distraction and compare the contributing factors for three typical distracted driving behaviours: drinking water, answering a phone and using mobile phone application (APP) while driving. An online survey including a driving behaviour scale and the Theory of Planned Behaviour Questionnaire (TPB Questionnaire) was conducted to obtain data related to these driving distractions. An integral structural equation model based on the Theory of Planned Behaviour (TPB) was established to explain the factors causing three typical distracted behaviours, and the causes of differences for three typical distracted behaviours were compared. The result shows that the attitudes and perceived behaviour control are the main factors causing distracted behaviours, and the subjective norm has a significant impact on answering a phone while driving. The occurrence of a distracted driving behaviour is the consequence of behaviour intention and perceived behaviour control. These conclusions provide insights for implementing behaviour modification and traffic laws education.</p

Dietary compounds in modulation of gut microbiota-derived metabolites

Gut microbiota, a group of microorganisms that live in the gastrointestinal tract, plays important roles in health and disease. One mechanism that gut microbiota in modulation of the functions of hosts is achieved through synthesizing and releasing a series of metabolites such as short-chain fatty acids. In recent years, increasing evidence has indicated that dietary compounds can interact with gut microbiota. On one hand, dietary compounds can modulate the composition and function of gut microbiota; on the other hand, gut microbiota can metabolize the dietary compounds. Although there are several reviews on gut microbiota and diets, there is no focused review on the effects of dietary compounds on gut microbiota-derived metabolites. In this review, we first briefly discussed the types of gut microbiota metabolites, their origins, and the reasons that dietary compounds can interact with gut microbiota. Then, focusing on gut microbiota-derived compounds, we discussed the effects of dietary compounds on gut microbiota-derived compounds and the following effects on health. Furthermore, we give our perspectives on the research direction of the related research fields. Understanding the roles of dietary compounds on gut microbiota-derived metabolites will expand our knowledge of how diets affect the host health and disease, thus eventually enable the personalized diets and nutrients

A micro neural network for healthcare sensor data stream classification in sustainable and smart cities

A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities

Preparation and PEGylation of recombinant human interferon lambda3

The purpose of this study was to express recombinant human interferon lambda3 (rhIFN-λ3) in Escherichia coli, and prepare PEGylated recombinant human interferon lambda3 (PEG-rhIFN-λ3). The rhIFN-λ3 gene was inserted into pThioHisA vector after codon optimization and transformed into E. coli top10 strain, and then it was induced with isopropyl-β-D-thio-galactoside (IPTG). The recombinant protein was subjected to mPEG-ButyrALD modification after dialysis, renaturation and chromatographic purification. Subsequently, the modified product was preliminary isolated and purified for determining its activity. Results show that the recombinant protein was expressed in the form of inclusion bodies. After ion exchange, molecular sieve and other column chromatography purification, the purity of the purified rhIFN-λ3 was as high as 90% and the purity of the mono-PEGylated rhIFN-λ3 after cation-exchange chromatography was as high as 86%. The 50% effective concentration (EC50) of rhIFN-λ3 in WISH cells against vesicular stomatitis virus (VSV) was 8.43 ng/mL, while the EC50 of mono-PEGylated rhIFN-λ3 was 49.19 ng/mL, which reserved 17.14% of the in vitro activity and supported further studies of this new type of investigational interferon. Further study is needed to better understand the in vivo immunogenicity, antigenicity, stability and antiviral activity of PEG-rhIFN-λ3.Keywords: Recombinant human interferon lambda3, prokaryotic expression, purification, mPEG-ButyrALD, antiviral activity

Design and preparation of a novel colon-targeted tablet of hydrocortisone

The objective of this research was to design a new colon-targeted drug delivery system based on chitosan. The properties of the films were studied to obtain useful information about the possible applications of composite films. The composite films were used in a bilayer system to investigate their feasibility as coating materials. Tensile strength, swelling degree, solubility, biodegradation degree, Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), Scanning Electron Microscope (SEM) investigations showed that the composite film was formed when chitosan and gelatin were reacted jointly. The results showed that a 6:4 blend ratio was the optimal chitosan/gelatin blend ratio. In vitro drug release results indicated that the Eudragit- and chitosan/gelatin-bilayer coating system prevented drug release in simulated intestinal fluid (SIF) and simulated gastric fluid (SGF). However, the drug release from a bilayer-coated tablet in SCF increased over time, and the drug was almost completely released after 24h. Overall, colon-targeted drug delivery was achieved by using a chitosan/gelatin complex film and a multilayer coating system