Estimating and source analysis of surface PM2.5 concentration in the Beijing–Tianjin–Hebei region based on MODIS data and air trajectories

Particulate matter (PM) with an aerodynamic diameter of <2.5 μm (PM_2.5) has become the primary air pollutant in most major cities in China. Some studies have indicated that there is a positive correlation between the aerosol optical thickness (AOT) and surface-level PM_2.5 concentration. In order to estimate PM_2.5 concentration over large areas, a model relating the concentration of PM_2.5 and AOT has been established. The scale height of aerosol and relative humidity as well as the effect of surface temperature and wind velocity were introduced to enhance the model. 2013 full year Moderate Resolution Imaging Spectroradiometer (MODIS) AOT data and ground measurements of the PM_2.5 concentration in the Beijing–Tianjin–Hebei region were used to fit a seasonal multivariate linear equation relating PM_2.5 concentration and AOT, and the accuracy of the model has been determined. When comparing MODIS-estimated PM_2.5 with the measurements from ground monitoring stations during spring, summer, autumn and winter, we found the R² values were 0.45, 0.45, 0.37, and 0.31, respectively. Based on this model, the spatial distribution of PM_2.5 concentration during four typical haze events sampled by seasons was derived, and displayed with the backward air trajectories calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We undertook a preliminary analysis about the source of surface-level PMs and the process of its accumulation and dispersion during the haze episodes by analysing the effect of terrain and topography in the specific location of the Beijing–Tianjin–Hebei region. The spatial distribution of the PM_2.5 concentration showed that the high value region was generally in the southeast of the study area, which approximately overlapped an area of lower vegetation coverage, and the temporal variation of PM_2.5 concentration indicated that the air pollution was more severe during winter and spring than summer and autumn. The results of the analysis of backward air trajectories suggested that the hazy weather in the Beijing–Tianjin–Hebei region was mainly caused by unfavourable terrain and weather conditions.     

Identification and spatio-temporal analysis of earthquake clusters using SOM–DBSCAN model

Neural Computing and Applications - Seismic catalogs are vital to understanding and analyzing the progress of active fault systems. The background seismicity rate in a seismic catalog, strongly...     

GRDM—A digital field-mapping tool for management and analysis of field geological data

Field geological observations have both spatial and non-spatial aspects and recording them directly on a personal computer using a digital mapping tool has become a practical and effective alternative to traditional methods of field data collection and mapping. This paper presents the design of a cost-effective, stand-alone digital field-mapping tool named GRDM that caters to special requirements of field-based studies concerned with spatial disposition of the statistics of field measurements. Such studies require recording multiple observations for individual attributes at each field location to capture the inter-site variability and automatic computation of their statistics. Field observations include directional data that are circular in nature. Therefore, computation of their exclusive statistics within the field system is also necessary. To meet these requirements, GRDM was designed for field personnel lacking expertise in customizing a GIS. Its design automatically accommodates a list of values for each non-spatial attribute attached to individual location points and generates statistics from the lists. The system treats the orientation values as a distinct numeric data type and computes circular statistics for them. It makes both the original data as well as their statistics simultaneously available for extraction of thematic information.     

Vibrotactile and vibroacoustic communications: pairs in interaction and play—an interactive structure and bodies in an urban environment

We designed a vibrotactile vest and The Humming Wall, a vibroacoustic interactive furniture set in an urban environment to interact with each other. We developed the vibrotactile patterns in the vest as a form of vibrotactile language to convey information to the wearer. In addition, we designed a set of interactive movements on The Humming Wall that would trigger patterns on the vest and elicit sensations and encourage body movements onto the wearer’s body. We invited people to interact in pairs at The Humming Wall, with one at the wall and one wearing the vest (they later swapped roles). Actions by the one at the wall, such as swiping or knocking on the wall were repeated on the vest wearer’s body. In addition, participants could ‘feel’ (vibroacoustically) and hear their own heartbeats and breath rates at the wall. We conducted a field trial with 39 participants over a 5-week period. Participants wearing the vest (and their pair) completed a set of tasks. We logged use and responses, recorded all activities on video and conducted post-experiment interviews and questionnaires. The results depicted the participants’ experience, communication and connection while wearing the vibrotactile vest and interacting with the wall. The findings show convincing, strong and positive responses to novel interactions between the responsive vibroacoustic environment and the vibrotactile vest. This work constitutes the first field trial with people ‘working’ in pairs with a vibrotactile wearable responding to and driving vibroacoustic displays with an interactive vibroacoustic environment.     

Situating the “music of” households and firemen in semi-open spaces: contribution to the analysis of collective activity’s spatio-temporal dynamics

Cognition, Technology & Work - Examining different team dynamics and understanding collective activities in home environments are two important challenges for ergonomics and its related fields....     

A proposal for future data organization in enterprise systems—an analysis of established database approaches

The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.

     

Chance discovery in medicine — Detection of rare risky events in chronic diseases —

One of the most important characteristics of chance discovery is that it focuses on the specific events or patterns in which the essential nature of an applied domain is implicitly included. The understanding and forecasting of such patterns and events will have a significant impact on decision making in the applied domain. This paper discusses the meaning of chance discovery from the viewpoint of medicine. Since chance discovery in medicine can be viewed as the way to find a suitable occasion for some critical actions or to check the dangerous possibilities, called rare risky events, detection and interpretation of rare but important events are ones of the components that supports chance discovery. According to this observation, several approaches for detecting rare events were introduced and evaluated by a small dataset on neurological diseases. Experimental results show that a set of events which include rare risky events can be detected by the introduced detection method, though interpretation by domain experts is required for selection of such events. Shusaku Tsumoto, Ph.D.: He graduated from Osaka University, School of Medicine in 1989. After residents of neurology in Chiba University Hospital, he was involved in developing hospital information system in Chiba University Hospital. He moved to Tokyo Medical University in 1993 and started his research on rough sets and data mining in medicine. He received his Ph.D (Computer Science) from Tokyo Institute of Technology in 1997, and is now a Professor at Department of Medical Informatics, Shimane Medical University. His interests include approximate reasoning, data mining, fuzzy sets, knowledge acquisition, mathematical theory of data mining, and rough sets (alphabetical order).     

SEDRET—an intelligent system for the diagnosis and prediction of events in power plants

Artificial Intelligence applications in large-scale industry, such as fossil power plants, require the ability to manage uncertainty and time. In this paper, we present an intelligent system to assist an operator of a power plant. This system, called SEDRET, is based on a novel knowledge representation of uncertainty and time, called Temporal Nodes Bayesian Networks (TNBN), a type of Probabilistic Temporal Network. A set of temporal nodes and a set of edge define a TNBN, each temporal node is defined by a value of a variable and a time interval associate to the change of variable value. A TNBN generates a formal and systematic structure for modeling the temporal evolution of a process under uncertainty. The inference mechanism is based on probabilistic reasoning. A TNBN can be used to recognize events and state variables with respect to current plant conditions and predict the future propagation of disturbances. SEDRET was validated with the diagnosis and prediction of events in a steam generator with a power plant training simulator. The results performed in this work indicate that SEDRET can potentially improve plant availability through early diagnosis and prediction of disturbances that could lead to plant shutdown.     

Student trajectories in high school mathematics: Issues of choice,support, and identity‐making

Abstract

This study explores the range of experiences students have when making two kinds of decisions in relation to high school mathematics courses: what course to take, and how to and how much to apply themselves. Looking at the choices of students about to enter Grade 10, the first decision is their choice of courses. In mathematics, students leaving Grade 9 selected one course (usually), from five possibilities, for their Grade 10 year: an advanced‐placement Honours stream, an academic course with a traditional symbol‐manipulation approach, an academic course with a technology‐based applications approach, a non‐academic mathematics‐for‐citizenship course, and (as an imposed choice) repeating the Grade 9 mathematics course. The second point of decision‐making occurred within their mathematics and science courses—students constantly made choices about how, and how much, to apply themselves to the challenges of succeeding in the courses they had chosen. These students’ course choices, to a considerable extent, conformed to expectations based on the influence of socioeconomic status and prior achievement. Overwhelmingly, students were concerned more in the credentialing value of courses than their educational value or structural nature. Within their courses, most students focused their attention on doing the work rather than the content or the learning process. Students reported being encouraged to do their work by teachers, but could not provide any indication of tactical support with becoming effective learners. The final outcomes of the students’ marks suggest that, in the context of the study, Grade 10 mathematics courses are much more effective as gate‐keeping mechanisms than as opportunities for students to improve and succeed.     

A climatological study of the spatio-temporal variability of land surface temperature and vegetation cover of Vadodara district of Gujarat using satellite data

In the present study, a comprehensive assessment of the spatio-temporal variation of day-time and night-time land surface temperature (LST) and normalized difference vegetation index (NDVI) of Vadodara district of Gujarat in India from 2001 to 2012 has been carried out using satellite data. A significant cooling trend was observed in the day-time LST, whereas the night-time LST showed a distinct warming trend. The entire geographical extent of Vadodara was classified into different night-time LST classes to quantify the extent of the hot pockets, and it showed a clear-cut warming pattern for all the months of the year with an increase in the geographical areas under higher temperature range. Further analysis of Diurnal Temperature Range (DTR) also revealed a strong impact of the urbanization process, with annual DTR showing a decreasing trend at the rate of 0.29°C year^?1. An analysis of the vegetation cover of the district showed that on an average, the NDVI of the district increased during the study period. However, a micro-level examination of NDVI values depicted that the type of vegetation cover had drastically changed. The maximum NDVI values for months from May to December for 2012 were much lower than those of 2001 and 2006, indicating a change in vegetation pattern of the district. An assessment of the area under different NDVI values exhibited that for all the months of the year (except September), the total area with NDVI values of higher range (i.e. +0.5 and above) had substantially decreased from 2001 to 2012. The analysis revealed that for some of the months like February, while in 2001, 45% of district exhibited NDVI values above +0.5, but by 2012, it had decreased to only 18%, showing a drastic change in vegetation type and deterioration of the extent of thick dense vegetation.     

Quality and the efficiency of data in “Smart-Cities”

As a new form of sustainable development, the concept “Smart Cities” knows a large expansion during the recent years. It represents an urban model, refers to all alternative approaches to metropolitan ICTs case to enhance quality and performance of urban service for better interaction between citizens and government. However, the smart cities based on distributed and autonomous information infrastructure contains millions of information sources that will be expected more than 50 billion devices connected by using IoT or other similar technologies in 2020. In Information Technology, we often need to process and reason with information coming from various sources (sensors, experts, models). Information is almost always tainted with various kinds of imperfection: imprecision, uncertainty, ambiguity, we need a theoretical framework general enough to allow for the representation, propagation and combination of all kinds of imperfect information. The theory of belief functions is one such Framework. Real-time data generated from autonomous and distributed sources can contain all sorts of imperfections regarding on the quality of data e.g. imprecision, uncertainty, ignorance and/or incompleteness. Any imperfection in data within smart city can have an adverse effect over the performance of urban services and decision making. In this context, we address in this article the problem of imperfection in smart city data. We will focus on handling imperfection during the process of information retrieval and data integration and we will create an evidential database by using the evidence theory in order to improve the efficiency of smart city. The expected outcomes from this paper are (1) to focus on handling imperfection during the process of information retrieval and data integration (2) to create an evidential database by using the evidence theory in order to improve the efficiency of smart city. As experimentation we present a special case of modeling imperfect data in the field of Healthcare. An evidential database will be built which will contain all the perfect and imperfect data. These data come from several Heterogeneous sources in a context of Smart Cities. Imperfect aspects in the evidential database expressed by the theory of beliefs that will present in this paper.     

Behavioural data in the design of ergonomic computer terminals and workstations—a case study

Abstract

Manufacturers of office automation equipment, in their design of ergonomic computer terminals and office furniture, are encountering needed design data which is sometimes weak, absent, erroneous or inapplicable to the office-worker situation under consideration. In some instances, office automation manufacturers simply follow standards. In others, manufacturers make assumptions as to the best design, and in others, design decisions were based on testing results and conclusions.This paper reviews the ergonomic tools and features incorporated into the design of one office automation manufacturer's terminals and workstations. The short fall of applicable design data relevant to comfort versus extremes of motion in biomechanics, to keyboard home row height and slope angle to viewing distance, as well as to reverse video and surface colour are presented, along with the design resolution of these parameters. Further, certain aspects of posture recently published are discussed.     

The role of social motivations in e-learning: How do they affect usage and success of ICT interactive tools?

There is an increasing interest among educational institutions and private organizations to understand the role of ICT interactive tools (e.g. forums, blogs, chats, blackboards, newsgroups) in the successful implementation of an e-learning system. In this study, we offer a social perspective in the study of e-learning, and posit that individuals’ actions are socially embedded. Therefore, we attempt to identify social motivations that underlie learners’ attitudes and usage behavior of ICT interactive tools. We propose a comprehensive conceptual framework that identifies two groups of social motivations: (1) anticipated reciprocal relationships and (2) anticipated extrinsic rewards. The empirical test of the framework in a university setting reveals that both types of social motivations significantly influence learners’ attitudes. Specifically, social influence and altruism, both of which relate to reciprocal relationships, and recognition by the instructor, which refers to extrinsic rewards and personal benefits, exert a strong positive effect on attitudes toward and usage of ICT interactive tools. The usage leads to improved intentions to continue using these technologies in the future.     

Optimization of sequences in CDMA systems: A statistical–mechanics approach

A statistical–mechanics approach is useful not only in analyzing the macroscopic system performance of wireless communication systems, but also in discussing design problems of wireless communication systems. We discuss a design problem of spreading sequences in code-division multiple-access (CDMA) systems, as an example demonstrating the usefulness of the statistical–mechanics approach. We analyze, via the replica method, the average mutual information between inputs and outputs of a randomly spread CDMA channel, and discuss the optimization problem with the average mutual information as the measure of optimization. It has been shown that the average mutual information is maximized by orthogonally-invariant random Welch bound equality (WBE) spreading sequences. This finding is an extension of the optimality of WBE spreading sequences with Gaussian inputs for non-Gaussian inputs.     

Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.     

Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 10¹² g) over a total area of 66.01 × 10⁴ km² between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R² = 0.05, p > 0.05). (2) The mean AGB density was 28.9 g C m^?2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R² = 0.41, p < 0.001), while temperature seemed to be a minor factor (R² = 0.14, p < 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R² = 0.45, p < 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.