This paper presents a cloud-based system framework based on Bigtable and MapReduce as the data storage and processing paradigms for providing a web-based service for viewing, storing, and analyzing ...massive building information models (BIMs). Cloud and Web 3D technologies were utilized to develop a BIM data center that can handle the big data of massive BIMs using multiple servers in a distributed manner and can be accessed by multiple users to concurrently submit and view BIMs online in 3D. Traditional BIM include only static information such as the geometric parameters, physical properties, and spatial relations for modeling a physical space. In this study, BIM was extended to dynamic BIM, which includes dynamic data such as historical records from the monitoring of the facility environment and usage. Owing to this extension, a dynamic BIM became a parametric model, which can be used to simulate user behaviors. On the client side, this study applied WebGL in the web interface development to achieve the display of BIMs in 3D on browsers. Users can access the services via various online devices anytime and anywhere to view the 3D model online. On the server side, this study used Apache Hadoop, which can utilize multiple servers to provide mass storage spaces in a distributed manner with Bigtable-like structured storage, to establish the BIM data center. A schema for storing the big data of massive dynamic BIMs in Bigtables was proposed. MapReduce, a Hadoop component for the parallel processing of large data sets, was utilized to process big data from dynamic BIMs. A big data analysis framework to effectively retrieve and calculate required information from dynamic BIMs in the data center for various applications by MapReduce distributed computing was proposed this study. We provide principle and architecture of the proposed framework along with its experimental assessment. The results confirmed that scalable and reliable management of massive BIMs can be achieved using the proposed framework.
•Introducing a cloud-based tool for viewing, storing, and analyzing massive Building Information Models online.•BIM was extended to dynamic BIM, which includes dynamic data of BIM.•WebGL technology was employed to display BIMs in 3D on browsers.•Schema of Bigtables for storing BIMs and dynamic data in HBase was developed.•MapReduce distributed computing was employed to process big data of dynamic BIMs.
Abstract
This paper introduces a method for analysing motion patterns that can be utilised to optimise data-driven systems. The aim is to use surveillance cameras and artificial intelligence to track ...multiple objects in a reliable manner, thereby preserving the authenticity of movement patterns for numerous and similar objects. In a case study, this method is applied to optimize lighting conditions in animal husbandry. Furthermore, this approach can be utilized not only in animal husbandry but also in other domains.
This paper discusses the concepts of aesthetic and nonaesthetic in streetscapes. The nonaesthetic refers to physical elements and spatial compositions that make the aesthetic emerge differently ...depending on the observer who sees them. Using Ashihara’s (1979) methodology for evaluating aesthetic townscapes and Sibley’s (1959) concept of aesthetic properties, this paper proposes a quantification of the nonaesthetic of streetscapes, focusing on streets in Ginza, Tokyo, and examining the spatial clustering of their nonaesthetic. For this, we used deep learning to quantify the distribution of physical elements and their spatial composition and a spatial clustering analysis to uncover hidden patterns in the streetscapes. These methodologies enabled us to assess streets across neighborhoods and districts and produce a comparative microscale analysis that covers a wider area. Thus, our study combines the fields of aesthetics, architecture, urban planning, and community design.
In this paper, we review some advances made recently in the study of
mobile phone datasets
. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized ...datasets, and has grown into a stand-alone topic. We survey the contributions made so far on the
social networks
that can be constructed with such data, the study of
personal mobility
,
geographical partitioning
,
urban planning
, and
help towards development
as well as
security and privacy issues
.
Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI ...resistance, but direct clinical evidence is lacking.
Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets.
Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets.
We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.
Ship energy consumption and emission prediction are critical for ship energy efficiency management and pollution gas emission control, both of which are major concerns for the shipping industry and ...hence continue to attract global attention and research interest. This article examined the energy efficiency data sources, big data analysis for energy efficiency, and analyzed the ship energy consumption and emission prediction models. The ship energy consumption and pollution gas emission prediction models are comprehensively summarized based on the modeling method and principles. The theoretical analysis and artificial intelligence-based ship energy consumption model, as well as the top-down and bottom-up ship emission prediction models, are thoroughly examined in terms of influencing factors, model accuracy, data sources, and practical applications. On this basis, the challenges of ship energy consumption and emission prediction are discussed, and future research suggestions are proposed, providing a foundation for the development of ship energy consumption and emission prediction technologies. The analysis results show that the principles, parameters of concern, and data quality all have a significant impact on the performance of the prediction models. Consequently, the prediction model's accuracy can be improved by combining intelligent algorithms and machine learning. In the future, high precision, self-adapting, ship fuel consumption and emission prediction models based on artificial intelligence technology should be further studied, in order to improve their prediction performance, and thus providing solid foundations for the optimization management and control of the ship energy consumption and emissions.
•The progress on the prediction of the ship's energy consumption and pollution gas emissions has been summarized in detail.•The characteristics and performance of different prediction models have been discussed.•The challenges are deeply analyzed, and the prospects are put forward.•This work will be regarded as an essential guidance for future research on the optimization methods and decision-making system development of ship energy consumption and emissions.
•Mobility behavior during 6 months of lockdown in Metro Manila is analyzed.•Changepoints in location patterns are determined.•Differences in driving, transit, and walking trips are examined.•Three ...transport policy implications are outlined and discussed.
The COVID-19 pandemic has affected human mobility via lockdowns, social distancing rules, home quarantines, and the full or partial suspension of transportation. Evidence-based policy recommendations are urgently needed to ensure that transport systems have resilience to future pandemic outbreaks, particularly within Global South megacities where demand for public transport is high and reduced access can exacerbate socio-economic inequalities. This study focuses on Metro Manila – a characteristic megacity that experienced one of the most stringent lockdowns worldwide. It analyzes aggregated cell phone and GPS data from Google and Apple that provide a comprehensive representation of mobility behavior before and during the lockdown. While significant decreases are observed for all transport modes, public transport experienced the largest drop (−74.5 %, on average). The study demonstrates that: (i) those most reliant on public transport were disproportionately affected by lockdowns; (ii) public transport was unable to fulfil its role as public service; and, (iii) this drove a paradigm shift towards active mobility. Moving forwards, in the short-term policymakers must promote active mobility and prioritize public transport to reduce unequal access to transport. Longer-term, policymakers must leverage the increased active transport to encourage modal shift via infrastructure investment, and better utilize big data to support decision-making.
Environmental exposure assessment is an important step in establishing a list of local priority pollutants and finding the sources of the threats for proposing appropriate protection measures. ...Exposome targeted and non-targeted analysis as well as suspect screening may be applied to reveal these pollutants. The non-targeted screening is a challenging task and requires the application of the most powerful analytical tools available, assuring wide analytical coverage, sensitivity, identification reliability, and quantitation.
Moscow, Russia, is the largest and most rapidly growing European city. That rapid growth is causing changes in the environment which require periodic clarification of the real environmental situation regarding the presence of the classic pollutants and possible new contaminants. Gas chromatography – high resolution time-of-flight mass spectrometry (GC-HR-TOFMS) with electron ionization (EI), positive chemical ionization (PCI), and electron capture negative ionization (ECNI) ion sources were used for the analysis of Moscow snow samples collected in the early spring of 2018 in nine different locations. Collection of snow samples represents an efficient approach for the estimation of long-term air pollution, due to accumulation and preservation of environmental contaminants by snow during winter period. The high separation power of GC, complementary ionization methods, high mass accuracy, and wide mass range of TOFMS allowed for the identification of several hundred organic compounds belonging to the various classes of pollutants, exposure to which could represent a danger to the health of the population. Although quantitative analysis was not a primary aim of the study, targeted analysis revealed that some priority pollutants exceeded the established safe levels. Thus, dibutylphthalate concentration was over 10-fold higher than its safe level (0.001 mg/L), while benzapyrene concentration exceeded Russian maximal permissible concentration value of 5 ng/L in three samples. The large amount of information generated during the combination of targeted and non-targeted analysis and screening samples for suspects makes it feasible to apply the big data analysis to observe the trends and tendencies in the pollution exposome across the city.
Display omitted
•Complementary ionization tools increase environmental analysis' scope and reliability.•Over 500 environmental pollutants were identified in Moscow snow samples.•Electron capture negative ionization helps detecting halogenated and nitro compounds.•Accurate mass measurements increase reliability of the non-targeted screening.
PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's ...competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.
Parallel to the increasing level of maturity of the field of research on higher education, an increasing number of scholarly works aims at synthesising and presenting overviews of the field. We ...identify three important pitfalls these previous studies struggle with, i.e. a limited scope, a lack of a content-related analysis, and/or a lack of an inductive approach. We take these limitations into account by analysing the abstracts of 16,928 articles on higher education between 1991 and 2018. To investigate this huge collection of texts, we apply topic models, which are a collection of automatic content analysis methods that allow to map the structure of large text data. After an in-depth discussion of the topics differentiated by our model, we study how these topics have evolved over time. In addition, we analyse which topics tend to co-occur in articles. This reveals remarkable gaps in the literature which provides interesting opportunities for future research. Furthermore, our analysis corroborates the claim that the field of research on higher education consists of isolated ‘islands’. Importantly, we find that these islands drift further apart because of a trend of specialisation. This is a bleak finding, suggesting the (further) disintegration of our field.
PDF
