Original Data Sources Research Articles

Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan

The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network.

Last Interglacial sea-level data points from Northwest Europe

Abstract. Abundant numbers of sites and studies exist in NW Europe that document the geographically and geomorphologically diverse coastal record from the Last Interglacial (Eemian, Ipswichian, Marine Isotope Stage 5e). This paper summarises a database of 146 known Last Interglacial sea-level data points from in and around the North Sea (35 entries in the Netherlands, 10 Belgium, 23 in Germany, 17 in Denmark, 9 in Britain) and the English Channel (24 entries for the British and 25 for the French side, 3 on the Channel Isles) believed to be a representative and fairly complete inventory and assessment from ∼80 published sites. The geographic distribution (∼1500 km SW–NE) across the near field of the Scandinavian and British ice sheets and the attention paid to relative and numeric age control are assets of the NW European database. The research history of Last Interglacial coastal environments and sea level for this area is long, methodically diverse and spread through regional literature in several languages. Our review and database compilation effort drew from the original regional literature and paid particular attention to distinguishing between sea-level index points (SLIPs) and marine and terrestrial limiting points. We also incorporated an updated quantification of background rates of basin subsidence for the central and eastern North Sea region, utilising revised mapping of the base Quaternary, to correct for significant basin subsidence in this depocentre. As a result of subsidence, lagoonal and estuarine Last Interglacial shorelines of the Netherlands and the German Bight are preserved below the surface. In contrast, Last Interglacial shorelines along the English Channel are encountered above modern sea level. This paper describes the dominant sea-level indicators from the region compliant with the WALIS database structure and referenced to original data sources (https://doi.org/10.5281/zenodo.6478094, Cohen et al., 2021). The sea-level proxies are mostly obtained from locations with good lithostratigraphic, morphostratigraphic and biostratigraphical constraints. Most continental European sites have chronostratigraphic age control, notably through regional pollen association zones with duration estimates. In all regions, many SLIPs and limiting points have further independent age control from luminescence, uranium series, amino acid racemisation and electron spin resonance dating techniques. Main foreseen usage of this database for the near-field region of the European ice sheets is in glacial isostatic adjustment modelling and fingerprinting Last Interglacial ice sheet melt.

Revisiting party system structuration in Latin America and Europe: Economic and socio-cultural dimensions

The degree to which party systems are ideologically and programmatically structured is central to democracy. This article analyzes differences in the extent and nature of programmatic structuration in Latin America and Europe, using a new original data source, the Chapel Hill Expert Survey-Latin America (CHES-LA), in combination with the long-standing CHES-Europe. First, we demonstrate the reliability of CHES-LA in relation to CHES-Europe, and substantiate its validity by comparing it to other expert, elite and party manifesto surveys in Latin America. Using confirmatory factor analysis, we then show that while party system structuration in Latin America is somewhat lower than in Europe, it is also of a decisively different nature. In Latin America economic and socio-cultural policy positions are largely captured in a single overlapping dimension; in Europe, by contrast, competition occurs overwhelmingly along two dimensions, each with distinct clusters of policy positions.

VDM-DA: Virtual Domain Modeling for Source Data-Free Domain Adaptation

Domain adaptation aims to leverage a label-rich domain (the source domain) to help model learning in a label-scarce domain (the target domain). Most domain adaptation methods require the co-existence of source and target domain samples to reduce the distribution mismatch. However, access to the source domain samples may not always be feasible in real-world applications due to different problems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , storage, transmission, and privacy issues). In this work, we deal with the source data-free unsupervised domain adaptation problem and propose a novel approach referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA), in which the virtual domain acts as a bridge between the source and target domains. Specifically, based on the pre-trained source model, we generate the virtual domain samples by using an approximated Gaussian Mixture Model (GMM) in the feature space, such that the virtual domain maintains a similar distribution with the source domain without access to the original source data. Moreover, we also design an effective distribution alignment method to reduce the distribution divergence between the virtual domain and the target domain by gradually improving the compactness of the target domain distribution through model learning. In this way, we successfully achieve the goal of distribution alignment between the source and target domains when training deep networks without access to the source domain data. We conduct extensive experiments on four benchmark datasets for both 2D image-based and 3D point cloud-based cross-domain object recognition tasks, where the proposed method referred to as Virtual Domain Modeling for Domain Adaptation (VDM-DA) achieves the promising performance on all datasets.

A framework for culturally appropriate and sustainable indigenous housing

Safe, secure and stable housing is central to social, emotional and economic well-being for individuals and families. This is recognised by the United Nations as a fundamental human right, yet when it comes to Indigenous housing individuals disproportionately experience unsafe, unstable and substandard housing. The absence of housing security is therefore a significant contributor to the intergenerational poverty experienced by some Indigenous individuals and families. The long-standing nature of this issue makes it appear intractable and systemic, however, there is increasing evidence that this is not the case, and that governments, working in partnership with Indigenous organisations and communities, can begin to change these dynamics. This brief is based on an extensive review of the multidisciplinary evidence base, including literature and policy analysis as well as empirical investigation. Sources include research articles, official reports and statistics and scholarly debates, as well as media commentary and debate. These, along with original data sources, are analysed to identify the key policy learnings for a framework for culturally appropriate and sustainable Indigenous housing.

Corrigendum] miR‑23a suppresses pancreatic cancer cell progression by inhibiting PLK‑1 expression

Subsequently to the publication of this paper, the authors have realized that they made an error during the sorting of the data panels shown for the migration and invasion assays shown in Fig. 2C; essentially, the 'Invasion/PLL3.7' panel was chosen from the same original data source as the panel selected to represent the 'Migration/Inhibitor‑NC' experiment. The authors have consulted their original data, and realize that the 'Invasion/PLL3.7' data panel was inadvertently selected incorrectly for Fig.2. The revised version of Fig.2, showing the data appropriate for the 'Invasion/PLL3.7' experiment, is shown on the next page. Note that the errors made in assembling Fig.2 did not significantly affect either the results or the conclusions reported in this paper, and all the authors agree to this Corrigendum. The authors are grateful to the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this corrigendum, and apologize to the readership for any inconvenience caused.[Molecular Medicine Reports 18: 105‑112, 2018; DOI: 10.3892/mmr.2018.8941].

Validity of data extraction in evidence synthesis practice of adverse events: reproducibility study.

ObjectivesTo investigate the validity of data extraction in systematic reviews of adverse events, the effect of data extraction errors on the results, and to develop a classification framework for data extraction errors to support further methodological research.DesignReproducibility study.Data sourcesPubMed was searched for eligible systematic reviews published between 1 January 2015 and 1 January 2020. Metadata from the randomised controlled trials were extracted from the systematic reviews by four authors. The original data sources (eg, full text and ClinicalTrials.gov) were then referred to by the same authors to reproduce the data used in these meta-analyses.Eligibility criteria for selecting studiesSystematic reviews were included when based on randomised controlled trials for healthcare interventions that reported safety as the exclusive outcome, with at least one pair meta-analysis that included five or more randomised controlled trials and with a 2×2 table of data for event counts and sample sizes in intervention and control arms available for each trial in the meta-analysis.Main outcome measuresThe primary outcome was data extraction errors summarised at three levels: study level, meta-analysis level, and systematic review level. The potential effect of such errors on the results was further investigated.Results201 systematic reviews and 829 pairwise meta-analyses involving 10 386 randomised controlled trials were included. Data extraction could not be reproduced in 1762 (17.0%) of 10 386 trials. In 554 (66.8%) of 829 meta-analyses, at least one randomised controlled trial had data extraction errors; 171 (85.1%) of 201 systematic reviews had at least one meta-analysis with data extraction errors. The most common types of data extraction errors were numerical errors (49.2%, 867/1762) and ambiguous errors (29.9%, 526/1762), mainly caused by ambiguous definitions of the outcomes. These categories were followed by three others: zero assumption errors, misidentification, and mismatching errors. The impact of these errors were analysed on 288 meta-analyses. Data extraction errors led to 10 (3.5%) of 288 meta-analyses changing the direction of the effect and 19 (6.6%) of 288 meta-analyses changing the significance of the P value. Meta-analyses that had two or more different types of errors were more susceptible to these changes than those with only one type of error (for moderate changes, 11 (28.2%) of 39 v 26 (10.4%) 249, P=0.002; for large changes, 5 (12.8%) of 39 v 8 (3.2%) of 249, P=0.01).ConclusionSystematic reviews of adverse events potentially have serious issues in terms of the reproducibility of the data extraction, and these errors can mislead the conclusions. Implementation guidelines are urgently required to help authors of future systematic reviews improve the validity of data extraction.

Syphilis Trigram: a domain-specific visualisation to combat syphilis epidemic and improve the quality of maternal and child health in Brazil

BackgroundThe Brazilian healthcare system is a large and complex system, specially considering its mixed public and private funding. The incidence of syphilis has increased in the last four years, in spite of the presence of an effective and available treatment. Furthermore, syphilis takes part in a group of disorders of compulsory notification to the public health surveillance. The epidemiological implications are especially important during pregnancy since it can lead to complications, related to prematurity stillbirth and miscarriage, in addition to congenital syphilis, characterized by multisystem involved in the newborn.MethodsThe Action Research methodology was applied to address the complexity of the syphilis surveillance scenario in Pernambuco, Brazil. Iterative learning cycles were used, resulting in six cycles, followed by a formal validation of an operational version of the syphilis Trigram visualisation at the end of the process. The original data source was analyzed and prepared to be used without any new data or change in the ordinary procedure of the current system.ResultsThe main result of this work is the production of a Syphilis Trigram: a domain-specific infographic for presenting gestational data and birth data. The second contribution of this work is the Average Trigram, an organized pie chart which synthesizes the Syphilis Trigram relationship in an aggregated way. The visualization of both graphics is presented in an Infographic User Interface, a tool that gathers an infographic broad visualization aspect to data visualization. These interfaces also gather selections and filters tools to assist and refine the presented information. The user can experience a specific case-by-case view, in addition to an aggregated perspective according to the cities monitored by the system.ConclusionsThe proposed domain-specific visualization amplifies the understanding of each syphilis case and the overall characteristics of cases of a chosen city. This new information produced by the Trigram can help clarify the reinfection/relapse cases, optimize resource allocation and enhance the syphilis healthcare policies without the need of new data. Thus, this enables the health surveillance professionals to see the broad tendency, understand the key patterns through visualization, and take action in a feasible time.

Tutorial in Longitudinal Measurement Invariance and Cross-lagged Panel Models Using Lavaan

In longitudinal studies involving multiple latent variables, researchers often seek to predict how iterations of latent variables measured at early time points predict iterations measured at later time points. Cross-lagged panel modeling, a form of structural equation modeling, is a useful way to conceptualize and test these relationships. However, prior to making causal claims, researchers must first ensure that the measured constructs are equivalent between time points. To do this, they test for measurement invariance, constructing and comparing a series of increasingly strict and parsimonious models, each making more constraints across time than the last. This comparison process, though challenging, is an important prerequisite to interpretation of results. Fortunately, testing for measurement invariance in cross-lagged panel models has become easier, thanks to the wide availability of R and its packages. This paper serves as a tutorial in testing for measurement invariance and cross-lagged panel models using the lavaan package. Using real data from an openly available study on perfectionism and drinking problems, we provide a step-by-step guide of how to test for longitudinal measurement invariance, conduct cross-lagged panel models, and interpret the results. Original data source with materials: https://osf.io/gduy4/. Project website with data/syntax for the tutorial: https://osf.io/hwkem/.

Unsupervised Domain Adaptation for Segmentation with Black-box Source Model.

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.

RaptorQ codes aided secure data delivery strategy for vehicular networks

Abstract This paper deals with security issues in the presence of an eavesdropper for a vehicular scenario. The proposed secure data delivery scheme implements Fountain Codes, namely RaptorQ (RQ) codes, at the application layer (AL) to increase communication security against eavesdroppers’ attacks. For RQ coded transmission scheme, the receiver has to collect a sufficient number of coded packets to reconstruct the original source data. Secure delivery can be achieved if the legitimate user obtains enough RQ coded packets before the eavesdropper does. To satisfy this condition, it is proposed to use a road side unit (RSU) cooperation method when the eavesdropper has better channel conditions than the legitimate user to scatter the coded packets from multiple RSUs. The aim is to reduce the probability that the eavesdropper receives the sufficient number of coded packets and recover the source data before the legitimate user. An optimisation framework which jointly selects the RQ code rate at the AL and Modulation and Coding Scheme (MCS) at the physical (PHY) layer to ensure the secure data transmission by allowing the user to decode the file with a certain probability of decoding success while minimising the intercept probability at the eavesdropper is presented. To evaluate the proposed system, a realistic end-to-end system level simulator is developed. Simulation results show that the proposed scheme can provide secure and efficient data transmission over vehicular networks by significantly reducing the intercept probability at the eavesdropper.

A Dynamic, Network Scale Sediment (Dis)Connectivity Model to Reconstruct Historical Sediment Transfer and River Reach Sediment Budgets

AbstractModeling network‐scale sediment (dis)connectivity and its response to anthropic pressures provides a baseline understanding of river processes and sediment dynamics that can be used to forecast future hydro‐morphological changes in river basins. However, this requires a solid understanding of how a system is currently operating, and how it operated in the past. We present the basin‐scale, dynamic sediment connectivity model D‐CASCADE, which combines concepts of network modeling with empirical sediment transport formulas to quantify spatiotemporal sediment (dis)connectivity in river networks. D‐CASCADE accounts for multiple factors affecting sediment transport, such as spatiotemporal variations in hydrological regime, different sediment grain sizes, sediment entrainment and deposition. Add‐ons are included in D‐CASCADE to model local changes in river geomorphology driven by sediment‐induced variations in features such as channel width. We apply D‐CASCADE to the well‐documented Bega River catchment, NSW, Australia, where significant geomorphic changes to rivers have occurred post European colonization (after 1850s), including widespread channel erosion and sediment mobilization. The Bega catchment provides a useful case study to test D‐CASCADE, as original source data on the historical sediment budget are available. By introducing historic drivers of change in the correct chronological sequence, the D‐CASCADE model successfully reproduced the timing and magnitude of major phases of sediment transport and associated channel adjustments over the last two centuries. With this confidence, we then ran the model to test how well it performs at estimating future trajectories of basin‐scale sediment transport and sediment budgets at the river reach scale.

A harmonized country‐level dataset to support the global stocktake regarding loss and damage from climate change

AbstractUnder the Paris Agreement, parties should undertake a global stocktake of progress toward meeting the goals of the agreement and tackling climate change. The first global stocktake will be undertaken in 2023, and an assessment of loss and damage from climate change is an important part of the process. Loss and damage refer to the impacts of climate change felt when mitigation and adaptation efforts are inadequate or absent. Much data, including metrics and indicators relevant for loss and damage, are held in existing global databases, but these are disparate and cannot easily be combined and compared to support the global stocktake. We combine relevant primary data sources to provide a harmonized country‐level global dataset containing relevant indicators of recorded losses and damages from climate‐related events; exposure to climate‐related events; country vulnerability and adaptation readiness; scientific studies of climate change attribution; financial support for climate adaptation; and contextual governance conditions. The indicators are standardized against country population and GDP where relevant. We describe original data sources, processing steps, and an overview of key indicators in the dataset. We also compare the assembled data to existing global risk databases; namely, the INFORM risk index and the World Risk Index. This comparison, provided in the Supporting Information, shows a large amount of redundancy among vulnerability and governance indicators, and we suggest that creators of new databases and risk indices be clear about data limitations and the gaps that specific indices attempt to fill in the global data landscape. We recommend the standard use of ISO codes in future databases of this nature, as well as clear metadata regarding how overseas territories are treated relative to their sovereign state, and information on dissolution and creation of states over time.

Language vitality scales and their applicability to specific language situations

While working on the description and documentation of a minority language, the researcher faces the practical task of defi ning its vitality status. This article is devoted to our experience of solving this problem. At the beginning, we give a brief overview of the concept of vitality and various scales that exist for the classifi cation of languages according to this parameter. Particular attention is paid to the ELCat scale, the use of which is shown on the example of Bezhta (the Bezhta proper dialect), Northern Selkup, Evenki and Khakas languages. We describe some controversial points in the ELCat methods and propose an adjustment for some of them in accordance with the specifi cs of the languages in question. For example, since the level of language vitality can be diff erent in diff erent communities, we estimate vitality level separately for each of them. In addition, a specifi cation of the values of the parameters “intergenerational transmission of language” and “the ratio of the number of speakers and ethnic group” is introduced. In the fi nal part of the article, we discuss general problems arising in determining the vitality level of languages associated with the sources of original data.

The Circular Data Centre Compass – modelling and assessing data centre sustainability

Data centres (DC) house electrical and electronic data processing and storage equipment. The data centre industry has grown from zero in the late 1980s into a global service provider with over 7 million sites in 2021. This rapid sectoral growth presents many challenges to environmental, social, and economic sustainability including the generation of large quantities of e-waste, (which is exacerbated by an under-developed recycling infrastructure, and a limited market for second life products) and potential threat to material supply chains, specifically Critical Raw Materials. There is an urgent need to improve sectoral sustainability, which includes development of a Circular Economy. The CEDaCI (a Circular Economy for the Data Centre Industry) project was launched in 2018 to initiate this by demonstrating its viability though business case studies, physical prototypes and digital tools, a key example being the Circular Data Centre Compass (CDCC) – a unique online tool that includes original primary source data. This tool allows users to compare and assess the overall life cycle sustainability of different servers, the criticality of embodied materials and the circularity of current and proposed designs. Therefore, the CDCC improves overall life cycle management of data centres, which increases supply chain security and reduces risk of DC service interruption.

A Novel Processing Model for P300 Brainwaves Detection

Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain’s sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific period, it requires a suitable signal processing application for interpretation. Moreover, multiple steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the processing model for brainwave applications based on P300 peak signal detection in multiple brain channels. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform (FFT) with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channel classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.

Last Interglacial sea-level data points from Northwest Europe

Abstract. Abundant numbers of sites and studies exist that document the Last Interglacial (Eemian, Ipswichian, MIS 5e) coastal record for geographically and geomorphologically diverse NW Europe. This paper documents a database of 141 known Last Interglacial sea-level indicative data points from in and around the North Sea (35 entries in Netherlands, 10 Belgium, 16 in Germany, 17 in Denmark, 8 in Britain) and the English Channel (28 entries for British and 25 for the French side, 3 on the Channel Isles), believed to be a representative and fairly complete inventory and assessment coming from some 80 published sites. The good geographic distribution (some 1500 km SW-NE) across the near field of the Scandinavian and British Ice Sheets and the attention paid to absolute and relative age control are assets of the NW European database compilation. The research history of Last Interglacial coastal environments and sea-level position for this area is long, methodically diverse and spread over regional literature in several languages. Last Interglacial high-stand shorelines of Dutch and German Bight parts of the North Sea, were of lagoonal and estuarine type and have preserved subsurface (data entry included estimates of non-GIA vertical land motion). In contrast, Last Interglacial high-stand shorelines along the English Channel are encountered above modern sea-level (data entry includes datum definitions). Our review and database compilation effort drew from the original regional literature, and paid particular attention to distinguishing between sea-level index points (SLIPs) and marine and terrestrial limiting-points. This paper describes the dominant sea-level indicators produced from region to region, compliant to the database structure of the special issue (WALIS), referenced to original source data. The sea level proxies in majority are obtained from localities with well-developed lithostratigraphic, morpho-stratigraphic and biostratigraphical constraints. Amino-Acid Racemization information is also prominent, especially in Britain, albeit for many sites the older, lesser quality applications of that technique. The majority of European continental sites have chronostratigraphic age-control, notably through regional Pollen Association Zones of known durations. This greatly helps to separate transgression, highstand (‘stillstand’) and regression subsets from within the interglacial, useful when summarizing and/or querying the dataset. In all regions, many SLIPs and limiting points have further independent age-control from luminescence (IRSL, OSL, TL), U-series and ESR dating techniques. Main foreseen usage of this database for the near field region of the European ice sheets is in GIA modelling.

Domain adaptive twin support vector machine learning using privileged information

In the fields of computer vision and machine learning, domain adaptation has been extensively studied and the main challenge in the case is how to transform the existing classifier(s) into an effective adaptive classifier to exploit the latent information in the new data source which typically has a different distribution compared with the original data source. Currently, the Adaptive Support Vector Machines (A-SVM) has been proposed to deal with the domain adaptation problem, which is an effective strategy. However, the resulting optimization task by minimizing a convex quadratic function in A-SVM can not effectively minimize the distance between a source and a target domain as much as possible and typically has high computational complexity. In order to handle these problems, in this paper, we extend the A-SVM by determining a pair of nonparallel up- and down-bound functions solved by two smaller sized quadratic programming problems (QPPs) to achieve a faster learning speed. Notably, our method yields two nonparallel separating hyperplanes to exploit the latent discriminant information based on SVM classification mechanism, which can naturally enhance the classification performance. This method is named as Adaptive Twin Support Vector Machine Learning (A-TSVM). Moreover, we consider a high-level learning paradigm with privilege information (LUPI) to learn a induced model that further constrains the solution in the target space. The learned model is named as domain Adaptive Twin Support Vector Machine Learning Using Privileged Information (A-TSVM+). Finally, a series of comparative experiments with many other methods are performed on three datasets. The experimental results effectively indicate that the proposed method can not only greatly improve the accuracy of classification, but also save computing time.

Accuracy comparison and driving factor analysis of LULC changes using multi-source time-series remote sensing data in a coastal area

Land use and land cover (LULC) products derived from remote sensing data are fundamental to the earth's surface ecological environment studies. Various LULC products have different data sources with large uncertainties and varying spatio-temporal resolutions. In this study, our objectives are to understand the accuracy of the current LULC products at the coastal area of Tianjin city. We conducted accuracy verification and comparative analysis of the LULC datasets available from different sources, MCD12Q1, CGLS-LC100, FROM_GLC, GLC_FCS30 and GlobeLand30 for the years of 2010, 2015, 2017 and 2020, at varying spatial resolutions 500–100–30–10 m. Using land cover dataset for these years, we conducted time-series analysis of land cover changes and driving factors. Results showed that GLC_FCS30-2020 has the highest classification accuracy (72.57%), followed by GlobeLand30-2020 (72.23%) and GlobeLand30-2010 (70.10%). The classification accuracy is related to the spatial resolution of original data source and the classification system. The verification accuracies of all products are found to be lower than their official announcements. Additionally, the accuracy comparison among different land types indicated that the classification accuracy of agricultural land, impervious surface, and water bodies is high, while that of forest, grassland, and shrubs is low. According to the spatio-temporal pattern analysis of land cover changes in Tianjin during 2010–2020, the area of agricultural land is slowly decreasing, and construction land is increasing, and the increase results mainly from the conversion of agricultural land and grassland. The rapid population growth, economic development and policy guidance are the main driving factors.

Nationally representative estimates of the participation of cancer patients in clinical research studies according to the commission on cancer.

74 Background: The successful conduct of cancer clinical trials hinges on the willingness of patients to participate. The rate of adult clinical trial participation has been regarded as being &lt; 5%. However, national estimates of trial participation are nearly two decades old, and no evidence based on original data sources has been examined for many years. Moreover, studies about trial participation have focused solely on enrollment to treatment trials, which does not reflect the willingness of patients to contribute to other key elements of clinical research, such as quality of life or biorepository studies. We determined inclusive, contemporary estimates of clinical trial participation for adults with cancer using a national sample of data from 1,200 institutions. Methods: The data were from the Commission on Cancer (CoC), a consortium of cancer-related organizations providing accreditation for both academic and community cancer care facilities across the U.S. CoC enrollment data represent 70% of all cases of cancer diagnosed each year. Deidentified, institution-level aggregate counts of annual enrollment to treatment, biorepository, diagnostic, economic, genetic, prevention, quality of life, registry, and screening studies were examined. Overall, study-type estimates for the period 2013-2017 were estimated. Multiple imputation by chained equations was used to account for missing data, with summary estimates calculated separately by type of program (e.g., NCI-designated cancer programs) and pooled. Results: Across the entire U.S. system, the estimated participation rate to cancer treatment trials was 6.3%. Enrollment to treatment trials was highest at NCI-designated comprehensive cancer centers (18.9%), while for community cancer programs (CCPs) and comprehensive CCPs, treatment trial rates were 4.4% and 3.6%, respectively. Nearly 1 in 7 patients participated in biorepository studies (13.4%), including 39.4% at NCI cancer centers. Patients participated in a wide variety of other study types, including registry (8.1%), prevention (6.4%), genetic (3.6%), quality of life (2.9%), economic (2.7%), diagnostic (2.7%), and screening studies (1.8%). At least 25.4% of adult cancer patients were estimated to participate in one or more cancer clinical research studies. Conclusions: In a first-time use of nationally representative enrollment data from the CoC, enrollment to cancer treatment trials was 6.3%, higher than historical estimates of &lt; 5%. Patients participated in a diverse set of other study types, and taken together, at least one quarter of patients participated in a study. Contributions of adult patients with cancer to clinical research is much more comprehensive than previously understood.