dataesr/scientific-paragraphs-categorization huggingface dataset:
To characterize the construction supply chain and drive the simulation of risks and cascading effects considering the above context, a series of risk-related components are extracted based on expertise and historical data (see Table  1 ). These components are organized using the danger-risk-consequence chain model, and the causal chains and propagation chains between them are also identified as knowledge. The initial state of the simulated system consists only two components: D1 and S1. Driven by the knowledge base, a simulation experiment is performed. There are two disruptions (E1 after State 1 and E2 after State 3) injected into the system during the simulation. The resulting trajectory of the state evolution over time is shown in Fig. 6. After each automatic deduction, the directed graph representing the instantaneous state becomes more and more complex.
Overview of research methodology In this section, we present two studies to test our conceptual model and hypotheses. In both studies, we operationalised website visual design consistently as a global measure by changing multiple design elements, echoing our theoretical conception of visual design, in accordance with how prior studies have operationalised this construct (e. g. Chopdar & Balakrishnan, 2020; Jiang et al. , 2016; Pengnate & Sarathy, 2017; Seo et al. , 2016; van der Heijden; 2003; Xu & Schrier, 2019). Data collection was carried out in the first semester of 2017. In Study 1, we used a fictional website as stimuli, manipulated simultaneously several design elements to achieve two levels of visual design (lowly vs. highly attractive) and surveyed potential customers via an online (Qualtrics) research panel.
Study 1 Results We tested our hypotheses using the program macro PROCESS  (Hayes, 2013). Specifically, we used a mediation model (Model 4) to test H5, which proposed the existence of an indirect relationship between usability and website evaluation outcomes, whereas all the other hypotheses were tested using a serial mediation model (Model 6). In a serial mediation model, the mediators are supposed to act in a serial order as a causal chain, whereby the independent variable influences the first mediator, which influences the second, which subsequently influences the dependent variables. Using 5,000 bootstrap samples  (Preacher & Hayes, 2008) , the 95% Monte Carlo confidence interval (CI) estimated the mediating role of web user experience (i. e. usability and pleasure) in the relationship between website visual design and website evaluation outcomes.
Study 2 Results As in Study 1, we tested our hypotheses using the program macro PROCESS  (Hayes, 2013, Models 4 and 6). Similarly, a mediation model (Model 4) was used to test H5 (i. e. indirect effect of usability on website evaluation), whereas a serial mediation model (Model 6) was used to test all the other hypotheses. We present the conceptual model with the causal path results in Figure  5 , then calculate the direct and indirect effects in Table  2. 766* (H4). 286 (H3a). 327* (H3c). 376* (H2).
It is the experience one would have upon realising, for example, how to fix a computer problem, master a dance step or resolve some other difficulty', according to the American Psychological Association or APA Dictionary of Psychology (2015). 4. For example, the Norman Nielsen Group -for which 'visual design' as an article-search keyword for its databasedistinguishes the overall impression conveyed by visual design as a whole (e. g. considering that 'when looking at a visual, we can usually immediately say whether it is appealing or amiss') and from the design features and principles (e. g. visual hierarchy, balance, contrast) on which the visual design of the interface is based (https://www. nngroup. com/articles/principlesvisual-design/). 5. Consumer adoption of online travel agencies has grown considerably over the past few years as a global phenomenon  (Talwar et al.
Statistical Analysis Statistical analyses were performed using the Prism software package (version 8, GraphPad) and the SPSS software (IBM_SPSS Statistics 21; 2020). All p-values < 0. 05 were considered significant. Data are expressed as means ± standard deviations (SD) for normally distributed variables and as medians and interquartile ranges (IQR). Categorical variables are described as frequencies and percentages. Differences in diameter for the efferent vein and the aneurysmal sac between pre-embolization and follow-up CT were calculated. Ordinal qualitative variables were compared between the two groups using a non-parametric Mann-Whitney test, and continuous variables using a two-paired Student t-test or a Wilcoxon rank-sum test, and as function of the normality of the variables using the d'Agostino-Pearson test.
Drawing upon anxiety/uncertainty management (AUM) theory and organization support theory (OST), we developed and tested a model on how globally mobile talentin this instance, selfinitiated expatriates (SIEs) -stay and perform well in the organization. We collected data from 276 superior-subordinate dyads, which were analyzed using structural equation modeling (SEM). Our results suggest that role clarity mediates the influence of perceived organizational support and work adjustment. Furthermore, we found that work adjustment mediates the influence of role clarity on task performance and organizational citizenship behavior. We draw out the implications for advancing theory and practice in expatriate management.
, 2015; Ng et al. , 2019; Panaccio and Vandenberghe, 2012). The participating dyadic sample was requested to fill in the survey questionnaire and keep it with her/himself, and the co-author will visit them after two weeks to collect it back. This way, we received 162 dyadic survey responses. However, those who did not return the survey questionnaire were asked to complete it and give it back in another two weeks. The second time, only 122 of the dyadic sample return it. At this stage, we stopped data collection and skimmed through dyadic sample responses and found that eight dyadic sample responses had few unanswered items that were not included, and only 276 usable dyadic sample responses were used to examine the hypotheses of this study. The subordinates, who were all SIEs, completed questionnaires on the measures of role clarity (RC) and perceived organizational support (POS), and work adjustment (WA).
Therefore, the resource flow of the secondary network is mapped on the Markov chain; the performance analysis of SUs is transformed into a mathematical problem  (4)  The conditions and implementation methods of channel assembling are given: the change dynamics of the users in the secondary system are divided and presented by the graphical method visually. Then, the dimension reduction is achieved by the marking method, and the analytic expressions of system performance metrics are given. MATLAB is used to evaluate and analyze the performance of the system.
The service rate of SUs can be increased As shown above, the total number of the currently available idle channels, N free , will be determined at the beginning of each phase. In the priority support stage, the SU h in L h will be served first. If L h is empty, the idle channels will be assembled for SUs according to the following Rule 1. Rule 1: the ongoing SU h with minimum aggregated channels (denoted as SU h -min) will assemble the idle channels until all of the ongoing SU h were assembled to the upper bound, v. If there are still idle channels, the remaining chan-nels will be assembled to the ongoing SU w with minimum aggregated channels (denoted as SU w -min) until all of the ongoing SU w reached the upper bound, u:. In the polling stage, the data packets in L w are marked, and a service request list (SRL) for SU w is generated.
reuters. com/article/us-china-economy-output-aluminium/chinadecember-aluminum-production-surges-to-record-monthly-high-idUSKCN1PF0C2. 4 Bell T. 2020. The biggest aluminum producers of 2018. thoughtco. com/ the-10-biggest-aluminum-producers-2339724. 5 The Aluminum Association. 2020. Aluminum sustainability lightweight, strong & highly recyclable. aluminum. org/aluminum-sustainability. the Fujistu Group), can eliminate the environmental burden generated in the semiconductor production lines at the Mie Plant. 6 ustainable supply chain and operations management, especially green sourcing and production, are key strategic decisions for manufacturers to achieve their emission reduction goals. Several prior works have explored policies or decisions to improve sustainable sourcing or production efficiency, for example,  Zhao, Hobbs, & Pang (2010)  ,  Gong & Zhou (2013)  ,  Konur & Schaefer (2014)  ,  Drake, Kleindorfer, & Wassenhove (2016)  ,  Guo et al.
The study suggests promoting higher education in BRICS countries. Balaguer and Cantavella [19] used higher education data to test the nonlinear relation between education and carbon emission (EKC) in Australia from 1950 to 2014. This paper not only confirmed a U-shaped correlation between income level and its environmental effects but also suggests two opposing effects: (1) in the early stages, education intensifies carbon emission as society continues to use non-renewable energies; (2) later on, education incorporates technical knowledge about cleaner energies: plus, environmental social awareness leads to environmental sustainability. This article notes that Australian society turned its consumption patterns after achieving 52,043 Australian Dollars per capita corresponding to the year 1997 and suggests investigating the education effects on the environment beyond the effects of economic growth.
, 2021). The method is designed to allow detailed and unsupervised classification of urban form at large scale. First, from input data on building footprint and street network, we generate 370 characters (or metrics) of urban form attributed to each building, and then cluster these characters to generate a numerical taxonomy of Amsterdam's urban types (UT from hereon), that is, homogeneous patterns of urban from. Second, we obtain pointbased data of Airbnb listings in Amsterdam for the year 2017. Third, we analyse UTs and density of Airbnbs via a composite ML method based on forward feature selection and gradient boosting. Finally, we provide detailed profiles of the most strongly associated UTs by reverse-engineering the clustering. In the following sections we: first, illustrate the methodology, including datasets, morphometric approach and ML techniques used to model the relationship between UTs and density of Airbnbs; second, we present the application to the city of Amsterdam.
Datasets The methodology presented in this paper relies on three georeferenced vector datasets, containing building footprints at Level of Detail 1  (Biljecki et al. , 2016) , street network and points of Airbnb listings, respectively. All these datasets are today easily retrievable from a variety of official and non-official sources, depending on the location of the case study. In the United Kingdom, buildings can be extracted from Ordnance Survey (OS) OpenMap Local 1 and streets from OS OpenRoads. 2 In countries where official data is uneven or lacks, one may want to extract both datasets from OpenStreetMap (OSM), the first free crowd-sourced geodataset of the world 3. Airbnb listings can be obtained from Inside Airbnb, a platform that provides this kind of data for research purposes. 4 To implement the analysis, street network and Airbnb listings need pre-processing.
, 2021). The method is designed as a reproducible, open-source, Python-based workflow called momepy  (Fleischmann, 2019). Since the goal is to detect patterns, characters' local tendencies are quantified in spatial contexts around each morphological cell. A context is a topological aggregation of cells within a third order of contiguity, which is 'an empirically determined value large enough to capture a cohesive pattern over a relatively wide spatial extent but small enough to generate sharp boundaries between different patterns'  (Fleischmann et al. , 2021). From each primary character, four contextual characters are derived: (1) interquartile mean: a measure of central tendency; (2) interquartile range: an indicator of distribution's spread in absolute values; (3) Theil index of inequality computed on the inter-decile range: it captures local diversity of values; (4) Simpson's index: diversity of values within local context compared to the rest of the dataset.
Data aggregation Airbnb listings from Inside Airbnb are made available with a displacement up to 150 m from their real location for privacy reasons. 5 This makes matching Airbnb listings to their respective buildings (and the analysis at this spatial level) impossible. For this reason, we aggregate the number of buildings belonging to each UT and Airbnbs for hexagons of 400 m width. This spatial unit is advantageous for three main reasons: (i) it subdivides the space regularly, unlike arbitrarily defined areas, such as administrative boundaries or postal code areas, which usually change in size and could even overlap; (ii) its size (i. e. 400 m) is aligned with the notional neighbourhood scale  (Mehaffy et al. , 2010) ; (iii) it allows aggregating information at a relatively small scale, reaching a compromise between an unachievable scale (buildings) and a too coarse one of officially designated neighbourhoods.
In brief, this divides the original dataset into tenfold and uses each fold nine times as train set and once as test set to be predicted. The performances of these sub-models guide the iterative process of the SFS technique, ultimately providing a robust selection of UTs to be used in the next step. Gradient boosting and SHAP. Gradient boosting, a robust non-parametric ML technique  (Friedman, 2001) , is used to analyse the relation between UTs and density of Airbnbs. Gradient boosting belongs to the family of random forests, that is, algorithms relying on multiple decision trees for prediction. However, while the performance of random forests is the average of the performances of the single base models, gradient boosting progressively fits new decision trees to minimise the error made at each iteration and, ultimately, produce a better prediction.
Since the purpose of this analysis is regressive (density of Airbnbs), the minimisation of these errors is based on a gradient descent that optimises a least squares regression cost function pointing to the negative gradient direction. As in SFS, gradient boosting is applied by splitting the original dataset in train (80%) and test (20%) sets and implementing a tenfold cross validation. Furthermore, to control the learning process and avoid overfitting, the maximum depth of each decision tree is set to a third of the number of predictors (the UTs selected via SFS), a value largely accepted in ML  (Franklin, 2005). Finally, to evaluate model performance, an overall adjusted R 2 value, representing the amount of variance of density of Airbnbs that is predicted from the UTs used in the model, is computed as the average of the partial adjusted R 2 values computed for each cross validated test set.
Since the aim of this work is not purely predictive, but rather explorative, a further ML technique, that is, SHapley Additive exPlanations (SHAP)  (Lundberg and Lee, 2017) , is implemented to render the gradient boosting model interpretable. SHAP belongs to a set of algorithms (additive feature attribution) that explains a specific prediction by selecting interpretable features sampling and then fitting a linear model in the local area around such prediction. Though, unlike other methods, it is the only one that guarantees robustness and quality of local approximations. By collecting these local feature importance values across the entire dataset, SHAP can quantify the overall impacts (negative or positive) that the UTs have on explaining density of Airbnbs.
Implementing the combined morphometric and ML approach to Amsterdam The 25 th most visited city in the world in 2019 according to the Euromonitor International's market research database  (Rabia, 2019) , with around 20 million international visitors every year, Amsterdam is a major international hub with an expanding overlook. Along with boosting growth, Amsterdam has long faced the downsides of 'over-tourism', to the detriment of local environment and population's ability to live and prosper. Amsterdam is likely to take the opportunity to re-think a few fundamentals of its own model of development and seek a more balanced way of profiting from its own assets. These famously include some of the most impressive concentrations of historical and cultural heritage in Europe and the world. For historical and environmental reasons, nowhere a compact and dense urban fabric has, like in Amsterdam, contributed to a living tradition of urbanity across all aspects of local culture, values, norms and habits.
Datasets The data representing all 252,385 building footprints at the required level of detail were extracted from a nationwide open dataset available from  Dukai (2018) , based on official cadastral data. The street network layer was obtained from the national open data repository Basisregistratie Grootschalige Topografie, BGT 6 and processed to clear the network of transportation-focused geometries to derive a morphological representation of 38,963 street edges. Inside Airbnb's data used in this work dates back to 2017 and consists of 23,107 geo-localised points representing all kinds of Airbnb accommodations (i. e. private rooms, entire homes, shared rooms) in the metropolitan area of Amsterdam. By visual inspecting the data, we observe a strong density in the area circumscribed by the canal Ij and the main ring road (i.
Modelling urban types and density of Airbnbs Selecting the most relevant urban types. Having created 1,317 hexagons covering the metropolitan area of Amsterdam and aggregated both number of buildings belonging to each UT and Airbnb listings for these spatial units, SFS is implemented to identify UTs that can best model density of Airbnbs. More specifically, SFS is set to use a gradient boosting regressor with a limited number of decision trees (i. e. 32), due to the relatively small size of the original dataset (i. e. 1,317  observations). Furthermore, SFS is set to explore the optimal number of UTs in the range 2-21. Outcomes (Supplemental Figure  S1  in the Supplementary Material) show that 15 UTs (i. e. UT0, UT12, UT14, UT15, UT16, UT18, UT19, UT2, UT20, UT3, UT4, UT5, UT6, UT8, UT9) reach the top performance in the selection process.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the  Agence Nationale de la Recherche  ( ANR-15-IDEX-01 ), Axel and  Margaret Ax :son  Johnson Foundation  ( GEN2971-100 ) and the  Ministry of Research, Innovation and Digitization, CNCS -UEFISCDI  ( PN-III-P4-PCE-2021-1878 : Institutions, digitalization and regional development in the European Union, within  PNCDI III ).
Motivated by these studies, we examine the value of fast-versus slow-turnover analysts in facilitating the price discovery of corporate disclosures. Using a comprehensive news database from Capital IQ Key Developments, we find that fast-revising analysts tend to update their recommendations following verifiable and less ambiguous corporate disclosures such as earnings announcements, or security issuances. On the other hand, slower-revising analysts tend to revise recommendations following news with potentially ambiguous price impact (i. e. , "soft" information), such as news about the product market competition, operation strategy, and legal issues. These news announcements are mostly unscheduled and tend to carry forward-looking information about their underlying firm value, which, we believe are harder to interpret by nonstock experts.
A. Data and Filters We obtain analyst recommendations and earnings forecast data from IBES. We restrict our attention to equity analysts that appear in both the detailed recommendation and forecast IBES files from 1993 to 2013. This initial sample contains 629,400 recommendations made by 14,242 unique analysts. Security returns data and firm-level information are obtained from CRSP and COMPUSTAT, respectively. We identify "star analysts" based on Institutional Investor's annual ranking of All-American team (see  Fang and Yasuda (2009), (2014) ). The gender of analysts is identified using their full names collected from the Institutional Investor magazine and verified against multiple sources (see  Kumar (2010) ,  Law (2013) ). We apply various filters to the IBES recommendation data file. We require that firms in our sample have records on the CRSP daily database and have CRSP share code of 10 or 11.
We remove 19,809 anonymous recommendations in IBES since it is not possible to track which analysts issue these recommendations. We require that analysts issue at least one forecast and one recommendation change on a given stock for the analyst-stock pair to be in our sample. Each recommendation in the IBES database is coded with a rating scale between 1 and 5, ranging from "strong buy" to "strong sell," respectively. We characterize each revision as an upgrade or a downgrade. We do not consider initiations and reiterations in our empirical analysis. Kadan, Madureira, Wang, and Zach (2009)  document a significant number of mechanical recommendations changes due to the migration of a five-tier rating system to a three-tier rating system in 2002 following the National Association of Securities Dealers (NASD) Rule 2711.
Analyst Characteristics Table 1 reports a sample descriptive of analyst characteristics. The sample consists of analysts that provide active recommendations coverage between 1996 and 2013. Recommendations and earnings forecast data are obtained from IBES. We require that an analyst provides active recommendation coverage on at least three stocks to remain the sample each year. Details of filters used to construct the sample can be found in the main text. The sample consists of 4,563 unique analysts providing active recommendation coverage in 1996-2013, resulting in 25,678 analyst-year observations. We summarize analyst characteristics calculated at the analyst-year level. All variables are defined in the Appendix. EXPERIENCE is the number of years since the analyst's first recommendation appears in the IBES database.
All other independent variables are lagged by one period. NEWS_INTENSITY is the number of firm-specific news observed in the previous week. We obtain news releases data from the Capital IQ Key developments database and the sample period begins in 2003. Therefore, regression specifications with NEWS_INTENSITY (i. e. , columns 2 and 3) are estimated using observations from 2003 through 2013. Regression specification in column 1 is estimated using observations from 1996 through 2013. coefficient estimate indicates that an increase in the independent variable will increase the rate at which a recommendation will be revised, and vice versa. We allow for unobserved heterogeneity across analysts through analystrandom effects. This is represented by the term η a in equation (  1 ). This modeling approach is known as the frailty model in survival analysis.
Columns 2 and 3 report results with NEWS_INTENSITY included as a control variable. This is the number of firm-specific news observed in the previous week. We obtain news releases data from the Capital IQ Key Development database (Capital IQ), a comprehensive database of company-specific news collected from public news sources. 10 They include firm-and nonfirm-initiated news found in newswire services, third-party sources (e. g. , newspaper articles), investor transcripts, or disclosure wires. News coverage in Capital IQ was relatively thin until the end of 2002. Therefore, the estimation sample used in columns 2 and 3 is from 2003 to 2013. Column 3 reports results with Broker Â Year fixed effects. Analysts' speedstyle may be influenced by the incentives given on the job due to the clientele that their employer serves, which could be time varying in nature.