dataesr/scientific-paragraphs-categorization huggingface dataset:
However, and although university institutions are the most important part of this process, actions for the development of entrepreneurship are usually complemented by initiatives by other institutions outside the education system. The entrepreneur engineer must be able to evaluate possible solutions, use software tools adequately, learn to work in team, and use efficiently communication skills to work with other people. In addition, the engineer must have the ability to conceptualize and turn the concept into reality. The development of the entrepreneurship in engineering requires a big change in the learning processes linking educational and research areas to enhance creative processes and practices  [8]. This paper presents the results of the activities carried out with the objective of developing business capacity in mechatronic engineering students through the introduction of new activities related to entrepreneurship in the field of new product development.
2 μm  [3]. The manufacturing cost is one of the most important design requirements, for many kinds of products, which drives most of the technical and technological solutions  [4]. For casting processes, different analytic cost models have been developed based on the process's peculiarities. Several researchers  [5] [6] [7]  have identified the major cost items of a casting process, such as material, tooling, labour, energy and overheads. The DFM Concurrent Costing® software developed by Boothroyd Dewhurst Inc. [8]  has an analytic and detailed investment casting cost estimation module aimed at product designers. It considers most phases of the process (pattern and core manufacturing, pattern and cluster assembly, cleaning and etching, investment operation, melting, sintering, break out, blast cleaning and cut off).
Cost Estimation in the Interactive Design Environment Workflow-Knowledge-Based Engineering Systems engineering methods are deployable in most engineering and manufacturing organisations because of the availability of substantial volumes of enterprise records and data. These may be processed and classified to model the product and enterprise systems and simulate alternative scenarios. This is possible thanks to the ubiquity of IT systems in engineering enterprises, which can augment their computing resources as needed by scalable cloud services. Knowledge-based engineering (KBE) software tools, reviewed by La Rocca et al. [15]  and Verhagen et al. [17]  are a proven technology to organise, process, and generate systems data for aiding the rapid generation of new designs. In some implementations, these are embedded tools in computer-aided design (CAD) software environments and thus directly integrate into a design workflow.
Research Methodology To characterize the contributions of Ind4. 0 in the construc tion sector, we first provide a review of the studies on this subject published prior to June 2019. To do so, we have analyzed the scientific publications using the SCOPUS database 1 and post-2010 studies. The query focuses on title, abstract and author keywords on terms related to the construction sector and Ind4. 0 in the broadest sense. This initial work made it possible to filter the available publications and to highlight 347 documents. In order to respond to our issue, we chose to study in detail only the articles using "allow or target or objective or aim or goal" in the title, abstract or keywords. This additional filter made it possible to keep only articles that openly claimed an intention or purpose.
This final synthesis makes it possible to highlight new functions to open, in detail, new perspectives for construction companies. Indeed, more and more real-time monitoring is being studied and used. These follow-ups of objects and participants now make real-time simulations and optimization possible (for example, for flows, time or resources)  [8, 9]. The monitoring of planning and the creation of a knowledge and experience database are also being tested and could be generalized  [10]. These elements are therefore likely to provide new sources of interest or questioning of companies… Whereas BIM is only occasionally used by less than one third of French companies  [11]. Finally, this observation opens up new research perspectives, which are described in more detail in the last part of this article.
…or will they remain without real mutual effects? we obtain a total of approximately 7,050 combinations per diameter. Table  1  lists the number of possible parameter solutions, without taking compatibility into account, when we use a bolted assembly. Given the large number of possible combinations, the behaviour of bolted connections can only be understood by creating a large database, in particular using design of experiments (DOE). Many research works have aimed to develop methods of constructing designs of experiments that make it possible to obtain a maximum of information with a minimum of known points, whilst remaining robust with respect to random and approximation errors  [7]. These include: -Mixed level DOEs, with as many factors as desired, each factor taking the number of levels necessary for proper execution of the study; -Box-Behnken DOEs  [8] , which respect the rotational isovariance criterion; -Rechtschaffner DOEs  [9] , simplified fractional DOEs which allow the effects of factors and second order interactions to be determined; -Taguchi DOEs, based on the most famous theory for quality, which allow the effects of factors and interaction to be determined.
Both configuration levels require supplementary costs during the production process that cannot be neglected  [6, 7]. Many researchers have proposed different methods to optimize the RMS configuration, considering system or machine-level independently or jointly. However, as far as we know, there is still no paper focused on understanding optimization issues of RMS configuration. To fill this gap, this paper presents a literature review to investigate (1) How RMS configuration optimization problems are modelled, (2) Which are the main methods used to solve them and (3) Which are the main focus of papers when optimizing the RMS configuration in terms of: (a) configuration-level, (b) objective function and (c) process/production planning. This review was conducted in four databases: SCOPUS, Science Direct, ISI Web of Science and Taylor & Francis with the keywords "Reconfigurable Manufacturing System" AND "configuration".
Since commercial software, like LINGO, are capable to find a global optimum for ILP and MILP problems, some papers compared their results with those obtained by approximate  [5, 53, 54]  or hybrid methods [51] to verify the reliability of those methods. Others just used these solvers singly  [28, 47, 48]. Linear problems were also solved by different exact approaches, like enumerative  [2, 24]  and iterative techniques  [11, 41, 44]. Some papers modelled their problems as Multi-Criteria Decision-Making (MCDM) ones, by comparing divergent criteria of multiple alternatives and ranking them according to its suitability. MCDM problems mainly compared different system configurations  [21, 55] , but comparison of resource  [16]  and scheduling  [25]  alternatives were also found. They mainly addressed qualitative attributes (e.
Acknowledgments. The research work reported here was made possible by the  Fonds Unique Interministériel (FUI Addimafil ). Acknowledgments. The research work reported here was made possible by  Airbus Operations. Acknowledgment. This work was carried out in the  LS2N laboratory at the University of Nantes. The author acknowledges the significant contributions to this paper by the following people:  Mme. Sana BAKLOUTI  and  Mr. Joachim MARAIS  at the  University of Nantes  and is also grateful to TRANSVALOR society for their assistance and technical expertise. Acknowledgments. The research work herein reported was funded by  Università degli Studi di Catania  within the  CRUI-CARE Agreement  (research path PIA. CE. RI. 2020-2022 Linea 3 STARTING GRANT 2020 ) and by " Smart main. of ind. Plants and civ.
Acknowledgments. The research work herein reported was funded by  Università degli Studi di Catania  within the  CRUI-CARE Agreement  (research path PIA. CE. RI. 2020-2022 Linea 4 ). Acknowledgments. This research has benefitted from the financial support of  China Scholarship Council  (first author). Acknowledgments. This work was partially funded by  Fondazione Cassa Di Risparmio di Padova e Rovigo (CARIPARO)  and grant " BIRD   190850 " by  Department of Civil, Environmental and Architectural Engineering, University of Padova. Acknowledgments. The authors of this paper thank the  Faculty of Engineering of Gipuzkoa forlocating the DEHI laboratory  in their facilities and the study was supported by the  Gipuzkoa Country Council  (grant number  70/19 ) and  MINECO Ministry of Economy and Competitiveness  (grant number  PID2019-108975RA-I00 ).
Acknowledgments. This research work was supported by the  University of Jaen  through the  Plan de Apoyo a la Investigación   2019-2020-ACCION1 PAIUJA2019-20 :  TIC-159. - Researchstay Cristina Martin-Doñate Fakultät Mechanical and Medical Engineering-Hochschule Furtwangen University. 2019. Acknowledgments. The authors would like to thank  Ihobe  and the  Basque Ecodesign Center for promoting economically and technically the Master Degree in Circular Economy: Business Application in the Faculty of Engineering in Bilbao. Acknowledgments. This research work was supported by the  University of Jaen  with the  Plan de Apoyo a la Transferencia del Conocimiento, el Emprendimiento y la Empleabilidad  ( 2019-2020 )  AC-10  through the project "Creation of an entrepreneurial ecosystem of microstart-ups based on innovative products".
Multi-modal imaging acquisition All subjects were scanned using a 3T Siemens Verio scanner. The following MR scans were acquired: A 3D T1-weighted magnetization prepared rapid gradient-echo (MPRAGE) image (TR = 2. 3 s, TE = 2. 98 ms, 160 slices, flip angle = 9 o , voxel size = 1 mm 3 isotropic); a diffusion weighted image (DWI) with 64 directions ( b = 1000 s/mm 2 ) in addition to a single non-diffusion weighted volume (TR = 11. 7 s, TE = 90 ms, 63 slices, voxel size = 2 × 2 × 2 mm, flip angle = 90°); a Fluid-attenuated inversion recovery (FLAIR) scan (TR = 9 s; TE = 94 ms, 27 slices, voxel size = 0. 4 × 0. 4 × 4 mm, TI = 2500 ms; flip angle = 150°) and an arterial spin labelling (ASL) dataset (PICORE Q2TIPS, 50 pairs of control/ tag images, one calibration image, TR = 2. 5 s / TE = 11 ms, inversion time = 1.
White matter microstructure The diffusion datasets were visually inspected for optimal coverage and to ensure minimal eddy-current distortions by 2 authors (EM and EMK). 9 subjects were excluded due to excessive EPI distortions, while 1 subject was excluded due to poor field-of-view coverage. DWI data were stripped of nonbrain tissue using the Brain Extraction Tool (BET). Eddy currents and head movements were corrected with "eddy " in FSL (Version 6. 0. 1). Quantitative identification of slices with signal loss was performed in "eddy " and these volumes were replaced by non-parametric predictions using the Gaussian process  ( Andersson et al. , 2016 ). In addition, we also corrected for intra-volume movement  ( Andersson et al. , 2017 ). The b-matrix was subsequently reoriented by applying the rotational part of the affine transformation used during eddy correction  ( Leemans and Jones, 2009 ).
Diffusion tensors were generated from the eddy-corrected DWI datasets using DTIFIT in FSL 6. 0. 1. We pursued an unbiased longitudinal approach for the spatial normalisation of diffusion datasets using the Diffusion Tensor Imaging Toolkit (DTI-TK). Firstly, tensor images at baseline and follow-up were created by converting the diffusion eigenvectors and eigenvalues using the function fsl_to_dtitk in DTI-TK. Secondly, for each participant, an unbiased intra-subject template was generated using the tensor datasets from his or her baseline and follow-up after an iterative process of rigid, affine and non-linear diffeomorphic registrations. Next, the within-subject templates were used to create a study-specific population template using the same iterative process. Each participant's DWI data was normalised to the groupspecific template via a single interpolation step.
Quantification of WMH WMH lesion maps were obtained using an automated script on the Statistical Parametric Mapping 8 (SPM8) suite ( http://www. fil. ion. ucl. ac. uk/spm/ ) on FLAIR MRI; details on the procedures involved have been described previously  ( Firbank et al. , 2003 ; Smart et al. , 2011 ). All lesion maps from both baseline and follow-up visits were reviewed by a single experienced rater (A. L. ) while blinded to all clinical information. Lesion maps obtained from the segmentation procedure were used as starting points for manual WMH delineation. Baseline and follow-up FLAIR images were evaluated side by side during delineation to ensure consistency. WMH volumes were normalised by total intracranial volume to account for individual differences in head size. To generate a descriptive voxelwise map representing the spatial topography of WMH occurrence in our sample, we normalised the binarized lesion maps to the MNI152 template using the forward transformations from T1-MPRAGE to MNI152 space.
Statistical analyses A summary of the key imaging outcomes is illustrated in Fig. 1. To characterise brain imaging changes in relation to EYO, we used all available longitudinal datasets for all participants. Demographic variables were compared between the APOE- 4 groups using t -tests, Wilcoxon Rank sum tests, chi-square tests where appropriate. To increase robustness of our correlations, EYO was transformed with the inverse normal transformation due its skewed distribution, increasing the robustness of correlations  ( Miller et al. , 2016 ). Voxelwise associations of EYO with imaging changes were investigated using the longitudinal Sandwich Estimator (SwE) toolbox ( http://www. nisox. org/Software/SwE )  ( Guillaume et al. , 2013 ). This marginal model describes expected variability as a function of predictors in a design matrix, while additionally accounting for correlations due to repeated measurements and unexplained variations across individuals.
Non-voxelwise analyses of global FA, MD, RD, mean grey matter perfusion, and mean cortical thickness were performed using Linear Mixed Effect models (LME), implemented with the lmer function of the lme4 Fig. 1. Multi-modal neuroimaging outcome measures at both baseline and follow-up. Global FA, MD and RD were estimated using DTI-TK after generating a group-specific template that was unbiased to both time-points. T1-MPRAGE datasets were processed using Freesurfer for the segmentations of cortical thickness and hippocampus. ASL data were processed using FSL-BASIL, co-registered to the T1-MPRAGE, and visually inspected by 2 independent operators for errors in alignment. Grey matter perfusion was quantified following PVC of the CBF maps using the grey matter mask. WMHs were segmented from FLAIR data.
Sample characteristics Demographic details about the sample with diffusion imaging datasets are summarised in Table  1. The EYO ranged from 38 years before the parental onset to -3 years after onset. There were no significant differences with regards to age, gender, education years or ACER between the APOE- 4 carriers and non-carriers. Abbreviations: ACE-III = Addenbrooke's Cognitive Examination, EYO = Estimated years to onset.
, 2010 ). Cross-sectional studies in late-onset AD and familial AD have demonstrated preferential vulnerability of the corpus callosum, the superior and inferior longitudinal fasciculus, and cingulum bundles of the limbic projections, and our findings extend those reports by documenting a similar profile of white matter degeneration in a cognitively normal cohort of midlife adults. To the extent that EYO is a valid stage marker of preclinical disease progression, it is conceivable that the pattern of white matter deficits in our data may reflect incipient neuropathologies (i. e. amyloid or early build-up of neurofibrillary tau tangles). While the EYO correlations preclude causal interpretations, our hypothesis is aligned with a recent study that included both ante-mortem DTI and histological analyses in cognitively normal subjects  ( Kantarci et al.
Participants aged 40 to 59 years old with or without a parent with dementia were recruited through multiple sources, such as the register database held at West London Mental Health National Health Service Trust, part of the UK National Health Service. Other participants were recruited via the Join Dementia Research website through information about the study on the Internet and public presentations. Consented participants were seen at the West London Cognitive Disorders Treatment and Research Unit, West London Mental Health NHS Trust, where they were given a standardised neuropsychiatric interview and life-style questionnaires. Cognitive assessments were performed with the Addenbrooke's Cognitive Examination (ACE-III) during the follow-up visit. Blood was taken for APOE- 4 genotyping with all members of the research and clinical teams remaining blind to the results.
Acknowledgements Participants were recruited at West London  Mental Health National Health Service (NHS) Trust  (now known as  West London NHS Trust ) and scanning was carried out at the  Clinical Imaging Facility, Imperial College London. We thank all the  PREVENT-Dementia  participants for their enthusiastic participation in this study. Infrastructure support was provided by the  High Performance Hubs for Clinical Informatics (HPHI) , funded by the  MRC   Research Infrastructure Award  ( MR/M009041/1 ).
Differentially expressed genes were analyzed using the ExDEGA software (EBIOGEN, Inc. , Seoul, Korea). Gene classification was based on searches performed using the DAVID (http://david. ncifcrf. gov/, accessed on 10 October 2021), Medline databases (http:// www. ncbi. nlm. nih. gov/, accessed on 10 October 2021), KEGG pathway (http://www. genome. jp/kegg/mapper. html/, accessed on 12 October 2021) and STRING (http://www. string-db. org/, accessed on 10 October 2021). Heat maps of hypoxia-induced genes in hESC-CMs were performed using software MultiExperiment Viewer (Mev)  [23]. Raw RNA-sequencing data have been deposited in the Gene Expression Omnibus (accession number GEO: GSE171824).
RNA-Seq Analysis Demonstrated That FGF4+AA-Treated BG01 hESC-CMs Are a Suitable In Vitro Hypoxic Stress Model To investigate whether FGF4+AA-treated BG01 hESC-CMs are suitable an in vitro hypoxic stress model at the molecular level, we analyzed the transcriptomes of FGF4+AAtreated hESC-CMs cultured under normoxic (21% O 2 ) or hypoxic (2% O 2 ) conditions for 24 h (Figure  4A ). In total, of 25,737 genes, 4673 differentially expressed genes (2731 upregulated and 1942 downregulated genes) with fold changes greater than 2 were found using the ExDEGA program (Figure  4B-D ). We focused on hypoxia-induced genes mimicking the AMI environment in BG01 hESC-CMs exposed to hypoxia using the DAVID, Medline, and KEGG pathway databases. Differentially expressed genes regulating glycolytic processes, hypoxia-inducible factor 1 (HIF-1) signaling, oxygen levels, and apoptotic processes in hESC-CMs by culturing under hypoxia for 24 h were clustered using Euclidean distance correlation and visualized by a heatmap (Figure  4E-F ).
Funding: This work was supported by the  Bio & Medical Technology Development Program of the National Research Foundation (NRF)  funded by the  Korean government (MSIT) ;  NRF-2016M3A9B6947892  (to Y. P. ), and by the  NRF  grants funded by the  Korea government ;  NRF-2015R1D1A1A01060473  (to S. -C. C. ) and  NRF-2018R1D1A1B07050742  (to S. -C. C. ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
To the best of our knowledge, we are the first to address this task in an automatic manner. We use the dataset of Buddha statues presented by Renoust et al. [22] , which, to the best of our knowledge, is the only dataset that comes with a rich set of annotations on built time, materials, etc. One major challenge in this dataset is its heterogeneous labels; that is, due to the inherent nature of cultural heritage as mentioned above, Buddha statue history can be lost, and thus some labels are completely missing or can only be roughly estimated by historians. As for their built time, many samples do not have relevant labels at all; some come with their exact built year, and the others only have more ambiguous expressions on the built time, such as the dynasty or century that they were established in.
DATASET We obtained the dataset from Renoust et al. [22] , which consists of 7,518 scanned Buddha images from 5 different books. The retina face detection algorithm  [8]  was deployed by the authors to extract the face of statues. These face images were then aligned and resize to 112 × 112, following the same process in  [7]. The algorithm found 4,949 Buddha face images in the dataset. Among them, only 1,887 have built time labels, while the remaining do not provide any information about their built time. The built time labels associated with respective images fall into three types: dynasty in which the statue was built, ranging from 40 years to 700 years, century, and exact year. Hereinafter, we call these labels built time collectively, while using built year whenever it pinpoints a certain year.
We picked out the label with the smallest range if multiple labels are available. The distribution of the labels is as follows: • dynasty: 320 samples • century: 316 samples • year: 1,251 samples We randomly split the dataset into 70% (i. e. , 3,464 samples) for training and 30% (i. e. , 1,485 samples) for testing, where 1,340 out of 3,464 samples have built time labels in the training set and 547 out of 1,485 samples in the testing set. Figure  2  shows some sample Buddha face images after alignment. Many of them have missing facial parts (Fig. 2 (h) ). All images of the dataset were collected by scanning printed books, which often leads to large colour distortion from the original statues (Fig. 2 (c) ). The original images in the books were captured by multiple cameras and their intrinsic parameters are unknown.
The mean for this dynasty is 1259 and the standard deviation is 37. The red line is the 10th century (from 901 to 1000), where the mean and standard deviation are 950 and 25, respectively. Our model consists of a ResNet50 variant of Arcface  [7] , pretrained with 17 million human face datasets  [1] , as backbone, of which the last batch normalisation layer is connected to a fully connected layer to predict built year. (a) (b) (c) (d) (e) (f) (g) (h) (i) To make full use of the dataset with labelled and unlabelled images, we design a dedicated semi-supervised loss function, consisting of three terms, which are the mean squared error (MSE) loss, a KL divergence-based loss to better incorporate the different ambiguity levels, and a regularisation to make a smoother manifold with unlabelled samples.
MSE loss. The MSE loss provides basic supervision merely for samples with exact built years. Let  () denote our model to regress the built year from image. Our MSE loss  is defined as: where   is the mean of. KL divergence-based loss. This loss utilises the different ranges of the built time labels in the dataset. Intuitively, an arbitrary pair of predicted built year must have a similar relationship (i. e. , the closeness) to the ground-truth built time, which is not as straightforward as computing the distance between two samples, since the built time labels come with different ambiguities. Inspired by t-SNE  [29] , we encode such pairwise relationships into conditional probability ( ( ′ ) |  ()) of prediction  ( ′ ) given  () (or conditional probability  ( ′ | ) of ground-truth  ′ given ), where (, ) and ( ′ ,  ′ ) are in D L.