全文获取类型
收费全文 | 472篇 |
免费 | 38篇 |
国内免费 | 1篇 |
专业分类
电工技术 | 8篇 |
综合类 | 2篇 |
化学工业 | 135篇 |
金属工艺 | 6篇 |
机械仪表 | 18篇 |
建筑科学 | 13篇 |
矿业工程 | 1篇 |
能源动力 | 6篇 |
轻工业 | 118篇 |
水利工程 | 1篇 |
石油天然气 | 2篇 |
无线电 | 23篇 |
一般工业技术 | 71篇 |
冶金工业 | 44篇 |
原子能技术 | 3篇 |
自动化技术 | 60篇 |
出版年
2024年 | 2篇 |
2023年 | 10篇 |
2022年 | 37篇 |
2021年 | 31篇 |
2020年 | 14篇 |
2019年 | 17篇 |
2018年 | 24篇 |
2017年 | 14篇 |
2016年 | 26篇 |
2015年 | 21篇 |
2014年 | 23篇 |
2013年 | 47篇 |
2012年 | 46篇 |
2011年 | 30篇 |
2010年 | 23篇 |
2009年 | 23篇 |
2008年 | 11篇 |
2007年 | 10篇 |
2006年 | 14篇 |
2005年 | 9篇 |
2004年 | 7篇 |
2003年 | 4篇 |
2002年 | 9篇 |
2001年 | 6篇 |
2000年 | 5篇 |
1999年 | 2篇 |
1998年 | 12篇 |
1997年 | 5篇 |
1996年 | 6篇 |
1995年 | 1篇 |
1993年 | 2篇 |
1992年 | 2篇 |
1991年 | 3篇 |
1987年 | 3篇 |
1985年 | 1篇 |
1984年 | 1篇 |
1981年 | 1篇 |
1980年 | 2篇 |
1979年 | 1篇 |
1978年 | 2篇 |
1977年 | 1篇 |
1975年 | 1篇 |
1972年 | 1篇 |
1971年 | 1篇 |
排序方式: 共有511条查询结果,搜索用时 15 毫秒
11.
Olga Azevedo Filipa Cordeiro Miguel Fernandes Gago Gabriel Miltenberger-Miltenyi Catarina Ferreira Nuno Sousa Damio Cunha 《International journal of molecular sciences》2021,22(9)
Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations of the GLA gene that result in a deficiency of the enzymatic activity of α-galactosidase A and consequent accumulation of glycosphingolipids in body fluids and lysosomes of the cells throughout the body. GB3 accumulation occurs in virtually all cardiac cells (cardiomyocytes, conduction system cells, fibroblasts, and endothelial and smooth muscle vascular cells), ultimately leading to ventricular hypertrophy and fibrosis, heart failure, valve disease, angina, dysrhythmias, cardiac conduction abnormalities, and sudden death. Despite available therapies and supportive treatment, cardiac involvement carries a major prognostic impact, representing the main cause of death in FD. In the last years, knowledge has substantially evolved on the pathophysiological mechanisms leading to cardiac damage, the natural history of cardiac manifestations, the late-onset phenotypes with predominant cardiac involvement, the early markers of cardiac damage, the role of multimodality cardiac imaging on the diagnosis, management and follow-up of Fabry patients, and the cardiac efficacy of available therapies. Herein, we provide a comprehensive and integrated review on the cardiac involvement of FD, at the pathophysiological, anatomopathological, laboratory, imaging, and clinical levels, as well as on the diagnosis and management of cardiac manifestations, their supportive treatment, and the cardiac efficacy of specific therapies, such as enzyme replacement therapy and migalastat. 相似文献
12.
Erika Carneiro Riqueza Alcino Palermo de Aguiar Luiz Claudio Santa Maria Mônica Regina Marques Palermo de Aguiar 《Polymer Bulletin》2002,48(4-5):407-414
Summary
The preparation of a chelating ion-exchange network based on acrylonitrile was carried out by chemical modification with hydroxylamine.
The beads of resin were synthesized by aqueous suspension copolymerization of acrylonitrile (AN), styrene (STY) and divinylbenzene
(DVB). The influence of diluent used in the suspension polymerization on the structure of the resulting copolymers was evaluated.
The diluents employed were heptane (HEP), toluene (TOL) and anisole (ANI). It was found that the AN incorporation into copolymer
structure was dependent on the diluent used. Conversion of nitrile groups into the amidoxime was conducted by treatment with
hydroxylamine under alkaline solution. The resins were characterized by apparent density, surface area, average pore diameter,
elemental analysis (CHN), FTIR and optical microscopy. Based on the results obtained, it was possible to control the porosity
by diluent employed in the synthesis and to modify chemically a resin containing nitrile groups by hydroxylamine reaction.
Received: 6 October 2001/Revised version: 2 April 2002/ Accepted: 11 April 2002 相似文献
13.
Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches. 相似文献
14.
In this paper we propose a nonlinear control approach for the path‐tracking of an autonomous underactuated airship. A backstepping controller is designed from the airship nonlinear dynamic model including wind disturbances, and further enhanced to consider actuators saturation. Control implementation issues related to airship underactuation are also addressed, namely control allocation and an attitude reference shaping to obtain a faster error correction with smoother input requests. The results obtained demonstrate the capacity of an underactuated unmanned airship to execute a realistic mission including vertical take‐off and landing, stabilization and path‐tracking, in the presence of wind disturbances, with a single robust control law. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
15.
Sebastiano Battiato Giovanni Maria Farinella Giovanni Giuffrida Catarina Sismeiro Giuseppe Tribulato 《Multimedia Tools and Applications》2009,42(1):5-30
Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience.
Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect
to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing
process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in
particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate
that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes
a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features
to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS)
show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed
approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
Giuseppe TribulatoEmail: |
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
16.
A method for analyzing production systems by applying multi-objective optimization and data mining techniques on discrete-event simulation models, the so-called Simulation-based Innovization (SBI) is presented in this paper. The aim of the SBI analysis is to reveal insight on the parameters that affect the performance measures as well as to gain deeper understanding of the problem, through post-optimality analysis of the solutions acquired from multi-objective optimization. This paper provides empirical results from an industrial case study, carried out on an automotive machining line, in order to explain the SBI procedure. The SBI method has been found to be particularly suitable in this case study as the three objectives under study, namely total tardiness, makespan and average work-in-process, are in conflict with each other. Depending on the system load of the line, different decision variables have been found to be influencing. How the SBI method is used to find important patterns in the explored solution set and how it can be valuable to support decision making in order to improve the scheduling under different system loadings in the machining line are addressed. 相似文献
17.
Maria Kelly Venezuela Mônica Carneiro SandovalDenise Aparecida Botter 《Computational statistics & data analysis》2011,55(4):1867-1883
Local influence diagnostics based on estimating equations as the role of a gradient vector derived from any fit function are developed for repeated measures regression analysis. Our proposal generalizes tools used in other studies (
[Cook, 1986] and [Cadigan and Farrell, 2002]), considering herein local influence diagnostics for a statistical model where estimation involves an estimating equation in which all observations are not necessarily independent of each other. Moreover, the measures of local influence are illustrated with some simulated data sets to assess influential observations. Applications using real data are presented. 相似文献
18.
Recent advances in microscopy and cytolabelling methods enable the real time imaging of cells as they move and interact in their real physiological environment. Scenarios in which multiple cells move autonomously in all directions are not uncommon in biology. A remarkable example is the swimming of marine spermatozoa in search of the conspecific oocyte. Imaging cells in these scenarios, particularly when they move fast and are poorly labelled or even unlabelled requires very fast three-dimensional time-lapse (3D+t) imaging. This 3D+t imaging poses challenges not only to the acquisition systems but also to the image analysis algorithms. It is in this context that this work describes an original automated multiparticle segmentation method to analyse motile translucent cells in 3D microscopical volumes. The proposed segmentation technique takes advantage of the way the cell appearance changes with the distance to the focal plane position. The cells translucent properties and their interaction with light produce a specific pattern: when the cell is within or close to the focal plane, its two-dimensional (2D) appearance matches a bright spot surrounded by a dark ring, whereas when it is farther from the focal plane the cell contrast is inverted looking like a dark spot surrounded by a bright ring. The proposed method analyses the acquired video sequence frame-by-frame taking advantage of 2D image segmentation algorithms to identify and select candidate cellular sections. The crux of the method is in the sequential filtering of the candidate sections, first by template matching of the in-focus and out-of-focus templates and second by considering adjacent candidates sections in 3D. These sequential filters effectively narrow down the number of segmented candidate sections making the automatic tracking of cells in three dimensions a straightforward operation. 相似文献
19.
Rasmussen CD Jørgensen MB Carneiro IG Flyvholm MA Olesen K Søgaard K Holtermann A 《Ergonomics》2012,55(2):256-264
Worksite health promotion is seldom offered to workers who are low-educated and multi-ethnic, possibly due to an assumption that they are more reluctant to participate. Furthermore, little has been done to promote health at female-dominated workplaces. The main aim of this study was to investigate differences in participation among immigrant and Danish cleaners throughout a 1-year randomised controlled study tailored to cleaners and carried out in predominantly female workplaces. No significant differences in ethnicity were found in consent and participation throughout the 1-year intervention. Dropout was equally distributed among Danish and immigrant cleaners. This study indicates that a worksite health promotion intervention among a female-dominated, high-risk occupation such as cleaning can be equally appealing for Danes and immigrants. PRACTITIONER SUMMARY: This study provides insight about participation of Danish and immigrant cleaners in a worksite health promotion intervention in a predominantly female occupation. For attaining high participation and low dropout in future worksite health promotion interventions among cleaners, the intervention ought to not only target the ethnic background of the workers, but also to be specifically tailored to the job group. 相似文献
20.
Bernardete Ribeiro Catarina Silva Ning Chen Armando Vieira João Carvalho das Neves 《Expert systems with applications》2012,39(11):10140-10152
Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM. 相似文献