74,576 research outputs found
Osteoinductive bone substitutes
No full textWismeyer, D. [Promotor]Gu, Z. [Promotor]Liu, Y. [Copromotor
New performing GC columns with unmatched separation capabilities
No full textGas chromatography (GC) is widely used for qualitative and quantitative analysis in numerous fields, such as petroleum, chemical industry, agriculture, environmental protection, medicine, and so on, due to its high
versatility, high selectivity, simplicity of use, analysis speed, and low sample consumption. The column is the heart of a GC instrumentation, which allows the analyte separation and their recognition and quantification. Commercial columns do not always allow a complete peak separation when compounds (i.e., isomers) are very
similar in molecular weight, polarity, and vapor pressure. The choice of the correct stationary phase, with high selectivity towards target analytes, is the key to obtaining the required chromatographic separation and the subsequent qualitative and quantitative analysis.
Considering the rapid polymer science development and the growing demand for new columns with
improved resolution capabilities, in this work novel stationary phases for capillary GC have been designed, synthesized, and characterized in terms of polarity range, resolution, column efficiency, thermal stability, filmforming properties, and support-deactivating capacity1-5. The separation features of these novel stationary phases allow high-resolution performances for a wide range of compounds, like aromatic anilines, xylenes, aromatic amines, halogenated benzenes, and aromatic aldehydes, with marked capabilities toward isomer separations.References:
[1] T. Sun, M. Ba, Y. Song, W. Li, Y. Zhang, Z. Cai, S. Hu, X. Liu, D. Nardiello, M. Quinto, Analytica Chimica Acta, 2024, 1291, art. no. 342221.
[2] T. Sun, R. Chen, Q. Huang, M. Ba, Z. Cai, H. Chen, Y. Qi, H. Chen, X. Liu, D. Nardiello, M. Quinto, Analytica
Chimica Acta, 2023, 1251, art. no. 340979.
[3] T. Sun, R. Chen, Q. Huang, M. Ba, Z. Cai, S. Hu, X. Liu, D. Nardiello, M. Quinto, ACS Applied Materials and Interfaces, 2022, 14 50, pp. 56132-56142
[4] R. Chen, Z. Cai, W. Li, Q. Huang, D. Nardiello, M. Quinto, X. Liu, S. Hu, T. Sun, Chemistry and Biodiversity, 2022,
19, art. no. e202200829
[5] Q. Huang, Z. Cai, R. Chen, W. Zhang, D. Nardiello, M. Quinto, X. Liu, S. Hu, T. Sun, Microchemical Journal, 2022,
183, art. no. 10808
Two-body open charm decays of Z(+)(4430)
No full textThe two-body open charm decays Z(+)(4430)-> D(+)(D) over bar*(0), D*(+)(D) over bar (0), D*(+)(D) over bar*(0) occur through the rescattering mechanism and their branching ratios are strongly suppressed if Z(+)(4430) is a D(1)(D) over bar* molecular state. In contrast, Z(+)(4430) falls apart into these modes easily with large phase space and they become the main decay modes if Z(+)(4430) is a tetraquark state. Experimental search of these two-body open charm modes and the hidden charm mode chi(cJ)rho will help distinguish different theoretical schemes.Astronomy & AstrophysicsPhysics, Particles & FieldsSCI(E)0ARTICLE11null7
Capacity results for a class of deterministic Z-interference channels with unidirectional receiver conferencing
No full textWe study the Z-interference channel in which there is an additional orthogonal link from the interference-free receiver to the interfered receiver. We call this channel model the Z-interference channel with unidirectional receiver conferencing. We find the capacity region when the Z-interference channel belongs to the class of deterministic Z-interference channels studied by El Gamal & Costa in 1982. Our results show that in the presence of unidirectional receiver conferencing, it is still optimal for the interfering transmitter to use superposition encoding to control the amount of interference it causes. For the interference-free receiver, it is optimal to forward part of the decoded message over the orthogonal cooperation link. We further note that the same scheme is also optimal for another class of Z-interference channels studied by Liu & Goldsmith in 2009. © 2011 IEEE
Confirmation of a charged charmoniumlike state Z(c)(3885)(-/+) in e(+)e(-) -> pi(+/-) (D(D)over-bar*)(-/+) with double D tag
No full textWe present a study of the process e(+)e(-) -> pi(+/-) (D (D) over bar*)(-/+) using data samples of 1092 pb(-1) at root s = 4.23 GeV and 826 pb(-1) at root s = 4.26 GeV collected with the BESIII detector at the BEPCII storage ring. With full reconstruction of the D meson pair and the bachelor pi(+) in the final state, we confirm the existence of the charged structure Z(c) (3885)(-/+) in the (D (D) over bar*)(-/+) system in the two isospin processes e(+)e(-) -> pi(+DD)-D-0*(-) and e(+)e(-) -> pi+D-D*(0). By performing a simultaneous fit, the statistical significance of Zc(3885)(-/+) signal is determined to be greater than 10 sigma, and its pole mass and width are measured to be M-pole = (3881.7 +/- 1.6(stat) +/- 1.6(syst)) MeV/c(2) and Gamma(pole) = (26.6 +/- 2.0(stat) +/- 2.1(syst)) MeV, respectively. The Born cross section times the (D (D) over bar*)(-/+) branching fraction (sigma(e(+)e(-) -> pi(+/-)Z(c)(3885)(-/+)) x Br(Z(c)(3885)(-/+) -> (D (D) over bar*)(-/+) )) is measured to be (141.6 +/- 7.9(stat) +/- 12.3(syst)) pb at root s = 4.23 GeV and (108.4 +/- 6.9(stat) +/- 8.8(syst)) pb at root s = 4.26 GeV. The polar angular distribution of the pi(+) - Z(c)(3885)(-/+) system is consistent with the expectation of a quantum number assignment of J(P) = 1(+) for Z(c)(3885)(-/+).Funding: The BESIII Collaboration thanks the staff of BEPCII and the IHEP computing center for their strong support. This work is supported in part by National Key Basic Research Program of China under Contract No. 2015CB856700; National Natural Science Foundation of China (NSFC) under Contracts No. 10935007, No. 11075174, No. 11121092, No. 11125525, No. 11235011, No. 11322544, No. 11335008, No. 11425524, No. 11475185; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; the CAS Center for Excellence in Particle Physics (CCEPP); the Collaborative Innovation Center for Particles and Interactions (CICPI); Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contracts No. 11179007, No. U1232201, No. U1332201; CAS under Contracts No. KJCX2-YW-N29, No. KJCX2-YW-N45; 100 Talents Program of CAS; National 1000 Talents Program of China; INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Contract No. Collaborative Research Center CRC-1044; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; Russian Foundation for Basic Research under Contract No. 14-07-91152; The Swedish Resarch Council; U.S. Department of Energy under Contracts No. DE-FG02-04ER41291, No. DE-FG02-05ER41374, No. DE-SC0012069, No. DESC0010118; U.S. National Science Foundation; University of Groningen (RuG) and the Helmholtzzentrum fuer Schwerionenforschung GmbH (GSI), Darmstadt; WCU Program of National Research Foundation of Korea under Contract No. R32-2008-000-10155-0.</p
Is Z(+)(4430) a loosely bound molecular state?
No full textSince Z(+)(4430) lies very close to the threshold of D*(D) over bar (1), we investigate whether Z(+)(4430) could be a loosely bound S-wave state of D*(D) over bar (1) or D*(D) over bar (1)' with J(P)=0(-),1(-),2(-), i.e., a molecular state arising from the one-pion-exchange potential. The potential from the crossed diagram is much larger than that from the diagonal scattering diagram. With various trial wave functions, we notice that the attraction from the one-pion-exchange potential alone is not strong enough to form a bound state with realistic pionic coupling constants deduced from the decay widths of D(1) and D(1)'.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000253764700017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Astronomy & AstrophysicsPhysics, Particles & FieldsSCI(E)32ARTICLE3null7
Paikallisten binäärikuvioiden inspiroima tehokas syvä konvoluutioneuroverkko visuaalisten piirteiden oppimiseen
No full textAbstractIn the past decades, deep neural networks (DNNs) have revolutionized the computer vision community with their significant success in a wide range of computer vision tasks. Recent work has focused intensely on accuracy, which has resulted in a large number of huge and complex models designed in the community. However, with the ubiquitous use of edge devices like mobile phones, robots, and embedded systems, efficiency is gradually becoming more and more important for modern computer vision models. In computer vision, the quality of feature representation learning directly determines the quality of the whole machine learning model. The core challenge is to develop feature representation learning algorithms both effectively and efficiently.In this thesis, we put our efforts into the following matters to meet the challenge. On the one hand, we take the merit of traditional local binary pattern (LBP) descriptors of being computationally simple and efficient, and propose improvement in the learnability of LBP to extract more discriminative features. On the other hand, taking advantage of DNNs of high representational capacity, we target building compact DNN modules with less computational cost and model size. These two aspects are either separately developed or combined, and both are considered in this thesis.We start by extending traditional LBP to learnable descriptors, allowing the new descriptors to be learned from the data rather than handcrafted. Based on that, our model obtains a better trade-off than earlier LBP variants including distinctiveness, computational cost, and robustness. Next, we propose two novel types of convolutions that combine LBP and the convolution operation. The new convolutions enjoy the following benefits: capturing higher-order local differential information, being computationally efficient, and being able to be integrated well into existing DNNs. Then, we propose an efficient convolutional neural network (CNN) module that benefits from group convolution and dynamic execution. It shares the efficiency of the standard group convolution without losing representational ability. Finally, we develop a novel binary DNN module for robust point cloud analysis. The proposed point cloud models achieve both running efficiencies through network binarization and rotation invariance at the same time.Original papersOriginal papers are not included in the electronic version of the dissertation.Su, Z., Pietikäinen, M., & Liu, L. (2019). BIRD: Learning binary and illumination robust descriptor for face recognition. In 30th British Machine Visison Conference : BMVC 2019, 1–12.Self-archived versionSu, Z., Fang, L., Kang, W., Hu, D., Pietikäinen, M., & Liu, L. (2020). Dynamic group convolution for accelerating convolutional neural networks. In Computer Vision – ECCV 2020, 138–155. https://doi.org/10.1007/978-3-030-58539-6_9Self-archived versionSu, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietikainen, M., & Liu, L. (2021). Pixel difference networks for efficient edge detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5097–5107. https://doi.org/10.1109/ICCV48922.2021.00507Self-archived versionSu, Z., Welling, M., Pietikäinen, M., & Liu, L. (2022). SVNet: Where SO(3) equivariance meets binarization on point cloud representation. In 2022 International Conference on 3D Vision (3DV), 547–556. https://doi.org/10.1109/3DV57658.2022.00084Self-archived versionSu, Z., Zhang, J., Wang, L., Zhang, H., Liu, Z., Pietikäinen, M., & Liu, L. (2023). Lightweight pixel difference networks for efficient visual representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/TPAMI.2023.3300513Su, Z., Müller, M., Wofk, D., Pietikäinen, M., & Liu, L. (2023). Spatial and temporal difference network for real-time salient object detection. Manuscript submitted for publication.TiivistelmäViime vuosikymmeninä syvät neuroverkot ovat mullistaneet konenäköä suurella menestyksellä useissa eri tehtävissä. Viimeaikaisin tutkimus on keskittynyt menetelmien tarkkuuteen, mikä on johtanut suuren määrään valtavan kokoisia ja kompleksisia malleja. Kaikella läsnä olevalla tekniikalla, kuten puhelimilla, roboteilla ja sulautetuilla järjestelmillä konenäkö on kasvavassa määrin tärkeämpää ja täten mallien tehokkuus on myös tärkeämpää. Konenäössä piirteiden oppiminen määrittää suoraan koko konenäkö mallin laadun. Keskeinen haaste on kehittää piirteiden oppimisalgoritmeja tehokkaasti.Tässä väitöskirjassa esitetään seuraavia ratkaisuja mallien tehokkuuden ongelmaan. Ensiksi parannetaan laskennallisesti tehokasta ja yksinkertaista paikallista binäärikuva -menetelmää lisäämällä sen piirteiden määrää. Toiseksi hyödynnetään suurta neuroverkkojen piirteiden kapasiteettia kehittämällä laskennallisesti tehokkaampia ja pienempiä moduuleja. Kumpiakin tekniikkoja käytetään erikseen ja yhdessä tässä väitöskirjassa.Perinteisestä paikallisesta binäärikuvio -menetelmästä tehdään oppiva, jolloin uusia piirteitä voidaan oppia datasta, sen sijaan että ne määriteltäisiin algoritmillisesti. Uusi kehitetty oppiva versio on laskennallisesti tehokkaampi, robustimpi ja erottelevaisempi. Seuraavaksi esitellään tekniikka, joka yhdistää paikallisen binäärikuvion ja konvoluution. Kehitetty konvoluutio pystyy irrottamaan korkeamman asteen paikallista informaatiota, se on laskennallisesti tehokas ja se voidaan integroida olemassa oleviin neuroverkkoihin vaivattomasti. Sen jälkeen esitellään konvoluutioneuroverkon moduuli, joka käyttää hyväkseen ryhmäkonvoluutiota ja dynaamista suoritusta. Moduuli pitää normaalin konvoluution piirteidenirrotus kyvyn ollen kuitenkin yhtä tehokas ryhmäkonvoluution kanssa laskennallisesti. Lopuksi esitetään binäärineuroverkko moduuli robustiin pistepilven analyysiin. Kehitetty moduuli parantaa laskennallista tehokkuutta hyödyntämällä sekä binarisointia että pyörimis-invarianttia konvoluutiota.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Su, Z., Pietikäinen, M., & Liu, L. (2019). BIRD: Learning binary and illumination robust descriptor for face recognition. In 30th British Machine Visison Conference : BMVC 2019, 1–12.Rinnakkaistallennettu versioSu, Z., Fang, L., Kang, W., Hu, D., Pietikäinen, M., & Liu, L. (2020). Dynamic group convolution for accelerating convolutional neural networks. In Computer Vision – ECCV 2020, 138–155. https://doi.org/10.1007/978-3-030-58539-6_9Rinnakkaistallennettu versioSu, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietikainen, M., & Liu, L. (2021). Pixel difference networks for efficient edge detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 5097–5107. https://doi.org/10.1109/ICCV48922.2021.00507Rinnakkaistallennettu versioSu, Z., Welling, M., Pietikäinen, M., & Liu, L. (2022). SVNet: Where SO(3) equivariance meets binarization on point cloud representation. In 2022 International Conference on 3D Vision (3DV), 547–556. https://doi.org/10.1109/3DV57658.2022.00084Rinnakkaistallennettu versioSu, Z., Zhang, J., Wang, L., Zhang, H., Liu, Z., Pietikäinen, M., & Liu, L. (2023). Lightweight pixel difference networks for efficient visual representation learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. Advance online publication. https://doi.org/10.1109/TPAMI.2023.3300513Su, Z., Müller, M., Wofk, D., Pietikäinen, M., & Liu, L. (2023). Spatial and temporal difference network for real-time salient object detection. Manuscript submitted for publication.Academic dissertation to be presented with the assent of the Doctoral Programme Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 16 October 2023, at 12 noonAbstract
In the past decades, deep neural networks (DNNs) have revolutionized the computer vision community with their significant success in a wide range of computer vision tasks. Recent work has focused intensely on accuracy, which has resulted in a large number of huge and complex models designed in the community. However, with the ubiquitous use of edge devices like mobile phones, robots, and embedded systems, efficiency is gradually becoming more and more important for modern computer vision models. In computer vision, the quality of feature representation learning directly determines the quality of the whole machine learning model. The core challenge is to develop feature representation learning algorithms both effectively and efficiently.
In this thesis, we put our efforts into the following matters to meet the challenge. On the one hand, we take the merit of traditional local binary pattern (LBP) descriptors of being computationally simple and efficient, and propose improvement in the learnability of LBP to extract more discriminative features. On the other hand, taking advantage of DNNs of high representational capacity, we target building compact DNN modules with less computational cost and model size. These two aspects are either separately developed or combined, and both are considered in this thesis.
We start by extending traditional LBP to learnable descriptors, allowing the new descriptors to be learned from the data rather than handcrafted. Based on that, our model obtains a better trade-off than earlier LBP variants including distinctiveness, computational cost, and robustness. Next, we propose two novel types of convolutions that combine LBP and the convolution operation. The new convolutions enjoy the following benefits: capturing higher-order local differential information, being computationally efficient, and being able to be integrated well into existing DNNs. Then, we propose an efficient convolutional neural network (CNN) module that benefits from group convolution and dynamic execution. It shares the efficiency of the standard group convolution without losing representational ability. Finally, we develop a novel binary DNN module for robust point cloud analysis. The proposed point cloud models achieve both running efficiencies through network binarization and rotation invariance at the same time.Tiivistelmä
Viime vuosikymmeninä syvät neuroverkot ovat mullistaneet konenäköä suurella menestyksellä useissa eri tehtävissä. Viimeaikaisin tutkimus on keskittynyt menetelmien tarkkuuteen, mikä on johtanut suuren määrään valtavan kokoisia ja kompleksisia malleja. Kaikella läsnä olevalla tekniikalla, kuten puhelimilla, roboteilla ja sulautetuilla järjestelmillä konenäkö on kasvavassa määrin tärkeämpää ja täten mallien tehokkuus on myös tärkeämpää. Konenäössä piirteiden oppiminen määrittää suoraan koko konenäkö mallin laadun. Keskeinen haaste on kehittää piirteiden oppimisalgoritmeja tehokkaasti.
Tässä väitöskirjassa esitetään seuraavia ratkaisuja mallien tehokkuuden ongelmaan. Ensiksi parannetaan laskennallisesti tehokasta ja yksinkertaista paikallista binäärikuva -menetelmää lisäämällä sen piirteiden määrää. Toiseksi hyödynnetään suurta neuroverkkojen piirteiden kapasiteettia kehittämällä laskennallisesti tehokkaampia ja pienempiä moduuleja. Kumpiakin tekniikkoja käytetään erikseen ja yhdessä tässä väitöskirjassa.
Perinteisestä paikallisesta binäärikuvio -menetelmästä tehdään oppiva, jolloin uusia piirteitä voidaan oppia datasta, sen sijaan että ne määriteltäisiin algoritmillisesti. Uusi kehitetty oppiva versio on laskennallisesti tehokkaampi, robustimpi ja erottelevaisempi. Seuraavaksi esitellään tekniikka, joka yhdistää paikallisen binäärikuvion ja konvoluution. Kehitetty konvoluutio pystyy irrottamaan korkeamman asteen paikallista informaatiota, se on laskennallisesti tehokas ja se voidaan integroida olemassa oleviin neuroverkkoihin vaivattomasti. Sen jälkeen esitellään konvoluutioneuroverkon moduuli, joka käyttää hyväkseen ryhmäkonvoluutiota ja dynaamista suoritusta. Moduuli pitää normaalin konvoluution piirteidenirrotus kyvyn ollen kuitenkin yhtä tehokas ryhmäkonvoluution kanssa laskennallisesti. Lopuksi esitetään binäärineuroverkko moduuli robustiin pistepilven analyysiin. Kehitetty moduuli parantaa laskennallista tehokkuutta hyödyntämällä sekä binarisointia että pyörimis-invarianttia konvoluutiota
Insights into the antibacterial mechanism of PEGylated nano-bacitracin A against Streptococcus pneumonia: both penicillin-sensitive and penicillin-resistant strains [Corrigendum]
No full textInsights into the antibacterial mechanism of PEG ylated nano-bacitracin A against Streptococcus pneumonia: both penicillin-sensitive and penicillinresistant strains [Corrigendum]Hong W, Liu L, Zhang Z, Zhao Y, Zhang D, Liu M. Int J Nanomedicine. 2018;13:6297–6309.The author wishes to advise that on page 6306, Figure 7 parts A and B are incorrect. The correct figure parts are included below:Read the original articl
INNOVATIVE PILLAR[6]ARENE-BASED STATIONARY PHASES FOR HIGH-RESOLUTION GAS CHROMATOGRAPHIC ANALYSES
No full textIn this work, the synthesis, fabrication, and characterization of new stationary phases based on pillar[6]arene derivative modified by long alkyl chains (P6A-C10) for high-resolution gas chromatographic (GC) analyses are reported. Pillar[n]arenes are a new class of macrocyclic hosts that can accommodate specific guests due to their highly symmetrical and rigid pillar architectures with π-electron rich cavities. Quantum chemistry calculations have been performed, showing a difference in non-covalent interactions with the P6A-C10 pillar framework, which leads to specific selectivity for aromatic compounds. The GC columns prepared with these innovative stationary phases exhibited a medium polarity, and good reproducibility for run-to-run, day-to-day, and column-to-column analyses [1], demonstrating great potential as new stationary phases in separation science. Furthermore, peculiar advantages are achieved if compared with the commercial HP-5, HP-35, DB-17, and PEG-20M columns, showing unmatched resolving capabilities toward chloroaniline, bromoaniline, iodoaniline, toluidine, and xylene isomers [2].
References:
1. Sun, T., Chen, R., Huang, Q., Ba, M., Cai, Z., Hu, S., Liu, X., Nardiello, D., & Quinto, M., ACS Appl. Mater. Interfaces 14 (2022) 56132−56142.
2. Sun, T., Chen, R., Huang, Q., Ba, M., Cai, Z., Chen, H., Qi, Y., Chen, H., Liu, X., Nardiello, D., & Quinto, M., Anal. Chim. Acta 1251 (2023) 340979
Un dialogo sul concetto di “Intangible Cultural Heritage” in Cina. Comparazioni e interpretazioni
No full text- …
