5916 research outputs found
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Model-predictive control of multilevel inverters: challenges, recent advances, and trends
No full textModel-predictive control (MPC) has emerged as a promising control method in power electronics, particularly for multiobjective control problems such as multilevel inverter (MLI) applications. Over the past two decades, improving the performance of MPC and tackling its technical challenges, such as computational load, modeling accuracy, cost function design, and weighting factor selection, have attracted great interest in power electronics. This article aims to discuss the current state of MPC strategies for MLI applications, describing the significance of each challenge with the reported effective solutions. Through this review, the MPC methods are categorized into two groups: direct MPC (without modulator) and indirect MPC (with modulator). The recent advances of each category are presented and analyzed, focusing on direct MPC as the most applied method for MLI topologies. In addition, some of the important concepts are experimentally validated through a case study and compared under the same operating conditions to evaluate the performance and highlight their features. Finally, the future trends of MPC for MLI applications are discussed based on the current state and reported developments.Agencia Nacional de Investigación y Desarroll
Foreign acquisition and credit risk: Evidence from the U.S. CDS market
No full textThis article empirically analyzes the effect of foreign block acquisitions on U.S. target firms' credit risk as measured by their credit default swap (CDS) spreads. Foreign block purchases lead to a greater increase in the target firms' CDS premia post-acquisition compared to domestic block purchases. This effect is stronger when foreign owners are geographically and culturally more distant, and when they obtain majority control. The findings are consistent with an asymmetric information hypothesis, in which foreign owners are less effective monitors due to information barriers.Publisher versio
Examining the contributions of parents’ daily hassles and parenting approaches to children’s behavior problems during the COVID-19 pandemic
No full textThe present study was designed to examine the direct and indirect contributions of parenting daily hassles and approaches to children’s externalizing and internalizing behavior problems during the COVID-19 pandemic. The sample for this study was 338 preschool children (53.6% girls, Mage = 56.33 months, SD = 15.14) and their parents in Turkey. Parents reported their daily hassles, parenting approaches, and children’s behavior problems. Findings from the structural equation model showed that higher levels of parenting daily hassles predicted higher levels of externalizing and internalizing behavior problems. In addition, we found an indirect effect of daily hassles on children’s internalizing behaviors via positive parenting. Further, there was an indirect path from parenting daily hassles to children’s externalizing behaviors through the negative parenting approach. Results are discussed in the context of the COVID-19 pandemic.Publisher versio
Multi-instance learning by maximizing the area under receiver operating characteristic curve
No full textThe purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-instance classification. The formulation is solved using a commercial mathematical optimization solver as well as a fast metaheuristic approach. When the data is not linearly separable, we illustrate how new features can be generated to tackle the problem. We present a comprehensive computational study to compare our methods against the state-of-the-art approaches in the literature. Our study reveals the success of an optimal linear ranking function through cross validation for several benchmark instances
Configurations of digital platforms for manufacturing: An analysis of seven cases according to platform functions and types
No full textWe analyze organizational configurations of digital platforms for manufacturing according to two dimensions: platform functions and platform types. Platform functions refer to the organizational functions of platforms: manufacturing, data sharing, market making, and innovation. Platform types refer to a typology of how platforms are organized: as internal, supply chain, or industry type. We combine those dimensions into a framework and use that to analyze seven cases of digital platforms from the manufacturing sector. Our research answers calls for conceptual clarity and scoping of the digital platform concept and mends relative lack of attention toward digital platforms for the manufacturing sector. We find that digital platforms for manufacturing come in different, partly unexpected, configurations: (1) not all functions are necessarily organizationally part of the platform, (2) not all functions are necessarily organized according to the same platform type, but (3) also not all random configurations of platform types and functions seem to be possible. This complexity highlights the importance of the innovation function for exploring effective configurations of digital platforms for manufacturing
Erratum: Measurement of prompt and nonprompt charmonium suppression in PbPb collisions at 5.02 TeV
No full textIn Fig. 3, the y axis titles were mistakenly written showing a single-differential cross section in either dimuon (Formula presented.) or rapidity, when in fact the cross section is normalized by both the (Formula presented.) and rapidity ranges used for a given measurement point. The corrected version is shown in the new Fig. 3 provided below. (Figure presented.) Differential cross section of prompt (Formula presented.) mesons (left) and (Formula presented.) mesons from b hadrons (nonprompt (Formula presented.) ) (right) decaying into two muons as a function of dimuon (Formula presented.) (upper) and rapidity (lower) in pp and (Formula presented.) collisions. The (Formula presented.) cross sections are normalised by (Formula presented.) for direct comparison. The bars (boxes) represent statistical (systematic) point-by-point uncertainties, while global uncertainties are written on the plots. © 2023, CERN for the benefit of the CMS Collaboration.Publisher versio
The role of art in the construction of public space: Istanbul biennials from 1987 to 2019
No full textThis study focuses on the interaction of public space and art, and uses the Istanbul Biennale (1987–2019) as an example to explore the role of art in public space. This study firstly examines public space, art in public space, and public-art issues, and discusses the relationship between public space and art from the past to the present. With its layered and dynamic structure, the Biennale provides a rich space for examining this relationship. The Istanbul Biennial, during its 32-year history, where this relationship can be observed in a certain continuum, was chosen as the field of study. In this study, the distribution of the Istanbul-Biennial in the city, the types of venues used and the relationship between these venues are investigated. For the analysis, firstly, the discourses, themes and curatorial expansions of the 16 biennials were searched through literature and printed media. Secondly, the exhibition venues and their locations/distributions in the city were mapped separately. Eventually, it has been determined that the biennials, which were initially located in the historical city center of Istanbul, have gradually expanded their area and even started to evolve into an open-air exhibition spreading to the peripheries and distant parts of the city in recent years
Guest editorial: The effect of COVID-19 on the performance of ethnic-minority firms in the UK and worldwide
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More learning with less labeling for face recognition
No full textIn this paper, we propose an improved face recognition framework where the training is started with a small set of human annotated face images and then new images are incorporated into the training set with minimum human annotation effort. In order to minimize the human annotation effort for new images, the proposed framework combines three different strategies, namely self-paced learning (SPL), active learning (AL), and minimum sparse reconstruction (MSR). As in the recently proposed ASPL framework [1], SPL is used for automatic annotation of easy images, for which the classifiers are highly confident and AL is used to request the help of an expert for annotating difficult or low-confidence images. In this work, we propose to use MSR to subsample the low-confidence images based on diversity using minimum sparse reconstruction in order to further reduce the number of images that require human annotation. Thus, the proposed framework provides an improvement over the recently proposed ASPL framework [1] by employing MSR for eliminating “similar” images from the set selected by AL for human annotation. Experimental results on two large-scale datasets, namely CASIA-WebFace-Sub and CACD show that the proposed method called ASPL-MSR can achieve similar face recognition performance by using significantly less expert-annotated data as compared to the state-of-the-art. In particular, ASPL-MSR requires manual annotation of only 36.10% and 54.10% of the data in CACD and CASIA-WebFace-Sub datasets, respectively, to achieve the same face recognition performance as the case when the whole training data is used with ground truth labels. The experimental results indicate that the number of manually annotated samples have been reduced by nearly 4% and 2% on the two datasets as compared to ASPL [1].TÜBİTA
Searches for additional Higgs bosons and for vector leptoquarks in τ τ final states in proton-proton collisions at √s = 13 TeV
No full textThree searches are presented for signatures of physics beyond the standard model (SM) in tau tau final states in proton-proton collisions at the LHC, using a data sample collected with the CMS detector at root s = 13 TeV, corresponding to an integrated luminosity of 138 fb(-1). Upper limits at 95% confidence level (CL) are set on the products of the branching fraction for the decay into tau leptons and the cross sections for the production of a new boson phi, in addition to the H(125) boson, via gluon fusion (gg phi) or in association with b quarks, ranging from O(10 pb) for a mass of 60 GeV to 0.3 fb for a mass of 3.5TeV each. The data reveal two excesses for gg phi production with local p-values equivalent to about three standard deviations at m(phi) = 0.1 and 1.2TeV. In a search for t-channel exchange of a vector leptoquark U-1, 95% CL upper limits are set on the dimensionless U1 leptoquark coupling to quarks and tau leptons ranging from 1 for a mass of 1TeV to 6 for a mass of 5TeV, depending on the scenario. In the interpretations of the M-h(125) and M-h,EFT(125) minimal supersymmetric SM benchmark scenarios, additional Higgs bosons with masses below 350 GeV are excluded at 95% CL.BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER, ERC PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science - EOS" - be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy - EXC 2121 "Quantum Universe" - 390833306, and under project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program - UNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe", and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).Publisher versio