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arXiv 2023年

作者： Islam, Saiful Khanra, Pitambar Nakuci, Johan Muldoon, Sarah F. Watanabe, Takamitsu Masuda, Naoki Department of Mathematics University at Buffalo State University of New York Buffalo United States Institute for Artificial Intelligence and Data Science University at Buffalo State University of New York Buffalo United States Neuroscience Program University at Buffalo State University of New York Buffalo United States School of Psychology Georgia Institute of Technology United States International Research Centre for Neurointelligence The University of Tokyo Institutes for Advanced Study Tokyo Japan

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of *** Codes 92F05 © 2023, CC BY.

关键词： Dynamics

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ON ANALYSTS’ EARNINGS FORECASTS FOR FAILING FIRMS

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Journal of Business Finance & Accounting 1990年第1期17卷 101-118页

作者： Moses, O. Douglas The author is Associate Professor in the Department of Administrative Science Naval Postgraduate School Monterey. Data on earnings forecasts used in this study were taken from the Institutional Brokers Estimate System (IBES) published by Lynch Jones and Ryan New York. Access to historic IBES data provided by Lynch Jones and Ryan is greatly appreciated. Support for this project was provided by the Naval Postgraduate School Foundation Research Program which is funded by the Office of Naval Research and the Office of Naval Technology.

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On the computational Bayesian survival spatial Dengue hemorrhagic fever (DHF) modelling with Fernandez–steel skew normal conditional autoregressive (FSSN CAR) frailty

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AIP Conference Proceedings 2023年第1期2536卷

作者： Dwi Rantini Nur Iriawan Irhamah Musofa Rusli Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Internal Medicine Faculty of Medicine Universitas Airlangga Surabaya 60115 Indonesia

Generally, infectious disease data has a spatial effect, meaning that areas that are close together affect each other. One of the infectious diseases is dengue hemorrhagic fever (DHF). The recovery time of DHF patients is interesting to study. In this research, DHF data in eastern Surabaya was used. The recovery time of this DHF patient can be modeled using one of the statistical methods, namely survival analysis. This recovery time data follows the Weibull distribution. On the data indicated spatial effect, the conditional autoregressive (CAR) model can be used to express dependencies between adjacent areas. Spatial random effects in the survival model were modeled with Normal CAR, Double-Exponential (DE) CAR, and Fernandez-Steel skew Normal (FSSN) CAR. In this research, Cox regression was used and parameter estimation was performed using the Bayesian analysis with Hamiltonian Monte Carlo (HMC) algorithm using the Stan programming language. Based on the comparison of the Watanabe-Akaike information criterion (WAIC), the spatial random effects on the Weibull Cox regression model are best modeled with the FSSN CAR. This is because the FSSN CAR is able to capture error patterns both symmetrical and asymmetrical, not with Normal CAR and DE CAR which can only capture symmetrical error patterns. In this research, several variables that allegedly affect the recovery rate of DHF patients are given. Then, based on the best model, variables that significantly affect the patient's recovery rate are age, the high schools in last education, housewife in the type of occupation, stadium-II in severity level, fever days before entering the hospital, pulse, temperature, and leukocytes.

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Estimating Heat Fluxes by Merging Profile Formulae and the Energy Budget with a Variational Technique

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Advances in Atmospheric sciences 2004年第4期21卷 627-636页

作者：张述文邱崇践张卫东 Department of Atmospheric Sciences Lanzhou University Lanzhou 730000 School of Physical Science & Technology Lanzhou University Lanzhou 730000 variational technique (VT) is applied to estimate surface sensible and latent heat fluxes based on observations of air temperature wind speed and humidity respectively at three heights (1 m 4 m and 10 m) and the surface energy and radiation budgets by the surface energy and radiation system (SERBS). The method fully uses all information provided by the measurements of air temperature wind and humidity profiles the surface energy budget and the similarity profile formulae as well. Data collected at Feixi experiment station installed by the China Heavy Rain Experiment and Study (HeRES) Program are used to test the method. Results show that the proposed technique can overcome the well-known unstablility problem that occurs when the Bowen method becomes singular in comparison with the profile method it reduces both the sensitivities of latent heat fluxes to observational errors in humidity and those of sensible heat fluxes to observational errors in temperature while the estimated heat fluxes approximately satisfy the surface energy budget. Therefore the variational technique is more reliable and stable than the two conventional methods in estimating surface sensible and latent heat fluxes.

A variational technique (VT) is applied to estimate surface sensible and latent heat fluxes based on observations of air temperature, wind speed, and humidity, respectively, at three heights (1 m, 4 m, and 10 m), and the surface energy and radiation budgets by the surface energy and radiation system (SERBS). The method fully uses all information provided by the measurements of air temperature, wind, and humidity profiles, the surface energy budget, and the similarity profile formulae as well. data collected at Feixi experiment station installed by the China Heavy Rain Experiment and study (HeRES) program are used to test the method. Results show that the proposed technique can overcome the well-known unstablility problem that occurs when the Bowen method becomes singular; in comparison with the profile method, it reduces both the sensitivities of latent heat fluxes to observational errors in humidity and those of sensible heat fluxes to observational errors in temperature, while the estimated heat fluxes approximately satisfy the surface energy budget. Therefore, the variational technique is more reliable and stable than the two conventional methods in estimating surface sensible and latent heat fluxes.

关键词： heat flux Bowen ratio profile method variational technique

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ROLE OF SIMULATION IN RAPID PROTOTYPING FOR CONCEPT DEVELOPMENT

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 204-211页

作者： KING, JF BARTON, DE J. Fred King:is the manager of the Advanced Technology Department for Unisys in Reston Virginia. He earned his Ph.D. in mathematics from the University of Houston in 1977. He has been principal investigator of research projects in knowledge engineering pattern recognition and heuristic problem-solving. Efforts include the development of a multi-temporal multispectral classifier for identifying graincrops using LANDSAT satellite imagery data for NASA. Also as a member of the research team for a NCI study with Baylor College of Medicine and NASA he helped develop techniques for detection of carcinoma using multispectral microphotometer scans of lung tissue. He established and became technical director of the AI Laboratory for Ford Aerospace where he developed expert scheduling modeling and knowledge acquisition systems for NASA. Since joining Unisys in 1985 he has led the development of object-oriented programming environments blackboard architectures data fusion techniques using neural networks and intelligent data base systems. Douglas E. Barton:is manager of Logistics Information Systems for Unisys in Reston Virginia. He earned his B.A. degree in computer science from the College of William and Mary in 1978 and did postgraduate work in London as a Drapers Company scholar. Since joining Unisys in 1981 his work has concentrated on program management and software engineering of large scale data base management systems and design and implementation of knowledge-based systems in planning and logistics. As chairman of the Logistics Data Subcommittee of the National Security Industrial Association (NSIA) he led an industry initiative which examined concepts in knowledge-based systems in military logistics. His responsibilities also include evaluation development and tailoring of software engineering standards and procedures for data base and knowledge-based systems. He is currently program manager of the Navigation Information Management System which provides support to the Fleet Ballistic Missile Progr

A valuable technique during concept development is rapid prototyping of software for key design components. This approach is particularly useful when the optimum design approach is not readily apparent or several known alternatives need to be rapidly evaluated. A problem inherent in rapid prototyping is the lack of a "target system" with which to interface. Some alternatives are to develop test driver libraries, integrate the prototype with an existing working simulator, or build one for the specific problem. This paper presents a unique approach to concept development using rapid prototyping for concept development and scenario-based simulation for concept verification. The rapid prototyping environment, derived from artificial intelligence technology, is based on a blackboard architecture. The rapid prototype simulation capability is provided through an object-oriented modeling environment. It is shown how both simulation and blackboard technologies are used collectively to rapidly gain insight into a tenacious problem. A specific example will be discussed where this approach was used to evolve the logic of a mission controller for an autonomous underwater vehicle.

关键词： Military Engineering

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Modeling the number of confirmed cases of COVID-19 in East Java using negative binomial regression based on least square spline estimator

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AIP Conference Proceedings 2024年第1期3083卷

作者： Arip Ramadan Nur Chamidah I. Nyoman Budiantara Student of Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Information System School of Industrial and System Engineering Telkom University Surabaya 60231 Indonesia. Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia. Department of Statistics Faculty of Sciences and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia.

The East Java Province has experienced a significant surge in number of confirmed cases of COVID-19. This study endeavors to investigate the potential correlation between weather conditions and the incidence of number of confirmed cases of COVID-19 in East Java. To achieve this, a nonparametric regression model, specifically, the Negative Binomial Regression (NBR) model based on the least squares spline estimator, was utilized. The outcomes of the study indicate that the Mean Absolute Percentage Error (MAPE) of nonparametic regression model is 0.30. Meanwhile, the MAPE for the parametric regression model is 0.34. The results show that a nonparametric regression model approach is better than parametric regression model approach. The study establishes that the truncated spline estimator based NBR model represents the best fit, with an MLCV value of -256.71. The findings of the study suggest that a temperature less than 21.75°C is associated with a decrease of 13.28 number of confirmed cases of COVID-19 per each 1°C increase, while a temperature between 21.75°C and 25.78°C is linked to an increase of 6.85 number of confirmed cases of COVID-19 per each 1°C increase. In contrast, a temperature greater than 25.78°C is associated with a decrease of 139.42 number of confirmed cases of COVID-19 per each 1°C increase. Similarly, a wind speed less than 5.57 m/s is related to a decrease of 12.99 number of confirmed cases of COVID-19 per each 1 m/s increase, whereas a wind speed between 5.57 m/s and 8.99 m/s is associated with a decrease of 10.29 number of confirmed cases of COVID-19 per each 1 m/s increase. Furthermore, a wind speed greater than 8.99 m/s is linked to a decrease of 19.16 number of confirmed cases of COVID-19 per each 1 m/s increase. The study provides evidence that higher temperatures and wind speeds result in a slower rise in the incidence of the number of confirmed cases of COVID-19. Consequently, it is recommended that the local government remains vigilant du

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RUDDER ROLL STABILIZATION FOR COAST GUARD CUTTERS AND FRIGATES

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NAVAL ENGINEERS JOURNAL 1983年第3期95卷 267-282页

作者： BAITIS, E WOOLAVER, DA BECK, TA Dr. Erich Baitis: a native of Germany came to the David W. Taylor Naval Ship Research and Development Center in 1957 as a co-operative student/trainee and received his Bachelor of Science Degree in Physics from Virginia Polytechnic Institute. As a 32-year-old naval architect in 1971 he received both the VietnamHonorServiceMedaland the Navy'sMeritoriousCivilianServiceAwardfor his eight months as liaison with the Vietnamese NAVVS ferro-cement ship program. As head of the Seakeeping and Stabilization Group of the Surface Ship Dynamics Branch his work has led to the development of a new standard Ship Motion Computer Program and the application of ship motions to ship habitability operability and survivability problems. A major area of this work has been the ship/aircraft interface which is particularly sensitive to ship motions wind and other environmental factors. Mr. Dennis A. Woolaver:has been employed as a Naval Architect at the David W. Taylor Naval Ship Research and Development Center since 1965 where his work has resulted in numerous patents. He received his Bachelor of Science Degree in Electrical Engineering from George Washington University where he also completed graduate work in Communication Theory. Additional fields of study include Computer Science and Personnel Management. Current efforts are directed toward surface ship motion mitigation data acquisition and statistical analysis. He is a member of the Society of Naval Architects and Marine Engineers. Lt. Tom A. Beck USCG: joined the UnitedStatesCoastGuardin 1966 where he trained as an Electronics Technician. He received a Bachelor of Science Degree in Electrical Engineering from Columbia University in 1978. From 1978 to 1979 he was an Electronics Project Officer at the CoastGuardElectronics Laboratory in Alexandria Virginia. Since 1980 he has been a Research and Development Project Officer in the Sensor Technology Branch of the CoastGuardsOffice of Research and Development. Lt. Beck is a member of the Institute of Electronic and Elect

The potential use of rudders as anti-roll devices has long been recognized. However, the possible interference of this secondary function of the rudder with its primary role as the steering mechanism has prevented, for many years, the development of practical rudder roll stabilizers. The practical feasibility of rudder roll stabilization has, however, in recent years been demonstrated by two systems designed and developed for operational evaluation aboard two different U.S. C oast G uard Cutters, i.e., Jarvis and Mellon of the 3,000-ton, 378-foot HAMILTON Class. The authors describe the major components of the rudder roll stabilization (RRS) system, along with the design goals and methodology as applied to these first two prototypes. In addition, a brief history of the hardware development is provided in order to show some of the lessons learned. The near flawless performance of the prototypes over the past four years of operational use in the North Pacific is documented. Results from various sea trials and reports of the ship operators are cited and discussed. The paper concludes with a discussion of the costs and benefits of roll stabilization achieved using both a modern anti-roll fin system, as well as two different performance level RRS systems. The benefits of roll stabilization are demonstrated by the relative expansion in the operational envelopes of the USS OLIVER HAZARD PERRY (FFG-7) Class. The varying levels of roll stabilization suggest that the merits of fins and RRS systems are strongly dependent on mission requirements and the environment. The demonstrated performance of the reliable RRS system offers the naval ship acquisition manager a good economical stabilization system.

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Search for Majorana Neutrinos with the Complete KamLAND-Zen dataset

arXiv

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arXiv 2024年

作者： Abe, S. Araki, T. Chiba, K. Eda, T. Eizuka, M. Funahashi, Y. Furuto, A. Gando, A. Gando, Y. Goto, S. Hachiya, T. Hata, K. Ichimura, K. Ieki, S. Ikeda, H. Inoue, K. Ishidoshiro, K. Kamei, Y. Kawada, N. Kishimoto, Y. Koga, M. Marthe, A. Matsumoto, Y. Mitsui, T. Miyake, Haruhiko Morita, D. Nakamura, K. Nakamura, R. Nakamura, R. Nakane, J. Ono, T. Ozaki, H. Saito, K. Sakai, T. Shimizu, I. Shirai, J. Shiraishi, K. Suzuki, A. Tachibana, K. Tamae, K. Watanabe, H. Watanabe, K. Yoshida, S. Umehara, S. Fushimi, K. Kotera, K. Urano, Y. Berger, B.E. Fujikawa, B.K. Learned, J.G. Maricic, J. Fu, Z. Ghosh, S. Smolsky, J. Winslow, L.A. Efremenko, Y. Karwowski, H.J. Markoff, D.M. Tornow, W. Dell'Oro, S. Donnell, T.O. Detwiler, J.A. Enomoto, S. Decowski, M.P. Weerman, K.M. Grant, C. Penek, O. Song, H. Li, A. Axani, S.N. Garcia, M. Sarfraz, M. Research Center for Neutrino Science Tohoku University Sendai980-8578 Japan Department of Human Science Obihiro University of Agriculture and Veterinary Medicine Hokkaido Obihiro080-8555 Japan The University of Tokyo Institutes for Advanced Study The University of Tokyo Chiba Kashiwa277-8583 Japan Graduate Program on Physics for the Universe Tohoku University Sendai980-8578 Japan Graduate School of Science Osaka University Toyonaka Osaka560-0043 Japan Research Center for Nuclear Physics Osaka University Osaka Ibaraki567-0047 Japan Department of Physics Tokushima University Tokushima770-8506 Japan Graduate School of Integrated Arts and Sciences Tokushima University Tokushima770-8502 Japan Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics and Astronomy University of Hawaii at Manoa HonoluluHI96822 United States Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Triangle Universities Nuclear Laboratory DurhamNC27708 United States Physics Departments Duke University DurhamNC27708 United States North Carolina Central University DurhamNC27707 United States The University of North Carolina at Chapel Hill Chapel HillNC27599 United States Center for Neutrino Physics Virginia Polytechnic Institute State University BlacksburgVA24061 United States Center for Experimental Nuclear Physics and Astrophysics University of Washington SeattleWA98195 United States Nikhef The University of Amsterdam Science Park Amsterdam Netherlands Boston University BostonMA02215 United States Halıcıoglu Data Science Institute Department of Physics University of California San Diego La Jolla CA92093 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States

We present a search for neutrinoless double-beta (0νββ) decay of 136Xe using the full KamLAND-Zen 800 dataset with 745 kg of enriched xenon, corresponding to an exposure of 2.097 ton yr of 136Xe. This updated search benefits from a more than twofold increase in exposure, recovery of photo-sensor gain, and reduced background from muon-induced spallation of xenon. Combining with the search in the previous KamLAND-Zen phase, we obtain a lower limit for the 0νββ decay half-life of T1/20ν > 3.8 × 1026 yr at 90% C.L., a factor of 1.7 improvement over the previous limit. The corresponding upper limits on the effective Majorana neutrino mass are in the range 28–122 meV using phenomenological nuclear matrix element calculations. © 2024, CC BY.

关键词： Xenon

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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