RUTILEA社内勉強会第3回 「意外な統計」
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This document discusses two statistical approaches: 1) using robust statistics like the median and median absolute deviation instead of the mean and standard deviation to make analyses less sensitive to outliers, and 2) taking a Bayesian perspective to update probabilities as new data is observed. Robust statistics provide more stable estimates than normal statistics when sample sizes are small and outliers are present. From a Bayesian viewpoint, initial probabilities are updated based on new observations and evidence to arrive at posterior probabilities.
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Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Compexometric titration/Chelatorphy titration/chelating titration
Classification
Metal ion ion indicators
Masking and demasking reagents
Estimation of Magnisium sulphate
Calcium gluconate
Complexometric Titration/ chelatometry titration/chelating titration, introduction, Types-
1.Direct Titration
2.Back Titration
3.Replacement Titration
4.Indirect Titration
Masking agent, Demasking agents
formation of complex
comparition between masking and demasking agents,
Indicators/Metal ion indicators/ Metallochromic indicators/pM indicators,
Visual Technique,PM indicators (metallochromic), Indicators of pH, Redox Indicators
Instrumental Techniques-Photometry
Potentiometry
Miscellaneous methods.
Complex titration with EDTA.
Pests of Storage_Identification_Dr.UPR.pdf
InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
100speciesofinsectpestsofstoredproductscauseeconomiclosses
Farming systems analysis: what have we learnt?.pptx
Presentation given at the official farewell of Prof Ken Gillet at Wageningen on 13 June 2024
11.1 Role of physical biological in deterioration of grains.pdf
Storagedeteriorationisanyformoflossinquantityandqualityofbio-materials.
Themajorcausesofdeteriorationinstorage
•Physical
•Biological
•Mechanical
•Chemical
Storageonlypreservesquality.Itneverimprovesquality.
Itisadvisabletostartstoragewithqualityfoodproduct.Productwithinitialpoorqualityquicklydepreciates
8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
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The cost of acquiring information by natural selection
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Immersive Learning That Works: Research Grounding and Paths Forward
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Basics of crystallography, crystal systems, classes and different forms
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Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf
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The binding of cosmological structures by massless topological defects
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EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
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Compexometric titration/Chelatorphy titration/chelating titration
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Farming systems analysis: what have we learnt?.pptx
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RUTILEA社内勉強会第3回 「意外な統計」
- 1. '¥EHt2¥Fn¥t, ¥ ¥ ¥ ¥¥I
- 2. ' IIOutlinee. # l Z s c o r e v i a robust s t a t i s t i c s # 2 Bayesian perspective
- 3. # l Z s c o r e v i a robust s t a t i s t i c s Z z=¥¥Y# He,I f z > 3 , w e regard × i t a s a n outlier. But, this method could be unstable. |CA# t r u e observation i 6,27, 6.34, 6,25, 6,31, 6.28 mistake observation: 6,27, 6,34, 6.25, 63, I , 6.28 -
- 4. ( l Non robust s t a t i s t i c s c a n n o t detect outliers' i n the c a s e of s m a l l samples." Actuall, m e a n ( x ) = T I L X = 17.65 Std ( X ) = 2 5 . 4 1 Z = - 0 . 4 5 , - 0 . 4 5 , - 0 . 4 5 , # - 0 , 4 5 W e w i l l modify this conventional s t a t i s t i c s with robust s t a t i s t i c s . m e a n → median S td → median absolute deviation (MAD)
- 5. median (X) 6. 2 5 I 6.27 E # E b. 3 4 E 63,10 stable e s t i m a t o r M A D (X) = 1.48 med: ahl X i - m e d i a n 1411 = 0 , 0 4 4 (true Std i s 0.035) stable e s t i m a t o r Robust z s c o r e - 0 . 2 2 E 1,35 E - O . 6 7 E / 2 7 7, 5 E O, O
- 6. Discussion a n d Conclusion * I n the c a s e of small dataset, normal s t a t i s t i c s i s affected by outliers to o heavily. * The influence could be asseseid by the influence analysis. Ref, M i a hubert, et.cl. 2017.
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- 9. 7.tt#AaFEtEnh5I- TEeiIiaETFieII'Its Tn.'F¥¥ 99% a.i÷÷÷±÷ ÷÷÷÷÷÷÷÷ ÷÷.".IE?::EEimiaT3 p i ¥ % , g = n 9 . 9 -I x 99 t - 9 9 . 9 × 3 0 = § = 0 . 3 % THE'En.:#oil %.ae#Ektinu2.tiEZniHaFEiA'EtEuE. # ¥ t.TT#a3FiFEEEth, 0.3%17.17
- 10. M2"E¥# P P i . P H # P oP( Y i ) = )I Bayese update↳ → Planewyaj.pl#l0Po P(newYi) i ' t h ' ' EL'¥G h t t , Iri'LEE'¥44,8'" -44. F''- '7¥42#' ' I ¥7232 ¥EiEE¥* a 4 ¥ 4 2 - - ¥ 1 9 ' 'Ref. k¥2,Fife'¥Eif¥t4¥ I t
- 11. Co h c l n s i o n B T ' t 2 " E-¥fi te c ' l Eat i n " . F i * I n Fife'¥5'' 4 2 ¥ t 3. A TEFiFEEL't ' I n Ea u EE.In i.FEEt . T '42" EL#h s 'iff'IA. A F K h ta 3 F'' - '7 t i if I ' I I 'Est's ¥'t#GET 5¥ o r YET'¥'t'Ite I F E u n 3.