| 1 |
Pérez-Montoto_2009 |
Scoring function for DNA–drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories |
Pérez-Montoto, Lázaro G.; Santana, Lourdes; González-Díaz, Humberto |
2009 |
2009-11-30 |
PMC |
N |
PMC7127518 |
19604606.0 |
10.1016/j.ejmech.2009.06.011 |
58p5b2vw |
0.836961 |
Saw_A_2019, Qi_Z_2018, Chang_2011, Zheng_2009, Liao_2006 |
González-Díaz_2009, Pérez-Montoto_2009 |
| 2 |
Liu_S_2012 |
Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1) |
Liu, Shu; Fu, Rao; Zhou, Li-Hua; Chen, Sheng-Ping |
2012 |
2012-06-11 |
COMM-USE |
N |
PMC3372491 |
22701601.0 |
10.1371/journal.pone.0038086 |
7vwpvcdd |
0.821681 |
Saw_A_2019, Qi_Z_2018, Dai_Q_2012, Feng_2010 |
|
| 3 |
Xie_H_2014 |
3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase Inhibitors |
Xie, Huiding; Qiu, Kaixiong; Xie, Xiaoguang |
2014 |
2014-11-14 |
COMM-USE |
N |
PMC4264204 |
25405729.0 |
10.3390/ijms151120927 |
afx7hacm |
0.804462 |
Saw_A_2019, Zhou_2011, Dai_Q_2012, Qi_Z_2018 |
|
| 4 |
Pereira_2015 |
QSAR-Assisted Virtual Screening of Lead-Like Molecules from Marine and Microbial Natural Sources for Antitumor and Antibiotic Drug Discovery |
Pereira, Florbela; Latino, Diogo A. R. S.; Gaudêncio, Susana P. |
2015 |
2015-03-17 |
COMM-USE |
N |
PMC6272462 |
25789820.0 |
10.3390/molecules20034848 |
okp8ofl0 |
0.768177 |
Dai_Q_2012, Saw_A_2019, Qi_Z_2018, Chang_2011 |
Pereira_2014, Zhou_2010 |
| 5 |
Zhou_2010 |
QSAR models for predicting cathepsin B inhibition by small molecules—Continuous and binary QSAR models to classify cathepsin B inhibition activities of small molecules |
Zhou, Zhigang; Wang, Yanli; Bryant, Stephen H. |
2010 |
2010-06-30 |
None |
N |
PMC2873115 |
20194042.0 |
10.1016/j.jmgm.2010.01.009 |
bs17igjs |
0.732579 |
Saw_A_2019, Chang_2011, Qi_Z_2018, Dai_Q_2012, Feng_2010 |
|
| 6 |
Wang_2018 |
A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs |
Wang, Ming-Yang; Liang, Jing-Wei; Mohamed Olounfeh, Kamara; Sun, Qi; Zhao, Nan; Meng, Fan-Hao |
2018 |
2018-09-18 |
COMM-USE |
N |
PMC6225272 |
30231506.0 |
10.3390/molecules23092385 |
fewy8y4a |
0.709895 |
Qi_Z_2018, Saw_A_2019, Dai_Q_2012, Chang_2011 |
|
| 7 |
González-Díaz_2009 |
QSAR for RNases and theoretic–experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein |
González-Díaz, Humberto; Dea-Ayuela, María A.; Pérez-Montoto, Lázaro G.; Prado-Prado, Francisco J.; Agüero-Chapín, Guillermín; Bolas-Fernández, Francisco; Vazquez-Padrón, Roberto I.; Ubeira, Florencio M. |
2009 |
2009-07-04 |
PMC |
N |
PMC7088557 |
19578942.0 |
10.1007/s11030-009-9178-0 |
vk8n8s2q |
0.694425 |
Saw_A_2019, Qi_Z_2018, Chang_2011 |
|
| 8 |
Xu_J_2010 |
QSAR Studies on Andrographolide Derivatives as α-Glucosidase Inhibitors |
Xu, Jun; Huang, Sichao; Luo, Haibin; Li, Guoji; Bao, Jiaolin; Cai, Shaohui; Wang, Yuqiang |
2010 |
2010-03-02 |
COMM-USE |
N |
PMC2869241 |
20479989.0 |
10.3390/ijms11030880 |
s0ps9rd0 |
0.680745 |
Qi_Z_2018, Saw_A_2019, Dai_Q_2012, Liao_2006, Feng_2010 |
|
| 9 |
Zhou_2011 |
Multi-conformation 3D QSAR study of benzenesulfonyl-pyrazol-ester compounds and their analogs as cathepsin B inhibitors |
Zhou, Zhigang; Wang, Yanli; Bryant, Stephen H. |
2011 |
2011-09-01 |
PMC |
N |
PMC3167229 |
21798778.0 |
10.1016/j.jmgm.2011.06.013 |
kiwi87c9 |
0.673384 |
Saw_A_2019, Dai_Q_2012, Qi_Z_2018, Chang_2011 |
|
| 10 |
Pereira_2014 |
A Chemoinformatics Approach to the Discovery of Lead-Like Molecules from Marine and Microbial Sources En Route to Antitumor and Antibiotic Drugs |
Pereira, Florbela; Latino, Diogo A. R. S.; Gaudêncio, Susana P. |
2014 |
2014-01-27 |
COMM-USE |
N |
PMC3944514 |
24473174.0 |
10.3390/md12020757 |
ud3x78u3 |
0.656333 |
Montgomery_2004, Sen_D_2018, Dai_Q_2012 |
Pereira_2015 |
| 11 |
Mei_H_2004 |
Identification of encoding proteins related to SARS-CoV |
Mei, Hu; Sun, Lili; Zhou, Yuan; Xiong, Qing; Li, Zhiliang |
2004 |
2004-01-01 |
PMC |
N |
PMC7089014 |
|
10.1360/03wb0198 |
d8onhd04 |
0.644314 |
|
|
| 12 |
Durrant_2013 |
Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening |
Durrant, Jacob D.; Friedman, Aaron\nJ.; Rogers, Kathleen\nE.; McCammon, J. Andrew |
2013 |
2013-06-04 |
PMC |
N |
PMC3735370 |
23734946.0 |
10.1021/ci400042y |
kauk7ze0 |
0.642955 |
Dai_Q_2012, Saw_A_2019, Qi_Z_2018 |
Li_H_2009 |
| 13 |
Pérez-Montoto_2009 |
Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks |
Pérez-Montoto, Lázaro Guillermo; Dea-Ayuela, María Auxiliadora; Prado-Prado, Francisco J.; Bolas-Fernández, Francisco; Ubeira, Florencio M.; González-Díaz, Humberto |
2009 |
2009-07-17 |
PMC |
N |
PMC7111648 |
|
10.1016/j.polymer.2009.05.055 |
zamrnne9 |
0.639384 |
Qi_Z_2018, Saw_A_2019, Chang_2011, Dai_Q_2012, Liao_2006 |
|
| 14 |
Roy_K_2015 |
Chapter 10 Other Related Techniques |
Roy, Kunal; Kar, Supratik; Das, Rudra Narayan |
2015 |
2015-12-31 |
PMC |
N |
PMC7149793 |
|
10.1016/b978-0-12-801505-6.00010-7 |
a9q0tcgz |
0.629084 |
Webb_2014, Sen_D_2018, Liou_2013 |
Ekins_2007, Zhang_2018 |
| 15 |
Kingsley_2016 |
Ranking Protein-Protein Docking Results using Steered Molecular Dynamics and Potential of Mean Force Calculations |
Kingsley, Laura J.; Esquivel-Rodríguez, Juan; Yang, Ying; Kihara, Daisuke; Lill, Markus A. |
2016 |
2016-05-27 |
PMC |
N |
PMC5015890 |
27232548.0 |
10.1002/jcc.24412 |
04t18oqp |
0.608428 |
Saw_A_2019, Qi_Z_2018, Chang_2011, Dai_Q_2012 |
Zhang_2018 |
| 16 |
Zhang_2018 |
Exploring the Binding Mechanism between Human Profilin (PFN1) and Polyproline-10 through Binding Mode Screening |
Leili Zhang; David R. Bell; Binquan Luan; Ruhong Zhou |
2018 |
2018-09-16 |
BioRxiv |
N |
|
|
10.1101/418830 |
40pe21lm |
0.590025 |
Qi_Z_2018, Chang_2011, Saw_A_2019, Plewczyński_2008 |
|
| 17 |
Shao_2007 |
Structure‐Based Design and Synthesis of Highly Potent SARS‐CoV 3CL Protease Inhibitors |
Shao, Yi‐Ming; Yang, Wen‐Bin; Peng, Hung‐Pin; Hsu, Min‐Feng; Tsai, Keng‐Chang; Kuo, Tun‐Hsun; Wang, Andrew H.‐J.; Liang, Po‐Huang; Lin, Chun‐Hung; Yang, An‐Suei; Wong, Chi‐Huey |
2007 |
2007-08-23 |
PMC |
N |
PMC7162026 |
17722121.0 |
10.1002/cbic.200700254 |
voem879q |
0.580915 |
Qi_Z_2018 |
|
| 18 |
Li_H_2009 |
An effective docking strategy for virtual screening based on multi-objective optimization algorithm |
Li, Honglin; Zhang, Hailei; Zheng, Mingyue; Luo, Jie; Kang, Ling; Liu, Xiaofeng; Wang, Xicheng; Jiang, Hualiang |
2009 |
2009-02-11 |
COMM-USE |
N |
PMC2753843 |
19210777.0 |
10.1186/1471-2105-10-58 |
udzz15bk |
0.577000 |
Chang_2011, Qi_Z_2018, Saw_A_2019, Dai_Q_2012 |
Liu_S_2012 |
| 19 |
Webb_2014 |
Comparative Modeling of Drug Target Proteins☆ |
Webb, B.; Eswar, N.; Fan, H.; Khuri, N.; Pieper, U.; Dong, G.Q.; Sali, A. |
2014 |
2014-12-31 |
PMC |
N |
PMC7157477 |
|
10.1016/b978-0-12-409547-2.11133-3 |
d66rz6dc |
0.574834 |
Eswar_2007, Sen_D_2018 |
Eswar_2007 |
| 20 |
Eswar_2007 |
4.10 Comparative Modeling of Drug Target Proteins |
Eswar, N.; Sali, A. |
2007 |
2007-12-31 |
PMC |
N |
PMC7151936 |
|
10.1016/b0-08-045044-x/00251-0 |
oqxh7egj |
0.557085 |
Webb_2014, Qi_Z_2018 |
Webb_2014 |
| 21 |
Li_B_2008 |
Flow-injection chemiluminescence determination of chrysin and baicalein assisted by theoretical prediction of chemiluminescence behavior of chrysin and baicalein |
Li, Baoxin; Guo, Lili; Xu, Chunli; Ma, Limei |
2008 |
2008-12-01 |
PMC |
N |
|
|
10.1016/j.saa.2008.02.007 |
y5j07feh |
0.542407 |
Qi_Z_2018, Chang_2011, Saw_A_2019, Mendizabal-Ruiz_2018 |
|
| 22 |
Zeng_2007 |
Mass spectral profiling: An effective tool for quality control of herbal medicines |
Zeng, Zhong-Da; Liang, Yi-Zeng; Chau, Foo-Tim; Chen, Shuo; Daniel, Mok Kam-Wah; Chan, Chi-On |
2007 |
2007-12-05 |
PMC |
N |
PMC7119444 |
17996528.0 |
10.1016/j.aca.2007.09.057 |
fk8vz0g0 |
0.541901 |
Qi_Z_2018, Dai_Q_2012, Mendizabal-Ruiz_2018, Chang_2011 |
|
| 23 |
Jalali-Heravi_2009 |
Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors |
Jalali-Heravi, M.; Asadollahi-Baboli, M.; Mani-Varnosfaderani, A. |
2009 |
2009-12-05 |
PMC |
N |
PMC7126869 |
19665859.0 |
10.1016/j.jpba.2009.07.009 |
bf5rct4q |
0.531819 |
Saw_A_2019, Liao_2006, Dai_Q_2012, Qi_Z_2018 |
Zhou_2010, Wang_2018 |
| 24 |
Kim_M_2013 |
A mass weighted chemical elastic network model elucidates closed form domain motions in proteins |
Kim, Min Hyeok; Seo, Sangjae; Jeong, Jay Il; Kim, Bum Joon; Liu, Wing Kam; Lim, Byeong Soo; Choi, Jae Boong; Kim, Moon Ki |
2013 |
2013-03-18 |
PMC |
N |
PMC3649262 |
23456820.0 |
10.1002/pro.2244 |
p8n8va3y |
0.518791 |
Qi_Z_2018, Kirillova_2009, Chang_2011, Liou_2013 |
|
| 25 |
Wąż_P_2013 |
Descriptors of 2D-dynamic graphs as a classification tool of DNA sequences |
Wąż, Piotr; Bielińska-Wąż, Dorota; Nandy, Ashesh |
2013 |
2013-09-03 |
PMC |
N |
PMC7088265 |
|
10.1007/s10910-013-0249-1 |
x4z2iz7b |
0.510448 |
Liao_2006, Dai_Q_2012, Chang_2011, Qi_Z_2018 |
Yu_J_2009, Zou_S_2014, Deng_2014, Qi_Z_2018 |
| 26 |
Li_Z_2007 |
Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED) |
Li, ZhiLiang; Wu, ShiRong; Chen, ZeCong; Ye, Nancy; Yang, ShengXi; Liao, ChunYang; Zhang, MengJun; Yang, Li; Mei, Hu; Yang, Yan; Zhao, Na; Zhou, Yuan; Zhou, Ping; Xiong, Qing; Xu, Hong; Liu, ShuShen; Ling, ZiHua; Chen, Gang; Li, GenRong |
2007 |
2007-01-01 |
PMC |
N |
PMC7089106 |
17879071.0 |
10.1007/s11427-007-0080-7 |
7t4lizde |
0.497618 |
Saw_A_2019, Qi_Z_2018, Chang_2011, Dai_Q_2012, Feng_2010 |
|
| 27 |
Fusani_2018 |
Active learning strategies with COMBINE analysis: new tricks for an old dog |
Fusani, Lucia; Cabrera, Alvaro Cortes |
2018 |
2018-12-18 |
PMC |
N |
PMC7087723 |
30564994.0 |
10.1007/s10822-018-0181-3 |
oeokiay7 |
0.494967 |
Qi_Z_2018, Chang_2011, Saw_A_2019 |
Zhou_2010, Ekins_2007, Durrant_2013 |
| 28 |
Barlow_2012 |
In-silico studies in Chinese herbal medicines’ research: Evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date |
Barlow, D.J.; Buriani, A.; Ehrman, T.; Bosisio, E.; Eberini, I.; Hylands, P.J. |
2012 |
2012-04-10 |
PMC |
N |
PMC7126886 |
22326356.0 |
10.1016/j.jep.2012.01.041 |
udvrdglk |
0.490041 |
|
|
| 29 |
Hou_Z_2016 |
Linear Quantitative Profiling Method Fast Monitors Alkaloids of Sophora Flavescens That Was Verified by Tri-Marker Analyses |
Hou, Zhifei; Sun, Guoxiang; Guo, Yong |
2016 |
2016-08-16 |
COMM-USE |
N |
PMC4987015 |
27529425.0 |
10.1371/journal.pone.0161146 |
60udcsa7 |
0.486170 |
Chang_2011, Qi_Z_2018, Saw_A_2019, Dai_Q_2012, Feng_2010 |
|
| 30 |
Lin_X_2020 |
A Review on Applications of Computational Methods in Drug Screening and Design |
Lin, Xiaoqian; Li, Xiu; Lin, Xubo |
2020 |
2020-03-18 |
COMM-USE |
N |
PMC7144386 |
32197324.0 |
10.3390/molecules25061375 |
xurmzmwj |
0.476200 |
|
Ekins_2007 |
| 31 |
Du_Q_2017 |
2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications |
Du, Qi-Shi; Wang, Shu-Qing; Xie, Neng-Zhong; Wang, Qing-Yan; Huang, Ri-Bo; Chou, Kuo-Chen |
2017 |
2017-08-01 |
COMM-USE |
N |
PMC5642577 |
29050302.0 |
10.18632/oncotarget.19757 |
jbmj21c5 |
0.446318 |
Kirillova_2009, Liou_2013, Qi_Z_2018 |
Zhou_2010, Jia_J_2016, He_Z_2010, Wang_2018 |
| 32 |
Perez-Bello_2009 |
Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices |
Perez-Bello, Alcides; Munteanu, Cristian Robert; Ubeira, Florencio M.; Lopes De Magalhães, Alexandre; Uriarte, Eugenio; González-Díaz, Humberto |
2009 |
2009-02-07 |
PMC |
N |
PMC7126577 |
18992259.0 |
10.1016/j.jtbi.2008.09.035 |
us6ay892 |
0.438958 |
Qi_Z_2018, Chang_2011, Saw_A_2019, Dai_Q_2012 |
|
| 33 |
Chou_2011 |
Applications of analytical ultracentrifugation to protein size-and-shape distribution and structure-and-function analyses |
Chou, Chi-Yuan; Hsieh, Yi-Hui; Chang, Gu-Gang |
2011 |
2011-05-31 |
PMC |
N |
PMC7128498 |
21087667.0 |
10.1016/j.ymeth.2010.11.002 |
cw2opick |
0.408841 |
Qi_Z_2018, Dai_Q_2012, Chang_2011, Webb_2014 |
|
| 34 |
Nguyen_2019 |
GraphDTA: Predicting drug–target binding affinity with graph neural networks |
Thin Nguyen; Hang Le; Thomas P. Quinn; Thuc Le; Svetha Venkatesh |
2019 |
2019-06-27 |
BioRxiv |
N |
|
|
10.1101/684662 |
zult69f2 |
0.397468 |
Lee_C_2017 |
|
| 35 |
Khanna_2011 |
In silico approach to screen compounds active against parasitic nematodes of major socio-economic importance |
Khanna, Varun; Ranganathan, Shoba |
2011 |
2011-11-30 |
COMM-USE |
N |
PMC3278842 |
22373185.0 |
10.1186/1471-2105-12-s13-s25 |
ufdgi67q |
0.397095 |
Lee_C_2017, Sen_D_2018, Chang_2011 |
|
| 36 |
Schuck_2009 |
On computational approaches for size-and-shape distributions from sedimentation velocity analytical ultracentrifugation |
Schuck, Peter |
2009 |
2009-10-06 |
PMC |
N |
PMC2892069 |
19806353.0 |
10.1007/s00249-009-0545-7 |
ip0fxvhp |
0.384261 |
Liou_2013, Qi_Z_2018, Mendizabal-Ruiz_2018 |
Chou_2011, Jonić_2016 |
| 37 |
Hu_L_2011 |
Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions |
Hu, Le-Le; Chen, Chen; Huang, Tao; Cai, Yu-Dong; Chou, Kuo-Chen |
2011 |
2011-12-29 |
COMM-USE |
N |
PMC3248422 |
22220213.0 |
10.1371/journal.pone.0029491 |
0mn1gbll |
0.380896 |
Qi_Z_2018, Chang_2011, Lee_C_2017, Saw_A_2019 |
Wang_2018 |
| 38 |
Ekins_2007 |
In silico pharmacology for drug discovery: applications to targets and beyond |
Ekins, S; Mestres, J; Testa, B |
2007 |
2007-09-01 |
PMC |
N |
PMC1978280 |
17549046.0 |
10.1038/sj.bjp.0707306 |
qah877pk |
0.379302 |
Sen_D_2018, Webb_2014 |
Wang_2018 |
| 39 |
Bakker_2018 |
Dynamic graphs, community detection, and Riemannian geometry |
Bakker, Craig; Halappanavar, Mahantesh; Visweswara Sathanur, Arun |
2018 |
2018-03-29 |
COMM-USE |
N |
PMC6214282 |
30839776.0 |
10.1007/s41109-018-0059-2 |
ear9cyri |
0.343438 |
Qi_Z_2018, Liou_2013 |
Coelho_2008 |


Jeremy Billay from Talan Consulting UK
Identify Person in a pdf document using Google Natural Language API.


| Overview | Module | Use | Devguide | Index |
| Services' Summary | Interfaces' Summary | Properties' Summary | Services' Details | Interfaces' Details | Properties' Details |
| service Paragraph |
|
| Included Services - Summary | |
| ::com::sun::star::style::ParagraphProperties | This service is present when the paragraph object refers to a text range (details) |
| ::com::sun::star::style::ParagraphPropertiesAsian | This service is present when the paragraph object refers to a text range (details) |
| ::com::sun::star::style::ParagraphPropertiesComplex | This service is present when the paragraph object refers to a text range (details) |
| ::com::sun::star::style::CharacterProperties | This service is present when the paragraph object refers to a text range (details) |
| ::com::sun::star::style::CharacterPropertiesAsian | This service is present when the paragraph object refers to a text range (details) |
| ::com::sun::star::style::CharacterPropertiesComplex | This service is present when the paragraph object refers to a text range (details) |
| TextTable | This service is present when the paragraph object refers to a text table (details) |
| TextContent |
| Exported Interfaces - Summary | |
| ::com::sun::star::beans::XPropertySet | This interface gives access to the properties of the paragraph itself (or its style sheet). So this interface may show property values which are not valid for any of the text portions of the paragraph, if the text itself has its own style properties set. |
| ::com::sun::star::beans::XPropertyState | This interface gives access to the state of the property values in these properties. So, it is possible to determine if a specific properties value is defined in the paragraph or its style sheet. |
| ::com::sun::star::container::XEnumerationAccess | This interface enumerates the text portions of this paragraph. (details) |
| ::com::sun::star::beans::XTolerantMultiPropertySet | gives access to a sequence of properties. (details) |
| Included Services - Details | |
::com::sun::star::style::ParagraphProperties
|
|
::com::sun::star::style::ParagraphPropertiesAsian
|
|
::com::sun::star::style::ParagraphPropertiesComplex
|
|
::com::sun::star::style::CharacterProperties
|
|
::com::sun::star::style::CharacterPropertiesAsian
|
|
::com::sun::star::style::CharacterPropertiesComplex
|
|
TextTable
|
|
| TextContent |
| Exported Interfaces - Details | |
::com::sun::star::beans::XPropertySet
|
|
::com::sun::star::beans::XPropertyState
|
|
::com::sun::star::container::XEnumerationAccess
|
|
::com::sun::star::beans::XTolerantMultiPropertySet
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


‘Goons in Black Coats’: Lawyers Beat Police Personnel, Civilians in Nationwide Protests - Video
New Delhi (Sputnik): Security at the Delhi’s Tis Hazari court complex has been beefed up after violence flared between police and lawyers.
A scuffle over car parking between police and lawyers at a court in New Delhi turned into nationwide protests on Monday.
Videos of lawyers beating police and civilians in different cities have started surfacing on social media. The incident, which triggered protests in different parts of the country, occurred at Tis Hazari Court in Delhi.
CCTV footage allegedly filmed in the court shows lawyers attacking police officers.
Another video supposedly shows a lawyer trying to attack the person making the video.
In one video, a policeman is being beaten by a lawyer in a court in Delhi’s Saket district.
In Uttar Pradesh, a group of lawyers can be seen attacking a cop on a bike.
The situation went from bad to worse when a group of lawyers started attacking commuters.
Twitter has been flooded with public outrage over the clashes and beatings.
Media reports state that police fired gunshots during the scuffle on Saturday, injuring a lawyer. Infuriated, lawyers allegedly responded by setting a police vehicle on fire.
According to India Today, the protesters allegedly misbehaved with a woman journalist who was covering the strike.
Six separate complaints have been made to the district judge of Tis Hazari court by four advocates, and a Delhi Police constable who was injured in the incident, India Today quoted Delhi Police spokesperson Anil Mittal as saying.
According to media reports both parties claim the trigger for the violence was a dispute over a lawyer parking his car near the court’s lock-up area. Police said the lawyer was asked not to park there because it would obstruct the movement of jail vans. It led to a heated argument that turned into a scuffle and soon erupted into violence.


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Ontimer Converter
One of the commands which was provided by the Jedi Academy modification, Makermodule, is called "ontimer". It's immensely useful.
The "ontimer" command allows you to perform other commands whilst the script is performing. This converter allows you to take existing scripts and to transform them into ontimer scripts.
Download ontimer converter
Ontimer
When you run the batch file, it'll ask you for a number. This should be an integer not a string (in short, it should be made up of numbers ranging from 0 to 9 not made up of a-z or anything else.) Entering an automatic delay of 20 would convert the following script:
mmarkfoot;
mplace factory/catw2_b;
mplace factory/catw2_b;
mmove 12 0 0
into the following:
ontimer 0 "mmarkfoot";
ontimer 20 "mplace factory/catw2_b";
ontimer 40 "mplace factory/catw2_b";
ontimer 60 "mmove 12 0 0"
Manual Delays
As well as the per-command delays, you can also use wait commands in the script itself. The ontimer will take this into consideration when it's working out the time to execute the next command. If you specified 30 to be your automatic delay, the following script would convert from:
mmarkfoot;
mplacefx env/fire 250;
wait 100;
mmove 100
to the following:
ontimer 0 "mmarkfoot";
ontimer 30 "mplacefx env/fire 250";
ontimer 130 "mmove 100"
Subtractive Waits
Subtractive waits are also compatible with the ontimer. Rather than getting you infinitely stuck like you normally would with a negative wait, it will take away the specified amount from the next automatic delay. As an example, if you have an automatic delay of 20ms and you use the following in a script:
mmarkfoot;
wait -2;
mplace factory/catw2_b
The converter will output:
ontimer 0 "mmarkfoot";
ontimer 0 "mplace factory/catw2_b"
Usage
Before you run the batch file, you'll need to put any files which need converting into the "src" folder. Once they are all set to go, simply run the batch file and proceed to read step 1 to 3 if you're not sure of how the converting process works. The
converted script will be spit out into the provided "bin" folder (you'll be prompted to create one if you forgot to extract it.)
Once you're happy with the script and don't want to make any more changes
to it, or want to pause the script development and want to use a different timing for a different script, move it out of the "bin" folder. I've provided a folder called "archive" if you can't think of a name for a folder to put them
in. Don't forget to include the folder names if you're going to execute the scripts directly from them.


As of right now, these installation options only work on macOS
For all of these options, you will have to have python installed. To do this, go to python.org and download the latest version of python.
Install Using pip
- Use the command
pip install cl-timer
Install Using Homebrew
- First, use the command
brew tap lol-cubes/tap
- Then, use the command
brew install cl-timer
Install by Downloading Source Code (Not recommended)
This method is very complicated, and it is much easier to install with pip.
- Download this file and unzip it.
- Use the 'mv' command to move the folder into the site-packages directory in your python interpreter. For me that is '/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/'. You may need to use 'sudo'.
- Download this file.
- Move this file to the bin of you python interpreter. For me, this is '/Library/Frameworks/Python.framework/Versions/3.8/bin/'.
- Run
chmod +x <path-to-cl-timer-executable>
After installing using one of the above methods, whenever you open terminal and enter the command "cl-timer", the program will start.
IMPORTANT
CL Timer has the ability to detect a spacebar held for a long time, then released as a single keypress. It does this by considering key repeat speed, and in order for it to work your delay until key repeat has to be as low as possible.
To shorten your delay until key repeat:
- Open the System Preference app
- Go to the Keyboard settings
- Move the slider below "delay until repeat" as far to the right (the side labeled "short") as possible


8BitDo's Zero 2 Is A Switch Controller That's Small Enough To Fit On Your Keychain
Coming late next month
by Damien McFerran8Bitdo has just refreshed its classic Zero mini-pad in a range of colours which make it the ideal companion for your Switch Lite.
The Zero 2 is a tiny Bluetooth controller which offers eight hours of battery life on a single charge and also works with Android, PC and MacOS.
It lacks analogue controls and only has a single D-pad – vibration is also off the table – but it should be perfect for games which only requite digital input and don't use the second analogue stick. Retro titles should be especially well-suited to the device.
The 8BitDo Zero 2 will cost $19.99 when it launches on December 31st.


'BJP has been planting lies to make people emotional'
by B J DhanyaprasadBy planting different lies, the BJP has been trying to make people emotional. But, the Congress party will fight against the mean political games of BJP, said KPCC President Dinesh Gundu Rao.
Speaking during the birth anniversary of former prime minister Indira Gandhi and the installation ceremony of the new District Congress Committee office bearers, at Vokkaligara Samudaya Bhavan in Chikkamagaluru on Tuesday, he accused the BJP of misleading the people.
He said that the Modi government was not able to bring ‘Acche Din’ so far. It has failed to fulfil the promises.
He further said, “It is true that the Congress party is in a bad position in Udupi, Dakshina Kannada, Chikkamagaluru and Shivamogga districts. But, Congress does not play with the emotions of people, as BJP.”
He wanted to know why the attacks by IT and ED are not happening on BJP leaders’ houses.
“The BJP’s Hippocratic stand on the disqualified MLAs is no more a secret,” he said.
He accused that the Central and the state governments have failed to compensate flood victims in the state.
The KPCC president meanwhile called upon the new office bearers of the district Congress Committee to strengthen the party from the booth level.
“There are a lot of expectations from the new District Congress Committee President DR K P Amshumanth,” he added.
Congress leaders Siddaramaiah, B L Shankar, S L Patil, Vishnunathan, U T Khader, Vinay Kumar Sorake, Sachin Meega, T H Shivashankarappa, G H Srinivas, T D Rajegowda, Motamma, B L Ramadas, D K Taradevi, Gayatri Shantegowda and others were present.


Every
day(s)
week(s)
month(s)
year(s)
on the
first
second
third
forth
last
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
day
on
Sun
Mon
Tue
Wed
Thu
Fri
Sat
from
for
until
for
occurences
until
Generate all
Generate sample (10)


Example: QQ 12 34 56 C
You can:
If you can't provide a National Insurance number, you will need to explain why. And after you've applied, you'll be asked to prove your identity by sending documents in the post (or providing them in person).


Since being a young child, music has played a significant role in my life; from the church to starting teenage bands. In my early twenties I moved from Scotland to London in the hope of "making it" but family life kicked in and meant leaving London for Lincolnshire. Throughout my adult life, I've continued to gig - mainly playing guitar and singing, alongside writing and producing my own music - and have maintained an income from it alongisde my day job.
All of this suddenly felt threatened when I found out that I had cancer, specifically cancer in the tonsil. The enormity of this was hard to get my head around - appointments, scans, radiotherapy, chemotherapy - what did it mean for me, my family, my music? I had an intense course of radio and chemotherapy, with no guarantee that my vocal chords would survive unscathed.
My relationship with music helped to give me focus and an escape during some of the most difficult times whilst enduring treatment. We all know that people go to gigs to have a good time but seeing people close up, fighting to keep going, smiling and being able to forget their surroundings for a few moments was profound. These experiences alongside my perspective since having cancer has given me the passion to help / work with / support others like me, creating something positive for all involved at a changing point in their lives.
Two years after being told the cancer had been treated, I am back in work, playing music, enjoying life with family and friends and slowly regaining my confidence - currently writing the soundtrack to a French pantomime! The physical side effects are continuously changing and improving now but the mental health effects still ripple close to the surface. Being able to share with others is known as a positive step in any recovery process; for me, sharing through music and creativity is what works best.
I would like to establish and coordinate an online network for people (patients, family members, carers of those living with cancer) to offer them a social, creative outlet. Online sessions would be coordinated offering the space to explore peoples' talents and link up with others, with the hope that individual projects would develop from here. Due to the nature of cancer treatment and the physical locations of this and peoples' homes, interested individuals would be supported to get involved online rather than creating a physical forum which may not be possible to suit everyone's needs.
This group will also enable members to publish and act as an outlet for their own material showcasing that even after treatment we can still pursue a path we feel passionately about all be it in some cases in a new direction.
As a cancer survivor, I feel I can empathise with both those diagnosed and their families/carers and would like the opportunity to create an encouraging and supportive environment led by those participating. My musical career has allowed me to work with people from all walks of life, all genres of music and many different approaches to creating music. I am flexible and although not classicaly trained I feel comfortable working with different levels of ability and ensuring that people get the best out of their time working together.
Helping others to regain their confidence, self-worth and value in their creativity is extremely important to me, having experienced the loss of these through cancer. Delivering this project would support both those who get involved in the idea as well as me in furthering my recovery.
Check out the latest music videos from Beyond the C by clicking visiting our Youtube Channel


--- layout: post status: publish published: true title: Panoramic Photos from Spring Training author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 4376 wordpress_url: http://ivyenvy.com/?p=4376 date: '2011-03-22 09:15:24 +0000' date_gmt: '2011-03-22 14:15:24 +0000' categories: - Minor Leagues - The Cubs - Spring Training tags: - Angels - Spring Training - Fitch Park - Panoramic Photo - Tempe Diablo Stadium comments: [] ---
These aren't great photos, but they are kind of cool. I have a panoramic photo app on my iPod and I snapped a few photos with it. They are very small because of their width. Click on them to see the larger version.
[caption id="attachment_4377" align="aligncenter" width="300" caption="Minor League Camp at Fitch Park"][/caption]
[caption id="attachment_4381" align="aligncenter" width="300" caption="Cubs vs Angels at Tempe Diablo Stadium"][/caption]


This is the changelog for Berkeley DB 11g Release 2 (library version <IP_ADDRESS>).
Fixed a bug which caused an AccessViolationException when populating a database with a compare delegate specified secondary database. [#18935]
Fixed a memory leak in the SQL API that happens when removing an in-memory or temporary database. [#19058]
Exclusive transactions will now block new transctions and will prevent existing transactions from making forward progress. [#19256]
Fixed a bug that would cause an assert error when opening an in-memory hash database with thread count configured when compiled with --enable-diagnostic. [#19357]
Fixed a bug that could cause a hang when deleting a table if there are multiple connections to a database from different processes. [#19419]
Fixed a bug in the SQL API when using a blob field with a lot of content and multiple concurrent connections to the database. [#19945]
Fixed a bug to correctly handle the DB_BUFFER_SMALL case on a delete operation when compression is enabled. [#19660]
Fixed the pragma "txn_priority" so it sets the priority of all SQL transactions in a session. [#19684]
Fixed a bug that could cause a crash in replication groups of more than 10 sites, with multiple processes sharing each DB environment concurrently. [#19818]
Modified db_hotbackup to not read from the file system as required on non-UNIX systems. Also provided the db_copy function for this purpose. [#19863]
Fixed a bug that could cause records in a queue extent to be missing if multiple threads raced to allocate a new page. [#20219]
Fixed a bug in the SQL API that could cause a corrupted environment to fail to run recovery automatically. [#20767]
Fixed a leak of log file ids when a database is closed before the end of a transaction that references it. [#15957]
Fixed a bug in page allocation where if a non-transactional update was being done, then we release the metadata page lock too early possibly leading to the corruption of the in-memory page list used by DB->compact. [#19036]
A log write failure on a replication master will now cause a panic since the transaction may be committed on some clients. [#19054]
Removed the possibility that checkpoints will overlap in the log, decreasing the time to recover. [#19062]
Fixed several bugs that could cause an update running with MVCC to get the wrong version of a page or improperly update the metadata last page number. [#19063]
The database open code will no longer log the open and close of the master database in a file when opening a sub database in that file [#19071]
Fixed a bug where an error during an update to a hash database with DB_NOOVERWRITE set could return DB_KEYEXIST rather than the correct error. [#19077]
Fixed a bug where an updater supporting DB_READ_UNCOMMITED might downgrade its lock too soon if there was an error during the update. [#19155]
Fixed a bug that could cause the wrong page number to be on a root or metadata page if DB->compact moved the page and the operation was later rolled forward. [#19167]
Fixed a bug that could cause the close of a secondary index database to fail if the transaction doing the open aborted. [#19169]
Changed SQL API library build on *nix to link with libpthreads when necessary. [#19098]
Fixed a JDBC error where building would fail on Solaris without the flag "-D_HAVE_SQLITE_CONFIG_H". [#19189]
Fixed some bugs in log_verify when there are in-memory database logs and subdb logs. [#19157]
Fixed a bug that would return DB_NOTFOUND while searching for an existing item using a cursor on a non-sorted duplicate btree database. [#19210]
Fixed a JDBC bug that the configure can not deal with CPPFLAGS. [#19234]
Fixed a bug in the SQL API that would cause a constraint violation when updating the primary key with the same value. [#18976]
Fixed a bug in the SQL BDBSQL_FILE_PER_TABLE option, to allow absolute path names. [#19190]
The database file format was unchanged in 11gR2 library version <IP_ADDRESS>.
The log file format was unchanged in 11gR2 library version <IP_ADDRESS>.
Added Performance event monitoring support for DTrace and SystemTap which can be enabled during configuration. Static probes have been defined where statistics values are updated, where mutex or transactional consistency lock waits occur, and where some other potentially lengthy operations may be initiated. [#15605]
Added a new acknowledge policy - DB_REPMGR_ACKS_ALL_AVAILABLE. [#16762]
Added transactional bulk loading optimization for non-nested transactions. [#17669]
Added exclusive transaction support for the SQL API. [#17822]
Added support for bulk update and delete in C# API. [#18011]
Added a db_replicate utility. [#18326]
Added an implementation of the Online Backup API. [#18500]
Added support in Berkeley DB SQL for the vacuum and incremental vacuum pragmas [#18545]
Added an option to automatically convert SQLite databases to Berkeley DB on opening. [#18531]
Added BDBSQL_SHARE_PRIVATE, an option to enable inter-process sharing of DB_PRIVATE environments using multiple-reader. [#18533]
Added database-level locking to optimize single-threaded operations and remove locking limitations for database load operations. [#18549]
Added support for DB_INIT_REP, DB_PRIVATE, DB_REGISTER and DB_THREAD in DB_CONFIG file.[#18555][#18901]
Added support for the BDBSQL_DEFAULT_PAGE_SIZE pragma to override Berkeley DB's choice of page size depending on the filesystem. Use SQLITE_DEFAULT_PAGE_SIZE rather than a hard-coded default. [#18577]
Added an extension that allows access to binary files stored outside of the database. What is stored in the database is a pointer to the binary file. [#18635]
Added .stat command to dbsql shell to print environment, table, and index statistics. [#18640]
Added enhancements to reduce the size of indexes in the SQL API by allowing duplicates in the index database and moving the rowid from the index key into the index data. [#18653]
Added a compile time flag BDBSQL_FILE_PER_TABLE that causes each table to be created in a separate file. This flag replaces the BDBSQL_SPLIT_META_TABLE flag. [#18664]
Added the handling of read only and read write open of the same database in BDB SQL [#18672]
Added an encryption implementation to the SQL API [#18683]
Fixed failchk behavior on QNX. [#17403]
Fixed a bug that prevented the same process from connecting to the database after recovery is performed. [#18535]
Fixed a bug which would occur when recovery checkpoint was not written because the cache ran out of space attempting to flush the memory pool cache. The environment would be recovered and all database where made available, but some databases would incorrectly closed. This would cause a subsequent recovery to fail on its backward pass with the error "PANIC: No such file or directory". [#18590]
Fixed a bug that could cause recovery to fail with the error "DB_LOGC->get: log record LSN %u/%u: checksum mismatch" if the last log file was nearly full and ended with a partially written log record which was smaller than a checkpoint record. It now erases the invalid partial record before switching to the new log file. [#18651]
Fixed a bug such that segementation fault does not occur if DB->set_partition_dirs is called before DB->set_partition. [#18591]
Fixed a bug such that the error "unknown path" does not occur if you put duplicate records into a duplicated sorted HASH database with DB_OVERWRITE_DUP option. [#18607]
Added the ability to specify that data should not be logged when removing pages from a database. This can be used if the ability to recover the data is not required after the database has been removed. [#18666]
Fixed a bug that caused an aborting transaction to fail if it aborted while a DB->compact of the same HASH database was compacting the dynamic hash table [#18695]
Fixed a bug that could cause DB->compact to loop on a DB_RECNO database or a database with an multilevel unsorted off page duplicate tree. [#18722]
Fixed a bug that could cause an illegal page type error when using a HASH database with MVCC and the HASH table was contracted and then extended. [#18785]
Fixed locking bugs: [#18789]
Fixed a bug that could cause data to not be returned in a HASH database that was one of multiple databases in a file and was opened prior to running DB->compact method on that database in another thread of control [#18824]
Fixed a bug where doing a bulk insert with secondaries could return an error incorrectly. [#18878]
Fixed a bug that would return DB_NOTFOUND instead of DB_BUFFER_SMALL when the first item in a HASH database is larger than the user supplied buffer. [#18829]
Fixed a bug that would cause handle locks to be left referencing the wrong metadata page if DB->compact moved the metadata page of a sub-database. [#18944]
Fixed a bug that could cause records in a queue extent to be missing if multiple threads raced to allocate a new page. [#20219]
Fixed various items uncovered by extending DB_CONFIG support: [#18720]
Fixed a leak when aborting a transaction containing a DB->close. [#18951]
Fixed a bug that would leave a cursor referencing the wrong hash bucket when the hash table was expanded or contracted. This would cause the new hash bucket not to be locked on subsequent reference but would not cause problems because the metadata page would be locked to the end of the transaction due to the reorganization. [#18986]
Fixed a bug that could leave a buffer pinned if a DB->compact operation on HASH had a I/O or deadlock error. [#18980]
Fixed a bug that failed to dirty a page when DB->compact moved records within a hash bucket [#18994]
Fixed a bug that might cause an update to a HASH database to fail with an "unpinned page returned" error if it first gets an I/O error while logging. [#18985]
Fixed a bug that could leave a hash bucket overflow page not linked to the bucket if the unlink of that page aborted. [#19001]
Fixed a bug that would leave the next page pointer of a hash bucket that was removed pointing to an invalid page [#19004]
Exclusive transactions will now block new transctions and will prevent existing transactions from making forward progress. [#19256]
Allowed SQL applications to attach to the same database multiple times unless shared cache mode is explicitly requested. [#18340]
Fixed a bug where auto-removal of log files after writing a checkpoint was not functioning correctly. [#18413]
Fixed a race between opening and closing SQL databases from multiple threads that could lead to the error "DB_REGISTER limits processes to one open DB_ENV handle per environment". [#18538]
Optimized the SQL adapter for joins. Reduce the number of Berkeley DB operations in a join by caching the maximum key in the primary. [#18566]
A SQLITE_LOCKED or SQLITE_BUSY error returned by a statement in an explict transaction will no longer invalidate the entire transaction, but just the statement that returned the error. [#18582]
Changed how multiple connections to the same database are detected. Used a fileid so that different paths can be used without error. [#18646]
Fixed a bug where the journal (environment) directory was being created prior to the actual environment. [#18656]
Added a new PRAGMA to allow tuning of when checkpoints are run. [#18657]
Fixed spurious "column <x> not unique" error messages.[#18667]
Fixed a segmentation fault that could happen when memory could not be allocated for the index key in the SQL API.[#18783]
Fixed a bug causing a segfault when releasing a savepoint that was already released. [#18784]
Fixed a bug where getAllowPopulate and getImmutableSecondaryKey method always returned false for SecondaryConfig objects returned by SecondaryDatabase.getSecondaryConfig method. [#16018]
Fixed a bug which made it impossible to (re)set VerboseConfig.REPLICATION_SYSTEM on the Java API. [#17561]
Fixed a bug where populating a SecondaryDatabase on open could lead to an OutOfMemoryException. [#18529]
Fixed a bug such that segementation fault does not occur when putting records into callback-partitioned database. [#18596]
Fixed a bug where DatabaseConfig.getUnsortedDuplicates method returned true when the datbase had been configured for sorted duplicates. [#18612]
Initialized DatabaseConfig.pageSize so that it can be queried. [#18691]
Fixed a bug by opening a write cursor for Direct Persistent Layer(DPL) entity's put operation in the Concurrent Data Store product. [#18692]
Synchronized Java persistence code and tests from Java Edition to Berkeley DB. [#18711]
Introduced the EnvironmentConfig.setReplicationInMemory method as a way to configure in-memory internal replication files before opening the Environment handle on the Java API. [#18719]
Fixed a bug in the bulk DatabaseEntry class, where it was possible to overflow the buffer. [#18850]
Added LEASE_TIMEOUT field to the ReplicationTimeoutType class that enables configuring the amount of time a client grants its Master Lease to a master. [#18867]
Fixed a bug in BTree prefix comparison method such that there is no problem when the application needs to save a number larger than or equal to 2^31. The BTree prefix comparison function now returns an unsigned int instead of a signed int. [#18481]
Fixed a bug which caused the HasMultiple method to throw an exception when there were multiple databases in a single database file. [#18483]
Fixed a bug to ensure the CachePriority is set for Database and Cursor objects. [#18716]
Fixed a bug that use leading to the error: "Transaction that opened the DB handle is still active" when applications used different transactional handles in the associate and open methods in a secondary database. [#18873]
Fixed a bug which caused AccessViolationException when populating db with a compare delegate specified secondary db. [#18935]
All setter methods in the DPL |StoreConfig| and |EvolveConfig| now return |this| rather than having a |void| return type. This change requires that applications using the DPL be recompiled. [#17021]
Improve performance of |StoredCollection.removeAll|. This method no longer iterates over all records in the stored collection. [#17727]
Several new tuple formats and binding classes have been added in the |com.sleepycat.bind.tuple| package. See the |com.sleepycat.bind.tuple| package description for an overview of the new bindings and a comparative description of all tuple bindings. The new tuple formats are: [#18379]
The following classes are now certified to be serializable. [#18738]
Replication Manager now uses the standard system implementation of getaddrinfo() when running on Windows, which means that it can support IPv6 addresses if support is present and configured in the operating system. [#18263]
Fixed a bug which caused a "full election" to fail if a majority of sites were not ready when the election started. [#18456]
Fixed a bug which could occur when using bulk transfer with Replication Manager. When closing a DB_ENV handle, any remaining bulk buffer contents are flushed, and Replication Manager could have tried to send the resulting messages even though its connections had already been closed, leading in rare circumstances to spurious EBADF error reports, or possibly even arbitrary memory corruption. [#18469]
Fixed a bug which caused Replication Manager to wait for acknowledgement from client, even if it had failed to send a log record, due to "queue limit exceeded". Replication Manager now returns immediately, with a PERM_FAILED indication, to avoid a pointless delay to the commit() operation. [#18682]
Fixed a bug where changes made in one process to Replication Manager configuration values (such as ack policy or ack timeout) were not observed in other processes sharing the same database environment. [#18839]
Fixed bugs that could prevent client synchronization from completing due to a failure to request missing log records. [#18849]
Fixed a bug where a client that had rolled back transactions to synchronize with a new master, failed to invalidate existing database handles later used for cursor operations based on an explicitly provided transaction. [#18862]
Fixed a bug where Replication Manager called for an election after a DUPMASTER event, even when using Master Leases. In such a case it now simply accepts the new (remote) master. [#18864]
Fixed a bug which would cause failure if client env attempted to perform sync-up recovery to a point in the log that happened to fall exactly on a log file boundary. [#18907]
Moved the wait mutex from the lock structure to the locker structure, reducing the number of mutexes required in the system. [#18685]
Fixed a race condition that was causing the error: "Unable to allocate space from the buffer cache". The error can only be triggered when multiple memory pool regions are used and there is a periodic gathering and clearing of statistics. This also fixes a second bug where if you compile without statistics and explicitly set the memoru pool default pagesize, other environment handles to that environment would not see the correct memory pool default pagesize. [#18386]
Fixed a bug where the get_cachesize method and the mpool_stat method returned the initial cache size, even if the cache size had been changed. [#18706]
Changed memory pool allocation so that the EIO error is returned rather than the ENOMEM error when the memory cannot be allocated because dirty pages cannot be written. [#18740]
Fixed problems with the printed statistics for DB_MUTEX_SHARED latches. The DB_STAT_CLEAR flag (as specified by db_stat -Z) did not clear the counts of the number of times a shared latch either had to wait to get access or was able to get the latch without waiting. Also, the ownership state of a test-and-set latch (not a hybrid one) was always displayed as not owned, even when it was held. [#17585] [#18743]
Fixed bugs that could caused PANIC or DB_RUNRECOVERY errors when the synchronization of the transaction log failed. [#18588]
Fix javadoc to note the exception to the rule that a transaction handle may not be accessed after commit() operaton (getCommitToken() is allowed). [#18730]
Removed the possibility that checkpoints will overlap in the log, decreasing the time to recover [#19062]
Modified the db_printlog and db_dump -da so that they use the same formatting. The db_logprint utility now uses the message stream. Both db_dump -da and db_printlog accept a -D flag to indicate the numer of bytes of data items to display. You can set this value when calling the DB_ENV->set_data_len method or in the DB_CONFIG. [#18365]
Fixed a bug that caused a segmentation violation when using the db_printlog utility. [#18694]
Fixed a bug in db_hotbackup that would cause a trap if the -D flag is used and the DB_CONFIG file does not specify a log directory. [#18841]
Fixed a bug that would cause verify to call the wrong compare function if there are user defined compare functions used and the database has multilevel off page sorted duplicate trees. [#20284]
Modified db_hotbackup to not read from the file system as required on non-UNIX systems. Also provided the db_copy function for this purpose. [#19863]
Fixed db_hotbackup so that when -d/-l or -D is not specified, DB_CONFIG is used to determine the locations of the databases and logs in the source environment. [#19994]
Added support and documentation for iPhone OS. [#18223]
Fix a bug to make the configuration option --enable-debug work when CFLAGS is set. [#18432]
Updated Visual Studio project files to enable ADO.NET support. [#18448]
Enhanced the source tree layout making it easier to navigate. [#18492]
Fixed the --enable-dbm argument to configure. [#18497]
Fixed Visual Studio project files so that they can load into Visual Studio 2010. [#18505]
Updated Windows CE build files to be consistent with desktop Windows build files. Added Windows Mobile 6.5.3 Professional as a target platform. [#18516]
Added a fix so that an error message is displayed when the 'ar' utility is missing when configured. [#18619]
Added tighter integration of JDBC on POSIX/autoconf by including an argument --enable-jdbc to configure. [#18621]
Fix build conflicts in log verify with other configurations. [#18658]
Upgraded Berkeley DB SQL to SQLite version 3.7.0 [#18857]
Provided a functionality on the Windows platform to choose a default page size based on the underlying file system sector size. [#16538]
Changed DB_NOSYNC from an "operation" constant to a flag value. [#17775]
Changed the default permissions for files in the release tree to allow write access. [#17974]
Fixed a bug which caused database verification to hang when verifying a database in a Concurrent Data Store environment that performs locking on an environment-wide basis (DB_CDB_ALLDB.) [#18571]
Fixed temporary file naming with negative process IDs (e.g., VxWorks). [#18975]
Hybrid shared latches have a race condition which can consume cpu unnecessarily. The bug is triggered when a thread requests exclusive access to a shared latch while it is locked for reading by other threads. If you run into this situation, contact us for a patch. [#18982]
When doing a C# API, 64 bit build, some of the data in the lock statistical field may appear incorrect. This is a data display issue. If you run into this situation, contact us for a patch. [#20769]


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License
Everything in this repo is BSD style license unless otherwise specified.
Copyright (c) 2015 The Polymer Authors. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the name of Google Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

Users
This is the users section
AddyRob
Chuck
Sam
User: {{params.name}}
Contact
This is the contact section
JINKYUNG LEE
<EMAIL_ADDRESS>| 1-408-707-0660
Summary
Front End Web Developer who understand about Software Development life cycle and self-motivated, self-start person. I have strong commitment to task and adapt well in the fast-paced collaborative environment. I have experience in web development and successfully manage many academic projects which involves quick learning of various technologies. Have excellent communication, interpersonal and problem solving skill
Skills
Languages/App.Development
: Java, HTML5, SQL, C, JavaScript
Databases : MySQL,
Oracle
Tools : Eclipse, VisualStudio2010
Framework
: Apache&TomCat
Experience
Front
End Developer at at Ziontech, Inc - intern
- Worked with UI components and best practices of designing reports and dashboards
- Accommodate changing requirements through continuous communication with customers
- Understood device friendly responsive websites
Software Developer at SerimTSG - intern
- Understand the principles of object-oriented development
- Enhanced ability to work with distributed teams in a collaborative and productive manner
Academic projects
Web
site creation project
- Enhanced strong understanding of web-based technologies
Web Programming project
- Understand of JavaScript, CSS, HTML5 and web application frameworks
- Gained ability to get stuff done
Education
Chungnam
University (2012 - Now)
Bachelor of Computer Science
Engineering
Portfolio
This is the portfolio section


Classification methods and models
In classification methods, we are typically interested in using some observed characteristics of a case to predict a binary categorical outcome. This can be extended to a multi-category outcome, but the largest number of applications involve a 1/0 outcome.
Below, we look at a few classic methods of doing this:
Logistic regression
Regression/Partitioning Trees
Linear Discriminant Functions
There are other methods that we will examine but these are probably the easiest to understand.
In these examples, we will use the Demographic and Health Survey Model Data. These are based on the DHS survey, but are publicly available and are used to practice using the DHS data sets, but don’t represent a real country.
In this example, we will use the outcome of contraceptive choice (modern vs other/none) as our outcome.
library(haven)
dat<-url("https://github.com/coreysparks/data/blob/master/ZZIR62FL.DTA?raw=true")
model.dat<-read_dta(dat)
Here we recode some of our variables and limit our data to those women who are not currently pregnant and who are sexually active.
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
model.dat2<-model.dat%>%
mutate(region = v024,
modcontra= as.factor(ifelse(v364 ==1,1, 0)),
age = v012,
livchildren=v218,
educ = v106,
currpreg=v213,
knowmodern=ifelse(v301==3, 1, 0),
age2=v012^2)%>%
filter(currpreg==0, v536>0)%>% #notpreg, sex active
dplyr::select(caseid, region, modcontra,age, age2,livchildren, educ, knowmodern)
| caseid | region | modcontra | age | age2 | livchildren | educ | knowmodern |
|---|---|---|---|---|---|---|---|
| 1 1 2 | 2 | 0 | 30 | 900 | 4 | 0 | 1 |
| 1 4 2 | 2 | 0 | 42 | 1764 | 2 | 0 | 1 |
| 1 4 3 | 2 | 0 | 25 | 625 | 3 | 1 | 1 |
| 1 5 1 | 2 | 0 | 25 | 625 | 2 | 2 | 1 |
| 1 6 2 | 2 | 0 | 37 | 1369 | 2 | 0 | 1 |
| 1 6 3 | 2 | 0 | 17 | 289 | 0 | 2 | 0 |
using caret to create training and test sets.
We use an 80% training fraction
## Loading required package: lattice
## Loading required package: ggplot2
set.seed(1115)
train<- createDataPartition(y = model.dat2$modcontra , p = .80, list=F)
model.dat2train<-model.dat2[train,]
## Warning: The `i` argument of ``[`()` can't be a matrix as of tibble 3.0.0.
## Convert to a vector.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
##
## 0 1
## 4036 1409
##
## 0 1
## 0.7412305 0.2587695
## caseid region modcontra age age2
## Length:5445 Min. :1.000 0:4036 Min. :15.00 Min. : 225.0
## Class :character 1st Qu.:1.000 1:1409 1st Qu.:21.00 1st Qu.: 441.0
## Mode :character Median :2.000 Median :29.00 Median : 841.0
## Mean :2.164 Mean :29.78 Mean : 976.8
## 3rd Qu.:3.000 3rd Qu.:37.00 3rd Qu.:1369.0
## Max. :4.000 Max. :49.00 Max. :2401.0
## livchildren educ knowmodern
## Min. : 0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:1.0000
## Median : 2.000 Median :0.0000 Median :1.0000
## Mean : 2.546 Mean :0.7381 Mean :0.9442
## 3rd Qu.: 4.000 3rd Qu.:2.0000 3rd Qu.:1.0000
## Max. :10.000 Max. :3.0000 Max. :1.0000
Logistic regression for classification
Here we use a basic binomial GLM to estimate the probability of a woman using modern contraception. We use information on their region of residence, age, number of living children and level of education.
This model can be written: \[ln \left ( \frac{Pr(\text{Modern Contraception})}{1-Pr(\text{Modern Contraception})} \right ) = X' \beta\]
Which can be converted to the probability scale via the inverse logit transform:
\[Pr(\text{Modern Contraception}) = \frac{1}{1+exp (-X' \beta)}\]
glm1<-glm(modcontra~factor(region)+scale(age)+scale(age2)+scale(livchildren)+factor(educ), data=model.dat2train[,-1], family = binomial)
summary(glm1)
##
## Call:
## glm(formula = modcontra ~ factor(region) + scale(age) + scale(age2) +
## scale(livchildren) + factor(educ), family = binomial, data = model.dat2train[,
## -1])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4073 -0.7103 -0.5734 1.0669 2.3413
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.91240 0.06807 -28.095 < 2e-16 ***
## factor(region)2 0.38755 0.08534 4.541 5.60e-06 ***
## factor(region)3 0.62565 0.09531 6.564 5.23e-11 ***
## factor(region)4 0.30066 0.09454 3.180 0.001471 **
## scale(age) 0.63678 0.26540 2.399 0.016425 *
## scale(age2) -0.98328 0.26194 -3.754 0.000174 ***
## scale(livchildren) 0.17004 0.05408 3.144 0.001665 **
## factor(educ)1 0.43835 0.10580 4.143 3.43e-05 ***
## factor(educ)2 1.38923 0.08646 16.068 < 2e-16 ***
## factor(educ)3 1.54061 0.16086 9.577 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6226.5 on 5444 degrees of freedom
## Residual deviance: 5629.0 on 5435 degrees of freedom
## AIC: 5649
##
## Number of Fisher Scoring iterations: 4
We see that all the predictors are significantly related to our outcome
Next we see how the model performs in terms of accuracy of prediction. This is new comparison to how we typically use logistic regression.
We use the predict()
function to get the estimated class probabilities for each case
## 1 2 3 4 5 6
## 0.22002790 0.31137928 0.15091505 0.20389088 0.08726724 0.18808481
These are the estimated probability that each of these women used modern contraception, based on the model.
In order to create classes (uses modern vs doesn’t use modern contraception) we have to use a decision rule. A decision rule is when we choose a cut off point, or threshold value of the probability to classify each observation as belonging to one class or the other.
A basic decision rule is if \(Pr(y=\text{Modern Contraception} |X) >.5\) Then classify the observation as a modern contraception user, and otherwise not. This is what we will use here.
tr_predcl<-factor(ifelse(tr_pred>.5, 1, 0))
library(ggplot2)
pred1<-data.frame(pr=tr_pred, gr=tr_predcl, modcon=model.dat2train$modcontra)
pred1%>%
ggplot()+geom_density(aes(x=pr, color=gr, group=gr))+ggtitle(label = "Probability of Modern Contraception", subtitle = "Threshold = .5")
pred1%>%
ggplot()+geom_density(aes(x=pr, color=modcon, group=modcon))+ggtitle(label = "Probability of Modern Contraception", subtitle = "Truth")
Next we need to see how we did. A simple cross tab of the observed classes versus the predicted classes is called the confusion matrix.
##
## tr_predcl 0 1
## 0 3761 1142
## 1 275 267
This is great, but typically it’s easier to understand the model’s predictive ability by converting these to proportions. The confusionMatrix()
function in caret
can do this, plus other stuff.
This provides lots of output summarizing the classification results. At its core is the matrix of observed classes versus predicted classes. I got one depiction of this here and from the Wikipedia page
Lots of information on the predictive accuracy can be found from this 2x2 table:
Generally, we are interested in overall accuracy, sensitivity and specificity.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 3761 1142
## 1 275 267
##
## Accuracy : 0.7398
## 95% CI : (0.7279, 0.7514)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 0.6046
##
## Kappa : 0.1517
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.9319
## Specificity : 0.1895
## Pos Pred Value : 0.7671
## Neg Pred Value : 0.4926
## Prevalence : 0.7412
## Detection Rate : 0.6907
## Detection Prevalence : 0.9005
## Balanced Accuracy : 0.5607
##
## 'Positive' Class : 0
##
Overall the model has a 73.9% accuracy, which isn’t bad! What is bad is some of the other measures. The sensitivity is really low 267/(267+1142) = .189
, so we are only predicting the positive class (modern contraception) in 19% of cases correctly. In other word the model is pretty good at predicting if you don’t use modern contraception, 3761/(3761+275)= .931
, but not at predicting if you do.
We could try a different decision rule, in this case, I use the mean of the response as the cutoff value.
tr_predcl<-factor(ifelse(tr_pred>.258, 1, 0)) #mean of response
pred2<-data.frame(pr=tr_pred, gr=tr_predcl, modcon=model.dat2train$modcontra)
pred2%>%
ggplot()+geom_density(aes(x=pr, color=gr, group=gr))+ggtitle(label = "Probability of Modern Contraception", subtitle = "Threshold = .258")
pred2%>%
ggplot()+geom_density(aes(x=pr, color=modcon, group=modcon))+ggtitle(label = "Probability of Modern Contraception", subtitle = "Truth")
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 2944 577
## 1 1092 832
##
## Accuracy : 0.6935
## 95% CI : (0.681, 0.7057)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.2859
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.5905
## Specificity : 0.7294
## Pos Pred Value : 0.4324
## Neg Pred Value : 0.8361
## Prevalence : 0.2588
## Detection Rate : 0.1528
## Detection Prevalence : 0.3534
## Balanced Accuracy : 0.6600
##
## 'Positive' Class : 1
##
Which drops the accuracy a little, but increases the specificity at the cost of the sensitivity.
Next we do this on the test set to evaluate model performance outside of the training data
pred_test<-predict(glm1, newdata=model.dat2test, type="response")
pred_cl<-factor(ifelse(pred_test>.28, 1, 0))
table(model.dat2test$modcontra,pred_cl)
## pred_cl
## 0 1
## 0 746 262
## 1 160 192
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 746 160
## 1 262 192
##
## Accuracy : 0.6897
## 95% CI : (0.6644, 0.7142)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.2609
##
## Mcnemar's Test P-Value : 8.806e-07
##
## Sensitivity : 0.7401
## Specificity : 0.5455
## Pos Pred Value : 0.8234
## Neg Pred Value : 0.4229
## Prevalence : 0.7412
## Detection Rate : 0.5485
## Detection Prevalence : 0.6662
## Balanced Accuracy : 0.6428
##
## 'Positive' Class : 0
##
Regression partition tree
As we saw in the first working group example, the regression tree is another common technique used in classification problems. Regression or classification trees attempt to
library(rpart)
library(rpart.plot)
rp1<-rpart(modcontra~factor(region)+(age)+livchildren+factor(educ),
data=model.dat2train,
method ="class",
control = rpart.control(minbucket = 10, cp=.01)) #lower CP parameter makes for more compliacted tree
summary(rp1)
## Call:
## rpart(formula = modcontra ~ factor(region) + (age) + livchildren +
## factor(educ), data = model.dat2train, method = "class", control = rpart.control(minbucket = 10,
## cp = 0.01))
## n= 5445
##
## CP nsplit rel error xerror xstd
## 1 0.04009936 0 1.0000000 1.0000000 0.02293618
## 2 0.01100071 2 0.9198013 0.9198013 0.02230305
## 3 0.01000000 4 0.8977999 0.9169624 0.02227934
##
## Variable importance
## factor(educ) livchildren age factor(region)
## 58 23 19 1
##
## Node number 1: 5445 observations, complexity param=0.04009936
## predicted class=0 expected loss=0.2587695 P(node) =1
## class counts: 4036 1409
## probabilities: 0.741 0.259
## left son=2 (3862 obs) right son=3 (1583 obs)
## Primary splits:
## factor(educ) splits as LLRR, improve=189.73590, (0 missing)
## livchildren < 0.5 to the right, improve= 84.51811, (0 missing)
## age < 23.5 to the right, improve= 52.42664, (0 missing)
## factor(region) splits as LLRL, improve= 36.53020, (0 missing)
## Surrogate splits:
## livchildren < 0.5 to the right, agree=0.772, adj=0.215, (0 split)
## age < 19.5 to the right, agree=0.753, adj=0.149, (0 split)
## factor(region) splits as LLRL, agree=0.713, adj=0.014, (0 split)
##
## Node number 2: 3862 observations
## predicted class=0 expected loss=0.174262 P(node) =0.7092746
## class counts: 3189 673
## probabilities: 0.826 0.174
##
## Node number 3: 1583 observations, complexity param=0.04009936
## predicted class=0 expected loss=0.46494 P(node) =0.2907254
## class counts: 847 736
## probabilities: 0.535 0.465
## left son=6 (868 obs) right son=7 (715 obs)
## Primary splits:
## livchildren < 0.5 to the right, improve=33.940940, (0 missing)
## age < 36.5 to the right, improve=20.441730, (0 missing)
## factor(region) splits as LRRL, improve= 2.382434, (0 missing)
## factor(educ) splits as --LR, improve= 0.556353, (0 missing)
## Surrogate splits:
## age < 20.5 to the right, agree=0.749, adj=0.443, (0 split)
##
## Node number 6: 868 observations
## predicted class=0 expected loss=0.3709677 P(node) =0.1594123
## class counts: 546 322
## probabilities: 0.629 0.371
##
## Node number 7: 715 observations, complexity param=0.01100071
## predicted class=1 expected loss=0.420979 P(node) =0.1313131
## class counts: 301 414
## probabilities: 0.421 0.579
## left son=14 (14 obs) right son=15 (701 obs)
## Primary splits:
## age < 32.5 to the right, improve=9.574909, (0 missing)
## factor(educ) splits as --LR, improve=1.650766, (0 missing)
## factor(region) splits as LRRL, improve=1.324512, (0 missing)
##
## Node number 14: 14 observations
## predicted class=0 expected loss=0 P(node) =0.002571166
## class counts: 14 0
## probabilities: 1.000 0.000
##
## Node number 15: 701 observations, complexity param=0.01100071
## predicted class=1 expected loss=0.4094151 P(node) =0.128742
## class counts: 287 414
## probabilities: 0.409 0.591
## left son=30 (137 obs) right son=31 (564 obs)
## Primary splits:
## age < 16.5 to the left, improve=7.933444, (0 missing)
## factor(educ) splits as --LR, improve=2.545437, (0 missing)
## factor(region) splits as LRRL, improve=1.768127, (0 missing)
##
## Node number 30: 137 observations
## predicted class=0 expected loss=0.4379562 P(node) =0.0251607
## class counts: 77 60
## probabilities: 0.562 0.438
##
## Node number 31: 564 observations
## predicted class=1 expected loss=0.3723404 P(node) =0.1035813
## class counts: 210 354
## probabilities: 0.372 0.628
rpart.plot(rp1, type = 4,extra=4,
box.palette="GnBu",
shadow.col="gray",
nn=TRUE, main="Classification tree for using modern contraception")
Each node box displays the classification, the probability of each class at that node (i.e. the probability of the class conditioned on the node) and the percentage of observations used at that node. From here.
predrp1<-predict(rp1, newdata=model.dat2train, type = "class")
confusionMatrix(data = predrp1,model.dat2train$modcontra )
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 3826 1055
## 1 210 354
##
## Accuracy : 0.7677
## 95% CI : (0.7562, 0.7788)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 3.566e-06
##
## Kappa : 0.2475
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9480
## Specificity : 0.2512
## Pos Pred Value : 0.7839
## Neg Pred Value : 0.6277
## Prevalence : 0.7412
## Detection Rate : 0.7027
## Detection Prevalence : 0.8964
## Balanced Accuracy : 0.5996
##
## 'Positive' Class : 0
##
We see the regression tree is performing a little better than the logistic regression on the test case using the summary below:
pred_testrp<-predict(rp1, newdata=model.dat2test, type="class")
confusionMatrix(data = pred_testrp,model.dat2test$modcontra )
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 947 263
## 1 61 89
##
## Accuracy : 0.7618
## 95% CI : (0.7382, 0.7842)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 0.0434
##
## Kappa : 0.2365
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.9395
## Specificity : 0.2528
## Pos Pred Value : 0.7826
## Neg Pred Value : 0.5933
## Prevalence : 0.7412
## Detection Rate : 0.6963
## Detection Prevalence : 0.8897
## Balanced Accuracy : 0.5962
##
## 'Positive' Class : 0
##
Linear discriminant function
Linear discriminant functions attempt to separate classes from each other using a strictly linear function of the variables. It attempts to reduce the dimensionality of the original data to a single linear function of the input variables, or the discriminant function. This is very similar to what PCA does when it creates a principal component, although in LDA, the function uses this linear transformation of the data to optimally separate classes.
In this case it performs better than the logistic regression but not as well as the regression tree.
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
lda1<-lda(modcontra~factor(region)+scale(age)+livchildren+factor(educ), data=model.dat2train,prior=c(.74, .26) , CV=T)
pred_ld1<-lda1$class
head(lda1$posterior) #probabilities of membership in each group
## 0 1
## 1 0.8153664 0.1846336
## 2 0.7387134 0.2612866
## 3 0.8673284 0.1326716
## 4 0.8080069 0.1919931
## 5 0.8976027 0.1023973
## 6 0.8387015 0.1612985
ld1<-data.frame(ppmod= lda1$posterior[, 2],pred=lda1$class, real=model.dat2train$modcontra)
ld1%>%
ggplot()+geom_density(aes(x=ppmod, group=pred, color=pred))+ggtitle(label = "Probabilities of class membership on the linear discriminant function")
ld1%>%
ggplot()+geom_density(aes(x=ppmod, group=real, color=real))+ggtitle(label = "Probabilities of class membership and the real class")
Accuracy on the training set
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 3625 1000
## 1 411 409
##
## Accuracy : 0.7409
## 95% CI : (0.729, 0.7525)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 0.5318
##
## Kappa : 0.2181
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.8982
## Specificity : 0.2903
## Pos Pred Value : 0.7838
## Neg Pred Value : 0.4988
## Prevalence : 0.7412
## Detection Rate : 0.6657
## Detection Prevalence : 0.8494
## Balanced Accuracy : 0.5942
##
## 'Positive' Class : 0
##
lda1<-lda(modcontra~factor(region)+scale(age)+livchildren+factor(educ), data=model.dat2train,prior=c(.74, .26) )
#linear discriminant function
lda1$scaling
## LD1
## factor(region)2 0.4580587
## factor(region)3 0.8545973
## factor(region)4 0.3495414
## scale(age) -0.3873869
## livchildren 0.1025140
## factor(educ)1 0.4535731
## factor(educ)2 1.9263226
## factor(educ)3 2.2956187
Accuracy on the test set
## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 906 254
## 1 102 98
##
## Accuracy : 0.7382
## 95% CI : (0.714, 0.7614)
## No Information Rate : 0.7412
## P-Value [Acc > NIR] : 0.6115
##
## Kappa : 0.2062
##
## Mcnemar's Test P-Value : 1.214e-15
##
## Sensitivity : 0.8988
## Specificity : 0.2784
## Pos Pred Value : 0.7810
## Neg Pred Value : 0.4900
## Prevalence : 0.7412
## Detection Rate : 0.6662
## Detection Prevalence : 0.8529
## Balanced Accuracy : 0.5886
##
## 'Positive' Class : 0
##


| pgpool-II 4.0.3 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 1. Getting Started | Next |
Pgpool-II allows an automatic fail over when PostgreSQL server goes down. In this case Pgpool-II sets the status of the server to "down" and continue the database operation using remaining servers.
$ pg_ctl -D data1 stop waiting for server to shut down.... done server stopped $ psql -p 11000 -c "show pool_nodes" test node_id | hostname | port | status | lb_weight | role | select_cnt | load_balance_node | replication_delay ---------+----------+-------+--------+-----------+---------+------------+-------------------+------------------- 0 | /tmp | 11002 | up | 0.500000 | primary | 2172 | true | 0 1 | /tmp | 11003 | down | 0.500000 | standby | 0 | false | 0 (2 rows) $ psql -p 11000 -c "SELECT sum(abalance) FROM pgbench_accounts" test sum -------- 192112 (1 row)
The standby node was shut down by pg_ctl command. Pgpool-II detects it and detaches the standby node. "show pool_nodes" command shows that the standby node is in down status. You can continue to use the cluster without the standby node:
$ psql -p 11000 -c "SELECT sum(abalance) FROM pgbench_accounts" test sum -------- 192112 (1 row)
What happens if the primary server goes down? In this case, one of remaining standby server is promoted to new primary server. For this testing, we start from the state in which both nodes are up.
$ psql -p 11000 -c "show pool_nodes" test node_id | hostname | port | status | lb_weight | role | select_cnt | load_balance_node | replication_delay ---------+----------+-------+--------+-----------+---------+------------+-------------------+------------------- 0 | /tmp | 11002 | up | 0.500000 | primary | 2173 | true | 0 1 | /tmp | 11003 | up | 0.500000 | standby | 0 | false | 0 (2 rows) $ pg_ctl -D data0 stop waiting for server to shut down.... done server stopped $ psql -p 11000 -c "show pool_nodes" test node_id | hostname | port | status | lb_weight | role | select_cnt | load_balance_node | replication_delay ---------+----------+-------+--------+-----------+---------+------------+-------------------+------------------- 0 | /tmp | 11002 | down | 0.500000 | standby | 2173 | false | 0 1 | /tmp | 11003 | up | 0.500000 | primary | 0 | true | 0 (2 rows)
Now the primary node is changed from 0 to 1. What happens inside? When the node 0 goes down, Pgpool-II detects it and executes "fail_over_script" defined in pgpool.conf. Here is the content of the file.
#! /bin/sh # Execute command by failover. # special values: %d = node id # %h = host name # %p = port number # %D = database cluster path # %m = new master node id # %M = old master node id # %H = new master node host name # %P = old primary node id # %R = new master database cluster path # %r = new master port number # %% = '%' character failed_node_id=$1 failed_host_name=$2 failed_port=$3 failed_db_cluster=$4 new_master_id=$5 old_master_id=$6 new_master_host_name=$7 old_primary_node_id=$8 new_master_port_number=$9 new_master_db_cluster=${10} mydir=/home/t-ishii/tmp/Tutorial log=$mydir/log/failover.log pg_ctl=/usr/local/pgsql/bin/pg_ctl cluster0=$mydir/data0 cluster1=$mydir/data1 date >> $log echo "failed_node_id $failed_node_id failed_host_name $failed_host_name failed_port $failed_port failed_db_cluster $failed_db_cluster new_master_id $new_master_id old_master_id $old_master_id new_master_host_name $new_master_host_name old_primary_node_id $old_primary_node_id new_master_port_number $new_master_port_number new_master_db_cluster $new_master_db_cluster" >> $log if [ a"$failed_node_id" = a"$old_primary_node_id" ];then # master failed ! new_primary_db_cluster=${mydir}/data"$new_master_id" echo $pg_ctl -D $new_primary_db_cluster promote >>$log # let standby take over $pg_ctl -D $new_primary_db_cluster promote >>$log # let standby take over fi
The script receives necessary information as parameters from Pgpool-II. If the primary server goes down, it executes "pg_ctl -D data1 promote", which should promote the standby server to a new primary server.


Introduction
The ongoing COVID-19 pandemic has profound economic and social impact worldwide. Following the outbreak of the novel coronavirus in China, various European countries became the next hotspot areas in March 2020. Social distancing and lockdowns disrupted most economic activities, including the services sector. The collapse of demand for tourism services was especially drastic during the spring months: flight numbers decreased by 88%, while hotel occupancy decreased by 85% in April relative to last year. The summer brought slightly better numbers with the reopening of economies, but the situation of the industry is still severe.
Without international travel, neither traditional hotels nor Airbnb hosts can operate within the previous rules of the game. According to industry reports by STR, hotels significantly decreased prices to attract visitors. How about Airbnb? Based on the characteristics of peer-to-peer services provision, we can expect that Airbnb hosts reacted differently to the crisis.
First, short-term rental is not the primary source of income for many hosts. In the absence of tourists, a large number of hosts could have decided to withdraw their homes from Airbnb and redirect them for long-term rental until tourism rebounds. This may have a positive effect on cities struggling with overtourism and shortage of housing.
Second, hosts that decided to stay on Airbnb could have also adjusted services. Various new potential use cases may have emerged during the months of social distancing, such as renting flats for home office or self isolation. According to Airbnb CEO Brian Chesky, the length of bookings increased during the pandemic, along with the role of rural areas.
How did hosts react to the pandemic? In this blog post, I present some data insights about Airbnb for the first phase of the COVID crisis. I analyse the following attributes in 12 EU cities:
- number of active listings (booked for min 1 night in next two months)
- average price per night
- length of minimum stays
I use data from Inside Airbnb that publishes web-scraped data on Airbnb listings in a large number of cities on a monthly basis. For each analysed city, datasets for each month were downloaded between the period April 2018 - April 2020.Analysis
Let's begin the analysis with changes in supply - the number of active listings (Figure 1). Airbnb was on a dynamic growth path until February 2020: on average, there were around 9% more active offers than a year ago. Even in March, the number of offers on the platform was higher than a year ago, although numbers fell in Milan, Venice and Stockholm. However, April brought a significant fall of active offers (5%, relative to April 2019). The strongest decrease occurred in Milan (-19%), Stockholm (-17%) and Prague (-15%). In some of the cities, the numbers did not fall below the level of the previous year, but previous growth was eroded, as in Paris or Brussels. The only cities where the size of the market did not fall relative to the previous year was Barcelona and Vienna. Therefore, the results suggest that a significant share of Airbnb hosts withdraw listings, and potentially moved to the long-term rental market.
Figure 1. Number of active listings: Percentage change from same month previous year (below: average across cities) Next, let's examine changes in prices (Figure 2). Prices for accommodation on Airbnb have been consistently higher than a year ago, also including the months of the pandemic. Unlike hotels, Airbnb hosts did not decrease prices, but kept around 8% higher relative to 2019. Somewhat surprisingly, rates stayed high even in the Italian cities. Therefore, these results suggest that Airbnb hosts were not forced to drop rates, and were able to flexibly adapt to the changing market conditions.
Figure 2. Airbnb average price: percentage change from same month previous year (below: average across cities)
Finally, let's analyse changes in the share of offers that require longer bookings. Potential new use-cases may include the rental of second homes for local residents, who are in need of flats for distant work or self-isolation. The length of such stays should be longer than the usual short-term trips, but shorter than a long-term lease. Therefore, the share of Airbnb offers with minimum 14 day stays is calculated (Figure 3). Before the pandemic, the average share of active offers with minimum 14 night stays had been between 4-5%, and has increased to 7% by April. The share of such offers has risen in all of the analysed cities.
Figure 3. Share of listings with minimum two week stays (below: average across cities)
Conclusions
The results support that Airbnb hosts were less constrained and were able to pivot towards other services than short-term accommodation provision. A significant share of flats returned to the long-term rental market that most probably contributed to the drop in rents experienced in various major cities.
Moreover, the share of offers for longer stays increased in all 12 analysed cities. This may suggest a greater importance of new use-cases, such as renting homes for local residents for home office or self isolation.
This preliminary analysis shows that Airbnb is transforming and adopting to the "new normal". In the absence of large fixed costs, hosts can relatively easily adjust supply to demand. The lower number of listings should lead to a decrease of problems related to overtourism and housing shortage. However, it is uncertain whether this process is permanent.


public class DatabaseEntry
extends java.lang.Object
Storage and retrieval for the Database
and Cursor
methods
are based on key/data pairs. Both key and data items are represented by
DatabaseEntry objects. Key and data byte arrays may refer to arrays of zero
length up to arrays of essentially unlimited length.
The DatabaseEntry class provides simple access to an underlying object whose elements can be examined or changed. DatabaseEntry objects can be subclassed, providing a way to associate with it additional data or references to other structures.
Access to DatabaseEntry objects is not re-entrant. In particular, if
multiple threads simultaneously access the same DatabaseEntry object using
Database
or Cursor
methods, the results are undefined.
DatabaseEntry objects may be used in conjunction with the object mapping
support provided in the com.sleepycat.bind
package.
DatabaseEntry objects are used for both input data (when writing to a
database or specifying a search parameter) and output data (when reading
from a database). For certain methods, one parameter may be an input
parameter and another may be an output parameter. For example, the
Database.get(com.sleepycat.db.Transaction, com.sleepycat.db.DatabaseEntry, com.sleepycat.db.DatabaseEntry, com.sleepycat.db.LockMode)
method has an input key parameter and an output
data parameter. The documentation for each method describes whether its
parameters are input or output parameters.
For DatabaseEntry input parameters, the caller is responsible for initializing the data array of the DatabaseEntry. For DatabaseEntry output parameters, the method called will initialize the data array.
For DatabaseEntry output parameters, by default the method called will
reuse the byte array in the DatabaseEntry, if the data returned fits in
the byte array. This behavior can be configured with setReuseBuffer(boolean)
or setUserBuffer(int, boolean)
. If an entry is configured to
reuse the byte array (the default behavior), the length of the underlying
byte array should not be used to determine the amount of data returned each
time the entry is used as an output parameter, rather the getSize()
call should be used. If an entry is configured to not reuse the byte array,
a new array is allocated each time the entry is used as an output parameter,
so
the application can safely keep a reference to the byte array returned
by getData()
without danger that the array will be overwritten in
a subsequent call.
By default the Offset property is zero and the Size property is the length of the byte array. However, to allow for optimizations involving the partial use of a byte array, the Offset and Size may be set to non-default values.
For DatabaseEntry output parameters, the Size will always be set to the length of the returned data and the Offset will always be set to zero.
However, for DatabaseEntry input parameters the Offset and Size are set to non-default values by the built-in tuple and serial bindings. For example, with a tuple or serial binding the byte array is grown dynamically as data is output, and the Size is set to the number of bytes actually used. For a serial binding, the Offset is set to a non-zero value in order to implement an optimization having to do with the serialization stream header.
Therefore, for output DatabaseEntry parameters the application can assume that the Offset is zero and the Size is the length of the byte array. However, for input DatabaseEntry parameters the application should not make this assumption. In general, it is safest for the application to always honor the Size and Offset properties, rather than assuming they have default values.
By default the specified data (byte array, offset and size) corresponds to
the full stored key or data item. Optionally, the Partial property can be
set to true, and the PartialOffset and PartialLength properties are used to
specify the portion of the key or data item to be read or written. For
details, see the setPartial(int,int,boolean)
method.
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method, nor will they every be set by bindings. Therefore, the application can assume that the Partial properties are not set, unless the application itself sets them explicitly.
| Constructor and Description |
|---|
DatabaseEntry()
Construct a DatabaseEntry with null data.
|
DatabaseEntry(byte[] data)
Construct a DatabaseEntry with a given byte array.
|
DatabaseEntry(byte[] data,
int offset,
int size)
Constructs a DatabaseEntry with a given byte array, offset and size.
|
DatabaseEntry(java.nio.ByteBuffer data)
Construct a DatabaseEntry with a given native I/O buffer.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
equals(java.lang.Object o)
Compares the data of two entries for byte-by-byte equality.
|
boolean |
getBlob()
Deprecated.
Replaced with
getExternalFile() .
|
byte[] |
getData()
Return the byte array.
|
java.nio.ByteBuffer |
getDataNIO()
Return the java.nio.ByteBuffer.
|
boolean |
getExternalFile()
Return whether this DatabaseEntry is configured to be stored as an external
file.
|
int |
getOffset()
Return the byte offset into the data array.
|
boolean |
getPartial()
Return whether this DatabaseEntry is configured to read or write partial
records.
|
int |
getPartialLength()
Return the byte length of the partial record being read or written by
the application, in bytes.
|
int |
getPartialOffset()
Return the offset of the partial record being read or written by the
application, in bytes.
|
boolean |
getReadOnly()
Return whether this DatabaseEntry is configured as read only.
|
int |
getRecordNumber()
Return the record number encoded in this entry's buffer.
|
boolean |
getReuseBuffer()
Return true if the whether the entry is configured to reuse the buffer.
|
int |
getSize()
Return the byte size of the data array.
|
boolean |
getUserBuffer()
Return true if the whether the buffer in this entry is owned by the
application.
|
int |
getUserBufferLength()
Return the length of the application's buffer.
|
int |
hashCode()
Returns a hash code based on the data value.
|
void |
setBlob(boolean externalFile)
Deprecated.
Replaced with
setExternalFile(boolean) .
|
void |
setData(byte[] data)
Sets the byte array.
|
void |
setData(byte[] data,
int offset,
int size)
Sets the byte array, offset and size.
|
void |
setDataNIO(java.nio.ByteBuffer data)
Sets the java.nio.ByteBuffer.
|
void |
setDataNIO(java.nio.ByteBuffer data,
int offset,
int size)
Sets the java.nio.ByteBuffer (or the backing array if passed a non-direct
ByteBuffer,) offset and size.
|
void |
setExternalFile(boolean externalFile)
Configure this DatabaseEntry to be stored as an external file.
|
void |
setOffset(int offset)
Set the byte offset into the data array.
|
void |
setPartial(boolean partial)
Configure this DatabaseEntry to read or write partial records.
|
void |
setPartial(int doff,
int dlen,
boolean partial)
Configures this DatabaseEntry to read or write partial records.
|
void |
setPartialLength(int dlen)
Set the byte length of the partial record being read or written by
the application, in bytes.
|
void |
setPartialOffset(int doff)
Set the offset of the partial record being read or written by the
application, in bytes.
|
void |
setReadOnly(boolean readonly)
Configure this DatabaseEntry as read only.
|
void |
setRecordNumber(int recno)
Initialize the entry from a logical record number.
|
void |
setReuseBuffer(boolean reuse)
Configures the entry to try to reuse the buffer before allocating a new
one.
|
void |
setSize(int size)
Set the byte size of the data array.
|
void |
setUserBuffer(int length,
boolean usermem)
Configures the entry with an application-owned buffer.
|
public DatabaseEntry()
public DatabaseEntry(byte[] data)
data
- Byte array wrapped by the DatabaseEntry.public DatabaseEntry(byte[] data, int offset, int size)
data
- Byte array wrapped by the DatabaseEntry.offset
- Offset in the first byte in the byte array to be included.size
- Number of bytes in the byte array to be included.public DatabaseEntry(java.nio.ByteBuffer data)
data
- NIO byte buffer wrapped by the DatabaseEntry.public void setBlob(boolean externalFile)
setExternalFile(boolean)
.
externalFile
- Whether this DatabaseEntry is configured to be stored as an external file.public boolean getBlob()
getExternalFile()
.
public byte[] getData()
For a DatabaseEntry that is used as an output parameter, the byte array will always be a newly allocated array. The byte array specified by the caller will not be used and may be null.
public java.nio.ByteBuffer getDataNIO()
Used to access the underlying data when the DatabaseEntry is configured to utilize a java.nio.ByteBuffer.
public void setData(byte[] data, int offset, int size)
data
- Byte array wrapped by the DatabaseEntry.offset
- Offset in the first byte in the byte array to be included.size
- Number of bytes in the byte array to be included.public void setData(byte[] data)
data
- Byte array wrapped by the DatabaseEntry.public void setDataNIO(java.nio.ByteBuffer data, int offset, int size)
data
- java.nio.ByteBuffer wrapped by the DatabaseEntry.offset
- int offset into the ByteBuffer where the DatabaseEntry data begins.size
- int size of the ByteBuffer available.public void setDataNIO(java.nio.ByteBuffer data)
setUserBuffer(int, boolean)
is required
after setting the ByteBuffer.
data
- java.nio.ByteBuffer wrapped by the DatabaseEntry.public void setExternalFile(boolean externalFile)
externalFile
- Whether this DatabaseEntry is configured to be stored as an external file.public boolean getExternalFile()
public int getOffset()
For a DatabaseEntry that is used as an output parameter, the offset will always be zero.
public void setOffset(int offset)
offset
- Offset in the first byte in the byte array to be included.public int getPartialLength()
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
setPartial(int,int,boolean)
public int getPartialOffset()
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
setPartial(int,int,boolean)
public boolean getPartial()
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
setPartial(int,int,boolean)
public void setPartialOffset(int doff)
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
doff
- The offset of the partial record being read or written by the
application, in bytes.setPartial(int,int,boolean)
public void setPartialLength(int dlen)
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
dlen
- The byte length of the partial record being read or written by the
application, in bytes.
public void setPartial(boolean partial)
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
partial
- Whether this DatabaseEntry is configured to read or write partial
records.setPartial(int,int,boolean)
public void setPartial(int doff, int dlen, boolean partial)
Do partial retrieval or storage of an item. If the calling application is doing a retrieval, length bytes specified by dlen, starting at the offset set by doff bytes from the beginning of the retrieved data record are returned as if they comprised the entire record. If any or all of the specified bytes do not exist in the record, the get is successful, and any existing bytes are returned.
For example, if the data portion of a retrieved record was 100 bytes, and a partial retrieval was done using a DatabaseEntry having a partial length of 20 and a partial offset of 85, the retrieval would succeed and the retrieved data would be the last 15 bytes of the record.
If the calling application is storing an item, length bytes specified by dlen, starting at the offset set by doff bytes from the beginning of the specified key's data item are replaced by the data specified by the DatabaseEntry. If the partial length is smaller than the data, the record will grow; if the partial length is larger than the data, the record will shrink. If the specified bytes do not exist, the record will be extended using nul bytes as necessary, and the store will succeed.
It is an error to specify a partial key when performing a put operation of any kind.
It is an error to attempt a partial store using the Database.put
method in a database that supports duplicate records. Partial
stores in databases supporting duplicate records must be done using a
cursor method.
Note that the Partial properties are set only by the caller. They will never be set by a Database or Cursor method.
doff
- The offset of the partial record being read or written by the
application, in bytes.
dlen
- The byte length of the partial record being read or written by the
application, in bytes.
partial
- Whether this DatabaseEntry is configured to read or write partial
records.public boolean getReadOnly()
setReadOnly(boolean)
public void setReadOnly(boolean readonly)
readonly
- Whether this DatabaseEntry is configured as read only.public int getRecordNumber()
This method may be called at any time during the life of the application.
public void setRecordNumber(int recno)
recno
- the record number to be encodedpublic boolean getReuseBuffer()
This method may be called at any time during the life of the application.
public void setReuseBuffer(boolean reuse)
reuse
- whether to reuse the bufferpublic int getSize()
For a DatabaseEntry that is used as an output parameter, the size will always be the length of the data array.
public void setSize(int size)
size
- Number of bytes in the byte array to be included.public boolean getUserBuffer()
This method may be called at any time during the life of the application.
public int getUserBufferLength()
This method may be called at any time during the life of the application.
public void setUserBuffer(int length, boolean usermem)
The data
field of the entry must refer to a buffer that is
at least length
bytes in length.
If the length of the requested item is less than or equal to that number
of bytes, the item is copied into the memory to which the
data
field refers. Otherwise, the size
field
is set to the length needed for the requested item, and a
MemoryException
is thrown.
Applications can determine the length of a record by setting
length
to 0 and calling DatabaseEntry.getSize
on the return value.
length
- the length of the buffer
usermem
- whether the buffer is owned by the applicationpublic boolean equals(java.lang.Object o)
In either entry, if the offset is non-zero or the size is not equal to the data array length, then only the data bounded by these values is compared. The data array length and offset need not be the same in both entries for them to be considered equal.
If the data array is null in one entry, then to be considered equal both entries must have a null data array.
If the partial property is set in either entry, then to be considered equal both entries must have the same partial properties: partial, partialOffset and partialLength.
equals
in class java.lang.Object
public int hashCode()
hashCode
in class java.lang.Object
Copyright (c) 1996, 2016 Oracle and/or its affiliates. All rights reserved.