| 1 |
Zheng_2020 |
Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label |
Chuansheng Zheng; Xianbo Deng; Qing Fu; Qiang Zhou; Jiapei Feng; Hui Ma; Wenyu Liu; Xinggang Wang |
2020 |
2020-03-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>27185 |
ll4rxd9p |
0.899603 |
Saw_A_2019, Dai_Q_2012, Saw_A_2019 |
Fu_M_2020, Jin_C_2020 |
| 2 |
Fu_M_2020 |
Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images |
Min Fu; Shuang-Lian Yi; Yuanfeng Zeng; Feng Ye; Yuxuan Li; Xuan Dong; Yan-Dan Ren; Linkai Luo; Jin-Shui Pan; Qi Zhang |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>46045 |
96r8l6vq |
0.896233 |
Lee_C_2017, Saw_A_2019 |
Jin_C_2020, Zheng_2020 |
| 3 |
Chen_2020 |
Early prediction of mortality risk among severe COVID-19 patients using machine learning |
Xingdong Chen; Zhenqiu Liu |
2020 |
2020-04-19 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>64329 |
z0vup2lr |
0.892973 |
Saw_A_2019 |
|
| 4 |
Jin_C_2020 |
Development and Evaluation of an AI System for COVID-19 |
Cheng Jin; Weixiang Chen; Yukun Cao; Zhanwei Xu; Xin Zhang; Lei Deng; Chuansheng Zheng; Jie Zhou; Heshui Shi; Jianjiang Feng |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>39834 |
k1lg8c7q |
0.849217 |
Lee_C_2017, Saw_A_2019, Chang_2011 |
Fu_M_2020, Zheng_2020 |
| 5 |
AlMoammar_2018 |
Selecting Accurate Classifier Models for a MERS-CoV Dataset |
AlMoammar, Afnan; AlHenaki, Lubna; Kurdi, Heba |
2018 |
2018-11-09 |
PMC |
N |
PMC7123473 |
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10.1007/978-3-030-01054-6_74 |
fridlqhv |
0.846691 |
Saw_A_2019, Lee_C_2017, Zuo_G_2011, Saw_A_2019 |
|
| 6 |
Pourhomayoun_2020 |
Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making |
Mohammad Pourhomayoun; Mahdi Shakibi |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>47308 |
x70501t3 |
0.834071 |
Chen_2010, Saw_A_2019 |
Andre_2020, AlMoammar_2018, Sarkar_2020 |
| 7 |
Andre_2020 |
COVID-19 diagnosis prediction in emergency care patients: a machine learning approach |
Andre Filipe de Moraes Batista; Joao Luiz Miraglia; Thiago Henrique Rizzi Donato; Alexandre Dias Porto Chiavegatto Filho |
2020 |
2020-04-07 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>52092 |
nvavj9gk |
0.834008 |
Saw_A_2019, Lee_C_2017, Chen_2010 |
AlMoammar_2018 |
| 8 |
Zhou_2020 |
Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia |
Min Zhou; Yong Chen; Dexiang Wang; Yanping Xu; Weiwu Yao; Jingwen Huang; Xiaoyan Jin; Zilai Pan; Jingwen Tan; Lan Wang; Yihan Xia; Longkuan Zou; Xin Xu; Jingqi Wei; Mingxin Guan; Jianxing Feng; Huan Zhang; Jieming Qu |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>43117 |
ilc2bzkx |
0.811921 |
Lee_C_2017, Saw_A_2019 |
Song_2020, Fu_M_2020 |
| 9 |
Kumar_2020 |
Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers |
Rahul Kumar; Ridhi Arora; Vipul Bansal; Vinodh J Sahayasheela; Himanshu Buckchash; Javed Imran; Narayanan Narayanan; Ganesh N Pandian; Balasubramanian Raman |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>63461 |
59ghorzf |
0.805020 |
Lee_C_2017 |
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| 10 |
Song_2020 |
Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images |
Ying Song; Shuangjia Zheng; Liang Li; Xiang Zhang; Xiaodong Zhang; Ziwang Huang; Jianwen Chen; Huiying Zhao; Yusheng Jie; Ruixuan Wang; Yutian Chong; Jun Shen; Yunfei Zha; Yuedong Yang |
2020 |
2020-02-25 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>26930 |
zqsy6r4i |
0.802363 |
Lee_C_2017, Chang_2011 |
|
| 11 |
Fang_2020 |
CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study |
Fang, Mengjie; He, Bingxi; Li, Li; Dong, Di; Yang, Xin; Li, Cong; Meng, Lingwei; Zhong, Lianzhen; Li, Hailin; Li, Hongjun; Tian, Jie |
2020 |
2020-04-15 |
PMC |
Y |
PMC7166002 |
|
10.1007/s11432-020-2849-3 |
p9w9fwak |
0.797150 |
Qi_Z_2018, Chang_2011, Feng_2010, Lee_C_2017 |
|
| 12 |
Hassanien_2020 |
Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine |
Aboul Ella Hassanien; Lamia Nabil Mahdy; Kadry Ali Ezzat; Haytham H. Elmousalami; Hassan Aboul Ella |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>47787 |
45dpoepu |
0.772044 |
Lee_C_2017 |
Fu_M_2020, Ozturk_2020, Zheng_2020 |
| 13 |
Bai_X_2020 |
Predicting COVID-19 malignant progression with AI techniques |
Xiang Bai; Cong Fang; Yu Zhou; Song Bai; Zaiyi Liu; Qianlan Chen; Yongchao Xu; Tian Xia; Shi Gong; Xudong Xie; Dejia Song; Ronghui Du; Chunhua Zhou; Chengyang Chen; Dianer Nie; Dandan Tu; Changzheng Zhang; Xiaowu Liu; Lixin Qin; Weiwei Chen |
2020 |
2020-03-23 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>37325 |
50oy9qqy |
0.752782 |
|
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| 14 |
Martin_2020 |
An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot |
Alistair Martin; Jama Nateqi; Stefanie Gruarin; Nicolas Munsch; Isselmou Abdarahmane; Bernhard Knapp |
2020 |
2020-03-26 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>805 |
52nw9gxq |
0.746301 |
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Wu_J_2020, Santosh_2020 |
| 15 |
Abbas_2020 |
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network |
Asmaa Abbas; Mohammed Abdelsamea; Mohamed Gaber |
2020 |
2020-04-01 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>47456 |
i5jk0407 |
0.742608 |
Qi_Z_2018, Lee_C_2017, Dai_Q_2012, Chang_2011 |
Fu_M_2020, Jin_C_2020, Zheng_2020 |
| 16 |
Chen_2010 |
Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system |
Chen, Miao-Sheng; Ying, Li-Chih; Pan, Mei-Chiu |
2010 |
2010-03-31 |
PMC |
N |
PMC7126321 |
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10.1016/j.eswa.2009.06.032 |
b2m3p3uh |
0.732535 |
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| 17 |
Stubblefield_2020 |
Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification |
Jonathan Stubblefield; Mitchell Hervert; Jason Causey; Jake Qualls; Wei Dong; Lingrui Cai; Jennifer Fowler; Emily Bellis; Karl Walker; Jason H. Moore; Sara Nehring; Xiuzhen Huang |
2020 |
2020-04-15 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>62091 |
w8joc5ye |
0.730247 |
Kurc_2006 |
Fu_M_2020, Zhou_2020, Abbas_2020 |
| 18 |
Sarkar_2020 |
A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19 |
Jit Sarkar; Partha Chakrabarti |
2020 |
2020-03-30 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>43331 |
0agldesf |
0.727947 |
|
Andre_2020 |
| 19 |
Huang_2020 |
Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China |
Chiou-Jye Huang; Yung-Hsiang Chen; Yuxuan Ma; Ping-Huan Kuo |
2020 |
2020-03-27 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>41608 |
yw81pkrq |
0.705739 |
Saw_A_2019, Chang_2011, Zheng_2009 |
Mock_2019 |
| 20 |
Amyar_2020 |
Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation |
Amine Amyar; Romain Modzelewski; Su Ruan |
2020 |
2020-04-21 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>64709 |
hiac6ur7 |
0.690140 |
Lee_C_2017, Dai_Q_2012, Qi_Z_2018, Mendizabal-Ruiz_2018 |
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| 21 |
Al-karawi_2020 |
Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients |
Dhurgham Al-karawi; Shakir Al-Zaidi; Nisreen Polus; Sabah Jassim |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>63479 |
w8nwj3u6 |
0.685551 |
Qi_Z_2018, Chang_2011 |
|
| 22 |
Razzak_2020 |
Improving Coronavirus (COVID-19) Diagnosis using Deep Transfer Learning |
Imran Razzak; Saeeda Naz; Arshia Rehman; Ahmed Khan; Ahmad Zaib |
2020 |
2020-04-17 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>54643 |
sgy03deg |
0.678214 |
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| 23 |
Xiaoxuan_2020 |
Can Search Query Forecast successfully in China's 2019-nCov pneumonia? |
Li Xiaoxuan; Wu Qi; Lv Benfu |
2020 |
2020-02-18 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>22400 |
vv1wjen0 |
0.677622 |
Lee_C_2017, Chang_2011 |
|
| 24 |
Wu_J_2020 |
Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results |
Jiangpeng Wu; Pengyi Zhang; Liting Zhang; Wenbo Meng; Junfeng Li; Chongxiang Tong; Yonghong Li; Jing Cai; Zengwei Yang; Jinhong Zhu; Meie Zhao; Huirong Huang; Xiaodong Xie; Shuyan Li |
2020 |
2020-04-06 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>51136 |
kjovtgua |
0.656154 |
|
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| 25 |
Ajayi_2019 |
Forecasting herd-level porcine epidemic diarrhea (PED) frequency trends in Ontario (Canada) |
Ajayi, Toluwalope; Dara, Rozita; Poljak, Zvonimir |
2019 |
2019-03-01 |
PMC |
N |
PMC7125872 |
30771890.0 |
10.1016/j.prevetmed.2019.01.005 |
f9w310tp |
0.641137 |
Chen_2010, Saw_A_2019 |
|
| 26 |
Guo_Z_2020 |
Early warning of some notifiable infectious diseases in China by the artificial neural network |
Guo, Zuiyuan; He, Kevin; Xiao, Dan |
2020 |
2020-02-19 |
COMM-USE |
N |
PMC7062078 |
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10.1098/rsos.191420 |
a1rd7kas |
0.610312 |
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| 27 |
Kolozsvari_2020 |
Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence |
Laszlo Robert Kolozsvari; Tamas Berczes; Andras Hajdu; Rudolf Gesztelyi; Attila TIba; Imre Varga; Gergo Jozsef Szollosi; Szilvia Harsanyi; Szabolcs Garboczy; Judit Zsuga |
2020 |
2020-04-22 |
BioRxiv |
Y |
|
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10.1101/2<IP_ADDRESS>69666 |
35xpmdbj |
0.606246 |
Chang_2011 |
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| 28 |
Liu_Y_2020 |
Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically |
Liu, Yuliang; Zhang, Quan; Zhao, Geng; Liu, Guohua; Liu, Zhiang |
2020 |
2020-03-11 |
NONCOMM |
N |
PMC7073442 |
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10.2147/dmso.s242585 |
1r4gm2d4 |
0.602157 |
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| 29 |
Yu_C_2018 |
Acral melanoma detection using a convolutional neural network for dermoscopy images |
Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho |
2018 |
2018-03-07 |
COMM-USE |
N |
PMC5841780 |
29513718.0 |
10.1371/journal.pone.0193321 |
u4ybz8ds |
0.578345 |
Qi_Z_2018, Saw_A_2019, Feng_2010 |
Fu_M_2020 |
| 30 |
Sher_2017 |
DRREP: deep ridge regressed epitope predictor |
Sher, Gene; Zhi, Degui; Zhang, Shaojie |
2017 |
2017-10-03 |
COMM-USE |
N |
PMC5629616 |
28984193.0 |
10.1186/s12864-017-4024-8 |
f3uo372d |
0.577017 |
Saw_A_2019, Lee_C_2017, Dai_Q_2012, Qi_Z_2018, Chang_2011 |
Mock_2019, Sollner_2008 |
| 31 |
Khobahi_2020 |
CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images |
Shahin Khobahi; Chirag Agarwal; Mojtaba Soltanalian |
2020 |
2020-04-17 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>65722 |
qw5ly6rl |
0.565558 |
Lee_C_2017, Chang_2011, Mendizabal-Ruiz_2018 |
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| 32 |
Al-qaness_2020 |
Optimization Method for Forecasting Confirmed Cases of COVID-19 in China |
Al-qaness, Mohammed A. A.; Ewees, Ahmed A.; Fan, Hong; Abd El Aziz, Mohamed |
2020 |
2020-03-02 |
None |
Y |
PMC7141184 |
32131537.0 |
10.3390/jcm9030674 |
x1zq2i9h |
0.553901 |
Saw_A_2019, Chen_2010, Chang_2011 |
|
| 33 |
He_X_2020 |
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans |
Xuehai He; Xingyi Yang; Shanghang Zhang; Jinyu Zhao; Yichen Zhang; Eric Xing; Pengtao Xie |
2020 |
2020-04-17 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>63941 |
l3f469ht |
0.552846 |
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| 34 |
Khanna_2019 |
Prediction of novel mouse TLR9 agonists using a random forest approach |
Khanna, Varun; Li, Lei; Fung, Johnson; Ranganathan, Shoba; Petrovsky, Nikolai |
2019 |
2019-12-20 |
COMM-USE |
N |
PMC6924143 |
31856726.0 |
10.1186/s12860-019-0241-0 |
wo6356vq |
0.544953 |
Saw_A_2019, Chang_2011, Qi_Z_2018, Dai_Q_2012, Feng_2010 |
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Santosh_2020 |
AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data |
Santosh, K. C. |
2020 |
2020-03-18 |
PMC |
Y |
PMC7087612 |
32189081.0 |
10.1007/s10916-020-01562-1 |
j97ugs3y |
0.541065 |
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AlMoammar_2018, Andre_2020, Wu_J_2020 |
| 36 |
Nguyen_2019 |
A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation |
Nguyen, Cuong V.; Le Quang, Truong; Vu, Trung Nguyen; Le Thi, Hoi; Van, Kinh Nguyen; Trong, Thanh Han; Trong, Tuan Do; Sun, Guanghao; Ishibashi, Koichiro |
2019 |
2019-12-31 |
PMC |
N |
PMC7103934 |
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10.1016/j.imu.2019.100225 |
yt302jyx |
0.495919 |
Saw_A_2019, Lee_C_2017, Mendizabal-Ruiz_2018 |
Fu_M_2020, Wu_J_2020, Ozturk_2020 |
| 37 |
Carrillo-de-Gea_2016 |
A Computer-Aided Detection System for Digital Chest Radiographs |
Carrillo-de-Gea, Juan Manuel; García-Mateos, Ginés; Fernández-Alemán, José Luis; Hernández-Hernández, José Luis |
2016 |
2016-05-31 |
COMM-USE |
N |
PMC5058572 |
27372536.0 |
10.1155/2016/8208923 |
fn929z9y |
0.492123 |
Liou_2013, Lee_C_2017 |
Javadi_2006, Hassanien_2020 |
| 38 |
Ozturk_2020 |
Classification of Coronavirus Images using Shrunken Features |
Saban Ozturk; Umut Ozkaya; Mucahid Barstugan |
2020 |
2020-04-06 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>48868 |
2l1zw19o |
0.451891 |
Qi_Z_2018, Chang_2011, Lee_C_2017, Mendizabal-Ruiz_2018 |
Song_2020 |
| 39 |
Salg_2020 |
A globally available COVID-19 - Template for clinical imaging studies |
Gabriel Alexander Salg; Maria Katharina Ganten; Matthias Baumhauer; Claus Peter Heussel; Jens Kleesiek |
2020 |
2020-04-07 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>48793 |
5s30ihv3 |
0.449061 |
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| 40 |
David_2010 |
Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies |
David, Maria Pamela C; Concepcion, Gisela P; Padlan, Eduardo A |
2010 |
2010-02-08 |
COMM-USE |
N |
PMC3098112 |
20144194.0 |
10.1186/1471-2105-11-79 |
6yvdv1xk |
0.442771 |
Saw_A_2019, Chang_2011, Qi_Z_2018, Lee_C_2017, Feng_2010 |
AlMoammar_2018 |
| 41 |
Al-Turaiki_2016 |
Building predictive models for MERS-CoV infections using data mining techniques |
Al-Turaiki, Isra; Alshahrani, Mona; Almutairi, Tahani |
2016 |
2016-12-31 |
PMC |
N |
PMC7102847 |
27641481.0 |
10.1016/j.jiph.2016.09.007 |
wqa98m4v |
0.438413 |
Chang_2011 |
AlMoammar_2018 |
| 42 |
Bai_L_2020 |
Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19) |
Bai, Li; Yang, Dawei; Wang, Xun; Tong, Lin; Zhu, Xiaodan; Zhong, Nanshan; Bai, Chunxue; Powell, Charles A.; Chen, Rongchang; Zhou, Jian; Song, Yuanlin; Zhou, Xin; Zhu, Huili; Han, Baohui; Li, Qiang; Shi, Guochao; Li, Shengqing; Wang, Changhui; Qiu, Zhongmin; Zhang, Yong; Xu, Yu; Liu, Jie; Zhang, Ding; Wu, Chaomin; Li, Jing; Yu, Jinming; Wang, Jiwei; Dong, Chunling; Wang, Yaoli; Wang, Qi; Zhang, Lichuan; Zhang, Min; Ma, Xia; Zhao, Lin; Yu, Wencheng; Xu, Tao; Jin, Yang; Wang, Xiongbiao; Wang, Yuehong; Jiang, Yan; Chen, Hong; Xiao, Kui; Zhang, Xiaoju; Song, Zhenju; Zhang, Ziqiang; Wu, Xueling; Sun, Jiayuan; Shen, Yao; Ye, Maosong; Tu, Chunlin; Jiang, Jinjun; Yu, Hai; Tan, Fei |
2020 |
2020-12-31 |
PMC |
Y |
PMC7148716 |
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10.1016/j.ceh.2020.03.001 |
wyz5jyjh |
0.432637 |
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paul_2020 |
A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks |
swarna kamal paul; Saikat Jana; Parama Bhaumik |
2020 |
2020-04-22 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>69898 |
nng76upj |
0.415750 |
Chang_2011, Qi_Z_2018, Dai_Q_2012 |
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Majumdar_2018 |
Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment |
Majumdar, Abhishek; Debnath, Tapas; Sood, Sandeep K.; Baishnab, Krishna Lal |
2018 |
2018-09-01 |
PMC |
N |
PMC7088392 |
30173290.0 |
10.1007/s10916-018-1041-3 |
1v3wl6ky |
0.408052 |
Lee_C_2017 |
AlMoammar_2018 |
| 45 |
Ardabili_2020 |
COVID-19 Outbreak Prediction with Machine Learning |
Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson |
2020 |
2020-04-22 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>70094 |
nu0pn2q8 |
0.408052 |
Liou_2013 |
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Tang_2015 |
Inferring the hosts of coronavirus using dual statistical models based on nucleotide composition |
Tang, Qin; Song, Yulong; Shi, Mijuan; Cheng, Yingyin; Zhang, Wanting; Xia, Xiao-Qin |
2015 |
2015-11-26 |
COMM-USE |
N |
PMC4660426 |
26607834.0 |
10.1038/srep17155 |
gl65i3s0 |
0.399216 |
Lee_C_2017 |
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Wagner_1984 |
A computer based system for collection, storage, retrieval and reporting accession information in a veterinary medical diagnostic laboratory |
Wagner, Joseph E.; Warriner, William J.; Bradfield, Sylvia A.; Farrar, Patricia L.; Morehouse, Lawrence G. |
1984 |
1984-12-31 |
PMC |
N |
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6380915.0 |
10.1016/0010-4825(84)90033-7 |
83f0ruku |
0.398245 |
UNKNOWN_2018 |
Stokes_1993, Chapman_2004 |
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John_2019 |
Shiny Framework Based Visualization and Analytics Tool for Middle East Respiratory Syndrome |
John, Maya; Shaiba, Hadil |
2019 |
2019-12-04 |
PMC |
N |
PMC7122755 |
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10.1007/978-3-030-36365-9_16 |
r7tg0s45 |
0.392251 |
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Sloane_2020 |
Chapter 83 Artificial intelligence in medical devices and clinical decision support systems |
Sloane, Elliot B.; J. Silva, Ricardo |
2020 |
2020-12-31 |
PMC |
N |
PMC7153310 |
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10.1016/b978-0-12-813467-2.00084-5 |
7kskldmn |
0.382156 |
Lu_Q_2006, Jiang_2009 |
Pourhomayoun_2020, Dórea_2013 |
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Milano_2020 |
Statistical and network-based analysis of Italian COVID-19 data: communities detection and temporal evolution |
Marianna Milano; Mario Cannataro |
2020 |
2020-04-22 |
BioRxiv |
Y |
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10.1101/2<IP_ADDRESS>68916 |
6n88mcbf |
0.381596 |
Chang_2011, Lee_C_2017, Zheng_2008, Qi_Z_2018 |
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Sachdeva_2005 |
SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks |
Sachdeva, Gaurav; Kumar, Kaushal; Jain, Preti; Ramachandran, Srinivasan |
2005 |
2005-02-15 |
PMC |
N |
PMC7109999 |
15374866.0 |
10.1093/bioinformatics/bti028 |
k5u6tdkg |
0.353514 |
Lee_C_2017 |
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Seo_J_2019 |
An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data |
Seo, Jungryul; Laine, Teemu H.; Sohn, Kyung-Ah |
2019 |
2019-10-20 |
COMM-USE |
N |
PMC6832442 |
31635194.0 |
10.3390/s19204561 |
vtktgyvh |
0.344487 |
Mendizabal-Ruiz_2018, Qi_Z_2018 |
Liu_Y_2020, Jung_2017 |
| 53 |
Zhang_2009 |
Automatic online news monitoring and classification for syndromic surveillance |
Zhang, Yulei; Dang, Yan; Chen, Hsinchun; Thurmond, Mark; Larson, Cathy |
2009 |
2009-11-30 |
PMC |
N |
PMC7114309 |
|
10.1016/j.dss.2009.04.016 |
r0lduvs1 |
0.341266 |
|
Lu_H_2009 |
| 54 |
Lopez-Rincon_2020 |
Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning |
Alejandro Lopez-Rincon; Alberto Tonda; Lucero Mendoza-Maldonado; Eric Claassen; Johan Garssen; Aletta D. Kraneveld |
2020 |
2020-03-14 |
BioRxiv |
Y |
|
|
10.1101/2<IP_ADDRESS>0242 |
c2lljdi7 |
0.329834 |
Lee_C_2017, Chang_2011, Qi_Z_2018, Cheng_2012 |
|
| 55 |
Javadi_2006 |
Diagnosing pneumonia in rural Thailand: Digital cameras versus film digitizers for chest radiograph teleradiology |
Javadi, Massoud; Subhannachart, Ponglada; Levine, Sunisa; Vijitsanguan, Chomphunut; Tungsagunwattana, Sutarat; Dowell, Scott F.; Olsen, Sonja J. |
2006 |
2006-03-31 |
PMC |
N |
PMC7110458 |
16243559.0 |
10.1016/j.ijid.2005.01.007 |
yzqcet2l |
0.304412 |
Kylberg_2009, Yeh_J_2008, Montgomery_2004 |
|


Select an API in the list to access the API's live documentation where you can experiment with the most
frequently used objects and functions.
Tap the API button in the upper right corner of the live documentation to access the API reference.


{% extends 'base.html' %} {% block content %} local_hospital Health Get Access to all government sites and services under housing agriculture Agriculture Get Access to all government sites and services under agriculture apartment Housing Get Access to all government sites and services under housing construction Industrialization Get Access to all government sites and services under housing local_police Security Get Access to all government sites and services under police school Education Get Access to all government sites and services under education tour Wildlife and tourism Get Access to all government sites and services under Wildlife and tourism computer ICT Get Access to all government sites and services under information and communication technology gavel Government and law Get Access to all government sites and services under law and goverment
Get Access to all government sites and services under housing
Get Access to all government sites and services under agriculture
Get Access to all government sites and services under police
Get Access to all government sites and services under education
Get Access to all government sites and services under Wildlife and tourism
Get Access to all government sites and services under information and communication technology
Get Access to all government sites and services under law and goverment


Performance Graphs
2 hours
8 hours
24 hours
48 hours
7 days
14 days
30 days
3 months
6 months
1 year
2 years
3 years
5 years
10 years
ADS-B 1090 Graphs
Airspy Graphs
UAT 978 Graphs
System Graphs


Splitting Audio
Fission can help you rapidly split one long audio file into multiple files.
Split Points and Clips
Fission uses split points, or splits, to separate clips in an audio file. When a file contains multiple clips, the split points are what separate each clip. Saving a file with multiple clips results in multiple audio files being saved out.
To adjust the length of a clip, just move the relevant split. Likewise, to merge two clips together, just remove the split point separating them.
A split point between two clips
Adding Split Points
Adding a split point can be done in several ways. Generally, placing the playhead where you wish to split is the first step. You can click once in the main waveform to move the playhead, or simply play the file until the desired point. Once the playhead is in place, you can click the Split button in the Toolbar, or select Split at Playhead from the Clip menu. A split point will be inserted into the file, separating the audio into two distinct clips.
If you know the exact timestamp where you'd like to split, you can also use the Add Split option in the Tools menu to add a precise split.
The Smart Split Tool
Fission's Smart Split tool can be used to automatically find split points in a file, based on where there are silences. This can be particularly useful for rapidly splitting up recordings from analog sources like records and tapes, as well as multi-song digital recordings. To access it, select Smart Split from the Tools menu:
Fission's Smart Split Window
Smart Split identifies silences based on the settings selected and shows where it will place split points. When you click the Insert Splits button, the split points will be added to the file, creating multiple clips. If needed, adjust these splits by moving them, adding more, or removing any which are erroneous. Once you've got the split points set, save the audio out and you'll get a distinct file for each clip.


'RELISH IT'
Celtic boss Neil Lennon insists he’s loving title fight against Rangers
by Derek McGregorCELTIC boss Neil Lennon last night told Rangers he’s LOVING their title fight.
The Hoops moved three points clear in what’s been a neck and neck battle at the top of the Premiership – with Steven Gerrard’s men at Hamilton tomorrow.
A James Forrest double and strikes from Odsonne Edouard and Scott Brown saw the Celts demolish Livingston – with Rennes up next in the Europa League at Parkhead on Thursday night.
And asked if he felt it was a proper title contest with Rangers, Lennon replied: “At the minute, no question, yes.
“Psychologically that’s a good win for us.
“We can enjoy our Sunday, you get 24 hours to enjoy, see what the result brings then you move on to the next one.
“We put that to bed, that was a tough game under the circumstances.
“But we’ve dealt with it absolutely brilliantly so we look forward to Rennes on Thursday.
“I think you’ve got to relish the pressure - you either relish it or fear it.
“So RELISH it. It’s great.
“Maybe it is something new for some of the players but I feel they are taking on the challenge well and I think the fans are enjoying it as well.
“There will be anxious moments as we go along, dips and troughs - but we are in a strong position currently and playing brilliantly.
“I don’t think about goal difference at the moment it is way too early.”
Lennon purred with satisfaction as his juggernaut team made it eight straight wins in all competitions, including five successive in the league without a goal lost since the defeat at Livingston early October.
He said: “We were very powerful today.
“Not just because of our fitness levels and the way we battled for second balls – but our football was superb.
“It was a comprehensive win against a tough team to beat at times.
“We were outstanding and when you come back from two weeks off you always worry that momentum will be lost.
“But there was no sign of that at all.
“There wasn't one weakness out there and I'm thrilled with that.
“Their attitude is first class, in terms of how we approached it.”
Lennon lost Jonny Hayes through injury but also welcomed back Leigh Griffiths.
He added: “You can see how popular Leigh is with the fans.
“It's a timely comeback from him with the volume of games we've got.
“Jonny Hayes popped his shoulder so we will have to scan it and see how it is, hopefully not too bad.
“We made the change and Greg Taylor came in and did a great job.”


Known Bugs
- Deletion / insertion with multiple iterators is a bit wonky
- Additional Dynamic Programming Algorithms
These are features or exensions that have been requested, but are unlikely to be added in the near future. If you're interested in adding them yourself, let me know!
- None yet!


Withdrawals for Singapore meet
NON-ACCEPTORS FOR TOMORROW'S KRANJI MEETING
RACE 2: Kruger
RACE 3: Born to Win and Sun Conqueror
RACE 4: Hero In The Wind and Diamond Mine
RACE 5: Robin Hood
RACE 6:Gratus
RACE 7: Mr Hooper
RACE 8: Dreamer Legend and Runminderbinderrun


Use of Thinning and Prescribed Fire for Ecological Restoration of Fire-Maintained Oak Woodlands; A Rare, Historic Habitat Type in Massachusetts Forests
EPA Grant Number: U915990Title: Use of Thinning and Prescribed Fire for Ecological Restoration of Fire-Maintained Oak Woodlands; A Rare, Historic Habitat Type in Massachusetts Forests
Investigators: Hawthorne, Brian H.
Institution: University of Massachusetts - Amherst
EPA Project Officer: Graham, Karen
Project Period: January 1, 2001 through January 1, 2003
Project Amount: $59,140
RFA: STAR Graduate Fellowships (2001) RFA Text | Recipients Lists
Research Category: Biology/Life Sciences , Ecological Indicators/Assessment/Restoration , Academic Fellowships , Fellowship - Forestry
Objective:
The objective of this research project is to develop the knowledge necessary for creating an alternative type of forest opening, referred to here as woodland openings, to further increase the diversity of vegetation types and forest stand structures in Massachusetts forests. These recreation and wildlife openings are not intended as replacements for the openings currently used to provide early seral vegetation.
1. Effects on berry-producing species: The first study objective is to quantify the effects of burning and different levels of overstory cutting on berry production and berry-producing flora, primarily blueberry (Vaccinium angustifolium, V. pallidum = vaccilans, V. staminium) and huckleberry (Gaylusacchia baccata).
2. Effects on horizontal openness: I also will quantify the effects of fire and overstory cutting on the openness of forest stands and openings by measuring horizontal foliar density. Efforts will be made to involve other researchers who might have an interest in examining effects on other elements of habitat for forest-dwelling species, such as vertical complexity, deadwood, and hollow trees.
3. Effects on wildlife usage: The third objective is to quantify changes in the diversity of birds and small mammals as indicators of changes in wildlife use among the treatment groups.
4. Publication and outreach: In addition to submitting the research project results to a peer-reviewed journal, I intend to create a management guide to aid landowners in meeting combined aesthetic, recreational, and wildlife goals.
Changes in land-use and cultural practices since European settlement have drastically altered the vegetation of southern New England. Despite the reforestation of much of Massachusetts in the latter part of the 20th century, once common habitats such as fire-maintained oak woodlands are nearly nonexistent. Many private and public forest landowners share the goals of creating habitat for selected wildlife species and allowing passive recreational use of their forest, but these objectives often seem to be in conflict. For example, forest stands managed for passive recreation may favor some wildlife species, but open understories often lack forage, browse, mast, and cover for species associated with early seral stages. Conversely, openings that create early successional habitat become impassable tangles of briars and brush making most recreational uses impossible.
Pre-European inhabitants faced a similar challenge—creating open woodlands that allowed passage for hunting while providing food sources for game species. American Indians used frequent low-intensity surface fires in the spring and fall to maintain areas of open forests for easier hunting and to stimulate the production of both hard and soft mast in the chestnut/oak (Castanea dentate/Quercus spp.) forests of the Northeast (Wade, 2000). At a landscape level, these frequently burned open areas occurred within a mosaic of less frequently burned mature forests (Patterson and Sassaman, 1988).
The structure of the forests of Massachusetts has become less diverse, with fewer acres comprising early seral stages. The proportion of seedling/sapling stands to total forested area decreased from over 20 percent in 1972 to 7 percent in 1985 (Dickson and McAfee, 1988) and even further in the recently completed 1998 Forest Inventory and Analysis survey (Kittredge, personal communication). The fire-maintained woodlands that greeted the first European settlers have all but disappeared, nearly eliminating a habitat type that was once prevalent in the Northeast (Rawinski, 2000).
Evaluating techniques to support multiple management objectives such as wildlife and recreation requires knowledge of the specific effects of those techniques on key attributes of the forest. Programs such as the Coverts Project attempt to educate landowners on the improvement of habitat for selected species by the creation of early seral vegetation through sound forest management practices (Kittredge, personal communication). Unfortunately, there is little existing knowledge regarding appropriate practices for combining habitat improvement with recreational and aesthetic goals or for the creation of fire-maintained woodlands.
Approach:
The primary study area will be at the Cadwell Memorial Forest (CMF or the Forest) in Pelham, Massachusetts. The Forest is on the slopes of Mt. Lincoln in the Pelham Hills between the Connecticut Valley and the Quabbin Reservoir, and is owned by the University of Massachusetts–Amherst. Cellar holes, wells, and a cemetery indicate probable agricultural use with subsequent abandonment, typical of many forests in the state. Timber operations continue on several private in-holdings. The Forest contains several plantations and experimental plots of eastern white pine (Pinus strobes), red pine (Pinus resinosa), eastern hemlock (Tsuga Canadensis), and other conifers. The remainder of the Forest is predominantly mixed oak (hardwoods), white pine (hardwoods), and red maple (Acer rubrum) swamp. Groundstory vegetation varies between sites, with dense mountain laurel (Kalmia latifolia) and other ericaceous shrubs common.
At a minimum, three sites, each approximately 2 ha in size, will be chosen at the CMF. Initial candidate sites will be selected based on data collected by a Massachusetts Agricultural Experiment Station-funded study of understory and overstory vegetation (O'Keefe, 1987). Recently completed MRES projects (Kelty, 1999; Wilson, 1999) will provide data on geology, soils, landform, shrub distribution and other parameters for the study area. Sites will be chosen to minimize variations in slope, aspect, soil, and vegetation within each site. Additional pre-treatment measurements will be made to enable final site selection, sample size determination, and preparation of cutting plans. To this end, I will measure the basal area of each overstory tree species and percent cover of understory shrubs.
A recent study of oak woodlands in Worcester, MA (Rawinsky, 2000) describes an ecosystem maintained by frequent anthropogenic brushfires that burn individual sites every 5 to 10 years. These sites support a highly diverse flora, several mammal species and many bird, butterfly, and unusual moth species, all within the city limits of Worcester. Although described as early successional, the woodlands, savannas, and grasslands remain visually open and easily traversed, supporting remarkable aesthetic and recreational features. Urban birdwatchers, berry-pickers, and hikers share the woods with wild turkeys (Meleagris gallopavo), cottontail rabbits (Sylvilagus floridanus), and the rare Harris' checkerspot butterfly (Chlosyne barrisii). In addition, one of the primary requirements of forests managed for recreation and aesthetics is a relatively open understory and low shrub layer, allowing easy passage for hikers and skiers. Many people also consider the open park-like atmosphere of woodlands and savannas to be aesthetically pleasing (Brush, 1979).
Many studies of the use of fire by pre-European inhabitants have suggested that similar woodland habitats may have resulted from frequent aboriginal burning of oak forests (Day, 1953; Pyne, 1982; Cronon, 1983; Patterson and Backman, 1988; Patterson and Sassaman, 1988; Clark and Royall, 1995; Fuller, et al., 1998). Research also has been completed on the effects of fire on oak species (Abrams, 1992), and on the use of prescribed fire for restoration of oak forests (Elliott, et al., 1999), suggesting that the use of fire may stimulate hard mast production. Several studies also have shown that fire improves the growth of certain soft-mast or berry-producing shrubs relative to other shrubs in forest understories (Swan, 1970; Patterson, unpublished data). Fire-responsive understory shrubs in Massachusetts oak forests include species such as blueberry (Vaccinium angustifolium, V. pallidum = vaccilans, V. staminium) and huckleberry (Gaylusacchia baccata). Of these, the blueberry species are likely to show the most rapid response in berry production during the first 2 years following treatments (Patterson, personal observations). Wildlife species present in Massachusetts that feed on these species include birds such as ruffed grouse (Bonasa umbellus) and ring-necked pheasants (Phasianus colchicus), and mammals such as white-tailed deer (Odocoileus virginianus), black bear (Ursus americanus), red fox (Vulpes vulpes), mice, shrews, and voles. The growth and propagation of low-bush blueberry in heathlands and the effects of burning on soil nutrients have been studied at the Maine Experiment Station (Litten, et al., 1997, Litten and Smagula, 2000). Less information is available for blueberry response to management of oak woodlands. Unpublished results from a long-term study of burning effects in oak woodlands on Cape Cod suggest that both fire and adequate light are required to stimulate berry production (Patterson, personal observation).


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Contiki 2.5
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uIP virtual network interface using UDP tunnels. More...
#include "net/ethernode.h"
#include "net/uip_arch.h"
#include "net/uip-fw.h"
#include "ether.h"
#include "dev/radio.h"
#include "net/netstack.h"
#include "net/packetbuf.h"
#include "node.h"
#include "lib/random.h"
#include <stdio.h>
#include <sys/select.h>
#include <unistd.h>
Go to the source code of this file.
Functions | |
| void | ethernode_init (int port) |
| Initialize the virtual UDP tunnel network interface. | |
| int | ethernode_poll (void) |
| Poll the network device to see if a packet has arrived. | |
| u8_t | ethernode_send (void) |
| Send a packet from the uip_buf buffer over the UDP tunnel. |
uIP virtual network interface using UDP tunnels.
Definition in file ethernode.c.
| void ethernode_init | ( | int | port | ) |
Initialize the virtual UDP tunnel network interface.
| sid | The ID number of this node. |
Definition at line 154 of file ethernode.c.
| int ethernode_poll | ( | void | ) |
Poll the network device to see if a packet has arrived.
Definition at line 173 of file ethernode.c.
| u8_t ethernode_send | ( | void | ) |
Send a packet from the uip_buf buffer over the UDP tunnel.
| UIP_FW_TOOLARGE | A transmission of packet that was too large was attempted. |
| UIP_FW_DROPPED | The packet is known to be dropped. |
| UIP_FW_OK | The packet was transmitted. |
Definition at line 228 of file ethernode.c.
References NULL, UIP_FW_OK, UIP_FW_TOOLARGE, and uip_len.


Introduction to Java Programming, Includes Data Structures, Eleventh Edition,
Y. Daniel Liang
This quiz is for students to practice. A large number of additional quiz questions is available for instructors from the Instructor's Resource Website.
Chapter 25 Binary Search Trees
Section 25.2 Binary Search Trees
25.1
A __________ (with no duplicate elements) has the property that for every node in the tree the value of any node in its left subtree is less than the value of the node and the value of any node in its right subtree is greater than the value of the node.
A.
binary tree
B.
binary search tree
C.
list
D.
linked list
25.2
The ________ of a path is the number of the edges in the path.
A.
length
B.
depth
C.
height
D.
degree
25.3
The _______ of a node is the length of the path from the root to the node.
A.
length
B.
depth
C.
height
D.
degree
25.4
The _______ of a nonempty tree is the length of the path from the root node to its furthest leaf + 1.
A.
length
B.
depth
C.
height
D.
degree
25.5
The ________ is to visit the left subtree of the current node first, then the current node itself, and finally the right subtree of the current node.
A.
inorder traversal
B.
preorder traversal
C.
postorder traversal
D.
breadth-first traversal
25.6
The _________ is to visit the left subtree of the current node first, then the right subtree of the current node, and finally the current node itself.
A.
inorder traversal
B.
preorder traversal
C.
postorder traversal
D.
breadth-first traversal
25.7
The _________ is to visit the current node first, then the left subtree of the current node, and finally the right subtree of the current node.
A.
inorder traversal
B.
preorder traversal
C.
postorder traversal
D.
breadth-first traversal
25.8
The _________ is to visit the nodes level by level. First visit the root, then all children of the root from left to right, then grandchildren of the root from left to right, and so on.
A.
inorder traversal
B.
preorder traversal
C.
postorder traversal
D.
breadth-first traversal
25.9
In the implementation of BST, which of the following are true?
A.
Node is defined as an inner class inside BST.
B.
Node is defined as a static inner class inside BST because it does not reference any instance data fields in BST.
C.
Node has a property named left that links to the left subtree and a property named right that links to the right subtree and a property named right
D.
BST contains a property named root. If tree is empty, root is null.
25.10
A new element is always inserted into a leaf node.
A.
True
B.
False
Section 25.3 Deleting Elements from a BST
25.11
The time complexity for searching an element in a binary search tree is _______.
A.
O(n)
B.
O(logn)
C.
O(nlogn)
D.
O(n^2)
25.12
The time complexity for inserting an element into a binary search tree is _______.
A.
O(n)
B.
O(logn)
C.
O(nlogn)
D.
O(n^2)
25.13
The time complexity for deleing an element into a binary search tree is _______.
A.
O(n)
B.
O(logn)
C.
O(nlogn)
D.
O(n^2)
Section 25.5 Iterators
25.14
True or False? You can traverse the elements in a BST using a for-each loop.
A.
True
B.
False
Section 25.6 Case Study: Data Compression
25.15
A Huffman tree is constructed using the ____________ algorithm.
A.
dynamic programming
B.
divide-and-conquer
C.
greedy
D.
back-tracking
25.16
Suppose the keys 3, 4, 45, 21, 92, 12 are inserted into a BST in this order. What is the inorder traversal of the elements?
A.
3 4 12 21 45 92
B.
3 4 45 21 12 92
C.
12 21 92 45 4 3
D.
4 45 21 12 92 3
E.
4 21 12 92 45 3
25.17
Suppose the keys 3, 4, 45, 21, 92, 12 are inserted into a BST in this order. What is the preorder traversal of the elements?
A.
3 4 12 21 45 92
B.
3 4 45 21 12 92
C.
12 21 92 45 4 3
D.
4 45 21 12 92 3
E.
4 21 12 92 45 3
25.18
Suppose the keys 3, 4, 45, 21, 92, 12 are inserted into a BST in this order. What is the postorder traversal of the elements?
A.
3 4 12 21 45 92
B.
3 4 45 21 12 92
C.
12 21 92 45 4 3
D.
4 45 21 12 92 3
E.
4 21 12 92 45 3
25.19
Suppose the keys 3, 4, 45, 21, 92, 12 are inserted into a BST in this order. What is the preorder traversal of the elements after inserting 2 into the tree?
A.
3 2 4 12 21 45 92
B.
3 2 4 45 21 12 92
C.
12 2 21 92 45 4 3
D.
4 2 45 21 12 92 3
E.
4 2 21 12 92 45 3
25.20
Suppose the keys 3, 4, 45, 21, 92, 12 are inserted into a BST in this order. What is the preorder traversal of the elements after deleting 45 from the tree?
A.
3 4 12 21 92
B.
3 4 21 12 92
C.
12 21 92 45 4 3
D.
4 21 12 92 3


There are three basic rules behind the grammar of Glosa: combined, they tell us 'position determines function'.
All other rules of Glosa usage follow from these. All sentences contain a 'verb': it is the word that tells us the action occurring in the sentence; it gives the sentence _life_.
In Glosa, the word in the middle of the sentence, describing the action, is the 'verb' ('verb' = word functioning as a verb).
 EG An fo hedo bibe. ....................... hedo 'verb' ~~~~ ............................ bibe 'gerund' (verb/noun) [He very-much enjoys to drink/drinking.] EG Mi don ad un equs id bibe. ............. don(a) 'verb' ~~~ ........................... bibe 'noun' [I give (to) the horse its drink.] N.B. 'verb' = word functioning as a verb 'noun' = word functioning as a noun ' X ' = word functioning as an X
This word is used in many different ways in English, but, in Glosa, the "is" concept is conveyed more accurately - using a range of 'verbs'. As an 'action' its original function was to represent an "equals" sign, EG "He is John." Glosa translations for various uses of "is" in English are:-
es - to be, is (has equivalence with), has the essence of habe - has the property (quality) of gene - gets to be, becomes eqa - equals, measures Examples: Fe es u ju-fe. [She is a girl.] An es u filosofi-pe. [He is a philosopher.] U meteo habe frigo. [The weather is cold/has coldness] [It is cold.] Mi habe fo lati. [I am very tall/have much tallness.] U meteo gene ma termo. [The weather is warmer/gets warmer/becomes more hot.] U ju-an gene fatiga. [The boy is tired/becomes tired.] Mi habe fo turba; e fu gene koleri, si tu ne sto dice. [I am very upset, and will be angry if you don't stop talking.] U temperatura eqa tri-ze grada (30`C). [The temperature is thirty degrees (30`C).] U preci de ki-in u teatra eqa mo-penta dolara ($15). [The price of entry into the theatre is fifteen dollars ($15).]
In Glosa, some words are used almost exclusively as verbs, and these are general-purpose verbs, such as:-
habe - to have, have has ki - to go, go, goes, went veni - to come, come, comes, came tena - to hold, grab, take
Some words, in Glosa, are "almost verbs" but are not "full verbs" in their own right: they are not 'stand-alone' verbs, but they add to the meaning of the "full verb", which is usually the final word in the Verb Phrase. These Verboids are half-way between Adverbs and Verbs. Examples:-
posi - possibly pote - can, has the ability to nece - needs to, must lice - be allowed, allow to debi - ought to hedo - enjoys, happily forti - strongly seqe - following, subsequently vo(lu) - wish to, would tanto - so much, almost tosto - soon EG Mi fu posi ki ad u boteka. [I will possibly go to the shop.] An pote kurso fo celero. [He can run very quickly.] Kron mi voka, fe tosto veni in u domi. [When I call, she soon comes into the house.] Mi gina fu hedo kanta pro vi. [My wife will happily sing for you.] Tu lice habe id. [You are allowed to have it.] Pa noktu, mi tanto ariva pre u pluvi. .. (Pastness implied by "Pa" so [Last night I almost arrived before the rain.] 'pa tanto ariva' wrong.)
These words 'help' the verb to do its job. But they usually follow the "main verb" as separate words, though they contribute to the formation of a "compound verb." Examples are:-
ana - up EG ki ana [ascend] kata - down EG ki kata [descend] retro - back EG kurso retro [run back] itera - again EG akti itera [do (it) again; repeat] ab - away EG greso ab [walk away] versi - back to start EG viagia versi [travel home] avanti - forward EG ki avanti [advance] EG An pa ki kata un orto-lo; sed an nece skende itera ana id, te [He fell down the cliff, but (he) must climb again up it, in order to gene an kefa-ve; qi veni ab an kefa pre an pa kade. get his hat, which came off his head before he fell.]
In Glosa, the word functioning as the verb can be modified by placing short words, called particles, before it. These particles might alter the time of the event, negate it, turn it back on the Subject, or make it conditional on some other event. As well as that, the action might act upon - or not act upon - an Object; and then, for a change, the positioning Subject and Object is sometimes switched around.
Dealt with, in detail - in the Tense Table - these short words, which come at the start of a Verb Phrase, usually singly but sometimes in a group, tell us WHEN an action occurs.
- No particle before the 'verb' suggest a present tense (but can mean that an earlier time indication logically applies). pa - Simple Past Tense (pa [did]) fu - Future Tense (fu [will]) nu - An immediate Present Tense (nu [just now]) du - Continuous Tense (du X [is X-ing, continues to X]) pra - Past Perfect Tense: fully completed. (pra [had]) EG Un andra nu pa veni in u domi. ~~~~~ [The man just now came into the house.]
When an action happens only if something else occurs before it, then we say that the second event is conditional on the first: in English, this is usually shown by the use of "would", "should" or "could".
sio - would sio debi - would ought, should sio posi - would possibly, could EG Si mu gene pluvi, plu-ci sperma sio posi kresce in alti fito. [If they get rain, these plants could grow into tall bushes.]
When an action does NOT happen, we indicate this using a negative word before the verb. This happens in English, and something very similar occurs in Glosa.
ne - does not no - un- nuli - never (also used with 'nouns': nuli ra [no thing]) ni X ni Y - neither X nor Y EG U gina ne ki a plu boteka; ka fe vagona ne ergo. [The woman did not go to the shops, because her car did not work.]
When an action affects the person or thing that caused it, we say that the action is a "reflexive" action. In Glosa there are two particles that are used after the 'verb' to indicate that the action is received by the Subject of the sentence: one if the Subject is human, the other for non-human Subjects:-
-se - to itself, itself auto - to himself/herself, himself/herself, themselves EG Jon vagona frakto-se, tem an du age ad an ergo-lo. [Jon's car broke itself while he was driving to work (his work-place).] Kron fe skende kata plu grada; fe pa kade, e noku auto. [When she descended the stairs, she fell and hurt herself.]
While the standard English sentence, has two Noun Phrases separated by a Verb Phrase, some sentences have no real Object - receiver of the action: these are called "intransitive" sentences. Glosa also follows this pattern:-
Transitive S-V-O EG U kani fago u karni. [The dog eats the meat.]   IO --> Indirect Object [The dog barks at the post-man.]
While most of English and Glosa has the normal structure where the thing doing the action comes first, and whatever receives the action comes last (called Active Voice), both languages allow the reciever of the action to get a mention at the start of the sentence (called Passive Voice). It can be thought of as an (O-) V (-S) sentence:-
X V Y - X does action V to Y EG U kani morda un andra. ................................. [The dog bites the man.] Y gets V-ed by X - Y has action V done to it by X EG Un andra gene ge-morda ex u kani. [The man gets bitten by the dog.]


Constructor Summary
| Public Constructor | ||
| public |
constructor(PopApi: PopApi, options: Object) Create a new Routes object. |
Method Summary
| Public Methods | ||
| public |
convertErrors(err: Error, req: IncomingMessage, res: ServerResponse, next: Function): ApiError Convert the thrown errors to an instance of ApiError. |
|
| public |
postRoutes(app: Express): undefined Hook method for setting up middleware post setting up the routes. |
|
| public |
Hook method for setting up middleware pre setting up the routes. |
|
| public |
Register the controllers found in the controllers directory. |
|
| public |
removeServerHeader(req: IncomingMessage, res: ServerResponse, next: Function): undefined Remove security sensitive headers. |
|
| public |
setErrorHandler(err: ApiError, req: IncomingMessage, res: ServerResponse, next: Function): Object Error handler middleware |
|
| public |
setNotFoundHandler(req: IncomingMessage, res: ServerResponse, next: Function): ApiError Catch the 404 errors. |
|
| public |
Setup the application service. |
Public Constructors
public constructor(PopApi: PopApi, options: Object) source
Create a new Routes object.
Params:
| Name | Type | Attribute | Description |
| PopApi | PopApi |
|
The PopApi instance to bind the routes to. |
| options | Object |
|
The options for the routes. |
| options.app | Express |
|
The application instance to add middleware and bind the routes to. |
| options.controllers | Array<Object> |
|
The controllers to register. |
Throw:
'app' are required options for the routes middleware! |
Public Methods
public convertErrors(err: Error, req: IncomingMessage, res: ServerResponse, next: Function): ApiError source
Convert the thrown errors to an instance of ApiError.
Params:
| Name | Type | Attribute | Description |
| err | Error |
|
The caught error. |
| req | IncomingMessage |
|
The incoming message request object. |
| res | ServerResponse |
|
The server response object. |
| next | Function |
|
The next function to move to the next middleware. |
public postRoutes(app: Express): undefined source
Hook method for setting up middleware post setting up the routes.
Params:
| Name | Type | Attribute | Description |
| app | Express |
|
The application instance to add middleware to. |
public preRoutes(app: Express): undefined source
Hook method for setting up middleware pre setting up the routes.
Params:
| Name | Type | Attribute | Description |
| app | Express |
|
The application instance to add middleware to. |
public registerControllers(app: Express, PopApi: PopApi, controllers: Array<Object>): undefined source
Register the controllers found in the controllers directory.
public removeServerHeader(req: IncomingMessage, res: ServerResponse, next: Function): undefined source
Remove security sensitive headers.
Params:
| Name | Type | Attribute | Description |
| req | IncomingMessage |
|
The incoming message request object. |
| res | ServerResponse |
|
The server response object. |
| next | Function |
|
The next function to move to the next middleware. |
public setErrorHandler(err: ApiError, req: IncomingMessage, res: ServerResponse, next: Function): Object source
Error handler middleware
Params:
| Name | Type | Attribute | Description |
| err | ApiError |
|
The caught error. |
| req | IncomingMessage |
|
The incoming message request object. |
| res | ServerResponse |
|
The server response object. |
| next | Function |
|
The next function to move to the next middleware. |
public setNotFoundHandler(req: IncomingMessage, res: ServerResponse, next: Function): ApiError source
Catch the 404 errors.
Params:
| Name | Type | Attribute | Description |
| req | IncomingMessage |
|
The incoming message request object. |
| res | ServerResponse |
|
The server response object. |
| next | Function |
|
The next function to move to the next middleware. |


Ever heard of this term - Growth hacking? Probably not. Growth hacking
refers to a procedure by which you artificially increase your circle on any
social-media platform in general. This is a really important factor that
start-ups have to consider, while they're still in their initial phase. And
the need to automate growth hacking is really immense. Keep reading, to
understand the logic of growth-hacking and functionality of this API.
Logic of Growth Hacking
Growth hacking has a very interesting logic behind its working. This is what a
step-by-step analysis of growth hacking would roughly resemble -
- Repeat steps 2 to 5 for each social-media platform.
- Get the list of users who're following your competitor in that
social-media platform. Most of them are your target audience too, as they
were interested in your competitor. Repeat steps 3 to 5 for each such
follower.
- Select a user from the above list.
- Follow that user. They receive a notification that you've followed them.
- If they follow you back within n days, great - mission accomplished.
Else,
- If you've already followed them for a critical cut-off of m times,
don't follow them anymore. That user is probably not interested in this
sector - let's not spam them with any more notifications.
- Else, unfollow them & repeat from step 3.
Pretty smooth, huh? Most social-media platforms such as Facebook, Linkedin,
Twitter, Google offer APIs for programmatic implementation of growth-hacking.
This Inbound API Ruby gem is a similar API for ruby language.
Working with the API
-
Setting up dependencies :
This API depends on 2 ruby gems - Mechanize & JSON. They should be
installed by default, while installing this gem. In any case,
considering that you already have ruby & rubygems setup, type the
below commands in your terminal to install the dependencies -
gem install mechanize
to install Mechanize gem.
gem install json
to install the native JSON gem.
Great! You're now ready to install the Inbound API Ruby gem.
-
Installation :
Type
gem install inbound_api
on your terminal.
=> Successfully installed inbound_api-1.0.3
=> 1 gem installed
After the gem is installed, try out the below commands in your ruby
console (irb
) or in a ruby file.
-
Initializing :
require 'inbound_api'
=> true
instance = InboundAPI.new()
=> #< InboundAPI: ... >
-
Logging in :
instance.login('inbound_email_id','inbound_password')
=> {'response'=>'Success'}
Note that this and also the below statements return a hash, whose
response
field contains value of Success
or Failure
- making it
easier to debug.
-
Fetch a user's data :
instance.fetch_user_data('dany_jon_tyrion')
=> {'response'=>'Success', 'userid'=>'1277018', 'name'=>'Athitya Kumar', 'imageurl'=>'https://www.filepicker.io/api/file/d1e3Zr61Q8ef8dRFMeUr', 'location'=>'Chennai', 'karma'=>4, 'numberfollowers'=>541, 'numberfollowing'=>11341, 'work'=>'Student at IIT Kharagpur', 'companylink'=>'https://athityakumar.github.io/', 'twitterlink'=>'https://twitter.com/danyjontyrion', 'facebooklink'=>'https://www.facebook.com/athitya.kumar', 'linkedinlink'=>nil, 'googlepluslink'=>nil, 'mylink'=>'https://athityakumar.github.io/blog/page1/', 'numberbadges'=>nil, 'badges'=>nil, 'recent_activity'=>nil}
Note that all information of a user that isn't available at
inbound.org portal is initialised as nil.
-
Fetch a user's followers :
followers = instance.fetch_followers('dany_jon_tyrion')['followers']
followers.count
=> 535
-
Fetch a list of usernames followed by a user :
followers = instance.fetch_following('dany_jon_tyrion')
=> {'response'=>'Failure'}
Note that the server 500 error encountered on Inbound.org portal
translates into a hash output with 'response' field as 'Failure'.
-
Fetch a list of usernames as search results with / without pagination :
instance.search_users(name,location,badges,skills,number_of_pages)
where,
name and location are strings initialised as ""
badges and skills are lists initialised as []
pages is an integer number of pages initialised as 1.
For example,
instance.search_users('Dany','Chennai',['Inbound','HubSpot'],['Web','Graphics'],100)
=> {'response'=>'Success', 'usernames'=>['valiantsystems', 'webdesigningco1', '11Ganapathy', 'DENTISTINDIA', 'RenegadeCricket']}
Note that the actual list of Badges & Skills can be found by
functions instance.get_all_badges()
& instance.get_all_skills()
.
-
Follow a user :
instance.follow_user('dany_jon_tyrion')
=> {'response'=>'Success'}
Note that you'd have to login first to follow / unfollow.
-
Unfollow a user :
instance.unfollow_user('dany_jon_tyrion')
=> {'response'=>'Success'}
Don't try to unfollow yourself - you would get 'Failure' as the
response.
Any suggestions?
You can find this gem on
RubyGems website. If there are any
suggestions / enhancements you'd like to see in this Ruby API gem, do comment
below, or open a new issue
here. Also, if you
liked this project, do star the
repository. And fellow
developers, you're welcome to contribute - for, this is an open-source
project.


objects
color
an object containing the 3 rgb values
| parameter | type | description |
|---|---|---|
| red | int | the value for red in the additive rgb-model |
| green | int | the value for green in the additive rgb-model |
| blue | int | the value for blue in the additive rgb-model |
led
an object containing all the values for an led
| parameter | type | description |
|---|---|---|
| name | string | the name of the led |
| value | object<color> | an object of the currently set rgb-values |
mode
an object containing all the values for a native mode of the device
| parameter | type | description |
|---|---|---|
| id | int | mode index |
| name | string | mode name |
| value | int | mode identifier |
| flags | int | ???? |
| speedMin | int | minimum speed the mode can be set to |
| speedMax | int | maximum speed the mode can be set to |
| colorMin | int | minimum colors for this mode, if fewer rest defaults to black |
| colorMax | int | maximum colors for this mode |
| speed | int | speed the mode is currently set to |
| direction | int | direction the mode is currently set to |
| colorMode | int | ???? |
| colorLength | int | ???? |
| colors | array | the colors the mode is currently set to |
| flagList | array | list of flags from flags attribute |
mode
an object containing all the values for a native mode of the device
| parameter | type | description |
|---|---|---|
| height | int | amount of lines |
| keys | array<array<int | undefined>> | array of lines, which are arrays of key-ids or undefined if not existent |
| width | int | amount of keys per line |
| size | int | amount of keys |
zone
an object containing every values a zone can have
| parameter | type | description |
|---|---|---|
| name | string | zone name |
| id | int | zone index |
| type | int | ???? |
| ledsMin | int | the minimum amount of leds this zone has to be assigned |
| ledsMax | int | the amount of leds this zone can maximally be assigned |
| ledsCount | int | the amount of leds currently distributed to this zone |
| resizable | boolean | if the zone is resizable |


<% include header.html %>
<% include slidebar.html %>
<% include pinfo.html %>
Calendar
Draggable Events
Default
Normal
Medium
Urgent
Important
Add Event
remove after drop


Cheshire3 Objects: Parser |
Normally a simple wrapper around an XML parser, these objects can be viewed as Record Factories. They take a Document containing some XML and produce the equivalent Record.
The following implementations are pre-configured and ready to use.
They may be used out-of-the-box in configurations for Workflows, or in code by getting the object from a Server.
Module: cheshire3.parser
| Function | Parameters | Returns | Description |
|---|---|---|---|
| __init__ | config, parent | ||
| process_document | session, document | Record | Return a record after parsing from an XML document |
Sub-package: graph
Module: cheshire3.graph.parser


Module etm
A pure Python version of Karney's Exact Transverse
Mercator
(ETM) projection.
Classes Etm,
ETMError and
ExactTransverseMercator, transcoded from Karney's
C++ class TransverseMercatorExact, abbreviated as
TMExact
below.
Class ExactTransverseMercator provides Exact Transverse
Mercator
projections while instances of class Etm represent ETM
(easting, northing)
locations. See also Karney's
utility TransverseMercatorProj and use "python[3] -m
pygeodesy.etm ..."
to compared the results.
Following is a copy of Karney's TransverseMercatorExact.hpp file
Header
.
Copyright (C) Charles Karney (2008-2023) and licensed under the
MIT/X11 License. For more information, see the GeographicLib documentation.
The method entails using the Thompson Transverse Mercator as an intermediate
projection. The projections from the intermediate coordinates to
phi, lam
and x, y
are given by elliptic
functions. The inverse of these projections are found by Newton's method
with a suitable starting guess.
The relevant section of L.P. Lee's paper Conformal Projections Based On Jacobian Elliptic
Functions in part V, pp 67-101. The C++ implementation and notation
closely follow Lee, with the following exceptions:
Lee here Description
x/a xi Northing (unit Earth)
y/a eta Easting (unit Earth)
s/a sigma xi + i * eta
y x Easting
x y Northing
k e Eccentricity
k^2 mu Elliptic function parameter
k'^2 mv Elliptic function complementary parameter
m k Scale
zeta zeta Complex longitude = Mercator = chi in paper
s sigma Complex GK = zeta in paper
Minor alterations have been made in some of Lee's expressions in an
attempt to control round-off. For example, atanh(sin(phi))
is replaced by asinh(tan(phi))
which maintains accuracy near
phi = pi/2
. Such changes are noted in the code.
|
|
parseETM5(strUTM,
datum=Datum(name='WGS84', ellipsoid=Ellipsoids.WGS84, transform=Tran... ,
Etm=<class 'pygeodesy.etm.Etm'>,
falsed=True,
name='')
Parse a string representing a UTM coordinate, consisting of
"zone[band] hemisphere easting northing" . |
|
|
|
|
toEtm8(latlon,
lon=None,
datum=None,
Etm=<class 'pygeodesy.etm.Etm'>,
falsed=True,
name='',
strict=True,
zone=None,
**cmoff)
Convert a lat-/longitude point to an ETM coordinate. |
|
|
parseETM5 (strUTM,
datum=Datum(name='WGS84', ellipsoid=Ellipsoids.WGS84, transform=Tran... ,
Etm=<class 'pygeodesy.etm.Etm'>,
falsed=True,
name='')
|
|
Parse a string representing a UTM coordinate, consisting of
"zone[band] hemisphere easting northing" .
- Arguments:
strUTM - A UTM coordinate (str ).
datum - Optional datum to use (Datum,
Ellipsoid, Ellipsoid2 or a_f2Tuple).
Etm - Optional class to return the UTM coordinate (Etm) or
None .
falsed - Both easting and northing are falsed
(bool ).
name - Optional Etm name (str ).
- Returns:
- The UTM coordinate (
Etm ) or if
Etm is None , a UtmUps5Tuple(zone, hemipole, easting,
northing, band) . The hemipole is the
hemisphere 'N'|'S' .
- Raises:
ETMError - Invalid strUTM .
TypeError - Invalid or near-spherical datum .
Example:
>>> u = parseETM5('31 N<PHONE_NUMBER>932')
>>> u.toRepr()
>>> u = parseETM5('31 N 448251.8 5411932.7')
>>> u.toStr()
|
toEtm8 (latlon,
lon=None,
datum=None,
Etm=<class 'pygeodesy.etm.Etm'>,
falsed=True,
name='',
strict=True,
zone=None,
**cmoff)
|
|
Convert a lat-/longitude point to an ETM coordinate.
- Arguments:
latlon - Latitude (degrees ) or an (ellipsoidal) geodetic
LatLon point.
lon - Optional longitude (degrees ) or None .
datum - Optional datum for this ETM coordinate, overriding
latlon 's datum (Datum,
Ellipsoid, Ellipsoid2 or a_f2Tuple).
Etm - Optional class to return the ETM coordinate (Etm) or
None .
falsed - False both easting and northing (bool ).
name - Optional Utm name (str ).
strict - Restrict lat to UTM ranges
(bool ).
zone - Optional UTM zone to enforce (int or
str ).
cmoff - DEPRECATED, use falsed . Offset longitude
from the zone's central meridian (bool ).
- Returns:
- The ETM coordinate (
Etm ) or a UtmUps8Tuple(zone, hemipole, easting,
northing, band, datum, gamma, scale) if
Etm is None or not
falsed . The hemipole is
the'N'|'S' hemisphere.
- Raises:
ETMError - No convergence transforming to ETM east- and northing.
ETMError - Invalid zone or near-spherical or incompatible
datum or ellipsoid .
RangeError - If lat outside the valid UTM bands or if
lat or lon outside the
valid range and pygeodesy.rangerrors set to True .
TypeError - Invalid or near-spherical datum or
latlon not ellipsoidal.
ValueError - The lon value is missing or
latlon is invalid.
|


Table of Contents
Overview
Reach is a lightweight reachability framework. We designed Reach to be simple to use and also very flexible. Written on Swift 3.1 and compatible with:
- iOS 8.0
- macOS 10.10
- tvOS 9.0
- watchOS 2.0
Features
- Super friendly API
- Singleton free
- No external dependencies
- Minimal implementation
- Support for
iOS/macOS/tvOS/watchOS/Linux
- Support for CocoaPods/Carthage/Swift Package Manager
Getting Started
Reach contains a status property to check the reachability from network. If you want to receive notifications from network changes, you can subscribe to ReachDelegate
protocol.
let reach = Reach()
reach.start()
print(reach.status)
You can check the Reach.playground
to experimental with more examples. If need to see deeply information you can check our Documentation
Installation
CocoaPods
Reach is available through CocoaPods. To install it, simply add the following line to your Podfile:
platform :ios, '10.0'
use_frameworks!
swift_version = '3.0'
target 'MyApp' do
pod 'Reach'
end
Carthage
You can also install it via Carthage. To do so, add the following to your Cartfile:
github 'therapychat/Reach'
Swift Package Manager
You can use Swift Package Manager and specify dependency in Package.swift
by adding this:
.Package(url: "https://github.com/therapychat/Reach.git", majorVersion: 0)
Author
Sergio Fernández<EMAIL_ADDRESS>Contribution
For the latest version, please check develop branch. Changes from this branch will be merged into the master branch at some point.
- If you want to contribute, submit a pull request against a development
develop
branch. - If you found a bug, open an issue.
- If you have a feature request, open an issue.
License
Reach is available under the Apache License 2.0
. See the LICENSE file for more info.
Changelog
See CHANGELOG file.


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PANOXYL - benzoyl peroxide soap
Physicians Total Care, Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
If swallowed, get medical help or contact a Poison Control Center right away.
cetearyl alcohol, cocamidopropyl betaine, glycerin, hydrogenated castor oil, lactic acid, mineral oil, PEG-14M, potassium lauryl sulfate, potassium phosphate, purified water, silica, sodium lauryl sulfate, titanium dioxide, zea mays (corn) starch
call 1-888-784-3335 (STIEFEL). Side effects associated with use of this product may be reported to this number.
Maximum Strength
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The convenient bar form of PanOxyl® gently removes dirt and excess oil to cleanse and unclog pores, making it an excellent choice for the management of acne on the face, chest and back.
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PANOXYL is a registered trademark of Stiefel Laboratories, Inc.
www.stiefelchc.com
©2010 Stiefel Laboratories, Inc.
Stiefel Laboratories, Inc.
Research Triangle Park, NC 27709
NDC 54868-2656-0
PanOxyl®
10% Benzoyl Peroxide
Maximum Strength
ACNE CLEANSING BAR
DERMATOLOGIST Recommended Ingredients
Therapeutic acne control
FACE
CHEST
BACK
Net Wt 4 Oz (113 g)
NDC 54868-2655-0
PanOxyl®
5% Benzoyl Peroxide
Regular Strength
ACNE CLEANSING BAR
DERMATOLOGIST Recommended Ingredients
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You only need to know the latter. Moving on...
Important note: We're not using nginx as a reverse proxy, we're using it as a "regular" HTTP proxy. "Regular" as in the kind you set in your browser's network options. This is called a "forward proxy". If you google for how to use nginx as a proxy, virtually all hits will tell you how to use it as a reverse proxy. This is different. Now that we've cleared that up, let's get started!
First things first: we need to install nginx. If you're on a sane operating system, you could just use your operating system's package manager. The thing is, as of the time of this writing, nginx's XSLT module can only handle well formed XML as input. Sadly, well formed XML is rather scarce on the net, because web browsers are rather lenient about enforcing XML rules. nginx's XSLT module is based on libxslt, however, which is actually able to handle typical HTML as input as well.
Fortunately, there is a patchset for this. There's talk about it on the nginx mailing list, so with a bit of luck it'll be included in the main release sometime soon. For now, we can just grab the nginx fork with this patch applied. Note that you only need to do this if you care for the XSLT filtering and you'd like to be able to filter HTML and shoddy XHTML. If you know you'll only be filtering proper XML, you can just grab a binary package or the regular distribution tarball.
Assuming you will compile from source, open a terminal, cd to the appropriate directory and use the typical compilation procedure:
$ ./configure --prefix=$HOME --with-http_xslt_module
$ make
$ make install
You may need to use su or sudo for the last step, depending on which --prefix you chose. Nginx will work fine from your home directory, however.
If you chose to use a binary package, it should come with instructions on how to install so follow those.
Open the nginx configuration file in a text editor. If you went with the $HOME directory above, then you should edit the file $HOME/conf/nginx.conf. If you used a binary installer from your distribution, it's probably in /etc/nginx/nginx.conf or similar.
$ vim $HOME/conf/nginx.conf
Substitute vim for your favourite text editor. Also, as above you may need to use sudo if it's installed anywhere but your home directory.
You need to either replace the "normal" http server section or create a new one, depending on whether you'd also like to use nginx as a regular web server. The following snippet, which should go in your http section, will create a normal web proxy on port 8080:
server {
listen 8080;
location / {
resolver <IP_ADDRESS>;
proxy_pass http://$http_host$uri$is_args$args;
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root html;
}
}
For a plain http proxy, your configuration file should now look something like this:
worker_processes 1;
events {
worker_connections 1024;
}
http {
include mime.types;
default_type application/octet-stream;
sendfile on;
keepalive_timeout 65;
gzip on;
server {
listen 8080;
location / {
resolver <IP_ADDRESS>;
proxy_pass http://$http_host$uri$is_args$args;
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root html;
}
}
}
Fairly simple so far, right? Save the file, run nginx and verify that it's working by setting your browser's http proxy to localhost, port 8080, and surfing to, e.g. google.com. You can verify that your browser was using nginx by opening the logs/access.log file, which should list the requests your browser sent. If it doesn't work, open the logs/error.log file and see why. Make sure it's working before heading to the next part.
Now that your shiny new proxy is working, let's get to the fun part! (Queue evil laughter right about here)
To apply your own XSLT stylesheet to pages on your favourite website you proceed very much like you would if you were configuring nginx as a web server and you'd like to add a new virtual host. Basically, all you need to do is define a new server section with a specific host name. I'm going to use xkcd.com as an example here, because it's one of my favourite web comics:
server {
listen 8080;
server_name xkcd.com;
server_name www.xkcd.com;
location /.css {
alias /path/to/local/directory/css;
try_files $uri.css =404;
}
location / {
resolver <IP_ADDRESS>;
proxy_set_header Accept-Encoding "";
proxy_pass http://xkcd.com$uri$is_args$args;
}
location ~ ^/([0-9]+/)?$ {
resolver <IP_ADDRESS>;
proxy_set_header Accept-Encoding "";
proxy_pass http://xkcd.com$uri$is_args$args;
xslt_types application/xhtml+xml text/html;
xslt_stylesheet /path/to/local/directory/xslt/xkcd.com.xslt;
}
}
This new server section goes right next to your previous server section, still in the http section. Make sure the listen port in both match up.
In this example, we're doing two things: first, we inject a /.css directory onto the server, which we serve from a local directory, and second we apply a style sheet to all the URLs that contain comics on xkcd.com. xkcd.com is, fortunately, serving proper xhtml, although it does so with the text/html mime type, which is why we use xslt_types to add text/html to the mime types to filter. If it were serving plain HTML or broken XHTML, we would use the following instruction right before xslt_types:
xslt_html_parser on;
This would require the patched version of nginx we compiled from scratch earlier, and this is why I recommended using that version.
As an example, we could use the following xkcd.com.xslt (in the xslt/ directory):
<?xml version="1.0" encoding="UTF-8"?>
<xsl:stylesheet
xmlns:xsl="http://www.w3.org/1999/XSL/Transform"
xmlns:xhtml="http://www.w3.org/1999/xhtml"
xmlns="http://www.w3.org/1999/xhtml"
version="1.0"
exclude-result-prefixes="xhtml">
<xsl:output method="xml" version="1.0" encoding="UTF-8"
doctype-public="-//W3C//DTD XHTML 1.1//EN"
doctype-system="http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd"
indent="no"
media-type="application/xhtml+xml" />
<xsl:strip-space elements="*" />
<xsl:preserve-space elements="xhtml:pre" />
<xsl:template match="@*|node()">
<xsl:copy>
<xsl:apply-templates select="@*|node()" />
</xsl:copy>
</xsl:template>
<xsl:template match="xhtml:form">
</xsl:template>
<xsl:template match="xhtml:div[@id='bottom']">
</xsl:template>
<xsl:template match="xhtml:div[@id='topContainer']">
</xsl:template>
<xsl:template match="xhtml:div[@id='transcript']">
</xsl:template>
<xsl:template match="xhtml:meta[@http-equiv]">
</xsl:template>
<xsl:template match="xhtml:link[@rel='shortcut icon']">
</xsl:template>
<xsl:template match="xhtml:link[@rel='icon']">
</xsl:template>
<xsl:template match="xhtml:link[@rel='apple-touch-icon-precomposed']">
</xsl:template>
<xsl:template match="xhtml:link[@rel='stylesheet']">
<link rel='stylesheet' type='text/css' href='/.css/xkcd.com' />
</xsl:template>
<xsl:template match="xhtml:link[@type='application/atom+xml']">
</xsl:template>
<xsl:template match="xhtml:ul[@class='comicNav'][position()>1]">
</xsl:template>
<xsl:template match="xhtml:br">
</xsl:template>
<xsl:template match="text()[preceding-sibling::xhtml:br]">
</xsl:template>
<xsl:template match="comment()">
</xsl:template>
<xsl:template match="xhtml:script">
</xsl:template>
<xsl:template match="xhtml:div[@id='ctitle']">

<xsl:apply-templates select="node()" />

</xsl:template>
<xsl:template match="xhtml:div[@id='middleContainer']">
<xsl:apply-templates select="node()" />
</xsl:template>
<xsl:template match="xhtml:a/@accesskey">
</xsl:template>
<xsl:template match="xhtml:ul">

<xsl:apply-templates select="node()" />

</xsl:template>
<xsl:template match="xhtml:div[@id='comic']">
<xsl:choose>
<xsl:when test="xhtml:noscript">

<xsl:copy-of select="xhtml:noscript/@href"/>
<xsl:copy-of select="xhtml:noscript/xhtml:img/@src"/>
<xsl:copy-of select="xhtml:noscript/xhtml:img/@alt"/>

<xsl:value-of select="string(xhtml:noscript/xhtml:img/@title)"/>

</xsl:when>
<xsl:when test="xhtml:a">

<xsl:copy-of select="xhtml:a/@href"/>
<xsl:copy-of select="xhtml:a/xhtml:img/@src"/>
<xsl:copy-of select="xhtml:a/xhtml:img/@alt"/>

<xsl:value-of select="string(xhtml:a/xhtml:img/@title)"/>

</xsl:when>
<xsl:otherwise>

<xsl:copy-of select="xhtml:img/@src"/>
<xsl:copy-of select="xhtml:img/@alt"/>

<xsl:value-of select="string(xhtml:img/@title)"/>

</xsl:otherwise>
</xsl:choose>
</xsl:template>
</xsl:stylesheet>
... and the following xkcd.com.css (in the css/ directory):
html
{
background: #96A8C8;
font-size:16px;
font-variant:small-caps;
font-family:Lucida,Helvetica,sans-serif;
font-weight:500;
text-decoration: none;
padding: 10px;
}
body
{
width: 780px;
margin: 0 auto;
background:white;
border-style:solid;
border-width:1.5px;
border-color:#071419;
border-radius: 12px;
padding: 10px 0;
text-align: center;
}
ul
{
padding:0;
list-style-type:none;
}
ul li
{
display: inline;
}
ul li a
{
background-color:#6E7B91;
color: #FFF;
border: 1.5px solid #333;
font-size: 16px;
font-weight: 600;
padding: 1.5px 12px;
margin: 0 4px;
text-decoration: none;
border-radius: 3px;
box-shadow: 0 0 5px 0 gray;
}
ul a:hover
{
background-color: #FFF;
color: #6E7B91;
box-shadow: none;
}
Now that you have nginx all set up, just save and restart nginx, then visit xkcd.com. The example style sheet should apply a transformation much like this:
... becomes ...
Now, admittedly, xkcd.com doesn't have any advertisements that would need removing and there is the mobile version of xkcd.com which looks pretty much the same as the output from our XSLT. This is just an example to demonstrate the technique as such. Remember: XSLT is turing complete. That means you can actually perform any transformation you could think of.
You should now be able to apply any custom XSLT style sheet to any website of your liking. Congratulations! Now you can actually do something about it if the webdesigner of a given page you need to use regularly should be taken behind the barn to meet a nice, friendly neighbourbood bullet... *cough*
About the only limitation at this point is that you can't transform https websites because those can't be intercepted. Fortunately, that's not too many of the annoying ones, mwahaha.
I may put up a demonstration web proxy on my server, I'm still undecided about that. Either way, enjoy and spread the news! And if you happen to write some awesome stylesheets for some annoying pages, do tell and I may put 'em up right here.


Why we decided to work in harmony with Executive – Lawan
by The Eagle OnlinePresident of the Senate, Ahmad Lawan, has said that the 9th National Assembly decided to work in harmony with the Executive to enhance productivity in governance.
Lawan stated this in Abuja at his investiture as a fellow of Geoinformation Society of Nigeria.
His Special Adviser (Media), Ola Awoniyi, made this known in a statement in Abuja on Thursday.
According to Lawan, a good working relationship between the two arms of government would also forestall the anxiety and instability that a rancorous relationship could bring about.
“The past uneasy relationship between the Legislature and the Executive was not helpful to anyone.
“We made a conscious decision this year, 2019 when we ran for the office of President of the Senate.
“We told ourselves that we are first and foremost representatives of Nigerians. We came on different platforms, vehicles that took us to the construction site.
“And of course, at the construction site, all of us are supposed to work together to construct that structure,”Lawan said.
He said that to avoid the pitfall of the past, there was a conscious decision to have a National Assembly, particularly the Senate that was bi-partisan and work for the collective interest of the people.
Lawan said that the 9th National Assembly would support President Muhammadu Buhari because “we believe that fight between the legislature and the executive does not help anyone”.
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Has anyone ever heard of hanging ziplock bags with water in them ...
Odd method persuades flies to fly away: Hanging water bag looks strange, but for some reason it repels pests. It is not clear why these water bags .
http://askville.amazon.com/heard-hanging-ziplock-bags-water-door-rid-flies-coming-house/AnswerViewer.do?requestId=12949986Why Do Plastic Bags Filled With Water Repel Flies? | eHow.com
Why Do Plastic Bags Filled With Water Repel Flies?. Should you see a doorway with a water-filled plastic bag hung over it, you should know that flies are not .
http://www.ehow.com/about_5531620_do-filled-water-repel-flies.html
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Water Bag Fly Deterrent - Part 1 - YouTube
Aug 10, 2009 . I learned several years ago at a peach orchard that a zip-loc bag with water will keep flies away. I'm posting a couple of with/without vids to .
http://www.youtube.com/watch?v=CWjnvpL8N8k -
FLY PROBLEM, try a zip-lock bag and some pennies
Many people swear that a zip-lock bag filled half-way with water and attached over entry-ways will repel flies. No one yet knows how or why it works, but there is .
http://www.pennypinching-grandma.com/zip-lock.html -
Repel Flies With A Bag Of Water | Lifehacker Australia
Jul 7, 2009 . Annoyed by the household fly? Turns out all you need to deal with that pest is a plastic bag or two. Photo by brookage. Reader RickyC wrote in .
http://www.lifehacker.com.au/2009/07/repel-flies-with-a-bag-of-water/michael vick home with black walls
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Fly Control Plastic Bags - Natural Organic Home Garden Health ...
Fly Control. I've seen the use of water in a plastic bag as a fly repellent for over a year and it seems to be growing in use. What an expert from my area claimed .
http://www.dirtdoctor.com/Fly-Control-Plastic-Bags_vq1849.htm -
An Eco Friendly, Safe Way to Repel Flies | Apartment Therapy
May 10, 2012 . Light is refracted by the water, the colours and movements of the bag are amplified and the fly is confused and flies away."


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