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Here is where you find what the development team is up to and where the website is heading. Also other random stuff we wish to share.
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Question
01/02
Below are three options on how to respond to the scenario above. Image question the client side pings you and tells that they have a personal emergency and will need to be away from work
You will...
Below are three options on how to respond to the scenario above. Please choose the response that resonates most with you and click Next to proceed.


DARLAM precipitation
Below this paragraph are plots of the 60-day accumulated precipitation for an observed data set and DARLAM, respectively. The precipitation from DARLAM is positioned slightly north of and has a slightly greater maximum than the observations. Many other models predict the heaviest precipitation north of the observations as well. Where DARLAM stands out is in the daily cycle of the mean precipitation, also included below along with the daily cycle of two analyses of observed precipitation.
Last Updated on 10/24/00
By Chris Anderson


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Suspicious Package Prompts Evacuation of Milwaukee Fire Station
A suspicious package of unknown content was found on Monday night near a fire station located at 4060 N. 27th St. in Milwaukee, Wisconsin, according to multiple reports.
Local media reported that, following the discovery of the package, the fire station and surrounding neighborhood were evacuated.
Hazardous materials crews along with police have arrived on the scene.
Investigators are reportedly working to determine the contents of the package.
No further details are immediately available.


why i cant get the answer?its different from what i calculate with the calculator...help me please...
/this program use to calculate the volume of the given shapes
#include<iostream>
#include<conio>
main()
{
float num, capsule, cone, cylinder, hemisphere, pyramid, length, height, radius;
const float pi=3.142;
cout<<"Please select your shape by entering the number"<<endl;
cout<<"1. Capsule"<<endl<<"2. Cone"<<endl<<"3. Cylinder"<<endl<<"4. Hemisphere"<<endl<<"5. Pyramid"<<endl;
cout<<"The number you have selected is ";
cin>>num;
if (num==1)
{
cout<<"Please enter the radius : ";
cin>>radius;
cout<<"Please enter the side length : ";
cin>>length;
capsule=(pi*(radius*radius)*((4/3)*radius+length));
cout<<"The volume of the capsule is "<<capsule<<endl;
}
else if (num==2)
{
cout<<"Please enter the radius : ";
cin>>radius;
cout<<"Please enter the height : ";
cin>>height;
cone=(1/3)*(pi*(radius*radius)*height);
cout<<"The volume of the cone is "<<cone<<endl;
}
else if (num==3)
{
cout<<"Please enter the radius : ";
cin>>radius;
cout<<"Please enter the height : ";
cin>>height;
cylinder=(pi*(radius*radius)*height);
cout<<"The volume of the cylinder is "<<cylinder<<endl;
}
else if (num==4)
{
cout<<"Please enter the radius : ";
cin>>radius;
hemisphere=((2/3)*pi*(radius*radius*radius));
cout<<"The volume of the hemisphere is "<<hemisphere<<endl;
}
else if (num==5)
{
cout<<"Please enter the length : ";
cin>>length;
cout<<"Please enter the height : ";
cin>>height;
pyramid=((1/3)*(length*length)*height);
cout<<"The volume of the pyramid is "<<pyramid<<endl;
}
else
{
cout<<"The number you have entered is not in the list"<<endl;
}
getch();
return 0;
}


JWT
https://github.com/thisconnect/jwt-example
token and expires from sessionStorage
1. sign in /token
you get a token that expires in 60 seconds :)
reload this page to see time remaining
2. get data /api/private
(/api/private deliberately accepts GET requests for testing,
token sent with 'x-access-token' header)
3. test token in sessionStorage


HIGH COURT OF JUDICATURE FOR RAJASTHAN AT JODHPUR S.B. Criminal Revision No. 1569 / 2017 Gurvinder Singh S/o Gurbax Singh, Aged About 55 Years, B/c Sikh, R/o 8, Sadul Colony, Bikaner, Rajasthan.
----Petitioner Versus
1. State of Rajasthan
2. Sh. Kulbhushan Mehra S/o Rameshwar Das Mehra, 12-13, Rathod Nagar, Queens Road, Vaishali Nagar, Jaipur.
3. Sh. RP Gupta S/o Sh. PR Gupta, Reliance Industries Ltd., A-3, Mohan Cooperative Industrial Area, Mathura Road, New Delhi-44.
4. Sh. P Raghvendra S/o Sh Kumarn Nair, Kritkaya Tower, R/o. Flat No 1102, 11th Floor, Sain, Trombay Road, Near RK Studio, Chembhur, Mumbai-400 071.
5. Sh. NB Deshmukh S/o Bhaskar Gunwant Deshmukh, R/o. Flat No.44, Sector 28, Vashi, New Bombay-400 703.
6. Sh. P M S Parasad S/o Panda Koteshwras Rao, R/o. 92-93, 22 Narayan Dabholkar Road, Mumbai-400 006.
----Respondents _____________________________________________________ For Petitioner(s) : Mr. Gurvinder Singh, present in person For Respondents(s) : Mr. O.P. Rathi, Public Prosecutor.
Mr. Mahesh Bora, Senior Advocate with Mr. Nishant Bora for other respondents.
_____________________________________________________ HON'BLE MR. JUSTICE P.K. LOHRA Order Reserved on 17/05/2018 Pronounced on 23/05/2018 Reportable Petitioner-complainant, by instant revision petition, has assailed order dated 29th January 2014, passed by Addl. District & Sessions Judge No.3, Bikaner (for short, 'learned Court below') upsetting the order taking cognizance against private respondents for offences under Section 39(1) & 45(b) of the Standards of (2 of 16) [CRLR-1569/2017] Weights & Measures (Enforcement) Act, 1985 (for short, 'Act of 1985'), rendered by Judicial Magistrate, 1st Class, Bikaner (for short, 'learned trial Court').
2. Succinctly stated, the facts of the case are that Reliance Marketing Pvt. Ltd. (for short, hereinafter referred to as 'Company'), in terms of its policy, retained Ex. Army Officers as Operator for handling and up-keeping of its petrol pumps all over India. The Company, in all, employed around 1200 Ex. Army Officers throughout Country with the stipulation that accounting be done by Automatic Tank Gauge (ATG), a non-standardized, non-registered equipment. The petitioner being one of the Operators, experienced that measurement of stock in Underground Tanks mostly gave erratic/wrong reading. Taking cognizance of discrepancies of the stock-in-hand vis-a-vis shown by ATG, petitioner lodged complaint with the Company officials. In response to the same, as per version of the petitioner, third respondent acknowledged variation in the stock. Be that as it may, when no encouraging steps were taken by the Company, petitioner laid a complaint with the Meteorology Department, Government of Rajasthan to ventilate his afflictions. Petition further unfurls that Inspector of Meteorology Department carried out inspection and unearthed that at the outlets unverified measuring equipments, i.e. ATG, are installed. The concerned Inspector then sent a communication to his superior officers on 18.03.2008 insinuating the Company for violation of Sections 19 & 22 of the Act of 1985.
(3 of 16) [CRLR-1569/2017]
3. As per version of the petitioner, communication of Inspector, Meteorology Department facilitated follow-up action and accused- respondents were served show cause notice on 05.04.2008. The notice was then replied by respondents intimating Assistant Controller, Legal Meteorology Department for granting 15 days' time in this regard. Petitioner also alleged that after receiving communication dated 07.04.2008 from the Company, Meteorology Department did not take any further action, presumably under the influence of Company and other respondents. This sort of situation prompted petitioner-complainant to lay criminal complaint against the respondents before learned trial Court. Complaint of the petitioner attributing offence under Section 39 of the Act of 1985 and offence under Sections 120, 166, 167 IPC (Annex.12) was supported by affidavit and other documents.
4. The learned trial Court, on receipt of the complaint, examined complainant on oath under Section 200 Cr.P.C. Later on, considering the statements of the complainant to be in consonance and conformity with the documents Ex.1 to 8 named in the complaint for offence under Section 39(1) & 45(b) of the Act of 1985, deferred proceedings qua respondents No.9 & 10, public servants, in absence of sanction for prosecution.
5. Feeling aggrieved by the order of learned trial Court, accused-respondents preferred two separate revision petitions before learned Court below. The learned Court below, by the (4 of 16) [CRLR-1569/2017] order impugned, allowed both the revision petitions and set aside order of cognizance. Learned trial Court recorded a definite finding in the impugned order that complainant-petitioner's grievance is concerned with some dispute of accounts and has no direct nexus with violation of any legal provisions. That apart, learned Court below also examined status of the petitioner as an aggrieved person within the meaning of Section 63 of the Act of 1985 and recorded a definite finding against him for upsetting the impugned order. Non-impleadment of Company as an accused too was found fetal by the learned Court below for setting aside order of cognizance.
6. Mr. Gurvinder Singh, petitioner, submits that learned Court below has seriously erred in upsetting the order of cognizance. Mr. Singh would contend that learned Court below has misconstrued the complaint while concluding that same is confined to dispute of accounts only. Petitioner, while relying on complaint, his statements under Section 200 Cr.P.C. and the documents in support thereof, contends that prima facie offences are made out against respondents. Petitioner has strenuously urged that learned Court below while reversing the order of cognizance completely overlooked his statements under Section 200 Cr.P.C. Mr. Singh, humble petitioner, while assailing the finding of learned Court below about his locus standi to maintain complaint, has urged that said finding is wholly perverse. It is argued by the petitioner that learned Court below has seriously erred in construing Section 63 of the Act of 1985 so as not to treat him an (5 of 16) [CRLR-1569/2017] aggrieved person. Relying on inspection report of the Inspector, Meteorology Department (Annex.5), petitioner submits that, on the face of it, prima facie, offences under the Act of 1985 are made out against the respondents.
7. Questioning the order impugned, petitioner would contend that his grievances were against officials of the Company, and therefore, finding of learned Court below to non-suit him solely for non-impleadment of Company, is not sustainable. Lastly, petitioner contends that learned Court below has over-stepped its jurisdiction in setting aside order of cognizance, against which scope of judicial review is very limited. In support of his arguments, learned counsel has placed on following judgments:
(1) Sheoratan Agarwal & Anr. Vs. State of Madhya Pradesh (AIR 1984 SC 1824) (2) State of Punjab Vs. Kasturi Lal & Ors. (AIR 2004 SC 4087) (3) U.P. Pollution Control Board Vs. M/s. Modi Distillery & Ors. (AIR 1988 SC 1128) (4) Raghubans Dubey Vs. State of Bihar (AIR 1967 SC 1167) (5) State of Madras Vs. C.V. Parekh & Anr. (AIR 1971 SC 447).
8. Per contra, learned Senior Counsel Mr. Mahesh Bora, appearing for accused-respondents, submits that bare perusal of the complaint makes it abundantly clear that petitioner is a proxy complainant, allegedly espousing cause pertaining to petrol pump of village Charkhada, Tehsil Nokha, District Bikaner, with which he has no nexus. Elaborating his submissions in this behalf, learned Senior Counsel has referred to Para 3 & 4 of the complaint and (6 of 16) [CRLR-1569/2017] communication from Inspector, Meteorology Department (Annex.6), which according him refers to same petrol pump. Learned Senior Counsel has further argued that expression "aggrieved person" envisaged under Section 63 of the Act of 1985 cannot be given expansive meaning so as to cover general grievance of any individual having no substantial grievance. Learned Senior Counsel, therefore, argued that the learned Court below has rightly set aside the order of cognizance by not treating the petitioner a person aggrieved within the four corners of Section 63 of the Act of 1985.
9. Mr. Bora, while laying emphasis on Para 9 of the complaint, submits that alleged afflictions of the petitioner are confined to accounts, which by no stretch of imagination can expose respondents for penal action within the four corners of Act of 1985 and therefore there is no illegality or impropriety in the impugned order warranting judicial review in exercise of revisional jurisdiction under Section 401 Cr.P.C. Learned Senior Counsel further submits that Company has closed its retail outlet at Khasra No.515, village Charkhada, Tehsil Nokha, District Bikaner, which is the edifice of entire complaint and order Annex.9 being anterior to the complaint itself, even proxy complaint does not bring the petitioner within the ambit of an aggrieved person. Learned Senior Counsel has, thus, argued that the order of cognizance by learned trial Court after eight months from the date of order Annex.9 has been rightly upset by the learned Court below. Learned Senior Counsel has vehemently argued that petrol pumps (7 of 16) [CRLR-1569/2017] are purchasing and getting supply of petrol from retail-outlets of the Company and therefore finding of the learned Court below that in absence of impleadment of Company as party to the complaint same is not maintainable, cannot be faulted being in conformity with Section 62 of the Act of 1985.
10. Learned Senior Counsel has also urged that complaint of the petitioner to competent authority of Meteorology Department, Government of Rajasthan, stood rejected before order of cognizance on 27th of November, 2008, therefore, it would be unjust to interfere with the impugned order. Romping in Section 9 & 22 of the Act of 1985, learned Senior Counsel has contended that equipment ATG, being installed in Underground Storage Tank, is clearly excluded from the rigors of Section 39 of the Act of 1985. In support of his arguments, learned Senior Counsel has placed reliance on following judgments:
i. State of Haryana Vs. Brij Lal Mittal & Ors. [(1998) 5 SCC 343] ii. Agarwal Agencies Mandi Gulwala Vs. State of Rajasthan [2004 (1) Criminal Law Reporter (Raj.) 774] iii. SMS Pharmaceuticals Vs. Neeta Bhalla [(2005) 8 SCC 89] iv. Saroj Kumar Poddar Vs. State (NCT of Delhi) & Anr. [(2007) 3 SCC 693].
11. I have given my anxious considerations to the arguments advanced at Bar, perused the impugned order and scanned materials available on record. The legal precedents relied upon (8 of 16) [CRLR-1569/2017] by the rival parties are also examined by me in the backdrop of factual matrix.
12. The term 'cognizance' is not defined under Cr.P.C. It's literal meaning is awareness, realization or knowledge; notice, perception. Under the criminal law, cognizance takes place at a point when the Magistrate first takes judicial notice of an offence. Supreme Court in case of Ajit Kumar Palit Vs. State of West Bengal (AIR 1963 SC 765), while construing the word 'cognizance" has observed that it has no esoteric or mystic significance in the criminal law or procedure. It merely means become aware of and when used with reference to a Court or Judge to take notice of judicially. The word "cognizance" occurring in various sections in the Cr.P.C. is a word of wide import. It embraces within itself all powers and authority in exercise of jurisdiction and taking of authoritative notice of the allegations made in the complaint or a police report or any information received that offence has been committed. In the context of sections 200, 202 and 203, the expression "taking cognizance" has been used in the sense of taking notice of the complaint or the first information report or the information that offence has been committed on application of judicial mind. In the instant case, learned Court below, while exercising its revisional jurisdiction under Section 397 Cr.P.C., has upset the order of cognizance by citing three pivotal reasons, which are: (1) Complainant is not an aggrieved person within the meaning of Section 63 of the Act of 1985, (2) Essentially, grievance of the petitioner is about (9 of 16) [CRLR-1569/2017] accounts, and (3) non-impleadment of Company, which is responsible for supply of petrol to various petrol pumps. The judgment in Raghubans Dubey (supra), relied upon by the petitioner, is simply dilating on taking cognizance of offence on a police report while making distinction that mere summoning an individual by the Magistrate does not amount to taking cognizance of an offence. The Court has further clarified that it means cognizance of offence and not offenders. Thus, this verdict in the context of facts of the instant case renders no assistance to the cause of the petitioner.
13. In order to make judicial scrutiny of finding recorded by the learned Court below to exclude petitioner from the purview of an "aggrieved person", it would be just and appropriate to examine Section 63 of the Act of 1985. Section 63 of the Act of 1985, reads as under:
63. Cognizance of offences.- Notwithstanding anything contained in the Code of Criminal Procedure, 1973 (2 of 1974),
(a) No court shall take cognizance of an offence punishable under this Act except upon a complaint, in writing, made by--
i. Controller;
ii. Any other officer authorised in this behalf by the Controller by general or special order; iii. Any person aggrieved; or iv. A recognised consumer association whether the person aggrieved is a member of such association or not.
Explanation.--For the purposes of this clause "recognised consumer association" means a voluntary consumer association registered under the Companies Act, 1956 (1 of 1956) or any other law for the time being in force;
(10 of 16) [CRLR-1569/2017]
(b) No court inferior to that of a Metropolitan Magistrate or Judicial Magistrate of the first class shall try any offence punishable under this Act.
A bare reading of above quoted Section 63 of the Act of 1985 makes it abundantly clear that Court can take cognizance of an offence punishable under the Act upon a complaint by any person aggrieved. The Act of 1985 nowhere defines "person aggrieved".
14. The expression "aggrieved person" denotes an elastic, and to an extent, an elusive concept. It cannot be confined within the bounds of a rigid, exact and comprehensive definition. In Re. SIDEBOTHAM. Ex. Parte SIDEBOTHAM [1874-80] All E.R. 588 : (1980) 14 Ch.D. 458, James LJ, while construing the words "person aggrieved" said, 'but the words 'person aggrieved' do not really mean a man who is disappointed of a benefit which he might have received if some other order had been made. A 'person aggrieved' must be a person who has suffered a legal grievance, a man against whom a decision has been pronounced which has wrongfully deprived him of something, or wrongfully refused him something, or wrongfully affected his title to something'.
The word "aggrieved" refers to a substantial grievance and not merely illusory. Therefore, for treating someone an "aggrieved person", his grievance must be substantial and a fanciful suggestion of the grievance is insufficient. A merely sentimental grievance does not make a person "aggrieved".
(11 of 16) [CRLR-1569/2017]
15. Supreme Court, in case of S.A. Venkataraman Vs. The State (AIR 1958 SC 107), while examining general power of a Court to take cognizance of an offence vis-a-vis prohibition to the exercise that power by any provision of law, laid emphasis on interest of the party in the prosecution or its grievance by the commission of offence. The Court held:
"In our opinion, if a general power to take cognizance of an offence is vested in a Court, any prohibition to exercise of that power, by any provision of law, must be confined to the terms of the prohibition. In enacting a law prohibiting the taking of cognizance of an offence by a Court, unless certain conditions were complied with, the legislature did not purport to condone the offence. It was primarily concerned to see that prosecution for offences in cases covered by the prohibition shall not commence without complying with the conditions contained therein, such as a previous sanction of a competent authority in the case of a public servant, and in other cases with the consent of the authority or the party interested in the prosecution or aggrieved by the offence."
16. For testing the alleged afflictions of the petitioner to ascertain his status as a person aggrieved, it would be just and appropriate to examine the complaint and other allied documents. A bare reading of the complaint (Annex.12) ex-facie transpires nothing pertaining to petitioner's grievances vis-a-vis Act of 1985. The averments made in paragraphs 3 to 5 of the complaint are confined to inspection of the petrol pump at village Chakarda, Tehsil Nokha, District Bikaner and follow-up action by Company in this behalf besides inactions on the part of the officials of Meteorology Department. Likewise, paragraph 9 of the complaint simply unfurls past status of the petitioner as an Operator of the (12 of 16) [CRLR-1569/2017] Company. Therefore, in substance, there is no whisper in the complaint showing direct or indirect nexus or proximity of the petitioner with the petrol pump situated at village Chakarda, Tehsil Nokha, District Bikaner. From a bare perusal of Inspection Report (Annex.5) dated 15th of March 2008 prepared by Inspector, Meteorology Department, it is crystal clear that petrol pump at NH 89 on Nokha-Nagaur Road, village Chakarda, Tehsil Nokha, District Bikaner, is operated by one Col. B.L. Choudhary. Inspection Report is also signed by Col. B.L. Choudhary in the capacity of Operator of the petrol pump. Thus, complaint (Annex.12) clearly and unequivocally depicts petitioner a proxy complainant or rather a pseudo complainant. In these circumstances, even if an expansive meaning is given to the term "person aggrieved", I am afraid, petitioner cannot be treated as an aggrieved person within the four corners of Section 63 of the Act of 1985.
17. Switching on to second reason for upsetting the order of cognizance, suffice it to observe that recitals contained in paragraph 9 of the complaint are clearly portraying some accounting dispute of the petitioner with the Company. Accounting dispute between the petitioner and the Company, even if genuine, does not furnish him substantial grievance to maintain a complaint against respondents for alleged violation of provisions contained in Act of 1985 as any "person aggrieved" within the meaning of Section 63 of the Act.
Taking into considerations all these aspects, the impugned order to the extent it has zeroed status of the petitioner (13 of 16) [CRLR-1569/2017] as any person aggrieved on these counts cannot be faulted. The finding of the learned Court below in this behalf is, therefore, neither infirm, nor improper.
18. The third reason spelt out in the order impugned, to reverse cognizance order of the learned trial Court, is non-impleadment of the Company as an accused in the complaint. In order to examine correctness, legality or propriety of this finding, yet again it has became imperative for the Court to scrutinize complaint (Annex.12) and other documents annexed in support thereof. The complaint right from its first paragraph makes a reference about Company and alleged insinuations against it in succeeding paragraphs to highlight violation of the provisions of the Act of 1985. Paragraph 12 of the complaint contains specific allegation that Company has not obtained verification and stamping of STG from the Controller but for conspicuously silent about consent or connivance of the respondents in this behalf.
19. At this juncture, it would be apposite to refer Section 62 of the Act of 1985, which reads as under:
62. Offences by companies.-1. If the person committing an offence under this Act is a company, every person who, at the time the offence was committed, was in charge of, and was responsible to, the company for the conduct of the business of the company, as well as the company, shall be deemed to be guilty of the offence and shall be liable to be proceeded against and punished accordingly:
Provided that nothing contained in this sub- section shall render any person liable to punishment if he proves that the offence was committed without his (14 of 16) [CRLR-1569/2017] knowledge, or that he had exercised all due diligence to prevent the commission of such offence.
2. Notwithstanding anything contained in sub- section (1), where any offence under this Act has been committed by a company and it is proved that the offence has been committed with the consent or connivance of, or is attributable to, any neglect on the part of, any director, manager, secretary or other officer of the company, such director, manager, secretary or other officer shall also be deemed to be guilty of that offence and shall be liable to be proceeded against and punished accordingly.
Explanation.--For the purposes of this section,--
a. "company" means anybody corporate and includes a firm or other association of individuals; and b. "director", in relation to a firm, means a partner in the firm.
A cumulative reading of above quoted Section 62 of the Act of 1985 makes it abundantly clear that every person in-charge of and responsible for conduct of business of the company at the time offence was committed, as well as the company, shall be liable to be proceeded against and punished accordingly.
20. The judgments relied upon by the petitioner, in case of Sheoratan Agarwal and Kasturi Lal (supra), are based on interpretation of Section 10 of the Essential Commodities Act, 1955, but the text of Section 10 aforesaid, quoted in these judgments, do not contain the words "as well as the company", therefore, clearly distinguishable. Likewise, judgment of Supreme Court in C.V. Parekh (Supra), which is relied upon in subsequent judgment of Sheoratan Agarwal (supra), is also based on (15 of 16) [CRLR-1569/2017] construing Section 10 of the Essential Commodities Act, 1955, without finding mention of the words "as well as the company", too, thus, cannot render any assistant to the cause of the petitioner. The judgment of Supreme Court in M/s. Modi Distillery (supra) is based on interpretation of Section 47 of the Water (Prevention & Control of Pollution) Act, 1974, which is pari materia to Section 62 of the Act of 1985, therefore, its ratio decidendi requires due consideration in the backdrop of facts of the instant case. While concurring with the ratio decidendi of the verdict of Supreme Court, in my considered view, the complaint (Annex.2) being bereft of any specific allegation against the respondents that alleged offences are committed by the Company with the consent or connivance of the respondents, or are attributable to any neglect on their part, the impugned order cannot be categorized as infirm on this count.
21. My aforesaid view is also fortified by a judgment of Supreme Court in Brij Lal Mittal & Ors. (supra), wherein, while construing Section 34 of the Drugs and Cosmetics Act, 1940, Court declined to fasten vicarious liability for an offence committed under the Act by the company on its Director in absence of prima facie allegations against them that they were in charge of the company and also responsible to the company for conduct of its business. The Court held:
"It is thus seen that the vicarious liability of a person for being prosecuted for an offence committed under the Act by a company arises if at the material time he was in-charge of and was also responsible to the company for the conduct of its business. Simply (16 of 16) [CRLR-1569/2017] because a person is a director of the company it does not necessarily mean that he fulfils both the above requirements so as to make him liable. Conversely, without being a director a person can be in-charge of and responsible to the company for the conduct of its business. From the complaint in question we, however, find that except a bald statement that the respondents were directors of the manufacturers, there is no other allegation to indicate, even prima facie, that they were in-charge of the company and also responsible to the company for the conduct of its business."
Same view is reiterated by Supreme Court in Neeta Bhalla and Saroj Kumar Poddar (supra) in respect of offence under Section 138 of the Negotiable Instruments Act, 1881.
22. On an overall analysis of the facts of the case, and in the context of elucidation of the law governing the province of the Court's power to take cognizance of an offence, duly circumscribed by any provision of law, unhesitatingly, in my opinion, no illegality or impropriety is forthcoming from the impugned order warranting interference in exercise of revisional jurisdiction.
Resultantly, the instant revision petition fails and the same is hereby dismissed.
(P.K. LOHRA)J.


User Testing of Mobile Site
For this assigment, I asked testers to navigate the mobile website for The Oxford Hotel, a boutique hotel in Bend, Oregon.
Purpose of Test:
- The main purpose of this test was to examine the usability of this mobile site and the room search feature in particular, and compare findings with my previous analysis of the desktop site.
Testers (5):
- Ages 14, 16, 40-49, 40-49, 50-59
As in the last study, these testers were asked to take look at the home page and gave their initial impressions. They were then asked to attempt some specific tasks.
I expected that this group of testers would encounter the same issues with finding a room with a kitchen, and might have trouble returning to the home screen from an incomplete reservation.
Home Page: Overall Impressions
Scrolling through the home page, three of the testers commented on the "bouncing" arrow. It wasn't clear to one if this meant scroll down, or click on the location button which the arrow was pointing to.
One tester said that the text that appeared over the images was distracting and got in the way of the pictures.
The testers commented that it seemed like a nice hotel, and somewhat fancy.
Two of the testers read some of the blurbs on the site out loud as they scrolled -- "eco-chic" and "urban-organic". The testers were amused by these phrases (which is probably not the reaction the hotel is hoping for).
Scenario 1: Find a room with a kitchen for 1 adult for 2 nights in May.
Most of the testers went to the hamburger menu and guessed that Sleep would get them to rooms, but seemed puzzled by use of the label "Sleep" here.
The Room option under Sleep displayed a list of rooms by name (Cascade Suite, for example). Two testers started clicking on the room titles which brought up pictures and a paragraph description of the room. One tester commented that a list of amenities rather than a paragraph to read through would be better. These testers abandoned this method and went to "Book Now" with specific dates. This brought up a few room options, and also the amenities icons where they found kitchens.
Two testers started with Book Now. After some frustration trying to find a kitchen by images and text, one guessed that "suites" would have a kitchen, starting looking at suites, and found images under one of the suites that included a kitchen. The other tester spent some time looking at images until finally finding one that included a kitchen.
The last tester also started with Book Now, scrolled past the photos and paragraphs of text, zeroing in on the icons and quickly found a kitchen.
Scenario 2: What activities or attractions are nearby?
As with the analysis of the full site, this turned out to be a test to see how easy it was to get back to the home page from an abandoned reservation. Two of the testers tried the back button which resulted in frustration. One eventually tried the logo, saying this wasn't obvious, but it worked. The other got stuck in endless buffering, and ended up typing in the url again. The three other testers used the hotel icon/logo to return home.
The testers were all able to find activities nearby through the Adventure and Experience menu options. One tester found the activity map and used that to find what was nearby. Another tester who had completed the test for me earlier and was watching, had not realized this map existed. It took some scrolling to get to, and this tester's device was older and smaller than everyone elses.
Scenario 3: Are there places to eat out nearby?
The testers all went back to Dining in the hamburger menu. One went to Experience first before remember there was a Dining option.
Usability Summary
Usability testing of the Oxford Hotel mobile website revealed that it has some of the same issues as the full website, and a few new issues. Three of the testers had buffering issues when searching for rooms by date. One eventually typed in the hotel website address again. Buffering/slowness wasn't an issue with the full site.
The hamburger menu and diplay of menu options vertically seems like a plus for the mobile site. The testers seemed to remember the options after seeing them once, and returned to the hamburger menu quickly. The horizontal display of options on the full site gives users more to think about, and seems fine for a full site where a user might be willing to spend more time poking around.
Using the label Sleep to find rooms doesn't really make sense, however. The testers seemed puzzled by this. A suggestion for improvement would be to keep this simple and use the expected term Rooms instead.
As was the case with the full site, returning home from the booking process wasn't easy for all the testers. Having a more obvious home button is another suggestion for improvement.
Some testers were more willing than others to read through text to find what they need. The preference seems stronger on a mobile site for information to be presented in lists or images rather than in paragraphs of text.
As with the full site, another suggestion for improvement would be to allow users to choose the amenities they want and filter their search by amenities. Another option could be to have the amenities icons display first, rather than the descriptive paragraphs.
Although this site seems mostly well designed for mobile use, some of the design features were distracting to the testers. The bouncing arrow, which is unobtrusive on the full site, is large and was annoying to some of the testers on the mobile version.
The text boxes which only cover a fraction of the images on the full site, end up almost covering the entire images on the mobile site.
What works well on a full screen, is a distraction on a mobile device. It doesn't seem like these design elements were adjusted for mobile use. The images on this site are very nice and it seems a shame to cover them up with unnecessary text.
A suggestion for improvement would be to eliminate or separate the text from the images.
I thought it was interesting that the younger testers this week, as well the youngest tester last week, did not use the back button to try and return home. They knew the hotel name in the upper left corner would get them home, because as one tester stated, "this is what usually works", even though they also noted that this particular logo did not look clickable. This makes me wonder if having the company logo as home button became standard later on, and some of us who are older might still be using the back button mostly out of habit.


Note: the errors listed above may actually appear due to reasons different from described ones.
Report itself shows errors matching specific pattern and groups them according to this common pattern. So, the information
viewed in the report may just provide an idea of what could probably happen but more precise analysis is still needed.


Use PgnViewerJS for displaying a game in typical notation, with diagrams and different styles for the moves, the boards, ... Looks similar to the style of some magazines or books. For the diagram, I have taken the NAG 'D' (or $220), see NAGs at Wikipedia for details


Protocols
The following protocols are available globally.
-
A protocol for specialized
ModalDismisser
s presented byGlobalModalPresenter
s. You can use some stubs or spies instead of actual classes for testing.See moreSee also
GlobalModalDismisserUsages
.Declaration
Swift
public protocol GlobalModalDismisserProtocol
-
A protocol for specialized ModalPresenters that can present a UIViewController on a top of view hierarchy unconditionally. You can use some stubs or spies instead of actual classes for testing.
See moreSee also
GlobalModalPresenterUsages
.Declaration
Swift
public protocol GlobalModalPresenterProtocol
-
A protocol for wrapper classes that encapsulate some methods of
UIWindow
. You can use some stubs or spies instead of actual classes for testing.See moreSee also
KeyWindowMakerUsages
.Declaration
Swift
public protocol KeyWindowMakerProtocol
-
A protocol for wrapper classes that encapsulate
UIViewController#dismiss(animated: Bool)
. You can use some stubs or spies instead of actual classes for testing.See moreSee also
ModalDismisserUsages
.Declaration
Swift
public protocol ModalDismisserProtocol
-
A protocol for wrapper classes that encapsulate
UIViewController#present(_: UIViewController, animated: Bool)
. You can use some stubs or spies instead of actual classes.See moreSee also
ModalPresenterUsages
.Declaration
Swift
public protocol ModalPresenterProtocol
-
A protocol for wrapper classes that encapsulate
UINavigationController#pushViewController(_:UIViewController, animated:Bool)
. You can use some stubs or spies instead of actual classes for testing.See moreSee also
NavigatorUsages
.Declaration
Swift
public protocol NavigatorProtocol
-
A protocol for wrapper class that encapsulate a getter for
UIViewController.presentingViewController
. You can use some stubs or spies instead of actual classes for testing.See moreSee also
PresentingViewControllerReaderUsages
.Declaration
Swift
public protocol PresentingViewControllerReaderProtocol
-
Declaration
Swift
public protocol PrinterProtocol
-
A type for wrapper classes of
UINavigationController#(_:UIViewController, animated:Bool)
. You can use some stubs or spies instead of actual classes for testing.See moreSee also
ReverseNavigatorUsages
.Declaration
Swift
public protocol ReverseNavigatorProtocol
-
A protocol for wrapper classes that encapsulate a getter of rootViewController. You can use some stubs or spies instead of actual classes for testing.
See moreDeclaration
Swift
public protocol RootViewControllerReaderProtocol : AnyObject
-
A protocol for wrapper class that encapsulate a setter for
See moreUIWindow.rootViewController
. You can use some stubs or spies instead of actual classes for testing.Declaration
Swift
public protocol RootViewControllerWriterProtocol : AnyObject
-
A type for wrapper classes that encapsulate an implementation of
UIApplication#open
.See moreSee also
URLOpenerUsages
Declaration
Swift
public protocol URLOpenerProtocol
-
A protocol for wrapper classes that encapsulate the implementation of
UIApplication#windows
. You can use some stubs or spies instead of actual classes for testing.See moreSee also
WindowsReaderUsages
Declaration
Swift
public protocol WindowsReaderProtocol


Home Island lies 1.5NM south of the Direction Island anchorage. It has a population of approximately 600.
The population is mainly Muslim. Please respect their dress code.
It is advisable to follow the channel markers when moving from Direction Island to Home Island as there are numerous shallow areas and coral outcrops along the route.
Position 12° 06'58"S, 96° 53'33"E
The FEES payable for anchoring in Cocos Keeling are paid at the Shire Office on Home island.
No garbage can currently be disposed of anywhere in Cocos Keeling. Yachts must take everything away with them.
Last updated July 2016.


3 references to EventWriteException
Microsoft.PowerShell.Commands.Diagnostics (3)
NewWinEventCommand.cs (3)
166
throw new
EventWriteException
(msg);
195
throw new
EventWriteException
(msg);
205
throw new
EventWriteException
(msg);


Q
- The type of the queries the cached resources should be associated
to.public abstract class Cache<R extends DataResource>
extends java.lang.Object
| Constructor and Description |
|---|
Cache()
Initializes the cache without a limitation for the count of contained
entries.
|
Cache(int maxEntries)
Initializes the cache limiting the count of resources to cache at
maximum.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
add(R resource) |
void |
clear()
Removes all entries from the cache.
|
R |
get(java.lang.String key)
Returns the element mapped to
key . |
java.util.Collection<R> |
getAll(java.lang.String key)
Returns the collection of resources associated with
key . |
int |
getMaxEntries()
Returns the maximum count of key-resources-associations permitted in cache.
|
void |
setMaxEntries(int maxEntries)
Sets the maximum count of key-resources-associations permitted in cache.
|
int |
size()
Returns the number of associations between a key and a resource currently cached.
|
public Cache()
public Cache(int maxEntries)
maxEntries
- The count of resources this cache should manage at maximum.public R get(java.lang.String key)
key
. If a collection of resources
was associated with the key only the first will be returned. In this case use
getAll()
to get the whole collection.key
- The key the resource is mapped to.key
or null
if there is
none.public java.util.Collection<R> getAll(java.lang.String key)
key
.key
- The key the collection is mapped to.key
or null
if there is none.public boolean add(R resource) throws java.lang.IllegalArgumentException
resource
- The resource to be added to the cache.java.lang.IllegalArgumentException
- Thrown if resource
is null
or the
dummy-flag in the resource is set and it is not a valid
resource.public void clear()
public int size()
public int getMaxEntries()
public void setMaxEntries(int maxEntries)
maxEntries
- The maximum count of key-resources-associations permitted in cache.


Experienced Senior QA Engineer with over 21 years of experience supporting, testing, and coordinating large test efforts in the financial services and logistics industries. In addition to the primary functions of application support and testing, this included leading and prioritizing the work of others, coordinating test strategies and release schedules across globally dispersed teams, defining and implementing QA processes and procedures, and mentoring junior QA Engineers and interns.  I’m currently enrolled in UNC Chapel Hill’s Full Stack Web Development Coding Boot Camp. Expanding my skill set to include development using HTML, CSS, JavaScript, jQuery, Bootstrap, Handlebars, Firebase, Node, Express, mySQL, Sequelize, MongoDB, and React.
The Crystals Collector Game is a jQuery-based game where you attempt to match your score to a random number by clicking 1 of 4 crystal buttons, each with their own random value.
View Deployed ProjectTithe is a modern web application for contributing to your favorite charity. Tithe is sourced by the Charity Navigator, Unsplashed, and Google Maps Embed APIs. It allows the user to choose a state and get a list of charities back for that state. Users can save and “spotlight” charities they like in a section that persists when the users leave the site. Tithe filters the charities returned by the Charity Navigator API to only return rated charities.
View Deployed ProjectAn interactive Amazon-like storefront with 3 "views" (Customer, Manager, Supervisor) that is run via Node in a Terminal window.
View Deployed ProjectLIRI is like iPhone's SIRI. However, while SIRI is a Speech Interpretation and Recognition Interface, LIRI is a Language Interpretation and Recognition Interface. LIRI is a command line Node app that takes in parameters and gives you back data from the Twitter, Spotify, and OMDB APIs.
View Deployed ProjectA compatibility-based "Friend Finder" application. This full-stack site will take in results from your users' surveys, then compare their answers with those from other users. The app will then display the name and picture of the user with the best overall match. Deployed via Heroku.
View Deployed ProjectsequelizeBurger is a sequelized (i.e., mySQL --> Sequelize) version of Eat-Da-Burger, a restaurant burger logger app that lets users input the names of burgers they'd like to eat. Click the [Devour it!] button to devour the burger, click the [YUM!] button to "resurrect" the burger and make it "devourable" again. The app will store every burger in the database, whether devoured or not.
View Deployed ProjectA web app that lets users view, save, and attach notes to the latest articles displayed at NPR's website. Whenever a user visits the site, the app scrapes stories and display them for the user. The user can then save articles for later viewing. Once an article is saved, the user can attach notes to the article. Each scraped article, and its corresponding notes, is saved to a Mongo database.
View Deployed ProjectA memory game created with React. This application renders different images to the screen. Each image listens for click events. The application keeps track of the user's score. The user's score is incremented when clicking an image for the first time. The user's score resets to 0 and the game restarts if they click the same image more than once. Every time an image is clicked, the images rendered to the page are shuffled in a random order.
View Deployed ProjectA personal digital cookbook created with React. YumBot allows the user to create an inventory of ingredients on hand, then submit a search to the Recipe Puppy API to return recipes that include one or more of those ingredients. Users can "favorite" recipes to have them saved to their own Favorite Recipes Page. They can also save and edit their own recipes via their own Personal Recipes Page. Once saved, the recipes can be printed, emailed or shared via Twitter.
View Deployed Project


Powered by Github Jantar is completly free and open source. Feel free to clone, fork,
contribute, watch and star.
The MIT Licence fully embraces the open source spirit and gives developer the
freedom they need and deserve.
Jantar is a passion project by a single person. Without deadlines Jantar can grow gracefully and evolve with changing needs and requirements.
While trying to stay light, the main focus is on security, stability and performance.
SSL 2.0 is broken in many ways and should never be used. Even SSL 3.0, while being better than 2.0 cannot be validated
under FIPS 140-2 because of its weak key derivation process.
That's why Jantar only accepts TLS 1.0 or higher.
Jantar provides strong prefered cipher by default to provide the best possible protection possible.
Some example cipher are:
Some bugs like Heartbleed are so critical that they require immediate actions taken. Jantar is always up to date and will warn you about any unusual events that need your attention.


EuroScorer - Home
Vote
EuroScorer
Online Edition for the Fans
Vote
The candidates
EuroScorer online Edition for the Fans Final Candidates Presentation
To use this website thank's to activate JavaScript / Pour utiliser ce site, merci d'activer JavaScript.


Types of Machine Learning:
1.Supervised Learning:
In this type of learning the model is trained to predict output from the future inputs.(There is a teacher involved to teach and correct the students)
Eg. For small children there is a teacher to who shows some pictures and make them to identify it. The output is well known before.
2.Unsupervised Learning:
In this type of learning the model is trained to find existing patterns in the data to learn more about it (There is no teacher involved to teach and correct the students, they all learn by them self).The system tries to get some output and compare it with desired output and update it till both become equal.
Eg. There is no teacher for fish to teach swimming. Fish knows it from its birth, it just observes other fishes pattern and improvise itself.
3.Reinforcement Learning:
Reinforcement learning is a type of machine learning algorithm that allows the agent to decide the best next action based on its current state, by learning behaviours that will maximize the reward.
Reinforcement algorithms usually learn optimal actions through trial and error. They are typically used in robotics – where a robot can learn to avoid collisions by receiving negative feedback after bumping into obstacles, and in video games – where trial and error reveals specific movements that can shoot up a player’s rewards. The agent can then use these rewards to understand the optimal state of game play and choose the next action.
Machine Learning Algorithm
1. Decision Tree:
This algorithm falls in the category of Supervised Learning and in this Algorithm a tree is plotted for all the features based upon their information gain. The attribute with highest Information gain will come at the Top as it is having the maximum influence in deciding the output.
Now in order to implement Decision Tree we need to find,
- Entropy
- Gini Index
- Information Gain
Step1: We have to find the Entropy of every attribute both parent as well as children attribute in the dataset with respect to the output attribute.
Entropy can be calculated by using the formula E(X) = ∑- p(x)log 2 p(x) for all x € X
Step2: Now we have to find the information gain for all the Target values with the formula
Information Gain(IG) = E(Target) - E(Target, Attribute)
Step3: Select the Attribute which is having higher Information Gain because, Attribute Priority(Feature) ∝ Information Gain Hence the Attribute with high Information Gain will come at the top(root node).
2. Random Forest
As shown in the above Figure, Random Forest is a collection of Random Decision Tree.
In order to implement Random Forest,
Step1: Select some random sets of Data from the available Dataset.
Step2: Apply the Decision Tree Algorithm on it.
Step3: Get the output by input the testing data in all Decision Tree.
Step4: Output from majority of the Trees will be the Result
3. Naive Bayse:
This whole Algorithm Works on the Concept of Probability.
Implementation:
Step 1: Find the probability for all the possibilities in all the features with respect to the output.
This can be done by using the formula:
P(A/B) = P(A ᴖ B )/P(B)
Here A is the different possibilities in the features, and B is the output value i.e. (either Yes or No)
Step 2: Once we have found all the possibilities, now test it for the values from the testing dataset.
eg.
4. K-Nearest Neighbors Algorithm:
It is one of the very simple Algorithm. It is based upon the concept of Distance between the new data points and training data points.
Implementation:
Step1: Check for all the different possibilities in the dataset.
Suppose you have a dataset with two variables, which when plotted, looks like the one in the following figure.
Step 2: Now check the Distance for the new input value, here the distance can be Euclidian distance or Manhattan distance. The testing attribute will belongs to the category with which it is at least distance.
Your task is to classify a new data point with 'X' into "Blue" class or "Red" class. The coordinate values of the data point are x=45 and y=50. Suppose the value of K is 3. The KNN algorithm starts by calculating the distance of point X from all the points. It then finds the 3 nearest points with least distance to point X. This is shown in the figure below. The three nearest points have been encircled.
The final step of the KNN algorithm is to assign new point to the class to which majority of the three nearest points belong. From the figure above we can see that the two of the three nearest points belong to the class "Red" while one belongs to the class "Blue". Therefore the new data point will be classified as "Red".
5. Linear Regression:
It is the most basic and renowned Machine Learning Algorithm. In this Algorithm, a line of best fit (line at a least distance from all the data points) is formed from all the data points.
Implementation:
Step 1: Form an Equation for line of best fit on the dataset. (For example: y = b0 + b1 * x + b2*x2)
Step 2: Input testing attributes in the equation and calculate the output.
6. Apriori Algorithm:
It is a statistical algorithm which has been developed to implement association rule mining.
It is a statistical algorithm which has been developed to implement association rule mining.
- Support
- Confidence
- Lift
Support refers to the default popularity of an item and can be calculated by finding number of transactions containing a particular item divided by total number of transactions.
Confidence refers to the likelihood that an item B is also bought if item A is bought.
Lift(A -> B) refers to the increase in the ratio of sale of B when A is sold.
Implementation:
- Set a minimum value for support and confidence.
- Filter all the attributes having higher value of support than minimum threshold.
- Select all the rules from the subsets with confidence value higher than minimum threshold.
- Order the rules by descending order of Lift.
7. Logistic Regression:
This Algorithm is very similar to Linear Regression but here it uses a logistic function to model a binary variable based on any kind of independent variables.
Implementation:
Step 1: Take the dataset inputs and apply the logistic function.
Step 2: Now a boundary will be formed for all the possible outputs.
Step 3: Test it with the testing dataset.
8. Support Vector Machines:
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. They can do linear classification, but can use other non-linear basis functions.
Implementation:
- Input the labelled dataset.
- This Algorithm will output the optimal hyperplane with proper categorization.
- Testing Value can fall in anyone of the above plane.
9. Principal Component Analysis:
Principal component analysis(PCA) is a statistical technique to convert high dimensional data to low dimensional data by selecting the most important features that capture maximum information about the dataset.
The features are selected on the basis of variance that they cause in the output. The feature that causes highest variance is the first principal component. The feature that is responsible for second highest variance is considered the second principal component, and so on. It is important to mention that principal components do not have any correlation with each other.
10. AdaBoost Classifier:
Adaboost stands for Adaptive Boosting.
Implementation:
Step 1: Start with 1 decision tree stump to make a decision on 1 input variable.
Step 2: Move to another decision tree stump to make a decision on another input variable.
Step 3: Train another decision tree stump to make a decision on another input variable.
Step 4: Combine the decision stumps.


Line data Source code
1 : use crate::{Uint32, Uint64}; 2 : use ckb_types::core::EpochNumberWithFraction as CkbEpochNumberWithFraction; 3 : 4 : /// Consecutive block number starting from 0. 5 : /// 6 : /// This is a 64-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint64](type.Uint64.html#examples). 7 : pub type BlockNumber = Uint64; 8 : /// Consecutive epoch number starting from 0. 9 : /// 10 : /// This is a 64-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint64](type.Uint64.html#examples). 11 : pub type EpochNumber = Uint64; 12 : /// The epoch indicator of a block. It shows which epoch the block is in, and the elapsed epoch fraction after adding this block. 13 : /// 14 : /// This is a 64-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint64](type.Uint64.html#examples). 15 : /// 16 : /// The lower 56 bits of the epoch field are split into 3 parts (listed in the order from higher bits to lower bits): 17 : /// 18 : /// * The highest 16 bits represent the epoch length 19 : /// * The next 16 bits represent the current block index in the epoch, starting from 0. 20 : /// * The lowest 24 bits represent the current epoch number. 21 : /// 22 : /// Assume there's a block, which number is 11555 and in epoch 50. The epoch 50 starts from block 23 : /// 11000 and have 1000 blocks. The epoch field for this particular block will then be 1,099,520,939,130,930, 24 : /// which is calculated in the following way: 25 : /// 26 : /// ```text 27 : /// 50 | ((11555 - 11000) << 24) | (1000 << 40) 28 : /// ``` 29 : pub type EpochNumberWithFraction = Uint64; 30 : /// The capacity of a cell is the value of the cell in Shannons. It is also the upper limit of the cell occupied storage size where every 100,000,000 Shannons give 1-byte storage. 31 : /// 32 : /// This is a 64-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint64](type.Uint64.html#examples). 33 : pub type Capacity = Uint64; 34 : /// Count of cycles consumed by CKB VM to run scripts. 35 : /// 36 : /// This is a 64-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint64](type.Uint64.html#examples). 37 : pub type Cycle = Uint64; 38 : /// The Unix timestamp in milliseconds (1 second is 1000 milliseconds). 39 : /// 40 : /// For example,<PHONE_NUMBER>000 is Thu, 30 Apr 2020 07:59:38 +0000 41 : /// 42 : /// This is a 64-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint64](type.Uint64.html#examples). 43 : pub type Timestamp = Uint64; 44 : /// The simple increasing integer version. 45 : /// 46 : /// This is a 32-bit unsigned integer type encoded as the 0x-prefixed hex string in JSON. See examples of [Uint32](type.Uint32.html#examples). 47 : pub type Version = Uint32; 48 : 49 : /// This trait is a restriction for type `Uint64`, so we can only get epoch_number, epoch_index 50 : /// and epoch_length from the type `EpochNumberWithFraction` instead of all `Uint64` 51 : pub trait AsEpochNumberWithFraction { 52 : /// Return the epoch number of current block 53 : fn epoch_number(&self) -> u64; 54 : /// Return the index in epoch of current block 55 : fn epoch_index(&self) -> u64; 56 : /// Return the epoch length of current block 57 : fn epoch_length(&self) -> u64; 58 : } 59 : 60 : impl AsEpochNumberWithFraction for EpochNumberWithFraction { 61 0 : fn epoch_number(&self) -> u64 { 62 0 : (self.value() >> CkbEpochNumberWithFraction::NUMBER_OFFSET) 63 0 : & CkbEpochNumberWithFraction::NUMBER_MASK 64 0 : } 65 : 66 0 : fn epoch_index(&self) -> u64 { 67 0 : (self.value() >> CkbEpochNumberWithFraction::INDEX_OFFSET) 68 0 : & CkbEpochNumberWithFraction::INDEX_MASK 69 0 : } 70 : 71 0 : fn epoch_length(&self) -> u64 { 72 0 : (self.value() >> CkbEpochNumberWithFraction::LENGTH_OFFSET) 73 0 : & CkbEpochNumberWithFraction::LENGTH_MASK 74 0 : } 75 : }


The hotel's original building was commissioned by Tata and first opened its doors to guests on 16 December 1903.
It is widely believed that Jamsetji Tata decided to build the hotel after he was refused entry to one of the city's grand hotels of the time, Watson's Hotel, as it was restricted to "whites only". However, this story has been challenged by some commentators that suggest that Tata was unlikely to have been concerned with 'revenge' against his British adversaries. Instead, they suggest that the Taj was built at the urging of editor of The Times of India who felt a hotel "worthy of Bombay" was needed
The original Indian architects were Sitaram Khanderao Vaidya and D. N. Mirza, and the project was completed by an English engineer, W. A. Chambers. The builder was Khansaheb Sorabji Ruttonji Contractor who also designed and built its famous central floating staircase. The cost of construction was £250,000 (£127 million in 2008 prices)
Originally the main entrance was on the other side, where now the pool exists, and the ocean was at the back, although it is now always viewed and photographed from the ocean side.
Between 1915 and 1919, work proceeded at Apollo Bundar to reclaim the land behind the hotel where the Gateway of India was built in 1924. Gateway of India soon became a major focal point in Bombay.
The original clientele were mainly the Europeans, the Maharajas and the elites. Many world-renowned personalities have since stayed there, from Somerset Maugham and Duke Ellington to Lord Mountbatten and Bill Clinton.
When it opened in 1903, the hotel was the first in India to have: electricity, American fans, German elevators, Turkish baths and English butlers. Later it also had the city’s first licensed bar, India’s first all-day restaurant, and the India’s first discotheque, Blow Up.
Initially in 1903, it charged Rs 13 for rooms with fans and attached bathrooms, and Rs 20 with full board.
During World War I the hotel was converted into a hospital with 600 beds.
Jinnah's estranged wife Ratanbai Petit lived here during her last days in 1929 (her mother belonged to the Tata family).By 1966, the building was run-down, perhaps as a results of losing the British customers in 1948.The Taj Hotel was home to legendary Jazz musician Micky Correa, "The Sultan of Swing" from 1936-1960.The Taj Mahal Tower, an additional wing of the hotel, was opened in 1973.It was designed by Melton Bekker.[19] Also in 1970s Taj Hotels Resorts and Palaces was organized that built new properties and converted palaces into heritage hotels. In 1980, it expanded overseas.
The hotel received extensive international exposure in 2008 and reopened after extensive repairs.


Example template of creating a blog with Blender (mimicking Bootstrap's).
blender.repo
to "/acrodrig/acrodrig.github.io"
the current HTML page loads those pages and blogs posts dynamically. Additionally, it displays the
plugin toc.html
on the right.


MRS STELLA ODUNWA OJIYOVWI
Western Deputy Zonal Director
National Directorate of Employment
Lagos State
08023186771.
MRS NGOZI JULIANNA EDET
Principal Executive Officer
National Directorate of Employment
Lagos State
08028898627.


[viewBag] snippetProperties[name][title] = "name" snippetProperties[name][type] = "string" snippetProperties[name][default] = "" snippetProperties[name][options][] = "" [emptyForm] messages_success = "Your form was successfully submitted" messages_errors = "There were errors with your submission" inline_errors = "disabled" sanitize_data = "disabled" anonymize_ip = "disabled" recaptcha_theme = "light" recaptcha_type = "image" recaptcha_size = "normal" ==


Join the numbers and get to the 2048 tile!
How to play: Use your arrow keys to move the tiles. When two tiles with the same number touch, they merge into one!
Created by Somacode Class.


How Well Do You Really Know Your Halogen
Torchiere?
For many of us, the halogen torchiere
is like an old friend. You may have met in college, and traveled
together to your first apartment. Now, the two of you may share
an office-either at home or at work. Across the country, an amazing
50 million torchiere-style halogen floor lamps are in use one for
every two households! Many people have
two or three. These graceful lamps are versatile enough to bring
the brilliance of the midday sun to the darkest room, or provide
just the right touch of ambiance with a soft, amber glow...all for
a mere $20, $15, or even $10 apiece.
Unfortunately, there is a catch. That
"warm glow" is a lot warmer than you'd probably think! As this infrared
photograph illustrates, the halogen torchiere on the right gets
far hotter than the fluorescent model next to it, yet produces a
similar amount of useful light.
Fire,
Safety and Energy Issues
The Heat Is On.
The bulb in your halogen torchiere
can get as hot as 700 to 1,100° F.  In fact, a halogen bulb is so hot
that it can cook an egg in three to five minutes. (Don't try this
at home!) This extreme heat isn't simply surprising--it's dangerous.
Halogen torchieres already have caused
350 fires, 32 deaths, and 114 injuries in the U.S. alone. Why do
these bulbs get so hot? Because they guzzle electricity. Every ten
watts of power consumed by a halogen torchiere bulb yield nine watts
of heat and only one watt of visible light. No wonder these lamps
require 300 or even 500 watts to light a room.
New halogen torchieres are equipped
with an ugly metal cage intended to keep the hazardous bulb away
from flammable items. Although it improves the safety of the old
products, it misses the real point: Why cover up a dangerous light
source when you can eliminate the problem entirely--and save money,
too?
As Temperatures Rise, So Does Your
Energy Bill.
When you waste that much energy, you're
also wasting a lot of money--more each year than you paid for the
fixture to begin with. A typical halogen torchiere can cost as much
as $25 to $100 per year to operate. So...that lamp of yours isn't
such a great bargain, after all!
Fire
Map
As the map above demonstrates, halogen
torchieres have been responsible for safety problems throughout
the country. To better understate the scope of the safety problem,
Ecos Consulting examined the fires database from the
U.S. Consumer Product Safety Commission, records from insurance
companies and law firms, and press clippings to complete this map.
The current safety toll from halogen torchieres is:
Fires: 350
Deaths: 30
Injuries: 114
Property Damage: $2 million
You can help us improve the accuracy and completeness of these estimates
by reporting any fires you've experienced or read about in the space
provided below.
Our Involvement
The LightSite Quest: Finding A Better
Alternative to Halogen Torchieres
December, 1994
- Building The Prototype
Chris Calwell, a founder of Ecos Consulting
and member of the LightSite team began his study of the halogen torchiere
while working at the Natural Resources Defense Council in 1994. He was helping
the California utilities design and coordinate their rebate programs
for energy efficient compact fluorescent bulbs. He soon discovered
that high wattage halogen torchieres were selling in rapidly increasing
numbers in home centers and discount stores. Recognizing that their
300 to 600 watt lamps were rapidly eroding all of the energy savings
that had been achieved with compact fluorescent bulbs, he resolved
to find a solution. In December 1994, he and Jon Koomey, a Lawrence Berkeley Laboratory
(LBNL) scientist, built the first three way compact fluorescent
prototype torchiere in Jon's workshop. The prototype was a jumble
of wires, electrical tape, and Velcro, but it worked, proving the
viability of the concept.
1995 - Spreading
The Word
Chris demonstrated the fixture at a national lighting meeting in
early 1995, and then embarked on a systematic effort to convince
utilities, government agencies, and manufacturers of the need for
more efficient, safer alternatives to halogen torchieres. Michael
Siminovitch and Erik Page of LBNL
took up the challenge as well, conducting vital original research
to design and test attractive, efficient, compact fluorescent torchieres.
1995-1997 - Doing
The Legwork
From 1995 to 1997, Chris conducted the first research into the nationwide
sales, energy consumption, and safety record of halogen torchieres.
Then he published his findings in two ground-breaking reports for
E SOURCE, a Home Energy
cover story, and an IAEEL newsletter cover story. His original Home
Energy magazine cover photo of an egg frying on a halogen torchiere
has been widely reprinted and duplicated for other media events,
and he has worked with numerous North American TV stations and the
Wall Street Journal to get out the word about torchieres.
1996-1997 - Achieving
ENERGY STAR Compliance
In 1996 and 1997, Chris worked with his Ecos colleagues to help
EPA develop an ENERGY STAR
label for high quality, energy efficient light fixtures, so
it would be easier for consumers to identify and purchase the products.
While teaching at Colby College in early 1997, Chris developed an
energy curriculum in which his students measured torchiere usage
in the residence halls and successfully petitioned campus administrators
to ban the products. For the last three years, Chris has traveled
throughout the U.S. and Europe, giving lectures and multimedia presentations
about torchieres.
1998 - Turning
The Public On
In 1998, Chris and the Ecos team conducted the first nationwide
survey of halogen torchiere use on college campuses, and built the
first comprehensive national database of torchiere fires, deaths,
injuries, and property damage. Chris recently helped conduct a highly
successful torchiere turn in event in Milwaukee, during which 690
ENERGY STAR torchieres were purchased by the public in 90 minutes
and 813 halogen torchieres were recycled over a period of 7 hours.
The event was covered by three local TV stations and subsequently
replicated by Home Depot throughout the Milwaukee area.
Today - Building
The Ultimate Online Light Source
Ecos Consulting now operates ENERGY STAR torchiere incentive and
promotion programs on behalf of utilities in six states. Ecos launched
LightSite jointly with Energy Federation Incorporated to complement those programs,
making the products available at reasonable prices to anyone in
the country. LightSite uses the power of the Internet to bring information
and utility incentives to all qualifying customers as well, without
the traditional hassles of paper-based coupons and rebate forms.
Halogen
vs. Energy Star
Make The Brighter Choice.
More than 50 million halogen torchieres
have been sold in the U.S. since the late 1980's.
Halogen torchieres have caused at
least 350 fires, 114 injuries, 30 deaths, and $2,000,000 in property
damage since 1991, yet they can still be legally sold in this
country.
1 out of 6 universities have experienced
fires caused by halogen torchieres. Two-thirds of the colleges
in the country have banned their use in residence halls because
of safety concerns.

America spends about $1.3 billion
a year on the electricity to operate halogen torchieres. This
is equivalent to the annual output of four massive coal-fired
power plants.
Halogen torchiere electricity use
has wiped out all of the energy savings nationwide from using
compact fluorescent light bulbs.
Torchiere electricity use causes
about 12 million tons of carbon dioxide emissions each year --
as much as 22 billion miles of driving.
ENERGY STAR torchieres save about
$30 to $60 apiece each year in energy bills, returning more money
per dollar invested in their purchase than savings accounts, mutual
funds, or stocks.


About the Project
Marie Stopes Madagascar (MSM) has partnered with more than 100 private doctors and clinics throughout Madagascar, collectively known as the BlueStar Network, to provide family planning and reproductive healthcare for low-income women and couples.
The Challenge
In the past, MSM collected data from each of the 100+ doctors
using a paper-based system. However, MSM
found this method error-prone, expensive and unreliable.
Data arrived at headquarters sporadically, making trend
analysis impossible. Overall, MSM received
less than half of all required reports.
To expand the network and collect data from even more
remote rural areas, MSM needed a new tool.
Action Taken
MSM hired HNI to build its own internal capacity to collect data
electronically.
Together with HNI, MSM staff chose a list of important
monthly indicators (e.g. client
contraceptive choice by method). HNI built and installed the
new application. Right away, MSM began
collecting valuable data from the doctors.
The Result
Now, more than 95% of all doctors send in their monthly reports via SMS by the specified deadline, compared with less than half prior to HNI’s intervention.

Once we had a solid baseline over several months for each
doctor, we started analyzing which of our
marketing efforts directly led to spikes in clients seen.
Thanks to HNI, we now have a much better idea where to
spend our limited resources.” - Miles
Kemplay, Country Director of MSM.
MSM management noted 2 additional advantages to SMS-based data collection.
- Doctors report motivational benefits from the confirmation messages they receive in response to submitted data.
- Regular reporting via mobile phones has helped network members grow accustomed to using technology as a part of their professional lives.
“We have many ideas regarding how to use technology in our work…stay tuned!”
About MSM
Marie Stopes Madagascar is a not-for-profit,
non-governmental organization that provides family
planning and reproductive healthcare for low-income
women and couples throughout Madagascar. They have 15
health centers, and more than 100 private sector
doctors offering reproductive health services and
consulting.
www.mariestopes.mg


Create A Movie
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Year
Image URL
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Choose the field(s) that will be used to match rows from each file.
Only fields that exist in both files are shown below


Spacious Saltbox This delightful Saltbox is one of the most efficient designs we build and the “Best Bang for the Buck!” View PlanGet A Price
Half Cape Ranch But it doesn’t look like a Ranch! Living doesn’t have to lack style and beauty. We call this a Half Cape Ranch because of its authentic look. View PlanGet A Price
Traditional Cape But it doesn’t look like a Ranch! Living doesn’t have to lack style and beauty. We call this a Half Cape Ranch because of its authentic look. View PlanGet A Price
Greek Revival But it doesn’t look like a Ranch! Living doesn’t have to lack style and beauty. We call this a Half Cape Ranch because of its authentic look. View PlanGet A Price
Traditional Modified Half Cape But it doesn’t look like a Ranch! Living doesn’t have to lack style and beauty. We call this a Half Cape Ranch because of its authentic look. View PlanGet A Price
Modified Victorian But it doesn’t look like a Ranch! Living doesn’t have to lack style and beauty. We call this a Half Cape Ranch because of its authentic look. View PlanGet A Price


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| Classes in org.jclouds.softlayer.domain used by org.jclouds.softlayer.compute.config | |
|---|---|
| ProductItemPrice
The SoftLayer_Product_Item_Price data type contains general information relating to a single SoftLayer product item prices. |
|
| ProductPackage
The SoftLayer_Product_Package data type contains information about packages from which orders can be generated. |
| Classes in org.jclouds.softlayer.domain used by org.jclouds.softlayer.compute.functions | |
|---|---|
| Datacenter
Class Datacenter |
|
| ProductItem
The SoftLayer_Product_Item data type contains general information relating to a single SoftLayer product. |
|
| ProductItemPrice
The SoftLayer_Product_Item_Price data type contains general information relating to a single SoftLayer product item prices. |
|
| VirtualGuest
The virtual guest data type presents the structure in which all virtual guests will be presented. |
|
| VirtualGuest.State
These states come from the powerState field. |
| Classes in org.jclouds.softlayer.domain used by org.jclouds.softlayer.compute.strategy | |
|---|---|
| Datacenter
Class Datacenter |
|
| ProductItem
The SoftLayer_Product_Item data type contains general information relating to a single SoftLayer product. |
|
| ProductItemPrice
The SoftLayer_Product_Item_Price data type contains general information relating to a single SoftLayer product item prices. |
|
| ProductPackage
The SoftLayer_Product_Package data type contains information about packages from which orders can be generated. |
|
| VirtualGuest
The virtual guest data type presents the structure in which all virtual guests will be presented. |
| Classes in org.jclouds.softlayer.domain used by org.jclouds.softlayer.domain | |
|---|---|
| Address
Class Address |
|
| Address.Builder
|
|
| Datacenter
Class Datacenter |
|
| Datacenter.Builder
|
|
| OperatingSystem
Extends the SoftLayer_Software_Component data type to include operating system specific properties. |
|
| OperatingSystem.Builder
|
|
| Password
Contains a password for a specific software component instance |
|
| Password.Builder
|
|
| PowerState
The power state class provides a common set of values for which a guest's power state will be presented in the SoftLayer API. |
|
| PowerState.Builder
|
|
| ProductItem
The SoftLayer_Product_Item data type contains general information relating to a single SoftLayer product. |
|
| ProductItem.Builder
|
|
| ProductItemCategory
The SoftLayer_Product_Item_Category data type contains general category information for prices. |
|
| ProductItemCategory.Builder
|
|
| ProductItemPrice
The SoftLayer_Product_Item_Price data type contains general information relating to a single SoftLayer product item prices. |
|
| ProductItemPrice.Builder
|
|
| ProductOrder
Class ProductOrder |
|
| ProductOrder.Builder
|
|
| ProductOrderReceipt
Class ProductOrderReceipt |
|
| ProductOrderReceipt.Builder
|
|
| ProductPackage
The SoftLayer_Product_Package data type contains information about packages from which orders can be generated. |
|
| ProductPackage.Builder
|
|
| Region
A region is made up of a keyname and a description of that region. |
|
| Region.Builder
|
|
| VirtualGuest
The virtual guest data type presents the structure in which all virtual guests will be presented. |
|
| VirtualGuest.BillingItem
|
|
| VirtualGuest.Builder
|
|
| VirtualGuest.State
These states come from the powerState field. |
| Classes in org.jclouds.softlayer.domain used by org.jclouds.softlayer.features | |
|---|---|
| Datacenter
Class Datacenter |
|
| ProductOrder
Class ProductOrder |
|
| ProductOrderReceipt
Class ProductOrderReceipt |
|
| ProductPackage
The SoftLayer_Product_Package data type contains information about packages from which orders can be generated. |
|
| VirtualGuest
The virtual guest data type presents the structure in which all virtual guests will be presented. |
| Classes in org.jclouds.softlayer.domain used by org.jclouds.softlayer.predicates | |
|---|---|
| ProductItem
The SoftLayer_Product_Item data type contains general information relating to a single SoftLayer product. |
|
| ProductPackage
The SoftLayer_Product_Package data type contains information about packages from which orders can be generated. |
|
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| PREV NEXT | FRAMES NO FRAMES |


#include <bakery.h>
◆ Company()
◆ ~Company()
◆ addBakery()
| void Company::addBakery |
( |
Bakery * |
bakery | ) |
|
Adds a new bakery to the company
- Parameters
-
◆ addClient()
| void Company::addClient |
( |
Client * |
client | ) |
|
◆ addCompletedOrder()
| void Company::addCompletedOrder |
( |
Order * |
order | ) |
|
◆ addDeliveredOrder()
| void Company::addDeliveredOrder |
( |
Order * |
order | ) |
|
◆ addEmployee()
| void Company::addEmployee |
( |
Employee * |
employee | ) |
|
◆ addIncompletedOrder()
| void Company::addIncompletedOrder |
( |
Order * |
order | ) |
|
◆ addProduct()
| void Company::addProduct |
( |
Product * |
product | ) |
|
◆ chooseBakery()
| Bakery * Company::chooseBakery |
( |
string |
address | ) |
const |
◆ chooseClient()
| Client * Company::chooseClient |
( |
int |
nif | ) |
const |
◆ chooseEmployee()
| Employee * Company::chooseEmployee |
( |
int |
nif | ) |
const |
◆ chooseOrder()
| Order * Company::chooseOrder |
( |
int |
id | ) |
const |
◆ chooseProduct()
| Product * Company::chooseProduct |
( |
string |
name | ) |
const |
◆ getBakeries()
| vector< Bakery * > Company::getBakeries |
( |
| ) |
const |
◆ getClients()
| vector< Client * > Company::getClients |
( |
| ) |
const |
◆ getClientsPremium()
| vector< Client * > Company::getClientsPremium |
( |
| ) |
const |
◆ getCompletedOrders()
| vector< Order * > Company::getCompletedOrders |
( |
| ) |
const |
◆ getDeliveredOrders()
| vector< Order * > Company::getDeliveredOrders |
( |
| ) |
const |
◆ getEmployees()
| vector< Employee * > Company::getEmployees |
( |
| ) |
const |
◆ getIncompletedOrders()
| vector< Order * > Company::getIncompletedOrders |
( |
| ) |
const |
◆ getProducts()
| vector< Product * > Company::getProducts |
( |
| ) |
const |
◆ printClientHistory()
| void Company::printClientHistory |
( |
Client * |
client | ) |
|
This will print the full history of the client
- Parameters
-
| client | The client we're analising |
◆ removeCompletedOrder()
| void Company::removeCompletedOrder |
( |
Order * |
order | ) |
|
◆ removeEmployee()
| void Company::removeEmployee |
( |
Employee * |
employee | ) |
|
◆ removeIncompletedOrder()
| void Company::removeIncompletedOrder |
( |
Order * |
order | ) |
|
◆ totalMoneyEarnedByCategory()
| double Company::totalMoneyEarnedByCategory |
( |
string |
category | ) |
|
Gives the total money earned from a certain product category
- Parameters
-
| category | The category of the product (only accpets "Beverage", "Bread" or "Cake") |
- Returns
- The total money earned from that product category
◆ totalMoneyEarnedFromBakery()
| int Company::totalMoneyEarnedFromBakery |
( |
Bakery * |
bakery | ) |
|
Returns the total money earned by a certain bakery
- Parameters
-
| bakery | The bakery we want to analise |
- Returns
- The total money earned by a certain bakery
◆ totalNumberOfClients()
| int Company::totalNumberOfClients |
( |
| ) |
|
◆ totalNumberOfNormalClients()
| int Company::totalNumberOfNormalClients |
( |
| ) |
|
◆ totalNumberOfPremiumClients()
| int Company::totalNumberOfPremiumClients |
( |
| ) |
|
◆ totalOrdersDeliveredFromBakery()
| int Company::totalOrdersDeliveredFromBakery |
( |
Bakery * |
bakery | ) |
|
This method will calculate how many orders a certain bakery has delivered
- Parameters
-
| bakery | The bakery we want to analise |
- Returns
- How many orders this bakery has delivered
◆ totalQuantitySoldByCategory()
| int Company::totalQuantitySoldByCategory |
( |
string |
category | ) |
|
Returns the total quantity sold of a certain product category
- Parameters
-
| category | The category of the product (only accpets "Beverage", "Bread" or "Cake") |
- Returns
- The total quantity sold of that product category
The documentation for this class was generated from the following files:
- /home/victor/Documentos/PADARIA_AEDA-FEUP/Tests/bakery.h
- /home/victor/Documentos/PADARIA_AEDA-FEUP/Tests/bakery.cpp


if jumbotron used outside of container then it will fill entire width of screen can change default settings/styling of jumbotron as done in css file
More Info BUTTON Primary Success Warning Link primary LINK


Python FastAPI Library
Over the last few months I have been learning Python’s Asyncio library. It came from a need at work. I needed to pull a large about of data from Sonarqube. The data is broke into pages of 500 issues each and it required about 50 calls to retrieve all the issues. Using Requests this took about 8-10 minutes via synchronous calls. Each page load being 4-8 seconds and the data processing at the end (about 10 seconds). Frankly, I wasn’t expecting this long time to get the data back and decided it was a perfect use for Asyncio.
I find it difficult to explain Asynchronous vs Synchronous programming. Simplest explanation is that the program drops everything into a loop and while it waits for the response it can do “something else”. Like make another call or work on the response. Michael Kennedy has some great tutorials on this topic, I suggest reviewing his courses.
I played with several Async frameworks and also tried writting basic “async” and “await” functions to get better at the concept. I my main goal was an API builder and “if” possible also build frontend sites with it.
- Flask with Celery: Not actually an async framework, but a way to run on another thread. I found this really cool, but a lot of overhead with needing RabbitMQ or Redis.
- Quart: This is a great idea. Flask that is Async, but not all of Flask is there. There are a lot of issues with existing libraries. Flask-RestPlus doesn’t work with it. I hope this becomes Flask Async… it has great promise.
- Aiohttp: This is how I solved the sonarqube report data issue at work. I find it harder to get into, but the client side was easiest.
- Sanic: This is interesting, but I am less focused on “pure speed” and more on ability to build out an API or application. It has good documentation, but I just never really got into Sanic.
- FastAPI: I stumbled on this while looking at Starlette/Responder. I found it to be easy to get into, easy to learn, and really quick to build out basic APIs. Since it is built over Starlette I can still build a frontend and stay within a single framework (starlette).
Over the last few weeks, I have been learning FastAPI and building a test api to simulate long waits. Just like my original problem above. I took inspiration from FakeResponse.com and would like to build the test api out more to fit more of my needs. You can find more information at the page for the Test API project.
As a note, I seem to hit a maximum threshold of ~2100 calls per second with Async on my notebook. I think this may be a limit of Python on a single thread. SSL on tends to reduce this by about 50% as the extra hope to confirm the cert. Running the benchmarks via WRK I am getting about 500 requests per second. I still need to work on performance and it is using a SQLite db, so I think it is reasonable.
I used this tutorial for WRK and I ran the command as below (please don’t use my server for your tests, just pull the docker image and run it on your server) and it worked.
docker run --rm williamyeh/wrk -t2 -c5 -d5s https://test-api.devsetgo.com/api/v1/todo/list/count --timeout 2s


--- date: 2011-05-27T00:00:00Z published: true title: Cookie law deferred for one year | This is going to be a pain if it applies outside the EU. url: /2011/05/27/cookie-law-deferred-for-one-year-this-is-going-to-be-a-pain-if-it-applies-outside-the-eu-/ ---
The EU's Privacy and Communications Directive comes into force on 26 May.
It requires user's consent before using cookies - the text files that help organise and store browsing information.
Technically all firm must comply with the law but the UK has said that it needs more time to find a workable solution.
The government said that it was looking for a "business-friendly" solution and believed in light-touch regulation.
"We recognise that some website users have real concerns around online privacy but also recognise that cookies play a key role in the smooth running of the internet," said communications minister Ed Vaizey.
"But it will take some time for workable technical solutions to be developed, evaluated and rolled out so we have decided that a phased in approach is right," he added.


Masked Text fields typically reside in forms but can appear in other places, like dialog boxes and search.
Input text can be grouped in different ways, with special characters added in appropriate places, such as hyphens added to a phone number. Once the first special character is reached, the cursor automatically jumps to the next group.
Input text grouped and formatted for a phone number
Input text grouped and formatted for a credit card number


Samuel Rowan
Project Lead
Life needs things to live.
- Taliesin Jaffe
Jason Schwartz
Backend Developer
Wisdom begins in wonder.
- Socrates
Hanina Syed
Front End Developer
You will find only what you bring in.
- Yoda
Joey Wilson
Front End Developer
It’s what you learn after you know it all that counts.
- Earl Weaver
Chris Corbin
Web UI Developer
Love sometimes expresses itself in sacrifice.
— Captain Kirk
Cory Thomas
Web UI Developer
Fix the cause, not the symptom.
– Steve Maguire


My Favorites
List of famous websites, popular opensource projects and useful online tools for software developers.
Online Tools
Online Learning
Open Source/Popular Projects
Free Text Editor/IDE


CardBean
objects to the local system.CardBean
objects to the local system.Image
object according a given height.Image
object according a given width.CardBean
object.SetBean
objects and provides
methods for interaction with this list.CardBean
object.CardBean
object which is used to provide corresponding
toolbar functionalities.JComboBox.setSelectedItem(Object)
in order to
work for objects of class SetBean
.


Pairs trading is exactly a 2-asset version of statistical arbitrage. Thus, we can just modify the algorithm from the Kalman Filter and Statistical Arbitrage tutorial, except we're using only a single cointegrating unit vector so no optimization of cointegration subspace is needed.
def Initialize(self) -> None: #1. Required: Five years of backtest history self.SetStartDate(2014, 1, 1) #2. Required: Alpha Streams Models: self.SetBrokerageModel(BrokerageName.AlphaStreams) #3. Required: Significant AUM Capacity self.SetCash(1000000) #4. Required: Benchmark to SPY self.SetBenchmark("SPY") self.assets = ["SCHO", "SHY"] # Add Equity ------------------------------------------------ for i in range(len(self.assets)): self.AddEquity(self.assets[i], Resolution.Minute) # Instantiate our model self.Recalibrate() # Set a variable to indicate the trading bias of the portfolio self.state = 0 # Set Scheduled Event Method For Kalman Filter updating. self.Schedule.On(self.DateRules.WeekStart(), self.TimeRules.At(0, 0), self.Recalibrate) # Set Scheduled Event Method For Kalman Filter updating. self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose("SHY"), self.EveryDayBeforeMarketClose) def Recalibrate(self) -> None: qb = self history = qb.History(self.assets, 252*2, Resolution.Daily) if history.empty: return # Select the close column and then call the unstack method data = history['close'].unstack(level=0) # Convert into log-price series to eliminate compounding effect log_price = np.log(data) ### Get Cointegration Vectors # Get the cointegration vector coint_result = engle_granger(log_price.iloc[:, 0], log_price.iloc[:, 1], trend="c", lags=0) coint_vector = coint_result.cointegrating_vector[:2] # Get the spread spread = log_price @ coint_vector ### Kalman Filter # Initialize a Kalman Filter. Using the first 20 data points to optimize its initial state. We assume the market has no regime change so that the transitional matrix and observation matrix is [1]. self.kalmanFilter = KalmanFilter(transition_matrices = [1], observation_matrices = [1], initial_state_mean = spread.iloc[:20].mean(), observation_covariance = spread.iloc[:20].var(), em_vars=['transition_covariance', 'initial_state_covariance']) self.kalmanFilter = self.kalmanFilter.em(spread.iloc[:20], n_iter=5) (filtered_state_means, filtered_state_covariances) = self.kalmanFilter.filter(spread.iloc[:20]) # Obtain the current Mean and Covariance Matrix expectations. self.currentMean = filtered_state_means[-1, :] self.currentCov = filtered_state_covariances[-1, :] # Initialize a mean series for spread normalization using the Kalman Filter's results. mean_series = np.array([None]*(spread.shape[0]-20)) # Roll over the Kalman Filter to obtain the mean series. for i in range(20, spread.shape[0]): (self.currentMean, self.currentCov) = self.kalmanFilter.filter_update(filtered_state_mean = self.currentMean, filtered_state_covariance = self.currentCov, observation = spread.iloc[i]) mean_series[i-20] = float(self.currentMean) # Obtain the normalized spread series. normalized_spread = (spread.iloc[20:] - mean_series) ### Determine Trading Threshold # Initialize 50 set levels for testing. s0 = np.linspace(0, max(normalized_spread), 50) # Calculate the profit levels using the 50 set levels. f_bar = np.array([None]*50) for i in range(50): f_bar[i] = len(normalized_spread.values[normalized_spread.values > s0[i]]) \ / normalized_spread.shape[0] # Set trading frequency matrix. D = np.zeros((49, 50)) for i in range(D.shape[0]): D[i, i] = 1 D[i, i+1] = -1 # Set level of lambda. l = 1.0 # Obtain the normalized profit level. f_star = np.linalg.inv(np.eye(50) + l * D.T@D) @ f_bar.reshape(-1, 1) s_star = [f_star[i]*s0[i] for i in range(50)] self.threshold = s0[s_star.index(max(s_star))] # Set the trading weight. We would like the portfolio absolute total weight is 1 when trading. self.trading_weight = coint_vector / np.sum(abs(coint_vector)) def EveryDayBeforeMarketClose(self) -> None: qb = self # Get the real-time log close price for all assets and store in a Series series = pd.Series() for symbol in qb.Securities.Keys: series[symbol] = np.log(qb.Securities[symbol].Close) # Get the spread spread = np.sum(series * self.trading_weight) # Update the Kalman Filter with the Series (self.currentMean, self.currentCov) = self.kalmanFilter.filter_update(filtered_state_mean = self.currentMean, filtered_state_covariance = self.currentCov, observation = spread) # Obtain the normalized spread. normalized_spread = spread - self.currentMean # ============================== # Mean-reversion if normalized_spread < -self.threshold: orders = [] for i in range(len(self.assets)): orders.append(PortfolioTarget(self.assets[i], self.trading_weight[i])) self.SetHoldings(orders) self.state = 1 elif normalized_spread > self.threshold: orders = [] for i in range(len(self.assets)): orders.append(PortfolioTarget(self.assets[i], -1 * self.trading_weight[i])) self.SetHoldings(orders) self.state = -1 # Out of position if spread recovered elif self.state == 1 and normalized_spread > -self.threshold or self.state == -1 and normalized_spread < self.threshold: self.Liquidate() self.state = 0


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