One of the most intriguing aspects of Angie Sarich’s memoir, Leaving Parma, is her contrasting of the battlefield events at Tay Ninh on March 18, 1970, the day her uncle Craig died in Vietnam, with the peace negotiations that President Nixon was orchestrating through Henry Kissinger in Paris that day of her uncle’s death. It was Richard Nixon who in his letter wrote on April 1, 1970, to console Angie’s grandparents’ over the death of their son, “Those who give their own lives to make the freedom of others possible live forever in honor.” The war that had been President Johnson’s war had become Nixon’s war, and he would sign this same letter to thousands upon thousands of parents.
The Farmhouse Prize is named after the Yorba Linda farmhouse where Richard Nixon was born. The house built by Nixon’s father, a lemon farmer by trade, stands now restored at the original site of its construction at Nixon Library in Yorba Linda, California. President Nixon says about his childhood in the farmhouse: “As a young boy in Yorba Linda, I never thought about becoming President of the United States or even entering politics. My goal was to become a railroad engineer. Sometimes at night, I was awakened by the sound of a train whistle and I would dream of the faraway places I wanted to visit someday.”
There is a connection between the childhood of President Nixon and the decisions that he made as president to continue the prosecution of the Vietnam War. A trip to Washington, D.C., to visit the Vietnam Veterans Memorial, demonstrates the impact the 37th President of the United States not just on those whose names are engraved on that wall, but on those who journey so many miles to feel the touch of a name beneath their fingertips. That piercing cold that is said to follow ghosts where they tread, that chill that Angie writes her mother must have felt that mid-March morning in 1970 when she watched from her car as two officers came to tell her father at his real estate office that his son, her brother, was dead—still lingers in greater or lesser degree for each of us.
The young boy who never dreamed of becoming president would grow up to sign his name to platitudes about freedom and honor forever, platitudes which he must have genuinely intended to console—but from the moment of his inauguration he had already decided that departure from Vietnam was inevitable. He would have peace with honor, which he knew at the time meant merely a decent interval between our withdrawal and the fall of the Government of Vietnam.
And so what was there, not only in that boy, but also what is there in each of us that could give rise to such a man? If you travel to Yorba Linda to stand in that farmhouse, you will feel, perhaps, as I did, that Richard Nixon is Everyman, and the reason why he can generate in us such antipathy still, is that in him we see ourselves.
Sincerely,
Rich Skalstad,
Editor, Colonus Publishing, Inc.
I'm still working on this section.


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Review the ACM Code of Ethics and Professional Conduct, paying particular attention to the additional explanations provided for Section 1 and Section 2, then answer the questions below.
Association for Computing Machinery (ACM). 2020. "ACM Code of Ethics and Professional Conduct"
J.C. Knight (2002) describes safety-critical systems as “those systems whose failure could result in loss of life, significant property damage or damage to the environment”.
As lead for a software development project involving a safety-critical system you are being pressured by your manager to release the system before it has been adequately tested.
Approving the release of the system before completing testing would be:
There are no hints for this question


BeagleBoard.org® PocketBeagle® is a small development board the size of a mint tin, perfect for a stocking stuffer. PocketBeagle® features an incredible low cost, slick design, and easy to follow instructions with projects for beginners and professionals alike.
 Available from Arrow, DigiKey, Element14, and Mouser. Visit beagleboard.org/pocket
BeagleBoard.org® is a 501c3 and was founded in 2008 by a group of enthusiastic engineers interested in creating powerful, open and embedded devices. BeagleBoard.org® pioneered new ground in open hardware/software and community education. Visit https://www.beagleboard.org/ or email<EMAIL_ADDRESS>to get started.


General support queries. User Guides: https://support.komodoplatform.com/ Support staff: Angelina#6231 gcharang#6833. If you want to ask JL777 a question, use the #ask-jl777 channel.
100 messages
Before 05-Dec-19 12:00 AM
SHossain30-Aug-18 11:24 AM
search on google
TonyL30-Aug-18 11:24 AM
You can google stuff like this or hire someone (I'm about SSH). We're providing support only for Komodo products and always ready to help with questions around but not making Computer Science courses atm.
Shinzo Abe30-Aug-18 11:24 AM
can you please just tell me how to log into both vps at the same time?
or do i do the process twice?
SHossain30-Aug-18 11:24 AM
search on google
Shinzo Abe30-Aug-18 11:24 AM
ok
doesnt explicitly say
can u just tell me this one thing please?
save me like 25 minutes of search with one sentence
i know how to connect to a vps now but i dont know how to connect to 2 VPS simultaneously
SHossain30-Aug-18 11:28 AM
you seem to be having issues. why do you want to try 2 at the same time when you can't complete one?
Shinzo Abe30-Aug-18 11:29 AM
i can complete one
im jusdt thinking ahead
do i do one by one?
SHossain30-Aug-18 11:30 AM
yes
Shinzo Abe30-Aug-18 11:30 AM
shh root@ipadresss1 then compile the code
then shh root@ipaddress2 then compile the same code again?
could you please explain how these 2 VPS servers will be connected?
seems like im doing 2 different things that dont work together
hi guys, i am updating my 2.41 beta wallet to 2.41d kmd wallet on windows. do i want to choose "uninstall agama and all of its modules" click yes or no?(edited)
SHossain31-Aug-18 11:31 AM
Yes. And don't delete any user data or wallet.dat
it is best to backup wallet.dat before any update.
sbucks31-Aug-18 11:32 AM
i backed up my wallet as well as the whole appdata/komodo, probably over did it
1
lost my agama icon on desktop after install, where is the location of the executable to load agama stored?(edited)
yorklab31-Aug-18 11:38 AM
"C:\Program Files\AgamaApp\Agama.exe" - for Windows.
sbucks31-Aug-18 11:41 AM
nothing there other than genbet, genhodl,genjumblr, genrevs, and gensupernet,.. no exe file
no problem, just reinstalled it
yorklab31-Aug-18 11:46 AM
I was just going to suggest that.  I ask because I like to download epubs of docs
Acura31-Aug-18 01:02 PM
Anybody know where i can find the file for the pin for Agama?
Nabob31-Aug-18 01:23 PM
@Acura <Agama Userdata>/shepherd/pin/
Acura31-Aug-18 01:27 PM
thank you
Mc Tau31-Aug-18 06:00 PM
hi all, what does the U and the S mean, when you check a transaction in the explorer.
It's always split up. Sometimes U and U , sometimes U and S
xRobeSx31-Aug-18 06:02 PM
I'm not positive but I think it stands for Unspent and Spent.
Deleted User31-Aug-18 06:03 PM
@SHossain once i deploy the VPS servers and connect to my first VPS IP adreess through terminal can i go ahead and run dependencies?
I am trying to launch an assett chain on KMD
xRobeSx31-Aug-18 06:05 PM
I'm not shossain but yes you can
1
1
Deleted User31-Aug-18 06:09 PM
dont i have to install komodo manually before that ?


Arduino projects were really fun and intersting because I have never done anything like that before. The fact that we were making different lights blink like some christmas lights was awesome.


Week #8: Tech Blog
What is Rails?
I'm now eight weeks into prepping for Dev Bootcamp. I've spend many hours learning the Ruby language but if you were to ask me what the 'Rails' part of 'Ruby on Rails' is, I wouldn't have much of an answer for you. It's time to figure it out.
'Ruby' and 'Ruby on Rails' are different. Ruby is just the programming language. Ruby on Rails (or just Rails) is the web application framework which runs the Ruby language. Let's go a little deeper.
Here are some of the characteristics of Rails:
- It's open source.
- It's a full-stack framework which means that it's able to gather multiple libraries to form a single cohesive software stack.
- It's capable of gathering information from the server, and it can interact and retrieve information from a database.
- It features a routing system that is independent of the web server.
- Using convention over configuration, Rails makes building web apps simpler. This generally means less code and less repetition.


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Jakob the Liar (1999) Reviewed by Eugene Novikov http://www.ultimate-movie.com/ Member: Online Film Critics Society
"I DON'T HAVE A RADIO!" Starring Robin Williams, Armin Mueller-Stahl, Alan Arkin, Liev Schreiber. Rated PG-13.
Robin Williams has the rarest of gifts: the ability to rise above the most inept material and suffuse it with his irreverent style. Overwhelmingly, his worst films are pleasantly diverting at worst and enjoyable at best (with the notable exception of Flubber). So when I, the one person who has refused to abandon him despite Patch Adams, tell you that not even Williams can save his latest project, you know it's in trouble.
Williams plays Jakob Haim, a Jew imprisoned in a Polish ghetto during World War II. One night he wanders outside after curfew and is promptly sent to the office of a high-ranking German officer for his punishment. Jakob gets off easy and he gets to hear approximately 30 seconds of a radio broadcast. The announcement (in English, but punctuated by a triumphant "Heil Hitler!") is that Russian troops are only miles away from Jakob's ghetto. Liberation! he thinks. The next day, Jakob tells the news to his closest friend, a volatile prize fighter named Misha (Liev Schreiber), who despite being sworn to secrecy passes the message along.
Soon, it is a common assumption in the ghetto that Jakob has a radio hidden in his home -- a crime punishable by death. This is absurdly false, but the more Jakob tells the people of the ghetto this, the more convinced they become that he is abreast of the latest developments in the war that is to decide their fate. The danger, of course, is that the Germans allegedly have informants throughout the ghetto, and rumors about the radio can get out and put Jakob in great danger.
In a curiously irrelevant subplot, Jakob finds a 11-year old girl who has separated from her parents and decides to hide her in his small home. Apparently he is afraid that she will be discovered, and goes to great length to make sure of that -- just why the idea frightens him is never made clear. He and the girl build an uninvolving, generic relationship that never goes anywhere and is as irrelevant at the end as it was in the beginning.
The moral of the story is that hope is the best medicine. But Jakob the Liar forgets that the hope that Jakob brings to his ghetto is false. Such hope inevitably leads to expectations of its realization and when those expectations aren't met, the results are far worse than if there was no hope. That's the film's biggest detriment: it is based on a false assumption and thus comes off as painfully false. It's never moving because it doesn't give us anything to be moved by. Jakob the Liar fails to tug any heartstrings which destroys its purpose for existing.
Robin Williams doesn't inject the film with life, as a matter of fact, he seems a little out of it, as if crippled by his fake accent. He is stragely unenthusiastic; his character is one who spreads hope but his performance is lifeless, hopeless. Not since Good Will Hunting has he abandoned his signature style to this extent; this isn't the Robin Williams we know and love. In Good Will Hunting he became a serious actor and won an Oscar for it. Here, he is more of a wannabe serious actor; an impostor.
Jakob the Liar will be compared with last year's Life is Beautiful because it is being marketed as a "Holocaust comedy". It's not a comedy. It tries, sometimes, but it rarely works. Is it a melodrama? A war movie? A character study? No, no, and no. Jakob the Liar is the kind of movie that can't be placed into a category; not because it covers so many different genres but because it fails at just about every one it attempts.
Grade: C-
©1999 Eugene Novikov
Sent via Deja.com http://www.deja.com/ Before you buy.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


ANALOG PINS
DIGITAL PINS
Add sensors and drag the pins of the sensor/actuator/device to the pins of the Arduino
Incompatible pins will throw error messages
To dettach a connection or delete a sensor use the right click of the mouse on the desired device
// No code generated, until now...


{% extends "boh/management/base_management.html" %} {% block title %}Manage Settings | Services | Add ThreadFix Service{% endblock %} {% block manage_content %}


FlaneurImageDescriptor
public enum FlaneurImageDescriptor
Descriptor of an image resource.
-
The image is stored remotely and accessible via a URL.
Declaration
Swift
case url(_: URL)
-
The image is in-memory (via a
UIImage
instance).Declaration
Swift
case image(_: UIImage)
-
The image is part of the user’s Photos library.
Declaration
Swift
case phAsset(_: PHAsset)
-
Declaration
Swift
public var hashValue: Int
-
Declaration
Swift
static public func == (lhs: FlaneurImageDescriptor, rhs: FlaneurImageDescriptor) -> Bool
-
Declaration
Swift
public var description: String
-
Fetches the images described by the instance.
The handler will be called synchronously or asynchronously depending on the nature of
self
.Declaration
Swift
public func fetchImage(resultHandler: @escaping (UIImage?, Error?) -> ()) -> ()
Parameters
resultHandler
the handler that is called with either an image or an error.


Hint
Can it be done by iterating through the placements a single time?
How can we keep track of the position number?
How do we know if it's a company's first time appearing in the dictionary?


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The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper.
An epidemic of Zika virus (ZIKV) illness that occurred in July 2007 on Yap Island in the Federated States of Micronesia prompted entomological studies to identify both the primary vector(s) involved in transmission and the ecological parameters contributing to the outbreak. Larval and pupal surveys were performed to identify the major containers serving as oviposition habitat for the likely vector(s). Adult mosquitoes were also collected by backpack aspiration, light trap, and gravid traps at select sites around the capital city. The predominant species found on the island was Aedes (Stegomyia) hensilli. No virus isolates were obtained from the adult field material collected, nor did any of the immature mosquitoes that were allowed to emerge to adulthood contain viable virus or nucleic acid. Therefore, laboratory studies of the probable vector, Ae. hensilli, were undertaken to determine the likelihood of this species serving as a vector for Zika virus and other arboviruses. Infection rates of up to 86%, 62%, and 20% and dissemination rates of 23%, 80%, and 17% for Zika, chikungunya, and dengue-2 viruses respectively, were found supporting the possibility that this species served as a vector during the Zika outbreak and that it could play a role in transmitting other medically important arboviruses.
Arthropod-borne viruses (arboviruses) cause significant human morbidity and mortality throughout the world. Zika virus, which is reported to be transmitted by Aedes ( Stegomyia) species mosquitoes, caused an outbreak on the island of Yap, in the Federated States of Micronesia in 2007. This was the first described outbreak of Zika in Oceania, which has had several arbovirus outbreaks in the past. Diagnosing the outbreak was difficult due to the similarity in clinical symptoms between disease caused by Zika virus and other viruses. This work describes the efforts to identify the mosquito species that were responsible for transmission of the virus. While no virus was isolated from any species of mosquito collected during the current study, the predominant species found was Aedes hensilli and through the complementary laboratory studies, this mosquito was implicated as a probable vector for Zika virus. In addition, this species was found to be susceptible to both the medically important dengue-2 and chikungunya viruses.
Outbreaks of arboviral disease have been documented in islands of the western Pacific including The Federated States of Micronesia (FSM) and Palau. Multiple dengue outbreaks have been reported in the western pacific [1] – [4] with an outbreak of dengue 4 virus occurring in Palau in 1995 after a 7 year absence of dengue on this island [5] . This first outbreak of dengue 4 in the Western Pacific also affected FSM the same year [6] . Additional dengue outbreaks occurred more recently in FSM during 2004 and 2012–13 [7] , [8] . In 2007, an outbreak of acute febrile illness characterized by rash, conjunctivitis, fever, and arthralgia was reported on the island of Yap in the Federated States of Micronesia. While dengue was originally suspected, clinicians noted differences from classical dengue fever and collected serum from acutely-ill individuals for diagnosis. Chikungunya virus (CHIKV) was also considered as the clinical presentation was representative of CHIKV infection and an ongoing epidemic of CHIKV was occurring in Southeast Asia. However, Zika virus (ZIKV) nucleic acid was detected in 14% of the samples tested and no evidence of alternate etiologies was identified [9] .
Zika virus (ZIKV) is a member of the family Flaviviridae. Presence of the virus in human specimens has been demonstrated by virus isolation (samples from Africa and Asia) and antibody presence (Asia) [10] – [15] ; however, only a handful of clinical disease cases were described in the literature prior to this 2007 outbreak [16] – [19] . Since the outbreak in Yap, additional ZIKV outbreaks have been documented in Gabon in 2007 and in French Polynesia in 2013 [20] , [21] . Mosquito vectors from which virus has been identified include (among others) Aedes africanus, Aedes luteocephalus, Aedes aegypti, and Aedes albopictus (all belonging to the subgenus Stegomyia) [15] , [21] – [25] . However, little else is known regarding the natural ecology of the virus.
Because this was the first documentation of the virus in Oceania, understanding the biological transmission of the virus was a public health priority. A team including epidemiologists, clinicians, entomologists, and public health personnel investigated the outbreak with the objectives of characterizing the epidemiology, course of clinical illness, and ecological factors contributing to the epidemic and transmission of the virus. Household surveys were performed to obtain serum specimens, to obtain clinical and epidemiological data, to identify risk factors for infection, and to collect entomological specimens for the purpose of determining the most probable epidemic vector [9] . The entomological studies included both immature (larval and pupal) and adult surveys to determine the species present on the island, contributions of distinct container types in mosquito maintenance, and to perform virus isolation. This report describes the entomologic findings from the field collected material as well as subsequent laboratory studies assessing the vector capacity of the likely outbreak vector.
The Federated States of Micronesia are located in the Western Pacific Ocean northeast of Papua New Guinea ( Figure 1 ). Yap State is the westernmost state and is comprised of a main island group consisting of four closely associated islands situated at 9° North and 138° East. It is approximately 6 km wide by 15 km long with a population of 7,391 persons during the 2000 census. The climate is tropical with warm temperatures and rainfall reported throughout the year. Mosquitoes were collected and household surveys performed between July 4, 2007 and July 16, 2007 at 170 randomly selected homes in 9 out of the 10 municipalities, representing 16% of the total households on the island. The outbreak was estimated to have begun in April, 2007 and continued through July of 2007 [9] .
Adult mosquito sampling was carried out using three collection methods. Host seeking mosquitoes were collected using light traps, resting mosquitoes were collected using vacuum aspiration, and mosquitoes looking for oviposition habitat were collected with gravid traps. Gravid and light traps (light only) were set in the evening at three sites in the state capital city of Colonia from July 4–9, and July 12–16. Collection bags from the light and gravid traps were recovered daily for 9 days in the early morning. A battery operated backpack or handheld mechanical aspirators were used to collect mosquitoes resting in and around random houses where serosurveys were being performed during daytime hours, July 9–15.
All collected mosquitoes were identified morphologically using keys from Bohart [26] , and Rueda [27] . They were then sorted by sex, species, collection method, and collection period and placed into cryovials at a temporary laboratory set up in Colonia. Specimens were frozen at −20°C on-site; they were later transported to the CDC at Fort Collins CO, USA where storage was at −70°C until processing.
All indoor and outdoor water containing receptacles at the randomly selected households [9] were inspected for mosquito larvae and pupae. Live larvae observed in receptacles were collected and identified to species and allowed to emerge to confirm identification. All pupae found were collected and reared to adulthood. Key habitat information was recorded and larval indices (Breteau and household [28] ) were calculated from the collected data for each of the species observed.
Each pool of mosquitoes (not exceeding 40 individuals) were placed into a 1.7 mL polypropylene tube (Eppendorf, Hauppauge, NY) and ground with a pestle (Kontes) and 500 µl of Dulbecco's minimal essential medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS), 100 U/mL of penicillin and streptomycin, 1 U/mL of fungizone and gentamycin. The homogenized mosquitoes were then centrifuged at 15,000 g for 1 min. Triturate was then transferred to a new tube and frozen at −70°C. 100 µl of thawed triturate was then plated onto a 96-well cell culture plate (Corning). 50 µl of a Vero cell suspension was then added to the same well and placed into an incubator at 37°C and 5% carbon dioxide. The cell and homogenized mosquito mixture was then monitored daily for cytopathic effect (CPE) for 10 days. Medium from those wells presenting signs of CPE (presumptive positives) were removed and placed at −70°C until use [29] .
The viruses used for laboratory mosquito infections were obtained from the Arbovirus Reference Collection at CDC, Fort Collins, CO ( Table 1 ). As no ZIKV field strain was obtained, we used the prototype strain for ZIKV laboratory infections.
| Virus | Strain | Origin/Source | Passage History * |
| Zika | MR 766 | Rhesus monkey, Uganda 1947 | P149, V2 |
| Dengue 2 | Jam 1409 | Human, Jamaica 1949 | P3, C6(2) |
| Dengue 2 | TR 1751 | Human, Trinidad 1953 | P55, C6 (1) |
| Chikungunya | COM 125 | Mosquito, Comoros 2005 | V2 |
*P = passage (culture type unspecified), V = Vero cells, C6 = Aedes albopictus C6/36 cells.
Viral RNA was isolated using the QiaAmp viral RNA protocol (Qiagen). Total RNA was extracted from 50 µl of cell supernatant (CPE positive pools) or 100 µl of mosquito homogenate (artificial infections) and eluted from the kit columns using 60 µl of elution buffer. The RNA was stored at −70°C until use.
Both reverse-transcription PCR (RT-PCR) and real-time RT-PCR assays were utilized to detect viral nucleic acid. The Titan one-step RT-PCR (Roche) kit was paired with the primers FU1 and cFD3 for detection of Zika [30] , [31] . Briefly, 5 µl of sample RNA was added to the kit components and 400 nM of primers. The manufacturer's protocol was followed with no modifications. The reactions were analyzed by gel electrophoresis. The real-time PCR assay was used on both the presumptive positive pools and the experimentally infected mosquitoes. The previously described Zika (800 series set) and chikungunya virus specific primer and probe sets were used [32] , [33] . The DENV-2 oligonucleotide set were designed with the Primer Select software program (DNASTAR) (1085
Mosquito eggs were collected at selected houses in Yap using oviposition cups. Briefly, black, plastic cups were lined with seed germination paper [35] and filled approximately half full with water. Cups were placed under foliage near selected homes (2–4 feet above the ground) and collected after 3–5 days. Field collected egg liners were wrapped in moist paper towels, sealed in Ziploc-style bags, and transported to the insectary at the Center for Disease Control and Prevention (CDC), Fort Collins Colorado for colonization. The eggs were washed with a 10% bleach solution prior to hatching in a pan of tap water to eliminate surface fungal and bacterial contaminants.
Larvae were supplied with either a liver powder solution or mouse pellets as appropriate for the developmental stage and identified to species as 4 th instar. All larvae collected were identified as Aedes (Stegomyia) hensilli. Pupation occurred between days 5–7 post hatching. Pupae were removed from the larval pans and allowed to emerge into 1 ft 3 adult mosquito cage (BioQuip). In order to produce the next generation, adults were provided an anesthetized mouse as a blood meal source and the engorged females were provided with an oviposition site (seed germination paper) to deposit their eggs. The process was repeated in order to get sufficient numbers of experimental mosquitoes. In addition, species verification was performed on F2 adult mosquitoes.
Three to four day-old adult Ae. hensilli mosquitoes (F12–15) were fed on blood meals containing ZIKV, CHIKV, or DENV-2. The blood meals contained equal parts of virus, FBS with 10% sucrose, and sheep blood (Colorado Serum CO) washed with phosphate-buffered saline and packed by centrifugation. A Hemotek feeding system (Discovery Workshops) was used to deliver the blood meal to the mosquitoes for 1 hour at 37°C. The fully engorged females were separated and placed into a humidified environmental chamber (Thermo Scientific) and held at 28°C for 8 days until processing. Blood meal titer was determined by plaque assay to determine input titer.
After the 8 day holding period, mosquitoes were cold anesthetized and decapitated with the heads and bodies placed into separate 1.7 mL tubes (Eppendorf). A 400 µl aliquot of Dulbecco's minimal essential medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS), 100 U/mL of penicillin and streptomycin, 1 U/mL of fungizone and gentamycin was added to each tube and the sample was homogenized using a micropestle (Kontes). The supernatant was clarified by filtration through a 0.2 µM syringe filter (Pall) and stored at −70°C until use [36] .
Virus presence was determined using the virus isolation method as described above. An infected mosquito exhibited a virus positive body [percent infected = (number positive bodies/total number of mosquitoes processed) X 100] while those with disseminated infections were the infected individuals with virus in the head [percent disseminated = (number of positive heads/number of positive bodies) X 100]. Quantities of viral RNA were determined using real-time RT-PCR (above) and correlated with viral titer.
Adult mosquitoes were captured using three different collection methods (light trap, gravid trap, and vacuum aspirations). A total of 879 mosquitoes were collected in 84 trap nights. Additionally, 475 individuals collected as larvae and/or pupae were reared to adults for confirmatory identification and processing. Nine species were identified in these collections ( Table 2 ). The most abundant adult species collected was Aedes hensilli (41.2%) followed by Culex quinquefasciatus (28.1%). All other species each comprised less than 10% of the total collection. All adult mosquitoes (field collected adults and those reared from immatures) were processed and subjected to virus isolation efforts. No viable virus was recovered from any of these mosquitoes.
| Species | Collection Method(s) | % of total adult collection (n) |
| Aedes aegypti | Aspiration | 0.1 (1) |
| Aedes hensilli | Aspiration, gravid trap, light trap | 41.2 (362) |
| Aedes vexans | Aspiration, gravid trap, light trap | 1.6 (14) |
| Coquillettidia crassipes | Gravid trap, light trap | 6.0 (52) |
| Culex gossi | Aspiration, gravid trap, light trap | 8.0 (70) |
| Culex nigropunctatus | Aspiration, gravid trap, light trap | 8.9 (78) |
| Culex quinquefasciatus | Aspiration, gravid trap, light trap | 28.1 (247) |
| Culex sitiens | Aspiration, gravid trap, light trap | 4.3 (38) |
| Lutzia fuscana | Light trap | 0.5 (4) |
From 170 randomly surveyed households (July 4–16, 2007), 1366 water holding habitats were identified. Larvae and/or pupae were collected from 586 of these containers and 85% of surveyed households had at least one infested habitat; individual habitats sometimes contained more than one species ( Figure 2 ). The most prevalent containers with larvae or pupae were discarded cans followed by coconut shells ( Table 3 ). Proportionally, containers including tires, tarps, floats, and bamboo had high percentages of immatures but several of these container types were found only infrequently ( Table 4 ). Containers such as water barrels, used to collect rainwater, while proportionally fewer in number than other containers, were actually major contributors to mosquito production due to the sheer number of larvae and pupae present (e.g. thousands of immature mosquitoes per water barrel in comparison with cans or shells which typically contained fewer than 10 individuals each). In total, ten different species were identified from the larval collections. Ae. hensilli was both the most abundant and most prevalently identified immature species being found in 83% of the infested containers distributed all over the island (household index of 81.2 and Breteau index of 282.9).
| Container | Aedes aegypti | Aedes hensilli | Aedes lamelliferus | Aedes maehleri | Aedes vexans | Culex gossi | Culex nigropunctatus | Culex quinquefasciatus | Culex spp. | Lutzia fuscana |
| Animal pan | 1 | 1 | 1 | |||||||
| Bamboo | 1 | 1 | ||||||||
| Boat | 2 | |||||||||
| Bottle | 2 | 31 | 1 | 1 | 1 | 1 | ||||
| Bucket | 5 | 55 | 2 | 2 | 3 | 5 | 3 | 2 | ||
| Can(s) | 2 | 113 | 4 | 3 | 5 | 4 | 1 | |||
| Coconut shell(s) | 1 | 90 | 7 | 2 | 1 | 6 | 4 | |||
| Cooking items | 1 | 50 | 2 | 1 | 3 | 7 | 3 | |||
| Float | 6 | 1 | 1 | |||||||
| Flower pot | 11 | 1 | ||||||||
| Ground pool | 1 | 17 | 4 | 1 | 2 | 1 | 1 | 2 | ||
| Live plant/axil | 12 | 3 | 3 | 4 | 2 | 2 | 1 | |||
| Plant frond | 1 | 10 | 1 | |||||||
| Tarp | 1 | 8 | 1 | 2 | 1 | |||||
| Tire(s) | 5 | 48 | 1 | 11 | 1 | 2 | 1 | 7 | ||
| Water barrel | 7 | 30 | 2 | 1 | 3 | 5 | 1 | |||
| Water tank | 2 | 1 |
| Number containing larvae/pupae | Number without larvae/pupae | Proportion infested (%) | |
| Tire(s) | 58 | 31 | 65 |
| Tarp | 11 | 7 | 61 |
| Float | 5 | 4 | 56 |
| Coconut shell(s) | 95 | 89 | 52 |
| Bamboo | 1 | 1 | 50 |
| Can(s) | 124 | 131 | 49 |
| Bottle | 32 | 35 | 48 |
| Cooking items | 57 | 65 | 47 |
| Bucket | 64 | 90 | 42 |
| Water barrel | 45 | 76 | 37 |
| Flower pot | 12 | 23 | 34 |
| Live plant/axil | 22 | 50 | 31 |
| Boat | 2 | 5 | 29 |
| Plant frond | 14 | 49 | 22 |
| Ground pool | 25 | 92 | 21 |
| Water tank | 2 | 8 | 20 |
| Animal pan | 2 | 10 | 17 |
Because no virus was found in any of the field collected material, laboratory infections were performed on the most common mosquito collected, Ae. hensilli, to determine if this species could have served as the epidemic vector. Cohorts of Ae. hensilli were infected with three different viruses during these studies: 1) ZIKV - to determine if this was the likely vector during the outbreak; 2) CHIKV- to ascertain whether Ae. hensilli could serve as a vector for this virus which was expanding through SE Asia and was considered as a possible etiology of the outbreak prior to ZIKV diagnosis; 3) DENV - as Ae. hensilli was previously postulated as the vector of the 1996 dengue outbreak in Yap [5] .
Cohorts of 3–4 day old adults were provided infectious blood meals with titers of at least 4.9 log
| Virus (Strain) | Rep. | Titer (log
10 pfu/mL)
|
% infection (n) | Average titer in log
10 pfu equivalents/mL (range)
|
||
| Body | Head | Body | Head | |||
| Zika (MR766) | 1 | 4.9 | 7.1 (14) | 0 (1) | 3.1 | na |
| 2 | 5.7 | 80.0 (20) | 12.5 (16) | 2.7 (1.0–3.3) | 1.8 (1.0–2.1) | |
| 3 | 5.9 | 86.1 (36) | 22.6 (31) | 3.2 (1.0–4.0) | 2.0 (0.6–2.7) | |
| DENV-2 (TR1751) | 1 | 5.3 | 20.7 (29) | 16.7 (6) | 2.6 (0.1–3.23) | 2.1 |
| DENV-2 (Jam1409) | 1 | 5.5 | 0 (20) | 0 | 0 | na |
| CHIKV (COM 125) | 1 | 5.7 | 62.5 (32) | 80.0(20) | 5.0 (1.0–5.6) | 4.1 (1.1–4.4) |
The 2007 outbreak of ZIKV in Yap prompted the investigation of vectorial capacity of the predominant local mosquito to transmit this virus and other related viruses that are present or threaten to affect FSM and other Western Pacific island countries. Yap State, the western-most part of FSM, has previously been affected by arboviral outbreaks [6] but the discovery of ZIKV on the island highlighted the risk of epidemics due to agents previously unknown to the area. During the entomological investigations, collection of mosquito larvae and pupae from over 15 distinct container types revealed a wide range of habitats, both natural and artificial, that could support development of a variety of mosquito species. Because the island extensively imports products via cargo ships, introduction of exotic species that could utilize the variety of habitats is a strong possibility. This could allow further novel arboviral introduction events on the island. For example, Ae. albopictus could easily be or have been introduced to the island due to the proximity and intense air and sea traffic with Guam and Mariana Islands where this species is widespread [37] . None were found during this study.
The overwhelmingly predominant mosquito species found on the island was Ae. hensilli. This mosquito was previously speculated to be the vector of DENV during the 1995 outbreak in Yap State as it was the only Aedes (Stegomyia) present on some affected islands [6] . However, like in this outbreak, no isolations were made from field-collected mosquitoes and no arboviruses have ever been reported from this species so incrimination as a vector could not be biologically confirmed. The collection of additional mosquitoes may have allowed virus isolation from field material but repeated strong rainstorms limited the number of adults collected. As in the previous dengue outbreak, Ae. hensilli is the most probable outbreak vector due to its high density, widespread distribution on the island, and its tendency to bite humans. Although transmission studies may have helped clarify vector status, laboratory infection studies reported here further suggest that this is a probable vector due to the high infection rates with ZIKV. While there is an admittedly suboptimal dissemination rate to indicate vector status for ZIKV, there has been documentation of other Aedes (Stegomyia) mosquitoes serving as outbreak vectors even with low susceptibility to infection or dissemination. For example, Ae. aegypti, which has been reported to be relatively resistant to infection to yellow fever virus, has nevertheless been implicated in outbreaks of yellow fever [38] . Vector status of Ae. hensilli for DENV-2 is more difficult to assert based on the laboratory data indicating less than 20% infection rates with virtually no dissemination. However, susceptibility to viruses in at least 2 distinct arboviral genera ( flavivirus and alphavirus) suggests that this species could possibly serve as a vector of other medically important arboviruses typically transmitted by Aedes (Stegomyia) species (e.g. yellow fever and chikungunya viruses). It could also serve as a vector of arboviruses in large population centers where the mosquito is found [39] . Aedes hensilli has a limited known distribution consisting of FSM, Palau, and Singapore [39] suggesting that these additional areas might also be potentially at risk due to arboviral pathogens vectored by this species.
Since little is known of the biology or zoonotic transmission of ZIKV, it is also possible that other Scutellaris group species (among others) could be possible vectors of the virus. This is supported by the findings that ZIKV has previously been associated with Ae. africanus [23] , [40] , [41] , Ae. luteocephalus [42] , and Ae. aegypti [15] mosquitoes. There are numerous Scutellaris group mosquitoes from island ecologies including Aedes cooki, Aedes polynesiensis, Aedes palauensis, Aedes rotumae, and Aedes scutellaris, and others, some of which have been implicated in arboviral transmission [42] – [48] . The range of the Scutellaris group mosquitoes should be considered as possible vectors of ZIKV in islands of the Pacific and elsewhere.
Aedes hensilli was found to be very susceptible to infection by CHIKV. This finding was interesting as the strain of CHIKV selected was a Central/East African genotype strain associated with the Indian Ocean lineage but not possessing the valine residue at E1that has been linked to increased infectivity in Ae. albopictus [49] . A strain without this mutation was specifically selected to evaluate the susceptibility of Ae. hensilli to a virus that may not have been adapted to alternate Scutellaris group mosquitoes. However, the high degree of susceptibility to CHIKV even without the valine reside at position 226 is not completely unexpected as distinct populations of Ae. albopictus have historically shown significant susceptibility to CHIKV [50] . The ability of Ae. hensilli to be infected with CHIKV again, like with ZIKV, indicates that geographic areas with less well characterized Scutellaris group mosquitoes should consider alternate species to be potential vectors of introduced arboviral diseases.
We gratefully acknowledge the people of Yap state for cooperating during the sampling operations in their homes. We would also like to thank the officials of Ministry of Health of FSM for inviting us and supporting our operations, Christina Fillmed for providing EPA laboratory space, and Ernie Beyan, Peter Fattamag, and Mathew Thigthen for assistance in collecting mosquitoes. We thank Jacob Kool for assistance with coordination of field efforts and Myrielle Dupont for comments on the manuscript. The findings and conclusions in this report are those of the authors only and do not necessarily reflect the views of their respective institutions.


| RB-946 |
22357 |
|
|
|
CE/VA |
20045 |
Corporate Support |
45, Install Fire AV`s in basement
|
2009 |
|
| RQ-2013-0017 |
|
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|
RQ |
20490 |
154 |
490, Establish Physical Chemistry Lab in room 154
|
2013 |
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| RZ-2009-8002 |
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|
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|
Not Selected |
20019 |
|
19, Establish ISIF power/thermal integrtaion facility
|
2008 |
|


The 44th uniform polyhedron is the Small rhombidodecahedron; it has Wythoff symbol 2 5/2 5|. Its dual is the small rhombidodecacron. The pattern of polygons round each vertex is (10,4,10/9,4/3); its symmetry group is the icosahedral group A5.
It has:
Euler characteristic: -18
It is one-sided; it has
Number of vertices: 60
Number of edges: 120
Number of faces: 42 (=610and 15 quadrilaterals and 154/3and 610/9).
Note: a polygon a/b means a shape made by taking a points and joining every bth of them; for instance 5/2 means a five-pointed star made by taking five points and joining every second point.


{% extends "layout.html" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Pay for application for nationality You will need the reference number included in the email. Reference number For example: QQ 123 456 A Surname This should be the name on your application {% endblock %}
You will need the reference number included in the email.


Question: Given the aroma/bitterness/etc of a cup of coffee, can we predict if it will be a ‘good’ cup of coffee as rated by professional coffee tasters?
Check out the Jupyter Notebook to follow along.
We’ll bring in the data, do preliminary exploration, transform it for analysis, separate it into training/test sets, determine which model results in the highest accuracy predictions, and predict the rating a new cup of coffee might receive.
Info from The Coffee Institute, data scraped by James LeDoux.
Let’s start with our initial imports. This will bring in everything we’ll need for the project, including ways to manipulate data (pandas), visualize data (matplotlib), and do ML analysis (sklearn)
#importing
##data tools
import pandas as pd
pd.set_option('display.max_columns', 500)
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
##ML tools
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
%matplotlib inline
Next let’s get the data and take a quick look at it.
coffeeRaw = pd.read_csv('arabica_data_cleaned.csv')
coffeeRaw.head()
| Unnamed: 0 | Species | Owner | Country.of.Origin | Farm.Name | Lot.Number | Mill | ICO.Number | Company | Altitude | Region | Producer | Number.of.Bags | Bag.Weight | In.Country.Partner | Harvest.Year | Grading.Date | Owner.1 | Variety | Processing.Method | Aroma | Flavor | Aftertaste | Acidity | Body | Balance | Uniformity | Clean.Cup | Sweetness | Cupper.Points | Total.Cup.Points | Moisture | Category.One.Defects | Quakers | Color | Category.Two.Defects | Expiration | Certification.Body | Certification.Address | Certification.Contact | unit_of_measurement | altitude_low_meters | altitude_high_meters | altitude_mean_meters | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Arabica | metad plc | Ethiopia | metad plc | NaN | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | guji-hambela | METAD PLC | 300 | 60 kg | METAD Agricultural Development plc | 2014 | April 4th, 2015 | metad plc | NaN | Washed / Wet | 8.67 | 8.83 | 8.67 | 8.75 | 8.50 | 8.42 | 10.0 | 10.0 | 10.0 | 8.75 | 90.58 | 0.12 | 0 | 0.0 | Green | 0 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950.0 | 2200.0 | 2075.0 |
| 1 | 2 | Arabica | metad plc | Ethiopia | metad plc | NaN | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | guji-hambela | METAD PLC | 300 | 60 kg | METAD Agricultural Development plc | 2014 | April 4th, 2015 | metad plc | Other | Washed / Wet | 8.75 | 8.67 | 8.50 | 8.58 | 8.42 | 8.42 | 10.0 | 10.0 | 10.0 | 8.58 | 89.92 | 0.12 | 0 | 0.0 | Green | 1 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950.0 | 2200.0 | 2075.0 |
| 2 | 3 | Arabica | grounds for health admin | Guatemala | san marcos barrancas "san cristobal cuch | NaN | NaN | NaN | NaN | 1600 - 1800 m | NaN | NaN | 5 | 1 | Specialty Coffee Association | NaN | May 31st, 2010 | Grounds for Health Admin | Bourbon | NaN | 8.42 | 8.50 | 8.42 | 8.42 | 8.33 | 8.42 | 10.0 | 10.0 | 10.0 | 9.25 | 89.75 | 0.00 | 0 | 0.0 | NaN | 0 | May 31st, 2011 | Specialty Coffee Association | 36d0d00a3724338ba7937c52a378d085f2172daa | 0878a7d4b9d35ddbf0fe2ce69a2062cceb45a660 | m | 1600.0 | 1800.0 | 1700.0 |
| 3 | 4 | Arabica | yidnekachew dabessa | Ethiopia | yidnekachew dabessa coffee plantation | NaN | wolensu | NaN | yidnekachew debessa coffee plantation | 1800-2200 | oromia | Yidnekachew Dabessa Coffee Plantation | 320 | 60 kg | METAD Agricultural Development plc | 2014 | March 26th, 2015 | Yidnekachew Dabessa | NaN | Natural / Dry | 8.17 | 8.58 | 8.42 | 8.42 | 8.50 | 8.25 | 10.0 | 10.0 | 10.0 | 8.67 | 89.00 | 0.11 | 0 | 0.0 | Green | 2 | March 25th, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1800.0 | 2200.0 | 2000.0 |
| 4 | 5 | Arabica | metad plc | Ethiopia | metad plc | NaN | metad plc | 2014/2015 | metad agricultural developmet plc | 1950-2200 | guji-hambela | METAD PLC | 300 | 60 kg | METAD Agricultural Development plc | 2014 | April 4th, 2015 | metad plc | Other | Washed / Wet | 8.25 | 8.50 | 8.25 | 8.50 | 8.42 | 8.33 | 10.0 | 10.0 | 10.0 | 8.58 | 88.83 | 0.12 | 0 | 0.0 | Green | 2 | April 3rd, 2016 | METAD Agricultural Development plc | 309fcf77415a3661ae83e027f7e5f05dad786e44 | 19fef5a731de2db57d16da10287413f5f99bc2dd | m | 1950.0 | 2200.0 | 2075.0 |
Ok. That’s a lot of info, far more than we need really. Remember, our goal is to build something that can predict a value using only information we might have about a cup of coffee right in front of us. We won’t know the elevation, processing method, etc. So let’s trim this down to a more useful dataset..
coffee = coffeeRaw[['Aroma','Flavor', 'Aftertaste', 'Acidity', 'Body', 'Balance',
'Total.Cup.Points']].copy()
coffee.head()
| Aroma | Flavor | Aftertaste | Acidity | Body | Balance | Total.Cup.Points | |
|---|---|---|---|---|---|---|---|
| 0 | 8.67 | 8.83 | 8.67 | 8.75 | 8.50 | 8.42 | 90.58 |
| 1 | 8.75 | 8.67 | 8.50 | 8.58 | 8.42 | 8.42 | 89.92 |
| 2 | 8.42 | 8.50 | 8.42 | 8.42 | 8.33 | 8.42 | 89.75 |
| 3 | 8.17 | 8.58 | 8.42 | 8.42 | 8.50 | 8.25 | 89.00 |
| 4 | 8.25 | 8.50 | 8.25 | 8.50 | 8.42 | 8.33 | 88.83 |
Much better.
Real quick, let’s make sure there’s no weirdness in our data.
coffee.info()
coffee.isnull().sum()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1311 entries, 0 to 1310
Data columns (total 7 columns):
Aroma 1311 non-null float64
Flavor 1311 non-null float64
Aftertaste 1311 non-null float64
Acidity 1311 non-null float64
Body 1311 non-null float64
Balance 1311 non-null float64
Total.Cup.Points 1311 non-null float64
dtypes: float64(7)
memory usage: 71.8 KB
Aroma 0
Flavor 0
Aftertaste 0
Acidity 0
Body 0
Balance 0
Total.Cup.Points 0
dtype: int64
Yay, no blanks!
Pre-Processing & Basic Analysis
Since all we really want to algorithmically determine is if a certain cup is ‘good’ or ‘bad’, let’s bin our data into those categories.
#preprocessing into Good/Bad coffee
bins = [-1, 85, 100] #anything scoring 85 or above is good coffee™
labels = ['bad', 'good']
coffee['goodCoffee'] = pd.cut(coffee['Total.Cup.Points'], bins = bins, labels = labels)
coffee = coffee.drop(columns=['Total.Cup.Points'])
coffee['goodCoffee'].unique()
[good, bad]
Categories (2, object): [bad < good]
coffee['goodCoffee'].value_counts()
bad 1215
good 96
Name: goodCoffee, dtype: int64
#transform 'good' and 'bad' to 1 & 0
encoder = LabelEncoder()
coffee['goodCoffee'] = encoder.fit_transform(coffee['goodCoffee'])
coffee.head()
| Aroma | Flavor | Aftertaste | Acidity | Body | Balance | goodCoffee | |
|---|---|---|---|---|---|---|---|
| 0 | 8.67 | 8.83 | 8.67 | 8.75 | 8.50 | 8.42 | 1 |
| 1 | 8.75 | 8.67 | 8.50 | 8.58 | 8.42 | 8.42 | 1 |
| 2 | 8.42 | 8.50 | 8.42 | 8.42 | 8.33 | 8.42 | 1 |
| 3 | 8.17 | 8.58 | 8.42 | 8.42 | 8.50 | 8.25 | 1 |
| 4 | 8.25 | 8.50 | 8.25 | 8.50 | 8.42 | 8.33 | 1 |
We can see we have way more bad coffee than good coffee. (Sad) Let’s quickly graph this.
# Plot the data
plt.xkcd()
plt.bar(coffee['goodCoffee'].unique(), coffee['goodCoffee'].value_counts(),
width=0.75, align='center', color=mcolors.XKCD_COLORS)
plt.xticks([0, 1], ['Good', 'Not Good'])
plt.yticks(coffee['goodCoffee'].value_counts())
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.ylabel('# of entries')
plt.title('HOW MUCH OF THIS COFFEE SUCKS?')
plt.show()
# and a pie chart
plt.xkcd()
plt.pie(coffee['goodCoffee'].value_counts(), explode=(0, 0.1), labels=(['Not Good', 'Good']), autopct='%1.1f%%',
shadow=True, startangle=90, colors=mcolors.XKCD_COLORS)
plt.title('HOW MUCH OF THIS COFFEE SUCKS?')
plt.show()
Finally, let’s finish getting our data ready for analysis by splitting it into training/test sets and standardizing the values to remove a bit of bias from the system.
#seperate feature/response vars
X = coffee.drop(columns=['goodCoffee'])
y = coffee['goodCoffee']
#train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
#standardize values
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
Test the models
Random Forest Classifier
#better for mid-size datasets
rfc = RandomForestClassifier(n_estimators = 200)
rfc.fit(X_train, y_train)
pred_rfc = rfc.predict(X_test)
#how well did model perform?
print(classification_report(y_test, pred_rfc))
print(confusion_matrix(y_test, pred_rfc))
precision recall f1-score support
0 0.98 0.98 0.98 244
1 0.74 0.74 0.74 19
accuracy 0.96 263
macro avg 0.86 0.86 0.86 263
weighted avg 0.96 0.96 0.96 263
[[239 5]
[ 5 14]]
SVM Classifier
#better for smaller datasets
clf = svm.SVC()
clf.fit(X_train, y_train)
pred_clf = clf.predict(X_test)
#how well did model perform?
print(classification_report(y_test, pred_clf))
print(confusion_matrix(y_test, pred_clf))
precision recall f1-score support
0 0.98 0.98 0.98 244
1 0.78 0.74 0.76 19
accuracy 0.97 263
macro avg 0.88 0.86 0.87 263
weighted avg 0.97 0.97 0.97 263
[[240 4]
[ 5 14]]
Neural Network (Multi-Layer Percepitron Classifier)
#better for huge amounts of data
mlpc= MLPClassifier(hidden_layer_sizes=(11, 11, 11), max_iter=500)
mlpc.fit(X_train, y_train)
pred_mlpc = mlpc.predict(X_test)
print(classification_report(y_test, pred_mlpc))
print(confusion_matrix(y_test, pred_mlpc))
precision recall f1-score support
0 0.99 0.98 0.98 244
1 0.76 0.84 0.80 19
accuracy 0.97 263
macro avg 0.87 0.91 0.89 263
weighted avg 0.97 0.97 0.97 263
[[239 5]
[ 3 16]]
What’s best?
from sklearn.metrics import accuracy_score
acc_rfc = accuracy_score(y_test, pred_rfc)
acc_clf = accuracy_score(y_test, pred_clf)
acc_mlpc = accuracy_score(y_test, pred_mlpc)
print(f"Random Forest was {acc_rfc:.2%} accurate")
print(f"SVM was {acc_clf:.2%} accurate")
print(f"Neural Network was {acc_mlpc:.2%} accurate")
Random Forest was 96.20% accurate
SVM was 96.58% accurate
Neural Network was 96.96% accurate
All our models preformed about the same, with the neural net just a little bit better than everyone else. We’ll implement that model. This is doubley nice because now we get to say “I implemented a neural network” which sounds very fancy indeed.
Predicting future values
#a reminder what our data looks like
coffee[90:100]
| Aroma | Flavor | Aftertaste | Acidity | Body | Balance | goodCoffee | |
|---|---|---|---|---|---|---|---|
| 90 | 8.42 | 8.00 | 7.42 | 8.00 | 7.92 | 7.92 | 1 |
| 91 | 7.58 | 7.83 | 7.83 | 7.92 | 7.83 | 8.17 | 1 |
| 92 | 7.83 | 8.00 | 7.75 | 7.75 | 7.83 | 7.83 | 1 |
| 93 | 8.08 | 8.17 | 7.92 | 8.00 | 8.08 | 8.00 | 1 |
| 94 | 7.67 | 8.00 | 7.83 | 8.00 | 7.92 | 7.83 | 1 |
| 95 | 7.50 | 7.92 | 8.25 | 7.83 | 7.92 | 7.75 | 1 |
| 96 | 8.17 | 7.92 | 7.75 | 7.75 | 7.67 | 7.75 | 0 |
| 97 | 8.00 | 7.92 | 7.75 | 7.92 | 7.75 | 7.83 | 0 |
| 98 | 7.83 | 8.00 | 7.58 | 7.92 | 7.92 | 7.83 | 0 |
| 99 | 7.83 | 8.00 | 7.83 | 7.75 | 7.67 | 7.83 | 0 |
#---------------
#A theoretical cup of coffee
new_Aroma = 8
new_Flavor = 7.5
new_Aftertaste = 8
new_Acidity = 8
new_Body = 7.9
new_Balance = 8
#---------------
X_new = [[new_Aroma, new_Flavor, new_Aftertaste, new_Acidity, new_Body, new_Balance]]
X_new = sc.transform(X_new)
y_new = mlpc.predict(X_new)
print(y_new)
if y_new[0] == 1:
print("it's good!")
else:
print ("it's not good!")
[1]
it's good!


-Save manager's time-Let knowledge flow freely and give a consistent experience from one rep to the rest.


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now known or in the future discovered, Company’s User Content as well as all
modified and derivative works thereof.
c) By
transmitting or submitting any User Content while using the Software, Company
affirms, represents and warrants that such transmission or submission is (i) accurate; (ii) not in violation of any laws, contractual
restrictions or other third party rights, and that
Company has permission from any third party whose information or intellectual
property is comprised in the User Content; and (iii) free of viruses, adware,
spyware, worms or other malicious code.
d) Company
acknowledges and agrees that any of Company’s personal information within such
content will at all times be processed by SAP in accordance with its Privacy
Policy (http://www.sap.com/corporate-en/our-company/legal/privacy.epx).
6. Warranty: As there is no charge for the
license of the Software hereunder, You are not
entitled to the elimination of any defects or to subsequent delivery of
Software. SAP will compensate for loss or wasted anticipatory expenditure
arising out of defects subject to the limitation of liability in section 7
herein.
7. Liability:
7.1. SAP is
liable for damages or wasted anticipatory expenditures, in tort or otherwise,
only (a) under German product liability legislation, (b) for SAP’s gross
negligence or intent, (c) for absence of an expressly assured or guaranteed
quality, or (d) for personal injury.
7.2 Where
SAP is liable for grossly negligent acts of its employees who are not directors
or officers of SAP, SAP’s total liability for damages is limited to that liability
and to not more than the quantum of loss that SAP ought, in the light of its
knowledge of circumstances at the time this License Agreement was made, to have
foreseen as typical in connection with provision and licensed use of Software.
7.3. Where
SAP is liable under section 7.2, it is not liable for indirect loss,
consequential damages, or loss of profit.
7.4 SAP and
Licensee agree that the typical foreseeable quantum of loss cannot exceed
€50,000 in any case.
7.5. Licensee
undertakes to take appropriate steps to safeguard its data and programs,
including without limitation making backup copies not less frequently than is
recommended by SAP. Notwithstanding the other limitations of this section 7,
SAP is not liable for the loss of data and its recreation, in so far as this
would have been avoidable through observance of this obligation.
7.6. The
benefit of exclusions and limitations in this section 7 also extends to claims
of Customer against employees, contractors, and agents of SAP.
8. Indemnification: SAP will defend at its
own expense any legal proceeding brought against Company, to the extent that
such proceeding is based on a claim that the use of the Software as permitted
under this Agreement represents an infringement or misappropriation of a third
party’s copyright, patent, or a trade secret, and will pay all damages and
costs awarded by a court of final appeal attributable to such claim; provided,
however, that Company: a) provide notice of the claim promptly to SAP (but no
later than one (1) month after Company received notice of the claim), b) give
SAP sole control of the defense and settlement of the claim, provided that
Company, at Company’s option and expense, may also be represented in the legal
proceeding by independent counsel, c) provides to SAP, at SAP’s expense, all
commercially reasonable information, assistance and authority to defend such
claim and d) have not compromised or settled such proceeding without SAP’s
prior written consent.SAP shall have no liability for
any infringement or claim which results from the use of the Software provided
hereunder in combination with any equipment, software or data not provided or
approved by SAP. This section states the entire liability of SAP with respect
to the indemnification of any intellectual property right infringement
hereunder and there shall be no additional liability with respect to any
alleged or proven infringement. Section 7 shall apply accordingly.
9. Export: The Software is subject to
German, EU and US export control regulations. Company confirms that: a) Company
will not use the Software for, and will not allow the Software to be used for,
any purposes prohibited by German, EU and US law, including, without
limitation, for the development, design, manufacture or production of nuclear,
chemical or biological weapons of mass destruction, b) Company is not located
in a country that is subject to a U.S. Government embargo, or that has been
designated by the U.S. Government as a "terrorist supporting"
country, c) you are not a citizen, national or resident of, and are not under
the control of, the government of: Cuba, Iran, Sudan, Iraq, North Korea, Syria,
nor any other country to which the United States has prohibited export, d)
Company will not download or otherwise export or re-export the Software,
directly or indirectly, neither to the above-mentioned countries nor to
citizens, nationals or residents of those countries, e) you are not listed on
the United States Department of Treasury lists of Specially Designated Nationals,
Specially Designated Terrorists, and Specially Designated Narcotic Traffickers,
nor listed on the United States Department of Commerce Table of Denial Orders
or any other U.S. government list of prohibited or restricted parties and f)
Company will not download or otherwise export or re-export the Software,
directly or indirectly, to persons on the above-mentioned lists.
10. Support: SAP is solely responsible for
providing, and Apple has no obligation to provide, maintenance and support for
the Software. SAP may offer support for the Software – at its sole discretion –
by e.g. provisioning of updates, patches, bug fixes and new versions via the
Apple App Store according to SAP then current existing support offerings. In
the event SAP provides such support via itunes, the
timely availability of this support is dependent on Apple cooperation in due
time for which SAP is not responsible.
11. Data
Protection: SAP
and Company must comply with the provisions of the law of data protection.
Where SAP is granted access (for example, for remote maintenance) to hardware
and software that Company uses, it is not the intention that SAP process or use
personal data for business purposes. Rather, transfer of personal data will
only take place in exceptional circumstances, as an incidental effect of SAP’s
performing its contractual duties. If in the course of contract
maintenance SAP stores, processes, or uses personal data, it will comply with
Company’s instructions in that respect. Additional remuneration is payable by
Company to the extent such instructions require goods, works, or services not
included in the contract. SAP and Company must implement the technical and
organizational measures required by the German Federal Data Protection Act,
section 9 and the schedule thereto, to protect personal data from misuse. SAP
will deploy on work involving the processing of personal data only personnel
bound to data secrecy in the meaning of the German Federal Data Protection Act,
section 5. SAP will bind to the applicable provisions of the law of data
protection any subcontractor with access to personal data. If the subcontractor
is not domiciled in the European Union (EU), SAP will take steps to achieve a
level of data protection that is appropriate in accordance with EU data protection
requirements. Company must ensure that all legal requirements on your part are
met so that SAP can perform its contractual services without infringing the
law. Company acknowledges and agrees that any of Company’s personal information
will at all times be processed by SAP in accordance with its Privacy Policy (http://www.sap.com/corporate-en/our-company/legal/privacy.epx).
12. Term
and Termination:
Company may terminate this Agreement by destroying all copies of the Software
on Company’s Apple mobile device. SAP shall be entitled to terminate
Company’s licenses to use the Software if Company or you fail to comply with
any of the terms of this Agreement. This Agreement terminates automatically
upon termination or expiration of the SAP Standard Software agreement between
Company and either the respective SAP entity or the authorized SAP reselling
partner. In case of termination or expiration of this Agreement, Company must
destroy all copies of the Software on Company’s Apple mobile devices.
13. Law/Venue: German law applies exclusively
to all claims in contract, in tort or otherwise, and the UN sales laws
convention is excluded. If You are a merchant within the meaning of the German
Commercial Code (HGB), section 1, or a public-law juristic person or special
fund, the sole place of jurisdiction for all differences arising out of or in
connection with the Software Agreement shall be Karlsruhe.


pymatgen.core.trajectory module¶
-
class
Trajectory
(lattice, species, frac_coords, time_step=2, site_properties=None, constant_lattice=True, coords_are_displacement=False, base_positions=None)[source]¶ Bases:
monty.json.MSONable
Trajectory object that stores structural information related to a MD simulation. Provides basic functions such as slicing trajectory or obtaining displacements.
Create a trajectory object
Parameters: - lattice – The lattice as any 2D array. Each row should correspond to a lattice vector. E.g., [[10,0,0], [20,10,0], [0,0,30]] specifies a lattice with lattice vectors [10,0,0], [20,10,0] and [0,0,30].
- species –
List of species on each site. Can take in flexible input, including:
- A sequence of element / specie specified either as string symbols, e.g. [“Li”, “Fe2+”, “P”, …] or atomic numbers, e.g., (3, 56, …) or actual Element or Specie objects.
- List of dict of elements/species and occupancies, e.g., [{“Fe” : 0.5, “Mn”:0.5}, …]. This allows the setup of disordered structures.
- frac_coords (MxNx3 array) – list of fractional coordinates of each species
- time_step (int, float) – Timestep of simulation in femtoseconds. Defaults to 2fs.
- site_properties (list) – Properties associated with the sites as a list of dicts of sequences, e.g., [{“magmom”:[5,5,5,5]}, {“magmom”:[5,5,5,5]}]. The sequences have to be the same length as the atomic species and fractional_coords. Number of supplied dicts should match number of frames in trajectory Defaults to None for no properties.
- constant_lattice (bool) – Whether the lattice changes during the simulation, such as in an NPT MD simulation.
- coords_are_displacement (bool) – Whether supplied coordinates are given in displacements (True) or positions (False)
- base_positions (Nx3 array) – The starting positions of all atoms in trajectory. Used to reconstruct positions when converting from displacements to positions. Only needs to be specified if coords_are_displacement=True. Defaults to first index of frac_coords if coords_are_displacement=False.
-
extend
(trajectory)[source]¶ Concatenate another trajectory
Parameters: trajectory (Trajectory) – Trajectory to add
-
classmethod
from_file
(filename, constant_lattice=True, **kwargs)[source]¶ Convenience constructor to obtain trajectory from XDATCAR or vasprun.xml file
Parameters: - filename (str) – The filename to read from.
- constant_lattice (bool) – Whether the lattice changes during the simulation, such as in an NPT MD simulation. True results in
Returns: (Trajectory)
-
classmethod
from_structures
(structures, constant_lattice=True, **kwargs)[source]¶ Convenience constructor to obtain trajectory from a list of structures. Note: Assumes no atoms removed during simulation
Parameters: - structures (list) – list of pymatgen Structure objects.
- constant_lattice (bool) – Whether the lattice changes during the simulation, such as in an NPT MD simulation. True results in
Returns: (Trajectory)
-
get_structure
(i)[source]¶ Returns structure at specified index :param i: Index of structure :type i: int
Returns: (Structure) pymatgen structure object


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clients ART < HIV South baseline counts mortality care cells/mm Johannesburg antiretroviral Mopani data initiating treatment therapy years Health initiation programme study low {border-style count ;():–. CPT adults px;} #ffffff;} African compared higher .collab: Africa disease stage advanced districts increased time, Africa. district late population risk rural time analysis AIDS HIV-infected older significantly clinical presenting proportion Department National associated guidelines likely presentation recent test viral Africa, III Med. Pretoria: TIER.Net analysis. healthcare improve initiated life lower px; routine {font-family (Figure -year Mortality NDoH; adult delivery findings font-size high immunodeficiency load mortality. outcomes patients primary quality records sub-Saharan testing using years, .). Afr Baseline World care, combination cotrimoxazole counts. demonstrated double; focus human improved interventions median men morbidity preventive services trends women (%) ART, Africa: Africa’s FDC Infect Kaplan–Meier Mopani. aged border-color border-width burden cohort date death declined drug early expansion expectancy included including medical monitoring newly number outcome programme, rates regimens single solid; start study. therapy; urban use virus year {font-style {font-weight (< (Table .–. ;():e–. AIDS. Acquir Active Antiretroviral Available CLIENTS Data Defic Delivery Died Dis. FIGURE Health. Immune Int Internet]. Interventions Soc. Syndr. United [], [cited [homepage age availability bold cells/mm, counts, courier; data. deaths decreased em; fixed-dose follow-up from: health high-risk hospital hospitals increase initiate known missing overall pregnant prevention pt;} rapid received reduced remaining respectively. scale-up set specifically stavudine strategies supported tenofovir transmission used


S2DBCP offers database connection pooling using POJO. By integrating with JTA, transactions are maintained even after connections are opened and closed several times. Hence, it is no longer necessary to have several classes pass connections to maintain JDBC transactions.
Furthermore, it is possible to use S2Tx, to automatically manage transactions with Aspect.
It is necessary to configure XADataSource and ConnectionPool to use S2DBCP. XADataSource may be use if it is supported by the JDBC driver. If it is not supported, S2's XADataSourceImpl interface may be used to emulate XA.
org.seasar.extension.dbcp.impl.XADataSourceImpl
Property
Description
Example
driverClassName
JDBC Driver class name
"oracle.jdbc.driver.OracleDriver"
URL
Oracle database URL
"jdbc:oracle:thin:@xxx:1521:yyy"
user
User name to connect to database
"hoge"
password
Password of user to connect to database
"password"
org.seasar.extension.dbcp.impl.ConnectionPoolImpl
Property
Description
Example
XADataSource
XADataSource instance. When using S2Container, set to component name of XADataSource.
xaDataSource
transactionManager
TransactionManager instance. When using S2Container, set to component name of TransactionManager.
transactionManager
timeout
Time interval in seconds for unused connections which are returned to the pool to be closed and destroyed. Default value is 600 seconds (10 minutes).
600
maxPoolSize
Maximum number of concurrent connections. When number of connections exceeds this number, a new request are blocked until a connection is returned to the pool. Default value is 10 connections.
10
Connections are set as follows. Please modify it as needed to make it specific for your application.


Related Functions |
(Note that these are not member functions.)
|
| template<class Char , class Traits > |
std::basic_ostream< Char,
Traits > & | operator<< (std::basic_ostream< Char, Traits > &os, const MinusView &x) |
| | Print float minus view.
|
Constructors and initialization |
|
| | MinusView (void) |
| | Default constructor.
|
| | MinusView (const FloatView &y) |
| | Initialize with float view y.
|
Value access |
|
| FloatVal | domain (void) const |
| | Return domain.
|
| FloatNum | min (void) const |
| | Return minimum of domain.
|
| FloatNum | max (void) const |
| | Return maximum of domain.
|
| FloatNum | med (void) const |
| | Return median of domain (closest representation).
|
| FloatVal | val (void) const |
| | Return assigned value.
|
| FloatNum | size (void) const |
| | Return size of domain (distance between maximum and minimum).
|
Domain tests |
|
| bool | zero_in (void) const |
| | Test whether 0 is contained in domain.
|
| bool | in (FloatNum n) const |
| | Test whether n is contained in domain.
|
| bool | in (const FloatVal &n) const |
| | Test whether n is contained in domain.
|
Domain update by value |
|
| ModEvent | lq (Space &home, int n) |
| | Restrict domain values to be less or equal than n.
|
| ModEvent | lq (Space &home, FloatNum n) |
| | Restrict domain values to be less or equal than n.
|
| ModEvent | lq (Space &home, FloatVal n) |
| | Restrict domain values to be less or equal than n.
|
| ModEvent | gq (Space &home, int n) |
| | Restrict domain values to be greater or equal than n.
|
| ModEvent | gq (Space &home, FloatNum n) |
| | Restrict domain values to be greater or equal than n.
|
| ModEvent | gq (Space &home, FloatVal n) |
| | Restrict domain values to be greater or equal than n.
|
| ModEvent | eq (Space &home, int n) |
| | Restrict domain values to be equal to n.
|
| ModEvent | eq (Space &home, FloatNum n) |
| | Restrict domain values to be equal to n.
|
| ModEvent | eq (Space &home, const FloatVal &n) |
| | Restrict domain values to be equal to n.
|
Delta information for advisors |
|
| FloatNum | min (const Delta &d) const |
| | Return minimum value just pruned.
|
| FloatNum | max (const Delta &d) const |
| | Return maximum value just pruned.
|
View-dependent propagator support |
|
| static ModEventDelta | med (ModEvent me) |
| | Translate modification event me to modification event delta for view.
|
Minus float view.


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{%extends "base.html"%} {% import "bootstrap/wtf.html" as wtf %} {%block title%}修改密码{%endblock%} {% block page_content%}


0.92 Release “Baby Steps”
The Pitivi team is proud to announce the second alpha release of the new version of Pitivi based on GES, the GStreamer Editing Services library. This is a incremental bugfixing release, so please make sure to see the 0.91 release notes to get the full picture.
WARNING: Please keep in mind that this is still an alpha release, so there may be some bugs we haven't found yet. We appreciate your feedback, so try it out, let us know how well it works for you and report detailed bugs for issues you may encounter. Again, this is not yet considered production-ready software.
That said, one month after 0.91, with the feedback we have received so far, we are pretty confident that the new release series is of much higher quality than the 0.15 series and older.
Changes and fixed bugs
Executive summary:
- Fix a bug where transitions would stop working
- Fix the handling of rendering parameters
- Fixes for the keyframes UI in the timeline
- Usability improvements for the welcome dialog
- Update the preview immediately when adding an effect
- Fixes for AppData XML spec compliance
- Various build and packaging fixes
- Drop the PyXDG dependency
- Translations have been updated
See the list of reported bugs that have been fixed in 0.92.
General fixes:
e33598c ui/startupwizard: Force ButtonBox items to have a homogeneous/uniform width
b9f62d7 Keep the welcome dialog shown when clicking “Missing dependencies...”\
4dcbf5c elements: Set clip inpoints to prevent keyframes from breaking on split/trim
1de8d96 previewers: stop waveforms and thumbnails generation when removing clips
2cb9298 mainwindow: Make it possible to save project when an asset moved
bfe4154 project: Avoid to work with read only caps
79fb9ab render: Take into account video size scaling value when rendering
e43c54d project: Always set auto-transition to true on newly created timelines
dcb9b68 effects: Commit the timeline when adding an effect.
4936135 keyframes: If the length of the line is inferior to one pixel, don't draw it.
Build/packaging fixes:
c316cc1 Apply minor changes to make AppData XML file friendlier to distributions
8159d23 Drop dependency on PyXDG, use GLib instead
5c94a24 build: Don't attempt locale-uninstalled bits if DESTDIR is set
6490a95 bin: Fix GI_TYPELIB_PATH mistakes
308f18c bin: Build gst-devtools only if in developer mode
078b71b bin: Better handling of gst version and add default scenario path
306e880 bin: Do not build GI and PyGobject if not necessary
Requirements
Dependencies/requirements have changed significantly since the 0.15 series, but have stayed mostly the same since 0.91 (pyxdg was dropped as a dependency).
Generally speaking, you can refer to Pitivi's check.py for the dependencies' versions specific to a given release. See also dependencies for additional tips.
Known issues
Please see the 0.91 release notes.
See the list of currently known issues, the 0.91 and 0.92-specific issues and the list of bugs that need re-testing with 0.91 and 0.92 (you can help!)
Contributors for this release
In Pitivi:
9 Thibault Saunier
6 Jean-François Fortin Tam
6 Mathieu Duponchelle
3 Colin Walters
In GES:
5 Thibault Saunier
2 Kishore Arepalli
2 Mathieu Duponchelle
Translations:
cs Marek Černocký
es Daniel Mustieles
fi Timo Jyrinki
fr Claude Paroz
gl Fran Diéguez
lv Rūdolfs Mazurs
pt_BR Rafael Ferreira
sl Martin Srebotnjak
sr Мирослав Николић
zh_HK Cheng-Chia Tseng
zh_TW Cheng-Chia Tseng
Download, additional information and feedback
A tarball is available on http://ftp.gnome.org/pub/GNOME/sources/pitivi/0.92/
See the Pitivi website and building with GES for download and installation instructions.
Feedback is welcome. See bug reporting for tips and tricks.
The results of the search are


Nisha and Brandon's Wedding
Please join us at our wedding on August 7th, 2010, 3:30-7:30pm - Mt. Tabor Amphitheatre, Portland, OR. Then celebrate with dancing, drinks and food immediately following the ceremony.
Répondez s'il vous plaît
Join us in our celebration and RSVP now. If you need help please contact us.Contact
Need to get in touch with us? Having trouble RSVPing? Please email us by clicking on the links below:


The devices are displayed on the Home page, and it is easy to enjoy them.
Now, it is easy to control your device. Let's do it after adding any device.
Helps, Feedback and Settings are here. Let us get started.


Broadco takes lead at Florida Keys powerboat races
KEY WEST, Fla. (AP) - Powerboat racers Charles Broaddus and Chris Hanley have piloted Broadco to a Super Cat-class lead on the second competition day of the Race World Offshore Key West Championships in the Florida Keys.
Broaddus of St. Clair Shores, Michigan, and Hanley of Orlando, dominated the Super Cat field Friday. Wednesday’s Super Cat victor WHM finished third, leaving Broadco leading the class based on points.
CR Racing, with Robert Unnerstall of Wentzville, Missouri, and Casey Boaz of Osage Beach, Missouri, won again in the Super Stock class.
Super Stock boat Jackhammer overturned on the fifth lap, but only throttleman Ricky Maldonado was transported to a local hospital. Race officials say the Palmetto, Florida, resident was treated for a fractured wrist and released.
The 2019 races conclude Sunday with double points.
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