Q:

Undo `lazyload()` with the relationship default

I have a Query object which was initially configured to lazyload() all relations on a model:
query = session.query(Article).options(lazyload('author'))

Is it possible to revert the relationship loading back to default? E.g. the relationship was configured with lazy='joined', and I want the query to have joinedload() behavior without using joinedload() explicitly.
I was expecting defaultload() to have this behavior, but in fact it does not: it references the query default instead of the relationship default. So I'm searching for kinda resetload() solution.

The reason for doing this is because I'm creating a JSON-based query syntax, and no relations should be loaded unless the user explicitly names them.
Currently, I'm using lazyload() on all relations that were not explicitly requested, but want to go the other way around:  lazyload() all relations first, and then override it for some of them.
This would have made the code more straigntforward.

A:

Just to be clear:

By default, all inter-object relationships are lazy loading.

http://docs.sqlalchemy.org/en/latest/orm/loading.html
So we are talking about a case in which a relation has been specifically marked as eager loading, then the queries are configured as lazy loading, then you want to "override the override" as it were.
Chaining calls to options will override earlier calls. I did test this a bit.
q = s.query(User)  # lazy loads 'addresses'
q = s.query(User).options(contains_eager('addresses'))  # eager loads
q = s.query(User).options(contains_eager('addresses'))\
        .options(lazyload('addresses'))  # lazy loads
q = s.query(User).options(contains_eager('addresses'))\
        .options(lazyload('addresses'))\
        .options(contains_eager('addresses'))  # eager loads

However, it sounds like you're talking about just reverting the lazyload option, whereas the above case involves an explicit change to eager loading.
The defaultload docstring says its use case is to be chained to other loader options, so I don't think it's related.
Based on a glance through the source, I don't think this behavior is supported. When you update the loading strategy option, it updates a dictionary with the new loading strategy and I don't think there's still a reference to the old strategy, at least as far as I can tell.
You could keep a reference to the query object before .options(lazyload(...)), or just have an option to generate the query with or without the lazyload on everything.


Hundreds attend funeral for California sheriff shot “execution-style”

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This is an archived article and the information in the article may be outdated. Please look at the time stamp on the story to see when it was last updated.

LANCASTER, Calif. — Hundreds of law enforcement officers, friends and relatives showed up Thursday morning in California to pay their respects to a sheriff who was shot in a “calculated execution.”

Steve Owen, 53, was killed in Lancaster, Calif. when responding to a robbery-in-progress call on Oct. 5, 2016. (Los Angeles County Sheriff’s Department)

Los Angeles County Sheriff’s Sgt. Steve Owen, 53, was gunned down last week when answering a burglary-in-progress call in Lancaster, California.

“This has been a very dark day for the LA County Sheriff’s Department,” Capt. Steve Katz said on Oct. 5, the day of the incident. “We’re all suffering now. His family certainly is suffering, his law enforcement family is suffering. It doesn’t get worse than this.”

Police arrested 27-year-old Trenton Lovell near the scene of the shooting in the Los Angeles suburb and charged him with Owen’s murder. Sheriff Jim McDonnell told reporters that Lovell shot Owen, a 29-year department veteran, then stood over his body and fired four more times.

Lovell tried to get away in Owen’s patrol car, but rear-ended another police car and fled on foot, officials said. Lovell was shot in the torso when trying to flee from a nearby house he was hiding in, and was charged after getting treated at a hospital, police said. He is being held without bail.

“Somebody has to go when somebody calls for help. In this case, Steve went and got there first,” sheriff’s Executive Officer Neal Tyler said. “He was shot for his efforts and made the ultimate sacrifice.”

Owen’s wife and two children were able to see Owen in the hospital before he died, officials said.

“We want to say thank you for the love and support you have shown our family as we mourn the loss of our husband, father, brother, uncle, son, and ultimately our HERO, Sgt. Steve Owen,” the family said in a statement.

The funeral Thursday morning in Lancaster was closed to the general public because of the large number of people expected to attend. A procession through the streets of Lancaster was held before the service, the route lined with police officers, and patrol cars and motorcycles.


Calycadenia micrantha

Calycadenia micrantha is an uncommon species of flowering plant in the daisy family known by the common name small-flowered calycadenia. It is endemic to California, where it is known from about 13 occurrences that are widely spread over several counties, including Napa, Siskiyou, Shasta, Mendocino, Trinity, and Monterey Counties, with several populations known in Lake County. It was described to science as a species in 2004. Some specimens of the plant had previously been included within the description of its relative, Calycadenia truncata. The fact that it has been found in relatively far-flung locations suggests it could easily be present in other areas between them and, as a newly classified species, has simply not been reported yet.

This annual plant produces a slender purplish stem 10 to 50 centimeters tall. The hairy leaves are 2 to 5 centimeters long. The inflorescence is a single flower head or small cluster of heads, each with 1 to 6 three-lobed yellow ray florets. Unlike other Calycadenia species, this plant is self-compatible, or able to fertilize itself.

References

External links
Jepson Manual Treatment

micrantha
Category:Flora of California
Category:Plants described in 2004


Adam Schefter doesn't understand why the Bengals didn't trade A.J. Green considering his contract is set to expire at the end of this season. (0:42)

CINCINNATI -- Bengals wide receiver A.J. Green was very clear about what he's looking for in his next contract.

And it's not a one-year franchise tag.

"I'm not into a one-year," Green said on Wednesday. "Give me a long-term (contract) or just let me go."

The 32-year-old Pro Bowler, who has not played this season, is currently in the final year of his four-year, $60 million contract with the Bengals. Green has missed the Bengals' 0-8 start to the season because of an ankle injury he suffered during the first practice of the preseason.

Green said that he decided to slow down contract negotiations between himself and the team while he worked to get healthy again. On Wednesday, Green participated in team drills for the first time since the injury occurred in July.

Green said he's against the concept of giving the franchise tag to veterans. He feels it should be eliminated for players who have been in the league for five or more years.

However, he understands that getting the franchise tag is up to the Bengals' front office, not him. And while he would disagree with the tag, he doesn't plan on holding out if that happens.

"That's not me," Green said. "I'm not giving free money away. I don't care what that is."

The former Georgia standout once again downplayed any trade speculation regarding him ahead of Tuesday's deadline. The Bengals were adamant about keeping Green despite their 0-8 start. Even though Cincinnati is in the midst of its worst start since 2008 and appears to be headed for a rebuild, Green appeared content.

"I'm not going to be a guy that says, 'Oh I want to get traded' and then go to a situation where I'm not happy," Green said. "Like, I'm genuinely happy here. We might not win, but I'm happy here. But at the end of the day, this is a business. I know my worth and I know what I bring to this team."

Green's health is one of the biggest questions surrounding his future negotiation. The ninth-year player is confident in his ability to be productive when healthy.

"If they're concerned about that, they don't need to sign me," Green said. "It's whatever. If they're concerned about me not being able to play 16, then take the risk not signing me and not franchising me. We'll go from there."


Do you agree with the city of Mobile's dining out policy for firefighters? (poll)

You could be seeing Mobile firefighters dining out at local McDonald's and other restaurants in the near future while they are on duty. It's something that hasn't been allowed in the past, but a new policy allows firefighters to dine out while on the job. (file photo).

MOBILE, Alabama – The Mobile Fire-Rescue Department has a
new policy in place that allows firefighters to dine out away from their fire
stations while on the job.

The new policy will be restrictive – firefighters will only
be allowed to eat in restaurants with the rest of their crew and cannot be
separate from their equipment or vehicles. Also, dining out is only allowed
within the district the firefighters are assigned.

Critics say that the policy is not important for
firefighters. They claim that a policy is needed more for police officers, who
need to be more visible in the communities they patrol. Also, in other cities
like Pensacola, similar policies have been temporarily suspended before because
of costs.

Tell us what you think, do you agree with the policy or
should firefighters remain stationed at their fire station?


Development of LC/MS/MS assay for the determination of 5-ethyl-2-{5-[4-(2-hydroxyethyl)piperazine-1-sulfonyl]-2-propoxyphenyl}-7-propyl-3,5-dihydropyrrolo[3,2-d]pyrimidin-4-one (SK3530) in human plasma: application to a clinical pharmacokinetic study.
5-Ethyl-2-{5-[4-(2-hydroxyethyl)piperazine-1-sulfonyl]-2-propoxyphenyl}-7-propyl-3,5-dihydropyrrolo[3,2-d]pyrimidin-4-one (SK3530) is a new phosphodiesterase type-5 inhibitor currently undergoing a Phase III investigation for the treatment of male erectile dysfunction. This study first describes a rapid and sensitive LC/MS/MS assay method for the quantification of SK3530 and its major metabolite, SK3541, in human plasma. The assay was validated to demonstrate the specificity, linearity, recovery, lower limit of quantification (LLOQ), accuracy, and precision. The multiple reaction monitoring was based on the transition of m/z=532.5-->99.1 for SK3530, 488.6-->295.5 for SK3541, and 520.3-->99.1 for SK3304 (internal standard). The assay utilized a single liquid-liquid extraction and isocratic elution, and the LLOQ was 1 ng/ml using 0.2 ml human plasma. The assay was linear over a range from 1 to 1000 ng/ml for both SK3530 and SK3541, with correlation coefficients >0.9999. The mean intra- and inter-day assay accuracy ranged from 94.7 to 101.6% and 96.8 to 101.1% for SK3530 and 92.6-105.7% and 97.4-107.8% for SK3541, respectively. The mean intra- and inter-day precision was between 7.2-12.1% and 5.7-7.4% for SK3530 and 4.6-13.2% and 5.0-14.1% for SK3541, respectively. The developed assay was applied to a clinical pharmacokinetic study after oral administration of SK3530 in healthy male volunteers (dose 100 mg).


Photoreceptor cell death from age-related or hereditary retinal degeneration remains the leading cause of blindness in the developed world. The inner retina is largely spared in these diseases. Photoswitch chemicals are specific pharmacologic agents whose activity can be modulated using visible light. We propose utilizing this class of compounds as an approach to reverse blindness from outer retinal degeneration. Preliminary work from our laboratories has demonstrated that the photoswitchable voltage gated potassium channel antagonist AAQ is able to restore electrophysiological retinal responses to several mouse models of outer retinal blindness, and is able to restore pupillary light responses and behavioral responses to light in blind mice. However, AAQ has limitations in terms of its wavelength sensitivity, kinetics, and potential toxicity. Two 'second generation' photoswitch compounds, DENAQ and PhENAQ, show improved spectral response, kinetics, and tolerance in the eye. We propose rigorously testing these two compounds against each other in vitro and in in vivo in mouse, rat, dog, and primate models to determine which to take forward into clinical development; propose an in depth analysis of their effects on the primate retina in vitro, in order to understand their potential for rescue of human blindness; and propose performing required preclinical toxicology and efficacy tests with the goal of applying for a new drug application to allow human clinical studies at the conclusion of this grant.


" vim-airline template by chartoin (http://github.com/chartoin)
" Base 16 Flat Scheme by Chris Kempson (http://chriskempson.com)
let g:airline#themes#base16_flat#palette = {}
let s:gui00 = "#2C3E50"
let s:gui01 = "#34495E"
let s:gui02 = "#7F8C8D"
let s:gui03 = "#95A5A6"
let s:gui04 = "#BDC3C7"
let s:gui05 = "#e0e0e0"
let s:gui06 = "#f5f5f5"
let s:gui07 = "#ECF0F1"
let s:gui08 = "#E74C3C"
let s:gui09 = "#E67E22"
let s:gui0A = "#F1C40F"
let s:gui0B = "#2ECC71"
let s:gui0C = "#1ABC9C"
let s:gui0D = "#3498DB"
let s:gui0E = "#9B59B6"
let s:gui0F = "#be643c"

let s:cterm00 = 23
let s:cterm01 = 59
let s:cterm02 = 102
let s:cterm03 = 109
let s:cterm04 = 146
let s:cterm05 = 253
let s:cterm06 = 15
let s:cterm07 = 15
let s:cterm08 = 167
let s:cterm09 = 172
let s:cterm0A = 220
let s:cterm0B = 41
let s:cterm0C = 37
let s:cterm0D = 68
let s:cterm0E = 97
let s:cterm0F = 131

let s:N1   = [ s:gui01, s:gui0B, s:cterm01, s:cterm0B ]
let s:N2   = [ s:gui06, s:gui02, s:cterm06, s:cterm02 ]
let s:N3   = [ s:gui09, s:gui01, s:cterm09, s:cterm01 ]
let g:airline#themes#base16_flat#palette.normal = airline#themes#generate_color_map(s:N1, s:N2, s:N3)

let s:I1   = [ s:gui01, s:gui0D, s:cterm01, s:cterm0D ]
let s:I2   = [ s:gui06, s:gui02, s:cterm06, s:cterm02 ]
let s:I3   = [ s:gui09, s:gui01, s:cterm09, s:cterm01 ]
let g:airline#themes#base16_flat#palette.insert = airline#themes#generate_color_map(s:I1, s:I2, s:I3)

let s:R1   = [ s:gui01, s:gui08, s:cterm01, s:cterm08 ]
let s:R2   = [ s:gui06, s:gui02, s:cterm06, s:cterm02 ]
let s:R3   = [ s:gui09, s:gui01, s:cterm09, s:cterm01 ]
let g:airline#themes#base16_flat#palette.replace = airline#themes#generate_color_map(s:R1, s:R2, s:R3)

let s:V1   = [ s:gui01, s:gui0E, s:cterm01, s:cterm0E ]
let s:V2   = [ s:gui06, s:gui02, s:cterm06, s:cterm02 ]
let s:V3   = [ s:gui09, s:gui01, s:cterm09, s:cterm01 ]
let g:airline#themes#base16_flat#palette.visual = airline#themes#generate_color_map(s:V1, s:V2, s:V3)

let s:IA1   = [ s:gui05, s:gui01, s:cterm05, s:cterm01 ]
let s:IA2   = [ s:gui05, s:gui01, s:cterm05, s:cterm01 ]
let s:IA3   = [ s:gui05, s:gui01, s:cterm05, s:cterm01 ]
let g:airline#themes#base16_flat#palette.inactive = airline#themes#generate_color_map(s:IA1, s:IA2, s:IA3)

" Here we define the color map for ctrlp.  We check for the g:loaded_ctrlp
" variable so that related functionality is loaded iff the user is using
" ctrlp. Note that this is optional, and if you do not define ctrlp colors
" they will be chosen automatically from the existing palette.
if !get(g:, 'loaded_ctrlp', 0)
  finish
endif
let g:airline#themes#base16_flat#palette.ctrlp = airline#extensions#ctrlp#generate_color_map(
      \ [ s:gui07, s:gui02, s:cterm07, s:cterm02, '' ],
      \ [ s:gui07, s:gui04, s:cterm07, s:cterm04, '' ],
      \ [ s:gui05, s:gui01, s:cterm05, s:cterm01, 'bold' ])


WDRM 102.1 FM

WDRM (102.1 FM, “102.1 WDRM”) is an American radio station licensed to serve the community of Decatur, Alabama, and owned by Clear Channel Communications. It serves the Huntsville, Alabama, area with a country music format. WDRM has been consistently ranked by Arbitron as the most-listened to radio station in the Huntsville market for two decades. The weekday morning show, currently hosted by Dan McClain and Jessica “Jess” Humpherville, was top-rated in the Huntsville market for two decades until falling to #2 in the Summer 2008 ratings.


…is wrong. Thanks for joining my TED talk.

Continuous integration is such a beautiful tool. Making sure that whatever change you implement does not break the software you’re contributing to. For more complex systems it even ensures you don’t break the interactions of your system with other systems. Usually follows quite the simple principle, starting with pulling the development branch code. Generally, it would be the latest version of the software stable. You create your feature branch from it, implement your changes and run your tests locally and update your code until the tests pass.

Eventually, you check in your code onto your remote and trigger a build to make sure your changes integrate well with the overall software. Once you get your green build, you open a pull-request and eventually get to merge your code into the main branch.

What if for some reason that ignores reason, you may end up working with an unstable continuous integration. It could be that the build or tests are flaky which is not great but could happen. Not much you can do about that at a high level without diving deep within every application constituting the software you’re working on. I would accept that as the sort of pain you may find yourself dealing with in any software organisation.

Now imagine, a world where one merges code before testing it through CI CI builds. Right. Now picture, several teams able to merge against that same branch every few hours. Yeah, now we’re cooking with the sweet spice of pain. And not the way I like. There could be a very good reason things are working this way so let’s say there is nothing you can do about it. Chances are when you create your branch the build broke beforehand. I’m guessing an environment where anyone can break the main development branch, finger-pointing might be a national sport.

Nobody likes fingers pointed at their direction especially knowing their changes aren’t anywhere near the broken tests. How do you protect yourself against that? It’s simple yet tedious. There are two options I can think of.

The first one is to wait for the main development branch to have a clean build again and create your branch from there. Not my favourite as I am not a big fan of waiting in general. Waiting for fixes to coming by monitoring slack and observing build runs is about as fun as watching paint dry. Not a painting, bland wall paint. Nobody wants that.

The other, more proactive option, would be to create a branch from the last working commit of the main branch. Basically, go through your CI, look up the latest commit from the last green build, check it in and create your branch from there. Not so fast though, make sure you can run tests before you do any work on that branch. If no local tests are available then trigger a CI build off your new branch. If it’s green and only then, you can start developing your changes.

It’s not foolproof but it seems like the safest way to develop changes in such an environment. Also, it helps to figure out issues when you actually introduce them. I should stop this post before I start delving on unstable CI builds behaving like Targaryens.

Every time a Targaryen is born, the gods toss a coin in the air and the world holds its breath to see how it will land. Ser Baristan Selmy, ASOIF

Hope you enjoyed the read, here is an example of a simple yet effective CI setup you can do for free. Go and get jiggy with it!

Cover by Kevin Ku from Pexels


1. Introduction {#sec1-ijerph-17-00525}
===============

In view of the merits of the VIKOR model in considering the compromise between group utility maximization and individual regret minimization, in recent years, it has been recognized as a meaningful tool that can be applied to many decision areas. In previous literature, some traditional decision models have been applied to MADM problems, such as the ELECTRE model \[[@B1-ijerph-17-00525],[@B2-ijerph-17-00525],[@B3-ijerph-17-00525],[@B4-ijerph-17-00525]\], the MABAC model \[[@B5-ijerph-17-00525],[@B6-ijerph-17-00525],[@B7-ijerph-17-00525]\], the COPRAS model \[[@B8-ijerph-17-00525],[@B9-ijerph-17-00525]\], the TOPSIS model \[[@B10-ijerph-17-00525],[@B11-ijerph-17-00525],[@B12-ijerph-17-00525]\], The TODIM model \[[@B13-ijerph-17-00525],[@B14-ijerph-17-00525],[@B15-ijerph-17-00525]\], and the GRA model \[[@B16-ijerph-17-00525],[@B17-ijerph-17-00525],[@B18-ijerph-17-00525]\]. Compared with the above methods, the VIKOR model not only considers the objectivity of the decision maker and the complexity of the decision-making environment, but also considers the conflict criteria, so as to obtain more effective and accurate evaluation results. Du and Liu \[[@B19-ijerph-17-00525]\] developed the traditional VIKOR model into intuitionistic trapezoidal fuzzy environment. Park, et al. \[[@B20-ijerph-17-00525]\] established the IVIF-VIKOR model for MADM problems. Qin, et al. \[[@B21-ijerph-17-00525]\] came up with an extension of VIKOR model on the basis of interval type-2 fuzzy information. Ghadikolaei, et al. \[[@B22-ijerph-17-00525]\] extended the VIKOR model from the real number environment to the hesitating fuzzy linguistic environment, so it can better reflect the fuzziness of decision makers in making decisions in MADM problems. Wang, et al. \[[@B23-ijerph-17-00525]\] tried to expand the VIKOR model to the neutrosophic environment of triangular fuzzy, and applied it to evaluate the potential commercialization of emerging technologies. In order to select industrial robots more effectively, Narayanamoorthy, et al. \[[@B24-ijerph-17-00525]\] used an expanding VIKOR model on the foundation of interval intuitionistic hesitating fuzzy entropy. Later, some scholars Yang, et al. \[[@B25-ijerph-17-00525]\] determined the VIKOR model of language hesitation intuition to deal with the problem of MADM. Wang, et al. \[[@B26-ijerph-17-00525]\] established a VIKOR model based on projection in the context of picture fuzzy environment and used it in the risk assessment of construction projects. Wu, et al. \[[@B27-ijerph-17-00525]\] created the HFLTS-VIKOR model with possibility distributions.

Because of the uncertainty and decision problem of decision support system (DSS), in the practical DSS problem, we often cannot give the accurate evaluation value of the alternative to choose the best one. To overcome this problem, in 1965, the fuzzy set theory defined by Zadeh \[[@B28-ijerph-17-00525]\], initially applied membership functions instead of precise real numbers to describe the estimation results. Atanassov \[[@B29-ijerph-17-00525],[@B30-ijerph-17-00525]\] added another metric that complements non-membership functions. In recent years, the proposed Pythagorean fuzzy set (PFS) \[[@B31-ijerph-17-00525],[@B32-ijerph-17-00525]\] further expanded the scope of IFS, making the sum of squares of its membership degree and non-membership degree less than or equal to 1. Obviously, PFS is more extensive than IFS and can express more decision-making information, and the decision problems of IFS are special cases of PFS decision problems. In the previous literature, a great deal of research has been done on PFS. For example, Zhang and Xu \[[@B33-ijerph-17-00525]\] presented a combination of PFS and TOPSIS models to deal with MADM problems. In order to better understand the new fuzzy set of PFS, Peng and Yang \[[@B34-ijerph-17-00525]\] primarily put forward the division and subtraction operations of PFS. Reformat and Yager \[[@B35-ijerph-17-00525]\] applied Pythagorean fuzzy information to collaborative recommendation systems. Gou, et al. \[[@B36-ijerph-17-00525]\] studied some precious properties of continuous PFS. Garg \[[@B37-ijerph-17-00525]\] defined some new aggregation operators of PFS on the foundation of Einstein operations. Wu and Wei \[[@B38-ijerph-17-00525]\] came out some Hamacher aggregation operators of PFS to merge fuzzy information. Zeng, et al. \[[@B39-ijerph-17-00525]\] utilized the PFOWAWAD operator to study MADM issues under the context of PFS. Ren, et al. \[[@B40-ijerph-17-00525]\] established the PF-TODIM model. Combining with Pythagorean fuzzy environment, Wei and Lu \[[@B41-ijerph-17-00525]\] proposed a new MSM \[[@B42-ijerph-17-00525]\] operator. Wei \[[@B43-ijerph-17-00525]\] innovated some fuzzy interactive aggregation operators for arithmetic and geometric operations based on PFS. Wei and Lu \[[@B44-ijerph-17-00525]\] proposed some fuzzy power aggregation operators in the Pythagorean theorem. Wei and Wei \[[@B45-ijerph-17-00525]\] created ten cosine similarity measures in the fuzzy context of the Pythagorean theorem. Liang, et al. \[[@B46-ijerph-17-00525]\] studied some Bonferroni mean operators using Pythagorean fuzzy information. Liang, et al. \[[@B47-ijerph-17-00525]\] presented the PFGA operation based on Bonferroni mean aggregation operator. Combining the PFSs \[[@B31-ijerph-17-00525],[@B32-ijerph-17-00525]\] and DHFSs \[[@B48-ijerph-17-00525],[@B49-ijerph-17-00525]\], Wei and Lu \[[@B50-ijerph-17-00525]\] brought in the definition of the DHPFSs and proposed some DHPF-Hamacher aggregation operators. Peng, et al. \[[@B51-ijerph-17-00525]\] created some new PF information measures of MADM problems.

Nevertheless, to describe more decision information, Yager \[[@B52-ijerph-17-00525]\] later defined q-rung orthopair fuzzy sets (q-ROFSs), and based on PFS, the condition that the square sum of its membership and non-membership is less than or equal to 1 becomes that the sum of the $qth$ power of the two is less than or equal to 1. Obviously, compared to IFS, q-ROFSs is more general, and PFS is a special case. Liu and Wang \[[@B53-ijerph-17-00525]\] put forward the q-ROFWA operator and the q-ROFWG operator. Wei, et al. \[[@B54-ijerph-17-00525]\] defined some q-rung orthopair fuzzy MSM operators including q-ROFMSM operator, q-ROFWMSM operator, q-ROFDMSM operator, q-ROFWDMSM operator. Wei, et al. \[[@B55-ijerph-17-00525]\] gave some q-ROF Heronian mean operators. Yang and Pang \[[@B56-ijerph-17-00525]\] provided some new definition of partitioned Bonferroni mean operators under q-ROFS. Wang, et al. \[[@B57-ijerph-17-00525]\] came up with some q-rung interval-valued fuzzy Hamy mean operators including q-RIVOFHM operator, q-RIVOFWHM operator, q-RIVOFDHM operator and q-RIVOFWDHM operator. Liu and Liu \[[@B58-ijerph-17-00525]\] offered some power Bonferroni mean operators with linguistic q-rung orthopair fuzzy information. Xu, et al. \[[@B59-ijerph-17-00525]\] gave the definition of q-RDHOFS and presented some q-RDHOF Heronian mean operators.

However, to date, it is clear that the VIKOR model with q-RIVOFNs information has not been studied. Therefore, it's essential to take q-RIVOF-VIKOR model into consideration. The aim of our manuscript is to create an enlarged VIKOR model with the original VIKOR method and q-RIVOF information to settle MADM problems more effectively. Our manuscript is structured as: the definition, score function, accuracy function, operation rules, and some aggregation operators of q-RIVOFSs are briefly given in [Section 2](#sec2-ijerph-17-00525){ref-type="sec"}. The calculation process of traditional VIKOR model is briefly depicted in [Section 3](#sec3-ijerph-17-00525){ref-type="sec"}. Integrating the original VIKOR model with q-RIVOFNs information, the q-RIVOF-VIKOR technique is built and the calculation processes are simply shown in [Section 4](#sec4-ijerph-17-00525){ref-type="sec"}. An example of a vendor selection of healthcare consumer products has been illustrated by this new model and some comparisons between the q-RIVOF-VIKOR model and two q-RIVOFNs aggregation operators---including q-RIVOFWA and q-RIVOFWG operators---are also carried out to further explain merit of the new method in [Section 5](#sec5-ijerph-17-00525){ref-type="sec"}. Some conclusions of our manuscript are made in [Section 6](#sec6-ijerph-17-00525){ref-type="sec"}.

2. Preliminaries {#sec2-ijerph-17-00525}
================

Based on the theorems of q-ROFSs and the interval values, the essential definition and theorems of q-RIVOFSs are retrospected in brief below.

2.1. The q-RIVOFSs {#sec2dot1-ijerph-17-00525}
------------------

*Let $X$ be a fix set. A q-RIVOFSs has the following definition*: $$\widetilde{P} = \left\{ {\left\langle {x,\left\langle {{\widetilde{\mu}}_{\widetilde{P}}\left\langle x \right\rangle,{\widetilde{\nu}}_{\widetilde{P}}\left\langle x \right\rangle} \right\rangle} \right\rangle\left| {x \in X} \right.} \right\}$$*where the function* $\left. {\widetilde{\mu}}_{\widetilde{P}}\left( x \right) = \left\lbrack {\mu_{\widetilde{P}}^{L}\left\lbrack x \right\rbrack,\mu_{\widetilde{P}}^{U}\left\lbrack x \right\rbrack} \right\rbrack:\quad X\rightarrow\left\lbrack {0,1} \right\rbrack \right.$ *defines the membership degree and the function *$\left. {\widetilde{v}}_{\widetilde{P}}\left( x \right) = \left\lbrack {v_{\widetilde{P}}^{L}\left\lbrack x \right\rbrack,v_{\widetilde{P}}^{U}\left\lbrack x \right\rbrack} \right\rbrack:\quad X\rightarrow\left\lbrack {0,1} \right\rbrack \right.$  *defines the non-membership degree of the element * $x \in X$ *to* $\widetilde{P}$  *respectively, and, for every x ∈ X, it meets that*  $$\left( {\mu_{\widetilde{P}}^{U}\left( x \right)} \right)^{q} + \left( {v_{\widetilde{P}}^{U}\left( x \right)} \right)^{q} \leq 1,q \geq 1.$$ ${\widetilde{\pi}}_{\widetilde{P}}\left( x \right) = \left\lbrack {\pi_{\widetilde{P}}^{L}\left\lbrack x \right\rbrack,\pi_{\widetilde{P}}^{U}\left\lbrack x \right\rbrack} \right\rbrack = \left\lbrack {\sqrt[q]{1 - \left\lbrack {\left\lbrack {\mu_{\widetilde{P}}^{L}\left\lbrack x \right\rbrack} \right\rbrack^{q} + \left\lbrack {\nu_{\widetilde{P}}^{L}\left\lbrack x \right\rbrack} \right\rbrack^{q}} \right\rbrack},\sqrt[q]{1 - \left\lbrack {\left\lbrack {\mu_{\widetilde{P}}^{U}\left\lbrack x \right\rbrack} \right\rbrack^{q} + \left\lbrack {\nu_{\widetilde{P}}^{U}\left\lbrack x \right\rbrack} \right\rbrack^{q}} \right\rbrack}} \right\rbrack$ * is the degree of indeterminacy membership. For convenience, we called * $\widetilde{p} = \left( {\left\lbrack {\mu^{L},\mu^{U}} \right\rbrack,\left\lbrack {\nu^{L},\nu^{U}} \right\rbrack} \right)$ * a q-RIVOFN*.

*Let* $\widetilde{p} = \left( {\left\lbrack {\mu^{L},\mu^{U}} \right\rbrack,\left\lbrack {\nu^{L},\nu^{U}} \right\rbrack} \right)$  *be a q-RIVOFN, a score function S can be written as follows*: $${S\left( \widetilde{p} \right) = \frac{1}{4}\left\lbrack {\left\lbrack {1 + \left\lbrack \mu^{L} \right\rbrack^{q} - \left\lbrack v^{L} \right\rbrack^{q}} \right\rbrack + \left\lbrack {1 + \left\lbrack \mu^{U} \right\rbrack^{q} - \left\lbrack v^{U} \right\rbrack^{q}} \right\rbrack} \right\rbrack}{,~S\left( \widetilde{p} \right) \in \left\lbrack {0,1} \right\rbrack.}$$

*Let* $\widetilde{p} = \left( {\left\lbrack {\mu^{L},\mu^{U}} \right\rbrack,\left\lbrack {\nu^{L},\nu^{U}} \right\rbrack} \right)$  *be a q-RIVOFN, an accuracy function* $H$ *can be written as follows:* $${H\left( \widetilde{p} \right) = \frac{\left( \mu^{L} \right)^{q} + \left( \nu^{L} \right)^{q} + \left( \mu^{U} \right)^{q} + \left( \nu^{U} \right)^{q}}{2}}{,H\left( \widetilde{p} \right) \in \left\lbrack {0,1} \right\rbrack,}$$

According to *S* and *H*, the order relation between two q-RIVOFNs will be obtained as below:

*Let* ${\widetilde{p}}_{1} = \left( {\left\lbrack {\mu_{1}^{L},\mu_{1}^{U}} \right\rbrack,\left\lbrack {\nu_{1}^{L},\nu_{1}^{U}} \right\rbrack} \right)$* and *${\widetilde{p}}_{2} = \left( {\left\lbrack {\mu_{2}^{L},\mu_{2}^{U}} \right\rbrack,\left\lbrack {\nu_{2}^{L},\nu_{2}^{U}} \right\rbrack} \right)$  *be two q-RIVO FNs, assume that* $S\left( {\widetilde{p}}_{1} \right) = \frac{1}{4}\left\lbrack {\left\lbrack {1 + \left\lbrack \mu_{1}^{L} \right\rbrack^{q} - \left\lbrack v_{1}^{L} \right\rbrack^{q}} \right\rbrack + \left\lbrack {1 + \left\lbrack \mu_{1}^{U} \right\rbrack^{q} - \left\lbrack v_{1}^{U} \right\rbrack^{q}} \right\rbrack} \right\rbrack$  *and*  $S\left( {\widetilde{p}}_{2} \right) = \frac{1}{4}\left\lbrack {\left\lbrack {1 + \left\lbrack \mu_{2}^{L} \right\rbrack^{q} - \left\lbrack v_{2}^{L} \right\rbrack^{q}} \right\rbrack + \left\lbrack {1 + \left\lbrack \mu_{2}^{U} \right\rbrack^{q} - \left\lbrack v_{2}^{U} \right\rbrack^{q}} \right\rbrack} \right\rbrack$ * be the scores of * ${\widetilde{p}}_{1}$ * and * ${\widetilde{p}}_{2}$, *and let* $H\left( {\widetilde{p}}_{1} \right) = \frac{\left( \mu_{1}^{L} \right)^{q} + \left( \nu_{1}^{L} \right)^{q} + \left( \mu_{1}^{U} \right)^{q} + \left( \nu_{1}^{U} \right)^{q}}{2}$ *and* $H\left( {\widetilde{p}}_{2} \right) = \frac{\left( \mu_{2}^{L} \right)^{q} + \left( \nu_{2}^{L} \right)^{q} + \left( \mu_{2}^{U} \right)^{q} + \left( \nu_{2}^{U} \right)^{q}}{2}$ *be the accuracy degrees of* ${\widetilde{p}}_{1}$ *and* ${\widetilde{p}}_{2}$, *respectively, then when*  $S\left( {\widetilde{p}}_{1} \right) < S\left( {\widetilde{p}}_{2} \right)$,  ${\widetilde{p}}_{1} < {\widetilde{p}}_{2}$  *when*  $S\left( {\widetilde{p}}_{1} \right) = S\left( {\widetilde{p}}_{2} \right)$, *(1) if* $H\left( {\widetilde{p}}_{1} \right) = H\left( {\widetilde{p}}_{2} \right)$, *then* ${\widetilde{p}}_{1} = {\widetilde{p}}_{2}$; *(2) if* $H\left( {\widetilde{p}}_{1} \right) < H\left( {\widetilde{p}}_{2} \right)$, ${\widetilde{p}}_{1} < {\widetilde{p}}_{2}$.

*Let* ${\widetilde{p}}_{1} = \left( {\left\lbrack {\mu_{1}^{L},\mu_{1}^{U}} \right\rbrack,\left\lbrack {\nu_{1}^{L},\nu_{1}^{U}} \right\rbrack} \right)$, ${\widetilde{p}}_{2} = \left( {\left\lbrack {\mu_{2}^{L},\mu_{2}^{U}} \right\rbrack,\left\lbrack {\nu_{2}^{L},\nu_{2}^{U}} \right\rbrack} \right)$ *and* $\widetilde{p} = \left( {\left\lbrack {\mu^{L},\mu^{U}} \right\rbrack,\left\lbrack {\nu^{L},\nu^{U}} \right\rbrack} \right)$ *be three q-RIVOFNs, and some basic rules about them are defined as follows*:$$\begin{array}{l}
{(1){{\widetilde{p}}_{1} \oplus {\widetilde{p}}_{2} = \left( {\left\lbrack \begin{array}{l}
{\sqrt[q]{\left\lbrack \mu_{1}^{L} \right\rbrack^{q} + \left\lbrack \mu_{2}^{L} \right\rbrack^{q} - \left\lbrack \mu_{1}^{L} \right\rbrack^{q}\left\lbrack \mu_{2}^{L} \right\rbrack^{q}},} \\
\sqrt[q]{\left\lbrack \mu_{1}^{U} \right\rbrack^{q} + \left\lbrack \mu_{2}^{U} \right\rbrack^{q} - \left\lbrack \mu_{1}^{U} \right\rbrack^{q}\left\lbrack \mu_{2}^{U} \right\rbrack^{q}} \\
\end{array} \right\rbrack,\left\lbrack {v_{1}^{L}v_{2}^{L},v_{1}^{U}v_{2}^{U}} \right\rbrack} \right);}} \\
{(2){\widetilde{p}}_{1} \otimes {\widetilde{p}}_{2} = \left( {\left\lbrack {\mu_{1}^{L}\mu_{2}^{L},\mu_{1}^{U}\mu_{2}^{U}} \right\rbrack,\left\lbrack \begin{array}{l}
{\sqrt[q]{\left\lbrack v_{1}^{L} \right\rbrack^{q} + \left\lbrack v_{2}^{L} \right\rbrack^{q} - \left\lbrack v_{1}^{L} \right\rbrack^{q}\left\lbrack v_{2}^{L} \right\rbrack^{q}},} \\
\sqrt[q]{\left\lbrack v_{1}^{U} \right\rbrack^{q} + \left\lbrack v_{2}^{U} \right\rbrack^{q} - \left\lbrack v_{1}^{U} \right\rbrack^{q}\left\lbrack v_{2}^{U} \right\rbrack^{q}} \\
\end{array} \right\rbrack} \right);} \\
{(3){\lambda\widetilde{p} = \left( {\left\lbrack {\sqrt[q]{1 - \left\lbrack {1 - \left\lbrack \mu_{}^{L} \right\rbrack^{q}} \right\rbrack^{\lambda}},\sqrt[q]{1 - \left\lbrack {1 - \left\lbrack \mu_{}^{U} \right\rbrack^{q}} \right\rbrack^{\lambda}}} \right\rbrack,\left\lbrack {\left\lbrack v_{}^{L} \right\rbrack^{\lambda},\left\lbrack v_{}^{U} \right\rbrack^{\lambda}} \right\rbrack} \right),\lambda > 0;}} \\
{(4){\left( \widetilde{p} \right)^{\lambda} = \left( {\left\lbrack {\left\lbrack \mu_{}^{L} \right\rbrack^{\lambda},\left\lbrack \mu_{}^{U} \right\rbrack^{\lambda}} \right\rbrack,\left\lbrack {\sqrt[q]{1 - \left\lbrack {1 - \left\lbrack v_{}^{L} \right\rbrack^{q}} \right\rbrack^{\lambda}},\sqrt[q]{1 - \left\lbrack {1 - \left\lbrack v_{}^{U} \right\rbrack^{q}} \right\rbrack^{\lambda}}} \right\rbrack} \right),\lambda > 0;}} \\
{(5){{\widetilde{p}}^{c} = \left( {\left\lbrack {v_{}^{L},v_{}^{U}} \right\rbrack,\left\lbrack {\mu_{}^{L},\mu_{}^{U}} \right\rbrack} \right).}} \\
\end{array}$$

2.2. Some q-RIVOF Aggregation Operators {#sec2dot2-ijerph-17-00525}
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*Let* ${\widetilde{p}}_{j} = \left( {\left\lbrack {\mu_{j}^{L},\mu_{j}^{U}} \right\rbrack,\left\lbrack {\nu_{j}^{L},\nu_{j}^{U}} \right\rbrack} \right)\left( {j = 1,2,\ldots,n} \right)$ *be a list of q-RIVOFNs with weighting vector be* $w_{j} = {(w_{1},w_{2},\ldots,w_{n})}^{T}$, *thereby satisfying* $w_{j} \in \left\lbrack {0,1} \right\rbrack$ *and* ${\sum_{j = 1}^{n}w_{j}} = 1$, *then the q-RIVOFWA operator can be written as*: $$\begin{array}{l}
{{q - {RIVOFWA}}\left( {{\widetilde{p}}_{1},{\widetilde{p}}_{2},\ldots,{\widetilde{p}}_{n}} \right) = {\sum\limits_{j = 1}^{n}{w_{j}{\widetilde{p}}_{j}}}} \\
{= \left( {\left\lbrack {\sqrt[q]{1 - {\prod\limits_{j = 1}^{n}\left\lbrack {1 - \left\lbrack \mu_{{\widetilde{p}}_{j}}^{L} \right\rbrack^{q}} \right\rbrack^{w_{j}}}},\sqrt[q]{1 - {\prod\limits_{j = 1}^{n}\left\lbrack {1 - \mu\left\lbrack u_{{\widetilde{p}}_{j}}^{U} \right\rbrack^{q}} \right\rbrack^{w_{j}}}}} \right\rbrack,\left\lbrack {{\prod\limits_{j = 1}^{n}\left\lbrack v_{{\widetilde{p}}_{j}}^{L} \right\rbrack^{w_{j}}},{\prod\limits_{j = 1}^{n}\left\lbrack v_{{\widetilde{p}}_{j}}^{U} \right\rbrack^{w_{j}}}} \right\rbrack} \right)} \\
\end{array}$$

*Let* ${\widetilde{p}}_{j} = \left( {\left\lbrack {\mu_{j}^{L},\mu_{j}^{U}} \right\rbrack,\left\lbrack {\nu_{j}^{L},\nu_{j}^{U}} \right\rbrack} \right)\left( {j = 1,2,\ldots,n} \right)$ *be a list of q-RIVOFNs with weighting vector be* $w_{j} = {(w_{1},w_{2},\ldots,w_{n})}^{T}$, *thereby satisfying* $w_{j} \in \left\lbrack {0,1} \right\rbrack$ *and* ${\sum_{j = 1}^{n}w_{j}} = 1$, *then the q-RIVOFWG operator can be written as*: $$\begin{array}{l}
{{q - {RIVOFWG}}\left( {{\widetilde{p}}_{1},{\widetilde{p}}_{2},\ldots,{\widetilde{p}}_{n}} \right) = {\prod\limits_{j = 1}^{n}\left( {\widetilde{p}}_{j} \right)^{w_{j}}}} \\
{= \left( {\left\lbrack {{\prod\limits_{j = 1}^{n}\left\lbrack \mu_{{\widetilde{p}}_{j}}^{L} \right\rbrack^{w_{j}}},{\prod\limits_{j = 1}^{n}\left\lbrack \mu_{{\widetilde{p}}_{j}}^{U} \right\rbrack^{w_{j}}}} \right\rbrack,\left\lbrack {\sqrt[q]{1 - {\prod\limits_{j = 1}^{n}\left\lbrack {1 - \left\lbrack v_{{\widetilde{p}}_{j}}^{L} \right\rbrack^{q}} \right\rbrack^{w_{j}}}},\sqrt[q]{1 - {\prod\limits_{j = 1}^{n}\left\lbrack {1 - \left\lbrack v_{{\widetilde{p}}_{j}}^{U} \right\rbrack^{q}} \right\rbrack^{w_{j}}}}} \right\rbrack} \right)} \\
\end{array}$$

3. Traditional VIKOR Model {#sec3-ijerph-17-00525}
==========================

The VIKOR model, which firstly define by Opricovic and Tzeng \[[@B60-ijerph-17-00525]\], is a meaningful tool to investigate MADM problems and has been broadly applied to in the fields of industry, business economy and management in recent years. Assume that there are $m$ alternatives $\left\{ {A_{1},A_{2},\ldots A_{m}} \right\}$, $n$ attributes $\left\{ {G_{1},G_{2},\ldots G_{n}} \right\}$ with weighting vector $\left\{ {w_{1},w_{2},\ldots w_{n}} \right\}$ which meets the condition of $0 \leq ~w_{i} \leq 1,{\sum_{i = 1}^{n}w_{i}} = 1$ and $\lambda$ experts with weighting vector $\left\{ {\omega_{1},\omega_{2},\ldots\omega_{\lambda}} \right\}$, respectively, satisfies $0 \leq ~\omega_{i} \leq 1,{\sum_{i = 1}^{t}v_{i}} = 1$. Set up the matrix $R^{\lambda} = \left\lbrack a_{ij}^{\lambda} \right\rbrack_{m \times n},$ $i = 1,2,\ldots,m,j = 1,2,\ldots,n$ which is used to evaluate each alternative on each indicator, then the traditional VIKOR model can be presented as below.

**Step 1.** Establish the decision matrixes $R^{\lambda} = \left\lbrack a_{ij}^{\lambda} \right\rbrack_{m \times n},i = 1,2,\ldots,m,j = 1,2,\ldots,n$ based on expert's decision making results, and fuse all the evaluation information by using some aggregation operators such as WA operator and WG operator to get fused results matrix $R = \left\lbrack a_{ij}^{} \right\rbrack_{m \times n},i = 1,2,\ldots,m,j = 1,2,\ldots,n$;

**Step 2.** Calculate PIS $a_{j}^{+}$ and NIS $a_{j}^{-}$ $$a_{j}^{+} = \left\{ {\max\limits_{i}\left\{ a_{ij} \right\}} \right\},a_{j}^{-} = \left\{ {\min\limits_{i}\left\{ a_{ij} \right\}} \right\},i = 1,2,\ldots,m,j = 1,2,\ldots,n$$

**Step 3.** According to the Formula (7) and the attribute weighting vector $w_{j}\left( {j = 1,2,\ldots,n} \right)$, the results of $\mathsf{\Psi}_{i}$ and $\mathsf{\mathrm{\Upsilon}}_{i}$ which represents the mean and worst group scores of the alternatives $A_{i}$ can be obtained as follows. $$\mathsf{\Psi}_{i} = {\sum\limits_{j = 1}^{n}{w_{j}\frac{d\left( {a_{j}^{+},a_{ij}^{}} \right)}{d\left( {a_{j}^{+},a_{j}^{-}} \right)}}},i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ $$\mathsf{\mathrm{\Upsilon}}_{i} = \max\limits_{j}\left( {w_{j}\frac{d\left( {a_{j}^{+},a_{ij}^{}} \right)}{d\left( {a_{j}^{+},a_{j}^{-}} \right)}} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ where $0 \leq w_{j} \leq 1$ indicates the weighting vector of attributes which satisfies ${\sum_{i = 1}^{n}\omega_{i}} = 1$ and $d$ denotes the q-rung orthopair fuzzy distance measures.

**Step 4.** Calculate the results of $\mathsf{\Theta}_{i}$ by following the Equation:$$\mathsf{\Theta}_{i} = \alpha \times \frac{\left( {\mathsf{\Psi}_{i} - \mathsf{\Psi}^{+}} \right)}{\left( {\mathsf{\Psi}^{-} - \mathsf{\Psi}^{+}} \right)} + \left( {1 - \alpha} \right) \times \frac{\left( {\mathsf{\mathrm{\Upsilon}}_{i} - \mathsf{\mathrm{\Upsilon}}^{+}} \right)}{\left( {\mathsf{\mathrm{\Upsilon}}^{-} - \mathsf{\mathrm{\Upsilon}}^{+}} \right)}$$ where $$\mathsf{\Psi}^{+} = \min\limits_{i}\left( \mathsf{\Psi}_{i} \right),\mathsf{\Psi}^{-} = \max\limits_{i}\left( \mathsf{\Psi}_{i} \right)$$ $$\mathsf{\mathrm{\Upsilon}}^{+} = \min\limits_{i}\left( \mathsf{\mathrm{\Upsilon}}_{i} \right),\mathsf{\mathrm{\Upsilon}}^{-} = \max\limits_{i}\left( \mathsf{\mathrm{\Upsilon}}_{i} \right)$$ where $\alpha$ denotes the coefficient of decision making strategic. $\alpha > 0.5$ means "the maximum group utility", $\alpha = 0.5$ means equality degree and $\alpha < 0.5$ means the minimum regret degree.

**Step 5.** Then according to $\mathsf{\Theta}_{i}$ to select the best alternative, obviously, the smaller the $\mathsf{\Theta}_{i}$, the best alternative $A_{i}$ is.

4. The VIKOR Model for q-RIVOFNs MAGDM Problems {#sec4-ijerph-17-00525}
===============================================

Assume that there are $m$ alternatives $\left\{ {A_{1},A_{2},\ldots A_{m}} \right\}$, $n$ projects $\left\{ {G_{1},G_{2},\ldots G_{m}} \right\}$ with weighting vector $\left\{ {w_{1},w_{2},\ldots w_{n}} \right\}$ which meets the condition of $0 \leq ~w_{i} \leq 1,{\sum_{i = 1}^{n}w_{i}} = 1$ and $\lambda$ experts with weighting vector $\left\{ {\omega_{1},\omega_{2},\ldots\omega_{\lambda}} \right\}$, respectively, the conditions are satisfied $0 \leq ~\omega_{i} \leq 1,{\sum_{i = 1}^{t}v_{i}} = 1$. Construct the q-RIVOF evaluation matrix $R^{\lambda} = \left\lbrack {\widetilde{r}}_{ij}^{\lambda} \right\rbrack_{m \times n} = \left( {\left\lbrack {\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack,\left\lbrack {\left\lbrack v_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack v_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack} \right)_{m \times n},i = 1,2,\ldots,m,j = 1,2,\ldots,n$, where ${\widetilde{r}}_{ij}^{\lambda} = \left( {\left\lbrack {\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack,\left\lbrack {\left\lbrack v_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack v_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack} \right)$ indicates the q-RIVOF information of the alternative $A_{i}\left( {i = 1,2,\ldots,m} \right)$ on account of the indicators $G_{j}\left( {j = 1,2,\ldots,n} \right)$ by expert $D^{\lambda}$. $\left\lbrack {\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack \in \left\lbrack {0,1} \right\rbrack$ denotes the membership degree of alternatives $A_{i}$ satisfies the attribute $G_{j}$ and $\left\lbrack {\left\lbrack v_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack v_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack \in \left\lbrack {0,1} \right\rbrack$ is the membership degree of alternatives $A_{i}$ and it indicates that the attribute $G_{j}$ given by the decision maker is not satisfied, respectively, $0 \leq \left( \left( \mu_{ij}^{\lambda} \right)^{U} \right)^{q} + \left( \left( v_{ij}^{\lambda} \right)^{U} \right)^{q} \leq 1\left( {i = 1,2,\ldots,m,j = 1,2,\ldots,n} \right).$ then, based on q-RIVOFSs and traditional VIKOR model, the q-RIVOF-VIKOR model is established to settle MADM problems more reasonably and effectively, the computing steps are simply depicted as follows.

**Step 1.** Give the q-RIVOFNs decision making matrixes $R^{\lambda} = \left\lbrack {\widetilde{r}}_{ij}^{\lambda} \right\rbrack_{m \times n} = \left( {\left\lbrack {\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack,\left\lbrack {\left\lbrack v_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack v_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack} \right)_{m \times n},i = 1,2,\ldots,m,j = 1,2,\ldots,n$ based on expert's evaluation results, and fuse all the evaluation information by utilizing q-RIVOFWA or q-RIVOFWG operators to obtain the fused matrix $R = \left\lbrack {\widetilde{r}}_{ij} \right\rbrack_{m \times n} = \left( {\left\lbrack {\mu_{ij}^{L},\mu_{ij}^{U}} \right\rbrack,\left\lbrack {v_{ij}^{L},v_{ij}^{U}} \right\rbrack} \right)_{m \times n},$ $i = 1,2,\ldots,m,j = 1,2,\ldots,n$;

**Step 2.** Calculate PIS ${\widetilde{r}}_{j}^{+}$ and NIS ${\widetilde{r}}_{j}^{-}$ by following the Equation:$${\widetilde{r}}_{j}^{+} = \left( {\left\lbrack {\left\lbrack \mu_{ij}^{L} \right\rbrack^{+},\left\lbrack \mu_{ij}^{U} \right\rbrack^{+}} \right\rbrack,\left\lbrack {\left\lbrack v_{ij}^{L} \right\rbrack^{+},\left\lbrack v_{ij}^{U} \right\rbrack^{+}} \right\rbrack} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ $${\widetilde{r}}_{j}^{-} = \left( {\left\lbrack {\left\lbrack \mu_{ij}^{L} \right\rbrack^{-},\left\lbrack \mu_{ij}^{U} \right\rbrack^{-}} \right\rbrack,\left\lbrack {\left\lbrack v_{ij}^{L} \right\rbrack^{-},\left\lbrack v_{ij}^{U} \right\rbrack^{-}} \right\rbrack} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$

For benefit attribute:$${\widetilde{r}}_{j}^{+} = \left( {\left\lbrack {\max\limits_{i}\left\lbrack \mu_{ij}^{L} \right\rbrack,\max\limits_{i}\left\lbrack \mu_{ij}^{U} \right\rbrack} \right\rbrack,\left\lbrack {\min\limits_{i}\left\lbrack v_{ij}^{L} \right\rbrack,\min\limits_{i}\left\lbrack v_{ij}^{U} \right\rbrack} \right\rbrack} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ $${\widetilde{r}}_{j}^{-} = \left( {\left\lbrack {\min\limits_{i}\left\lbrack \mu_{ij}^{L} \right\rbrack,\min\limits_{i}\left\lbrack \mu_{ij}^{U} \right\rbrack} \right\rbrack,\left\lbrack {\max\limits_{i}\left\lbrack v_{ij}^{L} \right\rbrack,\max\limits_{i}\left\lbrack v_{ij}^{U} \right\rbrack} \right\rbrack} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$

For cost attribute:$${\widetilde{r}}_{j}^{+} = \left( {\left\lbrack {\min\limits_{i}\left\lbrack \mu_{ij}^{L} \right\rbrack,\min\limits_{i}\left\lbrack \mu_{ij}^{U} \right\rbrack} \right\rbrack,\left\lbrack {\max\limits_{i}\left\lbrack v_{ij}^{L} \right\rbrack,\max\limits_{i}\left\lbrack v_{ij}^{U} \right\rbrack} \right\rbrack} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ $${\widetilde{r}}_{j}^{-} = \left( {\left\lbrack {\max\limits_{i}\left\lbrack \mu_{ij}^{L} \right\rbrack,\max\limits_{i}\left\lbrack \mu_{ij}^{U} \right\rbrack} \right\rbrack,\left\lbrack {\min\limits_{i}\left\lbrack v_{ij}^{L} \right\rbrack,\min\limits_{i}\left\lbrack v_{ij}^{U} \right\rbrack} \right\rbrack} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$

**Step 3.** On the basis of the Equations (17) and (18) and $w_{j}\left( {j = 1,2,\ldots,n} \right)$, the results of $\mathsf{\Psi}_{i}$ and $\mathsf{\mathrm{\Upsilon}}_{i}$ which represents the mean and worst group scores of the alternatives $A_{i}$ can be obtained as follows. $$\mathsf{\Psi}_{i} = {\sum\limits_{j = 1}^{n}{w_{j}\frac{d\left( {{\widetilde{r}}_{j}^{+},{\widetilde{r}}_{ij}^{}} \right)}{d\left( {{\widetilde{r}}_{j}^{+},{\widetilde{r}}_{j}^{-}} \right)}}},i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ $$\mathsf{\mathrm{\Upsilon}}_{i} = \max\limits_{j}\left( {w_{j}\frac{d\left( {{\widetilde{r}}_{j}^{+},{\widetilde{r}}_{ij}^{}} \right)}{d\left( {{\widetilde{r}}_{j}^{+},{\widetilde{r}}_{j}^{-}} \right)}} \right),i = 1,2,\ldots,m,j = 1,2,\ldots,n$$ where $0 \leq w_{j} \leq 1$ indicates the weighting vector of attributes which satisfies ${\sum_{i = 1}^{n}\omega_{i}} = 1$ and $d$ denotes the q-rung orthopair fuzzy distance measures. For the traditional normalized Hamming distance (HD) measures or Euclidean distance measures (ED) are limited to deal with some special situations, thus, we shall use the combination form of three distance measures mentioned as follows. $$d\left( {{\widetilde{r}}_{ij},{\widetilde{r}}_{tj}} \right) = {\sum\limits_{k = 1}^{3}{\chi_{k}d^{k}\left( {{\widetilde{r}}_{ij},{\widetilde{r}}_{tj}} \right)}},\chi_{k} \in \left\lbrack {0,1} \right\rbrack,{\sum\limits_{k = 1}^{3}\chi_{k}} = 1.$$ where $0 \leq \chi_{k} \leq 1$ indicates the weighting vector of distance measures $d^{k}$ and $$d^{1}\left( {{\widetilde{r}}_{ij},{\widetilde{r}}_{tj}} \right) = \frac{\left( {\left| {\left| \mu_{ij}^{L} \right|^{q} - \left| \mu_{tj}^{L} \right|^{q}} \right| + \left| {\left| \mu_{ij}^{U} \right|^{q} - \left| \mu_{tj}^{U} \right|^{q}} \right| + \left| {\left| v_{ij}^{L} \right|^{q} - \left| v_{tj}^{L} \right|^{q}} \right| + \left| {\left| v_{ij}^{U} \right|^{q} - \left| v_{tj}^{U} \right|^{q}} \right|} \right)}{4}$$ $$d^{2}\left( {{\widetilde{r}}_{ij},{\widetilde{r}}_{tj}} \right) = \frac{\left| {\left\{ {\left\{ \mu_{ij}^{L} \right\}^{q} + \left\{ \mu_{ij}^{U} \right\}^{q} - \left\{ v_{ij}^{L} \right\}^{q} - \left\{ v_{ij}^{U} \right\}^{q}} \right\} - \left\{ {\left\{ \mu_{tj}^{L} \right\}^{q} + \left\{ \mu_{tj}^{U} \right\}^{q} - \left\{ v_{tj}^{L} \right\}^{q} - \left\{ v_{tj}^{U} \right\}^{q}} \right\}} \right|}{4}$$ $$d^{3}\left( {{\widetilde{r}}_{ij},{\widetilde{r}}_{tj}} \right) = \left\{ \begin{array}{l}
{\max\left\{ \begin{aligned}
 & {\frac{2 - \left( \mu_{ij}^{L} \right)^{q} - \left( \mu_{ij}^{U} \right)^{q} - \left( v_{ij}^{L} \right)^{q} - \left( v_{ij}^{U} \right)^{q}}{4},} \\
 & \frac{2 - \left( \mu_{tj}^{L} \right)^{q} - \left( \mu_{tj}^{U} \right)^{q} - \left( v_{tj}^{L} \right)^{q} - \left( v_{tj}^{U} \right)^{q}}{4} \\
\end{aligned} \right\}} \\
{- \min\left\{ \begin{array}{l}
{\frac{2 - \left( \mu_{ij}^{L} \right)^{q} - \left( \mu_{ij}^{U} \right)^{q} - \left( v_{ij}^{L} \right)^{q} - \left( v_{ij}^{U} \right)^{q}}{4},} \\
\frac{2 - \left( \mu_{tj}^{L} \right)^{q} - \left( \mu_{tj}^{U} \right)^{q} - \left( v_{tj}^{L} \right)^{q} - \left( v_{tj}^{U} \right)^{q}}{4} \\
\end{array} \right\}} \\
\end{array} \right\}$$

**Step 4.** Calculate the results of $\mathsf{\Theta}_{i}$ by following the Equation:$$\mathsf{\Theta}_{i} = \alpha \times \frac{\left( {\mathsf{\Psi}_{i} - \mathsf{\Psi}^{+}} \right)}{\left( {\mathsf{\Psi}^{-} - \mathsf{\Psi}^{+}} \right)} + \left( {1 - \alpha} \right) \times \frac{\left( {\mathsf{\mathrm{\Upsilon}}_{i} - \mathsf{\mathrm{\Upsilon}}^{+}} \right)}{\left( {\mathsf{\mathrm{\Upsilon}}^{-} - \mathsf{\mathrm{\Upsilon}}^{+}} \right)}$$ where $$\mathsf{\Psi}^{+} = \min\limits_{i}\left( \mathsf{\Psi}_{i} \right),\mathsf{\Psi}^{-} = \max\limits_{i}\left( \mathsf{\Psi}_{i} \right)$$ $$\mathsf{\mathrm{\Upsilon}}^{+} = \min\limits_{i}\left( \mathsf{\mathrm{\Upsilon}}_{i} \right),\mathsf{\mathrm{\Upsilon}}^{-} = \max\limits_{i}\left( \mathsf{\mathrm{\Upsilon}}_{i} \right)$$ where $\alpha$ denotes the coefficient of decision making strategic. $\alpha > 0.5$ means "the maximum group utility", $\alpha = 0.5$ means equality degree and $\alpha < 0.5$ means the minimum regret degree.

**Step 5.** According to $\mathsf{\Theta}_{i}$ to select the best alternative, obviously, the smaller the $\mathsf{\Theta}_{i}$, the best alternative $A_{i}$ is.

5. The Numerical Example {#sec5-ijerph-17-00525}
========================

5.1. Numerical for q-RIVOFNs MAGDM Problems {#sec5dot1-ijerph-17-00525}
-------------------------------------------

The supplier terms of an enterprise is undoubtedly very important, and in the future will be an even more important influence on the quality of a vendor's business, as it will affect the business of purchasing, production, inventory and sales, and so on. The relationship between suppliers and future enterprise is not a simple relationship between management and managed, suppliers will become a strategic partner companies, it is a win-win relationship. So supplier preliminary evaluation and selection is quite important. Medical supplies products have their own characteristics to distinguish it from other types of products, which can be distinguished from their production, transportation, marketing, and other aspects. It can be seen that supplier selection of medical consumption products is the classical MADM or MAGDM issue \[[@B61-ijerph-17-00525],[@B62-ijerph-17-00525],[@B63-ijerph-17-00525],[@B64-ijerph-17-00525],[@B65-ijerph-17-00525],[@B66-ijerph-17-00525],[@B67-ijerph-17-00525],[@B68-ijerph-17-00525],[@B69-ijerph-17-00525],[@B70-ijerph-17-00525],[@B71-ijerph-17-00525],[@B72-ijerph-17-00525]\]. In this subsection, an example for supplier selection of medical consumption products with q-RIVOF information shall be presented in order to demonstrate the method proposed in this paper. There is a panel with five possible medical consumption products suppliers. $\eta_{i}\left( {i = 1,2,3,4,5} \right)$ to sort. Experts select four attributes to appraise the five feasible construction projects: ① G~1~ is the environmental improvement quality; ② G~2~ is the transportation convenience of suppliers; ③ G~3~ is the green image; ④ G~4~ is the environmental competencies. The five feasible medical consumption products suppliers $\eta_{i}\left( {i = 1,2,3,4,5} \right)$ are to be evaluated using the q q-RIVOF information under the above four attributes by three experts $D^{\lambda}$ (Assume the weighting vector of experts is $\left( {0.35,0.25,0.40} \right)$ and attribute index's weighting vector is $\left( {0.27,0.37,0.16,0.20} \right)$).

Next, we make use of the VIKOR technique with q-RIVOFNs developed for medical consumption products supplier selection.

**Step 1.** Give the q-RIVOFNs decision making matrixes $R^{\lambda} = \left\lbrack {\widetilde{r}}_{ij}^{\lambda} \right\rbrack_{m \times n} = \left( {\left\lbrack {\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack \mu_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack,\left\lbrack {\left\lbrack v_{ij}^{\lambda} \right\rbrack^{L},\left\lbrack v_{ij}^{\lambda} \right\rbrack^{U}} \right\rbrack} \right)_{m \times n},i = 1,2,\ldots,m,j = 1,2,\ldots,n$ as follows.

Then according to q-RIVOFWA operator and q-RIVOFNs given in [Table 1](#ijerph-17-00525-t001){ref-type="table"}, [Table 2](#ijerph-17-00525-t002){ref-type="table"} and [Table 3](#ijerph-17-00525-t003){ref-type="table"}, the fused results matrix can be obtained as follows (Suppose $q = 4$).

**Step 2.** Calculate PIS ${\widetilde{r}}_{j}^{+}$ and NIS ${\widetilde{r}}_{j}^{-}$ by Equations (13) and (14), for all attributes are benefit we can easily gain the results of (PIS) $\eta^{+}$ and (NIS) $\eta^{-}$ as follows; $$\eta^{+} = \left\{ \begin{array}{l}
{\left\{ {\left\lbrack 0.6709,0.8095 \right\rbrack,\left\lbrack 0.1275,0.2305 \right\rbrack} \right\},\left\{ {\left\lbrack 0.6243,0.7462 \right\rbrack,\left\lbrack 0.2430,0.2430 \right\rbrack} \right\},} \\
{\left\{ {\left\lbrack 0.6245,0.7335 \right\rbrack,\left\lbrack 0.7335,0.3824 \right\rbrack} \right\},\left\{ {\left\lbrack 0.6312,0.7766 \right\rbrack,\left\lbrack 0.1803,0.4302 \right\rbrack} \right\}} \\
\end{array} \right\}$$ $$\eta^{-} = \left\{ \begin{array}{l}
{\left\{ {\left\lbrack 0.3975,0.5017 \right\rbrack,\left\lbrack 0.6051,0.7445 \right\rbrack} \right\},\left\{ {\left\lbrack 0.3411,0.4487 \right\rbrack,\left\lbrack 0.4800,0.6448 \right\rbrack} \right\},} \\
{\left\{ {\left\lbrack 0.3108,0.5282 \right\rbrack,\left\lbrack 0.4278,0.5975 \right\rbrack} \right\},\left\{ {\left\lbrack 0.4630,0.5576 \right\rbrack,\left\lbrack 0.3903,0.5537 \right\rbrack} \right\}} \\
\end{array} \right\}$$

**Step 3.** According to the Formulas (19) and (20) and $w_{j}\left( {j = 1,2,\ldots,n} \right)$, the results of $\mathsf{\Psi}_{i}$ and $\mathsf{\mathrm{\Upsilon}}_{i}$ which denote the mean and the worst group scores of alternative $\eta_{i}$ can be obtained. Suppose the weights of distance measures $d^{k}$ are (0.3, 0.4, 0.3), then the results of combination distance can be calculated in [Table 4](#ijerph-17-00525-t004){ref-type="table"}.

Then the results of $\mathsf{\Psi}_{i}$ and $\mathsf{\mathrm{\Upsilon}}_{i}$ are calculated as:$$\mathsf{\Psi}_{1} = 0.6112,\mathsf{\Psi}_{2} = 0.0292,\mathsf{\Psi}_{3} = 0.8467,\mathsf{\Psi}_{4} = 0.4445,\mathsf{\Psi}_{5} = 0.5890,$$ $$\mathsf{\mathrm{\Upsilon}}_{1} = 0.5890,\quad\mathsf{\mathrm{\Upsilon}}_{2} = 0.0166,\quad\mathsf{\mathrm{\Upsilon}}_{3} = 0.3088,\quad\mathsf{\mathrm{\Upsilon}}_{4} = 0.1818,\quad\mathsf{\mathrm{\Upsilon}}_{5} = 0.2919.$$

**Step 4.** On the basis of $\mathsf{\Psi}_{i}$ and $\mathsf{\mathrm{\Upsilon}}_{i}$ obtained by above steps, we can calculate the results of $\mathsf{\Theta}_{i}$, the results are recorded as below. (Let $\alpha = 0.4$) $$\mathsf{\Theta}_{1} = 0.7172,\mathsf{\Theta}_{2} = 0.0000,\mathsf{\Theta}_{3} = 1.0000,\mathsf{\Theta}_{4} = 0.5424,\mathsf{\Theta}_{5} = 0.8392.$$

**Step 5.** According to $\mathsf{\Theta}_{i}$ to select the best alternative, obviously, the smaller value the $\mathsf{\Theta}_{i}$ is, the best alternative $\eta_{i}$ is. Apparently, the ordering of $\eta_{i}$ is $\mathsf{\Theta}_{2} > \mathsf{\Theta}_{4} > \mathsf{\Theta}_{1} > \mathsf{\Theta}_{5} > \mathsf{\Theta}_{3}$, and the best choice is $\eta_{2}$.

5.2. Comparative Analyses for q-RIVOFNs MAGDM Problems {#sec5dot2-ijerph-17-00525}
------------------------------------------------------

In this subsection, we shall compare our presented VIKOR model for q-RIVOFNs with other existing q-RIVOF decision making tools including q-RIVOFWA operator and q-RIVOFWG operator proposed by Wang, Gao, Wei and Wei \[[@B57-ijerph-17-00525]\] to explain the model we developed is scientifically valid. Using the fused q-RIVOFNs results of [Table 5](#ijerph-17-00525-t005){ref-type="table"} and the weights of attributes, the fused results depicted by q-RIVOFNs of each alternative are listed in [Table 6](#ijerph-17-00525-t006){ref-type="table"}.

According to the score function of q-RIVOFNs, the score results $s\left( \eta_{i} \right)$ of each alternative can be determined as follows.

For q-RIVOFWA operator:$$s\left( \eta_{1} \right) = 0.5461,s\left( \eta_{2} \right) = 0.6228,s\left( \eta_{3} \right) = 0.5092,s\left( \eta_{4} \right) = 0.5599,s\left( \eta_{5} \right) = 0.5489.$$

For q-RIVOFWG operator:$$s\left( \eta_{1} \right) = 0.5380,s\left( \eta_{2} \right) = 0.6188,s\left( \eta_{3} \right) = 0.4785,s\left( \eta_{4} \right) = 0.5462,s\left( \eta_{5} \right) = 0.5267.$$

Then the ordering of alternatives by q-RIVOFWA and q-RIVOFWG operators is listed in [Table 7](#ijerph-17-00525-t007){ref-type="table"}.

Compare the results of the q-RIVOF-VIKOR model with q-RIVOFWA and q-ROIVFWG operators, the aggregation results are a little bit different in ranking of alternatives but the optimal scheme is the same. However, q-RIVOF-VIKOR model has the remarkable characteristics of considering the compromise between group utility maximization and individual regret minimization and can be more accuracy and valid in MAGDM problems.

6. Conclusions {#sec6-ijerph-17-00525}
==============

In this manuscript, the q-RIVOF-VIKOR model based on the traditional VIKOR model is presented. Firstly, we started with a review of the concept of q-RIVOFSs and introduced the score function, accuracy function, operation rules, and some aggregation operators of q-RIVOFNs. Furthermore, we combined the traditional VIKOR technique with q-RIVOFNs information, the q-RIVOF-VIKOR model is built and the calculational steps are detailedly given. The proposed model considers the compromise between group utility maximization and individual regret minimization, which is proved to be more accurate and effective. Finally, the new model is illustrated by taking the supplier selection of medical consumer products as an example, and the advantages of the new method are further illustrated by comparing q-RIVOF-VIKOR model with two q-RIVOFNs aggregation operators. In the future, the q-RIVOF-VIKOR model can be used in many other uncertain and fuzzy environments, such as risk analysis \[[@B73-ijerph-17-00525],[@B74-ijerph-17-00525],[@B75-ijerph-17-00525],[@B76-ijerph-17-00525],[@B77-ijerph-17-00525],[@B78-ijerph-17-00525],[@B79-ijerph-17-00525],[@B80-ijerph-17-00525],[@B81-ijerph-17-00525],[@B82-ijerph-17-00525],[@B83-ijerph-17-00525],[@B84-ijerph-17-00525]\].

H.G., L.R., G.W., C.W. and J.W. conceived and worked together to achieve this work, G.W. compiled the computing program by Excel and analyzed the data, G.W. wrote the paper. Finally, all the authors have read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.

The work was supported by the National Natural Science Foundation of China under Grant No. 71571128 and the Humanities and Social Sciences Foundation of Ministry of Education of the People's Republic of China (14XJCZH002). The APC was funded by Humanities and Social Sciences Foundation of Ministry of Education of the People's Republic of China (14XJCZH002).

The authors declare no conflict of interest.

ijerph-17-00525-t001_Table 1

######

The q-RIVOFNs information given by $D^{1}$.

               G~1~                        G~2~                        G~3~                        G~4~
  ------------ --------------------------- --------------------------- --------------------------- ---------------------------
  $\eta_{1}$   (\[0.7,0.8\],\[0.4,0.5\])   (\[0.2,0.4\],\[0.5,0.8\])   (\[0.5,0.6\],\[0.3,0.4\])   (\[0.6,0.7\],\[0.6,0.8\])
  $\eta_{2}$   (\[0.8,0.9\],\[0.2,0.3\])   (\[0.5,0.6\],\[0.1,0.2\])   (\[0.6,0.7\],\[0.5,0.6\])   (\[0.6,0.8\],\[0.3,0.4\])
  $\eta_{3}$   (\[0.5,0.6\],\[0.7,0.8\])   (\[0.3,0.4\],\[0.4,0.5\])   (\[0.6,0.8\],\[0.5,0.7\])   (\[0.4,0.5\],\[0.2,0.3\])
  $\eta_{4}$   (\[0.6,0.7\],\[0.3,0.4\])   (\[0.2,0.5\],\[0.5,0.6\])   (\[0.5,0.7\],\[0.3,0.4\])   (\[0.6,0.8\],\[0.2,0.4\])
  $\eta_{5}$   (\[0.5,0.9\],\[0.3,0.6\])   (\[0.4,0.6\],\[0.7,0.8\])   (\[0.3,0.4\],\[0.5,0.6\])   (\[0.6,0.7\],\[0.2,0.3\])

ijerph-17-00525-t002_Table 2

######

The q-RIVOFNs information given by $D^{2}$.

               G~1~                        G~2~                        G~3~                        G~4~
  ------------ --------------------------- --------------------------- --------------------------- ---------------------------
  $\eta_{1}$   (\[0.6,0.7\],\[0.2,0.3\])   (\[0.1,0.4\],\[0.6,0.8\])   (\[0.3,0.7\],\[0.2,0.5\])   (\[0.4,0.6\],\[0.5,0.7\])
  $\eta_{2}$   (\[0.5,0.6\],\[0.1,0.2\])   (\[0.8,0.9\],\[0.5,0.6\])   (\[0.4,0.6\],\[0.2,0.3\])   (\[0.7,0.8\],\[0.4,0.5\])
  $\eta_{3}$   (\[0.3,0.4\],\[0.5,0.6\])   (\[0.2,0.4\],\[0.5,0.7\])   (\[0.5,0.6\],\[0.2,0.3\])   (\[0.6,0.7\],\[0.5,0.6\])
  $\eta_{4}$   (\[0.3,0.5\],\[0.5,0.7\])   (\[0.4,0.5\],\[0.7,0.8\])   (\[0.4,0.5\],\[0.2,0.5\])   (\[0.5,0.9\],\[0.3,0.4\])
  $\eta_{5}$   (\[0.4,0.8\],\[0.2,0.4\])   (\[0.2,0.3\],\[0.6,0.7\])   (\[0.4,0.7\],\[0.2,0.3\])   (\[0.3,0.6\],\[0.4,0.8\])

ijerph-17-00525-t003_Table 3

######

The q-RIVOFNs information given by $D^{3}$.

               G~1~                        G~2~                        G~3~                        G~4~
  ------------ --------------------------- --------------------------- --------------------------- ---------------------------
  $\eta_{1}$   (\[0.5,0.6\],\[0.3,0.4\])   (\[0.6,0.7\],\[0.1,0.2\])   (\[0.2,0.4\],\[0.5,0.8\])   (\[0.3,0.5\],\[0.2,0.3\])
  $\eta_{2}$   (\[0.5,0.7\],\[0.1,0.2\])   (\[0.4,0.6\],\[0.4,0.5\])   (\[0.7,0.8\],\[0.2,0.3\])   (\[0.6,0.7\],\[0.4,0.5\])
  $\eta_{3}$   (\[0.2,0.4\],\[0.6,0.8\])   (\[0.4,0.5\],\[0.1,0.2\])   (\[0.4,0.7\],\[0.6,0.8\])   (\[0.3,0.4\],\[0.6,0.9\])
  $\eta_{4}$   (\[0.4,0.5\],\[0.2,0.3\])   (\[0.7,0.8\],\[0.3,0.6\])   (\[0.6,0.8\],\[0.1,0.5\])   (\[0.1,0.2\],\[0.6,0.7\])
  $\eta_{5}$   (\[0.3,0.6\],\[0.4,0.5\])   (\[0.5,0.6\],\[0.3,0.4\])   (\[0.1,0.3\],\[0.4,0.5\])   (\[0.5,0.8\],\[0.1,0.4\])

ijerph-17-00525-t004_Table 4

######

The results of combination distance.

                                          G~1~     G~2~     G~3~     G~4~
  --------------------------------------- -------- -------- -------- --------
  $d\left( {\eta_{1},\eta^{+}} \right)$   0.0585   0.0750   0.0829   0.0860
  $d\left( {\eta_{2},\eta^{+}} \right)$   0.0062   0.0004   0.0012   0.0086
  $d\left( {\eta_{3},\eta^{+}} \right)$   0.1797   0.1019   0.0435   0.1031
  $d\left( {\eta_{4},\eta^{+}} \right)$   0.1142   0.0600   0.0235   0.0281
  $d\left( {\eta_{5},\eta^{+}} \right)$   0.0497   0.0964   0.0929   0.0398
  $d\left( {\eta^{-},\eta^{+}} \right)$   0.1797   0.1221   0.1024   0.1031

ijerph-17-00525-t005_Table 5

######

The fused q-RIVOFNs matrix.

               G~1~                                    G~2~
  ------------ --------------------------------------- ---------------------------------------
  $\eta_{1}$   (\[0.6171,0.7179\],\[0.2998,0.4025\])   (\[0.4834,0.5860\],\[0.2750,0.4595\])
  $\eta_{2}$   (\[0.6709,0.7975\],\[0.1275,0.2305\])   (\[0.6243,0.7462\],\[0.2604,0.3798\])
  $\eta_{3}$   (\[0.3975,0.5017\],\[0.6051,0.7445\])   (\[0.3411,0.4487\],\[0.2430,0.3770\])
  $\eta_{4}$   (\[0.4932,0.5993\],\[0.2899,0.4101\])   (\[0.5763,0.6855\],\[0.4434,0.6448\])
  $\eta_{5}$   (\[0.4224,0.8095\],\[0.3042,0.5041\])   (\[0.4316,0.5634\],\[0.4800,0.5864\])
               **G~3~**                                **G~4~**
  $\eta_{1}$   (\[0.3975,0.5884\],\[0.3326,0.5581\])   (\[0.4879,0.6171\],\[0.3695,0.5227\])
  $\eta_{2}$   (\[0.6245,0.7335\],\[0.2757,0.3824\])   (\[0.6312,0.7675\],\[0.3617,0.4625\])
  $\eta_{3}$   (\[0.5187,0.7277\],\[0.4278,0.5975\])   (\[0.4630,0.5576\],\[0.3903,0.5537\])
  $\eta_{4}$   (\[0.5337,0.7249\],\[0.1747,0.4625\])   (\[0.5005,0.7766\],\[0.3435,0.5004\])
  $\eta_{5}$   (\[0.3108,0.5282\],\[0.3637,0.4691\])   (\[0.5209,0.7335\],\[0.1803,0.4302\])

ijerph-17-00525-t006_Table 6

######

The fused results of each alternative $\eta_{i}$

  q-RIVOFWA Operator                                                                                q-RIVOFWG Operator
  ----------------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------
  $\begin{array}{l}                                                                                $\begin{array}{l}
  {\eta_{1} = \left( {\left\lbrack 0.5246,0.6383 \right\rbrack,\left\lbrack 0.3078,0.4693 \right\rbrack} \right)} \\                  {\eta_{1} = \left( {\left\lbrack 0.5013,0.6258 \right\rbrack,\left\lbrack 0.3162,0.4829 \right\rbrack} \right)} \\
  {\eta_{2} = \left( {\left\lbrack 0.6395,0.7642 \right\rbrack,\left\lbrack 0.2314,0.3456 \right\rbrack} \right)} \\                  {\eta_{2} = \left( {\left\lbrack 0.6380,0.7619 \right\rbrack,\left\lbrack 0.2799,0.3813 \right\rbrack} \right)} \\
  {\eta_{3} = \left( {\left\lbrack 0.4254,0.5631 \right\rbrack,\left\lbrack 0.3741,0.5267 \right\rbrack} \right)} \\                  {\eta_{3} = \left( {\left\lbrack 0.4041,0.5218 \right\rbrack,\left\lbrack 0.4706,0.6092 \right\rbrack} \right)} \\
  {\eta_{4} = \left( {\left\lbrack 0.5361,0.6984 \right\rbrack,\left\lbrack 0.3237,0.5143 \right\rbrack} \right)} \\                  {\eta_{4} = \left( {\left\lbrack 0.5307,0.6839 \right\rbrack,\left\lbrack 0.3726,0.5533 \right\rbrack} \right)} \\
  {\eta_{5} = \left( \left. {\left\lbrack 0.4411,0.6986 \right\rbrack,\left\lbrack 0.3337,0.5106 \right.} \right\rbrack \right)} \\   {\eta_{5} = \left( {\left\lbrack 0.4227,0.6482 \right\rbrack,\left\lbrack 0.3985,0.5258 \right\rbrack} \right)} \\
  \end{array}$                                                                                \end{array}$

ijerph-17-00525-t007_Table 7

######

Order of alternatives by q-RIVOFWA and q-RIVOFWG operators.

                  Order
  --------------- --------------------------------------------------------
  q-RIVOFWA       $\eta_{2} > \eta_{4} > \eta_{5} > \eta_{1} > \eta_{3}$
  q-RIVOFWG       $\eta_{2} > \eta_{4} > \eta_{1} > \eta_{5} > \eta_{3}$
  q-RIVOF-VIKOR   $\eta_{2} > \eta_{4} > \eta_{1} > \eta_{5} > \eta_{3}$


527 Pa. 448 (1991)
593 A.2d 410
Michael R. FRONTINI, Appellant,
v.
COMMONWEALTH of Pennsylvania, DEPARTMENT OF TRANSPORTATION, Appellee.
Supreme Court of Pennsylvania.
Submitted March 4, 1991.
Decided July 12, 1991.
*449 David H. Acker, New Castle, for appellant.
John Heaton, Dept. of Transp., Harold H. Cramer, Asst. Chief Counsel, Harrisburg, for appellee.
Before NIX, C.J., and LARSEN, FLAHERTY, McDERMOTT, ZAPPALA, PAPADAKOS and CAPPY, JJ.

OPINION OF THE COURT
CAPPY, Justice.
The question before the Court is whether a driver can be classified as an habitual offender[1] under the motor vehicle code, as the consequence of a single act, which resulted in his conviction in criminal court for three counts of homicide by motor vehicle.[2] We find that as the three convictions arose from a single act, they cannot be counted as separate offenses for the purpose of imposing penalties under the habitual offender statute. Accordingly, the decision of the Commonwealth Court is reversed.
*450 The underlying facts of this case can be simply stated. On August 31, 1985, the automobile operated by appellant, Michael Frontini, struck another vehicle, killing three of the occupants in the second vehicle. Subsequently, appellant was charged with and pled guilty to driving under the influence of alcohol or a controlled substance,[3] reckless driving[4] and three counts of homicide by motor vehicle. Appellant was sentenced on September 2, 1986 to eleven and one half to twenty three months imprisonment for driving under the influence (DUI), and eleven and one half to twenty three months for each count of homicide by vehicle. The three homicide by vehicle sentences were to be served concurrently with each other and consecutive to the DUI sentence.
Following the imposition of sentence, notice of appellant's convictions were forwarded to the Pennsylvania Department of Transportation (DOT) for further action pursuant to 75 Pa.C.S. § 1531. That section requires DOT to maintain records of every licensed Pennsylvania driver's convictions for offenses which are punishable under the motor vehicle code by suspension or revocation. DOT suspended appellants license for one year on the DUI conviction under 75 Pa.C.S. § 1532(b), and revoked his license for an additional year under 75 Pa.C.S. § 1532(a), for the first homicide by vehicle conviction. Then DOT imposed a five year revocation on the basis of the second homicide by vehicle conviction, by classifying appellant as a habitual offender pursuant to 75 Pa.C.S. § 1542(a). An additional two year revocation was imposed for the third conviction of homicide by vehicle under 75 Pa.C.S. § 1542(e). The suspension and revocations were all to run in consecutive sequence.
On appeal to the Court of Common Pleas of Lawrence County, DOT's imposition of penalties were sustained. The Commonwealth Court affirmed the order of the lower Court in a memorandum opinion. ___ Pa.Cmwlth. ___, 553 A.2d 540. This Court granted the petition for allowance of *451 appeal, to address the issue of appellant's designation as an habitual offender, where the convictions accumulated by appellant to achieve that status arose from a single act.
The relevant section of the statute regarding habitual offenders, provides:
§ 1542. Revocation of habitual offender's license
(a) General rule.  The department shall revoke the operating privilege of any person found to be a habitual offender pursuant to the provisions of this section. A "habitual offender" shall be any person whose driving record, as maintained in the department, shows that such person has accumulated the requisite number of convictions for the separate and distinct offenses described and enumerated in subsection (b) committed after the effective date of this title and within any period of five years thereafter.
(b) Offenses enumerated.  Three convictions arising from separate acts of any one or more of the following offenses committed either singularly or in combination by any person shall result in such person being designated as a habitual offender:
The specific question under review is whether the appellant's three convictions for homicide by vehicle are separate acts as contemplated by the language of subsection (b) of the statute. Whenever a court attempts to ascertain the meaning of certain language within a statute, its goal is to effectuate the intent of the Legislature. See, 1 Pa.C.S. § 1921.
The apparent thrust of the habitual offender statute is to punish persons who make a "habit" of violating the more serious provisions of the vehicle code, thus causing themselves to be a menace to the other licensed drivers in Pennsylvania. Thus, the habitual offender statute is recidivist in nature, concerning itself with the number of prior acts committed by the offender, as opposed to the multiple consequences of any one act. The habitual offender statute is consistent in purpose with the recidivist provisions of the sentencing code at 42 Pa.C.S. § 9714, and with the successive *452 grading provisions of the retail theft statute at 18 Pa.C.S. § 3929(b).
Section 9714 of the sentencing code is entitled "sentences for second and subsequent offenses." It provides for a mandatory minimum sentence of five years imprisonment for persons convicted of two or more, specifically designated crimes of violence. Section 3929(b) of the retail theft statute sets up a step system, whereby successive offenses become graded more severely for sentencing purposes. Statutes such as these, provide for enhanced penalties for individuals with a propensity to commit repeated offenses of the same type. Recidivist statutes serve the legitimate public policy of segregating from society those persons with propensities to commit crime, who by their repeated criminal acts demonstrate their unwillingness or inability to be rehabilitated.
In the present case we have an individual, who by a single act caused multiple consequences. The propriety of the multiple criminal sanctions he must suffer as a result of that act is without question. However, for the purpose of a recidivist penalty it would be unjust, and in derivation of the intent of the statute, to separate out the consequences of this one act in order to catagorize this individual as a person with a propensity to commit repeated offenses.[5] Wherefore, we find the three convictions for homicide by vehicle, having resulted from a single act, are not to be considered as separate offenses for the purpose of classifying this appellant as an habitual offender pursuant to 75 Pa.C.S. § 1542.
The Commonwealth Court is reversed, and the case is remanded to the trial court for a new hearing on the *453 revocation/suspension of appellant's license consistent with this opinion.
Reversed and Remanded.
LARSEN, J., files a concurring opinion, in which PAPADAKOS, J., joins.
LARSEN, Justice, concurring.
I join in the result. However, I take issue with the majority's gratuitous discourse on the word "habit." The Statutory Construction Act provides that "[w]hen the words of a statute are clear and free from all ambiguity, the letter of it is not to be disregarded under the pretext of pursuing its spirit." 1 Pa.C.S.A. § 1921. Since the words of the statute herein are clear, our inquiry ends there. To write about an "apparent thrust" concept does a disservice to the above-cited, time honored rule of construction.
PAPADAKOS, J., joins in this concurring opinion.
NOTES
[1]  An Habitual Offender is defined in the Pennsylvania Vehicle Code at 75 P.S. § 1542.
[2]  See 75 P.S. § 3732.
[3]  75 Pa.C.S. § 3731(a)(4).
[4]  75 Pa.C.S. § 3714.
[5]  The Commonwealth Court in their opinion affirming the designation of this defendant as an habitual offender relied upon their earlier decision in Commonwealth v. Frye, 88 Pa.Cmwlth. 380, 489 A.2d 984 (1985). We find the facts in the instant case to be completely distinguishable from those in Frye, where the defendant committed a series of separate and distinct acts within a narrow time frame.


Various vehicles, for example, cars, electric motorcycles, and electric scooters, are usually provided with lamp light. Generally, lamp light is manually controlled by a user. Problems that may be caused when the user manually controls the lamp light include:
The user forgets to turn on corresponding lamp light, causing a traffic accident.
The user needs to remember a rule for turning on the lamp light, so that it is difficult for the user to control the lamp light; and if the user remembers the rule wrongly, lamp light control may be caused to be chaotic, and a traffic accident may be caused likewise.


The major objectives are: (1) to search for functional evidence of local control mechanisms within the uteroovarian vascular complex in rhesus monkeys which may be involved in the transfer of hormones or other materials between the uterus and ovary and thus serve to regulate corpus luteum function. If evidence for the transfer of such physiologically important materials as progesterone or monkey chorionic gonadotropin is found, additional functional and anatomical determinations will be made to define the venoarterial or other local pathways involved. A latex injection and tissue clearing technique will be utilized for gross anatomical determinations. Methods for light and electron microscopy will also be applied. (2) to relate rate and distribution of blood flow within the ovary and uteroovarian vascular pedicle to the varying levels of corpus luteum activity found during the menstrual cycle, various stages of pregnancy and lactation. If major alterations in rate and/or direction of blood flow are noted, investigations designed to evaluate the physiological significance of these alterations as well as the hormonal and nervous controls involved will be performed. The technique employing radioactive microspheres will be the primary one utilized for these purposes. Ancillary techniques to be employed include radioimmunoassay of steroid and gonadotropic hormones for determination of the endocrine status of an animal, laparoscopy for gross confirmation of ovarian activity and cineangiography for evaluating vascular pathways and direction of blood flow.


Q:

Material UI Tooltip not working with react-select options

I want tooltip on react-select's options. I have seen @atlaskit/tooltip working exactly like this but I want to use material ui tooltip which is not working when I tried. Can anyone please help me with a solution
import React, { Component } from "react";
import Select, { components } from "react-select";
import {primaryColor,primaryColorLight} from "../app-resources/theme-overrides/global"
import Tooltip from '@material-ui/core/Tooltip';

const { Option } = components;

const customOption = props => {
  return (
        <Tooltip title={props.label} arrow>
          <Option {...props} />
        </Tooltip>
  );
};

export default class SingleSelect extends Component {
  render(props) {
    return (
      <>
      <Select
        components={{
          Option: customOption,
          }}
        className="basic-single"
        classNamePrefix="select"
        name={this.props.name}
        isClearable
        options={this.props.Options}
        placeholder={this.props.placeholder}
        styles={{
          menu: base => ({
            ...base,
            marginTop: 0
          }),
          control: (base, state) => ({
            // none of react-select's styles are passed to <Control />
            background: 'white',
            height: 45,
            border: "1px solid #ccc2c2",
            borderRadius: 25,
            display: 'flex',
            border: state.isFocused ? "2px solid "+primaryColor : "1px solid #ccc2c2", //${primaryColor}
            // This line disable the blue border
            boxShadow: state.isFocused ? 0 : 0,
            '&:hover': {
               border:  state.isFocused ?"2px solid "+primaryColor:"1px solid "+primaryColorLight
            },
            fontSize: '1rem'
           }),
          container: (provided, state) => ({
            ...provided,
            marginTop: 8
          }),
          valueContainer: (provided, state) => ({
            ...provided,
            overflow: "visible"
          }),
          placeholder: (provided, state) => ({
            ...provided,
            position: "relative",
            display: "block",
            top: state.hasValue || state.selectProps.inputValue ? -4 : "47%",
            transition: "top 0.1s, font-size 0.1s",
            fontSize: state.hasValue || state.selectProps.inputValue? 12 : "1rem",
            color: state.hasValue || state.selectProps.inputValue || state.isFocused?primaryColor:"#757575",
            marginBottom: 20,
            background: 'linear-gradient(to top,#ffffff 60%,#fff0 50%)',
            backgroundSize: "60% auto",
            fontFamily: '"Roboto", "Helvetica", "Arial", sans-serif',
            padding:"0px 5px",
            marginLeft: 0,
            borderTopRightRadius: 20,
            borderTopLeftRadius: 20
          })
        }}
      />
      </>
    );
  }
}

A:

This is my working codesandbox: https://codesandbox.io/s/strange-shape-kthxv?file=/src/App.js.
Basically we use the following list for options (for example). The key here is use Tooltip to wrap label.
const options = [
  {
    value: "chocolate",
    label: (
      <Tooltip placement="right" title="chocolate" arrow>
        Chocolate
      </Tooltip>
    )
  },
  {
    value: "strawberry",
    label: (
      <Tooltip placement="right" title="strawberry" arrow>
        Strawberry
      </Tooltip>
    )
  },
  {
    value: "vanilla",
    label: (
      <Tooltip placement="right" title="Vanilla" arrow>
        Vanilla
      </Tooltip>
    )
  }
];

Hope this help.


The U.K. has unveiled a new Wi-Fi system that blocks hardcore porn in all public areas. It’s being touted as “the world’s first public Wi-Fi accreditation scheme.”

Thanks to a measure requiring all Internet users to “opt out” with their providers to access hardcore porn online, it’s already difficult to access porn in the privacy of one’s own home in the U.K. According to the Mirror, now stores, cafes, and supermarkets in the U.K. are putting up “Friendly Wi-Fi” signs to assure customers that they’ve implemented the filters, and that their children won’t stumble on any naughty content. So far, Starbucks, the British supermarket chain Tesco, and Samsung have already signed up for the program.

Proudly working with #friendlywifi stakeholders @Tesco who are proud to offer safe public WiFi to their Customers pic.twitter.com/9ekgUiBgvb — getmedigital.com (@getmedigital) July 17, 2014

“The ‘Friendly WiFi’ logo will make clear to parents which cafes, restaurants and other businesses have internet access that is safe for their children to use,” Communications Minister Ed Vaizey told the Mirror. “It will help these firms ensure that families feel comfortable and make it clear to parents they are choosing a safe online environment.”

The idea for the “Friendly Wi-Fi” program, which is believed to be the first public Wi-Fi filtering program of this scale in the world, took root when Prime Minister David Cameron gave a speech to the National Society for the Prevention of Cruelty to Children (NSPCC) on online safety. In his speech, he advocated for businesses to advertise in their windows that their public Wi-FI system was safe for children to access.

Now, Friendly Wi-Fi members will filter websites based on a blacklist authored by the Internet Watch Foundation (IWF), a nonprofit organization that tracks down “potentially criminal” online content, particularly content that might depict child abuse.

In theory, the “Friendly Wi-Fi” program kind of seems like a no-brainer. Unless you’re a journalist who covers adult topics and occasionally works in coffee shops (i.e., unless you’re me), it’s almost never a good idea to access porn in a public venue, and it probably shouldn’t be easy for people to do so. And apparently, it is easy to access porn on existing public Wi-Fi networks: According to a 2013 Mirror investigation, nearly a quarter of public networks in the U.K. allow people to access adult content.

But free speech activists are objecting to the program, on the grounds that the measure constitutes online censorship. There seems to be some concern that, much like the mandatory British Internet service provider filters, which were revealed to have blocked one-fifth of the world’s 100,000 most popular websites, the Friendly Wi-Fi program will be so restrictive as to block not just porn but also any other content the U.K. government doesn’t want you to see.

Today is the official launch of “Friendly Wifi”, meaning public wifi will restrict what web pages you can see. Could this be more Orwellian? — Adam Banks (@adambanksdotcom) July 17, 2014

But let’s put the potential free speech/censorship issues posed by the filters aside for a second and concentrate on what enterprising young British lads and ladies are supposed to do now. If they can’t masturbate to online porn in their homes, and they can’t do it in Starbucks bathrooms, where are they supposed to masturbate, and what are they supposed to masturbate to? Wallpaper?

H/T The Mirror | Photo by dbrekke/Flickr (CC BY 2.0)


Call for community colleges to schedule classes in blocks

Monday

Jan 30, 2012 at 12:01 AMJan 30, 2012 at 4:48 AM

Students who have had trouble juggling community-college classes with work and family obligations might get some relief soon. Chancellor Jim Petro has asked the state's public two-year schools to consider scheduling some classes as a block, in which they are offered back-to-back at a set time. Students could take classes from 8 a.m. to noon Monday through Friday, for example, and earn an associate degree in 18 months - as long as they go through summer.

Students who have had trouble juggling community-college classes with work and family obligations might get some relief soon.

Chancellor Jim Petro has asked the state’s public two-year schools to consider scheduling some classes as a block, in which they are offered back-to-back at a set time. Students could take classes from 8 a.m. to noon Monday through Friday, for example, and earn an associate degree in 18 months — as long as they go through summer.

Block scheduling is attractive to many students because they wouldn’t have to worry about rearranging job and family responsibilities every term because of their classes, Petro said. More than 80 percent of community-college students across the country have a job, and about 60 percent work more than 20 hours per week. That can prolong their studies.

“ Every extra year that students take to complete a college degree diminishes the chance they will graduate,” Petro said. “Time truly is the enemy.”

His goal is to improve the state’s graduation rate, providing skilled workers who can help turn around Ohio’s economy, he said.

An average of 9 percent of the state’s full-time community-college students graduate within three years; 56 percent of Ohio’s four-year university students earn diplomas within six years. The three- and six-year timelines are standard measures for judging college success nationwide.

Tennessee has had huge success with block scheduling at its 27 Technology Centers, Petro said. Seventy-five percent of students complete their degrees at those centers, which specialize in one-year to 18-month certifications in high-demand fields. And 83 percent of the students move directly into a job.

Students at the Technology Centers sign up for a program rather than individual courses, and attend at least six hours a day, five days a week. Classes end by 3 p.m., allowing students to hold an evening job or care for children after school.

“ Students know at registration what courses they must take, how much the program will cost and for which specific program they are training,” said James King, the vice chancellor of the Technology Centers. “We’re not like Burger King: You don’t get it your way.”

Petro doesn’t plan to require that all 23 of Ohio’s community colleges offer block schedules. But he hopes they consider it for at least one or two of their most-popular programs.

Several of the state schools already do, said Ronald Abrams, executive director of the Ohio Association of Community Colleges. “It’s done mostly in nursing, allied health and some highly technical programs."

Abrams said the idea could be expanded and “has the potential to improve student success.”

Columbus State Community College doesn’t have any true block-scheduling programs, officials said, but it does have a few variations.

A select group of science and technology scholars, for instance, take some classes, such as English and math, in blocks.

“ We’re always very committed to adapting our schedules to the needs of every student,” said Tom Erney, Columbus State’s dean of distance learning.

In the fall, Columbus State students will be able to take more classes in unofficial blocks in the mornings, afternoons and evenings. That’s because the school will be offering more classes throughout the day, from 7 a.m. to 10 p.m., to accommodate the switch from quarters to semesters.

Central Ohio Technical College in Newark also has a limited number of programs that are offered in blocks, as well as courses that can be taken in pairs or sequences.

But the school stands out because it is the only area college that allows students to sign up for a whole year of classes at one time, said Richard Prystowsky, the vice president for academic affairs and chief academic officer.

“ We are very, very careful about putting together the master schedule so that students in a program can get the courses they need without having to come back all the time,” Prystowsky said. “Roughly, 50 percent of our students took advantage of it last school year.”

Starting in late spring, students will be able to sign up for two years of classes, he said.

epyle@dispatch.com

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package observer;

import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.regex.Pattern;
import java.util.stream.Stream;

public interface observer3 {
  public interface Observer {
    public void data(double value);
  }

  public class CSVParser {
    public static void parse(Path path, Observer observer) throws IOException {
      try (Stream<String> lines = Files.lines(path)) {
        lines
            .flatMap(Pattern.compile(",")::splitAsStream)
            .mapToDouble(Double::parseDouble)
            .forEach(observer::data);
      }
    }
  }

  public class SumCSV {
    private double sum;
    public double parseAndSum(Path path) throws IOException {
      CSVParser.parse(path, value -> sum += value);
      return sum;
    }
  }

  public static void main(String[] args) throws IOException {
    Path path = Paths.get("test/test.csv");
    double value = new SumCSV().parseAndSum(path);
    System.out.println(value);
  }
}


Socialization Goals, Parental Psychological Control, and Youth Anxiety in Chinese Students: Moderated Indirect Effects based on School Type.
With rates of adolescent anxiety on the rise in China, it is imperative to investigate whether certain parenting beliefs and practices may be related to anxiety. Specifically, we tested whether parents' socialization goals relate to parental psychological control, and subsequently, adolescents' anxiety. We also tested if attending a "key" school (i.e., more competitive and achievement-oriented) or typical school moderated relations. Two hundred forty-seven high-school students (Mage = 15.62, 57.5% girls) and a caregiver (59.5% mothers) participated. Caregivers completed measures of their socialization goals and their own psychological control. Adolescents reported on their perceptions of parental psychological control and their own anxiety. Psychological control was positively related to youth anxiety. Moderated indirect effects were found. For youth in typical schools, parents who strongly value academic achievement (i.e., achievement oriented goals) and those who strongly value broadening one's experiences in new places and with new people (i.e., self-development in context goals) had youth who experienced more anxiety, and this relation occurred indirectly through greater parental psychological control. For youth in key schools, only parents' achievement oriented goals were related to youth anxiety indirectly through parental psychological control. Parents' interdependence oriented socialization goals were unrelated to either psychological control or anxiety.


Q:

Magento 2 : How to Clear Previous Messages in MessageManager

I have added a below line in my observer file for the event checkout_cart_save_after
$this->_messageManager->addNotice(__("A Notice Message"));

Every time it hits observer, it displayes more than 1 message in frontend. I need to display only one message at a time. Can anyone pls help to clear the messages in message manager?
My observer logic is below:
 public function execute(Observer $observer)
    {
        allowCheckout = 1;
          //custom logic......
         if($allowCheckout == 0){
           $this->_messageManager->addNotice(__("A Notice Message"));

         }

    }

So Everytime when the event occurs it goes to observer and displays message manager notice message. It displays more than one message. I want only once the message to be displayed

A:

Try the following code:
$this->_messageManager->getMessages(true); //this will clear your messageManager container
$this->_messageManager->addNotice(__("A Notice Message"));


Arsene Wenger: In the market for defensive leaders

Arsenal manager Arsene Wenger says he is in the market for old fashioned defenders during the January transfer window.

Wenger admits he has a "few names" on his list of defensive targets and has pinpointed Chelsea's central defensive partnership of Gary Cahill and skipper John Terry as examples of the dying breed of defenders he is looking to buy.

"John Terry is a coach on the pitch," he said.

John Terry is a coach on the pitch, he's a Tony Adams, basically, organising the team because he has huge experience. Arsene Wenger

"He's a Tony Adams, basically, organising the team because he has huge experience now and reads the game well.

"Maybe (Gary) Cahill in five, six, seven years will be like that as well. He is the right age and you do not find many any more. You had more before than you do today.

"With people coming in from different countries, communication has gone from the back four, much more than before and you see less people who talk.

"They do not always speak the same language, they do not respond to situations in the same way. That has gone a little bit from our game."

Tony Adams, Steve Bould, Nigel Winterburn and Lee Dixon were Wenger's mainstays early on in his tenure at Arsenal and he has also highlighted the back four who featured in Arsenal's unbeaten season of 2003/04 as another outstanding unit.

Wenger also believes there are a lack of aggressive-minded players playing in the modern game in England.

"Certainly you had more before, yes," he said when asked if there were less leaders in the game today.

"If you look at the evolution of English football, when I arrived here I had the back four and all were English. But they were not only English, they communicated because they had the same culture, the same way to see the game,

they were educated together and communication is an important part.

"We had a great defence after that, nobody spoke about them. The 'Invincibles' team were physically fantastic. Lauren, Kolo (Toure), Sol Campbell, Ashley Cole - that was a top back four.

"I believe as well that the young boys practise well on quality pitches, whereas before it was muddy and you could throw your body in - it created opportunities for defenders to work naturally on their defensive technique.

"Today it's all more about standing up (to opponents) and there is less physical commitment because the quality of the pitches is much better.

"Maybe, as well, because our societies are less aggressive, in the football education system you cultivate less that desire, it is more about the quality of the technique and maybe that creates less defenders."


Congenital aortopulmonary artery fistulas combined with bilateral coronary artery fistulas.
We describe an extremely rare case of "congenital aorto-pulmonary artery fistulas" combined with bilateral coronary artery fistulas associated with clinically significant left-to-right shunt. A multi-detector row computed tomographic scan revealed that tortuous anomalous vessels arising from the aortic arch, left anterior descending coronary artery, diagonal branch, and right coronary artery were connected to the main pulmonary trunk. After surgical intervention, the systemic-to-pulmonary flow ratio nearly normalized from 2.02 to 1.05. We describe the case and speculate as to the embryological pathogenesis of these unique fistulas.