The AN/TPS-32 is a helicopter-transportable, long-range, three-dimensional, computer-controlled, S-Band radar. The radar set includes the Primary Radar, SSR, VHF/UHF/HF Ground to Air Communications, Power Conditioning System, Processing and Display System, Voice and Data Recording System. The transmitter can vary the interpulse period and peak power to better illuminate targets at long ranges and lower elevations. The AN/TPS-32 Radar Set is composed of three equipment shelters and a pedestal mounted antenna array.
The antenna is a horizontally polarized frequency scanning phased array that electronically stears the beam in elevation. Subdividing a long pulse into five sub-pulses of differing frequencies creates five overlapping elevation beams that are step-scanned as a group through a −1° to +18° range. Each beam is 0.84º high and 2.15º wide. The antenna gain is at least 41 Decibels, and the side lobes at least 25 Decibels below the peak of the main beam. The antenna is collapsible onto three pallets for movement.
The radar came in service in the US Marine Corps in 1969, and is retired from service. Turkey's sets were adapted for use within the NATO Air Defense Ground Environment (NADGE) system and designated AN/TPS-64.


text:
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Winter 2006 | Volume 29, Number 1
A Fairy Tale for All Ages
What makes The Lion, the Witch and the Wardrobe — and all the Narnia tales — so appealing to young and old alike? Earl Palmer, C.S. Lewis devotee and pastor of Seattle's University Presbyterian Church, begins with a simple answer: They are "darn good stories."
The Wardrobe in the Classroom
The Big-Screen Wardrobe
In an interview with Walden Media's Micheal Flaherty, Response gets an insider's view of the challenges involved in filming The Lion, the Witch and the Wardrobe. The greatest of these, filmmakers say, was preserving C.S. Lewis' storytelling.
Dear Mr. Lewis
After penning The Chronicles of Narnia, C.S. Lewis personally replied to approximately 12,000 letters, many of them from children. "We must meet children as equals in that area of our nature where we are their equals," he once wrote.
"What If I Love Aslan More Than Jesus?"
Jack's Life at The Kilns and in Oxford
For most of his adult life, C.S. Lewis lived at The Kilns, a country home located just east of the city of Oxford in England. Response takes readers on a photo tour of The Kilns today and some of the author's favorite sites in Oxford.
New Life for The Kilns
The Faith of the Next Generation
During a visit to SPU this fall, sociologist and author Christian Smith delivered a disturbing message: Even those teens who call themselves Christians believe in and practice a religion that bears little resemblance to Christianity.
Response Photography Competition 2005 Winners
The winner and the runner up for the Response Photography Contest take readers to Uganda and Guatemala.
Critic's Screening of The Wardrobe
Response writer and film critic Jeffrey Overstreet provides an opening-day review of Disney's long-awaited film adaptation of The Lion, the Witch and the Wardrobe.
Photos above copyright 2005 Disney Enterprises, Inc., and Walden Media, LLC. All rights reserved.
Send This Page Send-to-Printer
A Conversion of the Imagination
2014: A Blueprint for Excellence shows "Seattle Pacific will be a place that knows and understands what's going on in the world, and it will be a place that embraces the Christian story," says President Philip Eaton. [President]
Katrina's Call
SPU professors, graduate students, and staff help with hurricane relief efforts on the Gulf Coast and on SPU's campus. [Campus]
Allowing Scripture to Transform Our Lives
Kerry Dearborn, associate professor of theology, contributes introductions and study notes to the Renovar� Spiritual Study Bible. [Faculty]
Quality Always
Alumni of the Year, Kathi and Jerry Teel, live out their Vitamilk Diary slogan, "Quality Always," in all areas of their lives. [Alumni]
The Gospel According to Miller
Author of best-selling book, Blue Like Jazz, tells SPU students that "engaging the culture is not rocket science." [Books & Film]
Courted by Division I soccer teams while in high school, stand-out Falcon forward, Sarah Martinez, hits goals on and off the field. [Athletics]
John Perkins writes a letter to Seattle Pacific about God's grace during and after Hurricane Katrina.
Copyright © 2005 Seattle Pacific University. General Information: (206) 281-2000

redpajama_set_name: RedPajamaCommonCrawl


Aguilar J. S., Benvenuti S., Dall'Antonia L., McMinn-Grivè M. M., Mayol-Serra J. M. Preliminary results on the foraging ecology of Balearic shearwaters (Puffinus mauretanicus) from bird-borne data loggers. In: Scientia Marina, vol. 67 (supplemento 2) pp. 129 - 134. Institut de Ciències del Mar (CSIC), 2003.
(English) A data logger devised and manufactured by our research team in order to study the homing routes of carrier pigeons was subsequently modified to study the homing and foraging strategies of breeding marine birds. Recent versions of the data logger, equipped with a flight sensor and depth meter or saltwater switch, were used in a study of the foraging strategies of chick-rearing Balearic shearwaters (Puffinus mauretanicus) in the framework of the project LIFE-Puffinus financed by the Balearic Government amd the EU. Due to low recapture rates (only 3 out of 6 tagged birds were recovered), only preliminary data from a small sample are available. Data loggers have recorded data on the pattern of nest attendance (including departure time to foraging trips and return time) and the diurnal pattern of flight and dive activity (including depth and duration of dives). Despite the small sample size, the results show that our data loggers can successfully be applied to the study of the breeding biology and foraging ecology - including the diving pattern-of Balearic shearwaters and similar species.


We have no fixed dates nor groups to join. We offer private guiding. In this website we try to give you a good description of some excellent alternatives. However, we encourage you to provide us with information that will help finding the right program and get the most of your time in Northern Patagonia. We are happy to offer personalized programs.
Please let us know when are you planning to come, where would you stay, how many people would take part of the activity, ages, your outdoor activities background, expectations and motivations, how would you consider your level of fitness and if you can, give us an idea of: how many hours / kilometers (or miles) distance / elevation gain you would like to walk per day.
In order to ensure guides availability and to plan carefully the right program for you, please contact us as soon as you can.
Safety, medical issues and risks involved.
We´ll need you to inform us if have or have had any medical problems or if you are currently taking any medication. Also, we´ll need your signature on an Acknowledgement, Agreement AND ASSUMPTION OF RISK.
Wind, precipitations, high or low temperature exposure, steep terrain, rocky terrain, slippery terrain, exposed terrain, obstacles, insects, rock slides and avalanches, falling branches or trees, snow slopes, equipment malfunction or failure, river fording, and others such as theft or loss of, or damage to property. I agree to accept and assume all risk of personal injury, illness, death or damage to myself and my property arising from my participation in this activity. My participation is voluntary; I choose to participate in spite of these named and other unnamed risks. I hereby affirm that I am psychologically and physically healthy. I agree to follow the trip leader´s /guide´s instructions and that he/she has decision-making authority on route choosing and over whether to stay in the place, continue with or cancel the trip. I understand my responsibility in decision-making."
It is the responsibility of each traveler to take insurance and have medical assistance before trip departure.
Our services do not include medical assistance nor life insurance. The trips we offer take place in spots far from big cities, and eventually tourists can be at a great distance from medical or other kind of assistance. TREK PATAGONIA is committed to warranting tourist's safety and well-being, and each tourist accepts at the time of booking he/she fully understands the potential risks and dangers connected with this kind of active travel. TREK PATAGONIA will not accept any liability with respect to accidents, damages, illness, negligence and physical or mental harm.
TREK PATAGONIA reserves the right to make a major change in the trip.
intervening events. It is understood that the route, schedules, itinerary, amenities and mode of transport may be subject to alteration without prior notice due to local circumstances or events.
b) Cancel totally or partially the itinerary.
c) Postpone the trip waiting better weather conditions, subject to availability.
In all these cases no money refund will be made.


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text:
Q: Referencing same random value in a SELECT statement I have 2 functions I am calling form a SSRS dataset query:

*

*fRndNumber() returns a random value

*fCheckSum() returns a calculated value based in the value returned from fRndNumber.

The following statement does not work because the second call of fRndNumber() is generating a different value than the first call. I require the fCheckSum function to use the same value generated by fRndNumber()
Select
    fRndNumber() as SerialNumber,
    fCheckSum(fRndNumber) as CheckSum

Ideally I would like the statement to read as follows, but of course I cannot reference the 'SerialNumber' in this manner.
Select
    fRndNumber() as SerialNumber,
    fCheckSum(SerialNumber) as CheckSum

Any suggestions?
Doug P

A: Depending on your version of SQL, you could use a CTE...
;WITH RandomCte AS
(
    SELECT  fRndNumber() AS SerialNumber,
            * -- add your other columns here...
    FROM    someTableName -- replace with your table name
)
SELECT  fCheckSum(SerialNumber) AS [CheckSum],
            *
FROM    RandomCte;

See here for more info on CTEs

A: Using a CTE is considered best practices these days. But if your server doesn't support them, you could also use an inline view:
SELECT
  A.SerialNumber,
  fCheckSum(A.SerialNumber) AS CheckSum
FROM  (
 SELECT fRndNumber() as SerialNumber
) as A;

redpajama_set_name: RedPajamaStackExchange


Kaushal Manda started off his career as a small-time actor in Telugu films. He is a model who did numerous shows and also owns a model agency successfully. Kaushal did a lot of things in his life to reach to a stage and he is currently the hot favorite contestant in the second season of Bigg Boss Telugu TV show.
From the third week, Kaushal gained a lot of fan following and the fan base grew steadily. Those who usually mess with Kaushal in the house indirectly confirms their elimination as the fans will be on a mission to send them out of the house. Regarding Kaushal, Nani has tasted criticism a lot of times in the house too. People make an allegation that Nani is biased and he does not show his support to Kaushal.
A lot of people who hate started targeting Kaushal supporters saying that Kaushal Army (the fan base of Kaushal) is paid. After a lot of waiting, the Kaushal army came forward to prove a fact that their love towards Kaushal is genuine and they are not paid. A lot of supporters have come forward to support Kaushal today morning near Madhapur main road. The fans called for a 2K walk and crowd gathered in a larger number that everyone was literally shocked to see the unimaginable support for Kaushal in just not the world but the real world.
Interestingly, there is a buzz that the police officials have given permission for when they were told that the walk is for Kerala Floods victims but don't know how they converted the same to a walk for supporting Kaushal.
On the whole, more craze is garnered by the TV show and everyone is aware of the fact that Kaushal is certainly the strongest contestant in the house.


text:
Indian origin scientist develops robots that understand casual language
this story is from June 23, 2014
Subodh Varma| TNN | Updated: Jun 23, 2014, 13:19 IST
A team of scientists at Cornell University has developed a robot that can understand spoken commands given in natural language.
NEW DELHI: A team of scientists at Cornell University has developed a robot that can understand spoken commands given in natural language. The robot has the ability to understand variations in language, fill in missing parts and adapt to its immediate surroundings.
Normally, robots would need step-by-step instructions for tasks they haven't performed before. Before you can tell your household robot "Make me a bowl of ramen noodles," you'll have to teach it how to do that. Since we're not all computer programmers, we'd prefer to give those instructions in English, just as we'd lay out a task for a child.
But human language can be ambiguous, and some instructors forget to mention important details. Suppose you told your household robot how to prepare ramen noodles, but forgot to mention heating the water or tell it where the stove is.
In his Robot Learning Lab at Cornell, Indian origin computer scientist Ashutosh Saxena and graduate students Dipendra K. Misra and Jaeyong Sung have developed a robot with a software that can translate human sentences, such as "Fill a pan with water, put it on the stove, heat the water. When it's boiling, add the noodles." into robot language. Notice that you didn't say, "Turn on the stove." The robot has to be smart enough to fill in that missing step.
Saxena and his team will describe their methods at the Robotics: Science and Systems conference at the University of California, Berkeley, July 12-16.
Saxena's robot, equipped with a 3-D camera, scans its environment and identifies the objects in it, using computer vision software previously developed in Saxena's lab. The robot has been trained to associate objects with their capabilities: A pan can be poured into or poured from; stoves can have other objects set on them, and can heat things. So the robot can identify the pan, locate the water faucet and stove and incorporate that information into its procedure. If you tell it to "heat water" it can use the stove or the microwave, depending on which is available. And it can carry out the same actions tomorrow if you've moved the pan, or even moved the robot to a different kitchen.
Saxena's research group uses techniques computer scientists call "machine learning" to train the robot's computer brain to associate entire commands with flexibly defined actions. The computer is fed animated video simulations of the action — created by humans in a process similar to playing a video game — accompanied by recorded voice commands from several different speakers.
The computer stores the combination of many similar commands as a flexible pattern that can match many variations, so when it hears "Take the pot to the stove," "Carry the pot to the stove," "Put the pot on the stove," "Go to the stove and heat the pot" and so on, it calculates the probability of a match with what it has heard before, and if the probability is high enough, it declares a match. A similarly fuzzy version of the video simulation supplies a plan for the action: Wherever the sink and the stove are, the path can be matched to the recorded action of carrying the pot of water from one to the other.
Of course the robot still doesn't get it right all the time. To test, the researchers gave instructions for preparing ramen noodles and for making affogato — an Italian dessert combining coffee and ice cream: "Take some coffee in a cup. Add ice cream of your choice. Finally, add raspberry syrup to the mixture."
The robot performed correctly up to 64 percent of the time even when the commands were varied or the environment was changed, and it was able to fill in missing steps. That was three to four times better than previous methods, the researchers reported, but "There is still room for improvement."
You can teach a simulated robot to perform a kitchen task at the "Tell me Dave" website, and your input there will become part of a crowdsourced library of instructions for the Cornell robots. Aditya Jami, visiting researcher at Cornell, is helping Tell Me Dave to scale the library to millions of examples. "With crowdsourcing at such a scale, robots will learn at a much faster rate," Saxena said.
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Sinitta was brought up by her mother, actress and singer Miquel Brown.
In her website, Sinitta's page on spirituality outlined her transformation from exposure to drugs as a child to her Christian belief.
At 14, Sinitta was expelled from school but she carved a career in dancing and then singing.
Sinitta's first single So Macho was a huge hit.
Initially, she was looked after professionally by Simon Cowell. They ended up having a long-term relationship. Sinitta has also reportedly dated Brad Pitt and David Essex.
In the late 1980s and early 1990s Sinitta had a number of hits after coming under the wing of Pete Waterman.
However, in the mid-nineties, Sinitta turned more to theatre and TV work as well as developing new musical talent with Simon Cowell.
In 2007, Sinitta became a judge on ITV's Grease Is The Word produced by Simon Cowell.
In 2002 Sinitta married businessman Andy Willner.
In 2008, Sinitta became a judge mentor on The X Factor, advising Simon Cowell.
In 2010 she became a contestant on Dancing on Ice partnering Andrei Lipanov. However, Sinitta and Andre were the first couple to be voted off Dancing on Ice 2010.
In 2011, Sinitta became one of the celebrities on I'm A Celebrity Get Me Out Of Here.
In 2016 she was one of contestants on Celebrity Masterchef.


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1. Why do you think people get personalized license plates?
2. Would you rather be alone or with people?
3. Where does your family usually buy groceries?
4. Do you like to ride on ferris wheels?
5. Do you have trouble sleeping if there is a lot of noise?
1. To boost their ego, or because they think it looks nice.
2. Alone most of the time, but it really does depend on when you're talking about.
3. There is only one chain in town, we go to the closest one.
4. I have never ridden on one, and I have no interest.


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text:
The Free Library > Health > Health, general > Emerging Infectious Diseases > September 1, 2016
The Free Library > Date > 2016 > September > 1 > Emerging Infectious Diseases
Trends in pneumonia mortality rates and hospitalizations by organism, United States, 2002-20111.
<a href="https://www.thefreelibrary.com/Trends+in+pneumonia+mortality+rates+and+hospitalizations+by+organism%2c...-a0464045973
MLA style: "Trends in pneumonia mortality rates and hospitalizations by organism, United States, 2002-20111.." The Free Library. 2016 U.S. National Center for Infectious Diseases 17 Jan. 2021 https://www.thefreelibrary.com/Trends+in+pneumonia+mortality+rates+and+hospitalizations+by+organism%2c...-a0464045973
Chicago style: The Free Library. S.v. Trends in pneumonia mortality rates and hospitalizations by organism, United States, 2002-20111.." Retrieved Jan 17 2021 from https://www.thefreelibrary.com/Trends+in+pneumonia+mortality+rates+and+hospitalizations+by+organism%2c...-a0464045973
APA style: Trends in pneumonia mortality rates and hospitalizations by organism, United States, 2002-20111.. (n.d.) >The Free Library. (2014). Retrieved Jan 17 2021 from https://www.thefreelibrary.com/Trends+in+pneumonia+mortality+rates+and+hospitalizations+by+organism%2c...-a0464045973
Pneumonia is the leading cause of infection-related deaths in the United States, with potential for severe complications such as respiratory failure and sepsis (1). A recent nationwide study noted that prior studies may have overestimated a temporal reduction in mortality rate (2). As pneumonia epidemiology has changed, interest in following epidemiologic trends continues, particularly for the various etiologic organisms. Emergence of influenza A(H1N1)pdm09 virus has highlighted the role of influenza viruses as etiologic agents of pneumonia (3).
The epidemiology of pneumonia is constantly changing because of advances in preventive measures, diagnostic testing, and novel therapies. Although the epidemiology, by organism, of pneumonia in hospital patients was recently clarified (4), our study also considered the effects of influenza virus and included regional information. Our objective was to provide an updated population-based analysis of hospitalized pneumonia patients to determine the major etiologic agents and associated hospitalization rates, case-fatality rates, and patient demographic differences (age, sex, and region).
In a retrospective cohort study, we examined hospitalizations of pneumonia patients using discharge data from the publicly available Nationwide Inpatient Sample (NIS) database (5). The University of Louisville Institutional Review Board did not require a review because the project did not meet the common rule definition of human subjects' research. We completed Healthcare Cost and Utilization Project Data Use Agreement Training.
The study included patients [greater than or equal to] 18 years of age discharged with a principal diagnosis of pneumonia according to standards of the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM). We also included those with a principal diagnosis of sepsis or respiratory failure and a secondary diagnosis of pneumonia. Only ICD-9-CM codes for which the numbers of an organism were substantial were analyzed: pneumococci, Klebsiella spp., Pseudomonas spp., Haemophilus influenzae, Staphylococcus aureus, and influenza virus. We followed US Census Bureau definitions in classifying cases by US region (6).
The NIS database contains an [approximately equal to] 20% stratified sample of US community hospitals. From that sample, the Agency for Healthcare Research and Quality calculated national estimates by weighting each discharge on the basis of hospital location, teaching status, bed number, and ownership control, relative to all US hospitals, which permitted us to estimate the number of US hospitalizations. For each patient discharged, we recorded age, sex, region, principal and secondary diagnoses (up to 15), and whether patient had died from any cause. Population estimates were obtained from the US Census Bureau (7).
Primary outcomes were temporal trends in hospitalization and case-fatality rates for pneumonia based on infecting organism as determined by ICD-9 coding. We then calculated the rate for hospitalizations caused by each organism using the number of hospitalizations from the NIS database as the numerator and US Census Bureau population estimates as the denominator. Discharge codes do not specify where the pneumonia was acquired, and although hospitalization rate was used, this does not necessarily indicate community-acquired cases. We calculated the all-cause case-fatality rate for each organism similarly, using the number of deaths.
We extracted data from the NIS using SAS version 9.3 (SAS Institute, Inc., Cary, NC, USA) and calculated adjusted odds ratios for age ranges, sex, and region using multivariate logistic regression. Study period differences were determined with the online z test calculator of the Healthcare Cost and Utilization Project (p<0.05 was significant) (8). Other descriptive statistics were completed with Excel 2013 (Microsoft Corp., Redmond, WA, USA).
S. aureus was the most commonly identified organism that caused pneumonia, at 19.2 cases/100,000 population (Table 1). Pneumococcus and influenza virus were more frequent causes in the Midwest, Klebsiella and Pseudomonas spp. were more frequent causes in the South, and H. influenzae and S. aureus were more frequent agents in the West.
Hospitalization rates, based on etiologic agent, changed during the study period (Figure 1). Overall, the rate for pneumonia hospitalizations caused by pneumococcus decreased 23% (p<0.001) and for H. influenzae, 42% (p<0.001). For Klebsiella, the rate increased 35% (p<0.001); for Pseudomonas, 23% (p<0.001); for S. aureus, 23% (p<0.001); and for influenza virus, 132% (p<0.001).
Case-fatality rate was highest for pneumonia caused by S. aureus: 15.6 deaths/100 cases (Table 2). Pneumococcal pneumonia, Klebsiella, Pseudomonas, and S. aureus were more frequent in the Northeast, whereas pneumonia caused by H. influenzae and influenza virus was more frequent in the West.
Overall case-fatality rates, based on causal agent (Figure 2), decreased for pneumococcus by 18% (p = 0.01), for Pseudomonas spp. by 8% (p = 0.29), for H. influenzae by 3% (p = 0.85), and for S. aureus by 32% (p<0.001). Rates increased for Klebsiella spp. by 13% (p = 0.26); and for influenza virus by 67% (p<0.001). Hospitalization and case-fatality rates by age are shown in online Technical Appendix Tables 1, 2 (http://wwwnc.cdc.gov/EID/ article/22/9/15-0680-Techapp1.pdf).
We found that, from 2002 to 2011, the hospitalization rates for pneumonia caused by Klebsiella spp., Pseudomonas spp., S. aureus, and influenza virus increased, and those caused by pneumococcus and H. influenzae decreased. The case-fatality rate of influenza virus-caused pneumonia increased, while rates of pneumococcus-caused and S. aureus-caused pneumonia decreased. Hospitalization and case-fatality rates were higher for men (especially elderly) than for women.
Our results coincide with those of previous studies, which found that S. aureus, pneumococcus, and Pseudomonas spp. comprised a large percentage of all pneumonia cases (9,10). Our results also coincide with those of a similar study which used the NIS database during 1993-2011: a decrease in pneumonia caused by Streptococcus spp., H. influenzae, and Pseudomonas spp. and an increase in pneumonia caused by Staphylococcus spp. (4). However, our study also examined trends in pneumonia caused by Klebsiella spp. and influenza virus and regional variations. Another study that used similar methods found that adjusted odds ratio of an inpatient death from pneumonia decreased from 2002 to 2005 (11). Previous studies also found that male patients [greater than or equal to] 75 years old carried the greatest disease prevalence and considerably greater hospitalization rates (185/10,000 population in 2006) (12,13).
US regional data are limited, and comparable results, based on clinical data, were not found. However, 2 sets of data from the Centers for Disease Control and Prevention (Atlanta, GA, USA) showed the highest incidence of pneumonia in the Midwest and the lowest in the West (12,14). Drivers of geographic differences could include variations in coding practices between regions and vaccination recommendation adherence (15).
Known changes in the epidemiology of pneumonia over the study period include the emergence of influenza A(H1N1)pdm09 virus, which caused 60.8 million cases of influenza and 12,469 deaths in 2009-2010 alone (3). We found that patients with influenza virus infections had the highest hospitalization rate during 2009. Many previous studies excluded influenza because it likely represents a separate clinical entity and the hospitalization rate can be difficult to interpret using calendar years (because early or late influenza seasons can skew yearly trends) (2,11).
Our study was limited by its reliance on ICD-9-CM coding to identify cases, potentially allowing misclassification. ICD-9-CM coding is based on discharge data, so determining whether the patient was diagnosed with pneumonia at admission or during hospitalization is not possible. Thus, we could not differentiate between community- or hospital-acquired cases. In addition, we did not examine unspecified cases of pneumonia that did not include an etiologic organism, the most commonly used pneumonia code. Decreases in hospitalization rates and case-fatality rates could be explained by improved outpatient management or increased rates of vaccination. Last, case-fatality rates were calculated by using deaths during the inpatient stay and not beyond, because coding did not permit 30-day outcomes.
In conclusion, knowing which etiologic agents are increasing or have the highest case-fatality rate is critical because treatment options and prognosis vary by organism, and resources must be allocated accordingly. Knowing patient demographic characteristics for each organism is also essential, to clarify the populations at greatest risk.
June 2016: Respiratory Diseases
* Debate Regarding Oseltamivir Use for Seasonal and Pandemic Influenza
* Human Infection with Influenza A(H7N9) Virus during 3 Major Epidemic Waves, China, 2013-2015
* Integration of Genomic and Other Epidemiologic Data to Investigate and Control a Cross-Institutional Outbreak of Streptococcus pyogenes
* Extended Human-to-Human Transmission during a Monkeypox Outbreak in the Democratic Republic of the Congo
* Experimental Infection and Response to Rechallenge of Alpacas with Middle East Respiratory Syndrome Coronavirus
* Use of Population Genetics to Assess the Ecology, Evolution, and Population Structure of Coccidioides
* Infection, Replication, and Transmission of Middle East Respiratory Syndrome Coronavirus in Alpacas
* Rapid Detection of Polymyxin Resistance in Enterobacteriaceae
* Human Adenovirus Associated with Severe Respiratory Infection, Oregon, 2013- 2014
* Heterogeneous and Dynamic Prevalence of Asymptomatic Influenza Virus Infections
* Improved Global Capacity for Influenza Surveillance
* Prevalence of Nontuberculous Mycobacterial Pulmonary Disease, Germany, 2009- 2014
* Antibody Response and Disease Severity in Healthcare Worker MERS Survivors
* Epidemiology of Pulmonary Nontuberculous Mycobacterial Disease, Japan
* Elevated Pertussis Reporting in Response to 2011-2012 Outbreak, New York City, New York, USA
* Hemophagocytic Lymphohistiocytosis and Progressive Disseminated Histoplasmosis
* Next-Generation Sequencing of Mycobacterium tuberculosis
* MERS-CoV Infection of Alpaca in a Region Where MERS-CoV is Endemic
http://wwwnc.cdc.gov/eid/ articles/issue/22/06/ table-of-contents
Dr. Wuerth is currently a third-year internal medicine resident at the Medical University of South Carolina in Charleston, South Carolina. He plans to pursue a fellowship in gastroenterology with a research interest in inflammatory bowel diseases.
(1.) Heron M. Deaths: leading causes for 2010. Natl Vital Stat Rep. 2013; 62:1-96 .
(2.) Lindenauer PK, Lagu T, Shieh MS, Pekow PS, Rothberg MB. Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003-2009. JAMA. 2012; 307:1405-13. http://dx.doi.org/10.1001/jama.2012.384
(3.) Shrestha SS, Swerdlow DL, Borse RH, Prabhu VS, Finelli L, Atkins CY, et al. Estimating the burden of 2009 pandemic influenza A (H1N1) in the United States (April 2009-April 2010). Clin Infect Dis. 2011; 52(Suppl 1):S75-82. http://dx.doi.org/10.1093/cid/ciq012
(4.) Smith SB, Ruhnke GW, Weiss CH, Waterer GW, Wunderink RG. Trends in pathogens among patients hospitalized for pneumonia from 1993 to 2010. JAMA Intern Med. 2014; 174:1837-9 and. http://dx.doi.org/10.1001/jamainternmed.2014.4344
(5.) Agency for Healthcare Research and Quality. Overview of the National (Nationwide) Inpatient Sample (NIS) [cited 2012 Nov 24]. https://www.hcup-us.ahrq.gov/nisoverview.jsp
(6.) Henry J. Kaiser Family Foundation. Health coverage and care in the South in 2014 and beyond [cited 2014 Jun 19]. http://kff.org/disparities-policy/issue-brief/health-coverage-andcare-in-the- south-in-2014-and-beyond/
(7.) US Census Bureau. Population estimates, historical data [cited 2012 Dec 20]. https://www.census.gov/popest/data/historical
(8.) Z-test calculator [cited 2014 Jun 22]. http://hcupnet.ahrq.gov/ ZTestCalc.jsp
(9.) Micek ST, Kollef KE, Reichley RM, Roubinian N, Kollef MH. Health care-associated pneumonia and community-acquired pneumonia: a single-center experience. Antimicrob Agents Chemother. 2007; 51:3568-73. http://dx.doi.org/10.1128/ AAC.00851-07
(10.) DeFrances CJ, Lucas CA, Buie VC, Golosinskiy A. 2006 National Hospital Discharge Survey. Hyattsville (MD): National Center for Health Statistics; 2008. p. 1-20.
(11.) Ruhnke GW, Coca Perraillon M, Cutler DM. Mortality reduction among pneumonia patients still substantial despite the impact of coding changes. Ann J Med. 2013; 126:266-9.
(12.) Jokinen C, Heiskanen L, Juvonen H, Kallinen S, Kleemola M, Koskela M, et al. Microbial etiology of community-acquired pneumonia in the adult population of 4 municipalities in eastern Finland. Clin Infect Dis. 2001; 32:1141-54. http://dx.doi.org/10.1086/319746
(13.) Gutierrez F, Masia M, Mirete C, Soldan B, Rodriguez JC, Padilla S, et al. The influence of age and gender on the population-based incidence of community-acquired pneumonia caused by different microbial pathogens. J Infect. 2006; 53:166-74. http://dx.doi.org/10.1016/j.jinf.2005.11.006
(14.) Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices. N Engl J Med. 2010; 363:45-53. http://dx.doi.org/10.1056/NEJMsa0910881
(15.) Aronsky D, Haug PJ, Lagor C, Dean NC. Accuracy of administrative data for identifying patients with pneumonia. Am J Med Qual. 2005; 20:319-28. http://dx.doi. org/10.1177/1062860605280358
Brandon A. Wuerth, John P. Bonnewell, Timothy L. Wiemken, Forest W. Arnold
Author affiliation: University of Louisville, Louisville, Kentucky, USA
DOI: http://dx.doi.org/ 10.3201/eid2209.150680
[1] This manuscript was presented in part at the Infectious Diseases Society of America (IDWeek), October 2-5, 2013, San Francisco, California, USA.
Address for correspondence: Forest W. Arnold, University of Louisville, Division of Infectious Diseases, 501 E Broadway, Ste 140B, Louisville, KY 40202, USA; email: f.arnold@louisville.edu
Table 1. Hospitalization rates (hospitalization-100,000 population)
for 6 causative agents, by sex and region, United States, 2002-2011
Rate (adjusted odds ratio)
Category      Pneumococcus,   Klebsiella spp.,   Pseudomonas spp.,
n = 379,400      n = 109,515         n = 330,302
Overall           12.6              3.6                11.0
M             13.0 (1)          4.1 (1)            12.3 (1)
F            12.3 (0.68)       3.2 (0.57)         9.7 (0.57)
Northeast     11.9 (1)          3.1 (1)             8.8 (1)
Midwest      15.1 (1.33)       3.1 (1.05)         11.5 (1.37)
South        12.8 (1.16)       4.6 (1.65)         13.0 (1.62)
West         10.7 (1.32)       3.0 (1.48)         9.1 (1.55)
Category      Haemophilus      Staphylococcus      Influenza virus,
influenzae,    aureus, n = 575,573     n = 270,237
n = 80,281
Overall           2.7               19.2                 9.0
M             2.7 (1)           20.5 (1)             8.0 (1)
F            2.6 (0.72)        17.9 (0.64)         10.0 (0.90)
Northeast     2.1 (1)           15.9 (1)             8.8 (1)
Midwest      3.3 (1.61)        17.7 (1.17)         12.8 (1.51)
South        2.8 (1.46)        22.5 (1.57)          9.0 (1.10)
West         2.3 (1.64)        17.9 (1.71)          5.5 (0.90)
Table 2. All-cause case-fatality rate (deaths-100 cases for 6
causative agents, by sex and region, United States, 2002-2011
n = 25,112        n = 15,687         n = 39,975
Overall            6.6              14.3               12.1
M              6.8 (1)          15.4 (1)           12.6 (1)
F            6.5 (0.91)       12.9 (0.81)         11.5 (0.87)
Northeast      8.1 (1)          20.5 (1)           15.3 (1)
Midwest      5.6 (0.67)       12.0 (0.53)         9.8 (0.62)
South        6.4 (0.82)       12.4 (0.56)         11.7 (0.75)
West         7.2 (0.91)       16.2 (0.76)         13.2 (0.85)
Category      Haemophilus    Staphylococcus,     Influenza virus,
influenzae,   aureus, n = 89,540      n = 9,417
n = 3,935
Overall           4.9              15.6                3.5
M             5.1 (1)          16.6 (1)            3.8 (1)
F           4.7 (0.86)       14.4 (0.83)          3.3 (0.84)
Northeast     5.8 (1)          18.9 (1)            3.9 (1)
Midwest     3.8 (0.64)       13.1 (0.67)          3.3 (0.83)
South       4.8 (0.86)       14.9 (0.78)          3.3 (0.92)
West        5.9 (1.06)       16.9 (0.89)          3.8 (1.07)
Please note: Some tables or figures were omitted from this article.
COPYRIGHT 2016 U.S. National Center for Infectious Diseases
Wuerth, Brandon A.; Bonnewell, John P.; Wiemken, Timothy L.; Arnold, Forest W.
Persistent Bacillus cereus bacteremia in 3 persons who inject drugs, San Diego, California, USA.
Reduction of healthcare-associated infections by exceeding high compliance with hand hygiene practices.
Influenza viruses


text:
Luminoso is a Cambridge, MA-based text analytics and artificial intelligence company. It spun out of the MIT Media Lab and its crowd-sourced Open Mind Common Sense (OMCS) project.

The company has raised $20.6 million in financing, and its clients include Sony, Autodesk, Scotts Miracle-Gro, and GlaxoSmithKline.

History

Luminoso was co-founded in 2010 by Dennis Clark, Jason Alonso, Robyn Speer, and Catherine Havasi, a research scientist at MIT in artificial intelligence and computational linguistics. The company builds on the knowledge base of MIT's Open Mind Common Sense (OMCS) project, co-founded in 1999 by Havasi, who continues to serve as its director. The OCMS knowledge base has since been combined with knowledge from other crowdsourced resources to become ConceptNet. ConceptNet consists of approximately 28 million statements in 304 languages, with full support for 10 languages and moderate support for 77 languages. ConceptNet is a resource for making an AI that understands the meanings of the words people use.

During the World Cup in June 2014, the company provided a widely reported real-time sentiment analysis of the U.S. vs. Germany match, analyzing 900,000 posts on Twitter, Facebook and Google+.

Applications

The company uses artificial intelligence, natural language processing, and machine learning to derive insights from unstructured data such as contact center interactions, chatbot and live chat transcripts, product reviews, open-ended survey responses, and email. Luminoso's software identifies and quantifies patterns and relationships in text-based data, including domain-specific or creative language. Rather than human-powered keyword searches of data, the software automates taxonomy creation around concepts, allowing related words and phrases to be dynamically generated and tracked.

Commercial applications include analyzing, prioritizing, and routing contact center interactions; identifying consumer complaints before they begin to trend; and tracking sentiment during product launches. The software natively analyzes text in fourteen languages, as well as emoji.

Products

Luminoso's technology can be accessed via two products: Luminoso Daylight and Luminoso Compass. Luminoso Daylight enables a deep-dive analysis into batch or real-time data, whereas Luminoso Compass automates the categorization of real-time data. Both products offer a user interface as well as an API. Luminoso's products can be implemented through either a cloud-based or an on-premise solution.

Research
Luminoso continues to actively conduct research in natural language processing and word embeddings and regularly participates in evaluations such as SemEval. At SemEval 2017, Luminoso participated in Task 2, measuring the semantic similarity of word pairs within and across five languages. Its solution outperformed all competing systems in every language pair tested, with the exception of Persian.

Recognition
Luminoso has been listed as a "Cool Vendor in AI for Marketing" by Gartner, and has also been named a "Boston Artificial Intelligence Startup to Watch" by BostInno.

In May 2017, Luminoso was recognized as having the Best Application for AI in the Enterprise by AI Business, and was also shortlisted as the Best AI Breakthrough and Best Innovation in NLP.

Competitors

Major competitors include Clarabridge and Lexalytics.

Investors

The company raised $1.5 million from angel investors in 2012. Its first institutional funding round of $6.5 was completed in July 2014, led by Acadia Woods with participation from Japan's Digital Garage. The company followed that with a $10M series B funding round in December 2018, led by DVI Equity Partners, with participation from Liberty Global Ventures, DF Enterprises, Raptor Holdco, Acadia Woods Partners, and Accord Ventures, among others.

References

External links

American companies established in 2010
Natural language processing
2010 establishments in Massachusetts

redpajama_set_name: RedPajamaWikipedia


But I did not want you to go forever.
My thoughts and feelings were irrational.
I am trying to do all that you advised.
I immerse my self within the community.
I try to channel my problems through creativity.
I try to ignore psychosis with distraction techniques.
I try not to harm myself.
I have tried with old friends.
I am making new friends.
I try to be assertive.
None of the above works permenantly.
I am on the worlds worst roller coaster.
My mood swings up and down, then super up, then way, way down.
I try to ignore you.
Super happy followed by super unhappy.
I begin to cry and I ask myself why.
Exhausted and worn out from these eratic mood swings.
But who should I tell.
The professionals only tell me what I have mostly heard before.
Friends and family mostly try but do not understsnd.
How could you understand something so complex with out experiencing something first hand, or studying.
I never brought a ticket.
I did not volunteer for this ride.
Seem to only make me stronger.
And help me bury these secrets that I keep.
Allow me to enter the land of lullaby's.
Yet I am stuck in a rabbit hole.
I think I am having a panic attack.
Please cover me with your arms of feathers.
I only feel safe when I sleep.
If you are diagnosed with depression, is it because you have an inability to control your emotions or is it because you don't understand them? Is it weak to confess? Surrender to the heavy pain in which depression brings.
I sit here and I wonder… why am I unable to just get on with it like everyone else? My brain goes into replay mode where I shuffle and playback bits of my life, the memories contaminated with false ones, hallucinations and psychosis leave me spinning.
All the while I want to scream out but no one will hear.
I reach out but no one is here.
It passes… or does it?
Just pushed deep down, burried.


text:
Q: Delete all folders and subdirectories that doesn't have a file of certain extension I'm using this solution to delete all empty folders and subdirectories in a certain path:
static void Main(string[] args)
{
    processDirectory(@"c:\temp");
}

private static void processDirectory(string startLocation)
{
    foreach (var directory in Directory.GetDirectories(startLocation))
    {
        processDirectory(directory);
        if (Directory.GetFiles(directory).Length == 0 &&
            Directory.GetDirectories(directory).Length == 0)
        {
            Directory.Delete(directory, false);
        }
    }
}

It works perfectly. But I want to delete all empty folders and also folders which are not empty but also doesn't contain files with the .dvr extension.
For example, my folder has the files:

a.log
b.log
c.dvr
d.dat

So this folder can't be deleted, for it contains a file with the dvr extension.
How can I filter it? (I'm using GTK# but I believe C# code will work, since this solution is a C# code)

A: Unfortunately error handling is very exception based in IO operations. And Directory.Delete throws an IOException if the directory is not empty. So you'll have to delete the files manually:
private static bool processDirectory(string startLocation)
{
    bool result = true;
    foreach (var directory in Directory.GetDirectories(startLocation))
    {
        bool directoryResult = processDirectory(directory);
        result &= directoryResult;

        if (Directory.GetFiles(directory, "*.dvr").Any())
        {
             result = false;
             continue;
        }

        foreach(var file in Directory.GetFiles(directory))
        {
            try
            {
               File.Delete(file);
            }
            catch(IOException)
            {
               // error handling
               result = directoryResult = false;
            }
        }

        if (!directoryResult) continue;
        try
        {
            Directory.Delete(directory, false);
        }
        catch(IOException)
        {
            // error handling
            result = false;
        }
    }

    return result;
}

A: I would use Directory.EnumerateFiles to see if a directory contains the file you're looking for.  Changing your code to be:
static void Main(string[] args)
{
    processDirectory(@"c:\temp");
}

private static void processDirectory(string startLocation)
{
    foreach (var directory in Directory.GetDirectories(startLocation))
    {
        processDirectory(directory);
        if (Directory.GetDirectories(directory).Length == 0  ||
            Directory.EnumerateFiles(directory, "*.dvr").Length == 0
            )
        {
            Directory.Delete(directory, false);
        }
    }
}

A: Avitus answer is correct, but since you need it to work in .NET 2.0, you cannot use the EnumerateFiles method, however GetFiles gets the job done nicely. But requires a little more code.
Example:
static void Main(string[] args)
{
    processDirectory(@"c:\temp");
}

private static void processDirectory(string startLocation)
{
    foreach (var directory in Directory.GetDirectories(startLocation))
    {
        processDirectory(directory);

        if (Directory.GetDirectories(directory).Length == 0))
        {
            bool delete = false;

            var files = Directory.GetFiles(directory);
            // This will delete the directory if it contains a file with the Extension
            // of .dvr, regardless if there are other files in there. Might be something you want to change.
            for (int i = 0; i < files.Length && !delete; i++)
            {
                delete = files[i].Extension.Equals(".dvr", StringComparison.OrdinalIgnoreCase);
            }

            if (delete)
            {
                // Recursive must be set to true in order to
                // delete files and sub-directories in the folder.
                // This folder will not have any sub-directories
                // so it's only used to delete the files.
                Directory.Delete(directory, /*recursive*/ true);
            }
        }
    }
}

You could, in the spirit of less code move the Directory.Delete method call within the for loop, if you wanted, then break the loop.


text:
Earlier this month, we reported on the sinister efforts of Michigan Attorney General Dana Nessel to establish a "thought criminal" database in her state to keep tabs on conservatives, Christians, and others whose views run contrary to her Leftist agenda. Well, Nessel is now being sued by the American Freedom Law Center (AFLC) for unfairly and unjustly targeting her political opponents, which is a clear abuse of public office.
The lawsuit also names Agustin V. Arbulu, the Director of the Michigan Department of Civil Rights, who recently announced that his department would be cooperating with Nessel to create "a process to document hate and bias incidents that don't rise to the level of a crime or civil infraction" – meaning any action or statement with which Nessel and Arbulu disagree.
Nessel and Arbulu are planning to rely upon the corrupt Southern Poverty Law Center's (SPLC) "hate" groups map, which we also recently exposed as nothing more than a political and ideological hit list for targeting white people, conservatives, and Christians.
Filed in the U.S. District Court for the Western District of Michigan, the AFLC federal civil rights lawsuit contends that Nessel and Arbulu's policy directives violate the fundamental rights of all Americans that are protected by both the First and Fourteenth Amendments to the United States Constitution.
"The newly-announced policy directive of Michigan's Attorney General and Department of Civil Rights Director should be dubbed the 'Crimespeak Policy' because it takes a page right out of George Orwell's 1984, where the goal of 'Thinkpol,' the Thought Police, was to eradicate 'crimespeak,'" says AFLC co-founder and Senior Counsel Robert Muise.
"In 1984, to even consider any thought not in line with the principles of those in power was 'crimespeak,' subjecting the perpetrator to punishment," he adds. "The same is true of the challenged policy at issue here. Not surprisingly, this is what you get when you elect left-wing progressives to public office."
For related news, be sure to check out Censorship.news and FirstAmendment.news.
As usual, Michigan's attempt to punish "crimespeak" can be traced back to international mega-criminal George Soros
It's important to remember that the SPLC hate map that Nessel and Arbulu are planning to use in order to compile their "thought criminal" database is financed by none other than George Soros, who's perhaps the most well-known international criminal working to undermine our American Republic.
What this means is that both Nessel and Arbulu, as well as anyone who supports their endeavors, are criminal traitors and co-conspirators in committing treason, which is why the success of AFLC's lawsuit is so critically important.
"It is one thing for a radically-partisan private organization like SPLC to express its falsehoods about political opponents," the lawsuit states.
"However, when the Michigan Attorney General and the Director of the Michigan Department of Civil Rights join and officially endorse this partisan attack by lending government resources and thus becoming the government enforcement agency for SPLC's radical agenda, the protections of the United States Constitution are triggered."
No matter what the corrupt SPLC and its supporters claim, the organization's "hate map" has nothing to do with hate, and everything to do with targeting its political opponents – many of which are simply patriotic, America-supporting groups that don't want to see our country relent to the deception of globalism.
"SPLC is a fraudulent purveyor of hate claiming to be a non-profit public interest law firm," contends AFLC co-founder and Senior Counsel David Yerushalmi.
"What non-profit organization hoards hundreds of millions of dollars in assets and hides millions of dollars in secret, off-shore bank accounts? Non-profits are tax-exempt … there is no legitimate reason for SPLC to ship money to hidden overseas accounts. The Michigan Attorney General has partnered with a nefarious organization, and AFLC's lawsuit will expose this for the world to see."
AmericanFreedomLawCenter.org
DetroitNews.com
1984, AFLC, Agustin Arbulu, American Freedom Law Center, big government, Christians, conservatives, crimespeak, Dana Nessel, evil, First Amendment, Fourteenth Amendment, hate crimes, hate map, Leftists, Liberty, Libtards, Michigan, Michigan Department of Civil Rights, Orwellian, Southern Poverty Law Center, SPLC, Thinkpol, thought crimes, thought criminal database, Tyranny
America Unhinged: Conservatives and Christians should start making movies with great stories, says Marcus Pittman – Brighteon.TV
Simple nasal wash reduces risk of covid hospitalization by more than 8x
By Ethan Huff
Pfizer CEO, conman Albert Bourla tests positive for covid-19 again, after being sick throughout August


LYNX Anarchy Body Spray is a rich, spicy fragrance that combines fresh and fruity notes. Sweet sandalwood and white amber leave you feeling refreshed.
Its really nice, am not a girly girl and lynx does the job!


Difficult behavior takes many forms. It includes gossiping, going over your bosses head, foot dragging, ignoring orders, refusing to talk, being rude, yelling, ignoring, harassing, and much more.
At the core, most conflict is about needs that have not been satisfied-not just physical needs, but also psychological and procedural needs. Difficult behavior is often a result of psychological needs for control, recognition, affection, and respect.
In and of themselves there is nothing wrong with having these needs. Problems arise in the satisfaction of these needs when difficult behavior has been rewarded in the past. For example, if people always listen when we interrupt we will continue to use this as an effective strategy. We should try not to reward difficult behavior. Beyond reinforcement, if we don't have the communication skills to let people know how we feel, or we loose it when things get emotionally charged, then difficult behavior can be expected.
It would be easy if there was some magical cure that could be applied to all difficult behavior. The fact that there is no panacea, does not mean that we are helpless and that there is nothing to be done. Even so, one shouldn't expect instant results. Changing behavior takes tact and time.
The following ideas for dealing with difficult behavior are gleaned from Robert Bacal's book-The Complete Idiots Guide to Dealing with Difficult Employees (CWL Publishing, 2000). Lets start with ideas that don't work: ignoring the problem behavior despite its impact on performance, responding in kind, blaming rather than problem solving, labeling the person as difficult and trying to psychoanalyze.
If these are bad ideas what are things we can do that help?
When we loose our self-control and restraint the situation does not improve. In fact it is more likely to get worse. Decisions made in the heat of the moment are seldom the best, and lack the benefits of our creativity. Our challenge is to slow down, and resist a knee jerk reaction. Staying steady, stable and grounded gives us the strong foundation we need to take on the most difficult behavior.
When we indulge ourselves by taking it personally (forgetting that offense is 10% given and 90% taken) we start playing negative internal tapes in our head. We tell ourselves that the person is bad, unreliable, beyond reason. The danger is that these labels become self-fulfilling, and do not give any benefit of the doubt. Rather than putting our energy into problem solving we feel smug blaming the other. We forget that it takes two to make things worse.
An important question to consider as soon as possible is whether the behavior is really causing performance problems. If it is not, and left alone things will not get worse, then leaving things often makes sense. As we reality check it is important to consider the impact of the behavior on others and not just ourselves.
This is the key to dealing with difficult behavior. As tempting as it is to focus on the person this should be avoided. By separating the person from the behavior it enables one-to paraphrase Fisher, Ury and Patton in their best seller "Getting to Yes"-to be hard on the problem and soft on the person.
Listening is widely acknowledged as a core communication skill that affects the ways we prevent and resolve conflict. When dealing with people whose behavior is getting to us we should make a special effort to hear the other person out. Even when you disagree! This enables you to validate the psychological needs of the other, and to let them know that you can imagine how they are feeling.
In addition to validation and empathy, asking open and closed questions, rephrasing and summarizing, and using "I Statements" are all key listening activities.
This is an important preventative technique. A common format is the yearly performance review. It should be used on an ongoing basis and whenever expectations are not clear. The goal is to make sure that responsibility is placed where it belongs. For example, with naysayers it is crucial that responsibility for involvement be returned.
Where there has been discussion about performance expectations, a physical record that documents the fact of the meeting, the content and any agreements should be generated.
Difficult behavior can be intentional, aggressive, sustained and extreme. When responsible talk does not work it make sense to seek help. Start with Human Resource managers. Be prepared to give a detailed briefing about the situation. In some situations the support of senior management may be necessary.
Beyond internal line support, consider using mediation if you think you and the other person can find a solution yourselves. Arbitration may make sense if a solution to a particular problem is needed quickly and you and the other person are struggling to communicate.
As a general rule, it makes sense to use power only as a last resort. When you use power you win and the other loses. More often than not, resentment and alienation accompany this action. Unacceptable behavior that does not change should be addressed as a disciplinary matter. Ideally an organization will describe behavioral expectations in a code, and specify how infractions will be dealt with. It is possible to retain the right to terminate at will while using a progressive disciplinary procedure. Following a fair procedure can go a long way to defend a charge of discrimination.
In addition to using the above techniques to prevent and resolve difficult behavior, we should be mindful of things we can do to limit the chances of being perceived as difficult ourselves. Matching our actions with our words, and our words, with our tone and body language is important. Incongruencies lead to suspicion and mistrust. Consistent decision making and achievable promises and commitments will also go a long way.
The reality is that we can all be difficult from time to time. Dealing with difficult behavior is not easy and so we often procrastinate. We do so at our own peril. Being proactive and engaging the person in a conversation about their behavior is the first step toward conflict prevention.