adingbatponder 
👾Is there a #simulation of a #lora #reticulum #radio #mesh used with different numbers of #rnode #radio #nodes #rnodes for given #spreadingfactor ( #sf ) & given payload size? Am interested to know what happens to the routing, & likelyhood of #deliveryconfirmation for small LoRa packets (max 2 blocks), for longest transmission times i.e. spreading factor 12 #SF12 . Would like to know if my impressions, that SF 12 is very tricky under #RNS LoRa as compared with #SF10, have a clear explanation.
In a #lora #rns #reticulum #mesh using #lora with long transmission blocks due to a large #spreadingfactor of 12 #sf12 , does one have to change some default settings to make it work better than the defaults? Like time between trying alternative routes to a destination or time to wait for or send a delivery confirmation (to wait for #ismband channel to be free of those long transmissions so that a send operation is possible) ?
John Vaccaro (johniac)SciTech Chronicles. . . . . . . . .Mar 13th, 2025
#QNodeOS #programmable #independent #hardware #Paleolithic #vitamin-D # Ust'-Ishim #SF12 #Habsburg #intermarriage #outcomes #inbreeding-coefficient #mikvah #menorah #Rome #well-dated #SuperCrudes #macroalgae #seaweed #standardized
Jan R. BoehnkeNew paper out, great collaboration w former #UniOfYork colleague Tom Patton:
https://link.springer.com/article/10.1007/s11136-024-03729-6
We harmonized data from #SF12 and #EQ5D using data from the 2000-2001-2003 Medical Expenditure Panel Surveys. We then analysed data from 6 trials (N=1,777 ppl living with opioid use disorder).
Key among multiple findings:
the inclusion of people with lived experience and the measurement of withdrawal symptoms and anhedonia could improve cost-effectiveness models
Analyzing quality of life among people with opioid use disorder from the National Institute on Drug Abuse Data Share initiative: implications for decision making - Quality of Life Research
Purpose We aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature. Methods Data obtained from six trials representing 1,777 individuals with OUD. We implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). We performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms. Results There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively. Conclusion We observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.
December issue of Quality of Life Research published:
https://link.springer.com/journal/11136/volumes-and-issues/32-12
#ISOQOL
Opens with a #ScopingReview of the use of visual tools and #EasyRead approaches for #HRQL instruments
https://link.springer.com/article/10.1007/s11136-023-03450-w
Multi-stage development of the Norwegian Fatigue Characteristics and Interference Measure
https://link.springer.com/article/10.1007/s11136-023-03477-z
#Stroke #RaschModel #Psychometrics
Association of body mass index from childhood (12yrs) to mid-adulthood (20-25-30yrs later) w #HRQL
https://link.springer.com/article/10.1007/s11136-023-03497-9
#SF12 #SF6D
