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TUNISIE Aucun développement significatif n'est probable. LIBYE Aucun développement significatif n'est probable. (...) YÉMEN Des ailés en petits nombres peuvent probablement être présents dans le Tihama et une reproduction à petite échelle pourrait probablement voir lieu. OMAN Aucun développement significatif n'est probable. ÉMIRATS ARABES UNIS Aucun développement significatif n'est probable.
Language:English
Score: 754673.3 - https://www.fao.org/ag/locusts/common/ecg/1394/fr/DL159f.pdf
Data Source: un
 Page 38 - Methodology for measurement of Quality of Service (QoS) Key Performance Performance Indicators (KPIs) for Digital Financial Services           Basic HTML Version Table of Contents View Full Version Page 38 - Methodology for measurement of Quality of Service (QoS) Key Performance Performance Indicators (KPIs) for Digital Financial Services P. 38 MTDR Rate/ Probability Money Transfer Duplication Rate MTLR Rate/ Probability Money Transfer Loss Rate MTAST Time Money Transfer Account Stabilization Time MTASSR Rate/ Probability Money Transfer Account Stabilization Success Rate MTFTRR Rate/ Probability Money Transfer Failed Transaction Resolution Rate Start MTFNR Rate/ Probability Money Transfer False Negative Rate MTFPR Rate/ Probability Money Transfer False Positive Rate Start MTCT Time Money Transfer completion time Start X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) X (3) MTCR Rate/ Probability Money Transfer completion rate enter start USSD Start Prompt to select TA X (2) enter 1 to select “Transfer Money” X (2) Prompt to select X (2) enter 1 to select “to Mobile Money user” X (2) Prompt to select category of recipient X (2) enter 1 to select “to X (2) Prompt to select X (2) Enter B number X (2) Prompt to select recipient ID again X (2) Enter B number again and continue TABLE E-3: KPI/Trigger point reference Start DFS app DFS_P2P_ command AA_100 Prompt to select DFS_P2P_ type TA type AE_104 Select: Transfer DFS_P2P_ AA_108 Prompt to select DFS_P2P_ recipient type recipient type AE_112 Select: To mobile DFS_P2P_ user AA_116 0 DFS_P2P_ AE_120 0 DFS_P2P_ subscriber” AA_124 Prompt to select DFS_P2P_ recipient ID recipient ID AE_128 Enter B number DFS_P2P_ and continue and continue AA_132 P 36 • Methodology for measurement of Quality of Service (QoS) Key Performance Indicators (KPIs) for Digital Financial Services     33     34     35     36     37     38     39     40     41     42     43          
Language:English
Score: 753754 - https://www.itu.int/en/publica...t/files/basic-html/page38.html
Data Source: un
Kohler Population Studies Center University of Pennsylvania Overall Structure of the Survey on Aging in SSA 2 major components: • Household Interview • Individual Interview • Both will be linked based on HH ID and individual ID Overall Sampling Design  Nationally representative stratified random sample of households that include at least 1 household member age 60 years and older  Household sample surveys:  Key source for data on social phenomena  Are among the most flexible methods of data collections  In theory almost any population-based subject can be investigated through household surveys  Only probability samples following well-established sampling procedures are suitable for making inferences from the sample population to the larger population that it is designed to represent  Snow-ball or convenience samples are not suitable for this survey Overall Sampling Design cont’d  Probability sampling in the context of household surveys:  Refers to the means by which elements of the target population are selected for inclusion in the survey  In order to be cost-effective, most household surveys are not implemented as simple random samples  Sampling procedure usually includes stratification to ensure that the selected sample actually is spread over geographic sub-areas and population subgroups  This sampling design usually uses clusters of households in order to keep costs to manageable level General Principals of the Survey on Aging in SSA Target population: individuals age 60+ and older Household sample: Nationally representative clustered random sample of households that include household members age 60+ yrs. Selection of household members: All regular household members age 60+ in the sampled household and their spouses if these are age-eligible and co-resident General Principals of the Survey on Aging in SSA Use of an existing sampling frame: clustered random sample of households can only be obtained from existing sampling frame which is a complete list of statistical units covering the target population  Census frame, complete list of villages/communities or sampling list from other nationally representative surveys  Sampling frame: is a complete list of sampling units that entirely covers the target population  Conventional sampling frame: list of enumeration areas (EA) from a recently completed census  EA: geographic area which usually groups a number of households together for convenient counting purposes General Principals of the Survey on Aging in SSA Stratification: process in which the sample is designed into sub- groups or strata that are as homogeneous as possible;  Within each stratum the sample is designed and selected independently; Two-stage cluster sampling procedure:  Cluster: a group of adjacent households which serves as the primary sampling unit (PSU) General Principals of the Survey on Aging in SSA Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded; Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;  Only way to obtain unbiased estimation and to be able to evaluate the sampling errors  Excluded are purposive sampling, quota sampling, and other uncontrolled non-probability methods because they cannot provide evaluation of precision and confidence of survey findings General Principals of the Survey on Aging in SSA Full coverage of the target population: should be nationally representative and cover 100% of the target population; that is no subpopulations age 60+ are systematically excluded; Probability sampling: sample should be obtained as probabilistic sample based on existing sampling frame using established sampling procedures;  Only way to obtain unbiased estimation and to be able to evaluate the sampling errors  Excluded are purposive sampling, quota sampling, and other uncontrolled non-probability methods because they cannot provide evaluation of precision and confidence of survey findings Sample Size  Sample size must take into account competing needs so that costs and precisions are optimally balanced  Sample size must also address the needs of users who desire for sub-populations of sub-areas domains  Sample size is determined by the trade-offs between survey precision, data quality, organizational capacities and survey budget;  In the case of Malawi this is about 2,000 respondents (men and women) Conducting a household listing and pre-selection of households  Data quality is enhances if eligible households are preselected for participating in the study  In many SSA countries recent and reliable household listings in EAs that carefully enumerates older individuals is not available  Hence, we suggest to conduct a specific household listing in selected EAs that provides a well-grounded basis for selecting respondents  Interviewers than interview only pre-selected eligible households  STEPS:  Household listing operation conducted before the survey  Pre-selection of households from this list  Selected Households are interviewed Overall Sampling Design cont’d  Two stage sample design is well-established approach for implementing household surveys  1st stage: select a sample of EAs with probability proportional to size (PPS);  Within each stratum a sample of predetermined number of EAs is selected independently with probability proportional to size, where size is measured in terms of older individuals age 60+;  If size of pop age 60+ is not available, and variations in age structures are relatively modest, then total pop size can be used  All households in the EAs are listed  2nd stage: after complete listing in EAs, a fixed number of households with individuals age 60+ is selected by equal probability sampling in the EAs Interviewing all individuals age 60+ in the HH Advantages:  Maximize the number of respondents for a given sample of HH  Cost effective to achieve the sample size  Analytical advantages so that interactions among spouses, within and between household variation of outcomes can be investigated Disadvantages:  Lower statistical power given the within household correlation of observations  Logistical challenges in the fieldwork Sample Take per Cluster  How many eligible individuals to interview per EA  DHS recommends 25-30 individuals  Because there will be more than 1 age-eligible individual per household, less than 24-30 households per PA need to be selected  If a sampled HH has 1.5 age-eligible individuals on average, than a sample take per cluster of 25-30 individuals results in the selection of 17-20 households per cluster  With 2,000 individuals sample size: 67-80 clusters have to be selected  If sample is stratified, these considerations should be conducted stratum- specific Sample Take per Cluster  This fixed sample take per cluster is:  Easy for survey management and implementation  But requires sampling weights that vary within clusters
Language:English
Score: 753537 - https://www.un.org/development...07_ikohler-sampling-design.pdf
Data Source: un
The numerical impact of the covariates is small probably because we considered what respectively, as compared with the actual probability, 2.51 and 1.37 percent. (...) However, in column (2), we find that if the firm is currently exporting, the firm’s probability of be- coming a domestic firm is only 5 percent, whereas its probability of remaining an exporter is 91 percent. (...) In Table 9, column (1) indicates the actual probability that domestic firms are in each status in the next year, and column (2) the predicted probability of the average domestic firm in each industry.
Language:English
Score: 751295.9 - https://www.wto.org/english/re...e/gtdw_e/wkshop09_e/todo_e.pdf
Data Source: un
To encode a binary decision (bin) after binarization, context modelling determines a probability of the next bit by using a finite state model with 64 states, and the bin encoder based on BAC encodes the bit by using the estimated probability; in order to decode the bin at the decoder, the same context modeling process is used to determine the probability, and then the bin decoder based on BAC recovers the bin by using the probability. (...) This is however simpler as the probability associated to the first bin is applied to the following bins. (No probability update needed within the group of bins).
Language:English
Score: 750964.55 - https://www.itu.int/wftp3/av-a...010_07_B_Geneva/JCTVC-B036.doc
Data Source: un
The example of Figure 1 shows the probability distribution of MBtype corresponding to three sequences together with the ideal probability distribution of UVLC codewords. (...) By default, a frame is classified as Normal. If the probability of the ‘16x16’ MBtype exceeds the probability of the ‘Skip’ MBtype, the frame is classified as High Motion. When the probability of ‘Skip’ dominates we compute the average run of ‘Skip’ symbols.
Language:English
Score: 750964.55 - https://www.itu.int/wftp3/av-a...deo-site/0104_Aus/VCEG-M14.doc
Data Source: un
PowerPoint Presentation Decision Support for Cost-Effective Diagnosis and Treatment by Inverting Bayesian Probability Gerald E. Loeb, M.D., University of Southern California Jeremy A. (...) Frontiers in Neurorobotics, 2012 Intake Patient Collect basic info: Demographic Complaints Vital signs Create differential diagnosis (Dn) and probabilities P(Dn): Diseases & Treated Conditions Null = well patient For each Pconsideration < P(Dn) < Pconclusion: Identify Actions that affect P(Dn) Compute Cost of each Action Present Cost/Benefit analysis to physician Select Action(s) to be taken Perform Actions and obtain Results algorithm Confusion Matrices Extracted from EHR Database Physician Differential Diagnosis The process of identifying all possible causes of a patient’s condition and efficiently eliminating all but the most probable by acquiring diagnostic data Usually depends on the personal experiences and memory of the individual practitioner We propose a decision support system to inform the physician of • The currently most probable causes • The costs of various diagnostic and therapeutic interventions • The benefit of each as a probability of achieving a definitive diagnosis or outcome. (...) Create a Markov Chain from the EHR to • Estimate Probability P of obtaining Results R that lead to P>0.99 certainty of diagnoses A, B, C… • Compute Total Costs C of each possible Action: • Expense + • Morbidity + • Delay Medical Cost/Benefit 20-20™ Physician Interface INTAKE VISIT Pt. #12345 Name: John Doe _ Sex: M DOB: 01/01/1970 Zipcode: 11111 Presenting complaint: headache for 3 days Differential Diagnosis (code) % probability to consider:  Tension headache (D001) 90%  Viral encephalitis (D002) 7%  Meningioma (D003) 3% Diagnostic Actions (code) benefit/cost to consider:  Spinal tap (T001) 5.4  MRI (T002) 3.1 20-20™ Physician Interface FOLLOW-UP VISIT Pt. #12345 Name: John Doe Sex: M DOB: 01/01/1970 Zipcode: 11111 Presenting complaint: headache for 3 days Test results: MRI (T002) 4 cm diameter, well-circumscribed anterior fossa tumor Differential Diagnosis (code) % probability to consider:  Meningioma (D003) 99%  Tension headache (D001) <1%  Viral encephalitis (D002) <1% Therapeutic Actions (code) benefit/cost to consider:  Craniotomy (R003) 5.4  Acyclovir (R002) 0.01  Aspirin (R001) 0.5 Strengths • Unifies overlapping concepts: • disease vs. wellness • diagnostic test vs. therapeutic trial • Mines existing EHRs to benefit from all prior experience with all patients, procedures, diagnoses and treatments • Automatically incorporates new diagnoses, tests and treatments as they arise in practice • Tolerant of noise and errors in the EHR • Reveals inefficient behavior • Avoids liability for controversial or out-of-date expert knowledge Challenges • Needs an EHR that is reasonably complete: • Timing and results of all tests and procedures • Knowledge of final diagnoses and outcomes Conundrum • If we had really good EHRs, then we could use them to improve the efficiency of health care. • If we could improve the efficiency of health care with EHRs, then we might get really good EHRs.
Language:English
Score: 750597.3 - https://www.itu.int/en/ITU-T/W...s/Gerald_Loeb_Presentation.pdf
Data Source: un
S/5 Annex B Evaluating probability of non-compliance To evaluate the probability of non-compliance, i.e. the probability that a non-compliant product of a certain type can be found on the market, an enforcement authority should determine a list of factors that increase the likelihood of the event “non-compliant products present in the market” for each family of products, as shown in the picture below: "Non- compliant product present on the market" "There has been a change in the standard" Vulnerability 2 "Compliance checks are very expensive" Vulnerability N A vulnerability of a risk event “non-compliant product present on the market” can be called a probability factor PF. (...) Ranking the products according to levels of risk, using both the index and the pre-defined combinations 3. Calculating the probability index and choosing combinations of probability factors having specific value 2. Building a product-non-compliance likelihood matrix: evaluating each product in the list against each probability factor 1. Analyzing the vulnerabilities of the risk event "non-compliant product present at the market" and building a comprehensive list of probability factors The process similar to that described in Annex A, similar approaches for ranking the products according to their probability of non-compliance levels can be applied.
Language:English
Score: 749714.5 - https://unece.org/DAM/trade/wp6/Recommendations/Rec_S_en.pdf
Data Source: un
MTCT = T(AE_104, AE_300) – MTHI + TTHI MTHI stands for the measured and TTHI for the (as- 5.2 Money Transfer Completion Rate (MTCR) sumed) typical time for all human interaction in this use 5.2.1 Functional description case. Probability that a money transfer can be completed The meaning of this expression is “take the measured successfully. overall duration of the transaction, eliminate times caused by actual human interaction (which can vary 5.2.2 Formal definition from instance to instance) and replace them by a gener- alized (typical) value). (...) TABLE 5-1: KPI abbreviations and full names ABBREVIATION TYPE REFERENCE MTCR Rate/Probability Money Transfer completion rate MTCT Time Money Transfer completion time MTFPR Rate/Probability Money Transfer False Positive Rate MTFNR Rate/Probability Money Transfer False Negative Rate MTFTRR Rate/Probability Money Transfer Failed Transaction Resolution Rate MTASSR Rate/Probability Money Transfer Account Stabilization Success Rate MTAST Time Money Transfer Account Stabilization Time MTLR Rate/Probability Money Transfer Loss Rate MTDR Rate/Probability Money Transfer Duplication Rate 18 • Methodology for measurement of Quality of Service (QoS) Key Performance Indicators (KPIs) for Digital Financial Services     15     16     17     18     19     20     21     22     23     24     25          
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Score: 748112.8 - https://www.itu.int/en/publica...t/files/basic-html/page20.html
Data Source: un
Les forums régionaux sur l’évolution probable du climat sont encore une autre réussite de l’Afrique. (...) Le forum sur l’évolution probable du climat en Afrique du Nord (PRESANORD) a été lancé sous la direction de l’ACMAD. (...) Il est important de noter que le Forum régional sur l’évolution probable du climat en Afrique du Nord (PRESANORD), composé des cinq pays d’Afrique du Nord, a rejoint le Forum sur l’évolution probable du climat en Europe du Sud-Est (SEECOF) pour former le Forum sur l’évolution probable du climat dans 5 Forums régionaux sur l’évolution probable du climat en Afrique Les meilleures pratiques la région méditerranéenne (MedCOF), coordonné par le Service météorologique national d’Espagne (AEMET), avec une contribution de l’ACMAD.
Language:English
Score: 748105.66 - https://www.uneca.org/sites/de...ctices2_converted%20French.pdf
Data Source: un