979 resultados para cost estimating tools
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
Resumo:
The past actual tax receipts and future estimates of the General Fund used by the Revenue Estimating Conference to project incoming revenue to be used in future state budgeting.
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Soluble MHC-peptide complexes, commonly known as tetramers, allow the detection and isolation of antigen-specific T cells. Although other types of soluble MHC-peptide complexes have been introduced, the most commonly used MHC class I staining reagents are those originally described by Altman and Davis. As these reagents have become an essential tool for T cell analysis, it is important to have a large repertoire of such reagents to cover a broad range of applications in cancer research and clinical trials. Our tetramer collection currently comprises 228 human and 60 mouse tetramers and new reagents are continuously being added. For the MHC II tetramers, the list currently contains 21 human (HLA-DR, DQ and DP) and 5 mouse (I-A(b)) tetramers. Quantitative enumeration of antigen-specific T cells by tetramer staining, especially at low frequencies, critically depends on the quality of the tetramers and on the staining procedures. For conclusive longitudinal monitoring, standardized reagents and analysis protocols need to be used. This is especially true for the monitoring of antigen-specific CD4+ T cells, as there are large variations in the quality of MHC II tetramers and staining conditions. This commentary provides an overview of our tetramer collection and indications on how tetramers should be used to obtain optimal results.
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Supplement to HR-388 - "Total Cost of Transportation Analysis of Road and Highway Issues"
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Helping behavior is any intentional behavior that benefits another living being or group (Hogg & Vaughan, 2010). People tend to underestimate the probability that others will comply with their direct requests for help (Flynn & Lake, 2008). This implies that when they need help, they will assess the probability of getting it (De Paulo, 1982, cited in Flynn & Lake, 2008) and then they will tend to estimate one that is actually lower than the real chance, so they may not even consider worth asking for it. Existing explanations for this phenomenon attribute it to a mistaken cost computation by the help seeker, who will emphasize the instrumental cost of “saying yes”, ignoring that the potential helper also needs to take into account the social cost of saying “no”. And the truth is that, especially in face-to-face interactions, the discomfort caused by refusing to help can be very high. In short, help seekers tend to fail to realize that it might be more costly to refuse to comply with a help request rather than accepting. A similar effect has been observed when estimating trustworthiness of people. Fetchenhauer and Dunning (2010) showed that people also tend to underestimate it. This bias is reduced when, instead of asymmetric feedback (getting feedback only when deciding to trust the other person), symmetric feedback (always given) was provided. This cause could as well be applicable to help seeking as people only receive feedback when they actually make their request but not otherwise. Fazio, Shook, and Eiser (2004) studied something that could be reinforcing these outcomes: Learning asymmetries. By means of a computer game called BeanFest, they showed that people learn better about negatively valenced objects (beans in this case) than about positively valenced ones. This learning asymmetry esteemed from “information gain being contingent on approach behavior” (p. 293), which could be identified with what Fetchenhauer and Dunning mention as ‘asymmetric feedback’, and hence also with help requests. Fazio et al. also found a generalization asymmetry in favor of negative attitudes versus positive ones. They attributed it to a negativity bias that “weights resemblance to a known negative more heavily than resemblance to a positive” (p. 300). Applied to help seeking scenarios, this would mean that when facing an unknown situation, people would tend to generalize and infer that is more likely that they get a negative rather than a positive outcome from it, so, along with what it was said before, people will be more inclined to think that they will get a “no” when requesting help. Denrell and Le Mens (2011) present a different perspective when trying to explain judgment biases in general. They deviate from the classical inappropriate information processing (depicted among other by Fiske & Taylor, 2007, and Tversky & Kahneman, 1974) and explain this in terms of ‘adaptive sampling’. Adaptive sampling is a sampling mechanism in which the selection of sample items is conditioned by the values of the variable of interest previously observed (Thompson, 2011). Sampling adaptively allows individuals to safeguard themselves from experiences they went through once and turned out to lay negative outcomes. However, it also prevents them from giving a second chance to those experiences to get an updated outcome that could maybe turn into a positive one, a more positive one, or just one that regresses to the mean, whatever direction that implies. That, as Denrell and Le Mens (2011) explained, makes sense: If you go to a restaurant, and you did not like the food, you do not choose that restaurant again. This is what we think could be happening when asking for help: When we get a “no”, we stop asking. And here, we want to provide a complementary explanation for the underestimation of the probability that others comply with our direct help requests based on adaptive sampling. First, we will develop and explain a model that represents the theory. Later on, we will test it empirically by means of experiments, and will elaborate on the analysis of its results.
