Let n be a large composite number. Without factoring n, the computation of a 2 t (mod n)given a, t with gcd(a# n) = 1 and t!n can be done in t squarings modulo n.For t n (e.g., n?2 1024 and t!2 100 ), no lower complexity than t squarings is known to fulfill this task. Rivest et al suggested to use such constructions as good candidates for realising timed-release crypto problems. We argue the necessity for a zero-knowledge proof of the correctness of such constructions and propose the first practically efficient protocol for a realisation. Our protocol proves, in log 2 t standard crypto operations, the correctness of (a e ) 2 t (mod n) with respect to a e where e is an RSA encryption exponent. With such a proof, a Timed-release Encryption of a message M can be given as a 2 t M (mod n) with the assertion that the correct decryption of the RSA ciphertext M e (mod n) can be obtained by performing t squarings modulo n starting from a. Timed-release RSA signatures can be constructed analogously. Keywords Timed-release cryptography, Time-lock puzzles, Non-parallelisability, Efficient zero-knowledge protocols. 1
The note focuses on the leading role of \n local level institutions (LLIs) in Burkina Faso, in rural \n decentralization, and poverty eradication, to enhance \n equitable prosperity. It is based on the study undertaken by \n the National Decentralization Commission in Burkina Faso, \n which draws on case studies in Sanmatenga, Sissili, Houet, \n and Yatenga, and, presents sociological evidence that \n certain high-performing LLIs contribute to equitable \n economic development. Economic findings support this, \n showing that both lower inequality levels, and lower poverty \n levels, are linked to a high degree of internal village \n organization. Contextually, LLIs surround, connect, and \n manage communities, incorporating many different kinds of \n indigenous organizations, and functions. Three categories of \n institutions active at the local level are identified: value \n institutions, which focus on activating, and maintaining the \n stability of local governance, and values of the society; \n production institutions, focused on accessing resources from \n the national government, so as to increase productivity; \n and, service-asset management institutions, which integrate \n productivity, and growth values, focused on managing, and \n expanding local assets for sustainable development. External \n aid will need to map these LLIs to guide pro-poor \n investment, and financing of community driven development, \n and encourage local governments to formalize participation.
This paper examines the recent changes in collective bargaining in France and the characteristics and conditions of the emergence of a post-Fordist bargaining system. For the last two years, die system of collective bargaining in France has been through an accelerated phase of change. The Aubry laws on 35 hours have revitalized the collective bargaining on working time and work organization by expanding the decentralization movement observed since the early 1980s: the number of enterprise agreements increased from 6,400 in 1987 to 13,300 in 1998 and 31,000 in 1999. The revival of collective bargaining through the government's political agenda has also rekindled the controversy over the respective roles of die agreement and the law in the production of standards governing labour relations. Due to the social partners' reticence about contractual commitment as well as the state's influence, industry-wide collective bargaining has for a long time been confined to a secondary role in relation to die legal provisions on which it could only improve or complement. From the early 1980s onwards, this hierarchy of standards based on me principle of favour was gradually weakened as enterprise agreements that allowed for working time beyond standards were legalized. This radically changed the function of bargaining. As a law- improvement tool, it became an instrument of change and decentralized adaptation to work rules, especially in the firm. Since less than 10 per cent of French employees were unionized, this change gave rise to numerous questions about me unequal distribution of capacities of action between employers and employees' representatives. The greater autonomy of enterprise regulation vis-a-vis die legal standards and industry-wide agreements cardes the risk of a return to employer self-regulation. This risk is all the greater as collective bargaining has become more complex and tends to be more oriented towards job regulation than distributive management of the capital-labour relationship. The social compromise of the Golden Age was based on a scheme of statutory bargaining that entalled a trade-off between wages and contribution to production, the organization of which was left to management by die union actor. With a focus on a compromise between employment and competitiveness â through reduction of working time in the case of France â post-Fordist collective bargaining deals simultaneously with all the parameters of me employment relation: working time, qualifications, quantitative job evolution, reorganization of production, wage policy, and investment strategies. Such an extension of the field of bargaining inevitably leads to a rethinking of the actors' doctrines and strategies, inasmuch as they had strongly incorporated the division between economie and social matters inherent in the Fordist compromise. For employers, this implies sharing, if not only a part of their managerial power, then at least information on the firm's economie strategies. For trade unions, these new contractual dynamics imply greater expertise and a renewal of modes of legitimation, which were previously based mainly on conventional wage demands. The new paradigm of collective bargaining, which is more autonomous, more complex and more demanding for industrial relations actors, undoubtedly calls for a greater consideration of local or regional dynamics. Previously, the decentraHzation of industrial relations was not accompanied by a regional framework sensitive to the negotiated regulation of labour relations. Yet, the growing importance of local forms of coordination in the performance of social Systems of production, as well as in managerial practices, tends to erase the firm's physical borders. The forms of outsourcing of activities that often come with a triangulation of labour relations (dissociation between the worker, the user of the workforce and the person responsible for the employment relationship) make the regulation typical of the Fordist era quite ineffectual. Neither the firm nor the industry is up to the emerging challenges of employment regulation in these new production organizations. Although the legal resources necessary for regionalizing collective bargaining do exist, the hegemony of industry federations and the structural weakness of local inter-industry authorities, both workers' and employers', still constitute a formidable obstacle.
Many people think changes are always good. However, sometimes it is not so. Before making a change, one must ask what kind of change is it and how it will affect status quo. This paper tries to show how an institutional change that has been wrongly conceived could lead to a worse situation. The Venezuelan experience studied here is a ?show-window? of contradictory decentralization process, in which sub-national governments lack the faculties to generate their own income, and moreover, in which oil income continues to be almost the only source of governmental financing.
Digital content distribution systems will enable business models in the near future that cannot be predicted today. In this paper, we identify a new security problem that can be crucial to this enablement. The problem arises from the conflicting privacy and integrity goals of middlemen in digital distribution chains. Our solution is a novel system design that incorporates obfuscated digital contracts, semi-trusted contract certifiers, and zero-knowledge proofs of arithmetic relations. Our implementation and timing experiments demonstrate that our solution is practical and efficient.
OBJECTIVE: The results of an evaluative longitudinal study, which identified the effects of health care decentralization on health financing in Mexico, Nicaragua and Peru are presented in this article. METHODS: The methodology had two main phases. In the first, secondary sources of data and documents were analyzed with the following variables: type of decentralization implemented, source of financing, funds for financing, providers, final use of resources, mechanisms for resource allocation. In the second phase, primary data were collected by a survey of key personnel in the health sector. RESULTS: Results of the comparative analysis are presented, showing the changes implemented in the three countries, as well as the strengths and weaknesses of each country in matters of financing and decentralization. CONCLUSIONS: The main financing changes implemented and quantitative trends with respect to the five financing indicators are presented as a methodological tool to implement corrections and adjustments in health financing.
The international character of todayâs most pressing environmental problems has become a key challenge for environmental policy making. As regulation by a supranational authority is not a realistic option at present, policymakers have to rely on decentralized approaches to the management of international environmental resources. This study combines two core dimensions of international environmental policy: the traditional search for cost-effective policy instruments and the creation of incentives for voluntary cooperation among sovereign nations. The analysis offers some clear-cut policy recommendations for the design of environmental treaties and for the further development of existing international institutions to protect the global environment.
In the early 1970s physicians engaged in fierce debates over the most appropriate method of evaluating the efficacy of coronary artery bypass grafting (CABG). With millions of patients and billions of dollars at stake, CABG sparked fierce controversy. Skeptics demanded that randomized controlled trials (RCTs) be performed, while enthusiasts argued that they already had visual proof of CABG's efficacy. When RCTs appeared, they did not settle the controversy. Participants simply reasserted their preconceptions, defending a trial's strengths or exploiting its flaws. The debate centered on standards of knowledge for the evaluation of therapeutic efficacy. Specifically, cardiologists and cardiac surgeons struggled to assess the relevance of different measures of therapeutic success: physiological or clinical, visual or statistical. Many factors contributed to participants' decisions, including disciplinary affiliation, traditions of research, personal experience with angiography, and assessments of the history of cardiac therapeutics. Physicians had to decide whether angiography provided a meaningful representation of the disease and its treatment or whether demonstrations of therapeutic success could come only from long-term statistical evaluation of mortality data.
Open access
Health and Medical Research Impacts
Health Systems, Economic Evaluations, Quality of Life
The author studies the \n allocation-between a central government and a local \n authority--of responsibility for planning, financing, and \n operations for the delivery of health services, in the \n context of an incomplete contracts model. In this model, \n inputs are required of both the central government and local \n authorities but they are unable to write down, and commit \n to, a complete and binding contract describing the actions \n both should take. The model is meant to capture the tradeoff \n between central and local authority in decisions about both \n financing and the provision of services. Each party provides \n a specific input--for example, the central government \n establishes a drug procurement system while the local \n authority designs and implements an incentive scheme to get \n doctors to carry out their responsibilities appropriately. \n The responsibility for delivery of services is identified \n with the ownership of essential infrastructure, such as the \n clinic or hospital. The author finds that to maximize the \n joint surplus of the two public bodies: Ownership of the \n facility should be given to the party that most values the \n well-being of local residents. (This way, if ex post \n bargaining breaks down, each still enjoys some benefits from \n the other's actions.) Financing authority and \n responsibility for delivering services should be negatively \n correlated. Generally it is optimal to allocate tax \n authority to the party that values the residents' \n well-being less--in other words, separate spending \n responsibility (ownership) from financing authority. A \n heavier financing burden (access to a small and inefficient \n tax base) has the same incentive effect as asset ownership: \n It increases the return to effort. If transferring ownership \n of the physical asset is costly (because the party that \n builds the asset has an inherent advantage in operating \n it-that is, there is some human capital embodiment), it may \n be optimal for the party with the higher construction costs \n to have planning authority. Somewhat paradoxically, the \n greater the costs of transferring assets from one party to \n the other, the more likely that ownership of the facilities \n and their provision should be separated.
This paper reports the results of an experiment that examines how incentive-based compensation contracts compare to flat-wage compensation contracts in motivating individual learning and performance. I use a multiperiod cognitive task where the accounting system generates information (feedback) that has both a contracting role and a belief-revision role. The results suggest that incentives enhance performance and the rate of improvement in performance by increasing both: (1) the amount of time participants devoted to the task, and (2) participants' analysis and use of information. Further, I find evidence that incentives improve performance only after considerable feedback and experience, which may help explain why many prior one-shot decision-making experiments show no incentive effects. Collectively, the results suggest that incentives induce individuals to work longer and smarter, thereby increasing the likelihood that they will develop and use the innovative strategies frequently required to perform well in complex judgment tasks and learning situations.
Stan Chervin, Kelly D. Edmiston, Matthew N. Murray
Decentralized systems of government finance give rise to fiscal disparities due to interjurisdictional variations in tax bases and expenditure needs. Intergovernmental aid is used to address such disparities. This article explores changes in local tax capacity and intergovernmental aid resulting from urban shopping malls that extract retail sales and sales tax revenue away from surrounding areas, especially rural counties. A model is developed and estimated to determine the impact of urban malls on local government sales tax bases, controlling for sales tax rate differentials and other factors. The results reveal a 15.9% decline in the sales tax base for counties in close proximity to two newmalls. The analysis is extended to examine impacts of changing local tax capacity on state education aid. Based on the programconsidered here, less than 20% of the loss in own-source revenue is recovered through increased aid.
With an introduction to the overall underdevelopment of higher education in China compared with the American counterpart, this article briefly examines the main trends of over two decades of development of the governance and financing systems of China's higher education sector. This article analyzes the resource allocation from governments and revenue generation in institutions under the reform policies of administrative decentralization and financing diversification. The new "Great Leap Forward" in higher education in 1999 and beyond, i.e., the radical and, to a certain extent, desperate mass higher education policy and practice of expanding enrollments in order to spur domestic consumption, is critically analyzed. By examining the ongoing institutional merging and "co-building" and the most recent enrollment expansion, the writer points out the economic significance for higher education of overcoming diseconomies of scale and inefficiencies. However, the long-range outcomes of the seemingly exciting investment in and consumption of mass higher education are difficult to predict.
Charles F. Minto, Thomas W. Schnider, Timothy G. Short, Keith M. Gregg ¡ 6 authors
Click on the links below to access all the ArticlePlus for this article.Please note that ArticlePlus files may launch a viewer application outside of your web browser.DRUG interactions are the basis of anesthetic practice. For example, induction of anesthesia may consist of intravenous administration of a benzodiazepine before induction, a hypnotic to achieve loss of consciousness, and an opioid to blunt the response to noxious stimulation. Similarly, anesthesia often is maintained with a combination of a hypnotic (e.g. , propofol, isoflurane) and an analgesic (e.g. , fentanyl, nitrous oxide). Anesthetic drugs are often combined because they interact synergistically to create the anesthetized state.Pharmacodynamic drug interactions are typically described using mathematical models. The basic model is that of an isobole. Isoboles are iso-effect curves, curves that show dose combinations that result in equal effect. 1The combination of two doses (d1and d2) can be represented by a point on a graph, the axes of which are the dose axes of the individual drugs (fig. 1). The isobole connects isoeffective doses of the two drugs when administered alone, D1and D2. If the isobole is straight (fig. 1A), then the relation is additive. If the isobole bows toward the origin (fig. 1B), then smaller amounts of both drugs are needed to produce the drug effect when administered together, so the relation is supraadditive or synergistic. If the isobole bows away from the origin (fig. 1C), then greater amounts of both drugs are needed to produce the drug effect when administered together, so the relation is infraadditive. In table 1we propose a set of criteria that pharmacodynamic models of drug interactions should meet. In this article we propose an interaction model that meets these criteria, based on response-surface methodology. Response surfaces are a powerful statistical methodology for estimating and interpreting the response of a dependent variable to multiple inputs. 2Response-surface methodology is used for two principal purposes; to provide a description of the response pattern in the region of the observations studied and to assist in finding the region in which the optimal response occurs. Our model is a straightforward extension of the sigmoidal concentrationâresponse relation for individual drugs. We test the proposed model using data from a study of the interaction of midazolam, propofol, and alfentanil with loss of consciousness. 3This article only considers pharmacodynamic interactions, the type of interaction most relevant to the practice of anesthesia. Pharmacokinetic interactions are entirely different and will not be considered. Appendix 1 (which can be found on the Anesthesiology Web site at www.anesthesiology.com) reviews several commonly used pharmacodynamic models of drug interactions and shows areas in which existing models fail to meet the criteria in table 1.The effects of individual drugs are often modeled by relating drug effect (E) to drug concentration (C) using a sigmoid model:where E0is the baseline effect when no drug is present, Emaxis the peak drug effect, C50is the concentration associated with 50% drug effect, and Îł is a âsigmoidicity factorâ that determines the steepness of the relation.This relation is shown graphically in figure 2. The concentration term often is defined as the concentration at the site of drug effect, but the model can be generalized to any measure of exposure (e.g. , dose, plasma concentration, or area under the curve). For models of probability, such as the probability of moving in response to surgical incision, E0is 0 and Emaxis the maximal probability (usually assumed to be 1). Dividing the numerator and denominator of equation 1by C50Îł, we obtain an alternate form:In this model, concentration has been normalized to the concentration that results in 50% of maximal drug effect. This is a natural way to think about drug concentrationâas a fraction of some measure of potency. For example, anesthesiologists are accustomed to thinking about volatile anesthetics in terms of minimum alveolar concentration (MAC), rather than in absolute concentration terms. This is precisely the concept of normalizing drug concentration to potency.The basic concept of our proposed interaction model is simple. Consider two drugs, each of which has a sigmoidal concentrationâresponse relation. We will think of any given ratio (i.e. , B/(A + B), called θ herein) of the two drugs as behaving as a new drug. This new drug, which is actually a fixed ratio of the two drugs, has its own sigmoidal concentrationâresponse relation, as shown in figure 3. This is the basic premise of our interaction model. The mathematics are simply an extension of the model for a single drug to a model that considers each ratio of two drugs as a drug in its own right. We will express the concentrations of drugs A and B as [A] and [B]. As suggested by equation 2, we must first normalize each drug to its potency, C50, and express the results in units (U) of potency. where UAis the normalized concentration of drug A, and UBis the normalized concentration of drug B. We can define a family of âdrugs,â each being a unique ratio of UAand UB. Each drug will be defined in terms of θ, where θ is defined as By definition, θ ranges from 0 (drug A only) to 1 (drug B only). The âdrug concentrationâ is simply UA+ UB. We can extend equation 2to describe the concentrationâresponse relation for any ratio, θ, of the two drugs in combination:where θ is the ratio of the two drugs, the drug concentration is UA+ UB, Îł(θ) is the steepness of the concentrationâresponse relation at ratio θ, U50(θ) is the number of units (U) associated with 50% of maximum effect at ratio θ, and Emax(θ) is the maximum possible drug effect at ratio θ. Because Emax, C50, and Îł in equation 2have been replaced by functions of θ, each ratio has the potential to have its own Emax, C50, and Îł. This allows each ratio of drug A and drug B to behave as its own drug, with its own sigmoidal concentrationâresponse relation, which is the basic premise of the model.The term âU50(θ)â is the potency of the drug combination at ratio θ relative to the normalized potency of each drug by itself. This requires careful explanation. Let us assume that only drug A is present, in a concentration of C50,A. In this case, the drug effect is half of the maximal effect, UA= 1, UB= 0, θ= 0, and the drug concentration is UA+ UB= 1. Because we have 50% of the maximum drug effect, and 1 unit of drug, then the number of units associated with 50% drug effect when only drug A is present, U50(0), must be 1. Similarly, let us assume that only drug B is present and the concentration of drug B is C50,B. In this case, the drug effect is half of the maximal effect, UA= 0, UB= 1, θ= 1, and the drug concentration is UA+ UB= 1. Because we have 50% of the maximum drug effect, and 1 unit of drug, then the number of units associated with 50% drug effect when only drug B is present, U50(1), must again be 1. By definition, if only drug A or drug B is present, U50(θ) = 1.Now, let us assume that drug A and drug B both are present, each in exactly half of the concentration that would cause 50% of the drug effect when administered alone. In this case, UA= 0.5, UB= 0.5, θ= 0.5, and the drug concentration is UA+ UB= 1. If this causes 50% of maximum effect, then the drugs are simply additive at θ= 0.5, and U50(0.5) = 1. However, if this combination produces more than a half-maximal effect, then 1 unit of this combination, at θ= 0.5, is more potent than 1 unit of either drug alone (i.e. , synergistic). In this case, U50(0.5) < 1. Conversely, if this combination produces less than a half-maximal effect, then 1 unit of this combination, at θ= 0.5, is less potent than either drug alone (i.e. , infraadditive). In this case, U50(0.5) > 1. Thus, U50(θ) is the potency of the combination compared with the potency of either drug alone, which is 1 by definition.Thus, the units of U50(θ) are not concentration units, but rather the number of units, at ratio θ, associated with 50% of maximal drug effect. U50(θ) is 1 for θ= 0 and θ= 1. For all values of θ between 0 and 1 (i.e. , all possible ratios of the two drugs), U50(θ) assumes a value determined by the data. If this value is 1, then the interaction is additive at θ. If the value is less than 1, then the drug effect is synergistic at θ. If the value is greater than 1, then the interaction is antagonistic at θ.Figure 4shows the relation between a three-dimensional response surface and a conventional two-dimensional isobolographic analysis. The two-dimensional isobologram is a cut through the three-dimensional surface, generally taken at the 50% response level. In this particular example, synergy is evident in the three-dimensional model as a bowing of the surface toward the reader. This bowing causes the conventional isobologram to deviate toward the origin from the straight line of additivity. Much pharmacodynamic literature supports the sigmoid relation in equation 1, equation 2, and equation 5. There is only modest information specifying the functions Emax(θ), U50(θ), and Îł(θ). Our choice is to use functions that are capable of taking a variety of shapes, so that good approximations to the true relations can be determined empirically. To provide these flexible functions we chose fourth-order polynomials of the form where f(θ) is Emax(θ), U50(θ), or Îł(θ). The coefficients (β0, β1, β2, β3, β4) are model parameters that are either constrained by the model or estimated from the data. Fortunately, two of these terms, β0and β1, can be replaced by other terms already defined.We already defined the values Emax(θ), U50(θ), and Îł(θ) when only drug A is present, Emax,A, U50,A, and ÎłA, respectively. Note in equation 6that when θ= 0 (only drug A is present), f(0) =β0. Therefore, when f(θ) is Emax(θ), U50(θ), or Îł(θ), β0must be Emax,A, U50,A, and ÎłA, respectively.Similarly, we also defined the values Emax(θ), U50(θ), and Îł(θ) when only drug B is present, Emax,B, U50,B, and ÎłB, respectively. Referring again to equation 6, when θ= 1 (only drug B is present), f(1) =β0+β1+β2+β3+β4. We can rearrange this as β1= f(1) âβ0âβ2âβ3âβ4. Thus, when f(θ) is Emax(θ), U50(θ), or Îł(θ), β1must be Emax,Bâ Emax,Aâβ2,Emaxâβ3,Emaxâβ4,Emax, U50,Bâ U50,Aâβ2, U50âβ3,U50âβ4,U50, or ÎłBâÎłAâβ2,Îłâβ4,Îł, respectively.This permits us to develop models that incorporate the individual drug parameters for Emax(θ), U50(θ), and Îł(θ) as functions of θ. The equation for Emax(θ), using the substitutions previously mentioned for β0and β1, is U50,Aand U50,B, [equivalent to U50(θ) and U50(1)], are both 1 by definition. Thus, when f(θ) = U50(θ), the values of β0and β1in equation 6are 1 and âβ2âβ3âβ4, respectively. Therefore, the equation for potency as a function of θ can be simplified to Many isobolograms have a simple inward or outward curvature, which can be readily encompassed with a simple quadratic form of equation 8with just one coefficient:If β2,U50is 0, then the value of U50(θ) will be 1 for all values of θ. This means that the interaction will be additive. If β2,U50is a positive number, then U50(θ) will be less than 1 for all values of θ between 0 and 1. The effect is to magnify the term in equation 5, making it appear that there is more drug present. This will produce a greater than additive effect, i.e. , synergy. If β2,U50is a negative number, then U50(θ) will be greater than 1 for all values of θ between 0 and 1. This reduces the term in equation 5, making it appear that there is less drug present. This will produce a less than additive effect. This assumes that drugs A and B have the same maximal effect. It is possible for some approaches to synergy analysis to show apparent synergy if the maximal effects of drugs A and B are not identical, even if U50(θ) = 1 for all values of θ.The model for the steepness term, Îł(θ), can similarly be described from equation 6, with appropriate substitutions for β0and β1. The resulting equation is Equations 6â10describe straight lines (simple additivity) when the coefficients (i.e. , β2, β3, β4) are 0. They are the equations for parabolas if the respective β2coefficient is nonzero, and β3and β4are 0. More complex shapes are generated when β3and β4are nonzero.Figure 5shows Emax, U50, and Îł as functions of θ for the synergistic interaction seen in figure 4. Emaxand Îł are constant, and thus have no interaction. U50is necessarily 1 at θ= 0 and θ= 1, but is less than one between these extremes. This increases the potency of the drugs when administered in combination, resulting in the synergy seen in figure 4. The model can be readily expanded to show the interaction of more than two drugs. In the case of three drugs (A, B, and C) the proportion of each drug present can be expressed by θA, θB, and θC, where We can define the ratio of three drugs from just two of these ratios because θA+θB+θC=1. For our purposes here, we will use θBand θC. We again assume that for any fixed value of θBand θC, there is a sigmoidal relation between concentration and response. Therefore, if the three drugs could be administered to the effect site in an exactly fixed proportion, they would show a sigmoidal total concentrationâresponse relation, where the âconcentrationâ was the sum of the three normalized concentrations. This is precisely the notion that underlies the two-drug model. The equation for the model the model the parameters of the sigmoidal relation, Emax, and U50, are functions of θBand θC. The functions and are described in Appendix (which can be found on the Anesthesiology Web site at The point is that of a as in equations when three drugs are present, the parameters of the sigmoidal relation are surfaces for functions of θBand θC. The response-surface model was in the by the and as a for for also the model for of at 3This is on to the using and for the use of our response-surface model, we data previously by data are also the Anesthesiology Web site relations intravenous doses of midazolam, propofol, and alfentanil administered and in combination in for as to the to administration and or alfentanil to peak effect an intravenous the combination being midazolam, it was administered before the other drugs. The doses of midazolam, propofol, and alfentanil used and the proportion of for each dose are shown in table 2. This data set was because it three two-drug combinations that could be used to In the data are for the number of and the of the it the to a interaction model, the of the proposed response-surface model. We assumed in the model that all when no drug was 0 by definition. In we assumed that each drug was capable of if administered in a a of equation and can be constrained to 0 and 1 with no interaction the Thus, the probability of for any combination is where UB, and the doses of midazolam, propofol, and respectively. The units are of each dose to cause in 50% of the based on equation the data for the single and combination of and the data for the single and combination of and alfentanil the data for the single and combination of and alfentanil and the data set for the and combinations modeled parameters and estimated using by the for all the response of the either 0 to or 1 to and is the probability of response to for each dose be expressed in as the sum of the natural of the of response in the and in the The of the coefficients to the model was by the coefficients one at a by the model ratio and by of probability of for each dose and The response surfaces for the interactions and the surface was used to the synergistic combinations of the and the interactions based on the from the analysis of the data The intravenous doses to achieve probability of in this for each drug alone, for each combination, and for the To the application of the response-surface model with the parameters of , , and used to the for midazolam, propofol, and respectively. This was as the concentration at the of the respective then used to the of effect of in the these synergistic doses of midazolam, propofol, and administered alone and in The the and the from in of the to in of the The of effect was using for for by and shapes of the response surfaces generated by the equations are not readily We used three-dimensional to the response surface for a variety of interactions between two drugs, by the model parameters of equation 5. interactions and antagonistic interactions between two and interactions between and to the data for all in the in to the used by , the of the data for and for the analysis of the three drug interactions are shown in table 3. and for the analysis of the data set are shown in table 4. There no in the of the three drugs, there drug interactions the The for each drug to be the of the combination being modeled and This is an Because the data for single administration was in the interaction the entirely determined from each drug alone. the response surfaces for each of the drug interactions synergy. The synergy in the model was not Appendix on the Anesthesiology Web site at This when all three drugs are present, there is not synergy that from the interactions of all three drugs. The surface is shown in figure The maximum in values for the combinations are represented by the of the three of the as a = = = The maximum in for the combination is found at the point of the surface, which is at and This in figure and where the point as a on the 5shows the doses for the and combinations for maximum synergy associated with probability of a of based on the parameters shown in figure The between probability of to probability of no for each combination was based on the shown in figure the synergistic combination administration of one the dose, this results in a in the for to alone is the drug of choice when the point is a of A of the and of other such as and is to the drug combinations for other our response-surface model for an additive interaction a synergistic interaction and an interaction It also shows the interaction between a and a a and a and a and an way to describe our model for two drugs, A and B, is that the drug A and the drug B there are two sigmoid 1). Our proposed model connects these two sigmoid curves functions of θ interactions are then as coefficients of the polynomials that the to each each value of θ can have its own Emax, C50, and the model assumes that the sigmoidal is for all values of θ. This concept of a fixed ratio of two drugs own is not each drug ratio to be to a single response could be described by a single two-dimensional concentrationâresponse that when using combinations of combination should be as a new with individual rather than the of the individual use of functions to the parameters of drug A to drug B assumes that the response surface is and the basic model the parameters estimated in sigmoid the polynomials that U50(θ), Emax(θ), and Îł(θ) are more The use of response functions to complex response surfaces is in However, models with the variable present at than the are not often used because it to the proposed a flexible model. of the response surface, or application of the could result in of parameters that provide a but when more an for the are not unique to this model and can be by the one is in the terms can be from the model. The statistical of the terms should be the models using the ratio In to of model such as of and the pattern of the model should also be by the response surface, and by the individual model parameters as functions of θ (e.g. , equations to that the pharmacodynamic parameters not to For it must be that U50(θ) and Îł(θ) are positive in the of 0 1. In the case of three drugs, the surface should be as shown in figure a mathematical that if and only if the isobolograms are straight For this to be be with to θ. The case of Îł(θ) is more is with to θ, there is if Emax(θ) and U50(θ) are also not equal to ÎłB, there is no way to use our interaction model to test for defined by 1). we with of the surface, the description of an interaction as or antagonistic may be For example, a drug combination can be synergistic in and antagonistic in our the on drug interactions can be to simple such as or the The interaction has the potential to be and than about which to the relation, the should be to the response the surface one can the combination to produce the model is It no about the of interaction between the drugs. However, we assume that the concentrationâresponse relation for each of the drugs is described by a pharmacodynamic model. We have not to describe interactions between drugs that and that we are not of any that our could not be combined with the more of model for each drug not have to be the sigmoid 1). For example, the model could be a or response The model could also be a response as seen with some the model can be any so as it has parameters that the individual models. Thus, the only is that the interaction model reduces to the model for drug A when θ= 0 and to the model for drug B when θ= the of response-surface can For example, the of effects the of data in a to the model parameters for one of the drugs. to specifying a administered function for the concentrationâresponse relation is to use a as described by the use of flexible functions that are to example, a can be constrained to an they in the of antagonistic interactions, it can be to model additive and synergistic drug used our response-surface model to the drug interactions for the hypnotic point between midazolam, propofol, and on this a maximum effect of for all three drugs described the data the dose of and alfentanil the response to in of the dose of the response to in only of results not that alfentanil will or in response to a surgical they that doses of will response to in of the of our response-surface model to potential interactions of two or three drugs at the effect site We that these concentrations are based on information parameters and not possible interactions between the three drugs. this we to that the synergistic dose ratio is not necessarily if a hypnotic effect is the the three-dimensional may in and different used to study drug interactions, it not information that be by a of two-dimensional such as the isobologram of the response surface, of three-dimensional the response to concentration of one drug in the of fixed concentration of the other drug to one and the response to fixed concentration ratios of the two any of these a three-dimensional of the response surface can be if are In the case of three drugs, it is no possible to the response surface because it is a surface, the model parameters can be in three (fig. of using axes to be in the of the interactions between three or more drugs. the study of drug interactions in anesthesia has used isobolographic analysis or multiple approaches have In the multiple is so with described in Appendix 1 in the web with pharmacodynamic that it should be The application of response-surface methodology to the study of drug interactions has the potential to the of these models. We proposed a flexible model for drug interactions, which the relation between the concentrations of two or three drugs and drug effect. We our new model using previously data and that this model can also describe of interaction between an a a and an of response-surface methodology permits of the concentrationâresponse relation and can be used to develop for optimal drug
This thesis examines tactical lessons learned from recent military operations other than war (MOOTW) for implications on leadership development for junior leaders in the United States Marine Corps. A doctrinal examination of MOOTW provides the context for the study. The research questions focus on unique leadership capabilities and competencies necessary for junior Marine Corps leaders in the MOOTW environment. The research involved analysis of recent tactical experiential lessons. These tactical lessons learned, coupled with the doctrinal examination, result in MOOTW specific junior leader competencies necessary for MOOTW organizational effectiveness. The results synthesize into three key competency areas: (1) ability to adapt leadership roles to diverse environments, (2) independent decision-making skills for decentralized operations, and (3) ability to develop leadership skills in team members. Theoretical leadership development frameworks are reviewed for insight into improving these junior leader competencies in the Marine Corps. Recommendations include focusing MOOTW training on the characteristics of: (1) highly politicized environment at all levels of command, (2) high ambiguity between combatants and non-combatants, (3) decision-making at the lowest tactical levels in a decentralized environment, (4) development of teams to operate autonomously in this decentralized environment, and (5) reinforcement that tactical decisions by junior leaders have operational and even strategic impact.
This paper discusses the movement toward self-sufficiency in vaccine supply in developing countries (and countries in transition to new economic and political systems) and explains special supply concerns about vaccine as a product class. It traces some history of donor support and programmes aimed at self-financing, then continues with a discussion about self-sufficiency in terms of institutional capacity building. A number of deficiencies commonly found in vaccine procurement and supply in low- and middle-income countries are characterized, and institutional strengthening with procurement technical assistance is described. The paper also provides information about a vaccine procurement manual being developed by the United States Agency for International Development (USAID) and the World Health Organization (WHO) for use in this environment. Two brief case studies are included to illustrate the spectrum of existing capabilities and different approaches to technical assistance aimed at developing or improving vaccine procurement capability. In conclusion, the paper discusses the special nature of vaccine and issues surrounding potential integration and decentralization of vaccine supply systems as part of health sector reform.
The goal of the US Department of Energy (DOE) Clean Coal Technology (CCT) Program is to provide the energy marketplace with a suite of advanced, cost-effective, highly efficient, and environmentally responsible coal-utilization technologies through cooperatively implementing a series of demonstration projects with industry stake holders. These projects seek to establish at scale the commercial viability of the most promising advanced coal technologies that have developed beyond the proof-of-concept stage. This document serves as DOE's post-project assessment of a project selected in CCT Round 2, SNOX{trademark} Flue Gas Cleaning Demonstration Project. DOE's participation in this project through Cooperative Agreement No. DE-FC22-90PC89655 is consistent with Public Law 100-202 as amended by Public Law 100-446. The SNOX process is a combination of catalytic processes that remove sulfur dioxide (SO{sub 2}), nitrogen oxides (NO{sub x}) and residual particulate matter (PM) from flue gas that has been pre-cleaned with particulate removal. The process generates salable sulfuric acid (H{sub 2}SO{sub 4}) meeting an industry wide standard (US Government Specification O-S-801E) from the SO{sub 2} and converts the NO{sub x} to harmless nitrogen and water vapor. The integrated design of the process enables high-pollutant-removal efficiencies, no significant waste production (only very low quantities of flue gas ash and catalyst degradation fines), and significant heat recovery potential that can be used in the commercial application of the technology to attain increased thermal efficiency of the system. The host site chosen for this CCT demonstration project was Ohio Edison's Niles Station located along the Mahoning River in Niles, Ohio, just northwest of Youngstown. There are two cyclone coal-fired, steam electricity-generating units at the plant. The performance objectives of this project were as follows: to demonstrate SO{sub 2}-removal efficiency greater than 95%; to demonstrate NOx-removal efficiency greater than 90%; to demonstrate the commercial quality of the by-product H{sub 2}SO{sub 4}; to satisfy all Environmental Monitoring Plan requirements; to perform a technical and economic characterization of the technology.
I put forward a new theoretical framework to analyze the relationship between soft budget constraint syndrome and the economic performances of firms. It differs from the existing theoretical framework, Ă la Dewatripont and Maskin (1995), in the soft budget constraint literature. In this paper, soft budget constraint syndrome arises when firms that are expected to lose money are financed. The paper highlights a trade-off between hard and soft budget constraints. While soft budget constraints may compromise firms' incentives to improve performances, an all-out effort to harden budget constraints may put macro stability at risk, especially for economies suffering from allocative inefficiency. Based on this trade-off, the paper shows that a transition from centralized financing to decentralized financing in fact compromises firms' incentives to improve their performances, whereas a transition from centralized financing to a dual track system enhances efficiency. In the dual track system, budget constraints are soft in the centralized track but the macro stability of the economy is assured as a result. The macro stability enhances the disciplinary effect of hard budget constraints in the decentralized track, which in turn promotes firms' incentives to improve performances. The paper sheds light on a complementary relation between soft budget constraint syndrome in the state sector (i.e., the centralized track) and the remarkable growth of the non-state sector (i.e., the decentralized track) in China.
Reduced reimbursements from the federal government and third-party payors have threatened the financial viability of many hospitals. An increasing number of hospitals are losing money from their primary mission of caring for patients. The hospital âindustryâ is still viewed by many as inefficient. Hospitals are generally not run like businesses, nor is it really possible for them to function in the same manner because they have to provide services, to some extent unpredictable, 24 h a day, 7 days a week. Unlike businesses, they cannot increase the charges to their clients to any significant extent when their costs increase because fees are largely dictated by the federal government. For no other business is there the equivalent of capitation or dictation of prices by outside organizations as there is in the medical business. It is perhaps easier for hospital administrations to assess the productivity of their clinical laboratories than of most other hospital services. The number of tests, the number of staff, and the cost of running the service as determined by the supply and salary budgets can be readily quantified. Furthermore, these factors can be bench-marked against the performance of other institutions. However, clinical laboratories also have to contend with the absurd concept of the âbilled testâ beloved by the federal government, insurance carriers, and consulting companies lacking laboratory expertise. The âbilledâ test assigns equal weight to a multitest outpatient panel as it does to a dipstick urinalysis or to an elaborate genetic test that is labor-intensive and may take days to complete. This ridiculous concept makes comparisons of productivity between institutions impossible. Indeed, the billed test concept hides increases in productivity because one billed outpatient test may generate as much work as 12 inpatient tests. Successful efforts by hospitals to reduce their inpatient testing, because of non-reimbursability, then mask any increase in revenue-generating outpatient tests. This dual objective of reducing unnecessary inpatient testing and capitalizing on the potential for outpatient revenue has become a major charge for the responsible clinical laboratory director. Clinical laboratories everywhere have been faced with the challenge of doing more tests at less cost, i.e., boosting their productivity. Many laboratories have reached the point at which it is impossible to increase productivity using the equipment that they have. Although each generation of âautomatedâ analyzers usually provides some improvement in throughput and turnaround time for results, they do not have the ability to make the quantum improvements that are a prerequisite to significantly improving productivity. This has led to the concept of âtotal laboratory automationâ, as much a misnomer as âautomationâ is for a single laboratory instrument. Total laboratory automation goes beyond the automation of analyses but includes automation of much of the important hitherto labor-intensive manual preanalytical phase in the process. The concept was conceived in Japan and has been widely accepted there, so that many large Japanese hospitals now include robotized specimen processing and delivery systems. In the United States, only a very small proportion of even the largest hospital and reference laboratories have installed such systems. Clearly, many laboratory directors have been waiting to learn of the success, or otherwise, of the automated systems in daily operation before they, too, embark on such a major investment. Many also remain uncertain as to whether maximum centralization, as represented by total laboratory automation, is to be preferred over maximum decentralization, as represented by point-of-care testing. The 1999 Clinical Chemistry Forum was designed to present the arguments as to why a fresh approach to laboratory testing was needed and to detail the steps necessary to make the decision whether to commit to total laboratory automation and how to identify the steps involved in a successful installation. The presentations began, appropriately, with discussions of alternative approaches to coping with rapidly escalating workloads. These included total laboratory automation for both individual hospitals and for networks of hospitals. Within the laboratory, alternative approaches were presented, including the use of modular components and automation of selected fixed tasks. The topics covered included a discussion of the components of the necessary overall planning process by a senior administrator from an integrated health system. Another paper dealt with the internal marketing of the concept by the laboratory to the administration and medical staff who would have a major, and vested, interest in the successful operation of a new system. Two of the critical areas that can make or break a robotic system are the layout of the facility with its attendant demands, which involves providing an appropriate environment for both the operators and the analytical systems, and the design and implementation of a superior information system. The latter is essential for capitalizing on the rapid generation of test results. The planning for an automated laboratory entails much more than the operation of the system once it is installed. One of the difficulties in many laboratories is maintaining the daily processing and testing of specimens while a large part of the laboratoryâs space is taken out of service during construction. An especially difficult area to manage is ensuring the loyalty and productivity of staff. This is particularly true when they are aware that one of the objectives of installing a robotized laboratory is to reduce labor costs, which must inevitably impact some of the staff whose goodwill and cooperation are essential. This also is essential during all of the steps before the successful introduction of routine operation of the system on a daily basis. A majority of the forum papers are presented here in their full-length form. Four other papers are summarized below that address key problems in working toward an automated laboratory. We believe that the meeting achieved its objective of presenting all of the issues that need to be recognized by a laboratory director before embarking on the very challenging and expensive pathway leading to total laboratory automation. Although this concept has been well accepted in Japan, the small number of installations in the US to date means that those laboratory directors who have installed systems are still pioneers. We are grateful that they were willing to share their experience at the 1999 Clinical Chemistry Forum. In addition, the attendees and the readers of these Proceedings need to recognize the dedication and support given by Jean Rhame and Pamela Nash of the American Association for Clinical Chemistryâs staff, who made the meeting happen. Implementation of total automation of a laboratory is a formidable task. Not only does it ultimately require a large expenditure of money, it requires time and perseverance on the part of its proponents. Two of the papers presented at this forum addressed the very practical issues of getting buy-in from constituencies as diverse as a hospital administration to all of the individuals whose jobs may be threatened by an automated system. A third paper summarized the necessary steps for the overall planning process, and a fourth paper highlighted the critical importance of information handling in a successful robotic facility. These papers are summarized below. Julie A. Fisher, Mount Sinai Medical Center, New York City, discussed selling the concept of a totally automated laboratory to a hospitalâs administration and other stakeholders. Successful selling is based on extensive communication and detailed financial and other justifications. There are eight essential elements to successfully selling an automation concept. These are defining goals, assessing needs, obtaining stakeholder buy-in, the decision-making process, vendor selection, the financial planing process, implementation, and metrics. Continuous communication is essential throughout all phases of the project. The wishes of the laboratory must be congruent with those of the administration. The process may be protracted; the cycle between initial concept and routine operation may be as long as 6 years. The trigger for a laboratory to consider automation usually is pressure to reduce costs and improve its efficiency. Automation has the potential to enhance the economic survival of a laboratory, reduce its operating costs, improve the quality of services, and provide a safer work environment. The need for automation should be assessed in the context of whether the institution is planning to expand or to just cut costs. Every ramification must be considered. For example, contractual arrangements with unions must be taken into account. This will become particularly important when the system is fully implemented because contracts may determine who may or may not be laid off. Additionally, needs for upgrading or changing the laboratory information system and analytical instruments must be assessed. A successful automation project depends on stakeholder buy-in. The stakeholders include the laboratory staff, the hospital administration and Board of Trustees, and hospital physicians. It is important to communicate to each of the groups what automation will do for them. Each of these constituencies has different interests and concerns. The laboratory staff are most concerned about job security, but it is important to let them know that automation is a tool to help them perform their jobs differently, and perhaps better. For the administration and Board of Trustees, the focus needs to be on the financial bottom line, with emphases on the opportunity for both revenue enhancement and expense reduction. Other selling points for the administration can include the potential to perform tests for other hospitals and develop group purchasing arrangements with other hospitals for which laboratory services can be provided. Physicians are primarily concerned with turnaround times of test results as well as enhanced information. The financial planning process requires projections of revenue and expenses. A break-even analysis is essential and must demonstrate that automation will reduce costs and/or enhance revenue. Various approaches may be used. A traditional return on investment (ROI) analysis relates net income to investment capital. The formula for calculating a ROI may be refined to take into account sales as well, as in a DuPont analysis. This approach recognizes that it might not be beneficial to tie up assets, thereby lowering profitability. The same formula can be used for an expense analysis by keeping sales constant. The net profit margin increases with a reduction in expenses, and with automation, the key expense reduction is in labor. Technical productivity can be calculated by dividing the number of tests performed by the total number of paid full-time employees or equivalents (FTEs). The calculation of labor savings should take into account how the number of employees will be reduced. With layoffs, there often will be severance and/or retraining expenses to equip the laid-off employees for other jobs. Different laboratory areas will be affected differently. Thus, the laboratories in which automation will be implemented will be more impacted than others. For each laboratory area, a separate projection of staffing needs to be done. Recently, there has been a trend away from justifying automation solely on an ROI analysis because not all of the benefits can be quantified in financial terms. Automation provides added value through improved efficiency coupled with reduction in processing errors, improved turnaround times, automated repeat and reflex testing, enhanced safety, and improved specimen tracking. The active participation of stakeholders in the planning process enhances the laboratoryâs ability to sell the concept. Thus, an overall executive committee derives benefits when supported by laboratory management with information systems and instrumentation teams. It is advantageous to enlist stakeholders in vendor selection because acceptance of the system is critically dependent on the their involvement. The more people involved in different aspects of the planning process, the greater the probability of acceptance. Even during the implementation phase, it is important to involve the stakeholders, especially the staff who will be directly affected by the system. During the installation and after the system becomes operational, it is important to continue to communicate to the stakeholders. Information that should be communicated includes actual performance compared with projections, especially with regard to revenue projections and/or expense reductions, the quality of service, and whether a safer environment has been created. Patricia Abbott, Hospital of the University of Pennsylvania (HUP), Philadelphia, discussed the practical aspects of creating a robotized laboratory. Because acceptance of laboratory automation by a hospitalâs administration is, to a great extent, dependent on perceived financial benefits, an accurate estimate of the number of employees needed to operate the system is required. The greatest financial returns are likely to arise from reduced labor costs. Unfortunately, the estimate of the number of staff needed to operate a robotized laboratory must be made before the laboratory has any experience with the system or its impact. One of the first steps in the planning process is to decide which tests will be performed in the automated laboratory and which will be performed elsewhere. This decision requires not only an analysis of which tests are performed at each existing bench station but the proportion of tests requested stat vs routine per shift, the number of tests per shift, and the number of technologists working on each shift on each day of the week. With automation, it becomes feasible to combine the stat and routine workbenches for the high-volume tests, but for precise planning of staffing needs, the time of receipt of specimens in the laboratory must be considered. It is also necessary to consider physician needs in deciding which instruments should be interfaced with the robotized and to assess whether greater can be through the test on different analytical the of the planning process, it is essential to assess the and interests of the laboratory staff. This is especially important the laboratory been to a of separate laboratories because there may be a need for extensive of existing on the of the staff in the laboratory at it was to staff the automated laboratory with a staff who would be to operate all of the instruments in the and who would be by staff from the areas working in their areas of expertise. this the laboratory for example, be to on the of of the technologists who would be to operate only the in the automated laboratory to become in operating technologists who been to the and laboratories would not have to the needed to operate a was to assess the of the for working in the automated laboratory, it was on a small number of staff. The for the technologists to assess their to new and for management to assess each potential for a successful to a environment with new for the individuals selected to work in the automated laboratory was each existing The not only on instruments but also on the clinical of the that were new to them and of the results of these tests. before all technologists were to the it was on a selected staff and by their before it was out to all the staff. The of the automated laboratory the laboratory to turnaround time to the the and the in as well as from a processing to a of benefits through of test results possible to the efficiency of testing by the automated laboratory. a the turnaround times for and high-volume tests between in the laboratory information system of the receipt of a specimen and its test results to is now for and for the tests. It is important to have a committee of technologists to at all of work including and in work A of the a of the planning committee once the decision to been made to that the interests of all of the staff were The planning committee has been after the system to Because the staff from different the senior management has with the management of the automated laboratory to their and has with the staff on a as well as on a to that the of the staff are and The senior management a many of the staff a and that problems were to be A committee was as a to and assess problems and The ROI for the project at was based on the of the impact of on the staff, staff were to for all even those not directly affected by the automated laboratory, so that those staff from the automated laboratory be to laboratory the and of these benefits were to them. In the number of that to be was less than been for because of a to tests from other hospitals and the A. the concept of project management as to the of a robotized laboratory. management is as the of and to project to or needs and from a project. Thus, it is a approach to the management of costs, and However, it has only been management requires of a to and manage people and other One individual is to the and is given and to manage the project to its areas of or function are involved in project and the project should have and some in all of them. The primary areas involve the management of cost, and These are by the management of and management is concerned with the of the the overall and of management involves of the necessary the of and the for the project. It is concerned with all aspects of and requires critical and/or as management planning and cost and management all of the of total quality management to that the of the project will the needs of the of the project. management the most use of the people involved in the project and includes and management includes the to the and services needed to the project. management is the function of and to The project must manage or communication so that all of the appropriate people are about the of the project at the appropriate time in the appropriate both and in Each project has a cycle which may have different of and There is no single to manage a but the approach involves the phases of implementation, and the of the concept phase, there usually is only a of a but the of this phase is the for the project. The or design phase usually is when the project is to the project and is the critical detailed planning with planning is the need to develop to and manage of the project. Two critical require the of the people who must the project and the that many individuals working on a project are not working on it The of the phase is a project which should not be The must identify all the necessary and their costs The costs must be to the individual work times and must be with to to the overall project For large such as for installation of a costs with should be as part of the overall project. for costs are of the for for for and of the for the service for the instrument. For large it is to a work which the project to identify the and to to them. A is the for the and of time and cost to be based on is now readily to identify the through the and to determine the of the project. In of the most there is the of with of the project beyond the initial This is not a as long as the project the the cost, and quality and this to the stakeholders. A potential is and to develop management must be a and one of the most to manage is through to can be to whether the can be the probability of is or The latter requires the of a the objectives of the project are it is and its to an Mount Sinai Medical Center, New York City, discussed the critical of a laboratory information system in an automated laboratory. automation involves much more than a robotic system a laboratory. The in an automated laboratory is involved in both analytical and The latter includes both preanalytical such as the processing of and specimen and such as and The provides to the quality and and results and them to the In an automated laboratory, the of the must be integrated with the of the robotic processing and the robotic The each specimen on the robotic system and the robotic process to the and to the specimen and its they might be the system. It and from the robotic system the quality of each primary specimen and the of specimen in the so that specimens may be as It is for tests to be directly into the Not only does this reduce errors, it also has the potential to improve turnaround of to the also and testing. Furthermore, it enhances and provides an accurate time of specimen Within the laboratory, from the to the robotic information and the and system However, such an approach requires or of specimens for which tests were but not on the robotic of the provides in testing and reflex specimen testing. of different of specimens on the but the need to cost and may also a in the testing process because all specimens must through a single An automated laboratory is critically dependent on a and its and system should be in to to of some part of the system. An supply by an is essential to the impact of or in The should have a of that usually share the but with each one of handling the are also needed to provide in one become or to and from the to and and other should be for rapid the system one or more and should also be Each the system should be up to This should be in the at the same time operation of the in the and of the must be with and of the a is it should be in the of the before to the part of the a with the the laboratory staff should to but then should enlist the vendor for The same should be a The staff should provide the laboratory staff with an estimate of the likely so that alternative may be In the of a the medical staff must also be function is this should be communicated to all in the same manner that the was The papers summarized when taken with the full-length papers that will provide the an of the of and with regard to laboratory automation.