Resource intensity indicators

There are more indicators (e.g., mass intensity, MI, and mass productivity) that belong to this general class of resource intensity indicators, for example, which quantify greermess of chemical processes and products in terms of effectiveness of mass and energy intensity. 

The published literature on the effects of microbial activities on wine chemical composition is now considerable. Understanding the significance of wine chemistry is, however, heavily dependent on complex analytical strategies which combine extensive chemical characterization and sensory descriptive analysis. However, sensory analysis is extremely resource-intense, requiring many hours of panelists time. This prevents widespread application of these powerful analytical tools. Advanced statistical techniques have been developed that are closing the gap between chemical and sensory techniques. Such techniques allow the development of models, which should ultimately provide a sensory description based on chemical data. For example, Smyth et al. (2005) have developed reasonable models which can reveal the most likely compounds that relate to particular attributes that characterise the overall sensory profile of a wine. For wines such as Riesling and Chardonnay, the importance of several yeast volatile compounds has been indicated. Such information will allow yeast studies to target key compounds better rather than just those that are convenient to measure. 

Environmental impact indicators are used to characterize and aggregate the contribution of a given set of inputs and/or outputs to environmental impact, with the being latter understood as a general concept, or a particular problem such as climate change, toxicity, and so on. These indicators are more elaborate than those on resource intensity, since not only the inputs and outputs have to be identified and quantified, but also an impact function describing the unfriendliness of those inputs and outputs, and which allows their aggregation to be defined. 

The term simple should not be misunderstood as we use it in this book. It does not mean this category of PSSR is less important than a complex PSSR. It is intended to indicate the level of effort needed is well understood and fairly straightforward. Whenever we use the terms simple PSSR or short-form PSSR, they indicate a less resource-intensive approach to verifying readiness for startup when the trigger event has a lower level of risk. 

The analysis of the contribution made by increased pharmaceutical spending to the growth in the per capita intensity of health resources suffers from major measurement problems that deprive available indicators of any value. Traditional pharmaceutical price indexes (such as the Laspeyres index, used to calculate the pharmaceutical component of the consumer price index) provide little relevant information in a market in which the introduction of therapeutic innovations is of prime importance the indexes show an apparent freeze, and sometimes even a steep drop (as in the Spanish case). However, the steady rise in the average price per prescription paints a very different picture. 

Production indicators focus on production utilization and throughput levels in order to indicate how capital-intensive resources with high fixed costs are utilized. 

ESPS remains a computationally intensive strategy, though not prohibitively so on the scale of its competitors One explicit comparison (in the case hard-sphere crystals) indicates that ESPS and NIRM deliver similar precision for similar compute resource [34]. 

Inflammation is associated with various diseases such as rheumatoid arthritis, cancer, myocarditis, arteriosclerosis, bowel diseases, multiple sclerosis, asthma, and many others. While several inflammatory markers are commonly expressed during any inflammatory disorder, some are symptom specific. Therefore, the gene array data will be particularly helpful in indicating the appropriate disease model for subsequent preclinical and clinical tests. Only functional, active extracts with potentially safe and novel modes of actions may then be subjected to labor-intensive large-scale extraction, fractionation, characterization, and isolation of novel bioactive components. We believe that the strategy as described schematically in Figure 4.1 will allow efficient use of plant extracts and other natural resources toward identification of novel drug leads for human health care. 

The measured quantities, service requests, or resource demands that are used to characterize the workload, are called workload parameters. Examples of workload parameters ate transaction types, instruction types, packet sizes, source destinations of a packet, tmd page-reference patterns. The workload parameters can be divided into workload intensity and service demands. Workload intensity is the load placed on the system, indicated by the number of units of work contending for system resources. Examples include arrival rate or interarrival times of component (e.g., transaction or request), number of clients and think times, and number of processors or threads in execution simultaneously (e.g., file reference behavior, which describes the percentage of accesses made to each file in the disk system) The service demand is the total amount of service time required by each basic component at each resource. Examples include CPU time of transaction at the database server, total transmission time of replies from the database server in LAN, and total I/O time at the Web server for requests of images and video clips used in a Web-based learning system. 

Finally the importance of research and development as an activity within the chemical industry is confirmed by the enormous financial resources which are devoted to it. As indicated in section 3.2.3 the industry as a whole devotes approximately 7% of sales INCOME to R D, with this doubling for the most research intensive sector, pharmaceuticals. For comparison the figure for the engineering sector in the U.K. would be around 1%, and all manufacturing 2%. 

The agricultural assumptions and indicators depend on the actual and projected resources, including arable land, harvested land, irrigated land, cropping intensity, available labor, level of mechanization of agricultural labor, crop diversification related to the kind and number of livestock, permanent crops, etc. 

---