The Next Horizon

Lakatta EG. Cardiovascular aging research The next horizons. J Am Geriatr Soc 1999 47 613-25. 

A dynamic model of the process is used to predict the future outputs over a prediction horizon consisting of the next p samphng periods. 

At each sampling instant, a control policy consisting of the next m control moves is calculated. The control calculations are based on minimizing a quadratic or linear performance index over the prediction horizon while satisfying the constraints. 

A receding horizon approach is employed. At each sampling instant, only the first control move (of the m moves that were calculated) is actually implemented. Then the predictions and control calculations are repeated at the next sampling instant. 

Therefore, as it is mentioned in the AR4, climate projections for the end of the century depend on the scenario and the particular model used to develop them. Temperature changes, and especially precipitation changes, show, for such temporal horizon, a broad range of values. On the other hand, projections for the next 2-4-decades are quite robust, since they depend less on long-term feedbacks and also on future greenhouse gases emissions. In fact, the climate of the next few years is tightly determined by past and recent emissions (climate commitment). 

FIGURE 48 A soil profile. Many of the characteristics of soils vary with depth. A convenient way of representing its varying characteristics is by dividing the soil into layers, usually referred to as horizons, identified by letter symbols. The surface layer, which is known as the A horizon, is generally rich in organic matter. Next come the B and C horizons, each of which may have compositional characteristics and modifications. The deepest soil horizon (R) is solid rock. The illustration identifies clearly defined horizons, although in most soils the horizons are not as clear and in some they may be very diffuse. 

To get a better idea of the complexity of a real application scenario in these industries it makes sense to, once, exemplarily depict the planning processes in a typical production of active pharmaceutical ingredients (API production). Most pharmaceutical companies are looking at planning scenarios in which several hundred individual resources or facilities have to be accounted for, with demands and orders for some thousand final products. The planning horizon is often set to 2-5 years. Next to single equipment, there are facility pools, with one pool consisting of several individual units. 

The occurrence of the set-up procedure in period i is denoted by the binary variable Wi (0 = no, 1 = yes). The production costs per batch are denoted by p = 1.0 and the cost for a set-up is y = 3.0. Demands di that are satisfied in the same period as requested result in a regular sale Mi with a full revenue of a = 2.0 per unit of product. Demands that are satisfied with a tardiness of one period result in a late sale Mf with a reduced revenue of aL = 1.5 per unit. Demands which are not satisfied in the same or in the next period result in a deficit Bf with a penalty of a = 0.5 per unit. The surplus production of each period is stored and can be sold later. The amount of batches stored at the end of a period is denoted by Mf and the storage costs are a+ =0.1 per unit. The objective is to maximize the profit over a horizon of H periods. The cost function P contains terms for sales revenues, penalties, production costs, and storage costs. For technical reasons, the model is reformulated as a minimization problem ... 

During the next phase of construction, the basement and sub-basement of the house are dug. This excavation draws tens of thousands of tons of B (and sometimes C) Horizon soils to the surface. With little or no resale value, these soils are simply spread around the surface of the site to serve as the basis for landscaping after construction is complete. Starved of organic material and loaded with minerals (not to mention plaster, cement, lumber, and other debris), these soils are a prohibitively poor base for planting. It is little wonder mulch is an increasingly popular ground cover in the suburbs. 

The futnre is likely to be no less rewarding. Astronomers have not let success go to their heads and their thirst for discovery and knowledge has remained intact. They are certainly not so naive as to believe that the stars have delivered up all their secrets. The most ancient stars, those born before the Galaxy had assumed its present form, have now become a subject of intense interest. The next goal on the distant horizon is a complete picture of chemical evolution in space. In this context, it is quite clear that the early stages of this evolution are the least well understood. The end of the road is not yet in sight. 

We now consider hyperbolic discounters with willpower and personal rules to see how they will behave when they are faced with the same choice problem. Given their consumption history, we let the actors evaluate the two alternative consumption careers consuming little now and in the future and consuming much now and in the future. Actors have a fixed time horizon N, so they will consider the present and the next (N-1) consecutive consumption events. The welfare levels associated with the different options are assumed to be the ones given in figure 5.2. We assume that the time intervals between consumption events are one time unit. We start by considering consumers immediately before the first choice has to be made, that is, at t=0. 

With a time horizon of N = 2, they will take the present and the next event into consideration. If they have consumed little in the past, they obtain the following utility levels from the two alternatives ... 

The C02 capture and sequestration cannot meet these challenges since the expected time frame for deployment would be longer than are time horizons authorised for C02 emissions reduction, for which should also be considered the uncertainties lying in the world geological storage capacity. We will show in the next section that this is not the case with nuclear and electrolysis. 

Since the actual or potential phytotoxicity of a phenolic acid is determined by its physical and chemical properties and the susceptibility of the plant process involved, the actual or potential phytotoxicity of a given phenolic acid is best determined in nutrient culture in the absence of soil processes. The phytotoxicity observed in soil systems represents a realized or observed phytotoxicity, not the actual phytotoxicity, of a given phenolic acid. For example, the actual relative phytotoxicities (or potencies) for cucumber seedling leaf expansion were 1 for ferulic acid, 0.86 for p-coumaric acid, 0.74 for vanillic acid, 0.68 for sinapic acid, 0.67 for syringic acid, 0.65 for caffeic acid, 0.5 for p-hydroxybenzoic acid and 0.35 for protocatechuic acid in a pH 5.8 nutrient culture.5 In Portsmouth Bt-horizon soil (Typic Umbraquaalts, fine loamy, mixed, thermic pH 5.2), they were 1, 0.67, 0.67, 0.7, 0.59, 0.38, 0.35, and 0.13, respectively.19 The differences in phytotoxicity of the individual phenolic acids for nutrient culture and Portsmouth soil bioassays were due to various soil processes listed in the next paragraph and reduced contact (e.g., distribution and movement)36 of phenolic acids with roots in soils. 

To ensure quality diagnosis, immunohistochemistry quality control will become even more important. It is expected that the next five years will see increased participation in proficiency testing, as an increase in the number of laboratories that become accredited. New technologies on the horizon will also likely facilitate more efficient means of quality control. 

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