A spatiotemporal Data Envelopment Analysis (S-T DEA) approach:the need to assess evolving units

One of the major challenges in measuring efficiency in terms of resources and outcomes is the assessment of the evolution of units over time. Although Data Envelopment Analysis (DEA) has been applied for time series datasets, DEA models, by construction, form the reference set for inefficient units (lambda values) based on their distance from the efficient frontier, that is, in a spatial manner. However, when dealing with temporal datasets, the proximity in time between units should also be taken into account, since it reflects the structural resemblance among time periods of a unit that evolves. In this paper, we propose a two-stage spatiotemporal DEA approach, which captures both the spatial and temporal dimension through a multi-objective programming model. In the first stage, DEA is solved iteratively extracting for each unit only previous DMUs as peers in its reference set. In the second stage, the lambda values derived from the first stage are fed to a Multiobjective Mixed Integer Linear Programming model, which filters peers in the reference set based on weights assigned to the spatial and temporal dimension. The approach is demonstrated on a real-world example drawn from software development.

The time dimension of neural network models

This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented.

A comparison of beta-amyloid deposition in the medial temporal lobe in sporadic Alzheimer's disease, Down's syndrome and normal elderly brains

The density of beta-amyloid (A beta) deposits was studied in the medial temporal lobe in non-demented individuals and in sporadic Alzheimer's disease (SAD) and Down's syndrome (DS). No A beta deposits were recorded in six of the non-demented cases, while in a further eight cases, these were confined to either the lateral occipitotemporal or parahippocampal gyrus. The mean density of A beta deposits in the cortex was greater in SAD and DS than in non-demented cases but with overlap between patient groups. The mean density of A beta deposits was greater in DS than SAD consistent with a gene dosage effect. The ratio of primitive to diffuse A beta deposits was greater in DS and in non-demented cases than in SAD and the ratio of classic to diffuse deposits was lowest in DS. In all groups, A beta deposits occurred in clusters which were often regularly distributed. In the cortex, the dimension of the A beta clusters was greater in SAD than in the non-demented cases and DS. The data suggest that the development of A beta pathology in the hippocampus could be a factor in the development of DS and SAD. Furthermore, the high density of A beta deposits, and in particular the high proportion of primitive type deposits, may be important in DS while the development of large clusters of A beta deposits may be a factor in SAD.