Wages, productivity, and work intensity in the Great Depression

We show that U.S. manufacturing wages during the Great Depression were importantlydetermined by forces on firms' intensive margins. Short-run changes in work intensity and the longer-term goal of restoring full potential productivity combined to influence real wage growth. By contrast, the external effects of unemployment and replacement rates had much less impact. Empirical work is undertaken against the background of an efficient bargaining model that embraces employment, hours of work and work intensity

Developments in linear and integer programming

In this review we describe recent developments in linear and integer (linear) programming. For over 50 years Operational Research practitioners have made use of linear optimisation models to aid decision making and over this period the size of problems that can be solved has increased dramatically, the time required to solve problems has decreased substantially and the flexibility of modelling and solving systems has increased steadily. Large models are no longer confined to large computers, and the flexibility of optimisation systems embedded in other decision support tools has made on-line decision making using linear programming a reality (and using integer programming a possibility). The review focuses on recent developments in algorithms, software and applications and investigates some connections between linear optimisation and other technologies

Twin solution calorimeter determines heats of formation of alloys at high temperatures

Calvert-type, twin liquid metal solution calorimeter determines the heats of formation of transition metal alloys at high temperatures. The twin differential calorimeter measures the small heat effects generated over extended periods of time, has maximum operating temperature of 1073 degrees K and an automatic data recording system

Linear, integer separable and fuzzy programming problems: a united approach towards automatic reformulation

For mathematical programming (MP) to have greater impact as a decision tool, MP software systems must offer suitable support in terms of model communication and modelling techniques. In this paper modelling techniques that allow logical restrictions to be modelled in integer programming terms are described and their implications discussed. In addition it is demonstrated that many classes of non-linearities which are not variable separable may be after suitable algebraic manipulation put in a variable separable form. The methods of reformulating the fuzzy linear programming problem as a Max-Min problem is also introduced. It is shown that analysis of bounds plays a key role in the following four important contexts: model reduction, reformulation of logical restrictions as 0-1 mixed integer programs, reformulation of nonlinear programs as variable separable programs and reformulation of fuzzy linear programs. It is observed that as well as incorporating an interface between the modeller and the optimiser there is a need to make available to the modeller software facilities which support the model reformulation techniques described here

Constructive Heuristics for the Minimum Labelling Spanning Tree Problem: a preliminary comparison

This report studies constructive heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree that uses edges that are as similar as possible. Given an undirected labeled connected graph (i.e., with a label or color for each edge), the minimum labeling spanning tree problem seeks a spanning tree whose edges have the smallest possible number of distinct labels. The model can represent many real-world problems in telecommunication networks, electric networks, and multimodal transportation networks, among others, and the problem has been shown to be NP-complete even for complete graphs. A primary heuristic, named the maximum vertex covering algorithm has been proposed. Several versions of this constructive heuristic have been proposed to improve its efficiency. Here we describe the problem, review the literature and compare some variants of this algorithm

USING BAYESIAN STATISTICAL POSTPROCESSING METHODS TO IMPROVE LOCAL WIND FORECASTS

This thesis explores the use of Bayesian statistical postprocessing to rapidly train a highly accurate forecast from a 1 km resolution gridded WRF model forecast over a 100 km by 100 km area. These methods leverage three modeled forecast variables—10 m winds, sea-level pressure, and terrain elevation—in conjunction with downstream observations and prior model runs to identify model inaccuracies. Using only three days of data, a Bayesian corrected forecast is produced and analyzed for accuracy and improvement over the original model run relative to real-world observations. Over 90% of the resulting forecasts saw improvement over the raw model forecasts in root mean squared error, and over 87% of the forecasts saw improvement in mean error over the raw model forecasts. Extreme circumstances saw improvements in accuracy of over 9 knots while overall improvements were reliably seen both in accuracy and precision among Bayesian corrected forecasts. These findings are significant as they suggest that Bayesian statistical postprocessing methods work and should be both employable at rapid rates, and result in more accurate forecasts.First Lieutenant, United States Air ForceApproved for public release. distribution is unlimite

Guarentees and Limits of the Independence and Impartiality of the Judge

This Article looks at the role of judicial independence in the United States. The author examines the history of judicial independence in England and the Colonies, under the Articles of Confederation, and forward through history. The author concludes that judicial independence forms an integral part of the political doctrine of separation of powers. In addition, the author argues that judicial independence in any country must begin with a genuine commitment to democracy and the rule of law