A PORTFOLIO OF NUTRIENTS: SOIL AND SUSTAINABILITY

This paper develops a basic dynamic economic model that can be used for theoretical and numerical analysis of optimal soil management practices. A dynamic biophysical/economic optimal control model is developed in a multi-disciplinary framework, treating soil as a multi-pool portfolio of a particular limiting mobile nutrient (e.g. nitrogen). This specification allows for fertilizer to directly enter the active pool, while tillage initially affects the decadal pool, reflecting the realities of agricultural production. We examine the properties of the steady-state and the time paths of the optimal solutions. In addition, alternative sustainability criteria of farm-level agricultural practices are presented, and the optimal solution of the problem is evaluated to determine if it meets any or all of the definitions of sustainability.Farm Management,

A Linear Dynamic Analysis of Vent Condensation Stability

Pressure suppression systems in boiling water reactors are designed to condense a large amount of steam very rapidly by injecting it into a pool of water. It transpires that such condensing flows are unstable and can lead to large oscillatory pressures on the walls of the containment system. This paper presents a theoretical model whose purpose is to attempt to understand why these flows are unstable and to extract the important parameters and frequencies pertaining to the instability. A simple linear dynamic model is constructed comprising linear transfer function for (i) the unsteady steam flow in the vent (ii) the condensation interface and (iii) the pool hydrodynamics. The analysis demonstrates the existence of both stable and unstable regions of operation defined by several non-dimensional parameters including the ratio of the steam flow rate to the effective thermal diffusivity in the water just downstream of the condensation interface and the frictional losses in the vent. Instability frequencies are in the vicinity of the vent acoustic frequencies or the pool manometer frequency depending on the conditions. Though the qualitative dynamic behavior of the model is consistent with the experimental observations, quantitative comparison is hindered by difficulties in accurately assessing the effective thermal diffusivity in the water. Nevertheless the model provides insight into the nature of the instability

Default clustering in large portfolios: Typical events

We develop a dynamic point process model of correlated default timing in a portfolio of firms, and analyze typical default profiles in the limit as the size of the pool grows. In our model, a firm defaults at a stochastic intensity that is influenced by an idiosyncratic risk process, a systematic risk process common to all firms, and past defaults. We prove a law of large numbers for the default rate in the pool, which describes the "typical" behavior of defaults.Comment: Published in at http://dx.doi.org/10.1214/12-AAP845 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Understanding Terrorist Organizations with a Dynamic Model

Terrorist organizations change over time because of processes such as recruitment and training as well as counter-terrorism (CT) measures, but the effects of these processes are typically studied qualitatively and in separation from each other. Seeking a more quantitative and integrated understanding, we constructed a simple dynamic model where equations describe how these processes change an organization's membership. Analysis of the model yields a number of intuitive as well as novel findings. Most importantly it becomes possible to predict whether counter-terrorism measures would be sufficient to defeat the organization. Furthermore, we can prove in general that an organization would collapse if its strength and its pool of foot soldiers decline simultaneously. In contrast, a simultaneous decline in its strength and its pool of leaders is often insufficient and short-termed. These results and other like them demonstrate the great potential of dynamic models for informing terrorism scholarship and counter-terrorism policy making.Comment: To appear as Springer Lecture Notes in Computer Science v2: vectorized 4 figures, fixed two typos, more detailed bibliograph

Thermal Modeling and Experimental Validation in the LENS™ Process

Several aspects of the thermal behavior of deposited stainless steel 410 (SS410) during the Laser Engineered Net Shaping (LENSTM) process were investigated experimentally and numerically. Thermal images in the molten pool and surrounding area were recorded using a two-wavelength imaging pyrometer system, and analyzed using ThermaVizTM software to obtain the temperature distribution. The molten pool size, temperature gradient, and cooling rate were obtained from the recorded history of temperature profiles. The dynamic shape of the molten pool, including the pool size in both travel direction and depth direction, was investigated and the effect of different process parameters was illustrated. The thermal experiments were performed in a LENSTM 850 machine with a 3kW IPG laser for different process parameters. A three-dimensional finite element model was developed to calculate the temperature distribution in the LENS process as a function of time and process parameters. The modeling results showed good agreement with the experimental data.Mechanical Engineerin

A semiparametric factor model for electricity forward curve dynamics

In this paper we introduce the dynamic semiparametric factor model (DSFM) for electricity forward curves. The biggest advantage of our approach is that it not only leads to smooth, seasonal forward curves extracted from exchange traded futures and forward electricity contracts, but also to a parsimonious factor representation of the curve. Using closing prices from the Nordic power market Nord Pool we provide empirical evidence that the DSFM is an efficient tool for approximating forward curve dynamics.power market, forward electricity curve, dynamic semiparametric factor model

A semiparametric factor model for electricity forward curve dynamics

In this paper we introduce the dynamic semiparametric factor model (DSFM) for electricity forward curves. The biggest advantage of our approach is that it not only leads to smooth, seasonal forward curves extracted from exchange traded futures and forward electricity contracts, but also to a parsimonious factor representation of the curve. Using closing prices from the Nordic power market Nord Pool we provide empirical evidence that the DSFM is an efficient tool for approximating forward curve dynamics.power market, forward electricity curve, dynamic semiparametric factor model

CryptoKnight:generating and modelling compiled cryptographic primitives

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio

INSTITUTIONS INFLUENCE PREFERENCES: EVIDENCE FROM A COMMON POOL RESOURCE EXPERIMENT

We model the dynamic effects of external enforcement on the exploitation of a common pool resource. Fitting our model to the results of experimental data we find that institutions influence social preferences. We solve two puzzles in the data: the increase and later erosion of cooperation when commoners vote against the imposition of a fine, and the high deterrence power of low fines. When fines are rejected, internalization of a social norm explains the increased cooperation; violations (accidental or not), coupled with reciprocal preferences, account for the erosion. Low fines stabilize cooperation by preventing a spiral of negative reciprocation.Field experiments, common pool resources, cooperation, enforcement, regulation, social preferences, social norms, learning models