Interactions of Source State and Market Price Trends for Cattle of Korean, Japanese and USA Market Specifications

This study analyses the trends in the real prices of steers destined for the Japanese and Korean market, and cows destined for the USA market when sold in Queensland (QLD) or New South Wales (NSW). The aim of this paper is to explore how these prices have influenced each other when faced with the same overall economic and climatic conditions. A Vector Autoregressive model is first estimated to find linkages across six price series defined by source and destination. A Seemingly Unrelated Regressions Model for the real price series is then estimated including lagged prices of linked markets and indicators of macroeconomic and climatic conditions. From our empirical analysis, we find strong evidence of mean-reverting real prices, indicating they can be predicted by their historical mean. Further, the historical mean prices paid in QLD are higher than those paid in NSW for the Korean and US market cattle specifications. The price of cattle of Japanese market specification sold in NSW is solely determined by world conditions and historical values, and it influences directly or indirectly all other markets. The price trends for cattle of US market specifications do not seem to predict movements in the Japanese or Korean markets. This is expected as the Japanese market is a premium market while the US market accepts cattle from a wider range of specifications. This study does not find a systematic relationship between these prices and movements, the Southern Oscillation Index, the Asian financial crises, or the Australian Business Cycle

Neural Predictive Monitoring for Collective Adaptive Systems

Reliable bike-sharing systems can lead to numerous environmental, economic and social benefits and therefore play a central role in the effective development of smart cities. Bike-sharing models deal with spatially distributed stations and interact with an unpredictable environment, the users. Monitoring the trustworthiness of such a collective system is of paramount importance to ensure a good quality of the delivered service, but this task can become computationally demanding due to the complexity of the model under study. Neural Predictive Monitoring (NPM) [5], a neural-network learning-based approach to predictive monitoring (PM) with statistical guarantees, can be employed to preemptively detect violations of a specific requirement – e.g. a station has no more bikes available or a station is full. The computational efficiency of NPM makes PM applicable at runtime even on embedded devices with limited computational power. The goal of this paper is to demonstrate the applicability of NPM on collective adaptive systems such as bike-sharing systems. In particular, we first analyze the performance of NPM over a collective system evolving deterministically. Then, following [7], we tackle a more realistic scenario, where sensors allow only for partial observability and where the system evolves in a stochastic fashion. We evaluate the approach on multiple bike-sharing network topologies, obtaining highly accurate predictions and effective error detection rules

Mean-Field Limits Beyond Ordinary Differential Equations

16th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2016, Bertinoro, Italy, June 20-24, 2016, Advanced LecturesInternational audienceWe study the limiting behaviour of stochastic models of populations of interacting agents, as the number of agents goes to infinity. Classical mean-field results have established that this limiting behaviour is described by an ordinary differential equation (ODE) under two conditions: (1) that the dynamics is smooth; and (2) that the population is composed of a finite number of homogeneous sub-populations, each containing a large number of agents. This paper reviews recent work showing what happens if these conditions do not hold. In these cases, it is still possible to exhibit a limiting regime at the price of replacing the ODE by a more complex dynamical system. In the case of non-smooth or uncertain dynamics, the limiting regime is given by a differential inclusion. In the case of multiple population scales, the ODE is replaced by a stochastic hybrid automaton

Characterization methodology for re-using marble slurry in industrial applications

Nowadays calcium carbonate has a great importance in different industrial fields and currently there is the opportunity of appreciate the potential value of marble waste and convert it into marketable products. Marble slurry samples, collected from different dimension stone treatment plants in Orosei marble district (Sardinia - Italy), were chemically, physically, mineralogically, and morphologically analyzed and the obtained data were evaluated for compatibility with the marketable micronized CaCO3 specifications required by some industrial sectors, estimating the prospects of recovered CaCO3 utilization. Besides the economic benefits, transforming a waste into an important economic resource involves environmental advantages, due to reduced marble waste landfills, and sustainability promotion

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming

Matching Models Across Abstraction Levels with Gaussian Processes

Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, and whether such agreement holds for different parametrisations. Here we present a generally applicable statistical machine learning methodology to automatically reconcile the predictions of different models across abstraction levels. Our approach is based on defining a correction map, a random function which modifies the output of a model in order to match the statistics of the output of a different model of the same system. We use two biological examples to give a proof-of-principle demonstration of the methodology, and discuss its advantages and potential further applications.Comment: LNCS forma

Process algebra modelling styles for biomolecular processes

We investigate how biomolecular processes are modelled in process algebras, focussing on chemical reactions. We consider various modelling styles and how design decisions made in the definition of the process algebra have an impact on how a modelling style can be applied. Our goal is to highlight the often implicit choices that modellers make in choosing a formalism, and illustrate, through the use of examples, how this can affect expressability as well as the type and complexity of the analysis that can be performed