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The discrete element method has advanced significantly since it originated in 1979. This paper aims to provide a selective overview of some major developments on contact modelling methodologies for non-spherical particles in DEM over the last three decades. More attention is specifically paid towards developments in the early years. This review mainly focuses on the geometric aspects of contact modelling methods without touching upon material related physical issues. Various shape representation schemes for non-spherical particles are first presented using a classification system, followed by the critical review of contact modelling approaches for almost all contact types of non-spherical shapes. In addition to outlining key ideas, concepts and novel computational procedures that are proposed in these methodologies in a systematic and logically constructed manner, their evolutions and inter-relations in particular are also discussed in detail. Moreover, possible problems and unresolved issues for each method reviewed are highlighted, and possible solutions are pointed out where applicable.

Abstract The problem of maintaining organizational resilience in the face of ransomware attacks represents an important issue for modern organizations. Organizational networks and IT infrastructure have become increasingly complex, and it is often unclear how decisions about technology, policy, and recovery strategy will impact resilience. In this context, the paper focuses on two primary objectives. First, to offer security decision-makers a way of better understanding the impact of deploying different recovery solutions at organizational level by means of simulation modelling and comparative analysis of solutions. Second, to illustrate the suitability and benefits of using semantically justified, compositional system models together with a rigorously defined codesign model-construction methodology, in a complex scenario. Our choice of organizational recovery as modelling target is motivated through both form and complexity, allowing for illustrating the model conceptualization and construction methodology in a sufficiently rich context. We conceptualize the ransomware behaviour, organizational structure, IT infrastructure, and recovery choices and behaviour based on literature surveys and expert knowledge. Then, construct a modular, simulation model representing a generic target organization using our codesign approach. We execute the model over 9000 different parameter configurations, totalling an amount of 450 000 iterations. We analyse the results, both in three specific scenarios deemed organizationally relevant and at the general level—through sensitivity analysis—and, exemplify possible ways in which the model can help inform decision-makers about their possible recovery choices.

Mechanistic models (MMs) have served as causal pathway analysis and ‘decision-support’ tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches – access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

Abstract The nature of information security has been, and probably will continue to be, marked by the asymmetric competition of attackers and defenders over the control of an uncertain environment. The reduction of this degree of uncertainty via an increase in understanding of that environment is a primary objective for both sides. Models are useful tools in this context because they provide a way to understand and experiment with their targets without the usual operational constraints. However, given the technological and social advancements of today, the object of modelling has increased in complexity. Such objects are no longer singular entities but heterogeneous socio-technical systems interlinked to form large-scale ecosystems. Furthermore, the underlying components of a system might be based on very different epistemic assumptions and methodologies for construction and use. Naturally, consistent, rigorous reasoning about such systems is hard but necessary for achieving both security and resilience. The goal of this paper is to present a modelling approach tailored for heterogeneous systems based on three elements: an inferentialist interpretation of what a model is, a distributed systems metaphor to structure that interpretation and a co-design cycle to describe the practical design and construction of the model. The underlying idea is that an open-world interpretation, supported by a formal, yet generic abstraction facilitating knowledge translation and providing properties for structured reasoning and, used in practice according to the co-design cycle could lead to models that are more likely to achieve their pre-stated goals. We explore the suitability of this method in the context of two different security-oriented models: an organizational recovery under ransomware model and a surge capacity trauma unit model.

In animal sciences, the number of published meta-analyses is increasing at a rate of 15% per year. This current review focuses on the good practices and the potential pitfalls in the conduct of meta-analyses in animal sciences, nutrition in particular. Once the study objectives have been defined, several key phases must be considered when doing a meta-analysis. First, as a principle of traceability, criteria used to select or discard publications should be clearly stated in a way that one could reproduce the final selection of data. Then, the coding phase, aiming to isolate specific experimental factors for an accurate graphical and statistical interpretation of the database, is discussed. Following this step, the study of the levels of independence of factors and of the degree of data balance of the meta-design represents an essential phase to ensure the validity of statistical processing. The consideration of the study effect as fixed or random must next be considered. It appears based on several examples that this choice does not generally have any influence on the conclusions of a meta-analysis when the number of experiments is sufficient.

In the last decade, the functionally graded carbon nanotube reinforced composites (FG-CNTRCs) have attracted considerable interest due to their excellent mechanical properties, and the structures made of FG-CNTRCs have found broad potential applications in aerospace, civil and ocean engineering, automotive industry, and smart structures. Here we review the literature regarding the mechanical analysis of bulk CNTR nanocomposites and FG-CNTRC structures, aiming to provide a clear picture of the mechanical modeling and properties of FG-CNTRCs as well as their composite structures. The review is organized as follows: (1) a brief introduction to the functionally graded materials (FGM), CNTRCs and FG-CNTRCs; (2) a literature review of the mechanical modeling methodologies and properties of bulk CNTRCs; (3) a detailed discussion on the mechanical behaviors of FG-CNTRCs; and (4) conclusions together with a suggestion of future research trends.

This paper attempts to estimate a panel \"frontier\" whole economy aggregate energy demand function for 29 countries over the period 1978 to 2006 using parametric stochastic frontier analysis (SFA). Consequently, unlike standard energy demand econometric estimation, the energy efficiency of each country is also modeled and it is argued that this represents a measure of the underlying efficiency for each country over time, as well as the relative efficiency across the 29 OECD countries. This shows that energy intensity is not necessarily a good indicator of energy efficiency, whereas by controlling for a range of economic and other factors, the measure of energy efficiency obtained via this approach is. This is, as far as is known, the first attempt to econometrically model OECD energy demand and efficiency in this way and it is arguably particularly relevant in a world dominated by environmental concerns with the subsequent need to conserve energy and/or use it as efficiently as possible. Moreover, the results show that although for a number of countries the change in energy intensity over time might give a reasonable indication of efficiency improvements; this is not always the case. Therefore, unless this analysis is undertaken, it is not possible to know whether the energy intensity of a country is a good proxy for energy efficiency or not. Hence, it is argued that this analysis should be undertaken to avoid potentially misleading advice to policy makers.

The paper investigates the effects of oil price shocks on stock market volatility in Europe by focusing on three measures of volatility, i.e. the conditional, the realized and the implied volatility. The findings suggest that supply-side shocks and oil specific demand shocks do not affect volatility, whereas, oil price changes due to aggregate demand shocks lead to a reduction in stock market volatility. More specifically, the aggregate demand oil price shocks have a significant explanatory power on both current- and forward-looking volatilities. The results are qualitatively similar for the aggregate stock market volatility and the industrial sectors' volatilities. Finally, a robustness exercise using short- and long-run volatility models supports the findings.

The coincident rise in crude oil prices and increased number of financial participants in the crude oil futures market from 2000-2008 has led to allegations that \"speculators\" drive crude oil prices. As crude oil futures peaked at $147/bbl in July 2008, the role of speculators came under heated debate. In this paper, we employ unique data from the U.S. Commodity Futures Trading Commission (CFTC) to test the relation between crude oil prices and the trading positions of various types of traders in the crude oil futures market. We employ Granger Causality tests to analyze lead and lag relations between price and position data at daily and multiple day intervals. We find little evidence that hedge funds and other non-commercial (speculator) position changes Granger-cause price changes; the results instead suggest that price changes precede their position changes.

Among the variety of applications of exoskeletons, it is possible to mention motor rehabilitation, enhancement of human capabilities and providing support to different types of tasks. Despite the remarkable achievements in this field, two major issues still need to be improved in the exoskeleton design methodology, the mechanical design and the controller. Considering that the dynamic modelling approach plays a key role in these issues, this article proposes the use of modular modelling methodology for the development of exoskeletons. Initially, the conceptual design of a lower limb exoskeleton is presented, then its kinematic and dynamic models are calculated. Finally, some performed simulations demonstrate the model consistency and the actuator torques estimation.