Incorporating linear programing and life cycle thinking into environmental sustainability decision-making: a case study on anchovy canning industry

Life cycle assessment (LCA) is a powerful tool to support environmental informed decisions among product and process alternatives. LCA results reflect the process stage contributions to several environmental impacts, which should be made mutually comparable to help in the decision-making process. Aggregated environmental indexes enable the translation of this set of metrics into a one final score, by defining the attached weights to impacts. Weighting values reflect the corresponding relevance assigned to each environmental impact. Current weighing schemes are based on pre-articulation of preferences, without considering the specific features of the system under study. This paper presents a methodology that combines LCA methodology and linear programming optimisation to determine the environmental improvement actions that conduct to a more sustainable production. LCA was applied using the environmental sustainability assessment methodology to obtain two main indexes: natural resources (NR) and environmental burdens (EB). Normalised indexes were optimised to determine the optimal joint of weighting factors that lead to an optimised global Environmental Sustainability Index. The proposed methodology was applied to a food sector, in particular, to the anchovy canning industry in Cantabria Region (Northern Spain). By maximising the objective function composed of NR and EB variables, it is possible to find the optimal joint of weights that identify the best environmental sustainable options. This study proves that LCA can be applied in combination with linear programing tools as a part of the decision-making process in the development of more sustainable processes and products.     

Empirical Comparisons of X‐bar Charts when Control Limits are Estimated

A control chart is a very common tool used to monitor the quality of business processes. An estimator of the process variability is generally considered to obtain the control limits of a chart when parameters of the process are unknown. Assuming Monte Carlo simulations, this paper first compares the efficiency of the various estimators of the process variability. Two empirical measures used to analyze the performance of control charts are defined. Results derived from various empirical studies reveal the existence of a linear relationship between the performance of the various estimators of the process variability and the performance of charts. The various Monte Carlo simulations are conducted under the assumption that the process is in both situations of in‐control and out‐of‐control. Copyright © 2015 John Wiley & Sons, Ltd.     

Effects of heating in air and chlorine atmosphere on the crystalline structure of pure Ta2O5 or mixed with carbon

Structure changes undergone by pure amorphous hydrated tantalum oxide mixed with different types of carbon when heated in air or chlorine atmospheres were monitored by X-ray diffraction (XRD) and scanning electronic microscopy (SEM). Heating in air of pure Ta2O5 causes the appearance of the hexagonal structure -Ta2O5 at 973 K and the ortho-rhombic structure -Ta2O5 at 1173 K. Heating in chlorine atmosphere markedly lowers the temperature at which transformation to the orthorhombic phase occurs. This effect is attributed to recrystallization of tantalum oxide from tantalum chloride and oxygen, both in gaseous phase, formed in a previous chlorination step of the amorphous oxide. When the thermal treatment is performed in chlorine atmosphere the presence of carbon permits the detection of the hexagonal form at 753 K; this temperature varies with the type of carbon and the oxide : carbon ration. © 1998 Kluwer Academic Publishers     

An integrated domain analysis approach for teleoperated systems

Teleoperated systems for ship hull maintenance (TOS) are robotic systems for ship maintenance tasks, such as cleaning or painting a ship’s hull. The product line paradigm has recently been applied to TOS, and a TOS reference architecture has thus been designed. However, TOS requirements specifications have not been developed in any rigorous way with reuse in mind. We therefore believe that an opportunity exists to increase the abstraction level at which stakeholders can reason about this product line. This paper reports an experience in which this TOS domain was analyzed, including the lessons learned in the construction and use of the TOS domain model. The experience is based on the application of extensions of well-known domain analysis techniques, together with the use of quality attribute templates traced to a feature model to deal with non-functional issues. A qualitative research method (action research) was used to carry out the experience.     

A multivariable control scheme in a two-stage anaerobic digestion system described by partial differential equations

This paper deals with the design and application of a nonlinear multivariable controller in an anaerobic digestion system (AD) carried out in two interconnected fixed bed bioreactors. The proposed control scheme is derived from a mathematical model of the AD system described by a set of partial differential equations and consists of an estimator and two nonlinear control laws. The first law is developed to regulate the volatile fatty acids in the first bioreactor while the second aims at maintaining the chemical oxygen demand at predetermined set-points in the second bioreactor. The performance of the control algorithm is evaluated via numerical simulations in the face of load disturbances, parameter kinetic uncertainties and set-point changes. Stability and convergence properties of the proposed control scheme are also addressed in this paper.     

Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

Abstract: Many real‐world visual tracking applications have a high dimensionality, i.e. the system state is defined by a large number of variables. This kind of problem can be modelled as a dynamic optimization problem, which involves dynamic variables whose values change in time. Most applied research on optimization methods have focused on static optimization problems but these static methods often lack explicit adaptive methodologies. Heuristics are specific methods for solving problems in the absence of an algorithm for formal proof. Metaheuristics are approximate optimization methods which have been applied to more general problems with significant success. However, particle filters are Monte Carlo algorithms which solve the sequential estimation problem by approximating the theoretical distributions in the state space by simulated random measures called particles. However, particle filters lack efficient search strategies. In this paper, we propose a general framework to hybridize heuristics/metaheuristics with particle filters properly. The aim of this framework is to devise effective hybrid visual tracking algorithms naturally, guided by the use of abstraction techniques. Resulting algorithms exploit the benefits of both complementary approaches. As a particular example, a memetic algorithm particle filter is derived from the proposed hybridization framework. Finally, we show the performance of the memetic algorithm particle filter when it is applied to a multiple object tracking problem.     

Applying intelligent agents and semantic web services in eGovernment environments

The increasing volume of eGovernment‐related services is demanding new approaches for service integration and interoperability in this domain. Semantic web (SW) technologies and applications can leverage the potential of eGovernment service integration and discovery, thus tackling the problems of semantic heterogeneity characterizing eGovernment information sources and the different levels of interoperability. eGovernment services will therefore be semantically described in the foreseeable future. In an environment with semantically annotated services, software agents are essential as the entities responsible for exploiting the semantic content in order to automate some tasks, and so enhance the user's experience. In this paper, we present a framework that provides a seamless integration of semantic web services and intelligent agents technologies by making use of ontologies to facilitate their interoperation. The proposed framework can assist in the development of powerful and flexible distributed systems in complex, dynamic, heterogeneous, unpredictable and open environments. Our approach is backed up by a proof‐of‐concept implementation, where the breakthrough of integrating disparate eGovernment services has been tested.     

Interactive Ray Tracing of Large Models Using Voxel Hierarchies

We propose an efficient approach for interactive visualization of massive models with CPU ray tracing. A voxel‐based hierarchical level‐of‐detail (LOD) framework is employed to minimize rendering time and required system memory. In a pre‐processing phase, a compressed out‐of‐core data structure is constructed, which contains the original primitives of the model and the LOD voxels, organized into a kd‐tree. During rendering, data is loaded asynchronously to ensure a smooth inspection of the model regardless of the available I/O bandwidth. With our technique, we are able to explore data sets consisting of hundreds of millions of triangles in real‐time on a desktop PC with a quad‐core CPU.     

On the differential benchmarking of promotional efficiency with machine learning modelling (II): Practical applications

The assessment of promotional sales with models constructed by machine learning techniques is arousing interest due, among other reasons, to the current economic situation leading to a more complex environment of simultaneous and concurrent promotional activities. An operative model diagnosis procedure was previously proposed in the companion paper, which can be readily used both for agile decision making on the architecture and implementation details of the machine learning algorithms, and for differential benchmarking among models. In this paper, a detailed example of model analysis is presented for two representative databases with different promotional behaviour, namely, a non-seasonal category (milk) and a heavily seasonal category (beer). The performance of four well-known machine learning techniques with increasing complexity is analyzed in detail here. In particular, k-Nearest Neighbours, General Regression Neural Networks, Multilayer Perceptron (MLP), and Support Vector Machines (SVM), are differentially compared. Present paper evaluates these techniques along the experiments described for both categories when applying the methodological findings obtained in the companion paper. We conclude that some elements included in the architecture are not essential for a good performance of the machine learning promotional models, such as the semiparametric nature of the kernel in SVM models, whereas other can be strongly dependent of the database, such as the convenience of multiple output models in MLP regression schemes. Additionally, the specificity of the behaviour of certain categories and product ranges determines the need to establish suitable and specific procedures for a better prediction and feature extraction.