Oxidative stress complex syndrome: The dark side of the malnutrition‐inflammation complex syndrome

Patients undergoing maintenance hemodialysis (HD) have a high prevalence of protein‐energy malnutrition and inflammation. As these 2 conditions often occur concomitantly in HD patients, they have been referred to together as the ‘malnutrition‐inflammation complex syndrome’ (MICS) or ‘malnutrition‐inflammation atherosclerosis’ (MIA) syndrome to emphasize its important association with atherosclerotic cardiovascular disease. Oxidative stress, which results from an imbalance between oxidants and antioxidant defense mechanisms, is well established in HD subjects and could contribute to the poor clinical outcome of these patients. The aim of the present review is to discuss in more detail the common consequences of MICS and oxidative stress and their possible relationships with the long‐term complications of HD patients, leading to the conclusion that a complex syndrome similar to the MICS or MIA is the oxidative stress‐inflammation association, which may be called the “oxidative stress complex syndrome.”     

A first step towards a simple in-line shape compensation routine for the roll forming of high strength steel

The roll forming process is increasingly used in the automotive industry for the manufacture of structural and crash components from Ultra High Strength Steel (UHSS). Due to the high strength of UHSS (<1GPa) even small and commonly observed material property variations from coil to coil can result in significant changes in material yield and through that affect the final shape of the roll formed component. This requires the re-adjustment of tooling to compensate for shape defects and maintain part geometry resulting in costly downtimes of equipment. This paper presents a first step towards an in-line shape compensation method that based on the monitoring of roll load and torque allows for the estimation of shape defects and the subsequent re-adjustment of tooling for compensation. For this the effect of material property variation on common shape defects observed in the roll forming process as well as measurable process parameters such as roll load and torque needs to be understood. The effect of yield strength and material hardening on roll load and torque as well as longitudinal bow is investigated via experimental trials and numerical analysis. A regression analysis combined with Analysis of Variance (ANOVA) techniques is employed to establish the relationships between the process and material parameters and to determine their percentage influence on longitudinal bow, roll load and torque. The study will show that the level of longitudinal bow, one of the major shape defects observed in roll forming, can be estimated by variations in roll load and torque.     

DEVS-BASED INTELLIGENT CONTROL: SPACE-ADAPTED MIXING SYSTEM EXAMPLE

This paper reviews a methodology for event-based intelligent control employing the DEVS (discrete event system specification) formalism. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. We apply the DEVS-based intelligent control paradigm to a space-adapted mixing system. The event-based approach is compared with conventional sequential control methods.     

A multi-objective programming approach,integrated into the TOPSIS method,in order to optimize product design; in three-dimensional concurrent engineering

This paper proposes a new method to address a three-dimensional concurrent engineering (3D-CE) approach. It uses a Multi-Objective Linear Programming (MOLP) model integrated to the TOPSIS method in order to determine the best configuration product design, assembly process and suppliers of components. This method is able to involve various supply chain strategies by considering qualitative and quantitative criteria in the early stages of the new product development (NPD) process. It also applies the Fuzzy Analytic Hierarchy Process (FAHP) method to determine the relative importance of criteria defined to evaluate configurations of design alternatives, assembly processes and suppliers of components. A sensitivity analysis from different parameters in the model that affect the selection of the best design alternative is performed by creating a computer program using visual studio. A numerical example is used to demonstrate method efficacy and finally, the paper suggests some areas for future research.     

THE QSM ALGORITHM AND ITS APPLICATION TO SOFTWARE BEHAVIOR MODEL INDUCTION

This article presents a novel application of grammatical inference techniques to the synthesis of behavior models of software systems. This synthesis is used for the elicitation of software requirements. This problem is formulated as a deterministic finite-state automaton induction problem from positive and negative scenarios provided by an end user of the software-to-be. A query-driven state merging (QSM) algorithm is proposed. It extends the Regular Positive and Negative Inference (RPNI) and blue-fringe algorithms by allowing membership queries to be submitted to the end user. State merging operations can be further constrained by some prior domain knowledge formulated as fluents, goals, domain properties, and models of external software components. The incorporation of domain knowledge both reduces the number of queries and guarantees that the induced model is consistent with such knowledge. The proposed techniques are implemented in the ISIS tool and practical evaluations on standard requirements engineering test cases and synthetic data illustrate the interest of this approach.     

Robust Visual Tracking via Structured Multi-Task Sparse Learning

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $\ell _{p,q}$ mixed norms $(\text{ specifically} p\in \{2,\infty \}$ and $q=1),$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $L_1$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259–2272, 2011) is a special case of our MTT formulation (denoted as the $L_{11}$ tracker) when $p=q=1.$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.     

A framework to support qualitative reasoning about COAs in a dynamic spatial environment

We propose a framework to reason qualitatively about courses of action (COAs) which need to be executed in a realistic geographic space that may change. Particularly, the framework aims to support human mental ‘What-If’ analysis by simulating the execution of COAs in a virtual geographic environment, which can change during the simulation, and by allowing the user to explore various scenarios (different COAs) and to analyse their outcomes using causal reasoning techniques. In this article, we first present a framework which is based on a conceptual model of spatio-temporal situations, a multi-agent geosimulation platform and qualitative spatio-temporal causal reasoning techniques. Then, we illustrate the framework using a case study.     

Mining association rules for the quality improvement of the production process

Academics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment’s results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.     

A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models

Soft classification techniques avoid the loss of information characteristic to hard classification techniques when handling mixed pixels. Sub‐pixel mapping is a method incorporating benefits of both hard and soft classification techniques. In this paper an algorithm is developed based on sub‐pixel/pixel attractions. The design of the algorithm is accomplished using artificial imagery but testing is done on artificial as well as real synthetic imagery. The algorithm is evaluated both visually and quantitatively using established classification accuracy indices. The resulting images show increased accuracy when compared to hardened soft classifications.