Failure rates modelling for heterogenous populations

Mixture of distributions, decreasing failure rate, increasing failure rate, proportional hazards model, accelerated life model, asymptotic behavior of mixture failure rate

Modelling and analysis of a production plant for low density polyethylene

Dynamic model, tubular reactor, polyethylene, LDPE, discretization, simulation, sensitivity analysis, nonlinear analysis

Precipitation of barium sulphate nanoparticles in microemulsion : experiments and modelling

Nanopartikel, BaSO4, Mikroemulsion, Fällung, Modellierung

Modelling the influence of microstructure on elastic properties and tensile damage behaviour of Mo-base silicide alloys

Mo-Si-B alloys, Real microstructures, Voronoi structures, Microstructural characterization, Modelling and finite element simulations, Effective material properties, Damage and Crack growth, tensile strength, fracture toughness

Simulations for modelling of population balance equations of particulate processes using the discrete particle model (DPM)

Magdeburg, Univ., Fak. für Mathematik, Diss., 2009

Elastic modelling of deformation twinning on the microscale

Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2009

Multimodal automatic user disposition recognition in human-machine interaction

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013

Context modelling for IT security in selected application scenarios

Magdeburg, Univ., Fak. für Informatik, Diss., 2015

Conceptual Foundations of Modelling of Innovative Production Projects

At the moment there is a lack of methodological approaches to formalization of management of innovative projects relating to production systems, as well as to adaptation and practical use of the existing approaches. This article is about one potential approach to the management of innovative projects, which makes the building of innovative process models possible based on objective approach. It outlines the frameworks for the building of innovative project models, and describes the method of transition from conceptual modelling to innovative project management. In this case, the model alone and together with parameters used for evaluation of the project may be unique and depends on the special features of the project, preferences of decision-making person, and production and economic system in which it is to be implemented. Unlike existing approaches, this concept does not place any restrictions on types of models and makes it possible to take into account the specificities of economic and production systems. Principles embodied in the model allow its usage as a basis for simulation model to be used in one of specialized simulation systems, as well as for information system providing information support of decision-making process in production and economic systems both newly developed by the company (enterprise) and designed on the basis of available information systems that interact through the exchange of data. In addition, this article shows that the development of conceptual foundations of innovative project management in the economic and production systems is inseparable from the development of the theory of industrial control systems, and their comprehensive study may be reduced to a set of elements represented as certain algorithms, models and evaluations. Thus, the study of innovative process may be conducted in both directions: from general to particular, and vice versa.

Locations and Relocations:Modelling, Determinants, and Interrelations

Empirical studies on industrial location do not typically distinguish between new and relocated establishments. This paper addresses this shortcoming using data on the frequency of these events in municipalities of the same economic-administrative region. This enables us to test not only for differences in their determinants but also for interrelations between start-ups and relocations. Estimates from count regression models for cross-section and panel data show that, although partial effects differ, common patterns arise in “institutional” and “neoclassical” explanatory factors. Also, start-ups and relocations are positive but asymmetrically related. JEL classification: C25, R30, R10. Keywords: cities, count data models, industrial location

Individual-based modelling of bacterial cultures to study the lag ohase

Els bacteris són la forma dominant de vida del planeta: poden sobreviure en medis molt adversos, i en alguns casos poden generar substàncies que quan les ingerim ens són tòxiques. La seva presència en els aliments fa que la microbiologia predictiva sigui un camp imprescindible en la microbiologia dels aliments per garantir la seguretat alimentària. Un cultiu bacterià pot passar per quatre fases de creixement: latència, exponencial, estacionària i de mort. En aquest treball s’ha avançat en la comprensió dels fenòmens intrínsecs a la fase de latència, que és de gran interès en l’àmbit de la microbiologia predictiva. Aquest estudi, realitzat al llarg de quatre anys, s’ha abordat des de la metodologia Individual-based Modelling (IbM) amb el simulador INDISIM (INDividual DIScrete SIMulation), que ha estat millorat per poder fer-ho. INDISIM ha permès estudiar dues causes de la fase de latència de forma separada, i abordar l’estudi del comportament del cultiu des d’una perspectiva mesoscòpica. S’ha vist que la fase de latència ha de ser estudiada com un procés dinàmic, i no definida per un paràmetre. L’estudi de l’evolució de variables com la distribució de propietats individuals entre la població (per exemple, la distribució de masses) o la velocitat de creixement, han permès distingir dues etapes en la fase de latència, inicial i de transició, i aprofundir en la comprensió del que passa a nivell cel•lular. S’han observat experimentalment amb citometria de flux diversos resultats previstos per les simulacions. La coincidència entre simulacions i experiments no és trivial ni casual: el sistema estudiat és un sistema complex, i per tant la coincidència del comportament al llarg del temps de diversos paràmetres interrelacionats és un aval a la metodologia emprada en les simulacions. Es pot afirmar, doncs, que s’ha verificat experimentalment la bondat de la metodologia INDISIM.

Primary localized rectal/pararectal gastrointestinal stromal tumors: results of surgical and multimodal therapy from the French Sarcoma group.

BACKGROUND: Rectal and pararectal gastrointestinal stromal tumors (GISTs) are rare. The optimal management strategy for primary localized GISTs remains poorly defined. METHODS: We conducted a retrospective analysis of 41 patients with localized rectal or pararectal GISTs treated between 1991 and 2011 in 13 French Sarcoma Group centers. RESULTS: Of 12 patients who received preoperative imatinib therapy for a median duration of 7 (2-12) months, 8 experienced a partial response, 3 had stable disease, and 1 had a complete response. Thirty and 11 patients underwent function-sparing conservative surgery and abdominoperineal resection, respectively. Tumor resections were mostly R0 and R1 in 35 patients. Tumor rupture occurred in 12 patients. Eleven patients received postoperative imatinib with a median follow-up of 59 (2.4-186) months. The median time to disease relapse was 36 (9.8-62) months. The 5-year overall survival rate was 86.5%. Twenty patients developed local recurrence after surgery alone, two developed recurrence after resection combined with preoperative and/or postoperative imatinib, and eight developed metastases. In univariate analysis, the mitotic index (≤5) and tumor size (≤5 cm) were associated with a significantly decreased risk of local relapse. Perioperative imatinib was associated with a significantly reduced risk of overall relapse and local relapse. CONCLUSIONS: Perioperative imatinib therapy was associated with improved disease-free survival. Preoperative imatinib was effective. Tumor shrinkage has a clear benefit for local excision in terms of feasibility and function preservation. Given the complexity of rectal GISTs, referral of patients with this rare disease to expert centers to undergo a multidisciplinary approach is recommended.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.