Search

The design of production plants in the form of a Cyber Physical Production System (CPPS) promises rapid adaptability to changing market requirements, high flexibility during production, robust behavior in the event of failure and offers the possibility of integrating customers into the production process. In contradiction to these advantages there are both a low penetration and a still low Technology Readiness Level (TRL) of operational CPPS. This article therefore presents a production system for customized beverage bottling, consisting of industrial resources from well-known machine builders and designed as a CPPS through the use of a Multi-Agent System (MAS). The premise that all products are to be manufactured in a saleable form should clarify the claim of the CPPS to be classified as an industrial plant. Its functionality is tested through an evaluation based on two use cases with different requirements. This conceptual proof should enable the future, practice-oriented investigation of urgent questions from research and industry. As a further result, a complete test method for the development and commissioning of an CPPS, starting with the design phase and ending with a valid, reliable production system, is shown.

The Manufacturing Execution System (MES) is a process-oriented IT solution collecting and managing information from the shop floor manufacturing processes. Because of the programming and customization effort required for specific production processes, the MES implementation is not widespread in the food and beverage industry, as most food and beverage manufacturers are small- and medium-sized enterprises with limited resources to invest in MES. For engineering the MES that should be customized according to the target production processes, a model-driven approach has been presented in this paper with six phases covering the entire lifecycle of the MES engineering process. By using this approach, MES can be automatically generated and sustainably improved, which has the potential to reduce the complexity of implementation as well as the resources required for the engineering of customizable MES. Based on two use cases in the processing and packaging areas in the food and beverage industry, the feasibility and practicality of the presented approach have been proven.

Facing environmental challenges, high energy costs and a growing public awareness, the global brewing industry is increasingly publishing ambitious targets toward a more sustainable production. Small and medium-sized enterprises of the brewing and beverage industry cannot ensure energy and media efficiency mainly due to capital and knowledge inadequacy. This article addresses this problem and presents a pragmatic method to determine the energy and media demand. Accordingly, a modeling editor as well as a standardized data structure and automatic simulation parameter determination tools were developed to implement the method. A given production plant can be modeled with adequate details using the presented editor. Based on a configuration file, a holistic simulation model can be generated automatically in a simulation environment. A beverage bottling plant was studied, and the necessary datasets were obtained for implementing the proposed editor and, thereby, the method. It was confirmed that the simulated values of electrical energy and compressed air consumption match the measured empirical data. The measures to increase energy and media efficiency were also found effective. Using the presented method, enterprises of the brewing and beverage industry can easily uncover avenues for potential savings, test the effectiveness of optimization strategies, and substantiate possible investment decisions.

Marketing Automation in der Touristik ermoglicht signifikante Effizienzsteigerungen im hochkomplexen Tourismus-Geschaftsmodell. Wahrend weltweit die voranschreitende digitale Transformation im Wachstumsmarkt Tourismus fur eine rasante Implementierung immer neuer Toolstacks im Marketing sorgt, beschrankt man sich in der DACH-Region bisher eher auf den E-Commerce-Anteil im touristischen Umfeld. Ziel des Beitrags ist es, ein Bewusstsein fur die Marketing Automation entlang der gesamten Marketing Supply Chain in der Touristik zu schaffen und Unternehmen in der Touristik zu motivieren, Marketing- und Vertriebsaufgaben zu automatisieren.

The future powertrain mix will be derived above all from a CO2- and emissionoptimized vehicle fleet. The results of a powertrain variation study show that a scenario with high HEV share in the fleet is robust in regard to the external boundary conditions, particularly the electricity mix and the share of renewables. The high share of combustion engines also makes it easy to reduce CO2 emissions using synthetic fuels. A low share of PHEV, BEV and FCV also results in reduced manufacturing costs on the powertrain level. Furthermore, the analysis which powertrain mix would be expedient for WtW-CO2-legislation. Finally, modular powertrain systems were allocated to various vehicle segments to allow for a combination of combustion engines with dedicated hybrid transmissions with one or two electric motors and various electric axle configurations from single-speed to seamless-shifting multi-speed transmissions.

Manufacturing execution systems (MES) are process-oriented IT solutions that collect and manage information from manufacturing processes to improve transparency. Owing to the considerable programming effort required for the implementation of custom systems for specific production processes, investing in MES is not an option for many food and beverage manufacturers. Model-driven engineering (MDE) is one solution for reducing the implementation costs involved in custom systems. However, a concrete MDE approach that fulfills the requirements of the food and beverage industry has not yet been established for MES. With this background, this paper introduces an MDE approach for MES and focuses on the first step in implementing this approach by defining the modeling elements of MES functions that enable the automatic transformation from models into an operational MES. The modeling elements are defined for the four components of an MES solution: the plant model illustrating the technical systems, the process model describing the production processes, the MES function model representing the required MES functions and the report model showing the results of MES functions. A use case in the domain of beer brewing is presented to evaluate the proposed approach. This use case demonstrates the feasibility and suitability of predefined modeling elements in the modeling phase for automatic MES generation.

Quantum devices depend on addressable elements, which can be modified separately and in their mutual interaction. Self-assembly at surfaces, for example, formation of a porous (metal-) organic network, provides an ideal way to manufacture arrays of identical quantum boxes, arising in this case from the confinement of the electronic (Shockley) surface state within the pores. We show that the electronic quantum box state as well as the interbox coupling can be modified locally to a varying extent by a selective choice of adsorbates, here C60, interacting with the barrier. In view of the wealth of differently acting adsorbates, this approach allows for engineering quantum states in on-surface network architectures.

Sustainability is a megatrend influencing the beverage industry. Knowledge of the consumption behavior and suitable metrics are required for energy optimization strategies. Machine efficiency and energy consumption are intermixed in common parameters, e.g., customary specifications refer to the energy consumption for a specific number of products (e.g., kWh/1000 fillings). This does not reflect the influence that inevitable breakdown times have on the energy consumption (e.g., malfunction, lack, and tailback situations within the material flow). While specific energy performance indicators are useful as a benchmark, it does not provide reliable information to verify plant specifications, or to have a source-related cost allocation as a basis for a weak point analysis. In this work, energy and operational data were analyzed, in order to find a generic description of the operational-state related consumption behavior. Therefore, empirical data on the effective electrical energy and operational state data were collected on machine level of two representative bottling plants and for additional single machines. In the frequency distributions of the discrete values of the measured electrical energy data, three main peaks were found. These can be correlated to operational states such as state-related energy demand level. The change from one demand level to another was found to be reproducible.

Modern food companies focus on green value. While modelling and simulation methods for the prognosis and optimisation of the production efficiency of bottling plants are well-established and published, applicable state based models for the energy demand are missing. To close this gap this study presents an enhanced modelling and simulation approach for the electrical energy demand of food packaging machines and plants. It follows the hypothesis, that the electrical energy demand of bottling machines may be represented by a limited number of discrete energy demand levels correlating to their current operating states. A generic model was found defining energy demand level (DL), state transitions taking place through a finite number of constant intermediate level (IL) and a generic mapping to the operational states. Constant demand levels were parameterised empirically using operational state and active power data collected during a three month period in industrial bottling plants. For plants in the food and beverage industry electrical energy saving potentials up to 35% are expected. The presented simulation model was validated using industrial plant state data and measured power demand values. As Theil's index was found to be below a critical value of 0.4 for all machines and the total line and the total deviation for the total line with a 95%-confidence interval [0,3; 4,38] for α = 0,05 for three validation runs of each fourteen days the simulation was found satisfactory for analysing electrical energy optimisation strategies in a future application.

Die Rechtmasigkeit einer palliativmedizinischen Behandlung ist mangels besonderer Bestimmungen an den allgemeinen Regeln der Arzthaftung zu messen. Danach muss die medizinische Behandlung drei Grundvoraussetzungen genugen, um beruflich legitimiert zu sein und vor dem Recht bestehen zu konnen: Sie muss indiziert sein, den Regeln des Faches entsprechen (Verfahren „lege artis“) und mit Einwilligung des aufgeklarten Patienten erfolgen („informed consent“). Die Beurteilung der Standardgemasheit einer konkreten Behandlung ist hochgradig patienten- und einzelfallabhangig, weshalb die Gerichte zur Klarung von Behandlungsfehlervorwurfen in der Regel medizinische Sachverstandige zuziehen mussen. Indikation sowie Aufklarung und Einwilligung des Patienten sind demgegenuber auch abstrakt klarer konturierte Aspekte des arztlichen Pflichtenprogramms, denen hier aus palliativmedizinischer Sicht nachzugehen ist. Zu erortern ist schlieslich die arztliche Pflicht zur Information des Patienten uber Kostenaspekte der Behandlung bei fehlender Kostenubernahme, insbesondere durch die Gesetzliche Krankenversicherung.

Manufacturing Execution Systems (MES) are process-oriented information-technology (IT) solutions for collecting and managing information from manufacturing processes. Due to the individual programming effort and the complex integration with other manufacturing systems, though the food and beverage manufacturers can benefit from the MES, its implementation is not widespread in this industry. To simplify the implementation and engineering process, the concept of model-driven engineering (MDE) is considered as a solution. However, a feasible model-driven approach for MES engineering has not been established, not to mention for the food and beverage industry. This paper presents an approach for the automatic MES generation according to the MDE concept providing MES functions that are relevant to the food and beverage manufacturing processes primarily. It consists of necessary phases to cover the whole engineering process of the MES. Based on the application of the presented approach to the brewing process in a brewhouse, the feasibility and practicality of this approach were proven.

The Manufacturing Execution System (MES) is a production management system serving as the information center in the enterprise to improve manufacturing transparency. It is the middle layer connecting the manufacturing process on the shop floor and the business process on the Enterprise Resource Planning (ERP) level. On the one hand, the MES guides the execution of rough production plans into detailed operations on the shop floor. On the other hand, it provides the firm with critical key performance indicators (KPIs), enabling commercial decisions. The support from the MES, such as production fine planning, performance analysis, and product tracing, can help manufacturers to be efficient and gain more competitiveness in the global market. However, in the food and beverage industry, which faces strict regulations, growing competitiveness, customer demand changing, and suffer from low-profit margins, the implementation of the MES did not become widespread. This article intends to present the particular characteristics of the food and beverage manufacturing process, analyze the potential benefits and barriers of the MES implementation in the food and beverage industry through literature review. The solutions to solve the MES implementation issues and the research areas that need to be explored in order to meet the MES requirements from the food and beverage industry are also discussed in this article.

We introduce a new way to construct variables for classification in a setting of astronomy. The newly constructed variables complement the currently used Hillas parameters and are specifically designed to improve the classification. They are based on fitting elliptic or skewed bivariate distributions to images gathered by imaging atmospheric Cherenkov telescopes and evaluating the distance between the observed and the fitted distribution. As distance measures we use the Chi-square distance, the Kullback–Leibler divergence and the Hellinger distance. The new variables lead to an improved classification in terms of misclassification errors.

We created a customizable solution for automatic fault diagnosis in bottling plants.The model-based approach allows the generation of individual diagnosis solutions.New validated models for all relevant plant components were developed.The diagnosis solution was tested in practical scale and judged as a useful tool.Up to 87.1% of compliant diagnosis results were obtained. The bottling of beverages is carried out in complex plants that consist of several machines and material flows. To realize an efficient bottling process and high quality products, operators try to avoid plant downtimes. With actual non-productive times of between 10% and 60%, the operators require diagnosis tools that allow them to locate plant components that cause downtime by exploiting automatically acquired machine data.This paper presents a model-based solution for automatic fault diagnosis in bottling plants. There are currently only a few plant-specific solutions (based on statistical calculations or artificial neural networks) for automatic bottling plant diagnosis. In order to develop a customizable solution, we followed the model-based diagnosis approach which allows the automatic generation of diagnosis solutions for individual plants. The existing stochastic and discrete-event models for bottling plants are not adequate for model-based diagnosis. Therefore, we developed new first-principle models for the relevant plant components, validated them numerically, and abstracted them to qualitative diagnosis models. Based on the diagnosis engine OCC'M Raz'r, application systems for two real plants and one virtual plant (based on discrete-event simulation) were generated and evaluated. Compared to the reasons for downtime identified by experts, we obtained up to 87.1% of compliant diagnosis results. The diagnosis solution was tested by practitioners and judged as a useful tool for plant optimization.

Wahrend der Jubilar sein siebzigstes Lebensjahr vollendet hat, muss das berufsstandische Konzept „Individueller Gesundheitsleistungen“ (IGeL) noch fur heranwachsend gelten, durchlauft es doch gerade erst das zwanzigste Jahr seit seiner erstmaligen medialen Inszenierung. Bereits lange bevor davon die Rede war, hat Franz‐Josef Dahm sich neben seinem sozialrechtlichen Schwerpunkt den Rechtsfragen privat zu finanzierender Zusatzleistungen zugewendet und sich anhaltend bis in jungste Zeit immer wieder mit unterschiedlichen Facetten der Problematik befasst. Davon angeregt, geht dieser Beitrag auf Entwicklung, Gestalt und Perspektiven der Arztvergutung als Hintergrund auch des Phanomens privater Zusatzleistungen ein.

We introduce a method to minimize the mean square error (MSE) of an estimator which is derived from a classification. The method chooses an optimal discrimination threshold in the outcome of a classification algorithm and deals with the problem of unequal and unknown misclassification costs and class imbalance. The approach is applied to data from the MAGIC experiment in astronomy for choosing an optimal threshold for signal-background-separation. In this application one is interested in estimating the number of signal events in a dataset with very unfavorable signal to background ratio. Minimizing the MSE of the estimation is a rather general approach which can be adapted to various other applications, in which one wants to derive an estimator from a classification. If the classification depends on other or additional parameters than the discrimination threshold, MSE minimization can be used to optimize these parameters as well. We illustrate this by optimizing the parameters of logistic regression, leading to relevant improvements of the current approach used in the MAGIC experiment.

Eine wesentliche Herausforderung von Industrie 4.0 ist die vertikale Integration der Produktionsebenen mit ihren eingebetteten Systemen. Manufacturing Execution Systeme (MES) spielen dabei ein zentrale Rolle, da sie das Bindeglied zwischen dem Enterprise Ressource Planning (ERP) System und der Produktionswelt darstellen.

ObjectivesThe aim of this study was to examine the value of native and post-contrast T1 relaxation in the differentiation between healthy and diffusely diseased myocardium in 2 model conditions, hypertrophic cardiomyopathy and nonischemic dilated cardiomyopathy.BackgroundT1 mapping has been proposed as potentially valuable in the quantitative assessment of diffuse myocardial fibrosis, but no studies to date have systematically evaluated its role in the differentiation of healthy myocardium from diffuse disease in a clinical setting.MethodsConsecutive subjects undergoing routine clinical cardiac magnetic resonance at King's College London were invited to participate in this study. Groups were based on cardiac magnetic resonance findings and consisted of subjects with known hypertrophic cardiomyopathy (n = 25) and nonischemic dilated cardiomyopathy (n = 27). Thirty normotensive subjects with low pre-test likelihood of cardiomyopathy, not taking any regular medications and with normal cardiac magnetic resonance findings including normal left ventricular mass indexes, served as controls. Single equatorial short-axis slice T1 mapping was performed using a 3-T scanner before and at 10, 20, and 30 minutes after the administration of 0.2 mmol/kg of gadobutrol. T1 values were quantified within the septal myocardium (T1native), and extracellular volume fractions (ECV) were calculated.ResultsT1native was significantly longer in patients with cardiomyopathy compared with control subjects (p < 0.01). Conversely, post-contrast T1 values were significantly shorter in patients with cardiomyopathy at all time points (p < 0.01). ECV was significantly higher in patients with cardiomyopathy compared with controls at all time points (p < 0.01). Multivariate binary logistic regression revealed that T1native could differentiate between healthy and diseased myocardium with sensitivity of 100%, specificity of 96%, and diagnostic accuracy of 98% (area under the curve 0.99; 95% confidence interval: 0.96 to 1.00; p < 0.001), whereas post-contrast T1 values and ECV showed lower discriminatory performance.ConclusionsThis study demonstrates that native and post-contrast T1 values provide indexes with high diagnostic accuracy for the discrimination of normal and diffusely diseased myocardium.

Purpose To investigate the effect of magnetic field strength on the validity of two assumptions (namely, the “transceive phase assumption” and the “phase-only reconstruction”) for electrical properties tomography (EPT) at 1.5, 3, and 7T. Theory Electrical properties tomography is a method to map the conductivity and permittivity using MRI; the B1+ amplitude and phase is required as input. The B1+ phase, however, cannot be measured and is therefore deduced from the measurable transceive phase using the transceive phase assumption. Also, earlier studies showed that the B1+ amplitude is not always required for a reliable conductivity reconstruction; this is the so-called “phase-only conductivity reconstruction.” Methods Electromagnetic simulations and MRI measurements of phantoms and the human head. Results Reconstructed conductivity and permittivity maps based on B1+ distributions at 1.5, 3, and 7T were compared to the expected dielectric properties. The noise level of measurements was also determined. Conclusion The transceive phase assumption is most accurate for low-field strengths and low permittivity and in symmetric objects. The phase-only conductivity reconstruction is only applicable at 1.5 and 3T for the investigated geometries. The measurement precision was found to benefit from a higher field strength, which is related to increased signal-to-noise ratio (SNR) and increased curvature of the B1+ field. Magn Reson Med 71:354–363, 2014. © 2013 Wiley Periodicals, Inc.

A Prins cyclization between a polymer-bound aldehyde and a homoallylic alcohol served as the key step in the synthesis of tetrahydropyran derivatives. A phenotypic screen led to the identification of compounds that inhibit mitosis (as seen by the accumulation of round cells with condensed DNA and membrane blebs). These compounds were termed tubulexins as they target the CSE1L protein and the vinca alkaloid binding site of tubulin.

In beverage bottling plants, buffering conveying systems are used to decouple the various machines. They avoid propagation of machine breakdowns along the plant by assimilating or releasing bottles or containers. Today, they are controlled in steps by mechanical jam switches. In many cases, they enable an enduring filling process, which is necessary to gain good product quality and high plant efficiency. But with longer faults, the filling process still has to stop or slow down. Additionally, today's conveyors run very noisy. This affects the operator's health and conflicts with legal regulations. This paper presents an alternative control approach for buffering conveyors. It was created to improve the buffering effect and to reduce the noise emissions of today's systems. Instead of using jam switches, it uses counting sensor systems to obtain stepless information on the bottles entering and leaving the buffering conveyor. On this basis, the first continuous control algorithm for buffering conveyors was developed. It was described mathematically for all possible buffer situations. It was programmed and implemented in an industrial scale pilot plant. Finally, experiments in comparison with the commonly used jam switch control were carried out for all relevant settings. The practical experiments showed a stable behaviour of the new control algorithm. It reacted appropriately to state changes. Readjustment times and an almost minimal dead time (shortest transportation time of bottles) proved a high buffering effect. Above all, the new control algorithm resulted in a clearly audible and measureable reduction of the noise level. Copyright © 2012 John Wiley & Sons, Ltd.

The current gold standard to estimate local and global specific energy absorption rate for MRI involves numerically modeling the patient and the transmit radiofrequency coil. Recently, a patient-individual method was presented, which estimated specific energy absorption rate from individually measured B(1) maps. This method, however, was restricted to quadrature volume coils due to difficulties distinguishing phase contributions from radiofrequency transmission and reception. In this study, a method separating these two phase contributions by comparing the electric conductivity reconstructed from different transmit channels of a parallel radiofrequency transmission system is presented. This enables specific energy absorption rate estimation not only for quadrature excitation but also for the nonquadrature excitation of the single elements of the transmit array. Though the contributions of the different phases are known, unknown magnetic field components and tissue boundary artifacts limit the technique. Nevertheless, the high agreement between simulated and experimental results found in this study is promising. B(1)-based specific energy absorption rate determination might become possible for arbitrary radiofrequency excitation on a patient-individual basis.

Myocardial fibrosis is a common denominator in a wide spectrum of cardiomyopathies; it plays a major role in the pathophysiology of structural remodelling and as a predictor of adverse outcome. Quantification of myocardial fibrosis by invasive biopsy is limited in clinical practice due to the commonly scattered tissue distribution and procedural risks. Late gadolinium enhancement by cardiac magnetic resonance has revolutionized the assessment of ischemic cardiomyopathy by visualization of regional scarring after myocardial infarction. The binary (white-black) principle of contrast development and a requirement for well separated layers of healthy and diseased myocardium is straightforward for clinical decision making in ischemic heart disease, but renders this technique less powerful in conditions where the myocardium is affected diffusely. Recently emerged T1 mapping techniques allow for an individualized quantification of global and regional myocardial signal and are promising tools to separate between healthy and diseased. In this article we review the emerging evidence for T1 mapping techniques and outline the necessary future directions for a successful translational pathway.

Tissue heating during magnetic resonance measurements is a potential hazard at high-field MRI, and particularly, in the framework of parallel radiofrequency transmission. The heating is directly related to the radiofrequency energy absorbed during an magnetic resonance examination, that is, the specific absorption rate (SAR). SAR is a pivotal parameter in MRI safety regulations, requiring reliable estimation methods. Currently used methods are usually based on models which are neither patient-specific nor taken into account patient position and posture, which typically leads to the need for large safety margins. In this work, a novel approach is presented, which measures local SAR in a patient-specific manner. Using a specific formulation of Maxwell's equations, the local SAR is estimated via postprocessing of the complex transmit sensitivity of the radiofrequency antenna involved. The approximations involved in the proposed method are investigated. The presented approach yields a sufficiently accurate and patient-specific local SAR measurement of the brain within a scan time of less than 5 min.

The electric properties of human tissue can potentially be used as an additional diagnostic parameter, e.g., in tumor diagnosis. In the framework of radiofrequency safety, the electric conductivity of tissue is needed to correctly estimate the local specific absorption rate distribution during MR measurements. In this study, a recently developed approach, called electric properties tomography (EPT) is adapted for and applied to in vivo imaging. It derives the patient's electric conductivity and permittivity from the spatial sensitivity distributions of the applied radiofrequency coils. In contrast to other methods to measure the patient's electric properties, EPT does not apply externally mounted electrodes, currents, or radiofrequency probes, which enhances the practicability of the approach. This work shows that conductivity distributions can be reconstructed from phase images and permittivity distributions can be reconstructed from magnitude images of the radiofrequency transmit field. Corresponding numerical simulations using finite-difference time-domain methods support the feasibility of this phase-based conductivity imaging and magnitude-based permittivity imaging. Using this approximation, three-dimensional in vivo conductivity and permittivity maps of the human brain are obtained in 5 and 13 min, respectively, which can be considered a step toward clinical feasibility for EPT. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc.

This work presents a new approach toward a fast, simultaneous amplitude of radiofrequency field (B(1)) and T(1) mapping technique. The new method is based on the "actual flip angle imaging" (AFI) sequence. However, the single pulse repetition time (TR) pair used in the standard AFI sequence is replaced by multiple pulse repetition time sets. The resulting method was called "multiple TR B(1)/T(1) mapping" (MTM). In this study, MTM was investigated and compared to standard AFI in simulations and experiments. Feasibility and reliability of MTM were proven in phantom and in vivo experiments. Error propagation theory was applied to identify optimal sequence parameters and to facilitate a systematic noise comparison to standard AFI. In terms of accuracy and signal-to-noise ratio, the presented method outperforms standard AFI B(1) mapping over a wide range of T(1). Finally, the capability of MTM to determine T(1) was analyzed qualitatively and quantitatively, yielding good agreement with reference measurements.

Over the past few years new rules regarding noise exposure of workers have been enacted. In the brewing and beverage industry, this especially concerns the area of bottling. Bottling plants in most cases exceed the new levels. The transport of glass bottles has long been recognized one of the main sources of noise within a bottling hall. This paper examines a new continuous approach for controlling multi-lane bottle conveyors. In contrast to the nowadays used stepwise control, the filling level is detected continuously. Thus the conveyor speed can be adapted exactly to the situation occurring on the conveyor. This leads to noticeable lower noise emissions of the conveyor, without influencing the buffer and transporting tasks in a negative way. The new control concept may help the beverage industry avoid hearing disorders of workers, without decreasing the efficiency of the plants.

The efficiency of bottling plants typically ranges between 40 to 70%. Automatic conditioning monitoring helps to find critical points in a plant and supports the operator in optimizing the plant. But bottling plants are complex lines of several linked machines and currently critical points can only be identified manually. This paper presents a model-based efficiency analysis tool. It automatically localizes critical points that decrease the efficiency of the whole plant and the tool is adaptable to different plants solely through parameterization. The algorithm compares the behaviour of a plant with an OK-model of the plant. If there are inconsistencies, the commercial tool RAZ'R finds a failure model that is consistent with the observed plant behaviour, thus localizing the component that causes downtime of the filler. The algorithm succeeds in identifying the cause of the downtime in 90% of the cases. A demonstrator application, which runs on different plants, has been implemented and requires only simple adaptation steps. The algorithm only needs information about the configuration of the plant and the production data, which normally exists in automated plants. In the future, it is expected that the project partners will integrate the algorithm with their PDA-systems, such that the model-based analysis will help to increase the efficiency of bottling plants.

The self-assembly of three porphyrin derivatives was studied in detail on a Cu(111) substrate by means of scanning tunneling microscopy (STM). All derivatives have two 4-cyanophenyl substituents in diagonally opposed meso-positions of the porphyrin core. but differ in the nature of the other two meso-alkoxyphenyl substituents. At coverages below 0.8 monolayers, two derivatives form molecular chains, which evolve into nanoporous networks at higher coverages. The third derivative self-assembles directly into a nanoporous network without showing a one-dimensional phase. The pore-to-pore distances for the three networks depend on the size and shape of the alkoxy substituents. All observed effects are explained by 1) different steric demands of the alkoxy residues, 2) apolar (mainly dispersion) interactions between the alkoxy chains, 3) polar bonding involving both cyanophenyl and alkoxyphenyl substituents, and 4) the entropy/enthalpy balance of the network formation.

Industrial manufacturing processes are complex processes, where transparency of every process step is necessary to achieve a high level of quality and efficiency. In order to achieve this transparency, manufacturing execution systems (MES) are used. However, as these systems are very expensive, mainly due to individual programming effort, MES usage is oftentimes limited to larger companies. To ultimately reduce implementation costs for MES, the current research project AutoMES proposes a standardized, model-based approach to facilitate automatic generation of MES functions. This paper presents requirements on a suitable modeling language, as well as how these requirements are fulfilled by the modeling language used in the AutoMES project. The modeling language is an extension of the MES Modeling Language (MES-ML), a modeling language for the specification of MES. With the use of the extended MES-ML it is possible to generate a generic, machine-usable MES specification, suitable for code generation. To evaluate the proposed modeling language extensions, an industrial brewing process has been modeled and verified by MES engineers during the project AutoMES.

PURPOSE: To validate ironload T2* by automatic inline Maximum Likelihood Estimate (MLE) with k-space Rician noise correction, against the manual and automated truncation, as well as offset methods, in phantoms and in heart and liver in patients.METHODS: Twenty-five patients and an iron-oxide phantom were scanned at 1.5T using 2 multi-echo gradient-echo sequences. All parameters were identical (voxel 2-3 × 2-3 × 10 mm(3) , 10 echoes, TR = 26 ms, FA = 20°, BW = 833 Hz, SENSE = 2) except for TE (cardiac: TE1 = 2·5 ms, ΔTE = 2·5 ms; liver: TE1 = 1·2 ms, ΔTE = 1·5 ms). Phantoms were scanned at 1 and 32 signal averages (NSA), with NSA32 representing low-noise reference.RESULTS: Phantoms: MLE showed low variability between NSA1 and NSA32 (0·02 ± 0·29 ms, CI ±0·21 ms). Between methods, no difference was shown (MLE versus all