Static and Dynamic Integrated Expert Systems: State of the Art, Problems and Trends

Systemized analysis of trends towards integration and hybridization in contemporary expert systems is conducted, and a particular class of applied expert systems, integrated expert systems, is considered. For this purpose, terminology, classification, and models, proposed by the author, are employed. As examples of integrated expert systems, Russian systems designed in this field and available to the majority of specialists are analyzed.

Using the Simulation Modeling Methods for the Designing Real-Time Integrated Expert Systems

Certain theoretical and methodological problems of designing real-time dynamical expert systems, which belong to the class of the most complex integrated expert systems, are discussed. Primary attention is given to the problems of designing subsystems for modeling the external environment in the case where the environment is represented by complex engineering systems. A specific approach to designing simulation models for complex engineering systems is proposed and examples of the application of this approach based on the G2 (Gensym Corp.) tool system are described.

Timed Transition Automata as Numerical Planning Domain

A general technique for transforming a timed finite state automaton into an equivalent automated planning domain based on a numerical parameter model is introduced. Timed transition automata have many applications in control systems and agents models; they are used to describe sequential processes, where actions are labelling by automaton transitions subject to temporal constraints. The language of timed words accepted by a timed automaton, the possible sequences of system or agent behaviour, can be described in term of an appropriate planning domain encapsulating the timed actions patterns and constraints. The time words recognition problem is then posed as a planning problem where the goal is to reach a final state by a sequence of actions, which corresponds to the timed symbols labeling the automaton transitions. The transformation is proved to be correct and complete and it is space/time linear on the automaton size. Experimental results shows that the performance of the planning domain obtained by transformation is scalable for real world applications. A major advantage of the planning based approach, beside of the solving the parsing problem, is to represent in a single automated reasoning framework problems of plan recognitions, plan synthesis and plan optimisation.

High-Order Control Variations and Small-Time Local Controllability

The importance of “control variations” for obtaining local approximations of the reachable set of nonlinear control systems is well known. Heuristically, if one can construct control variations in all possible directions, then the considered control system is small-time locally controllable (STLC). Two concepts of control variations of higher order are introduced for the case of smooth control systems. The relation between these variations and the small-time local controllability is studied and a new sufficient STLC condition is proved.

The immune system as drug target

The immune system is perhaps the largest yet most diffuse and distributed somatic system in vertebrates. It plays vital roles in fighting infection and in the homeostatic control of chronic disease. As such, the immune system in both pathological and healthy states is a prime target for therapeutic interventions by drugs-both small-molecule and biologic. Comprising both the innate and adaptive immune systems, human immunity is awash with potential unexploited molecular targets. Key examples include the pattern recognition receptors of the innate immune system and the major histocompatibility complex of the adaptive immune system. Moreover, the immune system is also the source of many current and, hopefully, future drugs, of which the prime example is the monoclonal antibody, the most exciting and profitable type of present-day drug moiety. This brief review explores the identity and synergies of the hierarchy of drug targets represented by the human immune system, with particular emphasis on the emerging paradigm of systems pharmacology. © the authors, publisher and licensee Libertas Academica Limited.

Scalable harmonization of complex networks with local controllers

Computational and communication complexities call for distributed, robust, and adaptive control. This paper proposes a promising way of bottom-up design of distributed control in which simple controllers are responsible for individual nodes. The overall behavior of the network can be achieved by interconnecting such controlled loops in cascade control for example and by enabling the individual nodes to share information about data with their neighbors without aiming at unattainable global solution. The problem is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, that can be implemented adaptively and which provide a systematic rich way to information sharing. This paper elaborates the overall solution, applies it to linear-Gaussian case, and provides simulation results.

Glia-augmented artificial neural networks: foundations and applications

Information processing in the human brain has always been considered as a source of inspiration in Artificial Intelligence; in particular, it has led researchers to develop different tools such as artificial neural networks. Recent findings in Neurophysiology provide evidence that not only neurons but also isolated and networks of astrocytes are responsible for processing information in the human brain. Artificial neural net- works (ANNs) model neuron-neuron communications. Artificial neuron-glia networks (ANGN), in addition to neuron-neuron communications, model neuron-astrocyte con- nections. In continuation of the research on ANGNs, first we propose, and evaluate a model of adaptive neuro fuzzy inference systems augmented with artificial astrocytes. Then, we propose a model of ANGNs that captures the communications of astrocytes in the brain; in this model, a network of artificial astrocytes are implemented on top of a typical neural network. The results of the implementation of both networks show that on certain combinations of parameter values specifying astrocytes and their con- nections, the new networks outperform typical neural networks. This research opens a range of possibilities for future work on designing more powerful architectures of artificial neural networks that are based on more realistic models of the human brain.

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Molecular Insights of CD4+ T Cell Differentiation, Effector Formation and Helper Function

CD4+ T cells play a crucial in the adaptive immune system. They function as the central hub to orchestrate the rest of immunity: CD4+ T cells are essential governing machinery in antibacterial and antiviral responses by facilitating B cell affinity maturation and coordinating the innate and adaptive immune systems to boost the overall immune outcome; on the contrary, hyperactivation of the inflammatory lineages of CD4+ T cells, as well as the impairments of suppressive CD4+ regulatory T cells, are the etiology of various autoimmunity and inflammatory diseases. The broad role of CD4+ T cells in both physiological and pathological contexts prompted me to explore the modulation of CD4+ T cells on the molecular level.

microRNAs (miRNAs) are small RNA molecules capable of regulating gene expression post-transcriptionally. miRNAs have been shown to exert substantial regulatory effects on CD4+ T cell activation, differentiation and helper function. Specifically, my lab has previously established the function of the miR-17-92 cluster in Th1 differentiation and anti-tumor responses. Here, I further analyzed the role of this miRNA cluster in Th17 differentiation, specifically, in the context of autoimmune diseases. Using both gain- and loss-of-function approaches, I demonstrated that miRNAs in miR-17-92, specifically, miR-17 and miR-19b in this cluster, is a crucial promoter of Th17 differentiation. Consequently, loss of miR-17-92 expression in T cells mitigated the progression of experimental autoimmune encephalomyelitis and T cell-induced colitis. In combination with my previous data, the molecular dissection of this cluster establishes that miR-19b and miR-17 play a comprehensive role in promoting multiple aspects of inflammatory T cell responses, which underscore them as potential targets for oligonucleotide-based therapy in treating autoimmune diseases.

To systematically study miRNA regulation in effector CD4+ T cells, I devised a large-scale miRNAome profiling to track in vivo miRNA changes in antigen-specific CD4+ T cells activated by Listeria challenge. From this screening, I identified that miR-23a expression tightly correlates with CD4+ effector expansion. Ectopic expression and genetic deletion strategies validated that miR-23a was required for antigen-stimulated effector CD4+ T cell survival in vitro and in vivo. I further determined that miR-23a targets Ppif, a gatekeeper of mitochondrial reactive oxygen species (ROS) release that protects CD4+ T cells from necrosis. Necrosis is a type of cell death that provokes inflammation, and it is prominently triggered by ROS release and its consequent oxidative stress. My finding that miR-23a curbs ROS-mediated necrosis highlights the essential role of this miRNA in maintaining immune homeostasis.

A key feature of miRNAs is their ability to modulate different biological aspects in different cell populations. Previously, my lab found that miR-23a potently suppresses CD8+ T cell cytotoxicity by restricting BLIMP1 expression. Since BLIMP1 has been found to inhibit T follicular helper (Tfh) differentiation by antagonizing the master transcription factor BCL6, I investigated whether miR-23a is also involved in Tfh differentiation. However, I found that miR-23a does not target BLIMP1 in CD4+ T cells and loss of miR-23a even fostered Tfh differentiation. This data indicate that miR-23a may target other pathways in CD4+ T cells regarding the Tfh differentiation pathway.

Although the lineage identity and regulatory networks for Tfh cells have been defined, the differentiation path of Tfh cells remains elusive. Two models have been proposed to explain the differentiation process of Tfh cells: in the parallel differentiation model, the Tfh lineage is segregated from other effector lineages at the early stage of antigen activation; alternatively, the sequential differentiation model suggests that naïve CD4+ T cells first differentiate into various effector lineages, then further program into Tfh cells. To address this question, I developed a novel in vitro co-culture system that employed antigen-specific CD4+ T cells, naïve B cells presenting cognate T cell antigen and BAFF-producing feeder cells to mimic germinal center. Using this system, I were able to robustly generate GC-like B cells. Notably, well-differentiated Th1 or Th2 effector cells also quickly acquired Tfh phenotype and function during in vitro co-culture, which suggested a sequential differentiation path for Tfh cells. To examine this path in vivo, under conditions of classical Th1- or Th2-type immunizations, I employed a TCRβ repertoire sequencing technique to track the clonotype origin of Tfh cells. Under both Th1- and Th2- immunization conditions, I observed profound repertoire overlaps between the Teff and Tfh populations, which strongly supports the proposed sequential differentiation model. Therefore, my studies establish a new platform to conveniently study Tfh-GC B cell interactions and provide insights into Tfh differentiation processes.

Welke 'acteur' neemt de controllerrol als extraverte business partner?

Which 'actor' takes the management accountant role as an extravert business partner? Does a relation between the personal trait Extraversion and fulfilling a management accountant role as a business partner exist? Open Universiteit Nederland End thesis MSc Management, Accounting & Finance Support 1: Prof. dr. A.C.N. van de Ven RA Support 2: dr. P.C.M. Claes Examinator: dr. P. Kamminga Date of approval: September 3, 2014 student: P.R. van der Wal (studentnumber 839104017 email petervanderwal2003@yahoo.com The main question of this research is: Does a relation between the personal trait Extraversion and fulfilling a management accountant role as a business partner exist? This research is based on the dataset obtained by the controller survey 2013, executed in commission of the 'Open Universiteit' (Bork & van der Wal, 2014). From the literature review it is clear: among other management accountant roles we need business partners. And there is a relation between the personal trait Extraversion and fulfilling the role as business partner. At the same time a lack of necessary personal traits for this role has been noticed, among which is Extraversion. The factor- and cluster analyses reported by Bork & van der Wal (2014) resulted in the identification of two types of management accountant roles. In this extended research TYPE II is identified as a business partner because (s)he practices activity-combinations which are related to strategy, analyzing, supporting management in decision making, advisory, change-agency and representing the organization. 36% of the population of Dutch management accountants with a master degree (or similar) meet with the role of the business partner. Although the fulfillment of the role (TYPE II) is not purely business partnering. E.g. reporting and scorekeeping are still activities executed by TYPE II and it is not clear to what extent. Apart from that, role TYPE I executes change management and risk-management activities, which are (according to the definition) activities that belong to the business partner. The role as business partner is practiced but not that optimal as defined in theory. The logistic regression analyses on the survey-data show that Extraversion among three other triggers is significant for the prediction of the fulfillment of the management accountant role (Bork & van der Wal, 2014). A more extravert personal trait predicts a preference for TYPE II, which relates to the business partner. This 'in depth research' concentrated on the relation between the Big Five personal traits and the six activity-combinations (factors) instead of on the two clusters (I and II). The statistic analyses confirm the predicting influence of Extraversion on the business partner role. Although, except for one factor, no extra significance has been found in this additional research. The essential question can be confirmed positively: the management accountant role business partner exists in practice, some management accountants are more extravert then others, and there is a positive relation between extraversion and fulfilling the business partner role. Some formulated research limitations are related to the statistical weakness of some prediction outcomes and to interpretation differences that might occur. Further research can e.g. concentrate on the other personal traits and the significance for role-differentiation in education programs. The management accountant survey 2013 Management accountant roles in 2013 in the Netherlands Open Universiteit Nederland End thesis MSc Management, Accounting & Finance Support 1: Prof. dr. A.C.N. van de Ven RA Support 2: dr. P.C.M. Claes Examinator: dr. P. Kamminga Date of approval: September 3, 2014 student: P.R. van der Wal and H.J. Bork studentnumber: 839104017 and 838532340) email: petervanderwal2003@yahoo.com and hjbork@hotmail.com This paper describes the conceptual model and results of the 'management accountants survey 2013'. The survey is part of a longitudinal survey, earlier executed in 2004, 2007 and 2010 under responsibility of the 'Open Universiteit Nederland'. Secondly the dataset of this survey will be used by us to do our own analyses on the predicting value of the triggers 'personality factor: extraversion' and 'lever of control: interactive controls' on the management accounting role that comes close to a role defined as 'Business Partner'. Scientific research shows that there are different management accounting roles, and that these roles change and that preferences exist for certain roles (Verstegen B. , Loo, Mol, Slagter, & Geerkens, 2007). The main question that will be answered in this paper is which coherent combinations of activities are being executed by management accountants in 2013 in the Netherlands by master-graduates? And secondly which triggers of management accountants' activities predict to which cluster a management accountant belongs? The conceptual model of this research has been developed in 2004 (Verstegen B. , Loo, Mol, Slagter, & Geerkens, 2007). For this research the same 37 activities as in the former researches are included (appendix 1). In the trigger-set (appendix 1) some adaptations have been made for reasons of restricting the length of the survey and to pinpoint on particular research goals (e.g. personality and levers of control). The coherent combinations of activities were found by a factor-analysis and the groups of controllers by a cluster analysis. A regression analysis shows which trigger-items are most significant. The survey has been sent to 2.353 students that finished a controller-study on a Dutch University. There was a 9% (211) response with a completely filled survey. 137 of which indicated to work in a controller-function at the moment. These controllers have been included in the results. The factor-analysis results in six different coherent combinations of activities (factors). Shortly these factors are: advising top management on strategic level with result-effecting information (1), organizing internal reporting (2) organizing and representing the organization on external reporting (3), advising and managing changes by shortcomings in processes and control systems (4), maintaining and managing administrative organization- , information- and control systems (5) and organizing/executing risk management and internal audit (6). Factors 4, 5 and 6 are clustered in cluster TYPE I (125 controllers) and factors 1, 2 and 3 in cluster TYPE II (69 controllers). TYPE II can be associated with the management accountant role 'Business Partner', although the accountant keeps partly active in a scorekeeper role. The four most significant triggers for predicting being a TYPE II controller are 'Executing a risk-management task in order to meet compliance standards' (1), extraversion (2), company size in terms of fte (3) and gender (4).

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Residence Time Distribution (Rtd) Associated with Statistical Weighted Moments to Fit Mathematical Models Using In Industrial Systems Diagnosis

This work presents a computational, called MOMENTS, code developed to be used in process control to determine a characteristic transfer function to industrial units when radiotracer techniques were been applied to study the unit´s performance. The methodology is based on the measuring the residence time distribution function (RTD) and calculate the first and second temporal moments of the tracer data obtained by two scintillators detectors NaI positioned to register a complete tracer movement inside the unit. Non linear regression technique has been used to fit various mathematical models and a statistical test was used to select the best result to the transfer function. Using the code MOMENTS, twelve different models can be used to fit a curve and calculate technical parameters to the unit.

Dimensionality hyper-reduction and machine learning for dynamical systems with varying parameters

Thesis (Ph.D.)--University of Washington, 2016-08

Development of a toolbox for the reduction of hordeins in barley malt beers

Gluten sensitive consumers and people suffering from coeliac disease account for up to 6% of the general population (Catassi et al., 2013). These consumers must avoid foods which contain gluten and related proteins found in wheat, rye or barley. Beer is produced from barley malt and therefore contains hordeins, (gluten like proteins). Beers labelled as gluten-free must contain below 10 mg/kg hordeins (10 mg/kg hordeins = 20 mg/kg gluten under current regulations) to be considered safe for gluten sensitive consumers. Currently there are a limited number of methods available for reducing beer hordeins, the studies outlined in this thesis provide a range of tools for the beverage industry to reduce the hordein content of beer It is well known, that during malting and brewing hordeins are reduced, but they still remain in beer at levels above 10 mg/kg. During malting, hordeins are broken down to form new proteins in the growing plant. Model malting and brewing systems were developed and used to test, how the modification of the malting process could be used to reduce beer hordeins. It was shown, that by using a controlled malting and brewing regime, a range of barley cultivars produced beer with significant differences in levels of hordeins. Beer hordeins ranged from 10 mg/kg to 60 mg/kg. Another study revealed that when malting was prolonged, to maximise breakdown of proteins, beer hordeins can be reduced by up to 44%. The natural breakdown of hordein during malting enhanced in a further study, when a protease was added to support the hordein degradation during steeping and germination. The enzyme addition resulted in a 46% reduction in beer hordeins 2 when compared to the control. All of the malt treatments had little or no impact on malt quality. The hordein levels can also be reduced during the beer stabilisation process. Levels of beer hordein were tested after stabilisation using two different concentrations of silica gel and tannic acid. Silica gel was very effective in reducing beer hordeins, 90% of beer hordeins were removed compared to the control beer. Beer hordeins could be reduced to below 10 mg/kg and the beer qualities such as foam, colour and flavour were not affected. Tannic acid also reduced beer hordein by up to 90%, but it reduced foam stability and affected beer flavours. A further study described treatment of beer with microbial transglutaminase (mTG), to create bonds between hordein proteins, which increased particle size and allowed removal during filtration. The addition of the mTG led to a reduction of the beer hordein by up to 96% in beer, and the impact on the resulting beer quality was minimal. These studies provide the industry with a toolbox of methods leading to the reduction of hordein in the final beer without negatively affecting beer quality.

Navegação de um robô móvel baseado em um modelo de consciência artificial

This work presents a study about a the Baars-Franklin architecture, which defines a model of computational consciousness, and use it in a mobile robot navigation task. The insertion of mobile robots in dynamic environments carries a high complexity in navigation tasks, in order to deal with the constant environment changes, it is essential that the robot can adapt to this dynamism. The approach utilized in this work is to make the execution of these tasks closer to how human beings react to the same conditions by means of a model of computational consci-ousness. The LIDA architecture (Learning Intelligent Distribution Agent) is a cognitive system that seeks tomodel some of the human cognitive aspects, from low-level perceptions to decision making, as well as attention mechanism and episodic memory. In the present work, a computa-tional implementation of the LIDA architecture was evaluated by means of a case study, aiming to evaluate the capabilities of a cognitive approach to navigation of a mobile robot in dynamic and unknown environments, using experiments both with virtual environments (simulation) and a real robot in a realistic environment. This study concluded that it is possible to obtain benefits by using conscious cognitive models in mobile robot navigation tasks, presenting the positive and negative aspects of this approach.