939 resultados para integrated lot sizing and scheduling models


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This article is a transcription of an electronic symposium sponsored by the Brazilian Society of Neuroscience and Behavior (SBNeC). Invited researchers from the European Union, North America and Brazil discussed two issues on anxiety, namely whether panic is a very intense anxiety or something else, and what aspects of clinical anxiety are reproduced by animal models. Concerning the first issue, most participants agreed that generalized anxiety and panic disorder are different on the basis of clinical manifestations, drug response and animal models. Also, underlying brain structures, neurotransmitter modulation and hormonal changes seem to involve important differences. It is also common knowledge that existing animal models generate different types of fear/anxiety. A challenge for future research is to establish a good correlation between animal models and nosological classification.

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Digital business ecosystems (DBE) are becoming an increasingly popular concept for modelling and building distributed systems in heterogeneous, decentralized and open environments. Information- and communication technology (ICT) enabled business solutions have created an opportunity for automated business relations and transactions. The deployment of ICT in business-to-business (B2B) integration seeks to improve competitiveness by establishing real-time information and offering better information visibility to business ecosystem actors. The products, components and raw material flows in supply chains are traditionally studied in logistics research. In this study, we expand the research to cover the processes parallel to the service and information flows as information logistics integration. In this thesis, we show how better integration and automation of information flows enhance the speed of processes and, thus, provide cost savings and other benefits for organizations. Investments in DBE are intended to add value through business automation and are key decisions in building up information logistics integration. Business solutions that build on automation are important sources of value in networks that promote and support business relations and transactions. Value is created through improved productivity and effectiveness when new, more efficient collaboration methods are discovered and integrated into DBE. Organizations, business networks and collaborations, even with competitors, form DBE in which information logistics integration has a significant role as a value driver. However, traditional economic and computing theories do not focus on digital business ecosystems as a separate form of organization, and they do not provide conceptual frameworks that can be used to explore digital business ecosystems as value drivers—combined internal management and external coordination mechanisms for information logistics integration are not the current practice of a company’s strategic process. In this thesis, we have developed and tested a framework to explore the digital business ecosystems developed and a coordination model for digital business ecosystem integration; moreover, we have analysed the value of information logistics integration. The research is based on a case study and on mixed methods, in which we use the Delphi method and Internetbased tools for idea generation and development. We conducted many interviews with key experts, which we recoded, transcribed and coded to find success factors. Qualitative analyses were based on a Monte Carlo simulation, which sought cost savings, and Real Option Valuation, which sought an optimal investment program for the ecosystem level. This study provides valuable knowledge regarding information logistics integration by utilizing a suitable business process information model for collaboration. An information model is based on the business process scenarios and on detailed transactions for the mapping and automation of product, service and information flows. The research results illustrate the current cap of understanding information logistics integration in a digital business ecosystem. Based on success factors, we were able to illustrate how specific coordination mechanisms related to network management and orchestration could be designed. We also pointed out the potential of information logistics integration in value creation. With the help of global standardization experts, we utilized the design of the core information model for B2B integration. We built this quantitative analysis by using the Monte Carlo-based simulation model and the Real Option Value model. This research covers relevant new research disciplines, such as information logistics integration and digital business ecosystems, in which the current literature needs to be improved. This research was executed by high-level experts and managers responsible for global business network B2B integration. However, the research was dominated by one industry domain, and therefore a more comprehensive exploration should be undertaken to cover a larger population of business sectors. Based on this research, the new quantitative survey could provide new possibilities to examine information logistics integration in digital business ecosystems. The value activities indicate that further studies should continue, especially with regard to the collaboration issues on integration, focusing on a user-centric approach. We should better understand how real-time information supports customer value creation by imbedding the information into the lifetime value of products and services. The aim of this research was to build competitive advantage through B2B integration to support a real-time economy. For practitioners, this research created several tools and concepts to improve value activities, information logistics integration design and management and orchestration models. Based on the results, the companies were able to better understand the formulation of the digital business ecosystem and the importance of joint efforts in collaboration. However, the challenge of incorporating this new knowledge into strategic processes in a multi-stakeholder environment remains. This challenge has been noted, and new projects have been established in pursuit of a real-time economy.

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Meandering rivers have been perceived to evolve rather similarly around the world independently of the location or size of the river. Despite the many consistent processes and characteristics they have also been noted to show complex and unique sets of fluviomorphological processes in which local factors play important role. These complex interactions of flow and morphology affect notably the development of the river. Comprehensive and fundamental field, flume and theoretically based studies of fluviomorphological processes in meandering rivers have been carried out especially during the latter part of the 20th century. However, as these studies have been carried out with traditional field measurements techniques their spatial and temporal resolution is not competitive to the level achievable today. The hypothesis of this study is that, by exploiting e increased spatial and temporal resolution of the data, achieved by combining conventional field measurements with a range of modern technologies, will provide new insights to the spatial patterns of the flow-sediment interaction in meandering streams, which have perceived to show notable variation in space and time. This thesis shows how the modern technologies can be combined to derive very high spatial and temporal resolution data on fluvio-morphological processes over meander bends. The flow structure over the bends is recorded in situ using acoustic Doppler current profiler (ADCP) and the spatial and temporal resolution of the flow data is enhanced using 2D and 3D CFD over various meander bends. The CFD are also exploited to simulate sediment transport. Multi-temporal terrestrial laser scanning (TLS), mobile laser scanning (MLS) and echo sounding data are used to measure the flow-based changes and formations over meander bends and to build the computational models. The spatial patterns of erosion and deposition over meander bends are analysed relative to the measured and modelled flow field and sediment transport. The results are compared with the classic theories of the processes in meander bends. Mainly, the results of this study follow well the existing theories and results of previous studies. However, some new insights regarding to the spatial and temporal patterns of the flow-sediment interaction in a natural sand-bed meander bend are provided. The results of this study show the advantages of the rapid and detailed measurements techniques and the achieved spatial and temporal resolution provided by CFD, unachievable with field measurements. The thesis also discusses the limitations which remain in the measurement and modelling methods and in understanding of fluvial geomorphology of meander bends. Further, the hydro- and morphodynamic models’ sensitivity to user-defined parameters is tested, and the modelling results are assessed against detailed field measurement. The study is implemented in the meandering sub-Arctic Pulmanki River in Finland. The river is unregulated and sand-bed and major morphological changes occur annually on the meander point bars, which are inundated only during the snow-melt-induced spring floods. The outcome of this study applies to sandbed meandering rivers in regions where normally one significant flood event occurs annually, such as Arctic areas with snow-melt induced spring floods, and where the point bars of the meander bends are inundated only during the flood events.

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Nowadays, computer-based systems tend to become more complex and control increasingly critical functions affecting different areas of human activities. Failures of such systems might result in loss of human lives as well as significant damage to the environment. Therefore, their safety needs to be ensured. However, the development of safety-critical systems is not a trivial exercise. Hence, to preclude design faults and guarantee the desired behaviour, different industrial standards prescribe the use of rigorous techniques for development and verification of such systems. The more critical the system is, the more rigorous approach should be undertaken. To ensure safety of a critical computer-based system, satisfaction of the safety requirements imposed on this system should be demonstrated. This task involves a number of activities. In particular, a set of the safety requirements is usually derived by conducting various safety analysis techniques. Strong assurance that the system satisfies the safety requirements can be provided by formal methods, i.e., mathematically-based techniques. At the same time, the evidence that the system under consideration meets the imposed safety requirements might be demonstrated by constructing safety cases. However, the overall safety assurance process of critical computerbased systems remains insufficiently defined due to the following reasons. Firstly, there are semantic differences between safety requirements and formal models. Informally represented safety requirements should be translated into the underlying formal language to enable further veri cation. Secondly, the development of formal models of complex systems can be labour-intensive and time consuming. Thirdly, there are only a few well-defined methods for integration of formal verification results into safety cases. This thesis proposes an integrated approach to the rigorous development and verification of safety-critical systems that (1) facilitates elicitation of safety requirements and their incorporation into formal models, (2) simplifies formal modelling and verification by proposing specification and refinement patterns, and (3) assists in the construction of safety cases from the artefacts generated by formal reasoning. Our chosen formal framework is Event-B. It allows us to tackle the complexity of safety-critical systems as well as to structure safety requirements by applying abstraction and stepwise refinement. The Rodin platform, a tool supporting Event-B, assists in automatic model transformations and proof-based verification of the desired system properties. The proposed approach has been validated by several case studies from different application domains.

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Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

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Leishmaniasis is a disease caused by protozoa of the genus Leishmania, and visceral leishmaniasis is a form in which the inner organs are affected. Since knowledge about immunity in experimental visceral leishmaniasis is poor, we present here a review on immunity and immunosuppression in experimental visceral leishmaniasis in mouse and hamster models. We show the complexity of the mechanisms involved and differences when compared with the cutaneous form of leishmaniasis. Resistance in visceral leishmaniasis involves both CD4+ and CD8+ T cells, and interleukin (IL)-2, interferon (IFN)- gamma, and IL-12, the latter in a mechanism independent of IFN- gamma and linked to transforming growth factor (TGF)-ß production. Susceptibility involves IL-10 but not IL-4, and B cells. In immune animals, upon re-infection, the elements involved in resistance are different, i.e., CD8+ T cells and IL-2. Since one of the immunopathological consequences of active visceral leishmaniasis in humans is suppression of T-cell responses, many studies have been conducted using experimental models. Immunosuppression is mainly Leishmania antigen specific, and T cells, Th2 cells and adherent antigen-presenting cells have been shown to be involved. Interactions of the co-stimulatory molecule family B7-CTLA-4 leading to increased level of TGF-ß as well as apoptosis of CD4+ T cells and inhibition of macrophage apoptosis by Leishmania infection are other components participating in immunosuppression. A better understanding of this complex immune response and the mechanisms of immunosuppression in experimental visceral leishmaniasis will contribute to the study of human disease and to vaccine development.

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The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

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Carpotroche brasiliensis is a native Brazilian tree belonging to the Oncobeae tribe of Flacourtiaceae. The oil extracted from its seeds contains as major constituents the same cyclopentenyl fatty acids hydnocarpic (40.5%), chaulmoogric (14.0%) and gorlic (16.1%) acids found in the better known chaulmoogra oil prepared from the seeds of various species of Hydnocarpus (Flacourtiaceae). These acids are known to be related to the pharmacological activities of these plants and to their use as anti-leprotic agents. Although C. brasiliensis oil has been used in the treatment of leprosy, a disease that elicits inflammatory responses, the anti-inflammatory and analgesic activities of the oil and its constituents have never been characterized. We describe the anti-inflammatory and antinociceptive activities of C. brasiliensis seed oil in acute and chronic models of inflammation and in peripheral and central nociception. The mixture of acids from C. brasiliensis administered orally by gavage showed dose-dependent (10-500 mg/kg) anti-inflammatory activity in carrageenan-induced rat paw edema, inhibiting both the edema by 30-40% and the associated hyperalgesia. The acid fraction (200 mg/kg) also showed significant antinociceptive activity in acetic acid-induced constrictions (57% inhibition) and formalin-induced pain (55% inhibition of the second phase) in Swiss mice. No effects were observed in the hot-plate (100 mg/kg; N = 10), rota-road (200 mg/kg; N = 9) or adjuvant-induced arthritis (50 mg/kg daily for 7 days; N = 5) tests, the latter a chronic model of inflammation. The acid fraction of the seeds of C. brasiliensis which contains cyclopentenyl fatty acids is now shown to have significant oral anti-inflammatory and peripheral antinociceptive effects.

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Autosomal recessive polycystic kidney disease (ARPKD) is an inherited disease characterized by a malformation complex which includes cystically dilated tubules in the kidneys and ductal plate malformation in the liver. The disorder is observed primarily in infancy and childhood, being responsible for significant pediatric morbidity and mortality. All typical forms of ARPKD are caused by mutations in a single gene, PKHD1 (polycystic kidney and hepatic disease 1). This gene has a minimum of 86 exons, assembled into multiple differentially spliced transcripts and has its highest level of expression in kidney, pancreas and liver. Mutational analyses revealed that all patients with both mutations associated with truncation of the longest open reading frame-encoded protein displayed the severe phenotype. This product, polyductin, is a 4,074-amino acid protein expressed in the cytoplasm, plasma membrane and primary apical cilia, a structure that has been implicated in the pathogenesis of different polycystic kidney diseases. In fact, cholangiocytes isolated from an ARPKD rat model develop shorter and dysmorphic cilia, suggesting polyductin to be important for normal ciliary morphology. Polyductin seems also to participate in tubule morphogenesis and cell mitotic orientation along the tubular axis. The recent advances in the understanding of in vitro and animal models of polycystic kidney diseases have shed light on the molecular and cellular mechanisms of cyst formation and progression, allowing the initiation of therapeutic strategy designing and promising perspectives for ARPKD patients. It is notable that vasopressin V2 receptor antagonists can inhibit/halt the renal cystic disease progression in an orthologous rat model of human ARPKD.

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The pulp and paper industry is currently facing broad structural changes due to global shifts in demand and supply. These changes have significant impacts on national economies worldwide. Planted forests (especially eucalyptus) and recovered paper have quickly increased their importance as raw material for paper and paperboard production. Although advances in information and communication technologies could reduce the demand for communication papers, and the growth of paper consumption has indeed flattened in developed economies, particularly in North America and Western Europe, the consumption is increasing on a global scale. Moreover, the focal point of production and consumption is moving from the Western world to the rapidly growing markets of Southeast Asia. This study analyzes how the so-called megatrends (globalization, technological development, and increasing environmental awareness) affect the pulp and paper industry’s external environment, and seeks reliable ways to incorporate the impact of the megatrends on the models concerning the demand, trade, and use of paper and pulp. The study expands current research in several directions and points of view, for example, by applying and incorporating several quantitative methods and different models. As a result, the thesis makes a significant contribution to better understand and measure the impacts of structural changes on the pulp and paper industry. It also provides some managerial and policy implications.

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As increasing efficiency of a wind turbine gearbox, more power can be transferred from rotor blades to generator and less power is used to cause wear and heating in the gearbox. By using a simulation model, behavior of the gearbox can be studied before creating expensive prototypes. The objective of the thesis is to model a wind turbine gearbox and its lubrication system to study power losses and heat transfer inside the gearbox and to study the simulation methods of the used software. Software used to create the simulation model is Siemens LMS Imagine.Lab AMESim, which can be used to create one-dimensional mechatronic system simulation models from different fields of engineering. When combining components from different libraries it is possible to create a simulation model, which includes mechanical, thermal and hydraulic models of the gearbox. Results for mechanical, thermal, and hydraulic simulations are presented in the thesis. Due to the large scale of the wind turbine gearbox and the amount of power transmitted, power loss calculations from AMESim software are inaccurate and power losses are modelled as constant efficiency for each gear mesh. Starting values for simulation in thermal and hydraulic simulations were chosen from test measurements and from empirical study as compact and complex design of gearbox prevents accurate test measurements. In further studies to increase the accuracy of the simulation model, components used for power loss calculations needs to be modified and values for unknown variables are needed to be solved through accurate test measurements.

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The focus of the research is on the derivation of the valid and reliable performance results regarding establishment and launching of the new full-scale industrial facility, considering the overall current conditions for the project realization in and out of Russia. The study demonstrates the process of the new facility concept development, with following perfor-mance calculation, comparative analyzes conduction, life-cycle simulations, performance indicators derivation and project`s sustainability evaluation. To unite and process the entire input parameters complexity, regards the interlacing between the project`s internal technical and commercial sides on the one hand, and consider all the specifics of the Russian conditions for doing business on the other hand, was developed the unique model for the project`s performance calculation, simulations and results representation. The complete research incorporates all corresponding data to substantiate the assigned facility`s design, sizing and output capacity for high quality and cost efficient ferrous pipe-line accessories manufacturing, as well as, demonstrates that this project could be suc-cessfully realized in current conditions in Russia and highlights the room for significant performance and sustainability improvements based on the indexes of the derived KPIs.

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Foods behave as non-Newtonian fluids, but little is known about how corn and soybean mix behave under viscometric flow. In order to characterize the rheological behavior of animal feed under viscometric flow, a 70:30 (mass:mass) mixture of ground corn and soybean grains was submitted to a capillary rheometer at 3 different temperatures (80, 120, and 160 °C), different moisture levels (26.5 ± 0.08; 30.4 ± 0.31, and 33.4 ± 0.05%), and 4 shear rates (30.4; 72.9; 304.3, and 728.6/second). Different strain rates and die dimensions were used to obtain the target shear rates. The resulting data were fitted to Power Law, Casson, and Bingham models. Based on experimental data, water content, mass temperature, and the effects of shear rate on the apparent shear viscosity of corn-soy mix were fitted to a single expression (p < 0.001, R² = 0.93): η = 18,769.7 (y)-0.86 e (-9.34 U + 935 T), where y is shear rate, U is sample moisture, and T is sample temperature in Kelvin scale. As expected, such mixture presented a pseudoplastic (shear-thinning) behavior.

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The chemical composition of apple juices may be used to discriminate between the varieties for consumption and those for raw material. Fuji and Gala have a chemical pattern that can be used for this classification. Multivariate methods correlate independent continuous chemical descriptors with the categorical apple variety. Three main descriptors of apple juice were selected: malic acid, total reducing sugar and total phenolic compounds. A chemometric approach, employing PCA and SIMCA, was used to classify apple juice samples. PCA was performed with 24 juices from Fuji and Gala, and SIMCA, with 15 juices. The exploratory and predictive models recognized 88% and 64%, respectively, as belonging to a mixed domain. The apple juice from commercial fruits shows a pattern related to cv. Fuji and Gala with boundaries from 0.18 to 0.389 g.100 mL-1 (malic acid), from 8.65 to 15.18 g.100 mL-1 (total reducing sugar) and from 100 to 400 mg.L-1 (total phenolic compounds), but such boundaries were slightly shorter in the remaining set of commercial apple juices, specifically from 0.16 to 0.36 g.100 mL-1, from 9.25 to 15.5 g.100 mL-1 and from 180 to 606 mg.L-1 for acidity, reducing sugar and phenolic compounds, respectively, representing the acid, sweet and bitter tastes.

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The aim of this research was to study the effect of air-temperature and diet composition on the mass transfer kinetics during the drying process of pellets used for Japanese Abalone (Haliotis discus hannai) feeding. In the experimental design, three temperatures were used for convective drying, as well as three different diet compositions (Diets A, B and C), in which the amount of fishmeal, spirulin, algae, fish oil and cornstarch varied. The water diffusion coefficient of the pellets was determined using the equation of Fick's second law, which resulted in values between 0.84-1.94×10-10 m²/s. The drying kinetics was modeled using Page, Modified Page, Root of time, Exponential, Logarithmic, Two-Terms, Modified Henderson-Pabis and Weibull models. In addition, two new models, referred to as 'Proposed' models 1 and 2, were used to simulate this process. According to the statistical tests applied, the models that best fitted the experimental data were Modified Henderson-Pabis, Weibull and Proposed model 2, respectively. Bifactorial analysis of variance ANOVA showed that Diet A (fishmeal 44%, spirulin 9%, fish oil 1% and cornstarch 36%) presented the highest diffusion coefficient values, which were favored by the temperature increase in the drying process.