A multidisciplinary reference framework to support innovative design, implementation and assessment of chronic care models

Hoy en día, por primera vez en la historia, la mayor parte de la población podrá vivir hasta los sesenta años y más (United Nations, 2015). Sin embargo, todavía existe poca evidencia que demuestre que las personas mayores, estén viviendo con mejor salud que sus padres, a la misma edad, ya que la mayoría de los problemas de salud en edades avanzadas están asociados a las enfermedades crónicas (WHO, 2015). Los sistemas sanitarios de los países desarrollados funcionan adecuadamente cuando se trata del cuidado de enfermedades agudas, pero no son lo suficientemente eficaces en la gestión de las enfermedades crónicas. Durante la última década, se han realizado esfuerzos para mejorar esta gestión, por medio de la utilización de estrategias de prevención y de reenfoque de la provisión de los servicios de atención para la salud (Kane et al. 2005). Según una revisión sistemática de modelos de cuidado de salud, comisionada por el sistema nacional de salud Británico, pocos modelos han conceptualizado cuáles son los componentes que hay que utilizar para proporcionar un cuidado crónico efectivo, y estos componentes no han sido suficientemente estructurados y articulados. Por lo tanto, no hay suficiente evidencia sobre el impacto real de cualquier modelo existente en la actualidad (Ham, 2006). Las innovaciones podrían ayudar a conseguir mejores diagnósticos, tratamientos y gestión de pacientes crónicos, así como a dar soporte a los profesionales y a los pacientes en el cuidado. Sin embargo, la forma en las que estas innovaciones se proporcionan no es lo suficientemente eficiente, efectiva y amigable para el usuario. Para mejorar esto, hace falta crear equipos de trabajo y estrategias multidisciplinares. En conclusión, hacen falta actividades que permitan conseguir que las innovaciones sean utilizadas en los sistemas de salud que quieren mejorar la gestión del cuidado crónico, para que sea posible: 1) traducir la “atención sanitaria basada en la evidencia” en “conocimiento factible”; 2) hacer frente a la complejidad de la atención sanitaria a través de una investigación multidisciplinaria; 3) identificar una aproximación sistemática para que se establezcan intervenciones innovadoras en el cuidado de salud. El marco de referencia desarrollado en este trabajo de investigación es un intento de aportar estas mejoras. Las siguientes hipótesis han sido propuestas: Hipótesis 1: es posible definir un proceso de traducción que convierta un modelo de cuidado crónico en una descripción estructurada de objetivos, requisitos e indicadores clave de rendimiento. Hipótesis 2: el proceso de traducción, si se ejecuta a través de elementos basados en la evidencia, multidisciplinares y de orientación económica, puede convertir un modelo de cuidado crónico en un marco descriptivo, que define el ciclo de vida de soluciones innovadoras para el cuidado de enfermedades crónicas. Hipótesis 3: es posible definir un método para evaluar procesos, resultados y capacidad de desarrollar habilidades, y asistir equipos multidisciplinares en la creación de soluciones innovadoras para el cuidado crónico. Hipótesis 4: es posible dar soporte al desarrollo de soluciones innovadoras para el cuidado crónico a través de un marco de referencia y conseguir efectos positivos, medidos en indicadores clave de rendimiento. Para verificar las hipótesis, se ha definido una aproximación metodológica compuesta de cuatro Fases, cada una asociada a una hipótesis. Antes de esto, se ha llevado a cabo una “Fase 0”, donde se han analizado los antecedentes sobre el problema (i.e. adopción sistemática de la innovación en el cuidado crónico) desde una perspectiva multi-dominio y multi-disciplinar. Durante la fase 1, se ha desarrollado un Proceso de Traducción del Conocimiento, elaborado a partir del JBI Joanna Briggs Institute (JBI) model of evidence-based healthcare (Pearson, 2005), y sobre el cual se han definido cuatro Bloques de Innovación. Estos bloques consisten en una descripción de elementos innovadores, definidos en la fase 0, que han sido añadidos a los cuatros elementos que componen el modelo JBI. El trabajo llevado a cabo en esta fase ha servido también para definir los materiales que el proceso de traducción tiene que ejecutar. La traducción que se ha llevado a cabo en la fase 2, y que traduce la mejor evidencia disponible de cuidado crónico en acción: resultado de este proceso de traducción es la parte descriptiva del marco de referencia, que consiste en una descripción de un modelo de cuidado crónico (se ha elegido el Chronic Care Model, Wagner, 1996) en términos de objetivos, especificaciones e indicadores clave de rendimiento y organizada en tres ciclos de innovación (diseño, implementación y evaluación). Este resultado ha permitido verificar la segunda hipótesis. Durante la fase 3, para demostrar la tercera hipótesis, se ha desarrollado un método-mixto de evaluación de equipos multidisciplinares que trabajan en innovaciones para el cuidado crónico. Este método se ha creado a partir del método mixto usado para la evaluación de equipo multidisciplinares translacionales (Wooden, 2013). El método creado añade una dimensión procedural al marco. El resultado de esta fase consiste, por lo tanto, en una primera versión del marco de referencia, lista para ser experimentada. En la fase 4, se ha validado el marco a través de un caso de estudio multinivel y con técnicas de observación-participante como método de recolección de datos. Como caso de estudio se han elegido las actividades de investigación que el grupo de investigación LifeStech ha desarrollado desde el 2008 para mejorar la gestión de la diabetes, actividades realizadas en un contexto internacional. Los resultados demuestran que el marco ha permitido mejorar las actividades de trabajo en distintos niveles: 1) la calidad y cantidad de las publicaciones; 2) se han conseguido dos contratos de investigación sobre diabetes: el primero es un proyecto de investigación aplicada, el segundo es un proyecto financiado para acelerar las innovaciones en el mercado; 3) a través de los indicadores claves de rendimiento propuestos en el marco, una prueba de concepto de un prototipo desarrollado en un proyecto de investigación ha sido transformada en una evaluación temprana de una intervención eHealth para el manejo de la diabetes, que ha sido recientemente incluida en Repositorio de prácticas innovadoras del Partenariado de Innovación Europeo en Envejecimiento saludable y activo. La verificación de las 4 hipótesis ha permitido demonstrar la hipótesis principal de este trabajo de investigación: es posible contribuir a crear un puente entre la atención sanitaria y la innovación y, por lo tanto, mejorar la manera en que el cuidado crónico sea procurado en los sistemas sanitarios. ABSTRACT Nowadays, for the first time in history, most people can expect to live into their sixties and beyond (United Nations, 2015). However, little evidence suggests that older people are experiencing better health than their parents, and most of the health problems of older age are linked to Chronic Diseases (WHO, 2015). The established health care systems in developed countries are well suited to the treatment of acute diseases but are mostly inadequate for dealing with CDs. Healthcare systems are challenging the burden of chronic diseases by putting more emphasis on the prevention of disease and by looking for new ways to reorient the provision of care (Kane et al., 2005). According to an evidence-based review commissioned by the British NHS Institute, few models have conceptualized effective components of care for CDs and these components have been not structured and articulated. “Consequently, there is limited evidence about the real impact of any of the existing models” (Ham, 2006). Innovations could support to achieve better diagnosis, treatment and management for patients across the continuum of care, by supporting health professionals and empowering patients to take responsibility. However, the way they are delivered is not sufficiently efficient, effective and consumer friendly. The improvement of innovation delivery, involves the creation of multidisciplinary research teams and taskforces, rather than just working teams. There are several actions to improve the adoption of innovations from healthcare systems that are tackling the epidemics of CDs: 1) Translate Evidence-Based Healthcare (EBH) into actionable knowledge; 2) Face the complexity of healthcare through multidisciplinary research; 3) Identify a systematic approach to support effective implementation of healthcare interventions through innovation. The framework proposed in this research work is an attempt to provide these improvements. The following hypotheses have been drafted: Hypothesis 1: it is possible to define a translation process to convert a model of chronic care into a structured description of goals, requirements and key performance indicators. Hypothesis 2: a translation process, if executed through evidence-based, multidisciplinary, holistic and business-oriented elements, can convert a model of chronic care in a descriptive framework, which defines the whole development cycle of innovative solutions for chronic disease management. Hypothesis 3: it is possible to design a method to evaluate processes, outcomes and skill acquisition capacities, and assist multidisciplinary research teams in the creation of innovative solutions for chronic disease management. Hypothesis 4: it is possible to assist the development of innovative solutions for chronic disease management through a reference framework and produce positive effects, measured through key performance indicators. In order to verify the hypotheses, a methodological approach, composed of four Phases that correspond to each one of the stated hypothesis, was defined. Prior to this, a “Phase 0”, consisting in a multi-domain and multi-disciplinary background analysis of the problem (i.e.: systematic adoption of innovation to chronic care), was carried out. During phase 1, in order to verify the first hypothesis, a Knowledge Translation Process (KTP) was developed, starting from the JBI Joanna Briggs Institute (JBI) model of evidence-based healthcare was used (Pearson, 2005) and adding Four Innovation Blocks. These blocks represent an enriched description, added to the JBI model, to accelerate the transformation of evidence-healthcare through innovation; the innovation blocks are built on top of the conclusions drawn after Phase 0. The background analysis gave also indication on the materials and methods to be used for the execution of the KTP, carried out during phase 2, that translates the actual best available evidence for chronic care into action: this resulted in a descriptive Framework, which is a description of a model of chronic care (the Chronic Care Model was chosen, Wagner, 1996) in terms of goals, specified requirements and Key Performance Indicators, and articulated in the three development cycles of innovation (i.e. design, implementation and evaluation). Thanks to this result the second hypothesis was verified. During phase 3, in order to verify the third hypothesis, a mixed-method to evaluate multidisciplinary teams working on innovations for chronic care, was created, based on a mixed-method used for the evaluation of Multidisciplinary Translational Teams (Wooden, 2013). This method adds a procedural dimension to the descriptive component of the Framework, The result of this phase consisted in a draft version of the framework, ready to be tested in a real scenario. During phase 4, a single and multilevel case study, with participant-observation data collection, was carried out, in order to have a complete but at the same time multi-sectorial evaluation of the framework. The activities that the LifeStech research group carried out since 2008 to improve the management of diabetes have been selected as case study. The results achieved showed that the framework allowed to improve the research activities in different directions: the quality and quantity of the research publications that LifeStech has issued, have increased substantially; 2 project grants to improve the management of diabetes, have been assigned: the first is a grant funding applied research while the second is about accelerating innovations into the market; by using the assessment KPIs of the framework, the proof of concept validation of a prototype developed in a research project was transformed into an early stage assessment of innovative eHealth intervention for Diabetes Management, which has been recently included in the repository of innovative practice of the European Innovation Partnership on Active and Health Ageing initiative. The verification of the 4 hypotheses lead to verify the main hypothesis of this research work: it is possible to contribute to bridge the gap between healthcare and innovation and, in turn, improve the way chronic care is delivered by healthcare systems.

On dendrites in Down syndrome and DS murine models: a spiny way to learn

Since the discovery in the 1970s that dendritic abnormalities in cortical pyramidal neurons are the most consistent pathologic correlate of mental retardation, research has focused on how dendritic alterations are related to reduced intellectual ability. Due in part to obvious ethical problems and in part to the lack of fruitful methods to study neuronal circuitry in the human cortex, there is little data about the microanatomical contribution to mental retardation. The recent identification of the genetic bases of some mental retardation associated alterations, coupled with the technology to create transgenic animal models and the introduction of powerful sophisticated tools in the field of microanatomy, has led to a growth in the studies of the alterations of pyramidal cell morphology in these disorders. Studies of individuals with Down syndrome, the most frequent genetic disorder leading to mental retardation, allow the analysis of the relationships between cognition, genotype and brain microanatomy. In Down syndrome the crucial question is to define the mechanisms by which an excess of normal gene products, in interaction with the environment, directs and constrains neural maturation, and how this abnormal development translates into cognition and behaviour. In the present article we discuss mainly Down syndrome-associated dendritic abnormalities and plasticity and the role of animal models in these studies. We believe that through the further development of such approaches, the study of the microanatomical substrates of mental retardation will contribute significantly to our understanding of the mechanisms underlying human brain disorders associated with mental retardation. (C) 2004 Elsevier Ltd. All rights reserved.

Wealth effects of mergers and acquisitions for US firms: using alternative pricing models

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods

Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing problem that arises in many practical scientific and engineering contexts. In the first paper (part I) of this series of two, we presented background theory building on results from the image processing community to show that the majority of these algorithms, and more proposed in the wider literature, are each associated with a special case of a generalized functional, that, when minimized, solves the PWC denoising problem. It shows how the minimizer can be obtained by a range of computational solver algorithms. In this second paper (part II), using this understanding developed in part I, we introduce several novel PWC denoising methods, which, for example, combine the global behaviour of mean shift clustering with the local smoothing of total variation diffusion, and show example solver algorithms for these new methods. Comparisons between these methods are performed on synthetic and real signals, revealing that our new methods have a useful role to play. Finally, overlaps between the generalized methods of these two papers and others such as wavelet shrinkage, hidden Markov models, and piecewise smooth filtering are touched on.

Models, Techniques and Applications of e-Learning Personalization

In recent years Web has become mainstream medium for communication and information dissemination. This paper presents approaches and methods for adaptive learning implementation, which are used in some contemporary web-interfaced Learning Management Systems (LMSs). The problem is not how to create electronic learning materials, but how to locate and utilize the available information in personalized way. Different attitudes to personalization are briefly described in section 1. The real personalization requires a user profile containing information about preferences, aims, and educational history to be stored and used by the system. These issues are considered in section 2. A method for development and design of adaptive learning content in terms of learning strategy system support is represented in section 3. Section 4 includes a set of innovative personalization services that are suggested by several very important research projects (SeLeNe project, ELENA project, etc.) dated from the last few years. This section also describes a model for role- and competency-based learning customization that uses Web Services approach. The last part presents how personalization techniques are implemented in Learning Grid-driven applications.

Application of Numerical Methods to Study Arrangement and Fracture of Lithium-Ion Microstructure

The focus of this work is to develop and employ numerical methods that provide characterization of granular microstructures, dynamic fragmentation of brittle materials, and dynamic fracture of three-dimensional bodies.

We first propose the fabric tensor formalism to describe the structure and evolution of lithium-ion electrode microstructure during the calendaring process. Fabric tensors are directional measures of particulate assemblies based on inter-particle connectivity, relating to the structural and transport properties of the electrode. Applying this technique to X-ray computed tomography of cathode microstructure, we show that fabric tensors capture the evolution of the inter-particle contact distribution and are therefore good measures for the internal state of and electronic transport within the electrode.

We then shift focus to the development and analysis of fracture models within finite element simulations. A difficult problem to characterize in the realm of fracture modeling is that of fragmentation, wherein brittle materials subjected to a uniform tensile loading break apart into a large number of smaller pieces. We explore the effect of numerical precision in the results of dynamic fragmentation simulations using the cohesive element approach on a one-dimensional domain. By introducing random and non-random field variations, we discern that round-off error plays a significant role in establishing a mesh-convergent solution for uniform fragmentation problems. Further, by using differing magnitudes of randomized material properties and mesh discretizations, we find that employing randomness can improve convergence behavior and provide a computational savings.

The Thick Level-Set model is implemented to describe brittle media undergoing dynamic fragmentation as an alternative to the cohesive element approach. This non-local damage model features a level-set function that defines the extent and severity of degradation and uses a length scale to limit the damage gradient. In terms of energy dissipated by fracture and mean fragment size, we find that the proposed model reproduces the rate-dependent observations of analytical approaches, cohesive element simulations, and experimental studies.

Lastly, the Thick Level-Set model is implemented in three dimensions to describe the dynamic failure of brittle media, such as the active material particles in the battery cathode during manufacturing. The proposed model matches expected behavior from physical experiments, analytical approaches, and numerical models, and mesh convergence is established. We find that the use of an asymmetrical damage model to represent tensile damage is important to producing the expected results for brittle fracture problems.

The impact of this work is that designers of lithium-ion battery components can employ the numerical methods presented herein to analyze the evolving electrode microstructure during manufacturing, operational, and extraordinary loadings. This allows for enhanced designs and manufacturing methods that advance the state of battery technology. Further, these numerical tools have applicability in a broad range of fields, from geotechnical analysis to ice-sheet modeling to armor design to hydraulic fracturing.

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)

Does a peer model’s task proficiency influence children’s solution choice and innovation?

The current study investigated whether 4- to 6-year-old children’s task solution choice was influenced by the past proficiency of familiar peer models and the children’s personal prior task experience. Peer past proficiency was established through behavioral assessments of interactions with novel tasks alongside peer and teacher predictions of each child’s proficiency. Based on these assessments, one peer model with high past proficiency and one age-, sex-, dominance-, and popularity-matched peer model with lower past proficiency were trained to remove a capsule using alternative solutions from a three-solution artificial fruit task. Video demonstrations of the models were shown to children after they had either a personal successful interaction or no interaction with the task. In general, there was not a strong bias toward the high past-proficiency model, perhaps due to a motivation to acquire multiple methods and the salience of other transmission biases. However, there was some evidence of a model-based past-proficiency bias; when the high past-proficiency peer matched the participants’ original solution, there was increased use of that solution, whereas if the high past-proficiency peer demonstrated an alternative solution, there was increased use of the alternative social solution and novel solutions. Thus, model proficiency influenced innovation.

Comparing simulation output accuracy of discrete event and agent based models: a quantitative approach

In our research we investigate the output accuracy of discrete event simulation models and agent based simulation models when studying human centric complex systems. In this paper we focus on human reactive behaviour as it is possible in both modelling approaches to implement human reactive behaviour in the model by using standard methods. As a case study we have chosen the retail sector, and here in particular the operations of the fitting room in the women wear department of a large UK department store. In our case study we looked at ways of determining the efficiency of implementing new management policies for the fitting room operation through modelling the reactive behaviour of staff and customers of the department. First, we have carried out a validation experiment in which we compared the results from our models to the performance of the real system. This experiment also allowed us to establish differences in output accuracy between the two modelling methods. In a second step a multi-scenario experiment was carried out to study the behaviour of the models when they are used for the purpose of operational improvement. Overall we have found that for our case study example both, discrete event simulation and agent based simulation have the same potential to support the investigation into the efficiency of implementing new management policies.

Impaired reverse cholesterol transport and hepatic steatosis contribute to pathogenesis of high fat dietinduced hyperlipidemia in murine models

Purpose: To investigate the pathogenesis of high fat diet (HFD)-induced hyperlipidemia (HLP) in mice, rats and hamsters and to comparatively evaluate their sensitivity to HFD. Methods: Mice, rats and hamsters were fed with high-fat diet formulation (HFD, n = 8) or a control diet (control, n = 8) for 4 weeks. Changes in body weight, relative liver weight, serum lipid profile, expressions of hepatic marker gene of lipid metabolism and liver morphology were observed in three hyperlipidemic models. Results: Elevated total cholesterol (TC), triglyceride, low density lipoprotein-cholesterol (LDL-C) and high density lipoprotein-cholesterol (HDL-C) levels and body weight were observed in all hyperlipidemic animals (p < 0.05), while hepatic steatosis was manifested in rat and hamster HLP models, and increased hepatic TC level was only seen (p < 0.05) in hamster HLP model. Suppression of HMG-CoA reductase and up-regulation of lipoproteinlipase were observed in all HFD groups. Hepatic gene expression of LDLR, CYP7A1, LCAT, SR-B1, and ApoA I, which are a response to reverse cholesterol transport (RCT), were inhibited by HFD in the three models. Among these models, simultaneous suppression of HMG-CR, LCAT, LDLR and SR-BI and elevated LPL were features of the hamster model. Conclusion: As the results show, impaired RCT and excessive fat accumulation are major contributors to pathogenesis of HFD-induced murine HLP. Thus, the hamster model is more appropriate for hyperlipidemia research.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Methods for integrating machine learning and constrained optimization

In the framework of industrial problems, the application of Constrained Optimization is known to have overall very good modeling capability and performance and stands as one of the most powerful, explored, and exploited tool to address prescriptive tasks. The number of applications is huge, ranging from logistics to transportation, packing, production, telecommunication, scheduling, and much more. The main reason behind this success is to be found in the remarkable effort put in the last decades by the OR community to develop realistic models and devise exact or approximate methods to solve the largest variety of constrained or combinatorial optimization problems, together with the spread of computational power and easily accessible OR software and resources. On the other hand, the technological advancements lead to a data wealth never seen before and increasingly push towards methods able to extract useful knowledge from them; among the data-driven methods, Machine Learning techniques appear to be one of the most promising, thanks to its successes in domains like Image Recognition, Natural Language Processes and playing games, but also the amount of research involved. The purpose of the present research is to study how Machine Learning and Constrained Optimization can be used together to achieve systems able to leverage the strengths of both methods: this would open the way to exploiting decades of research on resolution techniques for COPs and constructing models able to adapt and learn from available data. In the first part of this work, we survey the existing techniques and classify them according to the type, method, or scope of the integration; subsequently, we introduce a novel and general algorithm devised to inject knowledge into learning models through constraints, Moving Target. In the last part of the thesis, two applications stemming from real-world projects and done in collaboration with Optit will be presented.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Optimized methods to digital survey and 3D modelling of cultural heritage assets

Currently making digital 3D models and replicas of the cultural heritage assets play an important role in the preservation and having a high detail source for future research and intervention. In this dissertation, it is tried to assess different methods for digital surveying and making 3D replicas of cultural heritage assets in different scales of size. The methodologies vary in devices, software, workflow, and the amount of skill that is required. The three phases of the 3D modelling process are data acquisition, modelling, and model presentation. Each of these sections is divided into sub-sections and there are several approaches, methods, devices, and software that may be employed, furthermore, the selection process should be based on the operation's goal, available facilities, the scale and properties of the object or structure to be modeled, as well as the operators' expertise and experience. The most key point to remember is that the 3D modelling operation should be properly accurate, precise, and reliable; therefore, there are so many instructions and pieces of advice on how to perform 3D modelling effectively. It is an attempt to compare and evaluate the various ways of each phase in order to explain and demonstrate their differences, benefits, and drawbacks in order to serve as a simple guide for new and/or inexperienced users.

Possibilidades e limites da avaliação da qualidade de vida : análise dos instrumentos WHOQOL e modelos clássicos de qualidade de vida no trabalho = Possibilities and limits of quality of life assessment : analysis of WHOQOL instruments and quality of work life classical models

Universidade Estadual de Campinas . Faculdade de Educação Física