Internationalization process of TUBOFURO

The central objective of this case study was to formulate the strategy of internationalization of Tubofuro®, discriminating relevant points from its design to its implementation. This is a company located in Leiria, Ortigosa parish, which operates, among others, in the Portuguese PVC pipes industry for which currently the domestic market is clearly insufficient, given the oversupply compared to demand. Being Tubofuro® an exporting company since 2004, the work here developed specifically intended to increase sales to the foreign market, with this representing 45% of total company's business in 2018 increasing of the number of markets through new partners to enable the positioning of Tubofuro® among the main players in each market, particularly in South American markets, North African and European. To achieve the above objectives presented a case study was applied, centred on Tubofuro® company, target of the internationalization strategy. The search carried out for the formulation of the strategy has been supported on a thorough analysis of the external environment and internal characteristics of the company, for which were crossed different types of data, quantitative, qualitative, secondary data and primary data. From this work resulted the development of internationalization and international marketing plan for the next three years, whose objectives are based on entrance and consequent growth in new markets, including the market Chilean, Peruvian, Mexican, Argentine, Algerian and German, as well growth in the presence and turnover in the markets for which Tubofuro® already exports regularly, for example Spain, France, Tunisia and Morocco. Based on the production capacity of Tubofuro® company, which will not suffer any kind of investment for incrementing but only to update, it is expected that the appropriate response capacity for the company is 8 regular markets, and could eventually arise sporadic exports to other markets not interfering with the normal production capacity of the company. The suggestion of the presented markets resulted from the study of the final price based on the one that local customers purchase a product equal or similar to Tubofuro® and the number of potential existing customers in each market. The internationalization model known as Uppsala Model corresponds to the strategy adopted by the company to its internationalization process, taking into account the philosophy of senior management and the risk aversion of them. The sales team Tubofuro® demand for each market, export a full container registering customer feedback, including quality and flow capacity in the market in order to seek a partnership agreement with a local distributor, which allows the Tubofuro® go to step two above mentioned model. The partnership agreement is based on mutual commitment to technical cooperation and trade between the Tubofuro® and partner, in order to increase the performance capacity among local customers. Only if the market presents a greater demand to our supply capacity and be justified by cost / benefit ratio, the entry into this market through a joint venture or subsidiary is that the decision will be taken. Although this is a case study, which means that is adjusted to the concrete case Tubofuro® preventing generalization of findings, we believe that this work can be a useful example for other companies in the internationalization process or the methodology adopted in formulating strategy or the outputs and conclusions drawn.

Engineering design of an integrated sustainable process system for cassava biobased materials

Cassava contributes significantly to biobased material development. Conventional approaches for its bio-derivative-production and application cause significant wastes, tailored material development challenges, with negative environmental impact and application limitations. Transforming cassava into sustainable value-added resources requires redesigning new approaches. Harnessing unexplored material source, and downstream process innovations can mitigate challenges. The ultimate goal proposed an integrated sustainable process system for cassava biomaterial development and potential application. An improved simultaneous release recovery cyanogenesis (SRRC) methodology, incorporating intact bitter cassava, was developed and standardized. Films were formulated, characterised, their mass transport behaviour, simulating real-distribution-chain conditions quantified, and optimised for desirable properties. Integrated process design system, for sustainable waste-elimination and biomaterial development, was developed. Films and bioderivatives for desired MAP, fast-delivery nutraceutical excipients and antifungal active coating applications were demonstrated. SRRC-processed intact bitter cassava produced significantly higher yield safe bio-derivatives than peeled, guaranteeing 16% waste-elimination. Process standardization transformed entire root into higher yield and clarified colour bio-derivatives and efficient material balance at optimal global desirability. Solvent mass through temperature-humidity-stressed films induced structural changes, and influenced water vapour and oxygen permeability. Sevenunit integrated-process design led to cost-effectiveness, energy-efficient and green cassava processing and biomaterials with zero-environment footprints. Desirable optimised bio-derivatives and films demonstrated application in desirable in-package O2/CO2, mouldgrowth inhibition, faster tablet excipient nutraceutical dissolutions and releases, and thymolencapsulated smooth antifungal coatings. Novel material resources, non-root peeling, zero-waste-elimination, and desirable standardised methodology present promising process integration tools for sustainable cassava biobased system development. Emerging design outcomes have potential applications to mitigate cyanide challenges and provide bio-derivative development pathways. Process system leads to zero-waste, with potential to reshape current style one-way processes into circular designs modelled on nature's effective approaches. Indigenous cassava components as natural material reinforcements, and SRRC processing approach has initiated a process with potential wider deployment in broad product research development. This research contributes to scientific knowledge in material science and engineering process design.

Using Kofax Solutions for Process Improvement

One of the ways the South Carolina State Housing Finance and Development Authority fulfills this mission is through the purchase and servicing of mortgage loans. The 2007 Recession resulted in decreased revenues for the department while higher default, foreclosure and bankruptcy rates increased the department's manpower cost. The agency has since acquired different servicing software which complies with current industry regulations and is once again servicing the loans that it purchases. This project is to see if the department could improve any of their overall processes by using existing technologies and software to better utilize the new servicing system while minimizing manual tasks. This paper explores whether the existing Kofax Document Recognition system could improve this process and reduce overall employee time and effort?

The Selection Process of Applications to the Portuguese Innovation Incentive System: Who Gets Financial Support?

Public policies to support entrepreneurship and innovation play a vital role when firms have difficulties in accessing external finance. However, some authors have found evidence of long-term inefficiency in subsidized firms (Bernini and Pelligrini, 2011; Cerqua and Pelligrini, 2014) and ineffectiveness of public funds (Jorge and Suárez, 2011). The aim of the paper is to assess the effectiveness in the selection process of applications to public financial support for stimulating innovation. Using a binary choice model, we investigate which factors influence the probability of obtaining public support for an innovative investment. The explanatory variables are connected to firm profile, the characteristics of the project and the macroeconomic environment. The analysis is based on the case study of the Portuguese Innovation.Incentive System (PIIS) and on the applications managed by the Alentejo Regional Operational Program in the period 2007 – 2013. The results show that the selection process is more focused on the expected impact of the project than on the firm’s past performance. Factors that influence the credit risk and the decision to grant a bank loan do not seem to influence the government evaluator regarding the funding of some projects. Past activities in R&D do not significantly affect the probability of having an application approved under the PIIS, whereas an increase in the number of patents and the number of skilled jobs are both relevant factors. Nevertheless, some evidence of firms’ short-term inefficiency was found, in that receiving public financial support is linked to a smaller increase in productivity compared to non-approved firm applications. At the macroeconomic level, periods with a higher cost of capital in financial markets are linked to a greater probability of getting an application for public support approved, which could be associated with the effectiveness of public support in correcting market failings.

At the intersection of Behavioural Economics, Nudging and Regulation: Rethinking the process of nudge design for regulation

Alongside the developments in behavioural economics, the concept of nudge was introduced as an intervention able to guide individual behaviour towards better choices without using coercion or incentives. While behavioural teams were created inside governmental units and regulatory authorities, nudging emerged in regulatory discourse, being increasingly regarded as a regulatory instrument that could overcome the disadvantages of other tools. This thesis analyses the viability of incorporating nudges into regulation. In particular, it investigates the implications for regulators of bringing iterative experimental testing – a widespread nudge design methodology outside regulation – into their own design practices. Nudges outside regulation are routinely designed using experiments of all kinds. This thesis intends to answer whether design premises rooted in iterative experimentation are still valid in the regulatory space, an arena that nudging entered into and that is distinct from the one where it originally emerged. The design and provision of nudges using the premises of iterative experimental testing is possible, but at a cost and burden for regulatory nudge designers. Therefore, the thesis evaluates how this burden can be reduced, in particular how nudges can be feasibly designed and provided through regulation or, put differently, how to more efficiently design and provide nudging as a regulatory tool.

Dry grinding technology for automotive gears manufacturing: process modeling and optimization

The following thesis focused on the dry grinding process modelling and optimization for automotive gears production. A FEM model was implemented with the aim at predicting process temperatures and preventing grinding thermal defects on the material surface. In particular, the model was conceived to facilitate the choice of the grinding parameters during the design and the execution of the dry-hard finishing process developed and patented by the company Samputensili Machine Tools (EMAG Group) on automotive gears. The proposed model allows to analyse the influence of the technological parameters, comprising the grinding wheel specifications. Automotive gears finished by dry-hard finishing process are supposed to reach the same quality target of the gears finished through the conventional wet grinding process with the advantage of reducing production costs and environmental pollution. But, the grinding process allows very high values of specific pressure and heat absorbed by the material, therefore, removing the lubricant increases the risk of thermal defects occurrence. An incorrect design of the process parameters set could cause grinding burns, which affect the mechanical performance of the ground component inevitably. Therefore, a modelling phase of the process could allow to enhance the mechanical characteristics of the components and avoid waste during production. A hierarchical FEM model was implemented to predict dry grinding temperatures and was represented by the interconnection of a microscopic and a macroscopic approach. A microscopic single grain grinding model was linked to a macroscopic thermal model to predict the dry grinding process temperatures and so to forecast the thermal cycle effect caused by the process parameters and the grinding wheel specification choice. Good agreement between the model and the experiments was achieved making the dry-hard finishing an efficient and reliable technology to implement in the gears automotive industry.

On Reasonable Space and Time Cost Models for the λ-Calculus

Slot and van Emde Boas Invariance Thesis states that a time (respectively, space) cost model is reasonable for a computational model C if there are mutual simulations between Turing machines and C such that the overhead is polynomial in time (respectively, linear in space). The rationale is that under the Invariance Thesis, complexity classes such as LOGSPACE, P, PSPACE, become robust, i.e. machine independent. In this dissertation, we want to find out if it possible to define a reasonable space cost model for the lambda-calculus, the paradigmatic model for functional programming languages. We start by considering an unusual evaluation mechanism for the lambda-calculus, based on Girard's Geometry of Interaction, that was conjectured to be the key ingredient to obtain a space reasonable cost model. By a fine complexity analysis of this schema, based on new variants of non-idempotent intersection types, we disprove this conjecture. Then, we change the target of our analysis. We consider a variant over Krivine's abstract machine, a standard evaluation mechanism for the call-by-name lambda-calculus, optimized for space complexity, and implemented without any pointer. A fine analysis of the execution of (a refined version of) the encoding of Turing machines into the lambda-calculus allows us to conclude that the space consumed by this machine is indeed a reasonable space cost model. In particular, for the first time we are able to measure also sub-linear space complexities. Moreover, we transfer this result to the call-by-value case. Finally, we provide also an intersection type system that characterizes compositionally this new reasonable space measure. This is done through a minimal, yet non trivial, modification of the original de Carvalho type system.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.

Characterization of a 3D-printed low-cost flexible dielectric material for the realization of innovative WPT wearable applications

The aim of this thesis is to demonstrate that 3D-printing technologies can be considered significantly attractive in the production of microwave devices and in the antenna design, with the intention of making them lightweight, cheaper, and easily integrable for the production of wireless, battery-free, and wearable devices for vital signals monitoring. In this work, a new 3D-printable, low-cost resin material, the Flexible80A, is proposed as RF substrate in the implementation of a rectifying antenna (rectenna) operating at 2.45 GHz for wireless power transfer. A careful and accurate electromagnetic characterization of the abovementioned material, revealing it to be a very lossy substrate, has paved the way for the investigation of innovative transmission line and antenna layouts, as well as etching techniques, possible thanks to the design freedom enabled by 3D-printing technologies with the aim of improving the wave propagation performance within lossy materials. This analysis is crucial in the design process of a patch antenna, meant to be successively connected to the rectifier. In fact, many different patch antenna layouts are explored varying the antenna dimensions, the substrate etchings shape and position, the feeding line technology, and the operating frequency. Before dealing with the rectification stage of the rectenna design, the hot and long-discussed topic of the equivalent receiving antenna circuit representation is addressed, providing an overview of the interpretation of different authors about the issue, and the position that has been adopted in this thesis. Furthermore, two rectenna designs are proposed and simulated with the aim of minimizing the dielectric losses. Finally, a prototype of a rectenna with the antenna conjugate matched to the rectifier, operating at 2.45 GHz, has been fabricated with adhesive copper on a substrate sample of Flexible80A and measured, in order to validate the simulated results.

Optimization of a polyhydroxyalkanoates production process using a halotolerant mixed microbial culture

The rate at which petroleum based plastics are being produced, used and thrown away is increasing every year because of an increase in the global population. Polyhydroxyalkanoates can represent a valid alternative to petroleum based plastics. They are biodegradable polymers that can be produced by some microorganisms as intracellular reserves. The actual problem is represented by the production cost of these bioplastics, which is still not competitive if compared to the one of petroleum based plastics. Mixed microbial cultures can be fed with substrates obtained from the acidogenic fermentation of carbon rich wastes, such as cheese whey, municipal effluents and various kinds of food wastes, that have a low or sometimes even inexisting cost and in this way wastes can be valorized instead of being discharged. The process consists of three phases: acidogenic fermentation in which the substrate is obtained, culture selection in which a PHA-storing culture is selected and enriched eliminating organisms that do not show this property and accumulation, in which the culture is fed until reaching the maximum storage capacity. In this work the possibility to make the process cheaper was explored trying to couple the selection and accumulation steps and a halotolerant culture collected from seawater was used and fed with an artificially salted synthetic substrated made of an aqueous solution containing a mixture of volatile fatty acids in order to explore also if its performance can allow to use it to treat substrates derived from saline effluents, as these streams cannot be treated properly by bacterias found in activated sludge plants due to inhibition caused by high salt concentrations. Generating and selling the produced PHAs obtained from these bacterias it could be possible to lower, nullify or even overcome the costs associated to the new section of a treating plant dedicated to saline effluents.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Meta-learning for informed model searches in Automated Machine Learning

Day by day, machine learning is changing our lives in ways we could not have imagined just 5 years ago. ML expertise is more and more requested and needed, though just a limited number of ML engineers are available on the job market, and their knowledge is always limited by an inherent characteristic of theirs: they are humans. This thesis explores the possibilities offered by meta-learning, a new field in ML that takes learning a level higher: models are trained on other models' training data, starting from features of the dataset they were trained on, inference times, obtained performances, to try to understand the relationship between a good model and the way it was obtained. The so-called metamodel was trained on data collected by OpenML, the largest ML metadata platform that's publicly available today. Datasets were analyzed to obtain meta-features that describe them, which were then tied to model performances in a regression task. The obtained metamodel predicts the expected performances of a given model type (e.g., a random forest) on a given ML task (e.g., classification on the UCI census dataset). This research was then integrated into a custom-made AutoML framework, to show how meta-learning is not an end in itself, but it can be used to further progress our ML research. Encoding ML engineering expertise in a model allows better, faster, and more impactful ML applications across the whole world, while reducing the cost that is inevitably tied to human engineers.

[meanings Of The Sickening Process For Patients With Systemic Lupus Erythematosus: A Review Of The Literature.]

Systemic lupus erythematosus is an autoimmune disease that causes many psychological repercussions that have been studied through qualitative research. These are considered relevant, since they reveal the amplitude experienced by patients. Given this importance, this study aims to map the qualitative production in this theme, derived from studies of experiences of adult patients of both genders and that had used as a tool a semi-structured interview and/or field observations, and had made use of a sampling by a saturation criterion to determine the number of participants in each study. The survey was conducted in Pubmed, Lilacs, Psycinfo e Cochrane databases, searching productions in English and Portuguese idioms published between January 2005 and June 2012. The 19 revised papers that have dealt with patients in the acute phase of the disease showed themes that were categorized into eight topics that contemplated the experienced process at various stages, from the onset of the disease, extending through the knowledge of the diagnosis and the understanding of the manifestations of the disease, drug treatment and general care, evolution and prognosis. The collected papers also point to the difficulty of understanding, of the patients, on what consists the remission phase, revealing also that this is a clinical stage underexplored by psychological studies.

[in Process Citation].

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Conseqüências da análise incorreta de experimentos blocados

Errors are always present in experimental measurements so, it is important to identify them and understand how they affect the results of experiments. Statistics suggest that the execution of experiments should follow random order, but unfortunately the complete randomization of experiments is not always viable for practical reasons. One possible simplification is blocked experiments within which the levels of certain factors are maintained fixed while the levels of others are randomized. However this has a cost. Although the experimental part is simplified, the statistical analysis becomes more complex.