A model of the learning process with local knowledge externalities illustrated with an integrated graphical framework

In this paper we present a theoretical model of the learning process with knowledge externalities to R&D and other learning inputs within a region, a technological district, an industry or a technological cluster with fast rates of accumulation of new technological knowledge. As there are several definitions of localized technological and learning opportunities (according to the technical space, or to the regional space) and of localized technological knowledge, we can therefore find several possible applications to the generic model. The analysis of the learning firm interacting with a specific region in the production of new technological knowledge is just one of them. The analytical model we develop is amenable to a graphical representation. Thus we provide in the first place a unifying graphical framework, consisting of a four-quadrant picture to analyze the process of knowledge accumulation by learning firms located and operating in a specific region or industry, which simultaneously stresses the nature of the basic learning process and the importance of true knowledge spillovers in the generation of new knowledge. We adopt the following approach to the construction of spillover stocks or pools. First, the magnitude of the state of aggregate knowledge in a region or industry is reconstructed through the historic accumulation of flows of knowledge. Thus, the aggregate level of knowledge can always be updated after every learning loop, or at every moment of discrete time, whose unit of measurement we might assume at the outset of our analysis. Secondly, every firm within a region or industry is treated symmetrically regarding spillover effects and magnitudes. Such statement meaning that the amount of aggregate knowledge borrowed from any available source, either the region or industry under analysis or some other distant region or industry, is regarded as the same by every firm. And finally, we model both the loss of appropriation of benefits from innovation and the distance between different technological bases or regional sources in terms of single parameters, or instantaneous rates of growth, weighting respectively the leakage and the absorption intensities of flows and stocks of knowledge. Several theoretical predictions about the direction and magnitude of the knowledge spillovers can therefore be deducted from parametric changes in the leakage and absorption functions of our model arising from, among other things: - Improvements in information technology and falling communication costs observed in the economic system at general. - Improvements in technological communication systems within specific technological districts. - The establishment of explicit cooperative relations and effective access to the pool of collective knowledge, or instead any improvements of the mutuality and trust conditions, within the group of firms located and operating within a specific region. - The increasing of competitive pressures, or the working of any other mechanism for lowering the appropriation of a firm’s gains from innovation, in an array of industrial sectors. One interesting theoretical result is then derived from our full model. With such purpose in mind, we consider first the existence of a relevant competitive situation where appropriation and communication are both dependent upon the number of receiving and sending firms within the region. Whereas the amount of technological leakage per firm increases with the number of firms effectively operating within the region, ceteris paribus; the extent of absorption per firm also increases with the number of firms effectively communicating within the region, ceteris paribus. Apparently, there is a trade-off between such appropriation conditions and communication conditions. In the long-run, the addition of firms eventually exhausts the net positive effects of taking part in an effective network, and so we can establish an equilibrium number of firms operating in the region.

Integrated graphical framework accounting for the nature and the speed of the learning process: an application to MNEs strategies of internationalisation of production and R&D investment

Existing illustrations of the learning phenomenon either stress the relationship between flows and stocks, neglecting the chronological time variable, or the speed of knowledge accumulation along time, neglecting the nature of the underlying learning process. In this paper we present a graphical depiction stressing, in an explicit way, both the nature of interplay between flows and stocks and the intensity of the learning process. The four-quadrant graphs that we develop overcome considerable simplification in literature by deriving, by construction, a measure of dynamic gains of knowledge following the interplay of stock of scientific and technological knowledge and the flow of effort in R&D. This scheme is then applied to study the internationalisation of production and R&D, which are strategies followed by multinational firms. Two types of innovation – process innovation and product innovation – are therefore studied constructing, in each case, an industry performance measure adequately indexed to the cumulated knowledge stock at a given moment in time. In any case, the dynamic efficiency measure adopted naturally takes into account both the absolute changes in the technology indexes and the time delays to reach them, which are properly discounted. Regarding multinationals strategies - internationalisation of production and R&D investment -, we begin with the question of finding a new location for using a now well developed production technology, and then deal with the problem of selecting a region of excellence in research to take gains of concentration advantages and local externalities.Learning; knowledge; technology; R&D; MNEs

A Model of the Learning Process with Local Knowledge Externalities Illustrated with an Integrated Graphical Framework

We present a unified graphical framework accounting for the nature and impact of spillover effects. The dynamics of the learning process with a specific spillover transfer mechanism can be illustrated by recurring to this four-quadrant picture. In particular, a whole cycle of technological learning is explained with help of such a graphical representation of the basic learning process in the presence of knowledge spillovers. We hypothesize two different functional specifications of spillover exchanges among firms within a local innovation system. Each conceivable shape for the knowledge transfer relationship among firms expresses a possible mode and intensity of information processing arising from technology spillovers. A general proposition regarding the relative efficiency of the two alternative formal models with spillovers effects is derived. The basic models with spillover effects are then extended in several relevant directions.Learning; knowledge; technology spillovers; knowledge externalities; local innovation systems

Ensuring successful introduction of Wolbachia in natural populations of Aedes aegypti by means of feedback control

The control of the spread of dengue fever by introduction of the intracellular parasitic bacterium Wolbachia in populations of the vector Aedes aegypti, is presently one of the most promising tools for eliminating dengue, in the absence of an efficient vaccine. The success of this operation requires locally careful planning to determine the adequate number of individuals carrying the Wolbachia parasite that need to be introduced into the natural population. The introduced mosquitoes are expected to eventually replace the Wolbachia-free population and guarantee permanent protection against the transmission of dengue to human. In this study, we propose and analyze a model describing the fundamental aspects of the competition between mosquitoes carrying Wolbachia and mosquitoes free of the parasite. We then use feedback control techniques to devise an introduction protocol which is proved to guarantee that the population converges to a stable equilibrium where the totality of mosquitoes carry Wolbachia.Comment: 24 pages, 5 figure

Distribution-Based Categorization of Classifier Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples

Unravelling the behavior of nanostructures during digestion and absorption

The food industry is increasingly focused on preventing nutrition-related diseases and improving consumers’ wellbeing. As a result, there is a growing trend towards healthy foods, enriched with bioactive compounds (such as vitamins, probiotics, bioactive peptides and antioxidants) produced through the application of innovative and safe technologies. In this context, the development of novel delivery systems for food applications through the use of nanotechnology has been extensively explored [1]. In fact, the encapsulation of bioactive compounds in bio-based nanostructures have been reported as promising mean of protecting the valuable bioactive compounds and providing new functionalities (e.g. increase of bioavailability). However, the use of very small particle sizes may alter the biological fate of the ingested materials and bioactive compounds, which could potentially have adverse effects on human health [2]. Therefore, the emerging field of nanotechnology offers new challenges to food industry not only by offering novel tools to improve food quality and human health, but also by introducing questions about nanostructures’ behaviour within the human body. The challenges that must be overcome before nanotechnology can be entirely embraced by food industry, includes the optimisation of nanostructures’ formulations to increase stability and bioactive compounds’ bio availability and the risk assessment of their use in food. The understanding of the behaviour of different nano-based delivery systems (e.g. nanoemulsions, nanoparticles) under digestion conditions, assessing their efficiency and safety is therefore of utmost importance to enable its widespread application in the food industry. This evaluation can be challenging, however, there are opportunities to take advantage from the lessons learned from pharmaceutical industry and of the considerable progress in the development of more realistic in vitro models to more accurately predict the behaviour of bio-based nanostructures once ingestedinfo:eu-repo/semantics/publishedVersio

Development of a realistic in vitro digestion model (RGM) coupled UV-VIS-SWNIR fibre optics spectroscopy

Background: The development of realistic gastric models unlocked the possibility of studying important digestion phenomena occurring during the digestion of food (e.g., retropulsion). Understanding the dynamics of food digestion in real-time, without sample manipulation, is still a challenge, but brings a huge potential in providing important insights regarding the dynamic process of food digestion (e.g., real time nutrient release kinetics) This study presents a realistic 3D printed in vitro gastric model coupled with ultraviolet-visibleshort- wave-near-infrared (UV-VIS-SWNIR) spectroscope that can be used for real time quantification of nutrients/bioactive compounds. Methods: The INFOGEST semi-dynamic in vitro protocol was used to simulate the digestion of rice (model food). The spectroscope was calibrated for glucose analysis, and the spectra were pre-processed and both chemometric and machine learning techniques were used for glucose quantification using the correlation coefficient as assessment metric. Results: The machine learning algorithms showed to be more accurate at predicting glucose release during the in vitro gastric digestion. Conclusions: The gastric compartment development techniques provide the opportunity to develop a potential standard dynamic in vitro gastric model. Furthermore, it was possible to accurately measure and quantify glucose release during the in vitro digestion process, in real time, using UV-VIS-SWNIR fibre optics spectroscopic.info:eu-repo/semantics/publishedVersio