Rough surface scattering in many-mode conducting channels: Gradient versus amplitude scattering

We study the effect of surface scattering on transport properties in many-mode conducting channels (electron waveguides). Assuming a strong roughness of the surface profiles, we show that there are two independent control parameters that determine statistical properties of the scattering. The first parameter is the ratio of the amplitude of the roughness to the transverse width of the waveguide. The second one, which is typically omitted, is determined by the mean value of the derivative of the profile. This parameter may be large, thus leading to specific properties of scattering. Our results may be used in experimental realizations of the surface scattering of electron waves, as well as for other applications (e.g., for optical and microwave waveguides)Comment: 6 pages, no figure

En castellano, de Blas de Otero, veinticinco años después (1959-1984).

Sin resume

Individual, Organizational and Institutional Determinants of Formal and Informal Inter-firm Cooperation in SMEs

Inter-firm cooperation has been considered an important strategy for SMEs to overcome competitive difficulties. Despite the relevance of this strategy there are no studies that jointly consider how entrepreneurs’ characteristics, organizational factors and institutional features influence SMEs to establish cooperative agreements. In order to bridge this gap, we analyze what factors at these three levels explain inter-firm cooperation and whether formal and informal inter-firm agreements are explained by different factors. Our research is based on a survey of 1,587 Spanish SMEs and the results show that individual, organizational and institutional factors contribute to jointly shape the decisions concerning inter-firm cooperation

Ballistic Localization in Quasi-1D Waveguides with Rough Surfaces

Structure of eigenstates in a periodic quasi-1D waveguide with a rough surface is studied both analytically and numerically. We have found a large number of "regular" eigenstates for any high energy. They result in a very slow convergence to the classical limit in which the eigenstates are expected to be completely ergodic. As a consequence, localization properties of eigenstates originated from unperturbed transverse channels with low indexes, are strongly localized (delocalized) in the momentum (coordinate) representation. These eigenstates were found to have a quite unexpeted form that manifests a kind of "repulsion" from the rough surface. Our results indicate that standard statistical approaches for ballistic localization in such waveguides seem to be unappropriate.Comment: 5 pages, 4 figure

Determinants of digital transformation in the restaurant industry

This article aims to identify the factors that influence the digital transformation process in the restaurant industry. The proposed theoretical framework differentiates three groups of conditioning factors of digitalisation in small and medium-sized enterprises (SMEs) in this sector: (1) the personal characteristics of the entrepreneurs/managers; (2) the characteristics of the businesses; and (3) the spatial location of the restaurants. The data used in the empirical research were compiled from a representative survey of restaurant SMEs in Spain. The study uses an ordinal logistic regression specification to test the hypotheses. The results obtained indicate that the education of entrepreneurs/managers, their entrepreneurial motivations, and their ambition for growth condition the digital transformation of their businesses. Furthermore, the characteristics of the company, such as the number of establishments, belonging to a corporate group, and the employees’ educational level, influence the digitalisation of restaurants. Likewise, it is observed that the digitalisation process is stimulated in inland towns compared to coastal areas, as well as in intermediate municipalities with populations of between 10,000 and 100,000 inhabitants.Ministerio de Ciencia e Innovación (MICIN). España PID2020-113384GB-I00Ministerio de Economía y Competitividad (MINECO). España ECO2013-42889-

Optimal experimental design for process optimization with stochastic binary outcomes

Effective control of the end-use properties in order to guarantee product quality is of paramount importance. This is especially valuable for products such as medicament, polymers and nanomaterials. There are plenty of innovative processes whose principles of operation are unknown and for which is not possible to develop reliable models due to time and cost. Despite this, it is important to know the optimum operating zone where the process should work to ensure that the end product meets the required properties. To address this issue, a run-to-run optimization approach is proposed to find the reduced region of operation which guarantees obtaining end-use properties with high probability when a first-principles model for the batch process is not available a priori. In order to evaluate the effectiveness of the methodology to find optimal policies for runs involving stochastic binary outcomes, the well-known example of the emulsion polymerization of styrene has been addressed. Results obtained demonstrated that the proposed method is a powerful tool for optimal design of experiments aiming to guarantee end-use product properties.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Model-based run-to-run optimization for process development

Research and development of new processes is a fundamental part of any innovative industry. For process engineers, finding optimal operating conditions for new processes from the early stages is a main issue, since it improves economic viability, helps others areas of R&D by avoiding product bottlenecks and shortens the time-to-market period. Model-based optimization strategies are helpful in doing so, but imperfect models with parametric or structural errors can lead to suboptimal operating conditions. In this work, a methodology that uses probabilistic tendency models that are constantly updated through experimental feedback is proposed in order to rapidly and efficiently find improved operating conditions. Characterization of the uncertainty is used to make safe predictions even with scarce data, which is typical in this early stage of process development. The methodology is tested with an example from the traditional innovative pharmaceutical industry.Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Model-based run-to-run optimization under uncertainty of biodiesel production

A significant source of uncertainty in biodiesel production is the variability of feed composition since the percentage and type of triglycerides varies considerably across different raw materials. Also, due to the complexity of both transesterification and saponification kinetics, first-principles models of biodiesel production typically have built-in errors (structural and parametric uncertainty) which give rise to the need for obtaining relevant data through experimental design in modeling for optimization. A run-to-run optimization strategy which integrates tendency models with Bayesian active learning is proposed. Parameter distributions in a probabilistic model of process performance are re-estimated using data from experiments designed for maximizing information and performance. Results obtained highlight that Bayesian optimal design of experiments using a probabilistic tendency model is effective in achieving the maximum ester content and yield in biodiesel production even though significant uncertainty in feed composition and modeling errors are present.Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Reg.santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Optimal experimental design for process optimization with stochastic binary outcomes

Effective control of the end-use properties in order to guarantee product quality is of paramount importance. This is especially valuable for products such as medicament, polymers and nanomaterials. There are plenty of innovative processes whose principles of operation are unknown and for which is not possible to develop reliable models due to time and cost. Despite this, it is important to know the optimum operating zone where the process should work to ensure that the end product meets the required properties. To address this issue, a run-to-run optimization approach is proposed to find the reduced region of operation which guarantees obtaining end-use properties with high probability when a first-principles model for the batch process is not available a priori. In order to evaluate the effectiveness of the methodology to find optimal policies for runs involving stochastic binary outcomes, the well-known example of the emulsion polymerization of styrene has been addressed. Results obtained demonstrated that the proposed method is a powerful tool for optimal design of experiments aiming to guarantee end-use product properties.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Bayesian optimization of crystallization processes to guarantee end-use product properties

For pharmaceutical solid products, the issue of reproducibly obtaining their desired end-use properties depending on crystal size and form is the main problem to be addressed and solved in process development. Lacking a reliable first-principles model of a crystallization process, a Bayesian optimization algorithm is proposed. On this basis, a short sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization can take advantage of the full information provided by the sequence of experiments made using a probabilistic model of the probability of success based on a one-class classification method. The proposed algorithm's performance is tested in silico using the crystallization and formulation of an API product where success is about fulfilling a dissolution profile as required by the FDA. Results obtained demonstrate that the sequence of generated experiments allows pinpointing operating conditions for reproducible quality.Fil: Luna, Martín Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin