Abstract

This study aims to explore how Management 3.0 can assist and support the development of dynamic capabilities in SMEs. To this end, several dimensions of dynamic capabilities are considered, such as the process of identifying new market opportunities, idea generation, emergence of organizational changes, and support for innovation processes and the development of new markets. This research employs a qualitative methodology by conducting four case studies with software companies located in Portugal and Brazil. It identifies a set of key elements offered by Management 3.0 for the development of dynamic capabilities. Collaborative participation, teamwork, creativity, empowerment, agility, and entrepreneurial attitude stand out as fundamental and supported elements in the Management 3.0 paradigm for SMEs to develop their dynamic capabilities.

Abstract
Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 x 10(9) cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 x 10(9) cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer.

Abstract
We study the time-dependent behavior of dissipative solitons (DSs) stabilized by nonlinear gradient terms. Two cases are investigated: first, the case of the presence of a Raman term, and second, the simultaneous presence of two nonlinear gradient terms, the Raman term and the dispersion of nonlinear gain. As possible types of time-dependence, we find a number of different possibilities including periodic behavior, quasi-periodic behavior, and also chaos. These different types of time-dependence are found to form quite frequently from a window structure of alternating behavior, for example, of periodic and quasi-periodic behaviors. To analyze the time dependence, we exploit extensively time series and Fourier transforms. We discuss in detail quantitatively the question whether all the DSs found for the cubic complex Ginzburg-Landau equation with nonlinear gradient terms are generic, meaning whether they are stable against structural perturbations, for example, to the additions of a small quintic perturbation as it arises naturally in an envelope equation framework. Finally, we examine to what extent it is possible to have different types of DSs for fixed parameter values in the equation by just varying the initial conditions, for example, by using narrow and high vs broad and low amplitudes. These results indicate an overlapping multi-basin structure in parameter space. Published under an exclusive license by AIP Publishing.

Abstract
Tendo por base um conjunto de dados dos clientes de uma empresa de produtos alimentares, tentamos implementar duas estratégias de data mining com o objetivo de compreender quais os atributos que melhor podem segmentar estes consumidores. Aplicamos primeiro um algoritmo de segmentação (k-means) para agrupar estes clientes e, seguidamente, utilizamos um algoritmo de classificação (árvore de decisão) para análise visual dos atributos que definiram os clusters da segmentação. Através da análise visual dos gráficos resultantes da indução de árvores de decisão conseguimos verificar que só o valor do salário dos clientes pode segmentar este conjunto de dados.;From a dataset of customers of a food company, we tried to implement two data mining strategies to understand which attributes can best segment these consumers. First, we applied a segmentation algorithm (k-means) to segment these customers and then we applied a classification algorithm (decision tree) for visual analysis of the attributes that defined the segmentation clusters. Through the visual analysis of the graphs resulting from the decision tree induction, we were able to verify that only the value of the customers' salary can segment this dataset.

Abstract
The implementation of urban gardens, which are increasingly appearing in cities, aims to respond actively to the growing demand for urban spaces for the installation of urban gardens, creating conditions for the practice of sustainable agriculture in an urban context. Through these initiatives, it is intended to ensure that the needs of the population are met and to maximize the benefits arising from the practice of urban agriculture, both for the environment and for people's quality of life. Technology is a facilitating element in the process of acquiring and maintaining these urban gardens. This chapter presents an app that can be used by farmers to manage the production of consumer goods in this space, providing information about the status of crops, products to be grown, and types of required maintenance. This app simplifies the production process and also increases the sustainability of agriculture activities considering the economic, social, and logistical dimensions.

Abstract
This paper presents a two-level optimization model for the optimal scheduling of an active distribution system in day-ahead and real-time market horizons. The distribution system operator transacts energy and ancillary services with the electricity market, plug-in hybrid electric vehicle parking lot aggregators, and demand response aggregators. Further, the active distribution system can utilize a switching procedure for its zonal tie-line switches to mitigate the effects of contingencies. The main contribution of this paper is that the proposed framework simultaneously models the arbitrage strategy of the active distribution system, electric vehicle parking lot aggregators, and demand response aggregators in the day-ahead and real-time markets. This paper's solution methodology is another contribution that utilizes robust and lexicographic ordering optimization methods. At the first stage of the first level, the optimal bidding strategies of plug-in hybrid electric vehicle parking lot aggregators and demand response aggregators are explored. Then, at the second stage of the first level, the day-ahead optimization process finds the optimal scheduling of distributed energy resources and switching of electrical switches. Finally, at the second level, the real-time optimization problem optimizes the scheduling of system resources. Different case studies were carried out to assess the effectiveness of the algorithm. The proposed algorithm increases the system's day-ahead and real-time revenues by about 52.09% and 47.04% concerning the case without the proposed method, respectively.