Customer Orientation in Family Businesses

The purpose of this article is to investigate customer orientation of service employees (COSE) in family businesses. This study elaborates on the perception and importance of COSE in family-owned companies. The paper also proposes new consequences of COSE in the context of family business. The research is based on a qualitative study comprised of 13 interviews conducted on senior managers in family firms. The results are analysed using NVivo 11. This investigation confirms the relevance of the COSE construct in family businesses and the role of family influence over it. New consequences are elicited, including differentiation, customer experience, and customer well-being. The results show that practitioners consider COSE as a key element for success. This study sheds light on how COSE can be applied in a family business in order to enhance customer experience. This study expands on the potential of COSE with the use of family businesses for the first time and introduces new consequences from the original model

Precise OBS location at the sea bottom in active seismic profiles using the air gun shot records

The Norcaribe campaign, in November – December 2013, funded by Spanish Ministry of Innovation and Science (Norcaribe Project CGL2010-17715), was performed on board of the Spanish research vessel “Sarmiento de Gamboa” around the Hispaniola island, also with the participation of the Dominic Republic Navy patrol vessels and several Haiti and Dominic Republic institutions. During the campaign, a 200 km long, wide-amgle refraction seismic profile was carried out crossing the Beata ridge. The air gun signal (5100 ci) was recorded by 15 OBSs deployed along the seismic line in water depths between 2.300 meters and 4.320 meters. To obtain the section records, the OBS position is needed, usually the deployment location is used, but the OBS can drift while is sinking due to the deep oceanic currents. The recovery locations at surface could provide information about the drift, assuming a constant sea current since the deployment to the recovery, but it is imprecise. In this work we show a method to obtain a precise location of the OBS at the sea bottom using a high-resolution bathymetry and the OBS record of the closest air gun shots of the profile. Also, the preliminary results for the Norcaribe campaign (Beata ridge profile) are shown.Peer Reviewe

The action of obestatin in skeletal muscle repair: stem cell expansion, muscle growth, and microenvironment remodeling

The development of therapeutic strategies for skeletal muscle diseases, such as physical injuries and myopathies, depends on the knowledge of regulatory signals that control the myogenic process. The obestatin/GPR39 system operates as an autocrine signal in the regulation of skeletal myogenesis. Using a mouse model of skeletal muscle regeneration after injury and several cellular strategies, we explored the potential use of obestatin as a therapeutic agent for the treatment of trauma-induced muscle injuries. Our results evidenced that the overexpression of the preproghrelin, and thus obestatin, and GPR39 in skeletal muscle increased regeneration after muscle injury. More importantly, the intramuscular injection of obestatin significantly enhanced muscle regeneration by simulating satellite stem cell expansion as well as myofiber hypertrophy through a kinase hierarchy. Added to the myogenic action, the obestatin administration resulted in an increased expression of VEGF/VEGFR2 and the consequent microvascularization, with no effect on collagen deposition in skeletal muscle. Furthermore, the potential inhibition of myostatin during obestatin treatment might contribute to its myogenic action improving muscle growth and regeneration. Taken together, our data demonstrate successful improvement of muscle regeneration, indicating obestatin is a potential therapeutic agent for skeletal muscle injury and would benefit other myopathies related to muscle regeneration

Modeling of energy efficiency for residential buildings using artificial neuronal networks

The energy efficiency dataset used to support the findings of this study has been deposited in the GitHub repository https://github.com/mereshow/ann-energy-efficiency.git.[Abstract] Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the residential sector. The process of evaluation and qualification of the energy efficiency in existing buildings should contain an analysis of the thermal behavior of the building envelope. To determine this thermal behavior and its representative parameters, we usually have to use destructive auscultation techniques in order to determine the composition of the different layers of the envelope. In this work, we present a nondestructive, fast, and cheap technique based on artificial neural network (ANN) models that predict the energy performance of a house, given some of its characteristics. The models were created using a dataset of buildings of different typologies and uses, located in the northern area of Spain. In this dataset, the models are able to predict the U-opaque value of a building with a correlation coefficient of 0.967 with the real U-opaque measured value for the same building

Assisted surface redesign by perturbing its point cloud representation

[Abstract] This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured point clouds are proposed as a manipulation method of complex geometries. The intent of the algorithm is to improve the design experience, thus different solutions can be presented to designers. The main objective of this work is to provide examples to be adopted as user own or to help them in the creative process. This is not about providing them with a tool to ‘do’ the designer's creative work, but using it as a creative tool in which the user retains control of it. The powerfulness of this approach relies on the fact that the user can use any/diverse criteria (objective or subjective) to evaluate the individuals proposed as possible solutions. As part of this study, the convergence of the algorithm and the ability of diversity in the final populations of the search process will be demonstrated. Various examples of the use of the algorithm are displayed

Assisted surface redesign by perturbing its point cloud representation

[Abstract] This research study explores the use of point clouds for design geometrically complex surfaces based on genetic morphogenesis. To this end, a point-based genetic algorithm and the use of massive unstructured point clouds are proposed as a manipulation method of complex geometries. The intent of the algorithm is to improve the design experience, thus different solutions can be presented to designers. The main objective of this work is to provide examples to be adopted as user own or to help them in the creative process. This is not about providing them with a tool to ‘do’ the designer's creative work, but using it as a creative tool in which the user retains control of it. The powerfulness of this approach relies on the fact that the user can use any/diverse criteria (objective or subjective) to evaluate the individuals proposed as possible solutions. As part of this study, the convergence of the algorithm and the ability of diversity in the final populations of the search process will be demonstrated. Various examples of the use of the algorithm are displayed

Development and operation of a pixel segmented liquid-filled linear array for radiotherapy quality assurance

A liquid isooctane (C$_{8}$H$_{18}$) filled ionization linear array for radiotherapy quality assurance has been designed, built and tested. The detector consists of 128 pixels, each of them with an area of 1.7 mm $\times$ 1.7 mm and a gap of 0.5 mm. The small pixel size makes the detector ideal for high gradient beam profiles like those present in Intensity Modulated Radiation Therapy (IMRT) and radiosurgery. As read-out electronics we use the X-Ray Data Acquisition System (XDAS) with the Xchip developed by the CCLRC. Studies concerning the collection efficiency dependence on the polarization voltage and on the dose rate have been made in order to optimize the device operation. In the first tests we have studied dose rate and energy dependences, and signal reproducibility. Dose rate dependence was found lower than 2.5 % up to 5 Gy min$^{-1}$, and energy dependence lower than 2.1 % up to 20 cm depth in solid water. Output factors and penumbras for several rectangular fields have been measured with the linear array and were compared with the results obtained with a 0.125 cm$^{3}$ air ionization chamber and radiographic film, respectively. Finally, we have acquired profiles for an IMRT field and for a virtual wedge. These profiles have also been compared with radiographic film measurements. All the comparisons show a good correspondence. Signal reproducibility was within a 2% during the test period (around three months). The device has proved its capability to verify on-line therapy beams with good spatial resolution and signal to noise ratio.Comment: 16 pages, 12 figures Submitted to Phys. Med. Bio

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection Within Fruit Juice Classification

Research article[Abstract] Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Sh(k)) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.The authors acknowledge Basque Government (Eusko Jaurlaritza) grant (IT1045-16) - 2016-2021 for consolidated research groups. This work was supported by the "Collaborative Project in Genomic Data Integration (CICLOGEN)" PI17/01826 funded by the Carlos III Health Institute, as part of the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER). This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and "Drug Discovery Galician Network" Ref. ED431G/01 and the "Galician Network for Colorectal Cancer Research" (Ref. ED431D 2017/23), and finally by the Spanish Ministry of Economy and Competitiveness for its support through the funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER) by the European Union. CR Munteanu acknowledges the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research