International new ventures in the digital age: the case of a big data and analytics provider

Digital technologies offer new opportunities of business for entrepreneurs that possess big data and analytical skills. In particular, in the international competition context, new opportunities for new ventures internationalization arise in sectors related to Industry 4.0 digital technologies. However, when internationalization and digitalization merge, the dynamics can change, opening a new area of research still little investigated. To contribute to fill this gap, we adopted a case study approach to investigate the internationalization process of a digital International New Venture, which offers services related to Big Data and Analytics. Preliminary findings highlight factors influencing the growth process of this company, showing opportunities and challenges related to digitalization. The article concludes with a discussion of the implications of the authors´findings, opening new debates for international entrepreneurship research and practice

A feasibility study for a circular approach in oil refining: Metals recovery from hydrodesulphurization catalysts

The paper deals with a profitability analysis developed for a plant that recycles spent hydrodesulphurization (HDS) catalysts. Such catalysts contain molybdenum (Mo), nickel (Ni), and vanadium (V), supported by an alumina (Al2O3) carrier. The recycling process is based on a double thermal pre-treatment stage, followed by a series of hydrometallurgical steps that allow recovering Mo and V and a Ni concentrate that need further refining for separation and recovery of the metals. The economic analysis is based on the discounted cash flow method, and the baseline case analyses show that the net present value (NPV) is 14,877 thousand EUR. The selling price of vanadium pentoxide strongly influences the results. Alternative scenarios are also studied to strengthen the results obtained, considering the sensitivity, scenario and risk analyses. Profitability is confirmed in 87% of the considered scenarios, and in about 81.5%, the NPV of the baseline scenario is achieved. Circular economy models can be realized if products are recovered and if there are technologies that can recover metals. This study confirms that an example of a circular economy is met from the proposed viability analysis, and the economic benefits can be significant

In vitro biocompatibility of a new hydrogel with Crocin, powerful antioxidant found in Crocus Sativus L. flowers

Recently, attention has been paid to the identification of natural antioxidants from the petals of Crocus S. flowers that are normally considered waste[1]. The antioxidant activities are mainly attributed to carotenoid compounds, like crocin. Scientific evidences demonstrate that this kind of compounds are among the most important natural plant sources of antioxidant activity in the human diet, protecting the body against damages caused by reactive oxygen species (ROS). This antioxidant was extracted using methanol and ethanol. In this experiment, it was used a new hydrogel consisting of three polymers - Polyvinyl pyrrolidone (PVP), Agar and Polyethylene glycol (PEG) – and Crocin, mixed, reticulated and then sterilized by gamma irradiation at 25 kGy. For the in vitro experimental protocol, it was used a primary culture of fibroblasts taken from the subcutaneous tissue of a newborn mice, seeding the cells on a little square (1cm2 area) of both kinds of hydrogel. As the ISO protocol prescribes, the experiments were repeated 3 times for each kind of hydrogel, stopping the culture at the 3rd, 7th and 14th day after the seeding. For all steps, 3 Petri dishes were used as controls without biomaterials. At fixed deadline, all Petri dishes were stained, using the Wright method for cell counting and morphological evaluations. The microscopic analysis revealed the complete biocompatibility of the hydrogel. The Petri dishes with this kind of new hydrogel has an overproduction of collagen from the fibroblasts. The presence of this natural important compound in saffron petals, now offers new possibilities for the best use of the hole flower. This study is still in progress.http://www.fupress.net/index.php/ijae/article/view/1495

Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful

Objective: Adrenal venous sampling (AVS) is the gold standard to discriminate patients with unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and in cases of unsuccessful cannulation of adrenal veins, the results may not always be interpreted. The aim of our study was to develop diagnostic models to distinguish UPA from BPA, in cases of unilateral successful AVS and the presence of contralateral suppression of aldosterone secretion.Design: Retrospective evaluation of 158 patients referred to a tertiary hypertension unit who underwent AVS. We randomly assigned 110 patients to a training cohort and 48 patients to a validation cohort to develop and test the diagnostic models.Methods: Supervised machine learning algorithms and regression models were used to develop and validate two prediction models and a simple 19-point score system to stratify patients according to their subtype diagnosis.Results: Aldosterone levels at screening and after confirmatory testing, lowest potassium, ipsilateral and contralateral imaging findings at CT scanning, and contralateral ratio at AVS, were associated with a diagnosis of UPA and were included in the diagnostic models. Machine learning algorithms correctly classified the majority of patients both at training and validation (accuracy: 82.9-95.7%). The score system displayed a sensitivity/specificity of 95.2/96.9%, with an AUC of 0.971. A flow-chart integrating our score correctly managed all patients except 3 (98.1% accuracy), avoiding the potential repetition of 77.2% of AVS procedures.Conclusions: Our score could be integrated in clinical practice and guide surgical decision-making in patients with unilateral successful AVS and contralateral suppression

Machine learning applied to ambulatory blood pressure monitoring: a new tool to diagnose autonomic failure?

BACKGROUND: Autonomic failure (AF) complicates Parkinson’s disease (PD) in one-third of cases, resulting in complex blood pressure (BP) abnormalities. While autonomic testing represents the diagnostic gold standard for AF, accessibility to this examination remains limited to a few tertiary referral centers. OBJECTIVE: The present study sought to investigate the accuracy of a machine learning algorithm applied to 24-h ambulatory BP monitoring (ABPM) as a tool to facilitate the diagnosis of AF in patients with PD. METHODS: Consecutive PD patients naïve to vasoactive medications underwent 24 h-ABPM and autonomic testing. The diagnostic accuracy of a Linear Discriminant Analysis (LDA) model exploiting ABPM parameters was compared to autonomic testing (as per a modified version of the Composite Autonomic Symptom Score not including the sudomotor score) in the diagnosis of AF. RESULTS: The study population consisted of n = 80 PD patients (33% female) with a mean age of 64 ± 10 years old and disease duration of 6.2 ± 4 years. The prevalence of AF at the autonomic testing was 36%. The LDA model showed 91.3% accuracy (98.0% specificity, 79.3% sensitivity) in predicting AF, significantly higher than any of the ABPM variables considered individually (hypotensive episodes = 82%; reverse dipping = 79%; awakening hypotension = 74%). CONCLUSION: LDA model based on 24-h ABPM parameters can effectively predict AF, allowing greater accessibility to an accurate and easy to administer test for AF. Potential applications range from systematic AF screening to monitoring and treating blood pressure dysregulation caused by PD and other neurodegenerative disorders