Advancements in Neuroradiology via Artificial Intelligence and Machine Learning

Neuroradiology is significantly showing the broad impact in field of Artificial intelligence research and also in Machine learning. Neuro-radiology includes methods such as neuro-imaging which simply diagnose and characterize disorders of the CNS and PNS. Artificial Intelligence (AI) is one of the main attribute in the field of computer science generally focusing on creating "algorithms" which can be used to solve any arbitrary desired problem. AI has several applications in the field of Neuroradiolody and one of the most common and influencing application is machine learning. Machine learning is a data science approach that allows computers to learn without being programmed with specific rules. Some of the factors which shows neuroradiological impact on AI research are; (a) neuroimaging comprising rich, multicontrast, multidimensional, and multimodality data which fit themselves well to machine learning tasks; (b) consideration of well-established neuroimaging public datasets of various neural diseases such as Alzheimer disease, Parkinson disease, tumors, different forms of sclerosis etc. (c) quantitative neuroimaging research history which proves clinical practices. Another major application is Deep learning which is useful in management of information content of digital pictures that a human reader can only identify and use partially. Except this various limitations also come in the picture such as adoption in neuroradiology practice etc. Till now several research has been done which connects the concepts of Neuroradiology and Artificial intelligence and yet more to be done so as to overcome the limitations of AI in Neuroradiology

SRF Cavity Fabrication and Materials

The technological and metallurgical requirements of material for highgradient superconducting cavities are described. High-purity niobium, as the preferred metal for the fabrication of superconducting accelerating cavities, should meet exact specifications. The content of interstitial impurities such as oxygen, nitrogen, and carbon must be below 10{\mu}g/g. The hydrogen content should be kept below 2{\mu}g/g to prevent degradation of the Q-value under certain cool-down conditions. The material should be free of flaws (foreign material inclusions or cracks and laminations) that can initiate a thermal breakdown. Defects may be detected by quality control methods such as eddy current scanning and identified by a number of special methods. Conventional and alternative cavity fabrication methods are reviewed. Conventionally, niobium cavities are fabricated from sheet niobium by the formation of half-cells by deep drawing, followed by trim machining and Electron-Beam Welding (EBW). The welding of half-cells is a delicate procedure, requiring intermediate cleaning steps and a careful choice of weld parameters to achieve full penetration of the joints. The equator welds are particularly critical. A challenge for a welded construction is the tight mechanical and electrical tolerances. These can be maintained by a combination of mechanical and radio-frequency measurements on halfcells and by careful tracking of weld shrinkage. The established procedure is suitable for large series production. The main aspects of quality assurance management are mentioned. Another cavity fabrication approach is slicing discs from the ingot and producing cavities by deep drawing and EBW. Accelerating gradients at the level of 35-45 MV.m-1 can be achieved by applying Electropolishing (EP) treatment....Comment: 37 pages, contribution to the CAS-CERN Accelerator School: Superconductivity for Accelerators, Erice, Italy, 24 April - 4 May 2013, edited by R. Baile

Matching knowledge management and human capital management: Towards an integrative framework

The rapidly increasing economic dynamics that global market poses to modern organizations combined with the emergency to attract, develop and retain the best human capital (HC) led to more effective approach to knowledge management (KM). HC becomes the center of KM while the distribution of knowledge among organization's employees is considered its main activity. Effective KM use requires the ability to choose among all skills and knowledge those which contribute to creation of organizational key processes and activities. KM and Human Capital Management (HCM), two highly popular topics in current management discussions, are often bracketed together. An extensive literature review shows that knowledge plays a background role in HCM discussions, emphasizing the impact of KM practices on leadership, creativity, motivation, new ideas generation, recruitment, and employee competence. Some gaps are diagnosed in terms of absence of literature regarding an holistic approach to HC and KM processes, given the fragmentation on findings between the research in the two areas. The literature review of both KM and HC provides a deeper understanding of how KM practices contribute to develop, retain and renew organization’s HC, as part of a broader and more integrated effort to manage and develop human capability for business performance. Taken together, these two research domains are matched in a framework that intends to support the implementation of KM practices in order to promote HC development. A survey was administered to eight portuguese healthcare institutions to infer the most relevant KM practices to impact HC level contributing to the framework

Investigating causes of mortality in live export cattle

This research project was initiated to provide industry with current, credible, scientific data on causes of death and risk factors for mortality in Australian live export cattle on long-haul voyages. Animal data and necropsy samples were collected from animals that died on 20 research voyages during the study period March 2010 to September 2012. The average voyage mortality percentage was 0.37%. Respiratory disease was the most commonly diagnosed cause of death, accounting for 107/215 (49.8%) of deaths overall, and 107/181 (59.1%) of deaths for which a diagnosis could be made. In addition, pneumonia was identified in 33% of animals for which respiratory disease was not considered the primary cause of death. Other common causes of death included lameness (n = 22/181, 12.2%), ketosis (n = 12, 6.6%), septicemia (n = 11, 6.1%), and enteric disease (n = 10, 5.5%). Quantitative polymerase chain reaction (qPCR) assays were developed to detect viruses and bacteria known to be associated with bovine respiratory disease (BRD) in necropsy and nasal swab samples: Bovine coronavirus (BCoV, Betacoronavirus 1), Bovine herpesvirus 1 (BoHV-1), Bovine viral diarrhoea virus (BVDV), Bovine respiratory syncytial virus (BRSV), Bovine parainfluenza virus 3 (BPIV-3), Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica and Pasteurella multocida Two-thirds (130/195) of animals from which lung samples were collected had histological changes and/or positive qPCR results suggestive of infectious lung disease: 93/130 (72%) had evidence of primary bacterial infection, 4 (3%) with primary viral infection, 29 (22%) with concurrent bacterial and viral infections, and for 4 (3%) the causative organism could not be indentified. M. bovis, H. somni, P. multocida, M. haemolytica and BCoV were significantly associated with respiratory disease during voyages. Results from nasal swab and serological samples collected at entry to the pre-export assembly depot indicated that there were significant differences in nasal and seroprevalence between animals sourced from different properties. Combined nasal swab and serum results suggest that BCoV and BVDV are likely to be important infectious agents in the development of BRD in live export cattle while BPIV-3 is unlikely to play a major role. The contribution of BoHV-1, BRSV and bacteria of interest is difficult to determine. Analysis of animal and voyage data collected by industry between January 1995 and December 2012 revealed that while there has been an overall reduction in voyage mortality rates since 2000, there remain significant differences in mortality rate between load and discharge regions. Examination of daily mortality data available for research voyages revealed that peak daily mortality risk occurs at 3-4 weeks post-departure. The development of methods for spatial analyses coupled with data available in the National Livestock Identification System database allowed the description of patterns of animal movement prior to export. This study has improved our understanding of causes of death and risk factors for mortality in Australian live export cattle. We now have baseline data on the prevalence of BRD organisms in live export cattle that could be used to develop strategies for BRD prevention and control prior to loading and during voyages

The internationalization of family firms Jerónimo Martins´ expansion to Colombia

The internationalization of family businesses is a relevant topic in a global-oriented society, and the increasing interest in this field is expressed by a significant expansion on the number of research papers published recently. In fact, family firms are spread around the world and they have an unquestionable role in the functioning of global markets. This work project introduces, analyzes and summarizes the best practices responsible for family-driven internationalization. The case of Jerónimo Martins Group, a Portuguese-family business (FB) leader in the food retail and wholesale industry, is used to evaluate the impact that family-owned firms´ characteristics may have on their internationalization in a real-life scenario. It not only aims to verify if the theoretical frameworks suit in practice, but also tries to discover how FBs can optimize their resources and particularities, obtaining a competitive advantage in terms of internationalization. The family firms should exploit their advantages, while trying to correct and surpass their difficulties and disadvantages, in order to succeed in this kind of operation. Benefiting from their familiar dimension, FBs are better positioned to internationalize if they can optimize the characteristics that differentiate them from the others

Innovation and development after the earthquake in Emilia

The 2012 earthquake in Emilia-Romagna (Italy) has shaken up the collective understanding on the socioeconomic importance of a vast territory that generates almost 2% of Italian GDP. The area affected by the earthquake is characterized by the presence of important industrial and agricultural districts, and by good practices of local governance that are internationally renowned. Private and public buildings, factories, offices and retail shops, historical and cultural heritage sites have been severely damaged. Not only, but it set in motion transformations in the socio-economic system that might have unexpected consequences and that undermine the quick recovery of the local system: different agents, at different levels, taking individual and collective decisions, generate a cascade of changes that interact with its evolution path. Indeed, earthquakes pose challenges, but provide unprecedented opportunities: strategic decisions by economic and political agents, newly available financial resources, coordination or lack of coordination among main stakeholders, and so on. The following paper provides an overview of the first results of Energie Sisma Emilia research project: it aims at collecting and disseminating relevant knowledge and evidence in order to design policies. In particular, it identifies the agents propelling innovation processes, and analyses their strategies in ever-changing environment. The paper starts with a socio-economic analysis of the area struck by the earthquake, followed by the results of three of the focus groups conducted. Eventually, it illustrates a specific innovation: the introduction and implementation of the digital infrastructure “Mude”

The impact of machine learning on the efficiency of the B2B sales service in pharmaceutical companies

The explanatory study examines the possible value of Machine Learning in the B2B sales process in pharmaceutical companies. Sales representatives accounting for a wide range of activities, suffering from time consuming and repetitive tasks. This study investigates the potential of Machine Learning applications for B2B sales in order to facilitate sales representative’s daily tasks and enhance the entire sales process. The results have been obtained through qualitative research based on 8 interviews with AI-experts, pharma consultants and sales representatives as well as secondary data in form of academic articles and reports. The findings reveal that, compared to other departments, ML-applications in B2B sales are less applied at the current stage, but mostly in the customer service process. The interviews have shown that the usage of ML-applications is possible within all steps of the sales process and enhances its overall efficiency and effectivity in terms of time, costs and quality. Furthermore, tasks which increase the efficiency of the sales department through ML applications are outlined. By applying ML within the B2B sales process, the daily work of sales representatives can be facilitated, which ultimately could not only have a positive impact on customer satisfaction, but also on employee commitment leading to competitive advantage in the price intense environment of the pharmaceutical industry.O presente estudo foca-se na possível importância da Aprendizagem Automática no serviço de vendas B2B em Empresas Farmacêuticas. Representantes de vendas responsáveis por uma grande variedade de actividades, afectados pelas demoradas e longas tarefas. Esta dissertação examina o potêncial da Aprendizagem Automática nas vendas B2B a fim de facilitar as tarefas diárias dos representantes de vendas, e de melhorar ainda todo o processo de vendas. Os resultados são obtidos através de uma pesquisa qualitativa baseada em 10 entrevistas com AI-experts, consultantes farmacêuticos e representantes de vendas, assim como fichas de dados provenientes de artigos e relatórios. Os resultados revelam que, em comparação com outros departamentos, a aplicação da Aprendizagem Automática em vendas B2B são actualmente menos aplicadas, sobretudo no que diz respeito ao atendimento ao cliente. As entrevistas mostraram que o uso da Aprendizagem Automática é possível em todas as fases do processo de vendas sendo que melhora toda a sua eficiência e efetividade em termos de tempo, custos e qualidade. Posteriormente, as tarefas de vendas mais eficientes dentro das farmácias estão estabelecidas; pelo que, a introdução da Aprendizagem Automática dentro do processo de vendas B2B poderá facilitar e, inclusive, melhorar o trabalho dos representantes de vendas, sendo que esta otimazação poderá, por conseguinte, não só ter um impacto positivo na satisfação do cliente como também no compromisso dos empregados originando, desta forma, uma vantagem competitiva face ao intenso ambiente de preços na industria farmacêutica