High-skilled Experts Research Articles

An unsupervised approach for health index building and for similarity-based remaining useful life estimation

Predictive maintenance techniques attempt to trigger a maintenance intervention at the right moment by estimating the life expectation. Predictive maintenance is increasingly implemented by automated approaches able to perform diagnostics and prognostics. The main part of recent research in these approaches is focused in machine learning structures whose reasoning is implicit and cannot be easily explained. This poses a problem for their implementation in highly constrained area such as aeronautics. To overcome this constraint, explicit reasoning approaches such as the Similarity-Based Model (SBM) can be implemented. The SBM has been widely used for fault diagnostics and the remaining useful life (RUL) estimation, but the development of SBM includes tasks that often rely on high skilled experts. For instance, data reduction techniques required for SBM are often performed by experts judgment whose outcomes are not always consistent. The produced features from these techniques are used to build the Health Index that can be used to create the degradation trends that serve as a reference for the SBM. To overcome these difficulties, an automatic and unsupervised approach based on the Kernel Principal Component Analysis is proposed to enhance the Health Index creation. It preserves as much of the sensor information as possible improving the similarity-based RUL estimation. Additionally, when estimating the RUL of a system, the most similar degradation trends stored in the SBM library are used to compute individual RULs, the final RUL is obtained by a fusion rule technique that combines all these individual RULs into a consolidated value. For the fusion rule techniques, a self-adaptive method that does not rely on human expertize is proposed. This fusion rule can benefit of the accumulated knowledge over the SBM operation. This unsupervised approach to develop a SBM is validated with promising results against an equivalent and supervised algorithm that came out best in the 2008 prognostic health management challenge.

Effective scientific personnel training in the field of modern computer technologies for the implementation of advanced research projects of the Megascience class

Successful research projects of the Megascience class usually require a well-trained team of scientists from various fields of knowledge. These scientists must be high-skilled experts. Each member of a team like that must have the necessary, specialized cross-industry skills, for example, in such areas as artificial intelligence, convolutional neural networks, specialized intelligent search engines, and full-text analysis. One of the key aspects of effective personnel training for successful implementation of Megascience projects into reality is the acquisition by students professional skills, abilities, and knowledge to use tools of modern scientific technologies, containing, for example, libraries of programs (functions). In particular, convolutional neural networks and intelligent search systems can be applied in various research projects in the field of physics, chemistry, biology, and medicine, for example, in telemedicine, for effective decision-making in diagnosing a patient. Therefore, understanding the principles of neural networks and intelligent search systems is a necessary competence of researchers working in the framework of Megascience projects. Classic search engines are based on indexing the textual information of the database that is being searched. Intelligent search engines can improve the search experience through intelligent data processing, including using convolutional neural networks. This report examines practical examples and areas of the successful application of convolutional neural networks and information systems in practice.

The Relationship between M&Amp;A and Innovation

M&As have been used as one of the most preferable strategies for firm growth. However, the motivations for M&A have changed mainly because the nature of technologies is changing. Due to the complicated nature of the latest technologies such as IoT and AI, as well as their rapid pace of change, firms acquire innovative startups that already deal with the cutting-edge technologies like Googles acquisition of DeepMind or Facebooks purchase of WhatsApp. The implications of these M&As are two-fold. First, by acquiring firms with high-skilled experts, existing firms can save the time and costs necessary not only to educate workers lack of technological background but also to catch up with cutting-edge and promising businesses. Second, acquiring innovative firms with diverse technologies and services can attract more customers, meaning this could be the best strategy for existing digital platform firms seeking profit-maximization through innovation due to network effects. Therefore, it is a good option for firms to acquire innovative companies in order to achieve long-run growth based on continuous innovation.

Building Trust by Qualification in a Market for Expert Services

Markets for credence goods are classified by experts alone being able to identify consumers’ problems and determine appropriate services for solution. Examining a market where experts have to invest in costly diagnosis to correctly identify problems and consumers being able to visit multiple experts for diagnosis, we introduce heterogeneously qualified experts. We assume that high skilled experts can identify problems with some probability even with low effort, e.g. due to education or experience. In a laboratory experiment we show that, against our theoretical predictions, this does not lead to a drop in experts’ willingness for high diagnostic effort. However, consumers generally distrust experts’ diagnosis effort as they buy less often after their first recommendation than it would be optimal and show frequently other non-optimal behavior patterns, e.g. receiving recommendations but do not buy service. Our results indicate that, at some level, introducing higher qualified experts increases the quality of diagnosis, as well as consumers’ trust resulting in more and quicker service purchases.

Effects of Qualification in Expert Markets with Price Competition and Endogenous Verifiability

We investigate a market in which experts have a moral hazard problem because they need to invest in costly but unobservable effort to identify consumer problems. Experts have either high or low qualification and can invest either high or low effort in their diagnosis. High skilled experts are able to identify problems with some probability even with low effort while low skilled experts here always give false recommendations. Experts compete for consumers by setting prices for diagnosis and service. Consumers can visit multiple experts, which enables an endogenous verifiability of diagnosis. We show that with a sufficient number of high skilled experts, stable second-best and perfectly non-degenerate equilibria are possible even with flexible prices, although they depend on transactions costs being relatively low. By contrast, with a small share of high skilled experts in the market, setting fixed prices can be beneficial for society.

Expert Qualification in Markets for Expert Services: A Sisyphean Task?

Moral hazard in expert diagnoses is more complicated in credence goods markets because ex-post verification of service optimality is usually not possible. We provide an experimental framework to investigate expert and consumer behavior as well as market efficiency in a setting in which experts need to invest in costly but unobservable effort to identify consumer problems and consumers are able to visit multiple experts for diagnosis. We introduce heterogeneously-qualified experts, varying in their necessary effort to diagnose consumers. We examine how subjects react to expert qualification and the introduction of price competition. We find that our baseline market is more efficient and qualification is not necessarily the Sisyphean task, as theory predicted. Nevertheless, we observe high skilled experts investing significantly less effort in diagnoses than their low skilled counterparts. Qualifying experts increases efficiency with fixed prices but remains almost without influence in markets with price competition. Introducing price competition does not lead to the predicted market breakdown, but rather has negative effects on market efficiency. In sum, whether expert qualification should be pursued in credence goods markets depends on the market composition and existing institutions.

Feasibility of establishing a biosafety level 3 tuberculosis culture laboratory of acceptable quality standards in a resource-limited setting: an experience from Uganda.

BackgroundDespite the recent innovations in tuberculosis (TB) and multi-drug resistant TB (MDR-TB) diagnosis, culture remains vital for difficult-to-diagnose patients, baseline and end-point determination for novel vaccines and drug trials. Herein, we share our experience of establishing a BSL-3 culture facility in Uganda as well as 3-years performance indicators and post-TB vaccine trials (pioneer) and funding experience of sustaining such a facility.MethodsBetween September 2008 and April 2009, the laboratory was set-up with financial support from external partners. After an initial procedure validation phase in parallel with the National TB Reference Laboratory (NTRL) and legal approvals, the laboratory registered for external quality assessment (EQA) from the NTRL, WHO, National Health Laboratories Services (NHLS), and the College of American Pathologists (CAP). The laboratory also instituted a functional quality management system (QMS). Pioneer funding ended in 2012 and the laboratory remained in self-sustainability mode.ResultsThe laboratory achieved internationally acceptable standards in both structural and biosafety requirements. Of the 14 patient samples analyzed in the procedural validation phase, agreement for all tests with NTRL was 90% (P <0.01). It started full operations in October 2009 performing smear microscopy, culture, identification, and drug susceptibility testing (DST). The annual culture workload was 7,636, 10,242, and 2,712 inoculations for the years 2010, 2011, and 2012, respectively. Other performance indicators of TB culture laboratories were also monitored. Scores from EQA panels included smear microscopy >80% in all years from NTRL, CAP, and NHLS, and culture was 100% for CAP panels and above regional average scores for all years with NHLS. Quarterly DST scores from WHO-EQA ranged from 78% to 100% in 2010, 80% to 100% in 2011, and 90 to 100% in 2012.ConclusionsFrom our experience, it is feasible to set-up a BSL-3 TB culture laboratory with acceptable quality performance standards in resource-limited countries. With the demonstrated quality of work, the laboratory attracted more research groups and post-pioneer funding, which helped to ensure sustainability. The high skilled experts in this research laboratory also continue to provide an excellent resource for the needed national discussion of the laboratory and quality management systems.Electronic supplementary materialThe online version of this article (doi:10.1186/1478-4505-13-4) contains supplementary material, which is available to authorized users.