Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities

Institutions and Export Dynamics

We study the role of contract enforcement in shaping the dynamics of international trade at the firm level. We develop a theoretical model to describe how agents build reputations to overcome the problems created by weak enforcement of international contracts. We find that, all else equal, exporters start their activities with higher volumes and remain as exporters for a longer period in countries with better contracting institutions. However, conditional on survival, the growth rate of a firm's exports to a country decreases with the quality of the country's institutions. We test these predictions using a rich panel of Belgium exporting firms from 1995 to 2008 to every country in the world. We adopt two alternative empirical strategies. In one specification we use firm-year fixed effects to control for time-varying firm-specific characteristics. Alternatively, we model selection more explicitly with a two-step Heckman procedure using "extended gravity" variables as our exclusion restrictions. Results from both specifications support our predictions. Overall, our findings suggest that weak contracting institutions cannot be thought simply as an extra sunk or fixed cost to exporting firms; they also significantly affect firms' trade volumes and have manifold implications for firms' dynamic patterns in foreign markets.Firm exports, contract enforcement, contracting institutions, firm dynamics

Better safe than sorry? Reliability policy in network industries

This report develops a roadmap for reliability policy in network industries. Based on economic theory, we analyse the relationship between reliability and various types of government policy: privatisation, liberalisation, regulation, unbundling, and 'commitment policy'. We let government policy depend on (1) the feasibility of competition between networks, (2) contractibility of reliability, and (3) the relation between profit maximisation and public interests. We test this roadmap on the basis of the empirical literature and case studies on electricity, natural gas, drinking water, wastewater, and railways.

On the Viability of Quantitative Assessment Methods in Software Engineering and Software Services

IT help desk operations are expensive. Costs associated with IT operations present challenges to profit goals. Help desk managers need a way to plan staffing levels so that labor costs are minimized while problems are resolved efficiently. An incident prediction method is needed for planning staffing levels. The potential value of a solution to this problem is important to an IT service provider since software failures are inevitable and their timing is difficult to predict. In this research, a cost model for help desk operations is developed. The cost model relates predicted incidents to labor costs using real help desk data. Incidents are predicted using software reliability growth models. Cluster analysis is used to group products with similar help desk incident characteristics. Principal Components Analysis is used to determine one product per cluster for the prediction of incidents for all members of the cluster. Incident prediction accuracy is demonstrated using cluster representatives, and is done so successfully for all clusters with accuracy comparable to making predictions for each product in the portfolio. Linear regression is used with cost data for the resolution of incidents to relate incident predictions to help desk labor costs. Following a series of four pilot studies, the cost model is validated by successfully demonstrating cost prediction accuracy for one month prediction intervals over a 22 month period

Judgments of effort exerted by others are influenced by received rewards

Estimating invested effort is a core dimension for evaluating own and others’ actions, and views on the relationship between effort and rewards are deeply ingrained in various societal attitudes. Internal representations of effort, however, are inherently noisy, e.g. due to the variability of sensorimotor and visceral responses to physical exertion. The uncertainty in effort judgments is further aggravated when there is no direct access to the internal representations of exertion – such as when estimating the effort of another person. Bayesian cue integration suggests that this uncertainty can be resolved by incorporating additional cues that are predictive of effort, e.g. received rewards. We hypothesized that judgments about the effort spent on a task will be influenced by the magnitude of received rewards. Additionally, we surmised that such influence might further depend on individual beliefs regarding the relationship between hard work and prosperity, as exemplified by a conservative work ethic. To test these predictions, participants performed an effortful task interleaved with a partner and were informed about the obtained reward before rating either their own or the partner’s effort. We show that higher rewards led to higher estimations of exerted effort in self-judgments, and this effect was even more pronounced for other-judgments. In both types of judgment, computational modelling revealed that reward information and sensorimotor markers of exertion were combined in a Bayes-optimal manner in order to reduce uncertainty. Remarkably, the extent to which rewards influenced effort judgments was associated with conservative world-views, indicating links between this phenomenon and general beliefs about the relationship between effort and earnings in society