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Developing causal loop diagrams (CLDs) involves identifying stakeholders and endogenous variables and formulating variable causal relationships. Traditionally, the CLDs are developed mainly using a qualitative approach such as literature review, observations and interviews with stakeholders. However, modellers may question which stakeholders should be approached, whether the relevant variables are selected, and what to do when stakeholders perceive different variable relationships in the CLDs differently. Applying in a case study, this research proposes a multi-method approach by combining both quantitative and qualitative methods to select stakeholders, identify endogenous/exogenous variables, and develop the CLDs. The proposed quantitative method is expected to provide modellers with a justifiable stakeholder and variable selection process. The method also highlights possible hidden variables and relationships, which were further explored with a traditional qualitative approach.

AbstractObjectiveTo assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission.MethodsFollowing PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer‐reviewed, freely available and full‐text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review.ResultsTwenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors.ConclusionsDue to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems.

Abstract DC microgrids have received wide interest in recent times because of the advantages related to improved power transfer capability, high efficiency, compatibility with distributed energy resources (DER) and reduced losses. However, the adoption of DC microgrids has been restricted because of the challenges in designing a reliable protection scheme. The challenge results from the high magnitude of the fault current without zero crossing, coupled with the requirement of high rapidity in fault detection. The scenario is further complicated during the variation in operational dynamics of microgrid either due to weather intermittency or network reconfiguration arising out of DER outage. Non-addressal of the above issues results in reduced grid resilience because of possible relay malfunction during stressed scenarios. In this regard, an ensemble classifier-based protection scheme has been proposed for DC microgrid with adaptability to system reconfiguration and weather intermittency. The adaptiveness has been achieved by online identification of network topology, while immunity to weather variation is attained by stochastic modelling of solar irradiance and wind speed. The use of local information for executing the protection tasks avoids the issues related to data loss and latency in the communication link. The task of operating mode detection, topology identification, fault detection/classification and section identification have been formulated as a set of classification problems and further solved using a random subspace sampling-based ensemble classifier technique. The reliability of the proposed scheme has been extensively validated for a wide range of fault scenarios involving wide variation in network topology, weather condition and fault parameters.

This paper describes the application of IoT Technology for monitoring different parameters of battery of electric vehicle. Electric vehicle totally depends upon the source of energy from the battery. In this project, the idea of monitoring the performance of the vehicle using IoT techniques is proposed, so that monitoring can be done easily and directly. The objective of the project is to promote green power and to improve smartness of electric vehicle by monitoring the battery parameters such as voltage, temperature, current and charge avaibility. Also, these values displayed in cloud, which brings the concept of Internet of Things (IoT). The IoT based battery monitoring system consist of two major parts i) Monitoring device and ii) User interface. Based on experimental results, the system is capable to detect battery performance.

This paper presents a comparative study in predicting the monthly average solar radiation for Darwin, Australia (latitude 12.46 deg S longitude 130.84 deg E). The city of Darwin, Northern Territory (NT), has the highest and most consistent sunshine duration among all the other Australian states. This unique climate presents an opportunity for photovoltaic (PV) applications. Reliable and accurate predictions of solar radiation enable potential site locations, which exhibit high solar radiations and sunshine hours, to be identified for PV installation. Three predictive models were investigated in this study—the linear regression (LR), Angstrom–Prescott–Page (APP), and the artificial neural network (ANN) models. The mean global solar radiation coupled with the climate data (mean minimum and maximum temperatures, mean rainfall, mean evaporation, and sunshine fraction) obtained from the Australian Bureau of Meteorology (BoM) formed the basis of the dataset. Using simple and easily obtainable climate data presents an added advantage by reducing model complexity. Predictive results showed the root mean square errors (RMSEs) obtained were 6.72%, 13.29%, and 8.11% for the LR, APP, and ANN models, respectively. The predicted solar exposure from the LR model was then compared with the satellite-derived data to assess the accuracy of the LR method.

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Glacier retreat is evident on all continents, and the rate of retreat has accelerated during recent decades. Accurate, spatially explicit information on the position of glacier margins over time is useful for analyzing patterns of glacier retreat and measuring reductions in glacier surface area. This information is also essential for evaluating how mountain ecosystems are evolving due to climate warming and the attendant glacier retreat. Here, we present a non-comprehensive spatially explicit dataset showing multiple positions of glacier fronts since the Little Ice Age (LIA) maxima, including many data from the pre-satellite era. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings, and aerial or satellite images with a supplement of geochronology; and own field data. We provide ESRI shapefiles showing 728 past positions of 94 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8).

A natural holm oak forest was selectively thinned to test thinning as a tool to reduce tree mortality, increase productivity, and reverse the recent regression of the dominant species (Quercus ilex) induced by climate change. Thinning increased aboveground productivity and reduced stem mortality in this Mediterranean forest during four years just after thinning, contributing to the maintenance of forest functioning under changing climatic conditions. Q. ilex was the only species positively affected by the thinning: stem growth increased for all stem sizes, and mortality was significantly lower in thinned plots. On the contrary, mortality rates of Phillyrea latifolia and Arbutus unedo were not significantly lower. Stem growth increased for P. latifolia only in the smallest stem-size class. Our results highlight the suitability of selective thinning for improving the forest productivity and ensuring the conservation of Mediterranean coppices. Other benefits of selective thinning, such as a decrease in the risk of fire dispersion and an increase in the water supply for human populations, are also discussed.