Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints

This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. In this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement informatio

Expected global suitability of coffee, cashew and avocado due to climate change

Coffee, cashew and avocado are of high socio-economic importance in many tropical smallholder farming systems around the globe. As plantation crops with a long lifespan, their cultivation requires long-term planning. The evaluation of climate change impacts on their biophysical suitability is therefore essential for developing adaptation measures and selecting appropriate varieties or crops. In this study, we modelled the current and future suitability of coffee arabica, cashew and avocado on a global scale based on climatic and soil requirements of the three crops. We used climate outputs of 14 global circulation models based on three emission scenarios to model the future (2050) climate change impacts on the crops both globally and in the main producing countries. For all three crops, climatic factors, mainly long dry seasons, mean temperatures (high and low), low minimum temperatures and annual precipitation (high and low), were more restrictive for the global extent of suitable growing regions than land and soil parameters, which were primarily low soil pH, unfavourable soil texture and steep slopes. We found shifts in suitable growing regions due to climate change with both regions of future expansion and contraction for all crops investigated. Coffee proved to be most vulnerable, with negative climate impacts dominating in all main producing regions. For both cashew and avocado, areas suitable for cultivation are expected to expand globally while in most main producing countries, the areas of highest suitability may decrease. The study reveals that climate change adaptation will be necessary in most major producing regions of all three crops. At high latitudes and high altitudes, however, they may all profit from increasing minimum temperatures. The study presents the first global assessment of climate change impacts on cashew and avocado suitability

Dyslipidaemia in children on renal replacement therapy

Background Information on lipid abnormalities in end-stage renal disease (ESRD) mainly originates from adult patients and small paediatric studies. We describe the prevalence of dyslipidaemia, and potential determinants associated with lipid measures in a large cohort of paediatric ESRD patients. Methods In the ESPN/ERA-EDTA registry, lipid measurements were available for 976 patients aged 2-17 years from 19 different countries from the year 2000 onwards. Dyslipidaemia was defined as triglycerides >100 mg/dL (2-9 years) or >130 mg/dL (9-17 years), high-density lipoprotein (HDL) cholesterol 145 mg/dL. Missing data were supplemented using multiple imputation. Results The prevalence of dyslipidaemia was 85.1% in peritoneal dialysis (PD) patients, 76.1% in haemodialysis (HD) patients and 55.5% among renal allograft recipients. Both low and high body mass index (BMI) were associated with a less favourable lipid profile. Younger age was associated with a worse lipid profile among PD patients. HDL levels significantly improved after transplantation, whereas no significant improvements were found for triglyceride and non-HDL levels. In transplant recipients, use of cyclosporin was associated with significantly higher non-HDL and HDL levels than tacrolimus usage (P 90 mL/min/1.73 m2 (P < 0.0001). Conclusions Dyslipidaemia is common among paediatric ESRD patients in Europe. Young age and PD treatment are associated with worse lipid profiles. Although lipid levels generally improve after transplantation, dyslipidaemia may persist due to decreased graft function, high BMI or to the use of certain immunosuppressant

Challenges in supporting extraction of knowledge about environmental objects and events from geosensor data

Technologies for capturing large amounts of real-time and high-detail data about the environment have advanced rapidly; our ability to use this data for understanding the monitored settings for decision-making has not. Visual analytics, creating suitable tools and interfaces that combine computational powers with the human’s capabilities for visual sense making, is a promising approach. Geosensor networks monitor a range of different complex environmental settings, collecting heterogeneous data at different spatial and temporal scales. Similarly domain experts with specific preferences and requirements use the collected data. Additionally, long-term monitoring networks may aim to increase sensor node longevity by minimizing storage and communication load. Based on these aspects, four key challenges for the extraction of knowledge about environmental objects and events from geosensor data are identified: dynamics and uncertainty of the continuous stream of recorded data; different scales in data collection but also data analysis at a range of aggregation levels; decentralized data processing and storage; and evaluation of the effectiveness, efficiency and completeness of implemented decentralized visual analytics approaches