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Non‐steady‐state chambers are widely employed for quantifying soil emissions of CO2, CH4, and N2O. Automated non‐steady‐state (a‐NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high‐frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post‐measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high‐resolution data obtained from an a‐NSS sampling system. Applied to a 38‐day dataset, the algorithm captured concurrent field measurements of CO2, CH4, and N2O fluxes. The automated sampling system enables the acquisition of high‐frequency data, allowing the detection of episodic gas flux events. By using shape‐constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO2 and N2O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO2, CH4, and N2O fluxes, providing both instantaneous and diel values directly from raw, high‐temporal‐resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a‐NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics. [ABSTRACT FROM AUTHOR]

The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. Vital to the success of wireless microsensor networks is the ability of microsensors to “collectively perform sensing and computation”. In this paper, we study one of the fundamental challenges in sensor networks, node localization. The collaborative multilateration presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed.

We present a low cost wireless microsensor node architecture for distributed computation and sensing in massively distributed embedded systems. Our design focuses on the development of a versatile, low power device to facilitate experimentation and initial deployment of wireless microsensor nodes in deeply embedded systems. This paper provides the details of our architecture and introduces fine-grained node localization as an example application of distributed computation and wireless embedded sensing.

The recent advances in radio and embedded system technologies have enabled the proliferation of wireless micro-sensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

The urban heat island effect can be studied through satellite imaging, field measurements, or analytical and numerical tools. However, the latter methods are considered more comprehensive due to the complexity of the built environment and the large quantity of data required for an adequate analysis. This study aims to investigate the extent to which specific urban bioclimatic design concepts and strategies affect the urban heat island intensity in Mediterranean semi-arid environmental conditions, classified as subtropical. The case study site chosen was Kaimakli, an urban district in Nicosia, Cyprus, known for high urban heat island intensities due to its location and rapid growth characterized by more impervious materials and less green vegetation. The analysis of the specific site considers parameters such as urban density, vegetation, soil sealing effect, building age and materials, land coverage ratio, and orientation. A design scenario consisting of three mitigation policies of different building types, heights, and vegetated types was developed and investigated. The study found that under semi-arid conditions, the use of extensive vegetation in an urban block of a 200 × 200 m² area and the reduction of the built area by about 10% resulted in an air temperature reduction of 1.5 °C during the summer solstice at 3:00 pm. These findings quantify the impact of specific urban heat island mitigation practices on decreasing the intensity of the effect under subtropical climatic conditions. This study provides valuable insights into the potential of specific urban bioclimatic design concepts and strategies to mitigate the urban heat island effect. The use of extensive vegetation and a reduction in the built area have been shown to be effective in reducing air temperatures, which can have significant implications for public health, energy consumption, and overall urban sustainability. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the determinants of research output by European economics departments. We use data from a survey we conducted on the various characteristics of these departments. We pay particular attention to spillovers from North America to Europe and measure these in terms of visiting arrangements between academic institutions across the Atlantic and formal training received by European faculty in North America. We find strong evidence that European economics departments with North-American links have higher research output.

Does medical technology generated in frontier countries have a significant impact on health outcomes in the rest of the world? This paper considers a framework where non-frontier countries may benefit from medical innovation that is embodied in medical imports or diffuses in the form of ideas. Using a novel dataset from a cross-section of 63 technology-importing countries, we show that medical technology diffusion is an important contributor to improved health status, as measured by life expectancy and mortality rates. Keywords: International technology diffusion; Life expectancy; Medical imports; Medical students JEL classification: O30; O40