Aquaponics in the Built Environment

Aquaponics’ potential to transform urban food production has been documented in a rapid increase of academic research and public interest in the field. To translate this publicity into real-world impact, the creation of commercial farms and their relationship to the urban environment have to be further examined. This research has to bridge the gap between existing literature on growing system performance and urban metabolic flows by considering the built form of aquaponic farms. To assess the potential for urban integration of aquaponics, existing case studies are classified by the typology of their building enclosure, with the two main categories being greenhouses and indoor environments. This classification allows for some assumptions about the farms’ performance in their context, but a more in-depth life cycle assessment (LCA) is necessary to evaluate different configurations. The LCA approach is presented as a way to inventory design criteria and respective strategies which can influence the environmental impact of aquaponic systems in the context of urban built environments

Malignant Tumors of the Central Nervous System

Malignant tumors of the central nervous system in adults comprise a heterogeneous group of malignancies, the largest subgroups comprising astrocytomas, ependymomas, and oligodendrogliomas. Glioblastomas are the most common tumor type, and they have dismal prognosis. Due to differences in cell type of origin, as well as pathogenesis, it is plausible that their etiology also differs between tumor types. The etiology of malignant CNS tumors is largely unknown and no occupational risk factors have been definitively identified. High doses of ionizing radiation increase the risk, but in occupational settings the dose levels appear too small to result in discernible excesses. Several studies have assessed possible effect of extremely low frequency and radiofrequency electromagnetic fields, but the results are inconsistent. Increased brain tumor risk has been reported in agricultural workers, but no specific exposure has been linked to them. Pesticides have been analyzed in several studies without showing a clear increase in risk.acceptedVersionPeer reviewe

An optimisation model for spatially allocating commodity production and forecasting its impact on regional productivity

C1 - Journal Articles Referee

Radiofrequency Electromagnetic Radiation and Memory Performance: Sources of Uncertainty in Epidemiological Cohort Studies

Uncertainty in experimental studies of exposure to radiation from mobile phones has in the past only been framed within the context of statistical variability. It is now becoming more apparent to researchers that epistemic or reducible uncertainties can also affect the total error in results. These uncertainties are derived from a wide range of sources including human error, such as data transcription, model structure, measurement and linguistic errors in communication. The issue of epistemic uncertainty is reviewed and interpreted in the context of the MoRPhEUS, ExPOSURE and HERMES cohort studies which investigate the effect of radiofrequency electromagnetic radiation from mobile phones on memory performance. Research into this field has found inconsistent results due to limitations from a range of epistemic sources. Potential analytic approaches are suggested based on quantification of epistemic error using Monte Carlo simulation. It is recommended that future studies investigating the relationship between radiofrequency electromagnetic radiation and memory performance pay more attention to treatment of epistemic uncertainties as well as further research into improving exposure assessment. Use of directed acyclic graphs is also encouraged to display the assumed covariate relationship

Radiofrequency electromagnetic field exposure assessment: a pilot study on mobile phone signal strength and transmitted power levels

In many epidemiological studies mobile phone use has been used as an exposure proxy for radiofrequency electromagnetic field (RF-EMF) exposure. However, RF-EMF exposure assessment from mobile phone use is prone to measurement errors limiting epidemiological research. An often-overlooked aspect is received signal strength levels from base stations and its correlation with mobile phone transmit (Tx) power. The Qualipoc android phone is a tool that provides information on both signal strength and Tx power. The phone produces simultaneous measurements of Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Received Signal Code Power (RSCP), and Tx power on the 3G and 4G networks. Measurements taken in the greater Melbourne area found a wide range of signal strength levels. The correlations between multiple signal strength indicators and Tx power were assessed with strong negative correlations found for 3G and 4G data technologies (3G RSSI −0.93, RSCP −0.93; 4G RSSI −0.85, RSRP −0.87). Variations in Tx power over categorical levels of signal strength were quantified and showed large increases in Tx power as signal level decreased. Future epidemiological studies should control for signal strength or factors influencing signal strength to reduce RF-EMF exposure measurement error

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation

Purpose: The purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression). Methods: The search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression). Results: Twenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89. Conclusions: Current GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds. Translational Relevance: AI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions