Barriers and Opportunities for Residential Solar PV and Storage Markets - A Western Australian Case Study

Residents and businesses around the world are increasingly installing solar photovoltaic (PV) panels and battery storage systems, satisfying not just their interest in clean energy, but also taking advantage of reduced technology costs and mitigating against future electricity price rises. Solar PV panels coupled with storage systems present an opportunity to move towards a resilient, affordable, flexible and secure electricity network. Western Australia provides a unique set of conditions (isolated network, high solar radiation, and rising electricity prices), which has contributed to the rapid uptake of solar PV’s in the state. Yet, a number of issues are still obstructing the transition to renewables. Using Western Australia as a case study, this paper investigates the barriers inhibiting the network transformation and explores the role that solar PV and storage can play as a disruptive threat to the incumbent, centralised service model of electricity utilities

Carbon neutral policy in action: the case of Bhutan

Climate policy across the world is proceeding at a highly variable pace, with some places very committed to decarbonizing their economies and others just beginning. Emerging nations are generally just starting along this journey. However, among the few nation states that have pledged to achieve carbon neutrality, is Bhutan, a least developed country. Carbon neutrality is an ambitious climate policy that is increasingly being recognized as necessary in order to stabilize global temperature rise at 1.5°C. However, Bhutan is likely to face significant challenges in maintaining this status as the country balances its desire to grow in economic opportunities (GDP) and in human happiness (GNH). Little research has been conducted inside the policy processes to better understand how Bhutan will maintain carbon neutrality. Through open-ended, semi-structured interviews with key stakeholders, this study provides an inside view on the current situation and future challenges that Bhutan may face, along with the complexities associated with implementing and maintaining an ambitious carbon neutral policy. The paper highlights Bhutan's story and how it could be useful for policy learning and knowledge sharing, especially in the context of emerging nations’ climate governance

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

High elevation watersheds in the southern Appalachians: Indicators of sensitivity to acidic deposition and the potential for restoration through liming

Southern Appalachian high elevation watersheds have deep rocky soils with high organic matter content, different vegetation communities, and receive greater inputs of acidic deposition compared to low elevation sites within the region. Since the implementation of the Clean Air Act Amendment in the 1990s, concentrations of acidic anions in rainfall have declined. However, some high elevation streams continue to show signs of chronic to episodic acidity, where acid neutralizing capacity (ANC) ranges from 0 to 20 µeq L-1. We studied three 3rd order watersheds (North River in Cherokee National Forest, Santeetlah Creek in Nantahala National Forest, and North Fork of the French Broad in Pisgah National Forest) and selected four to six 1st order catchments within each watershed to represent a gradient in elevation (849–1526 m) and a range in acidic stream ANC values (11–50 leq L-1). Our objectives were to (1) identify biotic, physical and chemical catchment parameters that could be used as indices of stream ANC, pH and Ca:Al molar ratios and (2) estimate the lime required to restore catchments from the effects of excess acidity and increase base cation availability. We quantified each catchment’s biotic, physical, and chemical characteristics and collected stream, O-horizon, and mineral soil samples for chemical analysis seasonally for one year. Using repeated measures analysis, we examined variability in stream chemistry and catchment characteristics; we used a nested split-plot design to identify catchment characteristics that were correlated with stream chemistry. Watersheds differed significantly and the catchments sampled provided a wide range of stream chemical, biotic, physical and chemical characteristics. Variability in stream ANC, pH, and Ca:Al molar ratio were significantly correlated with catchment vegetation characteristics (basal area, tree height, and tree diameter) as well as O-horizon nitrogen and aluminum concentrations. Total soil carbon and calcium (an indicator of parent material), were significant covariates for stream ANC, pH and Ca:Al molar ratios. Lime requirement estimates did not differ among watersheds but this data will help select catchments for future restoration and lime application studies. Not surprisingly, this work found many vegetation and chemical characteristics that were useful indicators of stream acidity. However, some expected relationships such as concentrations of mineral soil extractable Ca and SO4 were not significant. This suggests that an extensive test of these indicators across the southern Appalachians will be required to identify high elevation forested catchments that would benefit from restoration activities

The Family Streptomycetaceae

The family Streptomycetaceae comprises the genera Streptomyces, Kitasatospora, and Streptacidiphilus that are very difficult to differentiate both with genotypic and phenotypic characteristics. A separate generic status for Kitasatospora and Streptacidiphilus is questionable. Members of the family can be characterized as non-acid-alcohol-fast actinomycetes that generate most often an extensively branched substrate mycelium that rarely fragments. At maturity, the aerial mycelium forms chains of few to many spores. A large variety of pigments is produced, responsible for the color of the substrate and aerial mycelium. The organisms are chemoorganotrophic with an oxidative type of metabolism and grow within different pH ranges. Streptomyces are notable for their complex developmental cycle and production of bioactive secondary metabolites, producing more than a third of commercially available antibiotics. Antibacterial, antifungal, antiparasitic, and immunosuppressant compounds have been identified as products of Streptomyces secondary metabolism. Streptomyces can be distinguished from other filamentous actinomycetes on the basis of morphological characteristics, in particular by vegetative mycelium, aerial mycelium, and arthrospores. The genus comprises at the time of writing more than 600 species with validated names. 16S rRNA gene sequence-based analysis for species delineation within the Streptomycetaceae is of limited value. The variations within the 16S rRNA genes—even in the variable regions—are too small to resolve problems of species differentiation and to establish a taxonomic structure within the genus. Comprehensive comparative studies including protein-coding gene sequences with higher phylogenetic resolution and genome-based studies are needed to clarify the species delineation within the Streptomycetaceae