Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach

To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Influence of Voltage Sags on the Continuity of the Operation and Lifespan of Single-Phase Industrial Robots

Due to the increase in the number of automated processes that employ industrial robots (especially in industrial and laboratory environments, including vacuum systems), and the resulting increase in the number of unresolved service requests, the purpose of the authors’ research was to confirm the occurrence of disturbances in the form of voltage sags that are not recorded by automation systems and which lead to the destruction of robots or their equipment in areas defined by the characteristics of ITIC/SEMI F47 and CBEMA as being free from such disturbances. The article also describes the environmental classification of robots by their process functionalities/features, and recommends equipment that is able to compensate for these disturbances. Such a classification approach can be an excellent tool for building an exploitation culture and assist the conscious selection of electrical equipment in robotised systems susceptible to disturbances (e.g., robots in load-lock in vacuum environment).

Environmental Classification in Hearing Aids

There are two parts to this article. The first is a general overview of how hearing aid classification works, including a comparison study of normal-hearing listeners and multiple manufacturers' hearing aids while listening to a sound parkour composed of a multitude of acoustic scenes. Most hearing aids applied nearly identical classification for simple listening environments. But differences began to appear across manufacturers' products when the listening environments became more complex. The second section reviews the results of a study of the acoustic ecology (listening environments) experienced by several cohorts of hearing aid users over a 4-month period. The percentages of time people spent in seven different listening environments were mapped. It was learned that they spent an average of 57% of their time in conversation and that age is not a good predictor of the amount of time spent in most listening environments. This is because, when grouped by age, there was little to no difference in the distribution of time spent in the seven listening environments, whereas there was tremendous variability within each age group.

High viral abundance and low diversity are associated with increased CRISPR-Cas prevalence across microbial ecosystems

CRISPR-Cas are adaptive immune systems that protect their hosts against viruses and other parasitic mobile genetic elements. Consequently, selection from viruses and other genetic parasites is often assumed to drive the acquisition and maintenance of these immune systems in nature, but this remains untested. Here, we analyse the abundance of CRISPR arrays in natural environments using metagenomic datasets from 332 terrestrial, aquatic and host-associated ecosystems. For each metagenome we quantified viral abundance and levels of viral community diversity to test whether these variables can explain variation in CRISPR-Cas abundance across ecosystems. We find a strong positive correlation between CRISPR-Cas abundance and viral abundance. In addition, when controlling for differences in viral abundance, we found that the CRISPR-Cas systems are more abundant when viral diversity is low. We also found differences in relative CRISPR-Cas abundance among environments, with environmental classification explaining ~24% of variation in CRISPR-Cas abundance. However, the correlations with viral abundance and diversity are broadly consistent across diverse natural environments. These results indicate that viral abundance and diversity are major ecological factors that drive the selection and maintenance of CRISPR-Cas in microbial ecosystems.

Climate Change Public Budget Tagging: Connections across Financial and Environmental Classification Systems

Identifying and evaluating climate expenditures in the public sector, known as budget tagging, has generated increasing attention from multiple stakeholders, not only to assess the governments climate change policy, but also to monitor fiscal risks associated with increasing and unpredictable climate change impacts. This paper explores the issues raised by climate change budget tagging in the context of a broader discussion on the connections with fiscal and environmental statistical classification systems. It argues that, for climate change budget tagging efforts to be successful, the definitions and classifications of climate change expenditures must be consistent with statistical standards currently in use, such as the Government Finance Statistics Framework and the System of National Accounts.

A Comparison of Environment Classification Among Premium Hearing Instruments

Hearing aids classify acoustic environments into multiple, generic classes for the purposes of guiding signal processing. Information about environmental classification is made available to the clinician for fitting, counseling, and troubleshooting purposes. The goal of this study was to better inform scientists and clinicians about the nature of that information by comparing the classification schemes among five premium hearing instruments in a wide range of acoustic scenes including those that vary in signal-to-noise ratio and overall level (dB SPL). Twenty-eight acoustic scenes representing various prototypical environments were presented to five premium devices mounted on an acoustic manikin. Classification measures were recorded from the brand-specific fitting software then recategorized to generic labels to conceal the device company, including (a) Speech in Quiet, (b) Speech in Noise, (c) Noise, and (d) Music. Twelve normal-hearing listeners also classified each scene. The results revealed a variety of similarities and differences among the five devices and the human subjects. Where some devices were highly dependent on input overall level, others were influenced markedly by signal-to-noise ratio. Differences between human and hearing aid classification were evident for several speech and music scenes. Environmental classification is the heart of the signal processing strategy for any given device, providing key input to subsequent decision-making. Comprehensive assessment of environmental classification is essential when considering the cost of signal processing errors, the potential impact for typical wearers, and the information that is available for use by clinicians. The magnitude of differences among devices is remarkable and to be noted.

Soil classification based on spectral and environmental variables

Given the large volume of soil data, it is now possible to obtain a soil classification using spectral, climate and terrain attributes. The idea was to develop a soil series system, which intends to discriminate soil types according to several variables. This new system was called Soil-Environmental Classification (SEC). The spectra data was applied to obtain information about the soil and climate and terrain variables to simulate the pedologist knowledge in soil-environment interactions. The most appropriate numbers of classes were achieved by the lowest value of AIC applying the clusters analysis, which was defined with 8 classes. A relationship between the SEC and WRB-FAO classes was found. The SEC facilitated the identification of groups with similar characteristics using not only soil but environmental variables for the distinction of the classes. Finally, the conceptual characteristics of the 8 SEC were described. The development of SEC conducted to incorporate applicable soil data for agricultural management, with less interference of personal/subjective/empirical knowledge (such as traditional taxonomic systems), and more reliable on automation measurements by sensors.