Accommodation in Crisis: Forgotten Women in Western Sydney

This research project set out to identify the need for crisis accommodation of single homeless women in Western Sydney. It was hoped that this need would be identified through records of requests for assistance by homeless women in the region and through interviews with service providers in contact with homeless single women. While an obvious service gap is identified in this report, it is also argued that the recognition and numeration of, and response to single women s needs is bound up with many broader conceptual and structural difficulties, including: the invisibility of single women as a category of deserving homeless people, the mismatching of crisis accommodation provision and models with likely crisis needs, and the lagging development of suburban infrastructure and growth of suburban homelessness

Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

The potential of acoustic signatures to be used for State-of-Charge (SoC) estimation is demonstrated using artificial neural network regression models. This approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual, and often arbitrary, waveform peak selection. For applications where computational economy is prioritised, simple metrics of statistical significance are used to formally identify the most informative waveform features. These alone can be exploited for SoC inference. It is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value, challenging the more common peak selection methods which focus on the latter. Although later echoes represent greater through-thickness coverage, and are intuitively more information-rich, their presence is not guaranteed. Holistic waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical states. It is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly, while also improving the estimation accuracy. Most importantly, it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates, without any complementary voltage information. This makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is needed. Data is obtained experimentally from a 210 mAh LiCoO2/graphite pouch cell. Mean estimation errors as low as 0.75% are achieved on a SoC scale of 0–100%

Analysis of rare copy number variation in absence epilepsies.

OBJECTIVE: To identify shared genes and pathways between common absence epilepsy (AE) subtypes (childhood absence epilepsy [CAE], juvenile absence epilepsy [JAE], and unclassified absence epilepsy [UAE]) that may indicate common mechanisms for absence seizure generation and potentially a diagnostic continuum. METHODS: We used high-density single-nucleotide polymorphism arrays to analyze genome-wide rare copy number variation (CNV) in a cohort of 144 children with AEs (95 CAE, 26 UAE, and 23 JAE). RESULTS: We identified CNVs that are known risk factors for AE in 4 patients, including 3x 15q11.2 deletion. We also expanded the phenotype at 4 regions more commonly identified in other neurodevelopmental disorders: 1p36.33 duplication, 1q21.1 deletion, 22q11.2 duplication, and Xp22.31 deletion and duplication. Fifteen patients (10.5%) were found to carry rare CNVs that disrupt genes associated with neuronal development and function (8 CAE, 2 JAE, and 5 UAE). Four categories of protein are each disrupted by several CNVs: (1) synaptic vesicle membrane or vesicle endocytosis, (2) synaptic cell adhesion, (3) synapse organization and motility via actin, and (4) gap junctions. CNVs within these categories are shared across the AE subtypes. CONCLUSIONS: Our results have reinforced the complex and heterogeneous nature of the AEs and their potential for shared genetic mechanisms and have highlighted several pathways that may be important in epileptogenesis of absence seizures

In Situ Ultrasound Acoustic Measurement of the Lithium-Ion Battery Electrode Drying Process

The electrode drying process is a crucial step in the manufacturing of lithium-ion batteries and can significantly affect the performance of an electrode once stacked in a cell. High drying rates may induce binder migration, which is largely governed by the temperature. Additionally, elevated drying rates will result in a heterogeneous distribution of the soluble and dispersed binder throughout the electrode, potentially accumulating at the surface. The optimized drying rate during the electrode manufacturing process will promote balanced homogeneous binder distribution throughout the electrode film; however, there is a need to develop more informative in situ metrologies to better understand the dynamics of the drying process. Here, ultrasound acoustic-based techniques were developed as an in situ tool to study the electrode drying process using NMC622-based cathodes and graphite-based anodes. The drying dynamic evolution for cathodes dried at 40 and 60 °C and anodes dried at 60 °C were investigated, with the attenuation of the reflective acoustic signals used to indicate the evolution of the physical properties of the electrode-coating film. The drying-induced acoustic signal shifts were discussed critically and correlated to the reported three-stage drying mechanism, offering a new mode for investigating the dynamic drying process. Ultrasound acoustic-based measurements have been successfully shown to be a novel in situ metrology to acquire dynamic drying profiles of lithium-ion battery electrodes. The findings would potentially fulfil the research gaps between acquiring dynamic data continuously for a drying mechanism study and the existing research metrology, as most of the published drying mechanism research studies are based on simulated drying processes. It shows great potential for further development and understanding of the drying process to achieve a more controllable electrode manufacturing process

Controlled versus free breathing for multiple-breath nitrogen washout in asthma.

The lack of comparability in indices of ventilation heterogeneity between free- and controlled-breathing MBNW protocols is confirmed in asthma https://bit.ly/3lmri4A

Correlative electrochemical acoustic time-of-flight spectroscopy and X-ray imaging to monitor the performance of single-crystal and polycrystalline NMC811/Gr lithium-ion batteries

LiNixMnyCozO2 (NMC) electrodes typically consist of anisotropic single-crystal primary particles aggregated to form polycrystalline secondary particles. Electrodes composed of polycrystalline NMC particles have a comparatively high gravimetric capacity and good rate capabilities but do not perform as well as single crystal equivalents in terms of volumetric energy density and cycling stability. This has prompted research into well-dispersed single-crystalline NMC products as an alternative solution for high-energy-density batteries. Here, for the first time known to the authors, electrochemical acoustic time-of-flight (EA-ToF) spectroscopy has been shown to be effective in distinguishing between Li-ion batteries composed of either single-crystal NMC811 (SC-NMC811) or polycrystalline NMC811 (PC-NMC811) electrodes. Cells composed of PC-NMC811 electrodes had a higher degree of gas evolution compared to cells containing SC-NMC811 electrodes. Cells composed of PC-NMC811 electrodes also underwent larger changes in the acoustic signal's time-of-flight (ToF) during constant current cycling at a range of C-rates indicating expansion, fracture or dislocation of the reflective interfaces inside the cell. In addition, X-ray computed tomography (X-ray CT) has been used to confirm significant morphological differences between SC-NMC811 electrodes and PC-NMC811 electrodes including the electrode's particle size distribution (PSD) that is suggested to have an effect on acoustic signal interaction with these electrode interfaces

Acoustic time-of-flight imaging of polymer electrolyte membrane water electrolysers to probe internal structure and flow characteristics

Acoustic time-of-flight (AToF) imaging has been demonstrated as a low-cost, rapid, non-destructive, operando tool to characterize processes in the flow channels and liquid-gas diffusion layer (LGDL) of polymer electrolyte membrane water electrolysers (PEMWEs). An array of 64 piezoelectric sensors was used, with all sensors emitting input pulses and detecting the acoustic wave reflected by the sample (pulse-echo mode). The shape and intensity of this reflected waveform depends on the ratio of reflection and transmission at phase interfaces and is strongly affected by resonant scattering of acoustic waves by gas bubbles. This AToF imaging technique was deployed to produce reflection intensity maps of the anode flow-field and LGDL; by measuring the AToF response for current densities ranging from 0.00 A cm−2 to 2.00 A cm−2, a close correlation was found between the acoustic attenuation in the flow-field and the production and removal of oxygen gas through the flow channels. Furthermore, a close link between the AToF response and water thickness in the LGDL was demonstrated, as supported by literature data. The application of the AToF technique has been established as a novel way of investigating PEMWE operation and as an alternative to more complex imaging techniques such as neutron imaging

Nitrogen addition alters composition, diversity, and functioning of microbial communities in mangrove soils : an incubation experiment

Mangrove ecosystems are important for carbon storage due to their high productivity and low decomposition rates. Waterways have experienced increased nutrient loads as a result of anthropogenic activities and it is unclear how this may affect carbon and nutrient cycles in downstream mangroves that receive these nutrient-rich waters. Using a laboratory-based incubation experiment, this study aimed to assess the effects of nutrient addition on the diversity and structure of mangrove soil bacterial communities, as well as biomass and activity of the soil microbial community, under different oxygen conditions. Bacterial community diversity and composition was characterised using 16S rRNA gene sequencing and microbial activity was examined through the measurement of microbial respiration and the activities of enzymes associated with organic matter decomposition. Nitrogen addition caused clear shifts in bacterial community composition, with decreases in bacterial diversity and the abundance of sulfate-reducing bacteria. Microbial biomass also decreased with nitrogen addition under reduced oxygen incubations. Changes in bacterial community structure were accompanied by changes in the activity of some enzymes involved in carbon, nitrogen and phosphorus cycling. Under reduced oxygen conditions, nitrogen addition resulted in a significant increase in the microbial metabolic quotient but no accompanying change in microbial respiration, which was explained by a decrease in microbial biomass. The findings of this study indicate that nitrogen loading has potential implications for microbial communities and carbon and nutrient cycling in mangrove environments that warrant further investigation under field conditions

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants

Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance between the small set of available disease-associated and the much larger set of non-deleterious genomic variation, especially in non-coding regulatory regions of human genome. Machine Learning (ML) methods for predicting disease-associated non-coding variants are faced with a chicken and egg problem - such variants cannot be easily found without ML, but ML cannot begin to be effective until a sufficient number of instances have been found. Most of state-of-the-art ML-based methods do not adopt specific imbalance-aware learning techniques to deal with imbalanced data that naturally arise in several genome-wide variant scoring problems, thus resulting in a significant reduction of sensitivity and precision. We present a novel method that adopts imbalance-aware learning strategies based on resampling techniques and a hyper-ensemble approach that outperforms state-of-the-art methods in two different contexts: the prediction of non-coding variants associated with Mendelian and with complex diseases. We show that imbalance-aware ML is a key issue for the design of robust and accurate prediction algorithms and we provide a method and an easy-to-use software tool that can be effectively applied to this challenging prediction task

Influential factors of aligning Spotify squads in mission-critical and offshore projects – a longitudinal embedded case study

Changing the development process of an organization is one of the toughest and riskiest decisions. This is particularly true if the known experiences and practices of the new considered ways of working are relative and subject to contextual assumptions. Spotify engineering culture is deemed as a new agile software development method which increasingly attracts large-scale organizations. The method relies on several small cross-functional self-organized teams (i.e., squads). The squad autonomy is a key driver in Spotify method, where a squad decides what to do and how to do it. To enable effective squad autonomy, each squad shall be aligned with a mission, strategy, short-term goals and other squads. Since a little known about Spotify method, there is a need to answer the question of: How can organizations work out and maintain the alignment to enable loosely coupled and tightly aligned squads? In this paper, we identify factors to support the alignment that is actually performed in practice but have never been discussed before in terms of Spotify method. We also present Spotify Tailoring by highlighting the modified and newly introduced processes to the method. Our work is based on a longitudinal embedded case study which was conducted in a real-world large-scale offshore software intensive organization that maintains mission-critical systems. According to the confidentiality agreement by the organization in question, we are not allowed to reveal a detailed description of the features of the explored project