Performance of pilot-scale microbial fuel cells treating wastewater with associated bioenergy production in the Caribbean context

Microbial fuel cell (MFC) technology represents a form of renewable energy that generates bioelectricity from what would otherwise be considered a waste stream. MFCs may be ideally suited to the small island developing state (SIDS) context, such as Trinidad and Tobago where seawater as the main electrolyte is readily available and economical renewable and sustainable electricity is also deemed a priority. Hence this project tested two identical laboratory-scaled MFC systems that were specifically designed and developed for the Caribbean regional context. They consisted of two separate chambers, an anaerobic anodic chamber inoculated with wastewater and an aerobic cathodic chamber separated by a proton exchange membrane. Domestic wastewater from two various wastewater treatment plants inflow (after screening) was placed into the anodic chamber, and seawater from the Atlantic Ocean and Gulf of Paria placed into the cathodic chambers respectively with the bacteria present in the wastewater attaching to the anode. Experimental results demonstrated that the bacterial degradation of the wastewaters as substrate induced an electron flow through the electrodes producing bioelectricity whilst simultaneously reducing the organic matter as biochemical oxygen demand and chemical oxygen demand by 30 to 75%. The average bioenergy output for both systems was 84 mW/m² and 96 mW/m² respectively. This study demonstrated the potential for simultaneous bioenergy production and wastewater treatment in the SIDS context

Factors associated with age at autism diagnosis in a community sample of Australian adults

Autism diagnosis in adulthood has become increasingly common due to a range of factors including changes in awareness, diagnostic criteria, and professional practices. Past research identified a range of demographic and autism-related factors associated with autism diagnosis age in children. However, it is unclear whether these apply to autistic adults. This study aimed to examine predictors of autism diagnosis age in adults while controlling for current age and autistic traits. We used a cross-sectional sample of 657 adults aged 15–80 from three self and carer-report studies: the Australian Longitudinal Study of Autism in Adulthood (ALSAA), Study of Australian School-Leavers with Autism (SASLA) and Pathways, Predictors and Impact of Receiving an Autism Spectrum Diagnosis in Adulthood (Pathways). Using hierarchical multiplicative heteroscedastic regression, we found that older current age and higher self-reported autistic traits predicted older diagnosis age, and that female gender, lack of intellectual disability, language other than English, family history of autism, lifetime depression, and no obsessive–compulsive disorder predicted older diagnosis age beyond current age and autistic traits. The paradoxical relationship between high autistic traits and older diagnosis age requires further investigation. Based on these findings, we recommended strategies to improve autism recognition in women and people from non-English-speaking backgrounds. Future studies could extend the findings by examining the effects of childhood and adulthood socioeconomic status on adult diagnosis age. Lay Summary: We studied the relationship between age at autism diagnosis and other characteristics in adults. We found that both older current age and higher autistic traits, female gender, language other than English, family history of autism, and history of depression were related to older age at diagnosis, while intellectual disability and history of obsessive–compulsive disorder were related to younger age at diagnosis. Our findings suggest more work is needed to help recognize autism in women and people from non-English-speaking backgrounds

Bayesian optimization for materials design

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality

Mental wellbeing amongst younger and older migrant workers in comparison to their urban counterparts in Guangzhou city, China: a cross-sectional study

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Deciphering the past to inform the future: preparing for the next (“really big”) extreme event

Climate change will bring more extremes in temperature and precipitation that will impact productivity and ecosystem resilience throughout agroecosystems worldwide. Historical events can be used to identify drivers that impact future events. A catastrophic drought in the US in the 1930s resulted in an abrupt boundary between areas severely impacted by the Dust Bowl and areas that were less severely affected. Historical primary production data confirmed the location of this boundary at the border between two states (Nebraska and Iowa). Local drivers of weather and soils explained production responses across the boundary before and after the drought (1926–1948). During the drought, however, features at the landscape scale (soil properties and wind velocities) and regional scale (the Missouri River, its floodplain, and the nearby Loess Hills) explained most of the observed variance in primary production. The impact of future extreme events may be affected by land surface properties that either accentuate or ameliorate the effects of these events. Consideration of large-scale geomorphic processes may be necessary to interpret and manage for catastrophic events

Transformation Pathways of Silica under High Pressure

Concurrent molecular dynamics simulations and ab initio calculations show that densification of silica under pressure follows a ubiquitous two-stage mechanism. First, anions form a close-packed sub-lattice, governed by the strong repulsion between them. Next, cations redistribute onto the interstices. In cristobalite silica, the first stage is manifest by the formation of a metastable phase, which was observed experimentally a decade ago, but never indexed due to ambiguous diffraction patterns. Our simulations conclusively reveal its structure and its role in the densification of silica.Comment: 14 pages, 4 figure

Serial optical coherence microscopy for label-free volumetric histopathology

The observation of histopathology using optical microscope is an essential procedure for examination of tissue biopsies or surgically excised specimens in biological and clinical laboratories. However, slide-based microscopic pathology is not suitable for visualizing the large-scale tissue and native 3D organ structure due to its sampling limitation and shallow imaging depth. Here, we demonstrate serial optical coherence microscopy (SOCM) technique that offers label-free, high-throughput, and large-volume imaging of ex vivo mouse organs. A 3D histopathology of whole mouse brain and kidney including blood vessel structure is reconstructed by deep tissue optical imaging in serial sectioning techniques. Our results demonstrate that SOCM has unique advantages as it can visualize both native 3D structures and quantitative regional volume without introduction of any contrast agents

Measurement of the Branching Fraction of J/psi --> pi+ pi- pi0

Using 58 million J/psi and 14 million psi' decays obtained by the BESII experiment, the branching fraction of J/psi --> pi+ pi- pi0 is determined. The result is (2.10+/-0.12)X10^{-2}, which is significantly higher than previous measurements.Comment: 9 pages, 8 figures, RevTex

First observation of psi(2S)-->K_S K_L

The decay psi(2S)-->K_S K_L is observed for the first time using psi(2S) data collected with the Beijing Spectrometer (BESII) at the Beijing Electron Positron Collider (BEPC); the branching ratio is determined to be B(psi(2S)-->K_S K_L) = (5.24\pm 0.47 \pm 0.48)\times 10^{-5}. Compared with J/psi-->K_S K_L, the psi(2S) branching ratio is enhanced relative to the prediction of the perturbative QCD ``12%'' rule. The result, together with the branching ratios of psi(2S) decays to other pseudoscalar meson pairs (\pi^+\pi^- and K^+K^-), is used to investigate the relative phase between the three-gluon and the one-photon annihilation amplitudes of psi(2S) decays.Comment: 5 pages, 4 figures, 2 tables, submitted to Phys. Rev. Let