A Theoretical Survey on the Potential Performance of a Perovskite Solar Cell Based on an Ultrathin Organic-Inorganic Electron Transporting Layer

An ultrathin perovskite solar cell with 29.33 % theoretical power conversion efficiency (PCE) is designed for flexible applications. The perovskite layer is sandwiched between two multijunctions, i.e. poly(3- hexylthiophene) (P3HT), nickel oxide (NiO), and copper (I) thiocyanate (CuSCN) as the hole transporting element, from one side, and zinc oxide (ZnO), tin (IV) oxide (SnO2) and phenyl-C61 butyric acid methyl ester (PCBM) as the electron transporting compartment, from the other side. This study uses a professional software package to accurately simulate a series of highly efficient perovskite-based solar cell structures that use both organic and inorganic materials. Calculations are simultaneously run with SCAPS (version. 3.3.07). The materials system for the electron transporting multijunction, bandgap of the perovskite layer, defection density, temperature of operating conditions, and concentration of charge doping are manipulated as the tuning parameters. An excellent fill factor (84.76 %), a potentially low entire thickness (⁓ 1 µm), and compatible nature for both organic and inorganic materials make this layout auspicious for a feasible and versatile high efficiency, but low-cost electronic devices. The constituent materials are selected based on the thickness and photoconversion efficiency. In order to assess the further potentials of materials system, we replaced CuSCN with PTAA (Polytriarylamine) and observed an increase in the theoretical efficiency, and we investigated the effect of varying the doping concentration in the PTAA layer. To simulate this structure, both the electrical and physical properties of the materials are considered, and the results are compared with those of previous works. These results should be of significant interest to experimentalists in the field

Effect of artemia density on cyst yields of fertilized ponds

One of the effective factors in cyst production on Artemia culture ponds is the number of oviparous females and density of biomass in view of adult's weight per liter of ponds water. In this study, the effect of oviparous female's abundance (Reproductive Females lit^-1) on daily cyst yields with using the ANOVA and correlation Analytical method were assayed. The result indicated that, with presence the small number of oviparous females (less than five Ind lit^-1) and Artemia density (between 0/1 to 0/2g/ lit) on culture ponds, the cyst yields at first sixty days culture period (160 Kg.dw/ha) were more than yields that harvested at three months later (47 Kg.dw/ha) and on the second three months of cultural period despite of presence the larger number of oviparous females (more than twenty Ind/lit) and presence the same Artemia population density, the daily cyst yields was declined.(Sig=0.000)

Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps

Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables

Dissecting the heterogeneous cortical anatomy of autism spectrum disorder using normative models

International audienceBACKGROUNDThe neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly owing to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort toward understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous.METHODS:Here, we used an innovative normative modeling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertexwise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this, we employed a heterogeneous cross-sectional sample of 206 typically developing individuals (127 males) and 321 individuals with ASD (232 males) (6-31 years of age).RESULTS:We found few case-control differences, but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing.CONCLUSIONS:Our results 1) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and 2) show that by focusing on common effects (i.e., the "average ASD participant"), the case-control approach disguises considerable interindividual variation crucial for precision medicine

Feasibility and acceptability of NIDUS-Professional, a training and support intervention for homecare workers caring for clients living with dementia: a cluster-randomised feasibility trial protocol.

INTRODUCTION: Most people living with dementia want to remain living in their own homes, and are supported to do so by family carers and homecare workers. There are concerns that homecare is often unable to meet the needs of this client group, with limited evidence regarding effective interventions to improve it for people living with dementia. We have developed a training and support programme for homecare workers (NIDUS-Professional) to be delivered alongside support sessions for people living with dementia and their family carers (NIDUS-Family). We aim to assess (1) its acceptability among homecare workers and employing agencies, and (2) the feasibility of homecare workers, people living with dementia and their family carers completing the outcomes of intervention in a future randomised controlled trial. METHODS AND ANALYSIS: This is a cluster-randomised (2:1) single-blind, multisite feasibility trial. We aim to recruit 60-90 homecare workers, 30-60 clients living with dementia and their family carers through 6-9 English homecare agencies. In the intervention arm, homecare staff will be offered six group sessions on video call over three months, followed by monthly group sessions over the subsequent three-month period. Outcome measures will be collected at baseline and at six months. ETHICS AND DISSEMINATION: The study received ethical approval on 7 January 2020 from the Camden & King's Cross Research Ethics Committee. Study reference: 19/LO/1667. Findings will be disseminated through a peer-reviewed journal, conference presentation and blog to research and clinical audiences; we will attend forums to present findings to participating homecare agencies and their clients. TRIAL REGISTRATION NUMBER: ISRCTN15757555

Earthquake vulnerability assessment for urban areas using an ann and hybrid swot-qspm model

Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies

Fractionating autism based on neuroanatomical normative modeling.

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6-31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case-control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism