Use of ethanol is attracting increasing attention both as primary feedstock and as an alternative to increase the feedstock flexibility in a given unit. However, steam reforming of ethanol is not a straight forward process. Equilibrium of the reaction is shifted towards the production of hydrogen even at low temperature. However, in practice, ethanol is also converted to ethylene. The present study is focused primarily upon the production of hydrogen in fixed-bed reactors by steam-ethanol reforming under different temperature conditions. Computational fluid dynamics is used to model fluid flow, heat and mass transfer, and chemical reactions. The governing integral equations are solved for the conservation of mass, momentum, and energy and other scalars such as laminar flow and chemical species. Steady-state analyses are performed using computational fluid dynamics. The reaction rates are computed by the laminar finite-rate model. The present study aims to explore how to effectively produce hydrogen in fixed-bed reactors by steam-ethanol reforming at different temperatures. Particular emphasis is placed upon the effect of temperature on the transport and reaction characteristics of fixed-bed reactors for polymer electrolyte membrane fuel cell applications. The results indicate that under a thermodynamic point of view, high temperatures and steam-ethanol molar ratios promote hydrogen yield. Low ethylene content is obtained at high pressure and low temperature. At high temperatures the contribution of steam reforming reactions results in a marked increasing of overall enthalpy, enhancing process endothermicity, whereas the exothermic contribution of water-gas shift and methanation reactions reduces the external heat supply and the overall energy penalty at lower temperature. Although the equilibrium of the water-gas shift reaction favors the products formation at lower temperatures, reaction kinetics are faster at higher temperatures. The typical products distribution of ethanol steam reforming reaction, according to thermodynamic evaluations, results in considerable hydrogen production rates at higher temperatures and high methane yields at lower temperatures. The majority of supported metals as catalysts expresses better performance at high temperatures, and the production of oxygenated products increases and the formation of coke is thermodynamically favored at low temperatures. Low reaction temperatures generally favor the Boudouard reaction mechanism while methane decomposition is the main route at high temperatures.Keywords: Hydrogen; Ethanol; Reformers; Burners; Reforming; Combustion

A document by Yanan Fu. Click on the document to view its contents.

When chicks in a brood hatch asynchronously, the first to hatch gets fed sooner, creating a hierarchy of asymmetries in the brood. In species that suffer from brood reduction, this asymmetry can facilitate the death of one chick during periods of low food availability. The aim of this study was to analyze the effect of initial asymmetries on the growth and survival of Magellanic penguin Spheniscus magellanicus chicks, after disentangling the effects of parental condition, during four breeding seasons. We manipulated chicks' asymmetries within the brood by performing a cross-fostering experiment, creating broods with different weight ratios in a colony located in Isla Quiroga, Santa Cruz, Argentina. We found that weight ratio determines the asymmetry within the brood throughout the entire breeding season, and that final asymmetry also depends on the study season. Chicks that hatch last have a greater chance of survival when the weight ratio with their older sibling is closer to 1. Parental body condition affects chick growth but not asymmetry. To conclude, parents do not reverse the effect of initial asymmetry in post-hatching stages, even during breeding seasons of high reproductive success.

Due to its importance in the individual and national economies, stock market prediction is an important study subject. A stock market's success is based on the rise or fall of industrial, financial, medical, local, and global stock prices in a certain region and globally. Electronic news and public opinion impact stock markets. Since the Covid-19 epidemic, global stock prices have fluctuated because to economic uncertainty. Social media has been used to spread pandemic news, comments, and forecasts. Such news has affected global stock marketplaces, which are prone to polarity, by affecting investors' decision-making and changing the perspective of stock-interested people. This paper proposes a methodology to examine the influence of Covid-19 internet news data on stock market performance. News data comes from news.pk and internet news sites, while stock data is from Yahoo Finance. Text characteristics are retrieved from news information using TF-IDF, and stock-related features are produced for stock value prediction. A hybrid approach that combines evolutionary algorithms such as Genetic Algorithm, Harris hawks Optimization, and Particle Swarm Optimization with various Machine Learning and Deep Learning-based models. The empirical analysis-based results reveal the proposed hybrid model outperforms traditional ML models using standard performance evaluation measures.

The rise of AI is viewed as the next important technology in human history that would serve as a driver for sustainable development. Accordingly, several organizations have incorporated AI into their operations – including healthcare, hence, attracting extant literature to AI discussions. However, AI literature in healthcare has focused on medical diagnosis, operations, and prescriptions – to the neglect of supply chain (SC). This study bridges this knowledge gap by exploring the drivers and success factors of AI-enabled medical drones’ adoption in public healthcare SC. Drawing on data from the world’s largest medical drone programme in Ghana, we find that the need to make the public healthcare SC efficient with the aim of improving the socio-economic life of the citizens is the main driver of the policy adoption. Several success factors are identified and categorized into three phases – policy, project, and operation. Long-term policy and operating sustainability are delineated.

Coral reef health is tightly connected to the coral microbiome. Coral bleaching and disease outbreaks have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. Coping mechanisms of the coral holobiont under high temperatures are not completely described, but the associated microbial community is a potential source of acquired heat-tolerance. The relationship between stress and stability in the microbiome is key to understanding the role that the coral microbiome plays in thermal tolerance. According to the Anna Karenina Principle (AKP), stress or disease will increase instability and stochasticity among animal microbiomes. Here we investigate whether heat stress results in microbiomes that follow the AKP. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the surface mucous layer (SML) microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat stress showed high similarity, indicating a deterministic and stable response of the coral microbiome to disturbance, in opposition to the AKP. We hypothesize that this stability is the result of a selective pressure towards a coral microbiome that is assisting the holobiont to withstand heat stress. The coral SML microbiome responded to heat stress with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance.

Ephemeral gully erosion is a primary mode of soil erosion that is highly visible, affecting soil productivity and restricting land use. Watershed is the basic unit of soil erosion control; existing research has focused on several typical ephemeral gullies or slopes, which do not fully display changes in ephemeral gullies at a watershed scale. This study analyzed the spatial-temporal evolution and development rate of ephemeral gully erosion at the watershed scale on the Loess Plateau from 2009 to 2021 using remote sensing images (0.5 m resolution), unmanned aerial vehicles (UAV), and field investigations. The results revealed that: (1) most ephemeral gullies occurred in southwestern parts of the watershed, with many hills and large slope gradients; (2) average growth rates of each ephemeral gully frequency, length, density, dissection degree, and width were 2.87 km 2 y –1, 1.66 m y –1, 0.12 km km –2 y –1, 0.0125% y –1, and 0.04 m y –1 , respectively; (3) ephemeral gully erosion volume ( V) and length ( L) had a good power function relationship: V = 0 . 0842 L 1 . 1932   ( R 2 = 0 . 80 ) . The root mean square error (RMSE) and coefficient of determination (R 2) between the measured and predicted ephemeral gully volumes suggest that the V–L relationship has a good predictive ability for ephemeral gully volume. Thus, the V–L model was used to evaluate the development rate of ephemeral gully erosion volume in small watersheds from 2009 to 2021, revealing an average value of 743.20 m 3 y –1. This study proposed a feasible model for assessing ephemeral gully volume and volume changes at a watershed scale using high-resolution remote sensing images, providing a reference for understanding the development of ephemeral gully erosion in small watersheds over time.

Background School-aged children and school reopening dates have important roles in community influenza transmission. Although many studies evaluated the impact of reactive closures during seasonal and pandemic influenza outbreaks on medically attended influenza in surrounding communities, few assess the impact of planned breaks (i.e., school holidays) which coincide with influenza seasons, while accounting for differences in seasonal peak timing. Here, we analyze the effects of winter and spring breaks on influenza risk in school-aged children, measured by student absenteeism due to influenza-like illness (a-ILI). Methods We compared a-ILI counts in the two-week periods before and after each winter and spring break over five consecutive years in a single school district. We introduced a “pseudo-break” of 9 days’ duration between winter and spring break each year when school was still in session to serve as a control. The same analysis was applied to each pseudo-break to support any findings of true impact. Results We found strong associations between winter and spring breaks and a reduction in influenza risk, with a nearly 50% reduction in a-ILI counts post-break compared to the period before break, and the greatest impact when break coincided with increased local influenza activity. Conclusions These findings suggest that brief breaks of in-person schooling, such as planned breaks lasting 9-16 calendar days, can effectively reduce influenza in schools and community spread. Additional analyses investigating the impact of well-timed shorter breaks on a-ILI may determine an optimal duration for brief school closures to effectively suppress community transmission of influenza.

Considering the spatial omnipresence of the threat to biodiversity and limited resources and time for conservation and restoration, it is crucial to prioritize conservation and restoration activities to maximize benefits. By transporting marine-derived nutrients to freshwater and surrounding ecosystems, anadromous salmonids contribute greatly to biodiversity maintenance; however, their abundance has been decreased by human activities in many regions. Salmon populations are mainly governed by their productivity in the freshwater life stage; therefore, freshwater productivity, namely, the number of juveniles migrating to the ocean per reproducing parent, should be investigated to maintain healthy populations. Given that productivity decreases dramatically in response to flooding, the flood disturbance intensity controlled by hydrogeomorphology at a watershed scale may strongly influence the freshwater productivity of salmonids. In this study, we evaluated the effect of watershed hydrogeomorphology on the productivity of pink salmon (Oncorhynchus gorbuscha). We surveyed the escapement and number of fry migrants of pink salmon and measured environmental factors, including the average watershed slope and stream power index, as parameters of hydrogeomorphology. The freshwater productivity of pink salmon differed among the streams investigated and was negatively affected by average watershed slope, stream power, and average watershed maximum daily precipitation. These results indicated that flood disturbance reduces the freshwater productivity of pink salmon and that salmon productivity in an individual stream can be predicted by watershed hydrogeomorphology. Our approach can be applied to other anadromous salmonids that have spawning behaviour similar to that of pink salmon, which bury eggs in gravel. Predicting highly productive habitats based on the present study can contribute to planning and prioritizing habitat conservation and restoration for anadromous salmonids.

Spatial behavior, including home-ranging behaviors, habitat selection, and movement, can be extremely informative in estimating how animals respond to landscape heterogeneity. Responses in these spatial behaviors to factors such as human modification and resources on the landscape can highlight a species' spatial strategy to maximize fitness and minimize risk. These strategies can vary on spatial, temporal, and individual scales, and the combination of behaviors on these scales can lead to very different strategies among species. Harnessing the variation present at these scales, we developed a framework for predicting how species may respond to changes in their environments on a gradient ranging from generic, where a species exhibits broad-stroke spatial responses to their environment, to nuanced, in which a species uses a combination of temporal and spatial strategies paired with functional responses in selection behaviors. Using 46 GPS-tracked bobcats and coyotes inhabiting a landscape encompassing a range of human modification, we evaluated where each species falls along the generic-to-nuanced gradient. Bobcats and coyotes studied occupied opposite ends of this gradient, using different strategies in response to human modification in their home ranges, with bobcats broadly expanding their home range with increases in human modification and clearly selecting for or avoiding features on the landscape with temporal consistency. Meanwhile, coyotes did not expand their home ranges with human modification, but instead displayed temporal and spatial adjustments in their functional responses to human modification. These differences in response to habitat, resources, and risk between the two species highlighted the variation in spatial behaviors animals can use to exist in anthropogenic environments influenced by interspecific variation in behavioral plasticity. Categorizing animal spatial behavior based on the generic-to-nuanced gradient can help in predicting how a species will respond to future change based on their current spatial behavior.

Spatial isolation is a key driver of population-level variability in traits and genotypes worldwide. Geographical distance between populations typically increases isolation, but organisms face additional environmental barriers when dispersing between suitable habitat patches. Despite the predicted universal nature of the causes of isolation, global comparisons of isolation effects across taxa and geographic systems are few. We assessed the strength of isolation due to geographic and macroclimatic distance for paired marine island and paired mainland populations within the same species. Our meta-analysis included published measurements of phenotypic traits and neutral genetic diversity from 1832 populations of 112 plant and animal species at a global scale. As expected, phenotypic differentiation was higher between marine islands than between populations on the mainland, but spatial patterns of neutral genetic diversity did not vary between the two systems. Geographic distance had comparatively weak effects on the spatial patterns of phenotypes and neutral genetic diversity, but only phenotypic trait variability showed signal of system-dependence. These results suggest that spatial patterns of phenotypic variation are determined by system-dependent eco-evolutionary pressures, while the spatial variability of neutral genetic diversity might be universal. Our approach demonstrates that global biodiversity models that include island biology studies may progress our understanding of the interacting effects of spatial habitat structure, geographic- and environmental distances on biological processes underlying spatial population variability. We formulate future research directions for empirical tests and global syntheses in the field.

Background Bipolar disorder (BD) is a severe mental illness characterised by recurrent manic, hypomanic and depressive episodes alternating with euthymic periods. The burden of BD is vast, and many patients have unmet needs in their treatment. To better support patients in their personal recovery and well-being, positive psychology interventions (PPIs) have shown to be a promising tool. Recently, a mobile application has been developed to offer PPIs: the WELLBE BD-app. Aim The current study was designed to study the acceptability of the WELLBE BD-app and evaluate the feasibility of the design for use in a larger controlled trial (CT). We also studied the potential effects on mental health. Method This pilot-study used a mixed-methods quantitative and qualitative approach in which participants were randomly assigned to an intervention- or a treatment-as-usual control group, each with 20 participants with BD. The study sample consisted of a seven weeks during intervention. To assess acceptability, we held semi-structured interviews in the intervention group and collected log data and questionnaire data on the actual use of the app and perceived value of the accompanying exercises. Feasibility was determined by the number of completers of the intervention in both the intervention and control groups. Potential effects on mental health outcomes were measured using an extensive set of pre and post-intervention questionnaires. Results The intervention was fully completed by 52.7% (n = 11) of the participants and partly completed (1 to 4 modules) by 37.8% (n = 8). The post-test response rate was 73% in both groups. On average, the exercises were rated with a value of 7.5 on a scale of 1 to 10 ( SD = 1.2). Users found the application easy to use, useful for people with BD, and to have an attractive design. Problems with installation, technical problems, and lack of support were barriers to using the app. Guidance by an expert by experience (in videos before the exercises) was preferred by 80% of the participants instead of guidance by a professional. Effects on mental health outcomes were small and statistically non-significant, both between- and within groups. Although we found no significant results in the quantitative part of our study, the qualitative results show that people with BD appreciated the content and design of the intervention. The minimal effects on mental health may be partly explained by the small sample size and the relatively high levels of mental health of the participants at baseline. Conclusions and Implications for Practice Based on this study a larger trial on the effects of the WELLBE-app appears feasible and warranted. Next to minor modifications based on this pilot study, to create optimal impact including patients with lower levels of well-being is recommended and the guidance by experts or peers needs to be considered.

Introduction: Psychopathological disorders such as anxiety, depression and body image distress are common in women with PCOS and negatively impact their mental health. It is important to identify mental health latent subgroups of PCOS females and provide tailored measures to reduce psychopathological distress and improve their subjective well-being. Methods: LPA was conducted in Mplus version 8.3 to identify mental health latent profiles in women with PCOS based on the dual-factor approach. Differences in demographic and anthropometric variables, cognitive reappraisal, expressive suppression, and social support across mental health profiles were examined through multinomial logistic regression. Results: The current study identified three distinct mental health profiles within women with PCOS: Symptomatic but Content Profile Complete Mental Health Profile and Troubled Profile, with group proportions of 52.3%, 35.7%, and 11.1 %, respectively. The results of the multinomial regression analysis revealed that cognitive reappraisal and social support as predictors of positive mental health adjustment and expression suppression is an indicator of negative barriers in women with PCOS. Conclusion: This study identified three distinct mental health profiles in women with PCOS, which provides evidence for more precisely targeted interventions to address PCOS women’s diverse needs of psychopathological symptoms and subjective well-being.

Nowadays, Z-source inverter has received several attentions compared to the other power converters owing to its simplicity and reliability. In this paper, a refine control methodology of capacitor voltage control is presented based on third-order small signal analysis. The proposed control is applied to the improved switched inductor Z-Source inverter (ISL ZSI). Proportional, integral and derivative controller (PID) is adopted due to its capability to operate with large operation levels , reliable operation, and cheaper in implementation. In addition, the function of this control can be achieved even with incomplete system data or parameters variations. ISL-ZSI has been presented because of its advantages such as the high gain of DC voltage, low voltage stress for both the Z-network and the inverter bridge switches and ensure good soft starting. in addition, the inrush current has been removed based on its configuration. The proposed control is validated by both experimental and simulation results under input voltage change, load change, and steady state operations using DSP F28335 and MATLAB SIMULINK Real-Time Workspace (RTW).

Single-cell RNA sequencing is an emerging technique in the field of biology that departs radically from the previous assumption of gene-expression homogeneity within a tissue. The large quantity of data generated by this technology enables discoveries of cellular biology and disease mechanics that were previously not possible, and calls for accurate, scalable, and efficient processing pipelines. In this work, we propose SSCAE (spiking single-cell autoencoder), a novel SNN-based autoencoder for sc-RNA-seq dimensionality reduction. We apply this architecture to a variety of datasets, and the results show that it can match and surpass the performance of current state-of-the-art techniques. Moreover, the potential of this technique lies in its ability to be scaled up and to take advantage of neuromorphic hardware, circumventing the memory bottleneck that currently limits the size of sequencing datasets that can be processed.

In this work, ensemble methods are presented and tested as universal ways to improve the performance of Mem-ristive Deep Neural Networks (MDNNs) with non-idealities. The Generalized Ensemble Method and Weighted Voting ensemble methods improve the accuracy of classification on the MNIST dataset by 6.5% and 6.6% respectively, thus showing that they are more effective than basic Ensemble Averaging which has been investigated before, as well as other methods such as Voting. Different weighting schemes for Weighted Voting were tested, and we present Algorithm 1 and 2, which are the theoretically and experimentally optimal weighting schemes respectively. Our work serves as a guideline for choosing ensemble methods for MDNNs.

In this work, we solve nonlinear systems of ordinary differential equations coupled to noisy forcing, commonly used for models of neurons such as the Hodgkin-Huxley equation, over a memristor crossbar based computing system. We demonstrate stability and faithfulness of the distributions even under the effects of nonidealities of the memristors and the system itself. We investigate the properties of the dynamical systems under quantization faithfulness, varying the level of precision of the fixed point integer representation and concluding that 24 bits is enough for solution of the Hodgkin-Huxley equations, demonstrating that our solver can operate with both high precision and achieve speedups with low precision approximate computation.

Von Neumann architecture-based deep neural network architectures are fundamentally bottlenecked by the need to transfer data from memory to compute units. Memristor crossbar-based accelerators overcome this by leveraging Kir-choff's law to perform matrix-vector multiplication (MVM) in-memory. They still, however, are relatively inefficient in their device programming schemes, requiring individual devices to be written sequentially or row-by-row. Parallel writing schemes have recently emerged, which program entire crossbars simultaneously through the outer product of bit-line and word-line voltages and pulse widths respectively. We propose a scheme that leverages singular value decomposition and low-rank approximation to generate all word-line and bit-line vectors needed to program a convolutional neural network (CNN) onto a memristive crossbar-based accelerator. Our scheme reduces programming latency by 90% from row-by-row programming schemes, while maintaining high test accuracy on state of the art image classification models.

A universal objective function to minimize mem-ristive crossbar deep neural network weight mapping errors through Hessian backpropagation (HessProp) is presented. Hes-sProp minimizes the L2 norm of the neural network gradient to achieve a flat minima in a neural network's weight space. We hypothesize that this leads to robustness against small perturbations of weights. The stochastic weight mapping phenomenon on memristor crossbars is simulated, and the proposed method was evaluated on image classification tasks using the MNIST dataset. The result demonstrates on average 40.81% and 41.45% groundbreaking accuracy increase for distilled and large memristive convolutional neural networks in worst-case scenarios.

Background: Vitamin D deficiency is one of the most common nutritional deficiencies. Cardiovascular disease patients are also prone to this condition. Recently, a relationship between vitamin D deficiency and cardiovascular diseases has been suggested. This study aims to compare the relationship between ventricular systolic function and vitamin D deficiency. Methods: This study investigated patients without obvious coronary artery disease between 2020 and 2021. First, vitamin D levels were measured in the patients. Then, they were divided into two groups based on a 30 ng/dl cut-off point. All patients underwent echocardiography and ventricular systolic function parameters were evaluated and compared. Results: In this study, 27 patients with normal vitamin D levels and 47 patients with vitamin D deficiency entered the study. There was no significant difference in demographic variables and underlying diseases between these two groups. There was no significant difference between left ventricular (LV) systolic function parameters including ejection fraction (EF), and LV end-systolic/diastolic volume. No significant difference was also observed between right ventricular (RV) systolic function parameters including Tricuspid Annular Plane Systolic Excursion (TAPSE), RV fractional area change (RVFAC), Right ventricular systolic velocity (RVSM) in tissue Doppler echocardiography as well as RV diastolic parameters such as A, E, E´, deceleration time (DT), right atrial volume (RAVi) as a precursor of right ventricular systolic dysfunction groups. Conclusion: Based on the results of this study, there is no relationship between vitamin D levels and ventricular systolic dysfunction.

A method using questionnaire data for estimating the level of under reporting during an outbreak is presented. It is based on rewriting the conditional probabilities for getting tested, being infected, and having symptoms. It shows very good agreement with seroprevalence studies of blood donors. On the one hand, this shows the strength of questionnaires when testing the general population during an outbreak as a means to find the true prevalence. On the other, applying it to covid-19 demonstrates that the asymptomatic cases likely make up around 50% of the infected.

A document by Abigail Rose Croker. Click on the document to view its contents.

A 31-year-old man with fever, dyspnea, abdominal pain, and jaundice has been admitted to the hospital in the southeast of Iran. Due to the presence of a pathognomonic skin lesion (Tache noire), the patient was diagnosed with Mediterranean spotted fever (MSF) and was treated with doxycycline.

A 66-year-old breast ductal carcinoma patient developed obstructive jaundice, presenting with epigastric discomfort and dark-colored urine. Contrast-enhanced computed tomography and endoscopic retrograde cholangiopancreatography revealed bile duct stenosis. Brushing cytology and tissue biopsy confirmed bile duct metastasis, and a self-expandable metallic stent was placed/replaced endoscopically, extending the patient’s life.