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AbstractThe common orientations of the plate-fin heat sink for natural convection cooling of electronics are vertical and upward-facing horizontal. However, depending on various use scenarios, the heat sink may be inclined, intentionally or otherwise. In our previous papers concerning this subject, the author proposed a set of correlations for plate-fin heat sinks covering all inclination angles backward and forward (pitch rotation) from the vertical position of the heat sink. The set was based on a series of computational simulations with a validated model. At the time, tilting the heat sink sideways (roll rotation) was not considered. In the present study, though, the sideways inclination of the plate-fin heat sinks is simulated using our previous model only by adjusting the direction of the gravitational acceleration vector, thus requiring no additional validation. It is determined that the previously proposed correlation is valid up to 80° sideways inclinations of the heat sink. Interesting flow structures are observed when the heat sink is tilted 90° sideways. Furthermore, it is demonstrated that the correlation surprisingly remains valid if the heat sink is simultaneously rotated in both axes (pitch and roll). [ABSTRACT FROM AUTHOR]

Since there has been concern about food security, accurate prediction of wheat yield prior to harvest is a key component. Random Forest (RF) has been used in many classification and regression applications, such as yield estimation, and the performance of RF has improved by tuning its hyperparameters. In this paper, different changes are made to traditional RF for yield estimation, and the performance of RF is evaluated. Accordingly, RFs constructed using various weak learners, as well as a combined RF consisting of different weak learners are assessed by growing weak Gaussian Process Regression (GPR), Decision Tree (DT), Neural Network (NN), and Stepwise Regression (SW) models in the forest. The input data to DTs are also partitioned into leaves in N (e.g., two) dimensional feature space by using clustering in each parent node. In addition, a subset of the training set is randomly sampled with replacement for training a learner in the forest, instead of randomly sampling the whole training set in traditional RF. Using clustering in DTs added flexibility while utilizing NN as a weak learner yielded the most favorable outcomes in our research. The number of input training samples (Itree) to each tree was also identified as a new hyperparameter to the forest, and the prediction results were more influenced by the Itree compared to the known hyperparameters, such as the number of trees in the forest (ntree) and the number of features for each tree (mtry). [ABSTRACT FROM AUTHOR]

Embedding data in hyperbolic spaces has proven beneficial for many advanced machine learning applications. However, working in hyperbolic spaces is not without difficulties as a result of its curved geometry (e.g., computing the Fréchet mean of a set of points requires an iterative algorithm). In Euclidean spaces, one can resort to kernel machines that not only enjoy rich theoretical properties but that can also lead to superior representational power (e.g., infinite-width neural networks). In this paper, we introduce valid kernel functions for hyperbolic representations. This brings in two major advantages, 1. kernelization will pave the way to seamlessly benefit the representational power from kernel machines in conjunction with hyperbolic embeddings, and 2. the rich structure of the Hilbert spaces associated with kernel machines enables us to simplify various operations involving hyperbolic data. That said, identifying valid kernel functions on curved spaces is not straightforward and is indeed considered an open problem in the learning community. Our work addresses this gap and develops several positive definite kernels in hyperbolic spaces (modeled by a Poincaré ball), the proposed kernels include the rich universal ones (e.g., Poincaré RBF kernel), or realize the multiple kernel learning scheme (e.g., Poincaré radial kernel). We comprehensively study the proposed kernels on a variety of challenging tasks including few-shot learning, zero-shot learning, person re-identification, deep metric learning, knowledge distillation and self-supervised learning. The consistent performance gain over different tasks shows the benefits of the kernelization for hyperbolic representations. [ABSTRACT FROM AUTHOR]

Background: The new WHO model for antenatal care (ANC) focuses on improving practice, organisation and delivery of ANC within health systems, which includes both clinical care and women's experiences of care. The goal of this review is to identify tools and measures on women's experiences of ANC. Methods: We conducted a scoping review to identify tools and measures on women's experiences of ANC. An iterative approach was used to review all tools in a series of four steps: (1) identify papers between 2007 and 2023; (2) identify the tools from these papers; (3) map relevant measures to conceptualizations of experiences of care, notably mistreatment of women and respectful maternity care and (4) identify gaps and opportunities to improve measures. Results: Across the 36 tools identified, a total of 591 measures were identified. Of these, 292/591 (49.4%) measures were included and mapped to the typology of mistreatment of women used as a definition for women's experiences care during ANC in this review, while 299/591 (44.9%) irrelevant measures were excluded. Across the included measures, the highest concentration was across the domains of poor rapport between women and providers (49.8%) followed by failure to meet professional standards of care (23.3%). Approximately, 13.9% of measures were around overall respectful care, followed by health systems (6.3%), and any physical or verbal abuse, stigma and/or discrimination (4.8%). Conclusion: This analysis provides an overview of the existing tools, gaps and opportunities to measure women's experiences during ANC. Expanding beyond the childbirth period, these findings can be used to inform existing and future tools for research and monitoring measuring women's experiences of ANC. [ABSTRACT FROM AUTHOR]

Percutaneous coronary intervention (PCI) in patients with chronic total occlusion is commonly performed despite unclear long-term benefits. The goal of this study was to evaluate the postprocedural outcome of patients with chronic total occlusion intervention. The National Inpatient Sample database, years 2016-2020, was studied using International Classification of Diseases, Tenth Revision codes. Patients with chronic total occlusion interventions were compared with patients without chronic total occlusion. We evaluated postprocedural mortality and complications. PCI in patients with chronic total occlusion was associated with higher total inhospital mortality and all postprocedural complications. A weighted total of 10,059,269 patients underwent PCI, with 259,574 having chronic total occlusion. The chronic total occlusion group had a 3.17% mortality rate vs 2.57% of nonchronic total occlusion PCIs (odds ratio [OR] 1.24; 95% confidence interval [CI], 1.18-1.31; P <.001). Using multivariate analysis adjusting for basline charcteristics and high risk features such as age, sex, race, diabetes mellitus, chronic kidney disease, systolic heart failure, 3-vessel PCI, hypertension, chronic obstructive pulmonary disease, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, prior PCI, history of coronary artery bypass graft, history of anemia, smoking status, atrial fibrillation/flutter, valvular heart disease, and , history of stroke, chronic total occlusion PCI remained significantly associated with higher total mortality (OR 1.07; 95% CI, 1.02-1.13; P =.02). Patients with chronic total occlusion compared with nonchronic total occlusion PCI had also higher rates of myocardial infarction (OR 2.85; 95% CI, 2.54-3.21; P <.001), coronary perforation (OR 6.01; 95% CI, 5.25-6.89; P <.001), tamponade (OR 3.36; 95% CI, 2.91-3.88; P <.001), contrast-induced nephropathy (OR 2.05; 95% CI, 1.45-2.90; P <.001), procedural bleeding (OR 3.57; 95% CI, 3.27-3.89; P <.001), and acute postprocedural respiratory failure (OR 2.07; 95% CI, 1.81-2.36; P <.001). All postprocedural complications were more than 3 times the nonchronic total occlusion patients (OR 3.45; 95% CI, 3.24-3.67; P <.001). Using a large national inpatient database, PCI performed in patients with chronic total occlusion was associated with significantly much higher mortality and postprocedural complications compared with PCI in nonchronic total occlusion patients. [ABSTRACT FROM AUTHOR]

Pervious concrete pavement is an environmentally friendly product and subjecting massive demands in the urban environment due to flooding in major cities. Understanding the effects of main important parameters, such as aggregate size, cement content, and water–cement ratio, are necessary to understand the behavior of the pervious concrete system. Hence, response surface methodology (RSM) was utilized to estimate the influences of these parameters on the three main output including, compressive strength, permeability, and porosity of mixtures. In this study, the influences of three independent numerical variables including cement content from 350 to 550 kg, water–cement ratio from 0.32 to 0.38, and aggregate size between 2.83 mm and 8.00 mm, all at three levels, were calculated over the central composite design (CCD). As it is shown in this paper, a quadratic model is efficiently fixed to the experimental data. Also, the effects of aggregate size and cement content variation are more drastic than the effect of water–cement ratio on the response performance of the pervious concrete and, 0.35 water–cement ratio was associated with maximum strength value. [ABSTRACT FROM AUTHOR]

Endoplasmic reticulum-associated degradation (ERAD) is a protein quality control pathway that ensures misfolded proteins are removed from the ER and destroyed. In ERAD, membrane and luminal substrates are ubiquitylated by ERresident RING-type E3 ubiquitin ligases, retrotranslocated into the cytosol, and degraded by the proteasome. Overexpression of ERAD factors is frequently used in yeast and mammalian cells to study this process. Here, we analyze the impact of ERAD E3 overexpression on substrate turnover in yeast, where there are three ERAD E3 complexes (Doa10, Hrd1, and Asi1-3). Elevated Doa10 or Hrd1 (but not Asi1) RING activity at the ER membrane resulting from protein overexpression inhibits the degradation of specific Doa10 substrates. The ERAD E2 ubiquitin-conjugating enzyme Ubc6 becomes limiting under these conditions, and UBC6 overexpression restores Ubc6-mediated ERAD. Using a subset of the dominant-negative mutants, which contain the Doa10 RING domain but lack the E2-binding region, we show that they induce degradation of membrane tail-anchored Ubc6 independently of endogenous Doa10 and the other ERAD E3 complexes. This remains true even if the cells lack the Dfm1 rhomboid pseudoprotease, which is also a proposed retrotranslocon. Hence, rogue RING activity at the ER membrane elicits a highly specific off-pathway defect in the Doa10 pathway, and the data point to an additional ERAD E3-independent retrotranslocation mechanism. [ABSTRACT FROM AUTHOR]

This study evaluated the potential effects of long-term land use and climate change on the quality of surface runoff and the health risks associated with it. The land use change projection 2030 was derived from the main changes in land use from 2009 to 2019, and rainfall data was obtained from the Long Ashton Research Station Weather Generator (LARS-WG) model. The Long-Term Hydrological Impact Assessment (L-THIA) model was then utilized to calculate the rate of runoff heavy metal (HM) pollutant loading from the urban catchment. It was found that areas with heavy development posed a significantly greater public health risk associated with runoff, with higher risks observed in high-development and traffic areas compared to industrial, residential, and commercial areas. Additionally, exposure to Lead (Pb), Mercury (Hg), and Arsenic (As) was found to contribute significantly to overall non-carcinogenic health risks for possible consumers of runoff. Carcinogenic risk values of As, Cadmium (Cd), and Pb were also observed to increase, particularly in high-development and traffic areas, by 2030. This investigation offers important insight into the health risks posed by metals present in surface runoff in urban catchment areas under different land use and climate change scenarios. [Display omitted] • Long-Term Hydrologic Impact Assessment was used to calculate runoff pollutant. • Long Ashton Research Station and Land-use Change Model predicted rainfall and land. • Human health impacts of runoff from different land uses were assessed. • Lead had the highest ingestion exposure, followed by Mercury, Arsenic and Cadmium. • Arsenic emerged as primary contributor to carcinogenic health risks from runoff. [ABSTRACT FROM AUTHOR]

Byconsuming35% of the final global energy, buildings are among major contributors to greenhouse gas emissions and global warming. If the economic justification challenge is addressed, zero energy buildings (ZEB), which are defined as buildings that generate as much renewable-based energy as they consume annually, can be a promising solution for energy efficiency improvement in the building sector. In this article, we propose a stochastic ZEB device sizing model considering uncertain parameters (i.e., building's electrical and thermal demands, solar irradiation, and outdoor temperature) and their correlation. The proposed model finds the optimal size of the thermal and electrical devices considering their nonlinear behaviors and temperature-dependent dynamics. Furthermore, two parallel computing-based solution algorithms (i.e., parallelism in the algebraic level using Schur complement decomposition and parallelism in the problem (scenario) level by progressive hedging) are proposed to solve the stochastic model. Using the real historical data, numerical studies on the Woodward library building, located on the University of British Columbia campus, illustrate the efficacy of the solution algorithms to handle large-scale nonlinear programming models with more than 32.8 million variables, which is the largest one reported in the literature so far. [ABSTRACT FROM AUTHOR]

Purpose: Engineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalable and interpretable machine learning framework to automate this process and fill the current gap. Design/methodology/approach: The essential principles of the proposed pipeline are mainly (1) scalability, (2) interpretibility and (3) robust probabilistic performance across engineering problems. The lack of interpretibility of complex machine learning models prevents their use in various problems including engineering computation assessments. Many consumers of machine learning models would not trust the results if they cannot understand the method. Thus, the SHapley Additive exPlanations (SHAP) approach is employed to interpret the developed machine learning models. Findings: The proposed framework can be applied to a variety of engineering problems including seismic damage assessment of structures. The performance of the proposed framework is investigated using two case studies of failure identification in reinforcement concrete (RC) columns and shear walls. In addition, the reproducibility, reliability and generalizability of the results were validated and the results of the framework were compared to the benchmark studies. The results of the proposed framework outperformed the benchmark results with high statistical significance. Originality/value: Although, the current study reveals that the geometric input features and reinforcement indices are the most important variables in failure modes detection, better model can be achieved with employing more robust strategies to establish proper database to decrease the errors in some of the failure modes identification. [ABSTRACT FROM AUTHOR]

To design a reliable and secure power system, it is necessary to have enough transmission capacity. The solution of transmission expansion planning (TEP) problem determines cost-optimal investment in future transmission equipment. In this paper, we propose a new global solver, named Global-TEP, for the TEP problem with an AC network representation (ACTEP), which is a mixed-integer nonlinear programming problem. The proposed solver is based on second-order cone relaxation, enhanced relaxation tightening constraints, and optimization-based/feasibility-based bound tightening techniques. Multiple enhanced relaxation tightening constraints are incorporated into the mixed-integer second-order cone relaxation of TEP in order to obtain a very strong relaxation as the lower bounding problem. In addition, a novel feasibility-based bound tightening technique is proposed to tighten the bounds of decision variables in a considerably short runtime. Finally, introducing a novel application of optimization-based bound tightening technique, Global-TEP is constructed that can solve the ACTEP problem efficiently with a guaranteed optimality gap. As illustrated by numerical case studies, Global-TEP is more scalable, more flexible, and much faster than the available global solvers. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the capability of high‐volume comprehensive emergency obstetric care (CEmOC) health facilities on the provision of comprehensive postabortion care (PAC) in Sub‐Saharan Africa and to determine the frequency of women with severe abortion‐related complications in high capability facilities. Methods: A cross‐sectional analysis conducted across 11 countries in Sub‐Saharan Africa, using facility‐level information from the World Health Organization (WHO) Multi‐Country Survey on Abortion‐related morbidity (MCS‐A) between 2017 and 2018. PAC signal functions were adapted to assess facilities' capability to deliver comprehensive PAC through infrastructure, standard comprehensive capability, and extended comprehensive capability to provide PAC. The percentage of facilities with each signal function and distribution of facilities by number of signal functions were calculated for the three capability categories. Distribution of severe abortion complications by facility capability score was assessed. Results: Of 210 high‐volume CEmOC facilities included, 47.9% (n = 100) had capability to provide all facility infrastructure signal functions, 54.4% (n = 105) for standard comprehensive PAC, reducing to 17.7% (n = 34) for extended comprehensive PAC capability. Overall, there were gaps in extended capabilities including availability of a functioning ICU (available in 37.3% of facilities) and providers 24/7 (65.5% of facilities reported an obstetrician available 24/7 dropping to 41.3% for anesthesiologists). Facilities' PAC capability varied across regions. Overall, 34.6% (n = 614) of women with severe abortion‐related complications were treated in facilities with the maximum capability score for extended comprehensive PAC. Conclusion: Although high levels of capability to provide abortion‐related care for most signal functions were evident, significant gaps that impact on the management of severe abortion‐related complications remain, particularly related to extended facility capabilities including specialized human resources and ICU. Synopsis: Although levels of availability of key resources to manage abortion‐related complications were high, important resources for the most severe abortion‐related complications were not readily available, notably specialized human resources and ICU. [ABSTRACT FROM AUTHOR]

Objective: To estimate the prevalence of infection among abortion‐related complications in health facilities, describe their management, and identify sociodemographic and clinical factors associated with abortion‐related infections. Methods: A secondary analysis of the WHO Multi‐Country Survey on Abortion‐related morbidity (MCS‐A) conducted in 210 health facilities of 11 Sub‐Saharan African countries between 2017 and 2018. The outcome variable was abortion‐related infections, categorized into three mutually exclusive groups of abortion‐related complications: infections only, infection with other complications, and other complications without infection. We described the sociodemographic and clinical characteristics and the management of abortion‐related infection and identified the factors associated with abortion‐related infections using a multinomial logistic model. Results: A total of 9232 women with abortion‐related complications were included, with infection occurring among 10.6% of women (n = 974). Infection was involved in 47.4% (n = 153) of severe maternal outcomes with a case fatality rate of 27.4% (n = 42). The most common management approach was antibiotics, uterine evacuation, and uterotonics combined: 43.2% (n = 384) in the group of women with infection only and 48.6% (n = 4235) among those with infection and other complications. In addition, 85.9% (n = 7095) of women without infection also received therapeutic antibiotics. Factors associated with an increased odds of infection only compared with complication without infection were age younger than 20 years compared with those aged over 30 (aOR 1.84; 95% CI,1.24–2.74), not living in a couple (aOR 2.05, 95% CI,1.52–2.76), and gestational age of 13 weeks or more (aOR 1.70, 95% CI,1.27–2.26). The same factors were associated with infection and other complications. Conclusion: Infection is frequent among severe abortion‐related complications, and its case fatality rate is high. Further research to assess the relationship between abortion‐related infections and outcomes is needed. There is also a need to question the quality of postabortion care and improve adequate use of antibiotics. Synopsis: While abortion‐related infections are not the leading complications, they are more frequent among severe complications and have a higher case fatality rate than other complications. [ABSTRACT FROM AUTHOR]

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30–68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets. Author summary: Image-based cell profiling experiments measure thousands of features in millions of cells in microscopy images. Measuring cell response to various treatments in this way has proven powerful for many applications in biological research and drug discovery. However, each cell population responding to a treatment is usually population-averaged, losing information about the diversity of cells within each sample. We developed CytoSummaryNet, a machine learning method that substantially improves the ability to predict a treatment's mechanism of action from cell imaging profiles. Rather than averaging all cells equally, it learns which cells are most informative; it appears to focus on large, uncluttered cells while downweighting dividing cells and debris. Training CytoSummaryNet only requires information about which samples were exposed to which treatments, making it simpler to implement than previous techniques. By learning biologically relevant ways to summarize single-cell data into treatment profiles, CytoSummaryNet could help scientists leverage cell heterogeneity to extract richer insights from cell imaging screens and ultimately accelerate the development of new therapies. [ABSTRACT FROM AUTHOR]

Introduction: The obesity paradox has been observed in patients with cardiovascular disease. The goal of this study was to evaluate whether obesity has a protective effect in patients presenting with an ST elevation myocardial infarction (STEMI). Method: Using the large Nationwide Inpatient (NIS) sample database, we evaluated the mortality in patients with a STEMI based on weight categories. Results: A total of 2,161,640 STEMI patients were found in the database over age 18. We found that overweight and obesity had the lowest mortality using univariate (overweight mortality of 5% vs. obesity mortality of 6.5% vs. 10.9% for normal weights) and multivariate analyses (overweight OR: 0.52, CI: 0.43–063; p < 0.001 and obesity OR: 0.7, CI: 0.67–0.74; p < 0.001), whereas cachexia was associated with the highest mortality in the univariate (cachexia 24.5%) and multivariate (OR: 2.28, CI: 2.13–2.44; p < 0.001) analyses, followed by morbid obesity in the multivariate analysis (OR: 1.07, CI: 1.02–1.12; p = 0.004). Conclusions: We observed a partial obesity paradox in patients with a STEMI showing that overweight have the best survival rates followed by obesity. Cachexia followed by morbid obesity had the lowest survival rates. [ABSTRACT FROM AUTHOR]

Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model's performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ( p < 0.005 for all structures using a Wilcoxon's signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method's generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted. [ABSTRACT FROM AUTHOR]

Triboelectrostatic separation is a promising technology for the fractionation of agricultural flours to produce protein- and starch-enriched fractions. Rapid determination of the protein and starch content of these powder fractions is required for process development, optimization and ultimately control. The suitability of near-infrared and fluorescence spectroscopy along with data fusion for the rapid determination of protein and starch content and the development of a robust multivariate regression model was the objective in this work. A total of 102 samples were collected following triboelectrostatic separation of bean flour and analyzed using NIR and fluorescence spectroscopy. The superior performance of partial least squares method for both protein and starch, confirmed that NIR spectroscopy is a reliable method for rapid determination of protein- and starch-rich powders. The NIR and fluorescence data fusion model performed with the highest accuracy to predict the protein content. Unlabelled Image • Feasibility of NIR and fluorescence spectroscopy for rapid determination of protein and starch was explored. • Low-level data fusion was implemented to combine NIR and fluorescence spectroscopy data. • Various multivariate methods were applied to develop an optimal regression model. • NIR and fluorescence spectroscopy could be a suitable rapid approach for monitoring triboelectrostatic separation processes. • Fused NIR and fluorescence data predicted protein content with a higher accuracy. [ABSTRACT FROM AUTHOR]

Background and Objectives: COVID-19 infection, which began since 2019 from China, is introduced as a medicinal problem in the world. There is no certain treatment and preventive way actually. In countries that have been successful in controlling the disease, people's participation has played an important role in controlling the disease. This study was a review on epidemiology, pathophysiology and clinical manifestations of COVID-19 infection in order to guide policy making and promotion of knowledge, attitude and practice of the society associated with COVID-19 Materials and Methods: This is a review study in which appropriate key words were researched within authentic scientific spaces such as PubMed, Google scholar, CDC, WHO, NEJM and Cell Press from December 2019 till August 2020 and then, subjects were collected and analyzed. Results: Rapid spread of COVID-19 virus, the relationship between disease severity and mortality with various factors, respiratory transmission, especially in short distance, masks, hand washing and physical distance observance as the most important way to prevent, diversify clinical symptoms and at the same time fever and cough as the most common clinical symptoms, treatment with non-specific antiviral drugs and treatment based on controlling clinical symptoms and strengthening immunity with known drugs, paying attention to children as asymptomatic carriers, and pretentious pregnancy in the event of COVID-19 are the most important findings of this study. Conclusion: The findings of this study are practical points that awareness of those points is effective in both policy making and promoting the necessary knowledge for COVID-19 infection in the community. [ABSTRACT FROM AUTHOR]

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-the-art results in semantic segmentation for numerous medical imaging applications. Moreover, batch normalization and Dice loss have been used successfully to stabilize and accelerate training. However, these networks are poorly calibrated i.e. they tend to produce overconfident predictions for both correct and erroneous classifications, making them unreliable and hard to interpret. In this paper, we study predictive uncertainty estimation in FCNs for medical image segmentation. We make the following contributions: 1) We systematically compare cross-entropy loss with Dice loss in terms of segmentation quality and uncertainty estimation of FCNs; 2) We propose model ensembling for confidence calibration of the FCNs trained with batch normalization and Dice loss; 3) We assess the ability of calibrated FCNs to predict segmentation quality of structures and detect out-of-distribution test examples. We conduct extensive experiments across three medical image segmentation applications of the brain, the heart, and the prostate to evaluate our contributions. The results of this study offer considerable insight into the predictive uncertainty estimation and out-of-distribution detection in medical image segmentation and provide practical recipes for confidence calibration. Moreover, we consistently demonstrate that model ensembling improves confidence calibration. [ABSTRACT FROM AUTHOR]

The important focus of the energy strategy of the European Union relies on the concept of zero energy building (ZEB), which is, by definition, a building that roughly produces yearly as much renewable energy as it consumes. This article proposes an enhanced mixed-integer nonlinear programming model for optimal sizing of photovoltaic (PV) and battery energy storage systems to comply with the definition of a ZEB. A salient novel feature of the proposed model is that it factors in the environmental impacts, computed through rigorous life cycle assessment methodology, of buying electricity from the grid and manufacturing battery and PV systems. Furthermore, an adjustable parameter is introduced to make the model adaptive from the perspective of the building owner's willingness-to-pay for environmental impacts. The proposed model is then rigorously reformulated, managing to accumulate its nonlinearity in only one constraint per time interval. Eventually, the reformulated model is linearized to a mixed-integer linear programming model using the McCormick relaxation technique. The case study conducted on archetypal buildings in Luxembourg reveals that the proposed McCormick-based linear model is able to provide high accuracy results with reasonable computational effort. [ABSTRACT FROM AUTHOR]

Although buildings are excited by translational and rotational earthquake components, only translational components are considered in seismic loads. The effects of earthquake torsional components on building responses were studied in this work. For this purpose, the torsional component of 14 earthquakes was extracted from their translational components. Symmetric and asymmetric idealised single-storey models were analysed by considering the torsional and translational components. The models considered five different values of the uncoupled torsional frequency to uncoupled translational frequency ratio (Ω = ωθ/ωx = 1·5, 1·25, 1, 0·75, 0·5), with their lateral period varying from 0·05 to 2 s. The displacements of the left-hand side, right-hand side and centre of the diaphragm, the diaphragm rotation about the centre of mass and the base shear were studied. The results indicated that the effect of the torsional component on the non-linear responses of the building was considerable, and ignoring this effect led to an underestimation of the responses. The increase in the building's responses due to the torsional component was found to depend significantly on the value of Ω and the stiffness eccentricity. The maximum growth of the displacement of torsionally stiff and torsionally flexible buildings was 51% and 155%, respectively. The diaphragm rotation can be increased to 154% in the asymmetric systems. [ABSTRACT FROM AUTHOR]

Background: Nowadays, the use of green tea supplements has increased. Studies have shown that green tea can have positive effects on anti-inflammatory and antioxidative factors, as well as improve aerobic performance capacity. Therefore, in this study, we investigate the effects of this supplement on inflammatory factors, total antioxidant capacity responses, and maximum oxygen uptake (VO2 Max) of healthy young men in summer. Methods: This study is a double-blind randomized controlled trial (RCT) in which 15 young men (age 25.06 ± 2.1) were randomly assigned into the green tea (GT) and placebo groups. Subjects performed maximum aerobic exercises (shuttle run 20 m) in separate workouts (14 days) in summer. They consumed 640 mg green tea extracts or maltodextrin 90 min before exercise in a double-blind design. Blood samples were collected before and after the exercise and then evaluated in the biochemistry laboratory. In this study, one way analysis of variance (ANOVA) tests were used for the statistical analysis. Results: The results of this study show that green tea supplement significantly slowed down the increasing tumor necrosis factor alpha (TNF-a) (GT: 15.03 ± 4.31 [pg/ml], placebo: 31.38 ± 7.18 [pg/ml], [P = 0.000]); increased the total antioxidant capacity (GT: 1.04 ± 0.06 [mm], placebo group: 0.72 ± 0.04 [mm], [P = 0.001 VO2]); and Max (GT: 44.43 ± 3.06 [ml/kg/min], placebo group: 34.88 ± 1.30 [ml/kg/min], [P = 0.001]) in the supplement group than placebo. In addition, no significant differences in interleukin 1 beta (IL-1ß) was observed between thee groups (GT: 26.86 ± 5.05 [pg/ml], placebo group: 23.47 ± 3.16 [pg/ml], [P = 0.251]). Conclusions: The consumption of green tea supplements 90 min before aerobic exercise may decrease inflammation and oxidative stress factors and improve VO2 Max in summer. [ABSTRACT FROM AUTHOR]

Purpose: Obstructive sleep apnea (OSA) is characterized by repetitive episodes of upper airway obstruction while asleep that leads to sleep fragmentation and hypoxemia. OSA is associated with an increased rate of cardiovascular disease, stroke, motor vehicle crashes, and all-cause mortality. Continuous positive airway pressure (CPAP) reduces the risk of upper airway collapse, and its use is associated with reduced daytime sleepiness and decreased rates of cardiovascular disease and motor vehicle crashes. However, one of the major challenges associated with CPAP success is adherence. To date, there have been no studies on the rate or predictors of CPAP adherence in Canadians. We sought to determine the rate and predictors of CPAP adherence amongst a sample of patients presenting to the University of British Columbia Sleep Disorders Clinic (UBC SDC). Methods: Patients referred for suspected OSA to the UBC SDC were recruited, underwent attended overnight polysomnography, and completed a health questionnaire. Patient charts were reviewed, and CPAP adherence was defined according to the recognized standard of CPAP use for ≥4 hours per night for ≥70% of nights. Results: In total, 1,250 patients were recruited. Of these, 844 were prescribed CPAP and 421 (49.9%) were adherent. The severity of OSA was the only significant predictor of CPAP adherence (p<0.01). Adherent patients had a mean apnea hypopnea index of 34 events/hr compared to 27 events/hr in non-adherent patients. Conclusion: CPAP adherence was 49.9% and OSA severity was its only significant predictor in this cohort. [ABSTRACT FROM AUTHOR]

Purpose: Obstructive sleep apnea (OSA) is characterized by repetitive episodes of upper airway obstruction while asleep that leads to sleep fragmentation and hypoxemia. OSA is associated with an increased rate of cardiovascular disease, stroke, motor vehicle crashes, and all-cause mortality. Continuous positive airway pressure (CPAP) reduces the risk of upper airway collapse, and its use is associated with reduced daytime sleepiness and decreased rates of cardiovascular disease and motor vehicle crashes. However, one of the major challenges associated with CPAP success is adherence. To date, there have been no studies on the rate or predictors of CPAP adherence in Canadians. We sought to determine the rate and predictors of CPAP adherence amongst a sample of patients presenting to the University of British Columbia Sleep Disorders Clinic (UBC SDC). Methods: Patients referred for suspected OSA to the UBC SDC were recruited, underwent attended overnight polysomnography, and completed a health questionnaire. Patient charts were reviewed, and CPAP adherence was defined according to the recognized standard of CPAP use for =4 hours per night for ≥70% of nights. Results: In total, 1,250 patients were recruited. Of these, 844 were prescribed CPAP and 421 (49.9%) were adherent. The severity of OSA was the only significant predictor of CPAP adherence (p<0.01). Adherent patients had a mean apnea hypopnea index of 34 events/hr compared to 27 events/hr in non-adherent patients. Conclusion: CPAP adherence was 49.9% and OSA severity was its only significant predictor in this cohort. [ABSTRACT FROM AUTHOR]

One important task in power distribution system expansion planning is feeder routing (FR), which is to determine the optimal route from a medium voltage substation to load points and the optimal size of the conductors to be installed. This paper proposes a novel graph-based model for the resilient feeder routing problem using geographical information system (GIS). We show that incorporating GIS data enhances the FR solution optimality. Moreover, by introducing a representing graph, radiality of the planned network is guaranteed. A second-order cone programming (SOCP) model is used to model power flows through feeders. Since the representing graph ensures radiality, the SOCP model is exact. The uncertainty of rooftop solar generations and demand forecasting errors are taken into account, and a stochastic programming-based solution algorithm is developed. The proposed model represents practical aspects such as economic objectives (installation cost, power losses, resiliency), technical constraints (voltage limits, radiality constraint, reliability), and geographical constraints. The efficiency of the algorithm is demonstrated using three case studies: a small test system, a realistic one, and a synthetic large test system. [ABSTRACT FROM AUTHOR]

Domain adaptation addresses the learning problem where the training data are sampled from a source joint distribution (source domain), while the test data are sampled from a different target joint distribution (target domain). Because of this joint distribution mismatch, a discriminative classifier naively trained on the source domain often generalizes poorly to the target domain. In this article, we therefore present a Joint Distribution Invariant Projections (JDIP) approach to solve this problem. The proposed approach exploits linear projections to directly match the source and target joint distributions under the $L^{2}$ -distance. Since the traditional kernel density estimators for distribution estimation tend to be less reliable as the dimensionality increases, we propose a least square method to estimate the $L^{2}$ -distance without the need to estimate the two joint distributions, leading to a quadratic problem with analytic solution. Furthermore, we introduce a kernel version of JDIP to account for inherent nonlinearity in the data. We show that the proposed learning problems can be naturally cast as optimization problems defined on the product of Riemannian manifolds. To be comprehensive, we also establish an error bound, theoretically explaining how our method works and contributes to reducing the target domain generalization error. Extensive empirical evidence demonstrates the benefits of our approach over state-of-the-art domain adaptation methods on several visual data sets. [ABSTRACT FROM AUTHOR]

Formulating power flow equations with linear sensitivity factors (LSFs) reduces the number of variables and constraints, and consequently, the computational burden of power systems' optimisation problems. This study proposes a transformative, computationally efficient model for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of LSFs to formulate an optimisation. LSFs allow to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Thus, the proposed model includes less number of variables and constraints compared with the classical angle-based model. These features significantly reduce computational costs of TEP and enhance its scalability, especially for large-scale systems. Load and generation uncertainties are modelled using a data-driven approach, and N - 1 security criteria are taken into account to ensure system security. All equations under normal and N - 1 conditions are considered using data of the complete network graph. Simulation results show that the proposed model provides the same results as the conventional angle-based model while being much faster (more than 58% based on the authors' case studies) and computationally more efficient. [ABSTRACT FROM AUTHOR]

Formulating power flow equations with linear sensitivity factors (LSFs) reduces the number of variables and constraints, and consequently, the computational burden of power systems' optimisation problems. This study proposes a transformative, computationally efficient model for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of LSFs to formulate an optimisation. LSFs allow to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Thus, the proposed model includes less number of variables and constraints compared with the classical angle‐based model. These features significantly reduce computational costs of TEP and enhance its scalability, especially for large‐scale systems. Load and generation uncertainties are modelled using a data‐driven approach, and N − 1 security criteria are taken into account to ensure system security. All equations under normal and N − 1 conditions are considered using data of the complete network graph. Simulation results show that the proposed model provides the same results as the conventional angle‐based model while being much faster (more than 58% based on the authors' case studies) and computationally more efficient. [ABSTRACT FROM AUTHOR]

Endoplasmic reticulum (ER) stress occurs when the abundance of unfolded proteins in the ER exceeds the capacity of the folding machinery. Despite the expanding cadre of characterized cellular adaptations to ER stress, knowledge of the effects of ER stress on cellular physiology remains incomplete. We investigated the impact of ER stress on ER and inner nuclear membrane protein quality control mechanisms in Saccharomyces cerevisiae. We analyzed the turnover of substrates of four ubiquitin ligases (Doa10, Rkr1/Ltn1, Hrd1, and the Asi complex) and the metalloprotease Ste24 in induced models of ER stress. ER stress did not substantially impact Doa10 or Rkr1 substrates. However, Hrd1-mediated destruction of a protein that aberrantly engages the translocon (Deg1-Sec62) and substrates with luminal degradation signals was markedly impaired by ER stress; by contrast, Hrd1-dependent degradation of proteins with intramembrane degrons was largely unperturbed by ER stress. ER stress impaired the degradation of one of two Asi substrates analyzed and caused a translocon-clogging Ste 24 substrate to accumulate in a form consistent with persistent translocon occupation. Degradation of Deg 1-Sec 62 in the absence of stress and stabilization during ER stress were independent of four ER stress-sensing pathways. Our results indicateERstress differentially impacts degradation of protein quality control substrates, including those mediated by the same ubiquitin ligase. These observations suggest the existence of additional regulatory mechanisms dictating substrate selection during ER stress. [ABSTRACT FROM AUTHOR]

Introduction: The effect of using intravascular ultrasound (IVUS) during percutaneous coronary intervention (PCI) on short-term mortality is not well established. Aim: To evaluate any association between the use of IVUS vs. no IVUS during PCI and mortality in a large inpatient database. Material and methods: We used the National Inpatient Sample (NIS) database for available ICD-10 codes from 2016-2020 for IVUS and PCIs. Results: A total of 10,059,56 PCIs were performed. In 206,910, IVUS-guided PCI was performed vs. 9,852,359 without IVUS. Mortality did not differ between the two groups, with 2.52% mortality in the IVUS arm vs. 2.59% in no IVUS cohort, p = 0.4. The mean age of patients with IVUS use was 65.5 vs. 70.1 years without IVUS, p < 0.001. Total in-hospital cost in the IVUS group was double that without IVUS ($141,920 vs. $71,568, p < 0.001). Furthermore, IVUS utilization was significantly higher in patients with private health insurance (28.3% vs. 17.2%, p < 0.001). Conclusions: In-patient all-cause mortality using IVUS during PCI was similar to that in patients without IVUS utilization, but the cost was doubled, with higher utilization in privately insured patients. [ABSTRACT FROM AUTHOR]

Background: The goal of this study was to evaluate the effect of extracorporeal membrane oxygenation (ECMO) on mortality in patients with cardiogenic shock excluding Impella and IABP use. Method: The large Nationwide Inpatient Sample (NIS) database was utilized to study any association between the use of ECMO in adults over the age of 18 and mortality and complications with a diagnosis of cardiogenic shocks. Results: ICD-10 codes for ECMO and cardiogenic shock for the available years 2016–2020 were utilized. A total of 796,585 (age 66.5 ± 14.4) patients had a diagnosis of cardiogenic shock excluding Impella. Of these patients, 13,160 (age 53.7 ± 15.4) were treated with ECMO without IABP use. Total inpatient mortality without any device was 32.7%. It was 47.9% with ECMO. In a multivariate analysis adjusting for 47 variables such as age, gender, race, lactic acidosis, three-vessel intervention, left main myocardial infarction, cardiomyopathy, systolic heart failure, acute ST-elevation myocardial infarction, peripheral vascular disease, chronic renal disease, etc., ECMO utilization remained highly associated with mortality (OR: 1.78, CI: 1.6–1.9, p < 0.001). Evaluating teaching hospitals only revealed similar findings. Major complications were also high in the ECMO cohort. Conclusions: In patients with cardiogenic shock, the use of ECMO was associated with the high in-hospital mortality regardless of comorbid condition, high-risk futures, or type of hospital. [ABSTRACT FROM AUTHOR]

Accurate lymph node size estimation is critical for staging cancer patients, initial therapeutic management, and assessing response to therapy. Current standard practice for quantifying lymph node size is based on a variety of criteria that use uni-directional or bi-directional measurements. Segmentation in 3D can provide more accurate evaluations of the lymph node size. Fully convolutional neural networks (FCNs) have achieved state-of-the-art results in segmentation for numerous medical imaging applications, including lymph node segmentation. Adoption of deep learning segmentation models in clinical trials often faces numerous challenges. These include lack of pixel-level ground truth annotations for training, generalizability of the models on unseen test domains due to the heterogeneity of test cases and variation of imaging parameters. In this paper, we studied and evaluated the performance of lymph node segmentation models on a dataset that was completely independent of the one used to create the models. We analyzed the generalizability of the models in the face of a heterogeneous dataset and assessed the potential effects of different disease conditions and imaging parameters. Furthermore, we systematically compared fully-supervised and weakly-supervised methods in this context. We evaluated the proposed methods using an independent dataset comprising 806 mediastinal lymph nodes from 540 unique patients. The results show that performance achieved on the independent test set is comparable to that on the training set. Furthermore, neither the underlying disease nor the heterogeneous imaging parameters impacted the performance of the models. Finally, the results indicate that our weakly-supervised method attains 90%− 91% of the performance achieved by the fully supervised training. • Proposed lymph node segmenters generalized well on an unseen heterogeneous dataset. • Patient disease and imaging parameters did not impact the segmentation performance. • Weakly supervision achieved comparable performance to fully supervised training. [ABSTRACT FROM AUTHOR]

Background: Women across the world are mistreated during childbirth. We aimed to develop and implement evidence-informed, validated tools to measure mistreatment during childbirth, and report results from a cross-sectional study in four low-income and middle-income countries.Methods: We prospectively recruited women aged at least 15 years in twelve health facilities (three per country) in Ghana, Guinea, Myanmar, and Nigeria between Sept 19, 2016, and Jan 18, 2018. Continuous observations of labour and childbirth were done from admission up to 2 h post partum. Surveys were administered by interviewers in the community to women up to 8 weeks post partum. Labour observations were not done in Myanmar. Data were collected on sociodemographics, obstetric history, and experiences of mistreatment.Findings: 2016 labour observations and 2672 surveys were done. 838 (41·6%) of 2016 observed women and 945 (35·4%) of 2672 surveyed women experienced physical or verbal abuse, or stigma or discrimination. Physical and verbal abuse peaked 30 min before birth until 15 min after birth (observation). Many women did not consent for episiotomy (observation: 190 [75·1%] of 253; survey: 295 [56·1%] of 526) or caesarean section (observation: 35 [13·4%] of 261; survey: 52 [10·8%] of 483), despite receiving these procedures. 133 (5·0%) of 2672 women or their babies were detained in the facility because they were unable to pay the bill (survey). Younger age (15-19 years) and lack of education were the primary determinants of mistreatment (survey). For example, younger women with no education (odds ratio [OR] 3·6, 95% CI 1·6-8·0) and younger women with some education (OR 1·6, 1·1-2·3) were more likely to experience verbal abuse, compared with older women (≥30 years), adjusting for marital status and parity.Interpretation: More than a third of women experienced mistreatment and were particularly vulnerable around the time of birth. Women who were younger and less educated were most at risk, suggesting inequalities in how women are treated during childbirth. Understanding drivers and structural dimensions of mistreatment, including gender and social inequalities, is essential to ensure that interventions adequately account for the broader context.Funding: United States Agency for International Development and the UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction, Department of Reproductive Health and Research, WHO. [ABSTRACT FROM AUTHOR]

Background and Objectives: This prospective case-control study was conducted to evaluate abnormal serum protein electrophoresis (SPEP) patterns in patients living with human immunodeficiency virus (HIV) and its relation with disease severity markers and anti-retroviral treatment status. Materials and Methods: Thirty-seven HIV-positive patients and 24 healthy individuals were evaluated in the course of this study. The healthy HIV-negative individuals were selected as control group. Pregnant women, patients with malignancies, children, hepatitis B- and/or C-positive patients, those with a history of an autoimmune disease, or previous corticosteroid administration were excluded. SPEP--which detects serum levels of albumin, total protein, gammaglobulin--, CD4+ T-cell counts, viral load, and antiretroviral treatment status were assessed. Data were analyzed by SPSS? software. Results: Twelve patients (32 percent) demonstrated polyclonal gammopathy on SPEP, while only 1 (4 percent) healthy individual had the same pattern (P-value = 0.007). No statistically significant connection between SPEP patterns and antiretroviral treatment status was observed (P-value > 0.05). Interestingly no statistically significant relationship between CD4+ T-cell counts and polyclonal gammopathy was discerned. No statistically significant difference was observed between the two groups with regards to serum albumin and total protein levels. The serum albumin to total protein percentage, serum gamma globulin to total protein percentage, and serum albumin to globulin ratio was compared between the groups and a statistically significant difference was observed. Conclusion: Polyclonal gammopathy on SPEP is common among HIV-infected patients. Moreover, the SPEP patterns cannot be used as an indication of a patient's negative or positive response to treatment. [ABSTRACT FROM AUTHOR]

Numerous nascent proteins undergo folding and maturation within the luminal and membrane compartments of the endoplasmic reticulum (ER). Despite the presence of various factors in the ER that promote protein folding, many proteins fail to properly fold and assemble and are subsequently degraded. Regulatory proteins in the ER also undergo degradation in a way that is responsive to stimuli or the changing needs of the cell. As in most cellular compartments, the ubiquitin-proteasome system (UPS) is responsible for the majority of the degradation at the ER―in a process termed ER-associated degradation (ERAD). Autophagic processes utilizing ubiquitin-like protein-conjugating systems also play roles in protein degradation at the ER. The ER is continuous with the nuclear envelope (NE), which consists of the outer nuclear membrane (ONM) and inner nuclear membrane (INM). While ERAD is known also to occur at the NE, only some of the ERAD ubiquitin-ligation pathways function at the INM. Protein degradation machineries in the ER/NE target a wide variety of substrates in multiple cellular compartments, including the cytoplasm, nucleoplasm, ER lumen, ER membrane, and the NE. Here, we review the protein degradation machineries of the ER and NE and the underlying mechanisms dictating recognition and processing of substrates by these machineries. [ABSTRACT FROM AUTHOR]

This study presents a distributed collaborative transmission expansion planning (TEP) algorithm for interconnected multi-regional power systems. The proposed algorithm is a multi-agent-based TEP. A local TEP is formulated for each region (agent) with respect to the region's local characteristic and interactions (i.e. tie-line flows) with its neighbours. Nodal power balances at border buses are modified to model the interactions. Realistic planning constraints and objectives such as budget constraints, operational costs, N − 1 security criterion, and uncertainties are modelled in the local TEPs. The information privacy is respected as each local planner needs to share limited information related to cross-border tie lines with other planners. To coordinate the local planners, a two-level distributed optimisation algorithm is proposed based on the concept of analytical target cascading (ATC) for multidisciplinary design optimisation. While the upper level solves the local TEPs in parallel, the lower level seeks to coordinate neighbouring regions. The lower-level problem is further replaced in the upper-level optimisation by Karush–Kuhn–Tucker conditions to relax the need for any form of central coordinator. This makes the proposed ATC-based TEP a fully parallelised distributed optimisation algorithm. An initialisation strategy is suggested to enhance the performance of the distributed TEP. [ABSTRACT FROM AUTHOR]

Z-source converters, as one of the most noteworthy power electronic interfaces, have become so popular in the last decade. In this paper, the dynamic model of a three-phase six-switch Z-source inverter is presented. An accurate dynamic model is necessary for both determining the appropriate Z-source inverter with the efficient size of inductor and capacitor and designing the control system which guarantees the stability of the system. In this study, the steady state model and AC small signal analysis were used to obtain the dynamic model. To continue previous studies, in the proposed model, the state variables of the AC side were considered. Therefore, the model was more comprehensive and could be also used for three phase Z-source converter in the inverting mode. To validate the proposed model, the dynamic model was solved by MATLAB and compared with the real circuit implemented in the PSCAD/EMTDC. The simulation results demonstrated the accuracy of the proposed model for the three-phase Z-source inverter. [ABSTRACT FROM AUTHOR]

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training–validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common disease which impacts quality of life, mood, cardiovascular morbidity, and mortality. Continuous positive airway pressure (CPAP) is the first-line treatment for patients with moderate to severe OSA. CPAP ameliorates respiratory disturbances, leading to improvements in daytime sleepiness, quality of life, blood pressure, and cognition. However, despite the high efficacy of this device, CPAP adherence is often sub-optimal. Factors including: socio-demographic/economic characteristics, disease severity, psychological factors, and side-effects are thought to affect CPAP adherence in OSA patients. Intervention studies have suggested that augmented support/education, behavioral therapy, telemedicine and technological interventions may improve CPAP adherence. In this paper, we will extensively review the most common factors including age, gender, race/ethnicity, socioeconomic status, smoking status, severity of OSA, severity of OSA symptoms, psychological variables, social support, marital status/bed partner involvement, dry nose and mouth, mask leak, and nasal congestion that may predict CPAP adherence. We will also extensively review interventions that may increase adherence to CPAP. [ABSTRACT FROM AUTHOR]

Errors in forecasting load and renewable-based generation in restructured power systems mean that independent system operators (ISOs) must procure sufficient operating reserves to keep the real-time operation of the system reliable and secure. But when procured reserves turn out to be insufficient in real-time due to the lack of resource capacity or ramp capability, operators often set higher prices for reserves and energy to encourage more supply, and to motivate consumers to decrease usage or shift it to other times. This procedure, which is called scarcity or shortage pricing, is a core feature of U.S. electricity markets. It is receiving increased attention from market designers and stakeholders because scarcity will become more important for spot price formation in the future with the increased penetration of zero-marginal cost renewables, and the shrinking role of fuel costs in setting prices. Scarcity pricing is implemented in various ways by different ISOs. These differences have practical implications for the level of prices and incentives for investment, operations, and demand modification. In this paper, general approaches and specific calculation procedures for reserve and energy scarcity pricing practices and calculations across the seven ISO-based U.S. power markets are reviewed and compared. A consistent terminology is used to facilitate the comparison. Current scarcity pricing practices are grouped into three approaches: (1) imposing an adder after the spot market is run; (2) including stepwise demand curves within market clearing procedures for non-contingency reserve products (e.g., the novel flexiramp product), which tends to yield longer right tails for energy scarcity premium curves; and (3) having stepwise demand curves for traditional contingency reserve products only, which results in shorter right tails in energy scarcity curves. A generic numerical example is presented to highlight the large practical differences among the reserve scarcity pricing approaches and specific implementations. To further investigate factors that contribute the most to demand curves differences among ISOs, a sensitivity analysis is performed. This analysis shows that the largest source of differences among the curves is the scarcity prices assumed in the case of severe scarcity, while the number of steps used and whether flexiramp is considered also yields important differences in scarcity prices. As renewable penetration increases, it will become increasingly crucial to employ administrative demand curves so that spot prices more effectively motivate supply and demand adjustments exactly when and where they are needed. This study shows that the different assumptions yield very different scarcity premiums for reserves and energy, and are likely to provide divergent incentives for resources to respond to shortages. It is concluded that to promote market efficiency, a reserve shortage demand curve should have at least three features: inclusion of the marginal value of reserve products at each shortage level, consideration of the magnitude and probability of supply contingencies, and avoidance of abrupt price discontinuities that can cause excessively volatile market outcomes. • Explains principles and specific calculation procedures for reserve scarcity pricing practices across U.S. power markets. • Proposes a novel classification of sources of penalty values applied in the markets, highlighting their relative advantages. • Presents a generic numerical example to illustrate practical differences among the reserve scarcity pricing approaches. • Performs a sensitivity analysis to further investigate the most significant factors in differences among ISO demand curves. • Makes recommendations for defining demand curves for reserve scarcity pricing that accurately reflect the value of reserves. [ABSTRACT FROM AUTHOR]

Aim of the study:Adipose tissue possesses a population of multi-potent stem cells which can be differentiated to a Schwann cell phenotype and may be of benefit for treatment of peripheral nerve injuries. Effects of local therapy of nonexpanded adipose stromal vascular fraction (SVF) on peripheral nerve regeneration was studied using allografts in a rat sciatic nerve model.Materials and Methods:Thirty male white Wistar rats were divided into three experimental groups (n= 10), randomly: Sham-operated group (SHAM), allograft group (ALLO), SVF-treated group (ALLO/SVF). In SHAM group left sciatic nerve was exposed through a gluteal muscle incision and after homeostasis muscle was sutured. In the ALLO group the left sciatic nerve was exposed through a gluteal muscle incision and transected proximal to the tibio-peroneal bifurcation where a 10 mm segment was excised. The same procedure was performed in the ALLO/SVF group. The harvested nerves of the rats of ALLO group were served as allograft for ALLO/SVF group and vice versa. The SHAM and ALLO groups received 100 μL phosphate buffered saline and the ALLO/SVF group received 100 μL SVF (2.25 ± 0.45 × 107cells) locally where the grafting was performed.Results:Behavioral, functional, biomechanical, and gastrocnemius muscle mass showed earlier regeneration of axons in ALLO/SVF than in ALLO group (p< .05). Histomorphometic and immunohistochemical studies also showed earlier regeneration of axons in ALLO/SVF than in ALLO group (p< .05).Conclusions:Administration of nonexpanded SVF could accelerate functional recovery after nerve allografting in sciatic nerve. It may have clinical implications for the surgical management of patients after nerve transection. [ABSTRACT FROM AUTHOR]

Locally repairable codes (LRCs) have been recently proposed and used in real-world distributed storage systems (DSSs) such as Microsoft Azure Storage and Facebook HDFS-RAID (Hadoop Distributed File System-Redundant Array of Independent Disks). Since information in DSSs is changed frequently, reducing update complexity (UC) of LRCs is of great interest. In this paper, we propose code design algorithms that can reduce UC of existing LRCs without sacrificing their important code parameters such as minimum distance, code rate, or locality. We establish bounds on UC, and use them to show that our algorithms can achieve optimal or near optimal UC for a large class of LRCs. [ABSTRACT FROM AUTHOR]

Durability of membranes is one of the concerns for widespread commercialization of polymer electrolyte fuel cells. Effects of membrane swelling on the durability pose important challenges for the fabrication of the catalyst-coated membrane. This study provides insight into vulnerable locations of the membrane under hygro-thermal loading, mechanical loading due to clamping and realistic conditions where a combination of both of these loadings are imposed. With a half rib-channel model, we simulate a polymer electrolyte fuel cell that operates under varying loads and clamping pressure. Model considers anisotropic diffusion in the gas diffusion layer as well as complex interactions of water transport dynamics between gas diffusion layers and the membrane. Mechanical responses of the membrane subject to conjugate hygro-thermo-mechanical loadings during typical scenarios of fuel cell operation reveal the effects of operating parameters as well as individual contributing factors on the development of local stresses in the membrane. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush–Kuhn–Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator. [ABSTRACT FROM AUTHOR]

This paper presents Accordion Metallic Damper (AMD) as a unique energy dissipating system that originated from the concept of thin-walled tubes used in machinery as the shock absorber. The AMD can perform as a repairable hysteretic fuse in structural frames to enhance the lateral ductility, energy-dissipation and damping potential of the frame systems during earthquakes. The AMD consists of the corrugated thin-walled tubes installed at the brace connection to the frame. The lateral displacement of the braced frame causes yielding of the AMD in axial deformation mechanism and dissipates energy due to forming of plastic hinges along the corrugated tubes in reversed cyclic deformations. In order to evaluate the performance of the AMD for upgrading the seismic behavior of the structures a series of quasi-static cyclic tests were conducted on pre-fabricated corrugated thin-walled tubes and hysteretic load-deformation response, lateral strength, initial stiffness, and dissipated energy was investigated. Numerical studies were also carried out to provide a large parameter results to explore the effect of geometry parameters such as shape, thickness, diameter and length of the corrugated tube on the mechanical properties. The analytical model was created based on finite elements method and nonlinear inelastic analysis with considering large deformation capacity was employed for these studies. The results showed that the AMD exhibited enhanced energy-dissipation and damping potential with stable hysteresis loops and confirmed that the AMD is an excellent energy-dissipating device that can be used for upgrading the seismic behavior of framed structures. [ABSTRACT FROM AUTHOR]

Representing images and videos with Symmetric Positive Definite (SPD) matrices, and considering the Riemannian geometry of the resulting space, has been shown to yield high discriminative power in many visual recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices –especially of high-dimensional ones– comes at a high cost that limits the applicability of existing techniques. In this paper, we introduce algorithms able to handle high-dimensional SPD matrices by constructing a lower-dimensional SPD manifold. To this end, we propose to model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection. This lets us formulate dimensionality reduction as the problem of finding a projection that yields a low-dimensional manifold either with maximum discriminative power in the supervised scenario, or with maximum variance of the data in the unsupervised one. We show that learning can be expressed as an optimization problem on a Grassmann manifold and discuss fast solutions for special cases. Our evaluation on several classification tasks evidences that our approach leads to a significant accuracy gain over state-of-the-art methods. [ABSTRACT FROM AUTHOR]