bluuebunny/crossref_metadata_2025_split huggingface dataset:
Teacher Burnout and Contextual and Compositional Elements of School Environment
Authors: Shackleton Nichola , Bonell Chris , Jamal Farah , Allen Elizabeth , Mathiot Anne , Elbourne Diana , Viner Russell 
Published: November 2019

ABSTRACTBACKGROUNDTeachers report higher levels of stress than most occupational groups. Burnout is a specific psychological condition that results from chronic job stress characterized by emotional exhaustion, low personal accomplishment, and depersonalization. This study considers associations between aspects of the school environment and teacher burnout.METHODSExploratory analysis of baseline data from a cluster randomized controlled trial of 40 schools and 2278 teachers in the United Kingdom. Multilevel methods were used to consider the associations between different compositional and contextual aspects of the school environment and teacher burnout.RESULTSThere was evidence for school effects on teacher burnout, evidenced by ICCs and likelihood ratio tests, supporting the association between school environment and teacher burnout. The factors most consistently associated with teacher burnout in our study were teachers' perceptions of the school's safety and support and student attitudes to learning.CONCLUSIONSThe school environment does influence teacher burnout. More research is needed to develop and test causal pathways between the school environment and teacher burnout, and to understand ecological and individual predictors of teacher burnout and the interaction between the two.
Filtering approaches for online train motion estimation with onboard power measurements
Authors: Sessa Pier Giuseppe , De Martinis Valerio , Corman Francesco 
Published: November 2019

AbstractFuture railway systems will need the support of more precise and reliable information on train motion during operation, to fully exploit the potential of new technologies. Train motion information are here intended as time series of train state characteristics, that is, position, speed, and acceleration. This paper investigates the use of two different filtering formulations for better online estimations of train state along the track. Specifically, extended Kalman filters (EKFs) and particle filters (PFs) are used to fuse kinematic measurements collected by means of Global Navigation Satellite Systems (GNSSs) with information collected by the trains on the used tractive power. The EKFs need linearity assumptions and are based on statistical evaluations of the train state, the PFs are instead conceived for nonlinear models and they are based on probability functions related to train state measurements and estimates. A set of experiments with real data of trains operating on a Swiss line is presented to analyze the capabilities of these two filters when applied to rail operation in non‐urban environments. We discuss the extent by which the proposed filtering approaches match the latest technical requirements for implementation. We expect such an enhanced train motion estimation to enable reliable and continuous positioning, available at train level and at the traffic control center, while trackside equipment will be gradually reduced.
BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data
Authors: van Raaphorst Renske , Kjos Morten , Veening Jan‐Willem 
Published: November 2019

AbstractHigh‐throughput analyses of single‐cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase‐contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell‐to‐cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post‐processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command‐line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z‐ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap.
Review and Evaluation of the J100‐10 Risk and Resilience Management Standard for Water and Wastewater Systems
Authors: Chen Thomas Ying‐Jeh , Washington Valerie Nicole , Aven Terje , Guikema Seth David 
Published: November 2019

AbstractRisk analysis standards are often employed to protect critical infrastructures, which are vital to a nation's security, economy, and safety of its citizens. We present an analysis framework for evaluating such standards and apply it to the J100‐10 risk analysis standard for water and wastewater systems. In doing so, we identify gaps between practices recommended in the standard and the state of the art. While individual processes found within infrastructure risk analysis standards have been evaluated in the past, we present a foundational review and focus specifically on water systems. By highlighting both the conceptual shortcomings and practical limitations, we aim to prioritize the shortcomings needed to be addressed. Key findings from this study include (1) risk definitions fail to address notions of uncertainty, (2) the sole use of “worst reasonable case” assumptions can lead to mischaracterizations of risk, (3) analysis of risk and resilience at the threat‐asset resolution ignores dependencies within the system, and (4) stakeholder values need to be assessed when balancing the tradeoffs between risk reduction and resilience enhancement.
Stories for Interdisciplinary Disaster Research Collaboration
Authors: Moezzi Mithra , Peek Lori 
Published: November 2019

AbstractWhat if we used the stories that researchers and practitioners tell each other as tools to advance interdisciplinary disaster research? This article hypothesizes that doing so could foster a new mode of collaborative learning and discovery. People, including researchers, regularly tell stories to relate “what happened” based on their experience, often in ways that augment or contradict existing understandings. These stories provide naturalistic descriptions of context, complexity, and dynamic relationships in ways that formal theories, static data, and interpretations of findings can miss. They often do so memorably and engagingly, which makes them beneficial to researchers across disciplines and allows them to be integrated into their own work. Seeking out, actively inviting, sharing, and discussing these stories in interdisciplinary teams that have developed a strong sense of trust can therefore provide partial escape from discipline‐specific reasoning and frameworks that are so often unconsciously employed. To develop and test this possibility, this article argues that the diverse and rapidly growing hazards and disaster field needs to incorporate a basic theoretical understanding of stories, building from folkloristics and other sources. It would also need strategies to draw out and build from stories in suitable interdisciplinary research forums and, in turn, to find ways to incorporate the discussions that emanate from stories into ongoing analyses, interpretations, and future lines of interdisciplinary inquiry.
Effects of cognitive behavioural therapy and return‐to‐work intervention for patients on sick leave due to stress‐related disorders: Results from a randomized trial
Authors: Salomonsson Sigrid , Santoft Fredrik , Lindsäter Elin , Ejeby Kersti , Ingvar Martin , Ljótsson Brjánn , Öst Lars‐Göran , Lekander Mats , Hedman‐Lagerlöf Erik 
Published: November 2019

The aim of this study was to evaluate specific effects for patients with adjustment or exhaustion disorder, the Stress subgroup (n = 152), regarding symptom severity and sick leave after CBT, a return‐to‐work intervention (RTW‐I), and a combination of them (COMBO), using data from a randomized trial. In the original study, primary care patients on sick leave (N = 211) were randomized to CBT (n = 64), RTW‐I (n = 67), or COMBO (n = 80). Blinded Clinician Severity Rating (CSR) of symptoms and sick leave registry data were primary outcomes. Subgroup analyses showed that for the Stress subgroup, CBT led to greater reduction of symptoms than RTW‐I posttreatment, but COMBO did not differ from CBT or RTW‐I. Regarding sick leave, there was no difference between treatments in the Stress subgroup. An exploratory analysis of the treatment effects in a subgroup of patients with depression, anxiety or insomnia indicates that RTW‐I reduced sick leave faster than CBT. We conclude that CBT may be promising as an effective treatment of stress and exhaustion disorder.
Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities
Authors: Haroz Emily E. , Walsh Colin G. , Goklish Novalene , Cwik Mary F. , O’Keefe Victoria , Barlow Allison 
Published: November 2019

ObjectiveSuicide prevention is a major priority in Native American communities. We used machine learning with community‐based suicide surveillance data to better identify those most at risk.MethodThis study leverages data from the Celebrating Life program operated by the White Mountain Apache Tribe in Arizona and in partnership with Johns Hopkins University. We examined N = 2,390 individuals with a validated suicide‐related event between 2006 and 2017. Predictors included 73 variables (e.g., demographics, educational history, past mental health, and substance use). The outcome was suicide attempt 6, 12, and 24 months after an initial event. We tested four algorithmic approaches using cross‐validation.ResultsArea under the curves ranged from AUC = 0.81 (95% CI ± 0.08) for the decision tree classifiers to AUC = 0.87 (95% CI ± 0.04) for the ridge regression, results that were considerably higher than a past suicide attempt (AUC = 0.57; 95% CI ± 0.08). Selecting a cutoff value based on risk concentration plots yielded 0.88 sensitivity, 0.72 specificity, and a positive predictive value of 0.12 for detecting an attempt 24 months postindex event.ConclusionThese models substantially improved our ability to determine who was most at risk in this community. Further work is needed including developing clinical guidance and external validation.
What you say, and how you say it: Preschoolers' growth in vocabulary and communication skills differentially predict kindergarten academic achievement and self‐regulation
Authors: Ramsook K. Ashana , Welsh Janet A. , Bierman Karen L. 
Published: November 2019

AbstractThe idea that language skills support school readiness, predicting later self‐regulation and academic success, is widely accepted. Although vocabulary is often emphasized in the developmental literature, the ability to use language appropriately in the classroom, or social communication skills, may also be critical. This article examined longitudinal contributions of children's vocabulary and social communication skills, from preschool to kindergarten, to kindergarten academic achievement (reading and math) and self‐regulation (executive functions and learning behaviors). Participants were 164 children (14% Latinx, 30% Black, 56% White; 57% girls) enrolled in Head Start programs. Results revealed that initial levels and growth in vocabulary and communication skills predicted better academic achievement. Social communication skills uniquely predicted self‐regulation, after accounting for vocabulary. We discuss potential mechanisms for these links and recommend that strategies to build social communication skills be incorporated in preschool interventions promoting school readiness.
Hypothesis Testing in the Bayesian Framework
Authors: Shikano Susumu 
Published: November 2019

AbstractWhile the Bayesian parameter estimation has gained a wider acknowledgement among political scientists, they seem to have less discussed the Bayesian version of hypothesis testing. This paper introduces two Bayesian approaches to hypothesis testing: one based on estimated posterior distributions and the other based on Bayes factors. By using an example based on a linear regression model, I demonstrate similarities and differences not only between the null‐hypothesis significance tests and Bayesian hypothesis tests, but also those among two different Bayesian approaches, which are also critically discussed.
How to Explain Behavior?
Authors: Gigerenzer Gerd 
Published: November 2019

AbstractUnlike behaviorism, cognitive psychology relies on mental concepts to explain behavior. Yet mental processes are not directly observable and multiple explanations are possible, which poses a challenge for finding a useful framework. In this article, I distinguish three new frameworks for explanations that emerged after the cognitive revolution. The first is calledtools‐to‐theories: Psychologists' new tools for data analysis, such as computers and statistics, are turned into theories of mind. The second proposesas‐iftheories: Expected utility theory and Bayesian statistics are turned into theories of mind, describing an optimal solution of a problem but not its psychological process. The third studies theadaptive toolbox(formal models of heuristics) that describes mental processes in situations of uncertainty where an optimal solution is unknown. Depending on which framework researchers choose, they will model behavior in either situations of risk or of uncertainty, and construct models of cognitive processes or not. The frameworks also determine what questions are asked and what kind of data are generated. What all three frameworks have in common, however, is a clear preference for formal models rather than explanations by general dichotomies or mere verbal concepts. The frameworks have considerable potential to inform each other and to generate points of integration.
Decellularized pig pulmonary heart valves—Depletion of nucleic acids measured by proviral PERV pol
Authors: Godehardt Antonia W. , Ramm Robert , Gulich Barbara , Tönjes Ralf R. , Hilfiker Andres 
Published: November 2019

AbstractBackgroundDecellularized human pulmonary heart valve (dhHV) scaffolds have been shown to be the gold standard especially for younger, adolescent patients. However, human heart valves are limited in availability. Xenogeneic decellularized pig heart valves (dpHV) may serve as alternative.MethodsThe efficacy of DNA reduction processes upon decellularization of heart valves from German Landrace pigs was analyzed by measurements of remaining nucleic acids including proviral porcine endogenous retrovirus (PERV) sequences. Porcine pulmonary heart valves (pPHV) were decellularized by three different protocols and further treated with DNaseI or Benzonase, at varying incubation times. DNA isolated from valve associated muscle (m), valve cusp (c), and pulmonary artery (pa) was monitored by PCR and qRT‐PCR using GAPDH and the PERV polymerase (pol) for read‐out.ResultsDecellularization of pPHV led to a significant reduction of DNA (>99%) which could be further significantly increased for (m) and (pa) by nuclease treatment, reducing proviral PERV pol from approximately 5 × 107 to 5 × 103 copies/mg in nuclease treated tissues.ConclusionsBoth nucleases demonstrated comparable activities. But DNaseI revealed to be less consistent for PERV, especially at muscular tissue. Noteworthy, remaining proviral sequences are still detectable by PCR; however, due to the absence of the cellular replication machinery the production of infectious particles is not expected. Decellularization and nuclease treatment of pPHV is an efficient procedure to reduce the DNA content including PERV, thus represents a valuable option to increase virus safety independently from the source animal background.
Product Sustainable Design: A Review From the Environmental, Economic, and Social Aspects
Authors: He Bin , Li Fangfang , Cao Xiaoyang , Li Tengyu 
Published: November 2019

AbstractAs a global concern, the sustainability of a product is the responsibility for manufacturing. Product design has become one of the sources and core drivers for manufacturing competition, and the international competitiveness of products would mostly depend on product design capabilities. The product design has essential and profound impacts on the manufacturing, and thus, many researchers focus on product design and make varies of contributions in this area. Product sustainable design is a design process for a product with the consideration of environmental, economic, and social sustainability during the product entire life cycle. The result of product sustainable design is the creation of products with high sustainability of environmental, economic, and social aspects. This paper reviews the state of the art in the product sustainable design methodologies and tools from the perspective of environmental, economic, and social aspects. For the environmental perspective, design for environment methodologies and tools would enable products in a more environmentally friendly manner in the manufacturing. For the economic perspective, this paper introduces the design methodologies for the economic sustainability with cost, assembly, manufacture, and supply chain. For the social perspective, this paper introduces sustainable social design and social responsibility design for social sustainability and social sustainability through social intervention and social innovation. In addition, it encourages future works.
Origami-Layer-Jamming Deployable Surgical Retractor With Variable Stiffness and Tactile Sensing
Authors: Banerjee Hritwick , Li Tai Kai , Ponraj Godwin , Kirthika Senthil Kumar , Lim Chwee Ming , Ren Hongliang 
Published: November 2019

Abstract
Origami-based flexible, compliant, and bio-inspired robots are believed to permit a range of medical applications within confined environments. In this article, we experimentally demonstrated an origami-inspired deployable surgical retractor with the controllable stiffness mechanism that can facilitate safer instrument–tissue interaction in comparison to their rigid counterparts. When controllable negative-pressure is applied to the jammed origami retractor module, it becomes more rigid, increasing its strength. To quantify origami-modules strength further, we demonstrated performances of retractor based on the Daler–Rowney Canford paper (38 grams per square meter (gsm)) and sandpaper of 1000 grit. Experiments on the proposed retractor prototype elucidated sandpaper-based retractor can outperform paper-38-gsm retractor for facelift incision with the width of more than 9 cm. Though 38 gsm Canford paper comprised of thin layers, 16 times lesser in thickness than sandpaper, experiments proved its comparable layer jamming (LJ) performance. We leverage the advantage of the LJ mechanism to tune retractor stiffness, allowing the instrument to hold and separate a facelift incision to mitigate the likelihood of surgical complications. The retractor is equipped with a custom-made printed conductive ink-based fabric piezoresistive tactile sensor to assist clinicians with tissue-retractor interaction force information. The proposed sensor showed a linear relationship with the applied force and has a sensitivity of 0.833 N−1. Finally, cadaver experiments exhibit an effective origami-inspired surgical retractor for assisting surgeons and clinicians in the near future.
Kinematic Geometry Description of a Line With Four Positions and Its Application in Dimension Synthesis of Spatial Linkage
Authors: Lin Song , Wang Hanchao , Zhang Yu , Jiang Jingyu 
Published: November 2019

Abstract
In the dimension synthesis of the spatial linkages, the geometric characteristics of floating links in mechanisms reveal the geometric relationship between the motion task and the dimensions of the mechanisms. In order to establish the kinematic geometry rules corresponding to the motion of the floating link, this paper transforms the kinematic problems of geometric elements on the floating link into geometric problems and uses the geometric procedure to solve the spatial linkages synthesis problem with four given positions. In a previous work, we have extracted the kinematic geometry rules of a line with two and three positions. However, as the number of task positions increases, the kinematic characteristics representing the position transformation become more complicated. The method proposed in this paper extends the previous work to four given positions and builds up the geometric relationships among the kinematic rules for two, three, and four positions. The establishment of this geometric relationship is helpful to unify the synthesis procedure of synthesis problems with different number of positions. After that, the two-plane projection system and the transformation of projection are introduced to establish a procedural graphical synthesis method.
Modelling the Effects of Reliability and Maintenance on Levelised Cost of Wind Energy
Authors: Dao Cuong D. , Kazemtabrizi Behzad , Crabtree Christopher J. 
Published: November 2019

Abstract
Levelised cost of energy is an important measure to evaluate the success of a wind energy project. It includes all the capital and operational expenditures of a wind turbine over its lifetime based on the expected power generated. In the literature, wind turbine reliability is largely neglected in levelised cost of energy estimation. This paper presents a model to evaluate levelised cost of energy while considering the reliability and maintenance of wind turbine subassemblies. The key concept behind this model is that the failure rate of a wind turbine subassembly depends on the preventive maintenance spending. The proposed model makes it possible to relate reliability data, such as failure rate and downtime of wind turbine subassemblies, to the operation and maintenance expenditure, as well as the annual energy production. The model is analysed using a sample set of recently published reliability data and it is observed that both the operation and maintenance expenditure and levelised cost of energy are convex functions of the subassembly’s failure rate and the preventive maintenance spending. This study can help wind turbine manufacturers and operators identify the level of reliability improvement and maintenance investment required to minimise the levelised cost of energy.