bluuebunny/crossref_metadata_2025 huggingface dataset:
Establishing the Ideal Conditions to Create an Airway Fire Using a Porcine Airway Model
Authors: Bysice Andrew M. , Oswald Tyler , Vasquez Luis E. Mendoza , Laxague Francisco , Elise Graham M. , Noppens Ruediger , Fung Kevin
Published: February 2023

Objective

Airway fires are a rare but devastating complication of airway surgery. Although protocols for managing airway fires have been discussed, the ideal conditions for igniting airway fires remain unclear. This study examined the oxygen level required to ignite a fire during a tracheostomy.

Study Design

Porcine Model.

Setting

Laboratory.

Methods

Porcine tracheas were intubated with a 7.5 air‐filled polyvinyl endotracheal tube. A tracheostomy was performed. Monopolar and bipolar cautery were used in independent experiments to assess the ignition capacity. Seven trials were performed for each fraction of inspired oxygen (FiO2): 1.0, 0.9, 0.7, 0.6, 0.5, 0.4, and 0.3. The primary outcome was ignition of a fire. The time was started once the cautery function was turned on. The time was stopped when a flame was produced. Thirty seconds was used as the cut‐off for “no fire.”

Results

The average time to ignition for monopolar cautery at FiO2 of 1.0, 0.9, 0.8, 0.7, and 0.6 was found to be 9.9, 6.6, 6.9, 9.6, and 8.4 s, respectively. FiO2 ≤ 0.5 did not produce a flame. No flame was created using the bipolar device. Dry tissue eschar shortened the time to ignition, whereas moisture in the tissue prolonged the time to ignition. However, these differences were not quantified.

Conclusion

Dry tissue eschar, monopolar cautery, and FiO2 ≥ 0.6 are more likely to result in airway fires.
Disconnection from nature: Expanding our understanding of human–nature relations
Authors: Beery Thomas , Stahl Olafsson Anton , Gentin Sandra , Maurer Megan , Stålhammar Sanna , Albert Christian , Bieling Claudia , Buijs Arjen , Fagerholm Nora , Garcia‐Martin Maria , Plieninger Tobias , M. Raymond Christopher
Published: February 2023

The human relationship with nature is a topic that has been explored throughout human history. More recently, the idea of connection to nature has merged as an important transdisciplinary field of study. Despite increased scholarly attention to connection to nature, the notion of disconnection from nature remains undertheorized and understudied.

In this perspective article, we argue for a more comprehensive understanding of disconnection from nature to strengthen theories of human‐nature relationships that goes beyond individual relationships and considers social and collective factors of disconnection, including institutional, socio‐cultural and power dimensions.

Drawing on case insights, we present the ‘wheel of disconnection’ to illustrate how disconnections from nature manifest across individual or societal meaning‐making processes, thereby problematizing existing research that seeks to create dualisms between human positive and negative impacts on the environment in isolation from cultural or political contexts.

We do not seek to discount research or important practical efforts to foster an individual's connection to nature by elevating disconnection. Instead, we hope that creating greater awareness and understanding of disconnection will be able to guide opportunities going forward for strengthening a connection to nature along a continuum from the individual to the social.

Read the freePlain Language Summaryfor this article on the Journal blog.
Thermochromic and temperature regulation properties of phase change materials derived from polymer composites loaded with VO2 particles
Authors: Srirodpai Onruthai , Wootthikanokkhan Jatuphorn , Nawalertpanya Saiwan
Published: February 2023

This research work concerns the development of phase change materials based on poly(ethylene terephthalate) (PET) mixed with vanadium dioxide particles (VO2). Monoclinic VO2 particles were synthesized via a hydrothermal process followed by modification of the particle surface with a silane coupling agent, namely 3‐(aminopropyl) triethoxysilane (APTES). After that, the metal oxide particles were mixed with PET flakes, derived from used PET bottles, in an internal mixer. In this study, the effects of surface modification and concentrations of VO2 particles on thermomechanical and optical properties of the composites were investigated. From the DSC results, it was found that the percentage crystallinity of PET increased after loading with VO2. This indicates that VO2 can act as a nucleating agent inducing the crystallization of the PET chains. From the scanning electron microscopy with energy dispersive X‐ray spectroscopy (SEM–EDX) images, better dispersion of VO2 particles in the PET matrix was noticed when the surface‐modified VO2 particles were used as a replacement for the normal VO2 particles. This contributed to the greater percentage of visible light transmittance through the composite sheets. Thermogravimetric analysis (TGA) profiles of both PET and PET/VO2 specimens are similar, indicating that the VO2 did not accelerate the thermal decomposition of the PET. When the specimens were exposed to an IR lamp, it was found that the temperature behind the PET/VO2 sheets dropped when compared to that of the neat PET sheets. Some shrinkage and deformation of the PET/VO2 specimens were also noticed. The higher the VO2 loading, the greater the deformation. The effect was ascribed to the fact that the actual temperature was above the critical transition temperature of VO2. Consequently, the NIR transmittance was suppressed at the expense of the greater NIR absorbance by the VO2 phase in the composites. Finally, when the specimens were exposed to solar irradiation at ambient temperature, the temperature underneath the PET/VO2 specimens became greater than that of the control specimen (the neat PET). The result was discussed in the light of the emissivity of VO2 at ambient temperature, which is considerably low. Overall, this study demonstrates that PET/VO2 can exhibit both cooling and warming effects, depending on the ambient temperature in relation to the critical transition temperature (Tc) of VO2.
Cepacia syndrome in cystic fibrosis: A systematic review of the literature and possible new perspectives in treatment
Authors: Daccò Valeria , Alicandro Gianfranco , Consales Alessandra , Rosazza Chiara , Sciarrabba Calogero S. , Cariani Lisa , Colombo Carla
Published: February 2023

Background

Cepacia syndrome (CS) is an acute, necrotizing pneumonia with a high mortality rate, occurring in patients with cystic fibrosis (CF) infected with Burkholderia cepacia complex (BCC). Due to its low incidence, data on this condition are limited.

Methods

We conducted a systematic review of the reported cases of CS by searching MEDLINE, Embase and the Cochrane Library to improve knowledge of this rare but potentially lethal condition.

Results

We included 15 eligible articles, describing 18 cases (9 females) of CS. Median age at onset was 22 years (range: 10–60 years); median time to CS after first infection by BCC was 5 years (range: 1–26 years). Burkholderia cenocepacia was the most frequently reported causative agent. All patients received intravenous antibiotic treatment (most frequently including cotrimoxazole), while inhaled antibiotics were used in five patients (27.8%). Immunosuppressant agents were the most commonly prescribed supportive treatment (n = 7, 38.9%). Half of the patients died (9/18, 50%).

Conclusions

This study describes epidemiological, clinical characteristics, and prognosis of CS cases reported over the last 24 years. CS is a rare yet severe complication of BCC infection in patients with CF, which occurs several years after BCC colonization and has a negative outcome in 50% of the patients. Data are too scanty to identify the most effective therapeutic approach.
A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data
Authors: Wang Bowen , Sun Jianzhong , Chen Dan , Ning Shungang
Published: February 2023

The data measured by the pitot tube (PT) is related to airspeed indication and flight safety. However, there are few methods to solve the problem of failure prediction and predictive maintenance of PT. This study proposes a method for predicting the remaining useful life (RUL) of the PT based on dynamic operating data. First, an exposure index (EI) characterizing the severity level of operating conditions is proposed based on multiple dynamic operating and environmental parameters, then a cumulative exposure model (CEM) is developed to calculate the cumulative exposure. Dynamic covariate information is incorporated into the time‐to‐failure distribution of PT through the CEM, the RUL distribution of the individual PT is then obtained by Monte Carlo simulation. A case study is carried out based on actual data of a commercial aircraft fleet and shows that the proposed CEM can effectively use historical aircraft environmental information and truncated failure data of the components. The relative error of the remaining life prediction is substantially improved compared to the traditional reliability analysis methods, which means that the proposed model can provide more reliable RUL results based on historical operating conditions, providing better support for PT risk assessment and condition‐based maintenance decisions.
Subcutaneous and muscle layer seroma complicated with thoracentesis
Authors: Sekimoto Yasuhito , Kohmaru Makiko , Okuma Tomoko , Tajima Manabu , Sekiya Mitsuaki
Published: February 2023

Key message

Diagnostic thoracentesis is a basic and relatively safe diagnostic method for patients with pleural effusion. However, complications of thoracentesis are rare and not well known because of the low incidence. Herein, we report a case of subcutaneous and muscle layer seroma following thoracentesis.
Identifying important gene signatures of BMI using network structure‐aided nonparametric quantile regression
Authors: Wu Peitao , Dupuis Josée , Liu Ching‐Ti
Published: February 2023

We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of the outcome and rely on unmet assumptions. To address these concerns, we propose a method for nonparametric additive quantile regression with network regularization to incorporate the information encoded by known networks. To account for nonlinear associations, we approximate the unknown additive functional effect of each predictor with the expansion of a B‐spline basis. We implement the group Lasso penalty to obtain a sparse model. We define the network‐constrained penalty by the total norm of the difference between the effect functions of any two linked genes in the known network. We further propose an efficient computation procedure to solve the optimization problem that arises in our model. Simulation studies show that our proposed method performs well in identifying more truly associated genes and less falsely associated genes than alternative approaches. We apply the proposed method to analyze the microarray gene‐expression dataset in the Framingham Heart Study and identify several 75 percentile BMI associated genes. In conclusion, our proposed approach efficiently identifies the outcome‐associated variables in a nonparametric additive quantile regression framework by leveraging known network information.
Robust and Temperature‐Sensitive Hydrogels for High‐Efficiency Water Harvesting Under Low Solar Energy Radiation
Authors: Wang Meng , Sun Zhiqiang , Zhou Jianhua , Chen Yulian , Mu Xiaojiang , Zhang Zhixiang , Wang Xiaoyang , Liu Jing , Gao Jie , Liu Wenping , Miao Lei
Published: February 2023

Solar‐driven evaporation using hydrogels and photothermal materials is a promising freshwater harvesting technology. Clean water is generally collected through evaporation and subsequent condensation, which requires high energy input due to the inherent high vaporization enthalpy of water. Therefore, it is a great challenge to harvest fresh water efficiently under natural irradiation. Herein, a temperature‐sensitive polyacrylamide‐poly(N‐isopropylacrylamide) (A‐PNIPAm) gel is designed to pursue a high water collection rate under low energy input conditions, where the facile and reversible hydrophilic/hydrophobic transition in gels enables the quick acquisition of liquid water. A multifunctional hydrogel (ADS‐PNIPAm) is prepared using polydopamine and sodium alginate with excellent adsorption/filtration properties, which can remove pollutants and generate fresh water rapidly. Consequently, the water collection rate of the ADS‐PNIPAm hydrogel reaches up to 5.89 and 9.8 kg m−2 h−1 under 0.6 and 1 sun irradiation, respectively, which are superior to the previously reported values. Furthermore, ADS‐PNIPAm displays an excellent effect on purifying sewage, such as oils, algae, and dyes pollutants. ADS‐PNIPAm is a promising material for a rapid freshwater generation with solar irradiation only, which provides a new avenue to alleviate water source scarcity.
Effects of S Content on Inclusion Formation in the Al and Ti–Mg Complex Deoxidized Steel
Authors: Jin Pengliang , Yang Yiming , Cao Lei , Yuan Xinghu , Wang Guocheng
Published: February 2023

Two groups of Al and Ti–Mg complex deoxidized steels with different S contents are designed, and inclusion characteristics of two groups of steel samples are observed by field‐emission scanning electron microscopy–energy‐dispersive spectroscopy. The results show that there are single TiS inclusions, MgAl2O4 (Al2O3)–TiS, and MgAl2O4–TiN–TiS complex inclusions in No. 1 steel (low‐sulfur content) and No. 2 steel (high‐sulfur content). However, there are also complex inclusions containing MnS in the No. 2 steel but not in No. 1 steel. In order to reveal the precipitation mechanism of MnS, equilibrium phase of inclusion from 1873 K to liquidus temperature is further analyzed, and the mass fractions of different inclusions from liquidus to solidus temperature are quantitatively calculated using the element segregation model combined with FactSage 7.2 thermodynamic software. Furthermore, the mismatch values between different crystal planes of MnS, TiS, and TiN are calculated. The results show that MnS (110) is most likely to precipitate on TiS (001), which is consistent with the observation that there is TiS–MnS interface in the complex inclusions containing MnS in No. 2 steel. This study could be helpful to the controlling sulfide precipitation in Al and Ti–Mg complex deoxidized steel.
Optogenetics and Light-Sheet Microscopy
Authors: Maddalena Laura , Pozzi Paolo , Ceffa Nicolò G. , Hoeven Bas van der , Carroll Elizabeth C.
Published: February 2023

Light-sheet microscopy is a powerful method for imaging small translucent samples in vivo, owing to its unique combination of fast imaging speeds, large field of view, and low phototoxicity. This chapter briefly reviews state-of-the-art technology for variations of light-sheet microscopy. We review recent examples of optogenetics in combination with light-sheet microscopy and discuss some current bottlenecks and horizons of light sheet in all-optical physiology. We describe how 3-dimensional optogenetics can be added to an home-built light-sheet microscope, including technical notes about choices in microscope configuration to consider depending on the time and length scales of interest.
Widefield Multiphoton Imaging at Depth with Temporal Focusing
Authors: Wijesinghe Philip , Dholakia Kishan
Published: February 2023

Optical imaging has the potential to reveal high-resolution information with minimal photodamage. The recent renaissance of super-resolution, widefield, ultrafast, and computational imaging methods has broadened its horizons even further. However, a remaining grand challenge is imaging at depth over a widefield and with a high spatiotemporal resolution. This achievement would enable the observation of fast collective biological processes, particularly those underpinning neuroscience and developmental biology. Multiphoton imaging at depth, combining temporal focusing and single-pixel detection, is an emerging avenue to address this challenge. The novel physics and computational methods driving this approach offer great potential for future advances. This chapter articulates the theories of temporal focusing and single-pixel detection and details the specific approach of TempoRAl Focusing microscopy with single-pIXel detection (TRAFIX), with a particular focus on its current practical implementation and future prospects.
The Suitability of Different Training Images for Producing Low Connectivity, High Net:Gross Pixel-Based MPS Models
Authors: Walsh Deirdre A. , López-Cabrera Javier , Manzocchi Tom
Published: February 2023

Pixel-based multiple-point statistical (MPS) modelling is an appealing geostatistical modelling technique as it easily honours well data and allows use of geologically-derived training images to reproduce the desired heterogeneity. A variety of different training image types are often proposed for use in MPS modelling, including object-based, surface-based and process-based models. The purpose of the training image is to provide a description of the geological heterogeneities including sand geometries, stacking patterns, facies distributions, depositional architecture and connectivity. It is, however, well known that pixel-based MPS modelling has difficulty reproducing facies connectivity, and this study investigates the performance of a widely-available industrial SNESIM algorithm at reproducing the connectivity in a geometrically-representative, idealized deep-water reservoir sequence, using different gridding strategies and training images. The findings indicate that irrespective of the sand connectivity represented in the training image, the MPS models have a percolation threshold that is the same as the well-established 27% percolation threshold of random object-based models. A more successful approach for generating poorly connected pixel-based MPS models at high net:gross ratios has been identified. In this workflow, a geometrical transformation is applied to the training image prior to modelling, and the inverse transformation is applied to the resultant MPS model. The transformation is controlled by a compression factor which defines how non-random the geological system is, in terms of its connectivity.
Artifacts in Localised Multivariate Uniform Conditioning: A Case Study
Authors: Rondon Oscar , Talebi Hassan
Published: February 2023

Localised Multivariate Uniform Conditioning (LMUC) is a technique designed for spatially locating Selective Mining Unit (SMU) grades derived using Multivariate Uniform Conditioning (MUC) for the assessment of recoverable resources. LMUC has the advantage of producing SMU estimates conforming to the MUC panel-specific grade-tonnage curves while preserving the spatial grade distribution at the selective mining level. However, LMUC results have two severe artifacts. This paper documents both artifacts using four grades from a large nickel–cobalt laterite deposit in Western Australia.
Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling
Authors: de Castro Moreira Gabriel , Costa João Felipe Coimbra Leite , Marques Diego Machado
Published: February 2023

Machine learning is a broad field of study that can be applied in many areas of science. In mining, it has already been used in many cases, for example, in the mineral sorting process, in resource modeling, and for the prediction of metallurgical variables. In this paper, we use for defining estimation domains, which is one of the first and most important steps to be taken in the entire modeling process. In unsupervised learning, cluster analysis can provide some interesting solutions for dealing with the stationarity in defining domains. However, choosing the most appropriate technique and validating the results can be challenging when performing cluster analysis because there are no predefined labels for reference. Several methods must be used simultaneously to make the conclusions more reliable. When applying cluster analysis to the modeling of mineral resources, geological information is crucial and must also be used to validate the results. Mining is a dynamic activity, and new information is constantly added to the database. Repeating the whole clustering process each time new samples are collected would be impractical, so we propose using supervised learning algorithms for the automatic classification of new samples. As an illustration, a dataset from a phosphate and titanium deposit is used to demonstrate the proposed workflow. Automating methods and procedures can significantly increase the reproducibility of the modeling process, an essential condition in evaluating mineral resources, especially for auditing purposes. However, although very effective in the decision-making process, the methods herein presented are not yet fully automated, requiring prior knowledge and good judgment.
Addressing Application Challenges with Large-Scale Geological Boundary Modelling
Authors: Ball Adrian , Zigman John , Melkumyan Arman , Chlingaryan Anna , Silversides Katherine , Leung Raymond
Published: February 2023

For banded iron formation-hosted deposits accurate boundary modelling is critical to ore-grade estimation. Key to estimation fidelity is the accurate separation of the different domains within the ore body, requiring modelling of the boundaries between domains. This yields both theoretical and application challenges. We present a series of solutions for application challenges that arise when modelling large-scale boundaries employing a composition of Gaussian Process models on exploration and production hole data. We demonstrate these in the banded iron formation-hosted iron ore deposits in the Hamersley Province of Western Australia. We present solutions to several challenges: the inclusion of information derived from a geologist-defined boundary estimate to incorporate domain knowledge in data sparse regions, the incorporation of unassayed production holes that are implicitly defined as waste to augment production hole assay data, and a more holistic method of defining regional bounds and spatial rotations for Gaussian Process modelling of local spaces. Solution are evaluated against a range of metrics to show performance improvements over the manually performed estimation by an expert geologist of the boundaries delineating the ore body domains. Reconcilliation scores are used for evaluating the quality of predicted domain boundaries against measured production data. The predicted and in situ surfaces are also qualitatively evaluated against production data to ensure that the models were evaluated to be geologically sound by an expert in the field. In particular, better fidelity is shown when separating mineralised and non-mineralised ore, consequently improving the estimation of the ore-grades present in the mine site.
Simulation of Stationary Gaussian Random Fields with a Gneiting Spatio-Temporal Covariance
Authors: Allard Denis , Emery Xavier , Lacaux Céline , Lantuéjoul Christian
Published: February 2023

The nonseparable Gneiting covariance has become a standard to model spatio-temporal random fields. Its definition relies on a completely monotone function associated with the spatial structure and a conditionally negative semidefinite function associated with the temporal structure. This work addresses the problem of simulating stationary Gaussian random fields with a Gneiting-type covariance. Two algorithms, in which the simulated field is obtained through a combination of cosine waves are presented and illustrated with synthetic examples. In the first algorithm, the temporal frequency is defined on the basis of a temporal random field with stationary Gaussian increments, whereas in the second algorithm the temporal frequency is drawn from the spectral measure of the covariance conditioned to the spatial frequency. Both algorithms perfectly reproduce the correlation structure with minimal computational cost and memory footprint.
Geometric and Geostatistical Modeling of Point Bars
Authors: Dawuda Ismael , Srinivasan Sanjay
Published: February 2023

Point bar reservoir geology is frequently encountered in oil and gas developments worldwide. Furthermore, point bar geology is encountered in many sites being considered for large scale CO2 injection for sequestration. A comprehensive modeling method that adequately preserves point bar internal architecture and its associated heterogeneities is still not available. Traditional geostatistical methods cannot adequately capture the curvilinear architecture of point bars. Even geostatistical simulation techniques that can be constrained to multiple point statistics cannot capture the architecture of the point bars because they use regular grids to represent the heterogeneity. If heterogeneities like the thinly distributed shale drapes within the point bar are represented using an extremely fine mesh, the computational cost for performing flow modeling escalates steeply. This paper proposes a modeling method that preserves the point bar internal architecture and heterogeneities, without these limitations. The modeling method incorporates a gridding scheme that adequately captures the point bar architecture and heterogeneities, without huge computational costs.
Application of Reinforcement Learning for Well Location Optimization
Authors: Dawar Kshitij , Srinivasan Sanjay , Webster Mort D.
Published: February 2023

The extensive deployment of sensors in oilfield operation and management has led to the collection of vast amounts of data, which in turn has enabled the use of machine learning models to improve decision-making. One of the prime applications of data-based decision-making is the identification of optimum well locations for hydrocarbon recovery. This task is made difficult by the relative lack of high-fidelity data regarding the subsurface to develop precise models in support of decision-making. Each well placement decision not only affects eventual recovery but also the decisions affecting future wells. Hence, there exists a tradeoff between recovery maximization and information gain. Existing methodologies for placement of wells during the early phases of reservoir development fail to take an abiding view of maximizing reservoir profitability, instead focusing on short-term gains. While improvements in drilling technologies have dramatically lowered the costs of producing hydrocarbon from prospects and resulted in very efficient drilling operations, these advancements have led to sub-optimal and haphazard placement of wells. This can lead to considerable number of unprofitable wells being drilled which, during periods of low oil and gas prices, can be detrimental for a company’s solvency. The goal of the research is to present a methodology that builds machine learning models, integrating geostatistics and reservoir flow dynamics, to determine optimum future well locations for maximizing reservoir recovery. A deep reinforcement learning (DRL) framework has been proposed to address the issue of long-horizon decision-making. The DRL reservoir agent employs intelligent sampling and utilizes a reward framework that is based on geostatistical and flow simulations. The implemented approach provides opportunities to insert expert information while basing well placement decisions on data collected from seismic data and prior well tests. Effects of prior information on the well placement decisions are explored and the developed DRL derived policies are compared to single-stage optimization methods for reservoir development. Under similar reward framework, sequential well placement strategies developed using DRL have been shown to perform better than simultaneous drilling of several wells.
Compression-Based Modelling Honouring Facies Connectivity in Diverse Geological Systems
Authors: Manzocchi Tom , Walsh Deirdre A. , López-Cabrera Javier , Carneiro Marcus , Soni Kishan
Published: February 2023

In object- or pixel-based modelling, facies connectivity is tied to facies proportion as an inevitable consequence of the modelling process. However, natural geological systems (and rule-based models) have a wider range of connectivity behaviour and therefore are ill-served by simple modelling methods in which connectivity is an unconstrained output property rather than a user-defined input property. The compression-based modelling method decouples facies proportions from facies connectivity in the modelling process and allows models to be generated in which both are defined independently. The two-step method exploits the link between the connectivity and net:gross ratio of the conventional (pixel- or object-based) method applied. In Step 1 a model with the correct connectivity but incorrect facies proportions is generated. Step 2 applies a geometrical transform which scales the model to the correct facies proportions while maintaining the connectivity of the original model. The method is described and illustrated using examples representative of a poorly connected deep-water depositional system and a well-connected fluid-driven vein system.
Introduction: Distributed Learning Ecosystems. Concepts, Resources, and Repositories
Authors: Otto Daniel , Scharnberg Gianna , Kerres Michael , Zawacki-Richter Olaf
Published: February 2023

The incisive developments during the COVID-19 pandemic have made the importance of online learning for education in the digital age evident, and it is unlikely that this wheel will be turned back in the foreseeable future (Brown, 2021). First experiences show, for instance, that online learning can support the development of digital education and the practices of student engagement (Gourlay et al., 2021) and can lead to an openness towards learning innovation that was not present before the COVID-19 pandemic (Rapanta et al., 2021). However, as the term “online” learning implies, the internet has played and will play a key role in developing and distributing new forms of teaching and learning.
The Technical Specifications and Requirements for Connecting OER Repositories Using the LOM Standard
Authors: Abdel-Qader Mohammad , Saleh Ahmed , Tochtermann Klaus
Published: February 2023

One of the goals of creating Open Educational Resources (OER) is to increase their accessibility for more learners. Connecting the different repositories that provide that these OER use one standard can help achieve that goal. In this chapter, we give detailed specifications and requirements for connecting different OER repositories using the Learning Object Metadata (LOM) standard from a technical point of view. We define the used technical terms and show how the process is working at the back end. More specifically, for each stage of connecting repositories, starting from harvesting the metadata from those repositories to storing the processed data in files ready to be used in the front end, we describe the functional requirements, what technologies are needed, and how the process works. In this chapter, we will describe the process of connecting the OER repositories using the LOM standard from start to end as simply as possible. The idea is to allow non-technical staff to replicate such a process, or maybe some stages of it. Afterwards, we give some examples of the tools that may help in the process of harvesting data from the web. Some of these tools are visual and do not require any programming skills. Finally, we briefly describe the EduArc project, which connects OER repositories using the LOM standard.
A Trusted Learning Analytics Dashboard for Displaying OER
Authors: Ahmad Atezaz , Yordanov Ivaylo Ivanov , Yau Jane , Schneider Jan , Drachsler Hendrik
Published: February 2023

Learning Analytics (LA) consists of miscellaneous steps that include data harvesting, storing, cleaning, anonymisation, mining, analysis, and visualisation so that the vast amount of educational data is comprehensible and ethically utilisable by educators or instructors to obtain the advantages and benefits that LA can bring to the educational scene. These include the potential to increase learning experiences and reduce dropout rates. In this chapter, we shed light on OER repositories, LA, and LA dashboards and present an implementation of a research-driven LA dashboard for displaying OER and their repositories that allows the visualisation of educational data in an understandable way for both educators and learners. Moreover, we present an LA dashboard for displaying OER that shows information about the existing German OER repositories as part of our EduArc project located in Germany. The LA dashboard consists of multiple adopted indicators and metrics such as the number of reading sessions, duration of reading sessions, number of reading interruptions, number of learning activities, student attendance, and student grades. The details of the research methodology, including a literature review to create this dashboard, as well as the display items of the dashboard are presented and further elaborated.
Typologies of (Open) Online Courses and Their Dimensions, Characteristics and Relationships with Distributed Learning Ecosystems, Open Educational Resources, and Massive Open Online Courses
Authors: Stracke Christian M. , Bozkurt Aras , Burgos Daniel
Published: February 2023

This chapter analyses the different typologies of online courses. First, we start with a reflection about the key terms of online learning, online courses, and distributed learning ecosystems (DLE). In our literature review, we cannot identify any existing typology framework for online courses. Consequently, we analyse and compare dimensions and categories of online courses from different sources: first, from the collected publications and studies identified in our literature review, second, from the current practices and platforms for online courses, and third, from standards for online courses, including the first international quality norm for online learning ISO/IEC 40180. As our key result, a framework proposal for the different typologies of online courses is developed based on these discussions and a comparison of several dimensions. The integration of our comparison results leads to the Typologies of Online Courses (TOC) framework with eight dimensions. The aim of the TOC framework is two-fold. First, it should support designers in the design, quality development, and evaluation of online courses. Second, it should enable learners to differentiate online courses according to the dimensions of these courses in comparison with their own preferences and demands. In the conclusion, an outlook on future research needs is provided. Finally, we come full circle and briefly discuss how (open) online courses and especially the two currently most important types, namely, Open Educational Resources (OER) and Massive Open Online Courses (MOOCs), can contribute to DLE and to addressing the general need for (equity and collaborative) education for all.
Quality of OER: Test Theoretical Development and Validation of an Assessment Tool
Authors: Lübben Sonja , Müskens Wolfgang , Zawacki-Richter Olaf
Published: February 2023

Due to their dynamic development process, Open Educational Resources (OER) pose a special challenge regarding quality assurance. While there are many approaches to developing procedures for quality assurance, there is still a lack of suitable instruments to measure the quality of OER. Zawacki-Richter and Mayrberger (2017) integrated seven internationally used instruments for measuring the quality of OER into a comprehensive quality model for OER in higher education. Quality of OER is understood as a multidimensional construct within this model. The model formed the basis for the Instrument for Quality Assurance of OER (IQOER). The article reports on the empirical validation of the German version of this instrument. The validation included the analysis of interrater reliabilities, internal consistencies, and an estimation of construct validity operationalised as convergent validity with the MERLOT Peer Reviewer Report Form (California State University 2019). Furthermore, the particular importance of quality assurance of OER within distributed learning ecosystems is discussed. It is argued that to ensure the quality of OER, three conditions must be met: there must be practicable procedures for measuring quality; reliable and valid instruments for measuring quality must be used; and finally, the results of the quality measurement must be communicated to the users of the OER.
Keeping Each Other Safe: Young Refugees’ Navigation Towards a Good Life in Finland, Norway, and Scotland
Authors: Haswell Nick , Kaukko Mervi , Fylkesnes Marte Knag , Sullivan Paul
Published: February 2023

The metaphor of navigation has been used to investigate the social and moral movements people make in changeable or fluctuating circumstances, as well as to shed light on the intersection of people, practices and the changing contexts and social forces around them. In this chapter, we first provide a short overview of navigation as a metaphor, and how the situations of young refugees might add to the multiple meanings of navigation. Using empirical data from the international NordForsk-funded project Drawing Together: Relational wellbeing in the lives of young refugees in Finland, Norway and Scotland, we explore how young refugees socially and morally navigate through the complex and unstable circumstances of building new lives and new social networks in host countries. Then, turning to our findings, we discuss how ‘living well’ involves not only movement towards individual goals, but also movement with, for the sake of, and in relation to important people locally and transnationally. We conclude the chapter by envisioning the destination of young refugees’ navigation as hinted at by the data: a world worth living in for all.
Leading for Love, Life, Wisdom, and Voice in Steiner Schools: Constraints and Conditions of Possibility
Authors: Moller Virginia
Published: February 2023

This chapter interweaves two stories: the first is the story of Steiner education as one important voice in keeping focus on what matters in education and its transformative promise through the core pedagogical values of love, life, wisdom, and voice. The second story is based on the author’s autoethnographical research on leading practices of Steiner school principals over a period of major change and crisis in a Steiner school’s life. This research included the use of the theory of practice architectures to uncover unsustainable contradictions in the way we work in Steiner schools which constrain the full promise of the educational approach as an engine room for social change and renewal. These contradictions include doubt and uncertainty about the role of the principal and of leadership itself; and the depth of the emotional load of the principal and teachers in holding the competing ideological and pedagogical tensions of the Steiner and broader educational policy environment. Maintaining the integrity of the higher purposes of Steiner education involves leading practices which move away from the unsustainable tensions to encompass intentional hierarchy and healthy collaboration, and a repositioning of Steiner education from the margins to a legitimate part of a diverse educational mainstream.
Leading by Listening: Why Aboriginal Voices Matter in Creating a World Worth Living in
Authors: Burgess Catherine , Grice Christine , Wood Julian
Published: February 2023

Tolive well in a world worth living in for all Australians, Aboriginal voices should be central to Australian schooling. This is a radical shift from the current education policy, where Aboriginal-informed knowledge, leadership, and practices are peripheral. Through the lens of the theory of practice architectures, this chapter proposes that Aboriginal leading practices differ from many taken-for-granted Western leadership practices. Aboriginal leading practices—founded on deep listening, reciprocity, and respect are key to creating a world worth living in for all in Australian schools. Our findings are supported by the Aboriginal Voices Project systematic review that discovered that intercultural, collective approaches to leading in schools are more effective than transformational leadership models, bringing shared power and authority through trusting collaborations with local Aboriginal communities. The Culturally Nourishing Schooling Project, emerging from this research, prioritises relationship-building practices grounded in Aboriginal community-led practices that support cultural identity, curriculum, pedagogy, and whole-school reform.
Partnering for Hope: Agentic Narrative Practices Shaping a World Worth Living in
Authors: Morgan Sally
Published: February 2023

People seeking asylum in Australia remain subject to restrictive policies and punitive government practices, constraining their opportunities to live well. In this chapter, I focus on conversations that took place within the early stages of a critical participatory action research (CPAR) project involving members of the asylum-seeker owned Hope Co-operative. I posit a hybrid theoretical lens locating human subjectivity and agency in practice, layering the theory of practice architectures, Stetsenko’s transformative activist stance, and Emirbayer and Mische’s temporally embedded agency with past, future, and present orientations. Drawing on conversations between eight Hope Co-Operative asylum-seeking members and me, I trace some of these conversations’ cultural-discursive, material-economic, and social-political arrangements. I identify three types of talking—mapping solidarities, reminiscing and talking-up—and discuss these particular types of talking as agentic narrative practice comprising people’s mutual becoming. The study speaks to how particular relational arrangements and types of talking might counter systemic exclusion of people seeking asylum through the inherent radical agency of iterative and dialogic self- and world-making in practice.
Perturbative unitarity of strongly interacting massive particle models
Authors: Kamada Ayuki , Kobayashi Shin , Kuwahara Takumi
Published: February 2023

Abstract

Dark pion is a promising candidate for the strongly interacting massive particle dark matter. A large pion self-coupling mπ/fπ tends to be required for correct relic abundance, and hence the partial-wave amplitudes can violate the perturbative unitarity even for the coupling within naïve perturbative regime. We improve the partial-wave amplitudes in order to satisfy the optical theorem. We demonstrate that the improvement is relevant only for semi-relativistic pions, and thus this does not affect the self-scattering cross section at the cosmic structures. We also discuss the impact of the improvement of the πππ → ππ scattering process, and we find that there is an upper bound on mπ at which the correct relic abundance is never achieved even at large mπ/fπ due to the optical theorem.
Exact thermal correlators of holographic CFTs
Authors: Bhatta Atanu , Mandal Taniya
Published: February 2023

Abstract

We compute the exact retarded Green’s functions in thermal CFTs with chemical potential and angular momenta using holography respectively. We consider the field equations satisfied by the quasi-normal modes in both charged and rotating black holes in AdS spacetime and mapped them to the Heun equations by appropriate changes of variables. The AGT correspondence allows us to find the connection formulae among the solutions of the Heun equations near different singularities by using the crossing relations of the five-point correlators in the Liouville CFT. The connection formulae associated with the boundary conditions imposed on the bulk field equations yield the exact thermal correlators in the boundary CFT.
Die Neonatologie/Pädiatrische Intensivmedizin – ein attraktiver Arbeitsplatz?
Authors: Dresbach Till , Müller Andreas , Trepels-Kottek Sonja , Soff Johannes , Hoffmann Jan , Scholten Nadine
Published: February 2023

Hintergrund

Die Arbeitszufriedenheit wie auch das Wohlbefinden der ärztlichen und pflegerischen Mitarbeiter*innen sind zum einen relevant zur Mitarbeiter*innenbindung, aber auch zur Aufrechterhaltung einer qualitativ hochwertigen Patient*innenversorgung. Aufgrund des Personalmangels in der Neonatologie ist es wichtig, mehr über den aktuellen Stand der Arbeitsplatzzufriedenheit, der Work-Life Balance wie auch der Wechselabsichten zu erfahren.

Methode

Hierzu sind insgesamt 389 Ärzt*innen und Pflegekräfte befragt worden.

Ergebnisse

Insgesamt arbeiten 74 % der Ärzt*innen in Vollzeit (≥ 38 h/Woche) (Ärzte: 80 %, Ärztinnen: 69 %), wobei dies jedoch nur von 49 % der befragten Ärzt*innen (Ärzte: 57 %, Ärztinnen: 41 %) gewünscht ist. Mit 56 % an Ärzt*innen mit einem klinisch auffälligen WHO-5-Wohlbefindens Index zeigt sich auch hier Handlungsbedarf. Circa 44 % der befragten Ärzt*innen unter 60 Jahren planen, in den nächsten 5 Jahren ihren Arbeitgeber zu wechseln. Dies steht im signifikanten Zusammenhang zu Burn-out und Work-Life-Balance-Konflikten.

Diskussion

Insgesamt zeigen sich große Herausforderungen in der Vereinbarkeit von Arbeit und Familie und hierdurch der vermehrte Wunsch nach Teilzeit.
Infektionen des Rückenmarks und der angrenzenden Strukturen
Authors: Pfausler Bettina , Rass Verena , Lindner Anna
Published: February 2023

Eine Entzündung des Myelons und der angrenzenden Strukturen kann durch Viren, Bakterien, Pilze und Parasiten verursacht werden. Während Viren bevorzugt das Myelon und die Radizes direkt infizieren oder eine sekundäre Immunantwort triggern, neigen Bakterien, Pilze und Parasiten zur Bildung von Abszessen, Granulomen und Zysten und können wie destruierende Osteomyelitiden zu einer sekundären Myelonkompression führen. Die ätiologische Eingrenzung eines akuten/subakuten spinalen Prozesses erfolgt anhand der klinischen Präsentation, der zeitlichen Dynamik der Symptomentwicklung, des Immunstatus, der Bildgebung und mikrobieller/molekularbiologischer Untersuchungen von Liquor und Serum. Aufgrund des Tropismus einzelner Erreger zu bestimmten Faserstrukturen und Zellverbänden im Rückenmark kann in Zusammenschau mit der Klinik, der Bildgebung und der Expositionsanamnese oft bereits zeitnah eine fokussierte Abklärung und Diagnose erfolgen. In diesem Artikel wird auf wichtige Erreger einer spinalen/paraspinalen Infektion, deren geografisches Vorkommen und die klinische und bildgebende Präsentation unter besonderer Berücksichtigung der anatomisch-topografischen Lokalisation und aktueller epidemiologischer Entwicklungen eingegangen. Der Ausbruch von Poliomyelitiserkrankungen durch zirkulierende Impfstoffpolioviren (cVDPV) sei hier im Speziellen erwähnt.