bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis of castor oil/poly (N-vinyl-2-pyrrolidone)/ammonium persulfate hybrids emulsion as textile softeners
Authors: Fahmy H. M. , Amr A. 
Published: October 2023

AbstractTwo castor oil / poly (N-vinyl-2-pyrrolidone) / ammonium persulfate (CAO/PVP/APS) hybrids were synthesized via reaction of castor oil with PVP of molecular weights 40000 and 1300000 Dalton in presence of APS as an initiator. The optimum reaction conditions are: PVP/CAO weight ratio, 15%; PVP concentration, 60%; APS conc., 4% (based on weight of CAO); reaction temperature, 80 °C; reaction time, 60 min. The synthesized CAO/PVP/APS hybrids were characterized via FTIR analysis whereas their emulsions particle sizes were determined using TEM analysis. Incorporation of either of the aforementioned hybrids emulsions in the easy care finishing formulations of cotton fabric results in promoting of fabric softness, tensile strength, flexibility, and antibacterial activities of treated fabric along with reducing fabric wettabiliy and resiliency. Inclusion of Ag-NPs in the aforementioned finishing bath significantly improves extent of the antibacterial activities of treated fabric. The treated fabric morphology was examined using SEM as well as EDX analysis.
Evaluating connectivity models for conservation: insights from African lion dispersal patterns
Authors: Finerty Genevieve E. , Cushman Samuel A. , Bauer Dominik T. , Elliot Nicolas B. , Kesch M. Kristina , Macdonald David W. , Loveridge Andrew J. 
Published: October 2023

Abstract
Context
Evaluating connectivity and identifying corridors for protection is a central challenge in applied ecology and conservation. Rigorous validation and comparison of how approaches perform in capturing biological processes is needed to guide research and conservation action.

Objectives
We aim to compare the ability of connectivity surfaces optimised using home range and dispersal data to accurately capture lion movement during dispersal, using cost-distance and circuit theory approaches.

Methods
We delineate periods of dispersal in African lions (Panthera leo) to obtain movement trajectories of dispersing individuals across the Kavango Zambezi Transfrontier Conservation Area, southern Africa. We use these trajectories to assess comparative measures of connectivity values at dispersal points across surfaces and the ability of models to discriminate between observed and randomised paths.

Results
Encouragingly, results show that on average, all connectivity approaches and resistance surfaces used perform well in predicting movements of an independent set of dispersing lions. Cost-distance approaches were generally more sensitive to resistance input than circuit theory, but differences in performance measures between resistance inputs were small across both approaches.

Conclusions
Findings suggest that home range data can be used to generate resistance surfaces for connectivity maps in this system, with independent dispersal data providing a promising approach to thresholding what is considered as “connected” when delineating corridors. Most dispersers traversed through landscapes that had minimal human settlement and are likely highly connected by dispersal. Research into limiting factors and dispersal abilities will be critical to understanding how populations will respond to increasing habitat fragmentation and human expansion.
ECGDT: a graphical software tool for ECG diagnosis
Authors: Mondelo Víctor , Lado María J. , Méndez Arturo J. 
Published: October 2023

AbstractWhile cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven.
Rates of robust superlinear convergence of preconditioned Krylov methods for elliptic FEM problems
Authors: Castillo S. J. , Karátson J. 
Published: October 2023

AbstractThis paper considers the iterative solution of finite element discretizations of second-order elliptic boundary value problems. Mesh independent estimations are given for the rate of superlinear convergence of preconditioned Krylov methods, involving the connection between the convergence rate and the Lebesgue exponent of the data. Numerical examples demonstrate the theoretical results.
Renewing the social contract for education: A personal point of view on the UNESCO report
Authors: Nóvoa António 
Published: October 2023

AbstractHaving contributed to the UNESCO report Reimagining our futures together: A new social contract for education as a member of the International Commission and as Chair of the research-drafting committee of this commission, the author now takes the liberty of making some personal notes on the reflections advanced therein. This brief article is divided into three sections. The first presents the main foundations of the social contract of educational modernity (nineteenth century). The second discusses how this contract has to be renewed today. The third advocates the need to value the common in education, based on five main ideas: cooperation (in pedagogy), convergence (in curriculum), collaboration (in teaching), conviviality (in schools), and capillarity (in society). Finally, the epilogue defends the urgency of placing human rights at the center of the process of renewing the social contract for education.
Open networks of infinite server queues with non-homogeneous multivariate batch Poisson arrivals
Authors: Mehra Somya , Taylor Peter G. 
Published: October 2023

AbstractIn this paper, we consider the occupancy distribution for an open network of infinite server queues with multivariate batch arrivals following a non-homogeneous Poisson process, and general service time distributions. We derive a probability generating function for the transient occupancy distribution of the network and prove that it is necessary and sufficient for ergodicity that the expected occupancy time for each batch be finite. Further, we recover recurrence relations for the transient probability mass function formulated in terms of a distribution obtained by compounding the batch size with a multinomial distribution.
How can policy document mentions to scholarly papers be interpreted? An analysis of the underlying mentioning process
Authors: Yu Houqiang , Murat Biegzat , Li Jiatong , Li Longfei 
Published: October 2023

AbstractPolicy document mentions are useful for assessing the societal impact of scholarly papers. However, how policy document mentions can be interpreted is unclear yet. In this study, content analysis was used to examine features (mentioned element, mentioning form, and mentioning location) and motivations of policy document mention to scholarly papers. 885 policy documents were sampled for analysis from the Altmetric.com database. Results reveal that: (1) The mentioned elements of policy document mentions can be divided as five categories, summarized content (26.9%) is the most frequent one. (2) We found five types of the mentioning form of policy document mentions, the major mentioning form is references (72.3%). (3) The mentioning locations in policy documents can be divided into twelve categories, expounding (47.4%) and review (22.6%) are the core mentioning locations. (4) Motivation of policy document mentions can be broken down into five major categories and seventeen minor categories, more than 30% of motivations are to support the policy argument by listing relevant work. Analysis of the mention features of the policy document mentions gives us another way to understand how it works and how policy document mentions are motivated, with these findings we can do more work to find out the relationship between scientific articles and policy documents.
Heyting $$\kappa $$-Frames
Authors: Freytes Hector , Sergioli Giuseppe 
Published: October 2023

AbstractIn the framework of algebras with infinitary operations, the equational theory of $$\bigvee _{\kappa }$$

⋁
κ

-complete Heyting algebras or Heyting $$\kappa $$
κ
-frames is studied. A Hilbert style calculus algebraizable in this class is formulated. Based on the infinitary structure of Heyting $$\kappa $$
κ
-frames, an equational type completeness theorem related to the $$\langle \bigvee , \wedge , \rightarrow , 0 \rangle $$

⟨
⋁
,
∧
,
→
,
0
⟩

-structure of frames is also obtained.
Data-efficient model-based reinforcement learning with trajectory discrimination
Authors: Qu Tuo , Duan Fuqing , Zhang Junge , Zhao Bo , Huang Wenzhen 
Published: October 2023

AbstractDeep reinforcement learning has always been used to solve high-dimensional complex sequential decision problems. However, one of the biggest challenges for reinforcement learning is sample efficiency, especially for high-dimensional complex problems. Model-based reinforcement learning can solve the problem with a learned world model, but the performance is limited by the imperfect world model, so it usually has worse approximate performance than model-free reinforcement learning. In this paper, we propose a novel model-based reinforcement learning algorithm called World Model with Trajectory Discrimination (WMTD). We learn the representation of temporal dynamics information by adding a trajectory discriminator to the world model, and then compute the weight of state value estimation based on the trajectory discriminator to optimize the policy. Specifically, we augment the trajectories to generate negative samples and train a trajectory discriminator that shares the feature extractor with the world model. Experimental results demonstrate that our method improves the sample efficiency and achieves state-of-the-art performance on DeepMind control tasks.
What do you mean by organizational structure? Acknowledging and harmonizing differences and commonalities in three prominent perspectives
Authors: Albert Daniel 
Published: October 2023

AbstractThe organizational design literature stresses the importance of organizational structure to understand strategic change, performance, and innovation. However, prior studies diverge regarding the conceptualizations and operationalizations of structure. Organizational structure has been studied as an (1) arrangement of activities, (2) representation of decision-making, and (3) legal entities. In this point-of-view paper, the three prominent perspectives of organizational structure are discussed in terms of their commonalities, differences, and the need to study their relationship more thoroughly. Future research may not only wish to integrate these dimensions but also be more vocal about what type of organization structure is studied and why.
Biomass residue to carbon dioxide removal: quantifying the global impact of biochar
Authors: Lefebvre David , Fawzy Samer , Aquije Camila A. , Osman Ahmed I. , Draper Kathleen T. , Trabold Thomas A. 
Published: October 2023

AbstractThe Climate Change Conference of Parties (COP) 21 in December 2015 established Nationally Determined Contributions toward reduction of greenhouse gas emissions. In the years since COP21, it has become increasingly evident that carbon dioxide removal (CDR) technologies must be deployed immediately to stabilize concentration of atmospheric greenhouse gases and avoid major climate change impacts. Biochar is a carbon-rich material formed by high-temperature conversion of biomass under reduced oxygen conditions, and its production is one of few established CDR methods that can be deployed at a scale large enough to counteract effects of climate change within the next decade. Here we provide a generalized framework for quantifying the potential contribution biochar can make toward achieving national carbon emissions reduction goals, assuming use of only sustainably supplied biomass, i.e., residues from existing agricultural, livestock, forestry and wastewater treatment operations. Our results illustrate the significant role biochar can play in world-wide CDR strategies, with carbon dioxide removal potential of 6.23 ± 0.24% of total GHG emissions in the 155 countries covered based on 2020 data over a 100-year timeframe, and more than 10% of national emissions in 28 countries. Concentrated regions of high biochar carbon dioxide removal potential relative to national emissions were identified in South America, northwestern Africa and eastern Europe.
Graphical abstract
Chemistry and microstructure of duplex stainless steel powders from recycled Z100 mixed with 316L steels
Authors: Kateusz Filip , Polkowska Adelajda , Polkowski Wojciech , Chrzan Konrad , Jaśkowiec Krzysztof , Sokołowski Paweł , Igartua Amaya , Leunda Josu , Bisztyga-Szklarz Magdalena , Dudziak Tomasz , Jedliński Jerzy 
Published: October 2023

Abstract
Recovered metallic waste can be used in additive manufacturing as a feedstock if the subsequent steps of the waste-to-product process are sufficiently mastered. In this study, impact of recycling of Z100 duplex steel mixed with 316L steel on the resulting powders microstructure and chemical composition was investigated. The utility of the original method of recycling stainless steels into a high-grade powder suitable for additive techniques has been demonstrated. By examining three gradations of powders, namely 20–50 μm, 50–100 μm and 125–250 μm, differences in selected properties in relation to the average particle size are shown. The results suggest that with increasing the particle diameter, fine-crystalline γ-austenite is favoured to precipitate at the boundaries and within the volume of the originally formed large δ-ferrite grains. It is reflected by a decrease of δ/γ fraction ratio from 0.64 in the 20–50 μm powders to 0.20 in the 125–250 μm, respectively. Obtained results indicate non-diffusional, shear or semi-shear character of δ → γ + δ phase transformation. The resulting fine-crystalline austenite is characterised by a significant dislocation density, which induces dislocation strengthening effect, responsible for an increase in Vickers hardness from 145 HV and Young's modulus from 29 GPa in the 20–50 μm group to 310 HV and 146 GPa in the 125–250 μm fraction, respectively.
Multicomponent analysis of dietary supplements containing glucosamine and chondroitin: comparative low- and high-field NMR spectroscopic study
Authors: Adels Klaudia , Elbers Gereon , Diehl Bernd , Monakhova Yulia 
Published: October 2023

AbstractWith the prevalence of glucosamine- and chondroitin-containing dietary supplements for people with osteoarthritis in the marketplace, it is important to have an accurate and reproducible analytical method for the quantitation of these compounds in finished products. NMR spectroscopic method based both on low- (80 MHz) and high- (500–600 MHz) field NMR instrumentation was established, compared and validated for the determination of chondroitin sulfate and glucosamine in dietary supplements. The proposed method was applied for analysis of 20 different dietary supplements. In the majority of cases, quantification results obtained on the low-field NMR spectrometer are similar to those obtained with high-field 500–600 MHz NMR devices. Validation results in terms of accuracy, precision, reproducibility, limit of detection and recovery demonstrated that the developed method is fit for purpose for the marketed products. The NMR method was extended to the analysis of methylsulfonylmethane, adulterant maltodextrin, acetate and inorganic ions. Low-field NMR can be a quicker and cheaper alternative to more expensive high-field NMR measurements for quality control of the investigated dietary supplements. High-field NMR instrumentation can be more favorable for samples with complex composition due to better resolution, simultaneously giving the possibility of analysis of inorganic species such as potassium and chloride.
Graphical abstract
The impact of dynamic managerial capabilities on firm performance: A moderated mediation analysis of German DAX firms
Authors: Heubeck Tim 
Published: October 2023

AbstractIn an era of hypercompetition, research and development (R&D) investments are vital for organizations to stay competitive. This microlevel study draws on dynamic managerial capability (DMC) theory to explore the mechanisms contributing to competitive advantages. It posits that DMCs enhance firm performance by increasing R&D spending, and explores the moderating role of slack resources due to their effect on resource availability. Employing hierarchical regression analysis and bootstrapping methods on a longitudinal sample comprising 31 German DAX firms, the findings robustly demonstrate that DMCs facilitate firm performance by fostering R&D expenditures and confirm the moderating effect of specific slack resources. However, only internal but not external slack resources amplify the relationship between DMCs and R&D intensity. Overall, this study emphasizes the critical role of managers’ microlevel capabilities in determining firm performance and sheds light on how different slack resources influence the relationships between DMCs, R&D intensity, and firm performance.
‘We deserve new things’: (anti-)bricolage in Lomé’s makerspaces
Authors: Santos Janine Patricia 
Published: October 2023

AbstractThis article traces bricolage in the city of Lomé, Togo, as it is given meaning, practised and resisted by the ‘makers’ involved in the city’s makerspaces. While the Lévi-Straussian definition of bricolage as ‘making do’ given limited resources is heralded as an innovative practice in the Euro-American Maker Movement, Lomé’s makers appear to distance themselves from the concept due to its perceived stigmatization in both Lomé and Francophone Africa as a devalued survival practice through improvisation. Through their identification as ‘makers but not bricoleurs’, and their expression ‘we deserve new things’, I unpack the ambiguous relationship Lomé’s makers have with bricolage, and how their disavowal of the concept reveals more about the global infrastructural inequalities that surround it. By foregrounding the critical self-awareness of Lomé’s makers, I explore how ethnography allows for the de-centring and decolonization of foundational concepts and ideologies, as the makers challenge and reclaim bricolage to arrive at a future where it is no longer a necessity but a choice.
Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning
Authors: Banerjee Samprit , Wu Yiyuan , Bingham Kathleen S. , Marino Patricia , Meyers Barnett S. , Mulsant Benoit H. , Neufeld Nicholas H. , Oliver Lindsay D. , Power Jonathan D. , Rothschild Anthony J. , Sirey Jo Anne , Voineskos Aristotle N. , Whyte Ellen M. , Alexopoulos George S. , Flint Alastair J. 
Published: October 2023

AbstractBackgroundRemitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.MethodOne hundred and twenty-six persons aged 18–85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.ResultsSeventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.ConclusionsResidual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
Automatic Recognition and Localization of Poleward Moving Auroral Forms (PMAFs) From All‐Sky Auroral Videos
Authors: Yang Qiuju , Wang Jiakai , Su Hang , Xing Zanyang 
Published: October 2023

AbstractPoleward Moving Auroral Forms (PMAFs) are one of the most common dayside auroral phenomena and are important for the study of dayside auroras and their dynamical processes. Accurate recognition and localization of PMAFs from a large number of all‐sky imager observations is the first critical step in PMAFs study, but is very difficult and tedious. This paper proposes an integrated model, namely RL‐PMAFs, for automatically recognizing whether an all‐sky auroral video contains PMAFs and, for the first time temporally locating PMAFs. RL‐PMAFs consists of a recognition network and a localization network. Taking the all‐sky auroral videos as input, the recognition network characterizes the morphology and motion of the aurora to determine whether the input videos contain PMAFs. Then, the feature sequences of the videos containing PMAFs are fed to the localization network to obtain the starting and ending times of PMAFs. RL‐PMAFs is evaluated using auroral observations at Arctic Yellow River Station from 2005 to 2007. RL‐PMAFs not only yields higher recognition accuracy of 91.67% than previous methods, but also achieves a precision of 81.90% and a recall of 79.62% for locating PMAFs in auroral videos. The experimental results show that it is a valuable attempt of artificial intelligence for space physics.