bluuebunny/crossref_metadata_2025_split huggingface dataset:
Are universal welfare policies really more popular than selective ones? A critical discussion of empirical research
Authors: Laenen Tijs , Gugushvili Dimitri 
Published: February 2021

PurposeIn the social policy literature, it is often assumed that universal policies are more popular than selective ones among the public, because they supposedly generate broader self-interested coalitions and are considered morally superior. The present article revisits and challenges this assumption.Design/methodology/approachThe article critically reviews the existing empirical literature on public support for universal and means-tested welfare schemes.FindingsThe main conclusion is that the popularity of universal vis-à-vis selective welfare remains very much an open question. First, the studies that are typically cited to support the claim that universalism is indeed more popular are inconclusive because they conflate the institutional design of welfare programs with their respective target groups. Second, there is considerable variation in public support for universal and selective welfare across countries, time and policy domains.Research limitations/implicationsThe findings suggest that future research should focus on scrutinizing under which circumstances – when, where and why – universal social policies are more popular than selective ones.Originality/valueThe article makes an original case for considering perceived welfare deservingness of social policies' target groups alongside the policy design when studying popular support for differently targeted welfare schemes.
Organizational problem-solving and school discipline: comparing the roles of schoolwide behavior management technologies
Authors: Cho Vincent , Borowiec Katrina , Tuthill Kaitlyn F. 
Published: February 2021

PurposeApplications for tracking and managing classroom behavior have become increasingly commonplace, thus making it possible to incorporate nonacademic data into collaborative problem-solving and school improvement. Whether or how these platforms might support such aims, however, is not known. Accordingly, this study explores practices involving these applications, focusing especially on problem-solving among educators and with students' families.Design/methodology/approachThis comparative case study took place in three schools. In total, 34 semistructured interviews were conducted with teachers and school leaders. Analysis included qualitative coding as well as the development of within- and cross-case summaries.FindingsSchools varied greatly when it came to using behavior management platforms as a part of problem-solving. At a basic level, it was not uncommon for educators to use behavioral data for classroom troubleshooting or check-ins with students and transactional communications with families. However, only two schools attempted to use behavioral data for more systemic, “big picture” problem-solving, such as to make discipline policies more equitable or to improve teacher practices. The richness of collaboration with families seemed especially shaped by how and how frequently data were shared (e.g. automated notifications and paper printouts).Originality/valueEmpirical research about behavior management applications has been limited and focused only at the classroom level. The present study contributes new knowledge about the school-level implications of these platforms, while also expanding conversations about how behavioral data may be incorporated into data-informed problem-solving. Implications for leadership and theory are also discussed.
Effects of ethical leadership, job satisfaction and affective commitment on the turnover intentions of accountants
Authors: Torlak Nuri Gökhan , Kuzey Cemil , Dinç Muhammet Sait , Güngörmüş Ali Haydar 
Published: February 2021

PurposeThe paper aims to analyze the relationships between ethical leadership (EL), job satisfaction (JS), affective commitment (AC) and turnover intention (TI) that might make accountants quit withdrawal and become productive and useful in private organizations operating in Istanbul.Design/methodology/approachData were collected through an online survey using a simple random sampling methodology, obtained from 153 accountants working in companies in Istanbul. The methodology included descriptive statistics, factor analysis, structural equation modeling and mediation analysis.FindingsConcerning direct relationships between EL, JS, AC and TI, EL has significant positive associations with JS and AC, whereas EL has a significant negative association with TI. JS has a significant positive association with AC, whereas JS has a significant negative association with TI. Also, AC has a weak significant negative association with TI. Given indirect relationships among EL, JS, AC and TI, JS and AC mediate the relationship between EL and TI. Finally, a similarity is found when comparing Generation X and Generation Y in terms of overall JS, AC and TI.Research limitations/implicationsThe study is limited solely to companies functioning in Istanbul and incorporates a low number of respondents. Therefore, the results cannot be considered to be accurate for the whole country. The study might guide both private and public organizations in which owners/managers develop strategic plans.Originality/valueThe study fills the gap in research on organizational behavior where little has existed until now that probes the EL–JS–AC–TI links in Turkey. A few studies measure the TIs of accountants. Furthermore, EL and AC are rarely evaluated in the field of accounting in Turkey.
A novel structure to model the distribution of relief items in a post-disaster situation
Authors: Sakiani Reza , Seifi Abbas , Ramezani Khorshiddost Reza 
Published: February 2021

Purpose
There is usually a considerable shortage of resources and a lack of accurate data about the demand amount in a post-disaster situation. This paper aims to model the distribution and redistribution of relief items. When the new data on demand and resources become available the redistribution of previously delivered items may be necessary due to severe shortages in some locations and surplus inventory in other areas.

Design/methodology/approach
The presented model includes a vehicle routing problem in the first period and some network flow structures for succeeding periods of each run. Thereby, it can produce itineraries and loading plans for each vehicle in all periods when it is run in a rolling horizon manner. The fairness in distribution is sought by minimizing the maximum shortage of commodities among the affected areas while considering operational costs. Besides, equity of welfare in different periods is taken into account.

Findings
The proposed model is evaluated by a realistic case study. The results show that redistribution and multi-period planning can improve efficiency and fairness in supply after the occurrence of a disaster.

Originality/value
This paper proposes an operational model for distribution and redistribution of relief items considering the differences of items characteristics. The model integrates two well-known structures, vehicle routing problem with pickup and delivery and network flow problem to take their advantages. To get more practical results, the model relaxes some simplifying assumptions commonly used in disaster relief studies. Furthermore, the model is used in a realistic case study.
Exploring consumers’ attitude formation toward their own brands when in crisis: cross-national comparisons between USA and China
Authors: Kim Sojung , Yim Mark Yi-Cheon 
Published: February 2021

Purpose
This study aims to examine how culture influences consumer attitudes toward the brands of products they own during a product-harm crisis. To this end, average consumers from two countries - the USA, representing a highly individualistic society and China, a less individualistic (i.e. collectivist) society – are compared.

Design/methodology/approach
The study conducts an invariance test of the measurement model for a more rigorous comparison of the two countries. Structural equation modeling is performed to identify how average consumers respond to a product-harm crisis (e.g. iPhone explosion) based on survey results of 188 American and 197 Chinese consumers.

Findings
These results reveal that in both countries, an individual’s susceptibility to a normative interpersonal influence determines their brand consciousness, which, in turn, enhances consumer attachment to well-known brands, resulting in favorable brand attitudes. During a brand crisis, an owned brand’s buffering effect is observed among consumers high in brand consciousness in collectivistic but not in individualistic societies. The moderating role of feelings of betrayal on the brand attachment-consumer attitude relationship is also reported.

Originality/value
Culture shapes consumer behavioral patterns. In today’s global market, a company’s decisions are no longer limited by borders and many companies experience product failures. Thus, findings that show consumers’ distinguishable psychological experiences between different cultures contribute to crisis management literature.
Long non‐coding RNA H19 promotes myoblast fibrogenesis via regulating the miR‐20a‐5p‐Tgfbr2 axis
Authors: Lin Jinrong , Luo Zhiwen , Liu Shaohua , Chen Qingyan , Liu Siyang , Chen Jiwu 
Published: February 2021

AbstractEmerging evidence has indicated long non‐coding RNAs (lncRNAs) play important roles in diverse biological processes, including fibrosis. Here, we report that lncRNA H19 is able to promote skeletal muscle fibrosis. lnc‐H19 was identified to be highly expressed in skeletal muscle fibrosis in vivo and in vitro; while lnc‐H19 knockdown attenuated fibrosis in vitro. The knockdown of lnc‐H19 was proved to inhibit the activation of the TGFβ/Smad pathway in C2C12 myoblasts by sponging miR‐20a‐5p to regulate Tgfbr2 expression through the competing endogenous RNA function. Our study elucidates the roles of the lnc‐H19‐miR‐20a‐5p‐Tgfbr2 axis in regulating the TGFβ/Smad pathway of myoblast fibrogenesis, which might provide a promising therapeutic target for skeletal muscle fibrosis.
Development and evaluation of a core genome multilocus sequence typing scheme for Paenibacillus larvae, the deadly American foulbrood pathogen of honeybees
Authors: Bertolotti Alicia C. , Forsgren Eva , Schäfer Marc O. ,   EuroPLarva Consortium, Sircoulomb Fabrice , Gaïani Nicolas , Ribière‐Chabert Magali , Paris Laurianne , Lucas Pierrick , de Boisséson Claire , Skarin Joakim , Rivière Marie‐Pierre 
Published: February 2021

SummaryPaenibacillus larvae is the causative agent of the fatal American foulbrood disease in honeybees (Apis mellifera). Strain identification is vital for preventing the spread of the disease. To date, the most accessible and robust scheme to identify strains is the multilocus sequence typing (MLST) method. However, this approach has limited resolution, especially for epidemiological studies. As the cost of whole‐genome sequencing has decreased and as it becomes increasingly available to most laboratories, an extended MLST based on the core genome (cgMLST) presents a valuable tool for high‐resolution investigations. In this study, we present a standardized, robust cgMLST scheme for P. larvae typing using whole‐genome sequencing. A total of 333 genomes were used to identify, validate and evaluate 2419 core genes. The cgMLST allowed fine‐scale differentiation between samples that had the same profile using traditional MLST and allowed for the characterization of strains impossible by MLST. The scheme was successfully used to trace a localized Swedish outbreak, where a cluster of 38 isolates was linked to a country‐wide beekeeping operation. cgMLST greatly enhances the power of a traditional typing scheme, while preserving the same stability and standardization for sharing results and methods across different laboratories.
The architecture of a mixed fungal–bacterial biofilm is modulated by quorum‐sensing signals
Authors: Ruiz Alberto , Herráez Marta , Costa‐Gutierrez Stefanie B. , Molina‐Henares María Antonia , Martínez María Jesús , Espinosa‐Urgel Manuel , Barriuso Jorge 
Published: February 2021

SummaryInterkingdom communication is of particular relevance in polymicrobial biofilms. In this work, the ability of the fungus Ophiostoma piceae to form biofilms individually and in consortium with the bacterium Pseudomonas putida, as well as the effect of fungal and bacterial signal molecules on the architecture of the biofilms was evaluated. Pseudomonas putida KT2440 is able to form biofilms through the secretion of exopolysaccharides and two large extracellular adhesion proteins, LapA and LapF. It has two intercellular signalling systems, one mediated by dodecanoic acid and an orphan LuxR receptor that could participate in the response to AHL‐type quorum sensing molecules (QSMs). Furthermore, the dimorphic fungus O. piceae uses farnesol as QSM to control its yeast to hyphae morphological transition. Results show for the first time the ability of this fungus to form biofilms alone and in mixed cultures with the bacterium. Biofilms were induced by bacterial and fungal QSMs. The essential role of LapA‐LapF proteins in the architecture of biofilms was corroborated, LapA was induced by farnesol and dodecanol, while LapF by 3‐oxo‐C6‐HSL and 3‐oxo‐C12‐HSL. Our results indicate that fungal signals can induce a transient rise in the levels of the secondary messenger c‐di‐GMP, which control biofilm formation and architecture.
Effect of the food matrix on the capacity of flavor enhancers in intensifying salty taste
Authors: Reis Rocha Renata Abadia , Reis Rocha Lenízy Cristina , Ribeiro Michele Nayara , Lima Ribeiro Ana Paula , Alves da Rocha Roney , Souza Carneiro João de Deus 
Published: February 2021

AbstractThe effect of the flavor enhancers monoammonium glutamate (MAG), monosodium glutamate (MSG), disodium guanylate (GMP), and disodium inosinate (IMP) on intensifying salty taste in food matrices (shoestring potatoes, requeijão cheese, and beef burgers) with a reduction in the amount of sodium chloride (NaCl) present was evaluated. Experiments were conducted using a central composite rotational design with two variables: the concentrations of flavor enhancer and NaCl added in the food matrix. The effect of IMP was not significant (P > 0.05) on the intensity of salty taste in any of the matrices analyzed. GMP presented lower performance compared to MAG and MSG in intensifying the salty taste of the treatments, regardless of the reduction of NaCl. Compared to MSG and GMP, MAG showed greater efficiency in intensifying the salty taste in requeijão cheese and beef burger with a reduction of 25%, 50%, 75%, and 100% of NaCl. MSG presented higher efficiency compared to MAG and GMP when applied in shoestring potatoes for all reductions of NaCl tested (25%, 50%, and 75%). The ability of flavor enhancers to improve the salty taste depends on the effect of the flavor enhancer, the complexity of the food matrix, and the reduction of NaCl in foods.Practical ApplicationsThe complexity of the food matrix plays a significant role in the perception of salty taste in sodium‐reduced products. In these products, sodium reduction may affect the taste enhancer's effect of enhancing salty taste. Therefore, this study broadens the knowledge of the effects of flavor enhancers on different foods, as well as the ability to enhance salty taste in food matrices with NaCl reduction. Moreover, it provides information on how to reduce the sodium content in these matrices while maintaining the same perception of salty taste as a conventional matrix.
Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance
Authors: Doser Jeffrey W. , Finley Andrew O. , Weed Aaron S. , Zipkin Elise F. 
Published: February 2021

Abstract

Monitoring wildlife abundance across space and time is an essential task to study their population dynamics and inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations and communities. While current acoustic data models provide information on the presence/absence of individual species, new approaches are needed to monitor population abundance, ideally across large spatio‐temporal regions.

We present an integrated modelling framework that combines high‐quality but temporally sparse bird point count survey data with acoustic recordings. Our models account for imperfect detection in both data types and false positive errors in the acoustic data. Using simulations, we compare the accuracy and precision of abundance estimates using differing amounts of acoustic vocalizations obtained from a clustering algorithm, point count data, and a subset of manually validated acoustic vocalizations. We also use our modelling framework in a case study to estimate abundance of the Eastern Wood‐Pewee (Contopus virens) in Vermont, USA.

The simulation study reveals that combining acoustic and point count data via an integrated model improves accuracy and precision of abundance estimates compared with models informed by either acoustic or point count data alone. Improved estimates are obtained across a wide range of scenarios, with the largest gains occurring when detection probability for the point count data is low. Combining acoustic data with only a small number of point count surveys yields estimates of abundance without the need for validating any of the identified vocalizations from the acoustic data. Within our case study, the integrated models provided moderate support for a decline of the Eastern Wood‐Pewee in this region.

Our integrated modelling approach combines dense acoustic data with few point count surveys to deliver reliable estimates of species abundance without the need for manual identification of acoustic vocalizations or a prohibitively expensive large number of repeated point count surveys. Our proposed approach offers an efficient monitoring alternative for large spatio‐temporal regions when point count data are difficult to obtain or when monitoring is focused on rare species with low detection probability.
The shape of sound: A new R package that crosses the bridge between Bioacoustics and Geometric Morphometrics
Authors: Rocha Pedro C. , Romano Pedro S. R. 
Published: February 2021

Abstract

The field of Bioacoustics has flourished in the past few decades, with numerous initiatives towards its application as a tool for biodiversity conservation. Despite the development of new methods involving computational programming and machine learning algorithms, higher accuracy is still needed for species identification, which is why we present herein the new SoundShape package for R platform.

The new package focuses on implementing the eigensound method, a promising, and yet little explored protocol for bioacoustical analysis. The eigensound function is the main feature of SoundShape, allowing its user to convert sound waves into a dataset that can be analysed similar to coordinate sets from Geometric Morphometrics Methods, thus enabling the direct comparison between stereotyped calls from different species. Besides, SoundShape also features complementary functions for basic analysis of quantitative variation and illustration of hypothetical sound shapes representing the sample of sounds.

Implementation of SoundShape is summarized through a workflow guide, using data from the new package. The sample study resulted in nearly 90% variation expressed in an ordination plot, therefore successfully summarizing complex sound waves previously described by large datasets of amplitude values. Moreover, we also introduce two addendums to eigensound: (a) three steps to prevent errors and biased results—aimed at securing a meaningful comparison between acoustic units from different species; and (b) the option of applying a logarithmic grid on the x‐axis (time)—which emphasizes short duration calls while also encompassing long ones.

SoundShape package now provide the tools required for anyone to replicate and implement the eigensound method on the r platform. This will enable future studies to focus on further exploring the applications of sound shape analysis on various scientific areas, including (but not limited to) taxonomy, systematics, evolution, acoustic niche partitioning, soundscape ecology and machine learning algorithms focused on species identification for conservation biology.
Automated detection of wildlife using drones: Synthesis, opportunities and constraints
Authors: Corcoran Evangeline , Winsen Megan , Sudholz Ashlee , Hamilton Grant 
Published: February 2021

Abstract

Accurate detection of individual animals is integral to the management of vulnerable wildlife species, but often difficult and costly to achieve for species that occur over wide or inaccessible areas or engage in cryptic behaviours. There is a growing acceptance of the use of drones (also known as unmanned aerial vehicles, UAVs and remotely piloted aircraft systems, RPAS) to detect wildlife, largely because of the capacity for drones to rapidly cover large areas compared to ground survey methods. While drones can aid the capture of large amounts of imagery, detection requires either manual evaluation of the imagery or automated detection using machine learning algorithms. While manual evaluation of drone‐acquired imagery is possible and sometimes necessary, the powerful combination of drones with automated detection of wildlife in this imagery is much faster and, in some cases, more accurate than using human observers. Despite the great potential of this emerging approach, most attention to date has been paid to the development of algorithms, and little is known about the constraints around successful detection (P. W. J. Baxter, and G. Hamilton, 2018, Ecosphere, 9, e02194).

We reviewed studies that were conducted over the last 5 years in which wildlife species were detected automatically in drone‐acquired imagery to understand how technological constraints, environmental conditions and ecological traits of target species impact detection with automated methods.

From this review, we found that automated detection could be achieved for a wider range of species and under a greater variety of environmental conditions than reported in previous reviews of automated and manual detection in drone‐acquired imagery. A high probability of automated detection could be achieved efficiently using fixed‐wing platforms and RGB sensors for species that were large and occurred in open and homogeneous environments with little vegetation or variation in topography while infrared sensors and multirotor platforms were necessary to successfully detect small, elusive species in complex habitats.

The insight gained in this review could allow conservation managers to use drones and machine learning algorithms more accurately and efficiently to conduct abundance data on vulnerable populations that is critical to their conservation.