bluuebunny/crossref_metadata_2025_split huggingface dataset:
Food systems narratives in Colombia: embracing diverse perspectives can enable hybrid innovation pathways that address food system challenges
Authors: Dueñas-Ocampo Sebastián , Hegwood Margaret , Rojas-Becerra Angela Daniela , Rodríguez-Pinilla Juan Pablo , Newton Peter 
Published: February 2025

Abstract
When analyzing food systems challenges, considering multiple different narratives might lead to solutions that are more innovative and grounded in the local context relative to considering just a single narrative. However, the relationship between narrative diversity and innovation in food systems is not fully understood. Understanding the structure of and interactions between different food systems narratives can help researchers to identify opportunities (e.g., policies, interventions, and institutions) that could facilitate food systems transformations. This paper explores how narratives about food systems converge and vary between different groups of actors in rural Colombia as well as the implications for the co-development of innovative solutions amongst these actors to address food system challenges. As a case study, we focus on an important national policy initiative in Colombia, A Ciencia Cierta, which connects public science and civil society to implement community-based innovations. We conducted 47 interviews with key stakeholders, including policymakers, facilitators, academics, community leaders, and community participants who had been part of the initiative in Colombia, and experts about narratives in food systems globally. We found that actors (both individuals and groups) possess multiple narratives about food systems which evolve over time and adjust according to varying circumstances. Our findings indicate that hybrid innovations enabled by diverse knowledge(s) provide alternative solutions to overcome challenges faced by actors in food systems. This paper generates theoretical and empirical contributions to the growing study of how epistemic plurality can enhance innovation pathways in food systems.
Still Waters Run Deep: How Employee Silence Affects Instigated Workplace Incivility Over Time
Authors: Weiss Mona , Zacher Hannes 
Published: February 2025

Abstract
Research has shown that employees who remain silent about important issues at work are likely to experience negative personal consequences (e.g., burnout, reduced job satisfaction). Less clear is whether silence, over time, could also lead to negative interpersonal consequences. Drawing on social identity theory, we propose that involuntary forms of silence (acquiescent and quiescent silence) lead to decreased organizational identification, which, in turn, leads to increased instigated incivility over time. We tested our model at the within-person level using five waves of longitudinal data across four months from N = 1156 employees working in different industries. In line with predictions, results of random-intercept cross-lagged panel modeling (RI-CLPM) showed that higher acquiescent silence at the within-person level predicted subsequent decreases in organizational identification, which, in turn, predicted subsequent increases in instigated incivility. Contrary to expectations, there was no significant effect of quiescent silence on organizational identification. Supplemental analyses also revealed non-significant indirect effects for voluntary forms of silence (prosocial and opportunistic silence). Overall, these findings suggest that acquiescent silence may not only impair employees’ own well-being, but can also result in interpersonal harm.
Integration of CNN Models and Machine Learning Methods in Credit Score Classification: 2D Image Transformation and Feature Extraction
Authors: Gür Yunus Emre , Toğaçar Mesut , Solak Bilal 
Published: February 2025

Abstract
The problem of accurately classifying credit scores is critical for financial institutions to assess individual creditworthiness and effectively manage credit risk. Traditional methods often face limitations when processing large datasets, resulting in lower accuracy and longer processing time. To address this issue, this paper proposes a novel approach to credit score classification by integrating convolutional neural networks (CNN) with machine learning methods. First, a 1D dataset of sequential text data is transformed into 2D greyscale images to use 2D CNN models for feature extraction and classification. Six CNN architectures—DenseNet201, GoogLeNet, MobileNetV2, ResNet18, ShuffleNet, and SqueezeNet—are implemented, and the features in the last layer (1000 features) of each CNN are classified using the softmax method. To further improve the performance, the two best CNN models were selected, and a new fully connected layer (NewFC) was added. A class-based feature set [3 × 31,695] representing three credit score types (good, poor, and standard) was extracted from each model and merged into a feature set [6 × 31,695]. This combined feature set was then reclassified using KNN, LDA, Naive Bayes, and SVM algorithms. The performance of both CNN and machine learning methods was evaluated using accuracy, precision, sensitivity, specificity, and F-score metrics. To optimize classification performance and reduce computational cost, the RelieF algorithm was used to select the best 5 out of 6 features. Compared to using all 6 features, significant improvements in accuracy and efficiency were observed, demonstrating the effectiveness of the proposed method in credit score classification.
Specialized Purpose of Each Type of Student Engagement: A Meta-Analysis
Authors: Reeve Johnmarshall , Basarkod Geetanjali , Jang Hye-Ryen , Gargurevich Rafael , Jang Hyungshim , Cheon Sung Hyeon 
Published: February 2025

Abstract
Students involve themselves in learning activities multidimensionally, including behaviorally, cognitively, emotionally, and agentically. This multidimensional involvement predicts important outcomes, but it is also possible that each type of engagement might have its own specialized purpose or function. To investigate this possibility, we proposed and tested the specialized purpose hypothesis, which is that each type of engagement has its own specialized function targeted toward a specific purpose, such as to boost achievement, social support, motivation, or well-being. To test this hypothesis, we conducted four meta-analyses, utilizing multilevel random effects models. Each meta-analysis tested whether type of engagement differentially predicted students’ achievement (meta-analysis #1), social support (meta-analysis #2), motivation (meta-analysis #3), or well-being (meta-analysis #4). The database included 652 effect sizes from 62 studies within 54 articles involving 32,403 P-16 student-participants (M
age = 16.8 years-old; 51.2% female). All 62 studies measured all four types of engagement so that we could compare the relative strength of association between each type of engagement and each correlate. Behavioral engagement was the strongest predictor of achievement. Agentic engagement was the strongest predictor of social support. Cognitive engagement did not show a specialized relation with any outcome. Emotional engagement was strongly associated with both motivation and well-being. These findings generally support the specialized purpose hypothesis, but they also raise important and challenging questions for future theory and research about how to better conceptualize and measure each type of engagement.
Maximal representations in lattices of the symplectic group
Authors: Audibert Jacques 
Published: February 2025

Abstract
We prove that all lattices of

$$\text {Sp}(2n,\mathbb {R})$$

Sp
(
2
n
,
R
)

, except those commensurable with

$$\text {Sp}(4k+2,\mathbb {Z})$$

Sp
(
4
k
+
2
,
Z
)

when

$$n=2k+1$$

n
=
2
k
+
1

, contain the image of infinitely many mapping class group orbits of Zariski-dense maximal representation that are continuous deformations of maximal diagonal representations. In particular, we show that

$$\text {Sp}(4k,\mathbb {Z})$$

Sp
(
4
k
,
Z
)

contain Zariski-dense surface subgroups for all

$$k\ge 1$$

k
≥
1

.
Effects of nutrient concentration, germanium dioxide and seeding density on sporophyte production in the kelp Ecklonia radiata
Authors: Lawton Rebecca J. , Praeger Christina 
Published: February 2025

Abstract
Overcoming low frequencies of transition from the gametophyte to the sporophyte life history phase is vital to enable efficient hatchery production of the kelp Ecklonia radiata (Phaeophyceae) for aquaculture, restoration and conservation. Therefore, we investigated the effects of nutrient strength (full to 1/20 strength Provasoli enriched seawater (PES)), germanium dioxide strength (full and half strength) and application period (no application, 9-16, 9-30, 16-23 and 16-30 days post seeding), and seeding density (2, 4, 12 and 18 spores mm-2) on sporophyte production in E. radiata in three laboratory experiments. Nutrient strength and seeding density significantly affected sporophyte transition frequency, sporophyte formation frequency, and sporophyte length, with all three metrics consistently decreasing with decreasing nutrient strength and increasing seeding density. Sporophyte transition frequencies were more than four times higher in the full-strength nutrient treatment (58.2 %) compared to the 1/8 and 1/20 strength treatments (12.5 and 3.0 %, respectively), and 18% higher in the 2 spores mm-2 seeding density treatment (100.0 %) compared to the 18 spores mm-2 treatment (84.3 %). Germanium dioxide application period but not strength significantly impacted sporophyte transition frequency and sporophyte formation frequency. Transition frequencies were more than two-thirds lower when GeO2 was applied from day 9 onwards (<25 %) compared to day 16 onwards and no application (>75 %). Based on these results, we recommended using full strength PES, half strength GeO2 from day 16 onwards only if diatom contamination is an issue and seeding at a density of ≤10 spores mm-2 to maximise sporophyte production.
Differential Attraction of Summer and Winter Morphs of Spotted Wing Drosophila, Drosophila suzukii, to Yeasts
Authors: Jones Rory , Goddard Matthew R. , Eady Paul E. , Hall David R. , Bray Daniel P. , Farman Dudley I. , Fountain Michelle T. 
Published: February 2025

Abstract

Drosophila suzukii is a pest of soft and stone fruits that is attracted to yeast volatile metabolites. Drosophila suzukii has distinct summer and winter morphs which are found in different habitats. Complex communities of yeasts likely differ between habitats and thus we hypothesized morphs differ in their attraction to yeast volatiles from different species and combinations of species. We presented D. suzukii with yeast species in isolation and in combinations, as either post-culture mixes or co-cultured, and measured activity of the flies in laboratory choice tests with a Locomotor Activity Monitor as a proxy for attraction. Candida zemplinina was more attractive to winter than summer morphs when cultured in both sterile strawberry juice (SSJ) and artificial culture media (YPD). No significant correlations were found between principle component scores derived from single yeast volatile profiles and fly activity. There was also differential attraction of morphs to certain yeast mixes, most notably post SSJ culture mixes of Hanseniaspora uvarum and C. zemplinina were consistently more attractive to winter morphs. Mixtures of Metschnikowia pulcherrima + Pichia pijperi + H. uvarum in SSJ and M. pulcherrima + P. pijperi yeasts in YPD were also preferred by winter morphs, whilst summer morphs were attracted to SSJ co-cultures of M. pulcherrima + H. uvarum. However, co-culturing yeasts did not enhance attraction compared to post-culture mixes of constituent yeasts for either morph. Differential attraction of morphs to certain yeasts suggests options for new bait formulations in integrated pest management strategies may be tailored to better target morphs.
A New Quaternized Chitosan-Based Catalyst Obtained by an Environmentally-Friendly Synthesis Route for Green Production of Carbonated Molecules from Terminal Epoxides
Authors: Inciarte Helen C. , Cortés Natalia , Echeverri David A. , Rios Luis A. 
Published: February 2025

Abstract
Using CO2 as a building block to produce monomers through carbonation (cycloaddition reaction) provides an attractive approach to carbon dioxide valorization. The design of an efficient, cheap, and reusable catalyst for epoxide carbonation remains a challenge. In this paper, a new quaternized chitosan catalyst, useful for the cycloaddition of CO2 to epoxides, was developed. The new catalyst was obtained using a short and straightforward route, including the use of non-depolymerized chitosan and benign reactants such as succinic anhydride (a food additive), at room temperature. The catalyst allowed the carbonation of glycidyl methacrylate at 100 °C, 2 MPa, and 6 h reaction without any solvent required, with 98% epoxide conversion and 87% selectivity to the carbonated product. The catalyst was reused during 3 cycles showing stable values of conversion and selectivity to the carbonated GMA. The selectivity to the carbonated GMA remained stable at 87% during seven recycles. The use of non-depolymerized chitosan as a precursor is a key point that brought durability to the catalyst in comparison to depolymerized chitosan. The catalyst was also used in the carbonation of two di-epoxides (1,4-butanediol diglycidyl ether and bisphenol A diglycidyl ether) at 100 °C and 2 MPa affording high yields to the carbonated products.
Enhanced Biological, Thermal and Dielectric Properties of Polyvinyl Alcohol by a Methacrylate Polymer and Green Synthesized Silver Nanoparticles
Authors: Erol Ibrahim , Hazman Ömer , Yılmaz Fadime , Khamidov Gofur 
Published: February 2025

Abstract
Due to the increasing adverse environmental effects of synthetic polymers, the need for environmentally friendly alternative biomaterials is increasing daily. In this context, the synthesis of novel Poly(vinyl alcohol) (PVA) -based composite materials was aimed. In this study, methacrylate-based poly(2-oxo-2-[4-(trifluoromethyl)anilino]ethyl-2-methylprop-2-enoate) (PTFMAM) polymer synthesized for the first time was blended with PVA by hydrothermal method. Biosynthesized silver nanoparticles (Ag NPs) were added to the PTFMAM-PVA blend using the hydrothermal method. Nanocomposites were characterized by XRD, SEM, TEM, and FTIR. The thermal stability of nanocomposites was determined by thermogravimetric analysis (TGA), and glass transition temperatures (Tg) were determined by differential scanning calorimetry (DSC) techniques. According to TGA data, the thermal stability of PVA was improved by blending with PTFMAM and loading with Ag NPs. While the Tg of PVA and PTFMAM-PVA were 78 °C and 103 °C, this value increased to 116 °C with 7% Ag NP loading. The dielectric properties of the nanocomposites also increased with the loading of Ag NPs. Ag NPs loading also decreased the solubility of PVA in water. Combining PVA with PTFMAM and Ag NP increased the oxidant/antioxidant activity. At the same time, increases in the antimicrobial activities of the nanocomposites were observed. The inhibition zones of the nanocomposites against E. coli, S. aureus, and C. albicans strains were between 8.56 and 15.08 mm. The results showed that PVA equipped with synthetic PTFMAM and biosynthesized Ag NPs caused improvements in thermal, dielectric, and biological properties. The produced PTFMAM-PVA/Ag nanocomposites showed that they could be alternative materials in areas where PVA is frequently used with their improved properties.
The Relationship Between Spiritual Caregiving and Compassion Levels Among Turkish Nurses: An ICU Case Study
Authors: Fırat Kılıç Hülya , Su Serpil , Cevheroğlu Seda 
Published: February 2025

Abstract
This descriptive and correlational study aimed to determine the levels of spiritual caregiving and compassion among Turkish Intensive Care Unit (ICU) nurses and evaluate the relationship between these two variables. This study included 135 ICU nurses working in a university hospital in Turkey. Descriptive information forms, the Compassion Scale (CS), and the Spiritual Care-Giving Scale (SCGS) were used for data collection. The participants’ mean CS score was high, with the separation and common humanity subscales yielding the lowest and highest scores, respectively. The mean SCGS score was high, with the lowest and highest scores obtained from the spiritual care attitudes and spirituality perspectives, respectively. There is a positive correlation between CS and SCGS scores. This study concluded that Turkish ICU nurses demonstrated high levels of compassion and spiritual care and that there is a positive relationship between them. Cultural factors can affect compassion and spiritual care; therefore, future studies in different cultures are necessary to provide more valid evidence, possibly through experimental studies.
«Doctors must live»: a care ethics inquiry into physicians’ late modern suffering
Authors: Engen Caroline 
Published: February 2025

Abstract
In 2023, thousands of young Norwegian physicians joined an online movement called #legermåleve (#doctorsmustlive) and shared stories of their own mental and somatic health issues, which they considered to be caused by unacceptable working conditions. This paper discusses this case as an extreme example of physicians’ and healthcare workers’ suffering in late modern societies, using Vosman and Niemeijer’s approach of rethinking care imaginaries by a structured process of thinking along, counter-thinking and rethinking, bringing to bear suffering as a heuristic device. Thinking along, taking the physicians’ stories and arguments literally, reveals an image of an unbearable workload. Counter-thinking resituates their suffering within the broader conditions of late modernity, suggesting that the root cause may lie not in the quantity of the workload itself but in its qualities and in its perceived threat to their integrity as caregivers through epistemic and moral injury and an inability to respond to this threat. In rethinking, the ambiguity of suffering– its dual potential as both a constraint and an opening– becomes central. Following the physicians’ own interpretations and the solutions emerging from this framing, both their suffering and that of their patients could paradoxically be exacerbated by further decentering physicians and reinforcing utilitarian, data-driven approaches. However, staying with their suffering and reinterpreting its causes opens possibilities to leverage critiques of medicalization at large and of their own suffering in particular, challenging the assumption that the weight of care must always grow heavier. From this reframing, I argue, it is possible to reclaim and reimagine care and the clinical space as a nexus of epistemic and moral privilege, recentering response-ability both relationally and socially.
Partitioned neural network training via synthetic intermediate labels
Authors: Karadag Cevat Volkan , Topaloglu Nezih 
Published: February 2025

Abstract
The proliferation of extensive neural network architectures, particularly deep learning models, presents a challenge in terms of resource-intensive training. GPU memory constraints have become a notable bottleneck in training such sizable models. Existing strategies, including data parallelism, model parallelism, pipeline parallelism, and fully sharded data parallelism, offer partial solutions. Model parallelism, in particular, enables the distribution of the entire model across multiple GPUs, yet the ensuing data communication between these partitions slows down training. Instead of using the entire model for training, this study advocates partitioning the model across GPUs and generating synthetic intermediate labels to train individual segments. These labels, produced through a random process, mitigate memory overhead and computational load. This approach results in a more efficient training process that minimizes data communication while maintaining model accuracy. The method is validated using 6-layer fully-connected networks, via the extended MNIST, CIFAR10 and CIFAR100 datasets. It is shown that the computational improvement to reach 90% of the cross-yield accuracy can be as high as 66%. Additionally, the improvement in training bandwidth compared to standard model parallelism is quantitatively demonstrated through an example scenario. This work contributes to mitigating the resource-intensive nature of training large neural networks, paving the way for more efficient deep learning model development.
Investigating the Capability Approach: How Long-Term Unemployed People in Finland Perceive Their Access to Commodities, Conversion Factors and Capabilities
Authors: Ahonen Tiina , Mäki-Opas Tomi , Kouvo Antti , Toikko Timo 
Published: February 2025

Abstract
Capability Approach (CA) extends our understanding of wellbeing by underlining the importance of freedoms. There is a need to operationalize CA components for empirical measurement in different settings and population groups. This study investigated the conversion process from perceived resources to perceived capabilities by investigating the role of perceived conversion factors (personal and contextual) among a particular population group of Finnish long-term unemployed persons (N = 511, year 2016), aged 20–64 years, not receiving activation services, recruited through a service system and registers (random sampling). We used the label “perceived” to highlight that our approach was subjective, meaning that we measured respondents’ own perceptions of their commodities, conversion factors and capabilities. Data were collected in the PROMEQ project using a structured, self-employed questionnaire. Perceived capabilities were measured on a 7 + 1 item scale of self-reported capabilities. The main statistical methods applied were crosstabs with chi² tests, Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM). Long-term unemployed perceived poorer capabilities compared to the general Finnish population. Long-term unemployed men perceived poorer capabilities compared to long-term unemployed women. CFA indicated the data fit with the CA. The SEM model supported the theoretical assumptions of CA: perceived commodities associated with perceived capabilities strongly but indirectly through perceived social and environmental conversion factors. Group analysis (SEM) demonstrated, that between genders the CA-models were slightly differentiated. The results indicate the need for more effective capability promotion, and for targeted practices acknowledging variety of circumstances of the long-term unemployed. CA could offer a comprehensive tool for this task.