bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trichoderma koningiopsis applied as seed coating protects sweet sorghum (Sorghum bicolor (L.) Moench) from fungal contaminants during storage
Authors: Osorio-Guerrero Karen Viviana , Patiño-Moscoso Manuel Alfonso , Flórez-Gómez Deisy Lorena , Cortés-Rojas Diego Francisco 
Published: April 2024

AbstractSeed coating is an effective method for delivering beneficial microorganisms to the soil. Additionally, coated seeds are protected against pathogens or microbial contaminants during storage. This work aimed to evaluate the effect of seed coating with the beneficial fungus Trichoderma koningiopsis against fungal contaminants of sweet sorghum (Sorghum bicolor (L.) Moench) during storage. Sorghum-treated seeds with three concentrations of Trichoderma koningiopsis were stored at two temperatures (5 °C and 18 °C) over 12 months to evaluate physiological-, and sanitary quality of seeds. The concentrations of the most prevalent contaminant fungi Diaporthe melonis, Curvularia lunata and Penicillium polinocum were monitored. The viability of Trichoderma koningiopsis was evaluated every three months and correlated with the seed quality variables. The results indicate that beneficial fungi can protect seeds against fungal contamination during storage avoiding the application of a chemical fungicide. The coating procedure did not affect the seed germination over the 12 months of storage.
Online grocery shopping adoption versus non-adoption among the over-50s in Germany
Authors: Braun Simone , Osman Dunia 
Published: April 2024

AbstractOnline grocery shopping (OGS) has significantly risen due to accelerated retail digitization and reshaped consumer shopping behaviors over the last years. Despite this trend, the German online grocery market lags behind its international counterparts. Notably, with almost half of the German population aged over 50 and the 55–64 age group emerging as the largest user segment in e-commerce, the over-50 demographic presents an attractive yet relatively overlooked audience for the expansion of the online grocery market. However, research on OGS behavior among German over-50s is scarce. This study addresses this gap, empirically investigating OGS adoption factors within this demographic through an online survey with 179 respondents. Our findings reveal that over a third of the over-50 demographic has embraced OGS, indicating a growing receptivity for OGS among the over-50s. Notably, home delivery, product variety, convenience, and curiosity emerged as primary drivers for OGS adoption among this demographic. Surprisingly, most adopters did not increase online grocery orders since 2020 and a not inconsiderable proportion have even stopped buying groceries online again. For potential OGS adopters, regional product availability turned out as a motivator, signaling substantial growth potential and providing online grocers with strategic opportunities to target this demographic. In light of our research, we offer practical suggestions to online grocery retailers, aiming to overcome barriers and capitalize on key drivers identified in our study for sustained growth in the over-50 market segment.
Graded causation and moral responsibility
Authors: Hoffmann-Kolss Vera , Rolffs Matthias 
Published: April 2024

AbstractTheories of graded causation attract growing attention in the philosophical debate on causation. An important field of application is the controversial relationship between causation and moral responsibility. However, it is still unclear how exactly the notion of graded causation should be understood in the context of moral responsibility. One question is whether we should endorse a proportionality principle, according to which the degree of an agent’s moral responsibility is proportionate to their degree of causal contribution. A second question is whether a theory of graded causation should measure closeness to necessity or closeness to sufficiency. In this paper, we argue that we should indeed endorse a proportionality principle and that this principle supports a notion of graded causation relying on closeness to sufficiency rather than closeness to necessity. Furthermore, we argue that this insight helps to provide a plausible analysis of the so-called ‘Moral Difference Puzzle’ recently described by Bernstein.
When Should Popular Views be Included in a Reflective Equilibrium?
Authors: Jølstad Borgar , Juth Niklas , Solberg Carl Tollef , Barra Mathias 
Published: April 2024

AbstractIt has become increasingly common to conduct research on popular views on ethical questions. In this paper, we discuss when and to what extent popular views should be included in a reflective equilibrium process, thereby influencing normative theory. We argue that popular views are suitable for inclusion in a reflective equilibrium if they approximate considered judgments and examine some factors that plausibly contribute to the consideredness of popular views. We conclude that deliberation and familiarity contribute to the consideredness of popular views, whereas prevalence does not. Furthermore, we raise the “fair trial” objection: that expert deliberation on popular views might fail to respect the consideredness of these views. Eliciting views approximating considered judgments directly from participants is therefore preferable to refining popular views or treating them as “bare” intuitions in the reflective process. These conclusions have important implications for which research methods will likely elicit popular views suitable for a reflective equilibrium process. Research focusing on the prevalence of views is unlikely to yield popular views suitable for a reflective equilibrium, whereas deliberative and in-depth work is more promising.
Loss of amphibian species alters periphyton communities in montane ponds
Authors: Alonso Alberto , Bosch Jaime , Boyero Luz 
Published: April 2024

AbstractAmphibian larvae can affect the structure and functioning of freshwater ecosystems, but their effects have been little explored although amphibian biodiversity is rapidly declining. Given that larvae of different amphibian species belong to different trophic levels, their effects on freshwater communities and processes can be expected to differ, with herbivores likely having direct effects on algae and predators having indirect effects through trophic cascades. We explored this question through a mesocosm experiment conducted in montane ponds, using an anuran and a urodele species affected by emergent diseases. We used different scenarios of reduction and loss of one or both species, and compared them to a control scenario representing a typical amphibian community in the study area composed of four species, with total larval density held constant. Loss of the anuran resulted in lower chlorophyll concentration and algal density, likely due to replacement by more efficient grazers. Loss of the urodele produced similar trends but weaker, possibly due to an increase of invertebrate grazing activity in the absence of this predator. Our study shows how the loss of amphibian species can alter the structure of montane ponds, but also how the mechanisms involved and the intensity of effects differ for different species.
Professionality Model for Vocational Teachers: Based on the Example of Estonian Vocational Teachers in the Changing VET Context
Authors: Sirk Meidi 
Published: April 2024

AbstractVocational teachers (VTs) have been seen as the key individuals executing changes, solving problems and achieving goals in vocational education and training (VET). Therefore, the work of VTs is fragmented between different types of tasks which influence their professionalism and professionality (individual views, experiences and professional practices of teachers). Despite this, their professionality has been studied remarkably little, and previous studies have focused on different narrow aspects of professionalism. However, it is not clear how various factors, including changes in VET, can shape VTs’ perception of their professionality. Based on a meta-analysis, this study aims to create comprehensive knowledge through an empirical model of vocational teacher professionality in the context of VET changes based on the example of Estonian VET. The results show that VTs have expressed different perceptions, evaluations and experiences of change in VET, and therefore different forms of collaboration and learning activities have been used to adapt to changes, which together shape the professionality of VTs. Therefore, three profiles of VT professionality have emerged and the wider scope of the collaboration of VTs fosters more positive attitudes, satisfaction and commitment to their work and profession, while also relating to their higher self-efficacy, which supports coping in demanding pedagogical situations and when faced with new tasks and roles in a changing context.
Conceptualizing the digital thread for smart manufacturing: a systematic literature review
Authors: Abdel-Aty Tasnim A. , Negri Elisa 
Published: April 2024

AbstractBusiness operations and supporting data analysis initiatives are impeded by the silos of data present within departments, systems, and business units. Consequently, the ability of managers and engineers to harness data for operational management and informed decision-making is curtailed. The rapid advancements in technology have revolutionized various aspects of product development, manufacturing, operations, and end-of-life treatment. One such transformative concept, the digital thread, has emerged as an important paradigm. It orchestrates the integration of information and data along the entire product lifecycle, spanning from initial design and engineering through production, maintenance, use, and eventual end of life. While the digital thread has garnered increasing attention within both the research community and industrial enterprises, there remains a notable lack of standardization concerning its utilization and applications. This comprehensive literature review aims to explore the role of the digital thread in manufacturing within the context of the product lifecycle. As a result, this review synthesizes insights into the technologies, roles, and functions of the digital thread throughout the product lifecycle. Furthermore, it proposes a structured framework designed to impart a standardized perspective of the digital thread’s relevance within the manufacturing product lifecycle. Ultimately, this framework is poised to serve as a guiding resource for practitioners and researchers in designing and implementing digital threads.
AI-supported Modelling of a Simple TPR System for Fusion Neutron Measurement
Authors: Gerenton V. , Jardin A. , Wiącek U. , Drozdowicz K. , Kulinska A. , Kurowski A. , Scholz M. , Woźnicka U. , Dąbrowski W. , Łach B. , Mazon D. 
Published: April 2024

AbstractThe system proposed to measure the tritium to deuterium ratio on the International Thermonuclear Experimental Reactor (ITER) is a high-resolution neutron spectrometer, partly composed of a system of three Thin-foil Proton Recoil (TPR) spectrometers. This system works on the principle of converting neutrons into protons using a thin foil of polyethylene, which is then detected in silicon detectors to obtain the scattering angles and energy spectrum of the protons. The objective of this article is to show the benefit of artificial intelligence for improving a simple TPR system model written in Python to an accuracy approaching MCNP simulations, while significantly decreasing the computational cost. The first step was to model a polyethylene converter to obtain the energy-angle distribution of outgoing protons for a given incident neutron beam. When compared with MCNP, this simplified model was found to fail to account for proton energy and angular scattering. Therefore, in a second step, two neural networks were successfully trained to include these effects based on the output data of the TRIM code, assuming Gaussian distributions. The Python model was able to produce results very close (differences up to a few percent) to those obtained with MCNP by integrating these neural networks. To extend the study, the energy spectra of the protons could be obtained and subsequently used to obtain information on the ratio of deuterium and tritium in the plasma.
Production of Mycelium-Based Papers from Carrot Pomace and Their Potential Applications for Dye Removal
Authors: Mousavi S. Najmeh , Ramamoorthy Sunil Kumar , Hakkarainen Minna , Zamani Akram 
Published: April 2024

AbstractThe Current study aimed at valorizing carrot pomace (CP), an abundant waste from the juice industry. A water-soluble fraction of CP was separated from solid fraction of CP (SFCP) and employed as feedstock for producing fungal biomass (FB) in bench-scale bioreactors. FB combined with SFCP were used to develop mycelium-based papers (MBP) using the wet-laid method. The potential and capacity of FB, SFCP and MBP to remove dye (methylene blue) from wastewater was then investigated. The maximum achieved dye removal was 92% when using a mixture of SFCP and FB in their suspended forms. The MBP with the lowest density (549 kg/m3) reached 83% dye elimination. The findings of this study support the valorization of carrot pomace, through environmentally benign processes, to mycelium-based papers with potential application in wastewater treatment.
Necropolitics of Death in Neurodegeneration
Authors: de la Rosa T. , Berrocoso E. , Scorza F. A. 
Published: April 2024

AbstractNeurodegenerative diseases (ND) pose significant challenges for biomedicine in the twenty-first century, particularly considering the global demographic ageing and the subsequent increase in their prevalence. Characterized as progressive, chronic and debilitating, they often result in higher mortality rates compared with the general population. Research agendas and biomedical technologies are shaped by power relations, ultimately affecting patient wellbeing and care. Drawing on the concepts of bio- and necropolitics, introduced by philosophers Foucault and Mbembe, respectively, this perspective examines the interplay between the territoriality and governmentality around demographic ageing, ND and death, focussing on knowledge production as a dispositif of power by highlighting the marginal role that the phenomenon of mortality plays in the ND research landscape. We propose a shift into acknowledging the coloniality of knowledge and embracing its situatedness to attain knowledge ‘from death’, understood as an epistemic position from which novel approaches and practices could emerge.
Quasi-free Isomorphisms of Second Quantization Algebras and Modular Theory
Authors: Conti Roberto , Morsella Gerardo 
Published: April 2024

AbstractUsing Araki–Yamagami’s characterization of quasi-equivalence for quasi-free representations of the CCRs, we provide an abstract criterion for the existence of isomorphisms of second quantization local von Neumann algebras induced by Bogolubov transformations in terms of the respective one particle modular operators. We discuss possible applications to the problem of local normality of vacua of Klein-Gordon fields with different masses.
Dynamic data hiding capacity enhancement for the Hybrid Near Maximum Histogram image steganography based on Multi-Pixel-Pair approach
Authors: Sondas Adnan , Erturk Necla Bandirmali 
Published: April 2024

Abstract
Increasing data hiding capacity and making it difficult to detect presence of any confidential data in stego images are the key objectives in contemporary image steganography research ever-improving the embedding efficiency. With regard to these crucial and challenging points, a new Multi-Pixel-Pair (MPP)-based data hiding approach is proposed in this paper. It can dynamically increase data embedding capacity of the classical Hybrid Near Maximum Histogram (HNMH) image steganography method while well-maintaining the embedding efficiency. In the proposed MPP approach, the peak value as a reference point in the histogram distribution of a cover image is obtained, and accordingly the pixel-pairs of interest where secret data is to be embedded are determined. Then, the pixels of the cover image are scanned sequentially and data hiding is performed on the relevant pixels by using the Least Significant Bit (LSB) method. In an extensive experimental study of the proposed MPP approach, it is shown that the classical HNMH data hiding capacity is dynamically improved 4.74 to 37.48 times while the Peak Signal to Noise Ratio (PSNR) decreases between 6.62 and 13.75 dB, implying both a reasonable trade-off and 7.61% enhanced embedding efficiency performance. Moreover, using the well-known cover image partitioning technique, offering significant improvements in image steganography, and the proposed MPP approach together, can further extend the data hiding capacity about 7.05%.
Encoding laparoscopic image to words using vision transformer for distortion classification and ranking in laparoscopic videos
Authors: AlDahoul Nouar , Karim Hezerul Abdul , Momo Mhd Adel , Tan Myles Joshua Toledo , Fermin Jamie Ledesma 
Published: April 2024

AbstractLaparoscopic videos are tools used by surgeons to insert narrow tubes into the abdomen and keep the skin without large incisions. The videos captured by a camera are prone to numerous distortions such as uneven illumination, motion blur, defocus blur, smoke, and noise which have impact on visual quality. Automatic detection and identification of distortions are significant to enhance the quality of laparoscopic videos to avoid errors during surgery. The video quality assessment includes two stages: classification of distortions affecting the video frames to identify their types and ranking of distortions to estimate the intensity levels. The dataset generated in ICIP2020 challenge including laparoscopic videos was utilized for training, validation, and testing the proposed solution. The difficulty of this dataset is caused by having five categories of distortions and four levels of severity. Additionally, the availability of multiple distortion categories in one video is considered the most challenging part of this dataset. The work presented in this paper contributes to solve the multi-label distortion classification and ranking problem. This paper aims to enhance the performance of distortion classification solutions. Vision transformer which is a deep learning model was used to extract informative features by transferring learning and representation from the general domain to the medical domain (laparoscopic videos). Additionally, six parallel multilayer perceptron (MLP) classifiers were added and attached to vision transformer for distortion classification and ranking. The experiment showed that the proposed solution outperforms existing distortion classification methods in terms of average accuracy (89.7%), average single distortion F1 score (94.18%), and average of both single and multiple distortions F1 score (96.86%). Moreover, it can also rank the distortions with an average accuracy of 79.22% and average F1 score of 78.44%. Hence, the high performance of the method proposed in this paper opens the door to integrate our solution in the intelligent video enhancement system.
GPT for medical entity recognition in Spanish
Authors: García-Barragán Álvaro , González Calatayud Alberto , Solarte-Pabón Oswaldo , Provencio Mariano , Menasalvas Ernestina , Robles Víctor 
Published: April 2024

AbstractIn recent years, there has been a remarkable surge in the development of Natural Language Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER). Models such as BERT have demonstrated exceptional performance, leveraging annotated corpora for accurate entity identification. However, the question arises: Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR). This study utilized a dataset of Spanish EHRs related to breast cancer and implemented both a traditional NER method using BERT, and a contemporary approach that combines few shot learning and integration of external knowledge, driven by LLMs using GPT, to structure the data. The analysis involved a comprehensive pipeline that included these methods. Key performance metrics, such as precision, recall, and F-score, were used to evaluate the effectiveness of each method. This comparative approach aimed to highlight the strengths and limitations of each method in the context of structuring Spanish EHRs efficiently and accurately.The comparative analysis undertaken in this article demonstrates that both the traditional BERT-based NER method and the few-shot LLM-driven approach, augmented with external knowledge, provide comparable levels of precision in metrics such as precision, recall, and F score when applied to Spanish EHR. Contrary to expectations, the LLM-driven approach, which necessitates minimal data annotation, performs on par with BERT’s capability to discern complex medical terminologies and contextual nuances within the EHRs. The results of this study highlight a notable advance in the field of NER for Spanish EHRs, with the few shot approach driven by LLM, enhanced by external knowledge, slightly edging out the traditional BERT-based method in overall effectiveness. GPT’s superiority in F-score and its minimal reliance on extensive data annotation underscore its potential in medical data processing.
Assessment of flood risk in Söğütlü stream watershed of Trabzon province in Turkey using geographic information systems and analytic hierarchy process approach
Authors: Koralay Necla , Kara Ömer 
Published: April 2024

AbstractFloods are significant natural disasters that have wide-ranging social, economic, and environmental consequences on a global scale. They lead to land degradation and increased erosion, which in turn negatively impacts water quality. In the present study, the Söğütlü stream watershed in the Eastern Black Sea Region of Turkey was chosen as the study area to create a flood risk map using Analytical Hierarchy Process and Weighted Overlay tools in the ArcGIS. The raster maps, including soil type, rainfall, landuse, slope, aspect, elevation, and distance to the stream, were utilized as the foundation for the analysis. The flood risk map generated from this analysis reveals five risk zones that is very high, high, moderate, low, and very low-risk zones. Statistical analysis was performed to evaluate the obtained data. The study results indicate that low and moderate areas encompass 95.98% of the watershed, while high-risk and very high-risk areas account for 4.02% of the watershed in the flood risk map of the Söğütlü watershed. It was observed that areas in close proximity to rivers, low slope end elevation were more susceptible to flooding. This study is great importance as it allows for the development of preventive, preparatory, and post-disaster plans before flood events occur by providing a flood risk map. Moreover, the creation of a flood risk map at the watershed scale helps planners in undertaking soil and water conservation efforts during watershed planning.