bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accounting choices in data envelopment analysis
Authors: Cui Claire Murong , Harrison Julie A. , Ng Frederick , Rouse Paul 
Published: January 2025

Abstract
Due to the increasing availability of large-scale digitalized databases containing summarized financial accounting measures, a growing number of DEA models are using these variables. Where only accounting measures are used, we term these “FinDEA” models. Accounting measures are subject to accounting choices regarding recognition and measurement, which means that they are not necessarily equivalent to direct measures of underlying physical measures more commonly used in DEA. This paper investigates the impact of accounting choices on FinDEA results related to alternative accounting measures of capital. Using both simulated and real-world data, we find that accounting choices impact FinDEA results, with the magnitude influenced by the heterogeneity of the accounting choices and sample sizes. Our results suggest that the variations in accounting choices need to be considered as part of an assessment of the homogeneity of inputs and outputs when designing DEA models using accounting measures.
Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting
Authors: Asif Muhammad , Kuglitsch Monique M. , Pelivan Ivanka , Albano Raffaele 
Published: January 2025

Abstract
Among natural hazards, floods pose the greatest threat to lives and livelihoods. To reduce flood impacts, short-term flood forecasting can contribute to early warnings that provide communities with time to react. This manuscript explores how machine learning (ML) can support short-term flood forecasting. Using two methods [strengths, weaknesses, opportunities, and threats (SWOT) and comparative performance analysis] for different forecast lead times (1–6, 6–12, 12–24, and 24–48 h), we evaluate the performance of machine learning models in 94 journal papers from 2001 to 2023. SWOT reveals that the best short-term flood forecasting was produced by hybrid, random forest (RF), long short-term memory (LSTM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) approaches. The comparative performance analysis, meanwhile, favors convolutional neural network, ANFIS, multilayer perceptron, k-nearest neighbors algorithm (KNN), hybrid, LSTM, ANN, and support vector machine (SVM) at 1–6 h; hybrid, ANFIS, ANN, and LSTM at 6–12 h; SVM, hybrid, and RF at 12–24 h; and hybrid and RF at 24–48 h. In general, hybrid approaches consistently perform well across all lead times. Trends such as hybridization, model selection, input data selection, and decomposition seem to improve the accuracy of models. Furthermore, effective stand-alone ML models such as ANN, SVM, RF, genetic algorithm, KNN, and LSTM, provide better outcomes through hybridization with other ML models. By including different machine learning models and parameters such as environmental, socio-economical, and climatic parameters, the hybrid system can produce more accurate flood forecasting, making it more effective for early warning operational purposes.
Indication of the sensitivity of Pinaceae species growing in Eastern Central Europe to ground-level ozone pollution
Authors: Lukasová Veronika , Varšová Svetlana , Žatková Lucia , Adamčíková Katarína , Buchholcerová Anna , Onderka Milan , Milovský Rastislav , Bilčík Dušan , Mináriková Veronika 
Published: January 2025

Abstract
This study focused on testing the response of the assimilation apparatus of evergreen Pinaceae species to increasing levels of oxidative stress simulated in manipulative experiments. Needles were collected from mature individuals of Pinus mugo, Pinus cembra, Pinus sylvestris, Abies alba, and Picea abies at the foothill (FH) and alpine treeline ecotone (ATE) in the High Tatras (Western Carpathians). The injury index (INX), quantified by the modified electrolyte leakage (EL) method, indicated severe needle damage due to exposure to extremely high levels of O3. Ozonation induced changes in the chemical composition of the needles, which were detected via gas chromatography-mass spectrometry. The oxidative stability (OxS) indicator derived from INXs was used to determine the stomatal O3 flux-based critical level CL(OxS), with the threshold value of OxS at -0.05, corresponding to 5% injury to the needles. Assessment of the phytotoxic ozone dose (POD0) under ambient O3 and field environmental conditions during the 2023 growing season via CL(OxS) revealed that the studied species utilised between 18% (Abies alba FH) and 33% (Pinus mugo ATE) of their O3 tolerance potential. These results support our hypothesis that Pinaceae species growing in the High Tatras, which are part of the Alpine biogeographical region of Eastern Central Europe, are vulnerable to O3 concentrations significantly higher than the typical ambient O3 level in the natural environment.
Molecular and Morphological Characterization of Two Clinostomum (Digenea: Clinostomidae) Species with the First Case of Clinostomum tilapiae from Turkey
Authors: Öztürk Mehmet , Umur Şinasi 
Published: January 2025

Abstract

Purpose
The aim of this study was to investigate the presence of Clinostomum species in wild birds in Turkey using morphological and molecular methods.

Methods
51 birds of 18 species from seven orders previously reported as definitive hosts of the Clinostomum spp. were collected. Identification of the species was made by morphological characteristics and partial sequence of the cox1 gene.

Results
This study concludes that Ardea alba and Ardea cinerea were infected with Clinostomum complanatum, while Ardea purpurea was infected with Clinostomum tilapiae.

Conclusion

Clinostomum complanatum has been reported for the first time in the definitive hosts in Turkey. This study is the first molecular report of C. tilapiae in definitive hosts and the first report in Turkey. The present work indicates that Clinostomum species in the Afrotropic and Palearctic regions can also be found in Turkey.
Factors associated with children’s attachment hierarchy in lesbian, gay, and heterosexual parent families through assisted reproduction
Authors: Quintigliano Maria , Carone Nicola , Speranza Anna Maria , Lingiardi Vittorio 
Published: January 2025

Abstract
With few exceptions, previous studies of children’s identification and hierarchy of attachment figures have involved only children born to heterosexual parents through unassisted conception, recognizing mothers as the primary attachment figures. Further research into diverse families may help to disentangle parents’ attachment role from their gender, sexual orientation, and biological relatedness to children. The present study aimed at exploring whether differences in children’s choice of primary attachment figure could be explained by parents’ gender, biological status, attachment state of mind, reflective functioning, parenting quality, or by the child’s gender, across diverse family types. In total, 152 parents (i.e., 60 lesbian mothers through donor insemination, 50 gay fathers through surrogacy, 42 heterosexual parents through gamete donation) and their 76 school-aged children participated in the study. Children were interviewed about their choice of attachment figure in different situations. Parents completed questionnaires about parenting stress, parental rejection, parent–child relational efficacy, and parent positivity; and were administered the Adult Attachment Interview as a measure of their attachment state of mind and reflective functioning. Overall, no significant differences emerged between mothers and fathers or between biological and non-biological parents for children’s choice of primary attachment figure. However, fathers with higher RF were more likely to be identified as a primary attachment figure, and gay fathers who demonstrated less parental rejection were more likely to be chosen as a primary attachment figure. The findings provide important insights for social policies aimed at promoting preventive parenting interventions targeting the child–parent attachment bond in diverse families.
Dry Fractionation for Sustainable Production of Functional, Nutritional and Palatable Grain Legume Protein Ingredients
Authors: Schutyser Maarten , Novoa Santiago Calderon , Wetterauw Koen , Politiek Regina , Wilms Patrick 
Published: January 2025

Abstract
Grain legumes such as pea, faba bean, lupin and soybean are an important protein source for the production of plant-based foods and thus facilitate the protein transition. For many food applications, the proteins are first isolated using conventional wet methods that are resource intensive. Dry fractionation processes are therefore developed to facilitate a more sustainable protein transition. This review discusses the status of dry fractionation of grain legumes to produce protein-rich ingredients for food production and how the use of these dry-enriched ingredients could be further enhanced. Dry fractionation includes dry milling and dry separation technologies which are first briefly described. There are different strategies to further improve the separation, which include pre-treatments and improving powder bulk behaviour. Pre- and post-treatments not only improve the functional properties of dry-enriched protein ingredients but also enhance the nutritional value of the ingredients and minimize off-flavours. Opportunities still exist to further optimise dry fractionation techniques and pre-treatments to increase the purity and yield. Finally, the use of dry-enriched fractions should be accelerated by development of 1) functionality-driven ingredient formulation strategies and 2) new physical post-modifications and food fermentation strategies to enhance functionality, nutritional value and taste of the ingredients to prepare attractive food products.
Design and Synthesizing of Hemoglobin-Based Multifunctional Fibers for Improved Carbon Monoxide Absorption Rates
Authors: Pham Anh , Subeshan Balakrishnan , Asmatulu Eylem , Asmatulu Ramazan 
Published: January 2025

Abstract
This study is aimed at developing advanced materials for carbon monoxide (CO) capture by producing hemoglobin (Hb)-based electrospun multifunctional micro- and nanofibers blended with polyvinylpyrrolidone (PVP). Unlike conventional CO trapping materials such as activated carbon, ammoniacal cuprous chloride, zeolites, and metal-organic frameworks (MOFs), Hb/PVP fibers leverage the simplicity and scalability of electrospinning to produce continuous, defect-free flexible fibers with tunable micron- to nanoscale diameters. The process enables precise control over fiber morphology, surface area, porosity, and hydrophilicity, providing significant advantages for optimizing CO adsorption rates. Moreover, the inclusion of Hb introduces a biomimetic advantage through its intrinsic CO-binding affinity, offering higher specificity and interaction potential compared to traditional physical adsorption or chemical frameworks. Experimental results revealed that fibers with 8 wt.% PVP exhibited the smallest and most uniform diameters, while higher PVP concentrations (16, 32 wt.%) enhanced hydrophilicity, with complete water absorption occurring within 400 and 200 seconds, respectively. Structural and compositional analyses using confocal laser scanning microscopy (CLSM) and Fourier transform infrared spectroscopy (FTIR) confirmed the integrity and chemical characteristics of the fibers. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) established their thermal stability, with critical transitions at approximately 80 ℃ (denaturation) and 200 ℃ (decomposition). Degradation was observed between 200 and 430 ℃, corresponding to significant weight loss. These findings demonstrate the potential of Hb/PVP fibers as exceptional alternatives for CO capture. This study may open new possibilities for increasing the absorption rate of highly porous fibers for toxic CO capture in the bloodstream and address other related concerns.
Deep learning enabled computer vision in remanufacturing and refurbishment applications: defect detection and grading for smart phones
Authors: Mohandas Reenu , Southern Mark , Fitzpatrick Colin , Hayes Martin 
Published: January 2025

Abstract
This work demonstrates the use of Deep Learning-based Computer Vision for Remanufacturing end-of-life consumer electronics products, considering smartphones as the use-case. We implemented automated detection of screen defects such as scratches and cracks. In turn, this could lead to increased reuse of smartphones in a secondary market alongside new ones to meet consumer demand. The refurbishment of smartphone devices is a growing industry heavily dependent on manual labor, making decisions subjective, especially in grading the severity of damage. A critical analysis of defect detection and smartphone grading from a remanufacturing perspective is conducted. This analysis is significant in a remanufacturing sector that deals with dynamically varying input of cores (used products for remanufacturing), characteristics, timing, and number of returns. The solution we propose here is novel in its own right, requiring data analysis and collection, data curing, defect parameterization, and dataset building to enable model-based training and detection experiments. We collected and annotated a dataset to detect and grade the various defects based on their severity. A range of deep learning models was trained on the dataset to obtain baseline results for the state-of-the-art deep learning detection models, including YOLOv7, YOLOv8, YOLO11 variants, and Mask R-CNN. Our experiments also showed improved precision values when the pre-trained models were pre-fine-tuned using a road crack segmentation dataset before training on our phone defect dataset. The inference time for the YOLOv8x model is 8ms. This reduced inference time with a high precision of 70.4% indicates that a consistent, fast, and accurate grading is achieved here, ensuring a high throughput rate in the remanufacturing process and ensuring sustainability.
New limits of the Lie product formula type in Banach algebras
Authors: Popa Dumitru 
Published: January 2025

Abstract
We find new limits of the Lie product formula type in Banach algebras with unit. Some sample results: Let X, Y, Z be Banach algebras with unit,

$$ \left( x_{n},y_{n}\right) _{n\in \mathbb {N}}\subset X\times Y$$

x
n

,

y
n

n
∈
N

⊂
X
×
Y

convergent sequences with

$$\lim \nolimits _{n\rightarrow \infty }x_{n}=x$$

lim

n
→
∞

x
n

=
x

,

$$ \lim \nolimits _{n\rightarrow \infty }y_{n}=y$$

lim

n
→
∞

y
n

=
y

and

$$T:X\times Y\rightarrow Z$$

T
:
X
×
Y
→
Z

a continuous bilinear operator with

$$T\left( \textbf{1},\textbf{1}\right) = \textbf{1}$$

T

1
,
1

=
1

. Then for all sequences of natural numbers

$$\left( a_{n}\right) _{n\in \mathbb {N}}$$

a
n

n
∈
N

with

$$\lim \nolimits _{n\rightarrow \infty }a_{n}=\infty $$

lim

n
→
∞

a
n

=
∞

we have

$$\begin{aligned} \lim \limits _{n\rightarrow \infty }\left[ T\left( \prod \limits _{k=1}^{n}e^{ \frac{x_{k}}{a_{n}\left( k+n\right) \left( k+2n\right) }},\prod \limits _{k=1}^{n}e^{\frac{y_{k}}{a_{n}\left( k+2n\right) \left( k+3n\right) } }\right) \right] ^{a_{n}}=e^{\left( \ln \frac{4}{3}\right) T\left( x,\textbf{ 1}\right) +\left( \ln 2\right) T\left( \textbf{1},y\right) }; \end{aligned}$$

lim

n
→
∞

T

∏

k
=
1

n

e

x
k

a
n

k
+
n

k
+
2
n

,

∏

k
=
1

n

e

y
k

a
n

k
+
2
n

k
+
3
n

a
n

=

e

ln

4
3

T

x
,
1

+

ln
2

T

1
,
y

;

$$\begin{aligned} \lim \limits _{n\rightarrow \infty }\left[ T\left( \prod \limits _{k=1}^{n}\cos \frac{x_{k}}{a_{n}\sqrt{n\left( n+k\right) }},\prod \limits _{k=1}^{n}\cos \frac{ky_{k}}{na_{n}\sqrt{n^{2}+k^{2}}}\right) \right] ^{na_{n}^{2}} \end{aligned}$$

lim

n
→
∞

T

∏

k
=
1

n

cos

x
k

a
n

n

n
+
k

,

∏

k
=
1

n

cos

k

y
k

n

a
n

n
2

+

k
2

n

a

n

2

$$\begin{aligned} =e^{-\frac{\left( \ln 2\right) T\left( x^{2},\textbf{1}\right) +\left( 1- \frac{\pi }{4}\right) T\left( \textbf{1},y^{2}\right) }{2}}. \end{aligned}$$

=

e

-

ln
2

T

x
2

,
1

+

1
-

π
4

T

1
,

y
2

2

.
Why you Should not use CI to Evaluate Socially Disruptive Technology
Authors: Prégent Alexandra 
Published: January 2025

Abstract
Contextual Integrity (CI) is built to assess potential privacy violations of new sociotechnical systems and practices. It does so by evaluating their respect for the context-relative informational norms at play in a given context. But can CI evaluate new sociotechnical systems that severely disrupt established social practices? In this paper, I argue that, while CI claims to be able to assess privacy violations of all sociotechnical systems and practices, it cannot assess the ones that cause severe changes and disruptions in the norms and values of a given context. These types of technology are known as socially disruptive technologies (SDTs) and this paper argues that they are beyond CI’s scope. It follows that at best, a privacy assessment of those technologies by CI would be useless and, at worst, lead to potential harm, including failure to identify privacy violations and unwarranted legitimisation of privacy-threatening technology. Government actors, policymakers, and academics should refrain from relying on CI to assess this type of technology.
Adolescent depression in school: risk factors and consequences on school functioning
Authors: Fassl Flora , Muth Joy , Hofleitner Maximilian , Lüftenegger Marko 
Published: January 2025

Abstract
Adolescents spend most of their formative years in school, making it an important context to consider when investigating crises. A personal crisis that often has its onset during adolescence is depression. To gain a better understanding of depression in school, we explore possible individual and school-related risk factors and outcomes of depressive symptoms in line with current etiology models. First, we investigate the intersection of possible demographic risk factors of depression (female gender, immigration background, SES). Second, we explore school-related factors (conscientiousness, parental expectations, social inclusion) that might be associated with students’ depressive symptoms, and whether depressive symptoms mediate the effects of these school-related factors on school-functioning (grades, test anxiety). The representative data was collected in 30 Austrian secondary schools in a survey study with three waves. The sample consisted of 1874 12th-grade students from 93 classes. We used well-established self-report scales for all constructs that showed good reliability and students’ mean grades on the most recent tests. Analyses were pre-registered. In general, students experienced heightened depressive symptoms. More specifically, calculating a dummy-coded regression, we found female students with an immigration background and low SES to be the most burdened. The results of the mediation model showed depressive symptoms to partially mediate the association of conscientiousness, social inclusion, and parental expectations with test anxiety; this was not observed for grades. Furthermore, we only found conscientiousness to be positively associated with grades. Directions for future research and implications are discussed.
Ozone in Dentistry: An Updated Overview of Current Applications and Future Perspectives
Authors: Veneri Federica , Lanteri Valentina , Consolo Ugo , Checchi Vittorio , Generali Luigi 
Published: January 2025

Abstract

Purpose of Review
Oxygen-ozone therapy (OOT) is increasingly applied due to its multiple therapeutic effects, but it is still controversial in the medical community. This review provides a comprehensive overview of the recent clinical literature on OOT in dentistry, exploring its efficacy and current issues.

Recent Findings
OOT has a wide range of applications in dentistry, including caries management, endodontics, periodontics, and oral surgery. Overall, the results are promising with very limited adverse effects. The formulations used include gaseous ozone, ozonized water, ozonized gel, and ozonization of various products. A large methodologic heterogeneity currently prevents definitive conclusions and meaningful comparisons between different protocols.

Summary
Although not conclusive, current evidence suggests that OOT may offer some advantages as an adjunctive or alternative treatment option in certain clinical conditions. Future research should aim to standardize the reporting methodology of OOT protocols in order to make meaningful comparisons and guide the selection of the best protocols for specific treatment endpoints.
Confronting stem cells with surface-modified magnetic nanoparticles and low-frequency pulsed electromagnetic field
Authors: Bayramli-Öner Burcu , Lalegül-Ülker Özge , Sezer Serap , Elçin Ayşe Eser , Elçin Yaşar Murat 
Published: January 2025

Abstract
The combined use of Low Frequency-Pulsed Electromagnetic Field (LF-PEMF) and magnetic nanoparticles (MNPs) represents an innovative approach for biomedical applications in recent years. Also, the surface properties of MNPs play a crucial role in understanding how they will interact with biological systems and determining their suitability for the intended applications. The aim of this study was to evaluate the interactions of MNPs with different surface charges with adipose-derived mesenchymal stem cells (AD-MSCs) under LF-PEMF stimulation. Intracellular localization and in vitro cytotoxicity of surface-modified MNPs were examined through their interaction with AD-MSCs. Calcium and histochemical analysis were performed to investigate the synergistic effect of LF-PEMF. It was determined that application of MNPs (50 µg/mL) with LF-PEMF (1.3 mT, 15 Hz) did not demonstrate a cytotoxic effect on AD-MSCs. The surface modification of MNPs ensured a homogeneous distribution within cells, with cationic nanoparticles being predominantly localized around the nucleus, while anionic nanoparticles were dispersed in the cytoplasm. Furthermore, LF-PEMF exposure influenced cell morphology, leading to increased cytoplasmic extensions, particularly in cells interacting with silica-coated MNPs. These results shed light on the importance of how the surface properties of MNPs interact with cells under LF-PEMF stimulation and pave the way for future studies.

Graphical abstract
Advances in wearable energy storage and harvesting systems
Authors: Zhang Qiang , Soham Das , Liang Zheng , Wan Jiayu 
Published: January 2025

Abstract
The development of wearable energy sto rage and harvesting devices is pivotal for advancing next-generation healthcare technologies, facilitating continuous and real-time health monitoring. Traditional wearable devices have been constricted by bulky and rigid batteries, limiting their practicality and comfort. However, recent advancements in materials science have enabled the creation of flexible, stretchable, and lightweight energy storage and harvesting solutions. The integration of energy storage and harvesting technologies is essential for developing self-sustaining systems that minimize reliance on external power sources and enhance device longevity. These integrated systems ensure the continuous operation of sensors and processors vital for real-time health monitoring. This review examines recent significant progress in wearable energy storage and harvesting, focusing on the latest advancements in wearable devices, solar cells, biofuel cells, triboelectric nanogenerators, magnetoelastic gene rators, supercapacitors, lithium-ion batteries, and zinc-ion batteries. It also discusses key parameters crucial for their wearable applications, such as energy density, power density, and durability. Finally, the review addresses future challenges and prospects in this rapidly evolving field, underscoring the potential for developing innovative, self-powered wearable systems for healthcare applications.

Graphical Abstract