bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rheological and morphological behavior of waxy crude with poly(octadecyl methacrylate) as flow modifier
Authors: Dutta Pranjal P. , Mohan Bitupan , Phukan Subasita , Saikia Subrat , Saikia Prakash J. 
Published: January 2025

AbstractFree radical and reverse atom transfer radical polymerization (ATRP) techniques has been applied in the synthesis of poly(octadecyl methacrylate) (POMA) to be evaluated as flow improver (FI) for waxy crude through rheological and morphological studies. Addition of POMA improves the flow ability of waxy crude as well as modulated the wax crystal morphology. The maximum reduction of apparent viscosity of waxy crude was observed at higher concentration of POMA that was synthesized via free radical polymerization and at lower concentration of POMA that was synthesized via reverse ATRP technique. The wax crystal morphological observation by polarized optical microscopy shows that POMA can strongly crystallize with wax molecules, and the size of wax crystals increases slightly and tends to form small wax crystal flocs, which greatly improves the rheological properties of the crude oil.Highlights
Comb‐like polymer POMA synthesized via free radical and reverse ATRP methods.
Effect of POMA on low‐temperature flow ability of crude oil was studied.
POMA via free radical method efficiently lowers the crude oil gelation point.
Crude oil viscosity significantly decreases with addition of synthesized POMA.
Adding POMA via both methods significantly alters wax crystal size in crude oil.
Assessing risk factors for drug storage practices in veterinary surgeries: A questionnaire study of UK veterinary professionals
Authors: Perry Jordan , Gouveia Kelly 
Published: January 2025

AbstractBackgroundSafe drug storage practices are essential in veterinary practice to maintain optimal standards of animal care. Practical challenges in clinic may impact their uptake, which could affect drug efficacy and the success of treatment. The UK is presumed to provide high standards for animal care and welfare in the veterinary profession and may provide an interesting case study to assess veterinary drug storage practices.MethodsAn online survey with 184 practice participants assessed UK veterinary professionals’ responses on drug storage practices. This included socio‐demographic information and questions or statements that examined storage practices compliant with the requirements established by the Royal College of Veterinary Surgeons (RCVS), the regulatory body for veterinary practice in the UK.ResultsOverall, practices followed RCVS‐recommended measures, though there was clear selectivity for stock temperature checks (72.2%), over other measures, particularly, replacing stock (54.4%) and returning medications to refrigerated storage (52.5%). Clinical experience and practice type impacted most on drug storage practices, with more experienced clinicians and small animal practices showing greater uptake of best measures.ConclusionsWe suggest that practices should adopt all of the RCVS measures in relation to veterinary drug storage practices. Socio‐demographic factors should be considered because they can act as risk factors influencing best practice in clinics. Our findings may have wider implications for the veterinary profession in general, given similar demographic trends in veterinary practice in many European countries.
Mediation Mechanisms in Serbian Intellectual Property Law: A Steady Progression
Authors: Popović Dušan V. 
Published: January 2025

Abstract
The introduction of mediation mechanisms in Serbian civil law was incited by the accession of the country to the Council of Europe in 2003. The chapter starts by examining the general rules on mediability in Serbian law and applying them to IP-related disputes. As a general rule, mediation may be used as an alternative dispute resolution mechanism: (i) in contentious legal matters in which the parties may freely dispose of their claims; (ii) unless the law stipulates exclusive authority of a court or other relevant entity. This rule sets the objective boundaries of mediation. The subjective boundaries of mediation, related to the criteria that the parties in mediation must meet, are not prescribed in Serbian law. The first criterion is quite extensively defined, given that the parties may freely dispose not only of their property claims but also of non-property claims, as long as this is in accordance with the imperative norms, public order, moral rules and customary rules. The Serbian intellectual property legislation explicitly refers to mediation mechanisms in two cases only. First, the copyright legislation allows for a mediation mechanisms to be employed if a broadcasting organization and a cable operator are unable to reach an agreement on cable retransmission conditions. Second, trademark legislation allows for the parties to trademark opposition proceedings to attempt to resolve the dispute by means of mediation within the time frame of 24 months. Even if an IP dispute is mediable, one should not necessarily conclude that it is always appropriate to resolve it by way of mediation. Accordingly, the chapter examines the advantages and disadvantages of mediation over litigation.
Assessing personal recovery in individuals with severe mental illness: validation of the Dutch Brief INSPIRE-O
Authors: Swildens Wilma E. , Visser Ellen , van Ens Welmoed , Schaefer Barbara , Nugter Annet , Delespaul Philippe , van Weeghel Jaap , Slade Mike , Sanches Sarita A. 
Published: January 2025

Abstract

Purpose
Recovery is a key objective in mental health services for people with severe mental illness (SMI). In addition to clinical and functional recovery, personal recovery has gained increasing attention. The CHIME Framework identifies five personal recovery processes—Connectedness, Hope, Identity, Meaning, Empowerment—and is the theoretical foundation for the Brief INSPIRE, a validated Patient-Rated Experience Measure (PREM) to evaluate recovery support. Brief INSPIRE was modified to a five-item Patient-Rated Outcome Measure (PROM) assessing recovery, called Brief INSPIRE-Outcome (Brief INSPIRE-O). Subject of this study are the psychometric properties of the Brief INSPIRE-O.

Methods
Data on validity and reliability gathered through annual routine outcome monitoring were collected for 861 individuals with SMI of Flexible Assertive Community Treatment teams and a follow-up measurement was available for 232 of these individuals. Test–retest reliability was evaluated in a separate subset of 30 individuals with SMI.

Results
The Brief INSPIRE-O shows good internal consistency (Cronbach’s alpha 0.77), test–retest reliability, construct validity, sensitivity to change and no floor or ceiling effects. Furthermore, change in Brief INSPIRE-O was positively related to changes in quality of life and negatively to problems in clinical functioning and unmet care need.

Conclusion
Brief INSPIRE-O can be used for research and monitoring to better understand and improve processes of personal recovery in individuals with SMI.
Characterization of chemical compounds of fractions from four different bee pollens with chemometric classification
Authors: Tel-Çayan Gülsen , Çayan Fatih , Deveci Ebru , Karataş Şükrü , Duru Mehmet Emin 
Published: January 2025

Abstract
Bee pollen continues to increase its popularity as a ‘complete food’ among natural resources with its high health value and therapeutic chemical compounds. This study focused on HPLC-DAD (high-performance liquid chromatography-diode array detection) based on the identification of chemical compounds of Cistus creticus, Helianthus annuus, Papaver somniferum and Salix sp. bee pollens. Also, water extracts and the fractions (hexane, dichloromethane, ethyl acetate, butanol, water) obtained from the methanol extracts of all bee pollens were investigated for chemical compounds. A total of 29 chemical compounds were screened and kaempferol was detected in all studied bee pollen samples at concentration of 0.4 and 331.6 µg/g. Luteolin (68.7–694.8 µg/g), trans-aconitic acid (12.2–479.1 µg/g), myricetin (160.4–1534.2 µg/g), quercetin (162.1–608.1 µg/g), rosmarinic acid (273.8–435.6 µg/g), rutin (3.6–202.8 µg/g), and trans-cinnamic acid (5.1–1854.6 µg/g) were found as the most abundant chemical compounds. Additionally, nonpolar and polar fractions of the bee pollens were chemometrically clustered for similarities and differences of chemical compounds via principal component analysis (PCA) and hierarchical cluster analysis (HCA). In nonpolar fractions, C. creticus, H. annuus, P. somniferum, and Salix sp. ethyl acetate fractions (CEA, HEA, PEA, SEA) were separated from other fractions, while in polar fractions, C. creticus, H. annuus, P. somniferum, and Salix sp. butanol fractions (CB, HB, PB, SB) were separated from other fractions. This study provides additional data on the characterization of valuable chemical compounds in bee pollens and constitutes a beginning in the chemometric classification of chemical compounds.
Using ground oyster mushroom and chia seeds for the fortification of wheat bread-Nelder–Mead simplex optimisation
Authors: Schneider Yannik , Zettel Viktoria 
Published: January 2025

Abstract
Wheat bread was enriched with ground chia and ground oyster mushroom to improve its nutritional properties regarding protein and fibre content within the context of food fortification while maintaining a high specific volume and a soft crumb. A bread made from 100% wheat flour was used as reference. The Nelder–Mead simplex algorithm was used to determine the optimal proportions of chia and oyster mushroom substitution in the recipe. Bread quality was analysed and rated. Chia has a positive effect on the baking result due to its water-binding capacity, while mushrooms have a rather negative effect caused by the enzymes they contain. It was possible to replace 1.6% of wheat flour with ground oyster mushroom and 1.2% chia without any deviations from the control within 7 steps Furthermore, the partially substituted breads showed 3.8% lower contents of carbohydrates with a reduction of 4.1% of total sugar and higher contents of dietary fibre and protein, 22.7% and 2.3% respectively in comparison with the control sample. The wheat flour in bread can be replaced by chia flour and ground oyster mushrooms in small quantities without compromising the quality of the bread while improving some nutritional values.
Abwehrmechanismen in der Brief Dynamic Interpersonal Therapy
Authors: Herrmann Pauline Leonie , Hüwe Lotta , Andreas Sylke 
Published: January 2025

Zusammenfassung
Die Brief Dynamic Interpersonal Therapy (DIT) ist ein 16-stündiges manualisiertes Psychotherapieverfahren mit psychodynamischem Hintergrund. Im Fokus steht die Bearbeitung eines maladaptiven Beziehungsmusters, das als Ursache der Symptome begriffen wird. Inwiefern Veränderungen im Funktionsniveau der psychischen Abwehr eine Rolle spielen, wurde bislang nicht untersucht und soll an einem Einzelfall herausgearbeitet werden. Hierzu wurden 11 Sitzungen aus verschiedenen Therapiephasen mithilfe der Defense Mechanism Rating Scales Q‑sort analysiert. Die Symptombelastung, die therapeutische Beziehung und die Mentalisierungsfähigkeit wurden vor jeder Sitzung erhoben. Es zeigte sich beim Patienten ein stabiles und adaptives Muster der Abwehr mit unsystematischen Schwankungen. Die Outcome-Instrumente lassen auf einen erfolgreichen Verlauf der Behandlung schließen. Es bleibt zu diskutieren, ob adaptive Funktionsniveaus der Abwehr ein Behandlungsziel oder vielmehr eine Behandlungsvoraussetzung für die DIT darstellen.
DeepLabv3 + method for detecting and segmenting apical lesions on panoramic radiography
Authors: Ketenci Çay Fatmanur , Yeşil Çağrı , Çay Oktay , Yılmaz Büşra Gül , Özçini Fatma Hasene , İlgüy Dilhan 
Published: January 2025

Abstract

Objective
This study aimed to apply the DeepLabv3 + model and compare it with the U-Net model in terms of detecting and segmenting apical lesions on panoramic radiography.

Methods
260 panoramic images that contain apical lesions in different regions were collected and randomly divided into training and test datasets. All images were manually annotated for apical lesions using Computer Vision Annotation Tool software by two independent dental radiologists and a master reviewer. The DeepLabv3 + model, one of the state-of-the-art deep semantic segmentation models, was utilized using Python programming language and the TensorFlow library and applied to the prepared datasets. The model was compared with the U-Net model applied to apical lesions and other medical image segmentation problems in the literature.

Results
The DeepLabv3 + and U-Net models were applied to the same datasets with the same hyper-parameters. The AUC and recall results of the DeepLabv3 + were 29.96% and 61.06% better than the U-Net model. However, the U-Net model gets 69.17% and 25.55% better precision and F1-score results than the DeepLabv3 + model. The difference in the IoU results of the models was not statistically significant.

Conclusions
This paper comprehensively evaluated the DeepLabv3 + model and compared it with the U-Net model. Our experimental findings indicated that DeepLabv3 + outperforms the U-Net model by a substantial margin for both AUC and recall metrics. According to those results, for detecting apical lesions, we encourage researchers to use and improve the DeepLabv3 + model.

Clinical relevance
The DeepLabv3 + model has the poten tial to improve clinical diagnosis and treatment planning and save time in the clinic.
Ultra-fast computation of fractal dimension for RGB images
Authors: Ruiz de Miras Juan , Li Yurong , León Alejandro , Arroyo Germán , López Luis , Torres Juan Carlos , Martín Domingo 
Published: January 2025

Abstract
The fractal dimension (FD) is a quantitative parameter widely used to analyze digital images in many application fields such as image segmentation, feature extraction, object recognition, texture analysis, and image compression and denoising, among many others. A variety of algorithms have been previously proposed for estimating the FD, however most of them are limited to binary or gray-scale images only. In recent years, several authors have proposed algorithms for computing the FD of color images. Nevertheless, almost all these methods are computationally inefficient when analyzing large images. Nowadays, color images can be very large in size, and there is a growing trend toward even larger datasets. This implies that the time required to calculate the FD of such datasets can become extremely long. In this paper we present a very efficient GPU algorithm, implemented in CUDA, for computing the FD of RGB color images. Our solution is an extension to RGB of the differential box-counting (DBC) algorithm for gray-scale images. Our implementation simplifies the box-counting computation to very simple operations which are easily combined across iterations. We evaluated our algorithm on two distinct hardware/software platforms using a set of images of increasing size. The performance of our method was compared against two recent FD algorithms for RGB images: a fast box-merging GPU algorithm, and the most advanced approach based on extending the DBC method. The results showed that our GPU algorithm performed very well and achieved speedups of up to 7.9× and 6172.6× regarding these algorithms, respectively. In addition, our algorithm achieved average error rates similar to those obtained by the two reference algorithms when estimating the FD for synthetic images with known FD values, and even outperformed them when processing large images. These results suggest that our GPU algorithm offers a highly reliable and ultra-fast solution for estimating the FD of color images.
Computational analysis of virus-host protein-protein interactions using gene ontology and natural language processing
Authors: Cihan Pınar , Ozger Zeynep Banu , Cakabay Zeynep 
Published: January 2025

Abstract
The role of in-silico computational methods in identifying protein-protein interactions (PPIs) between target and host proteins is crucial for developing effective infection treatments. These methods are essential for quickly determining high-quality and accurate PPIs, predicting protein pairs with the highest likelihood of physical interaction from a large pool, and reducing the need for experimental confirmation or prioritizing pairs for experiments. This study proposes using gene ontology and natural language processing (NLP) approaches to extract and quantify features from protein sequences. In the first step, proteins were represented using gene ontology terms, and a set of features was generated. In the second step, NLP techniques treated gene ontology terms as a word dictionary, creating numerical vectors using the bag of words (BoW), count vector, term frequency-inverse document frequency (TF-IDF), and information content methods. In the third step, different machine learning methods, including Decision Tree, Random Forest, Bagging-RepTree, Bagging-RF, BayesNet, Deep Neural Network (DNN), Logistic Regression, Support Vector Machine (SVM), and VotedPerceptron, were employed to predict protein interactions in the datasets. In the fourth step, the Max-Min Parents and Children (MMPC) feature selection algorithm was applied to improve predictions using fewer features. The performance of the developed method was tested on the SARS-CoV-2 protein interaction dataset. The MMPC algorithm reduced the feature count by over 99%, enhancing protein interaction prediction. After feature selection, the DNN method achieved the highest predictive performance, with an AUC of 0.878 and an F-Measure of 0.793. Sequence-based protein encoding methods AAC, APAAC, CKSAAPP, CTriad, DC, and PAAC were applied to proteins in the SARS-CoV-2 interaction dataset and their performance was compared with GO-NLP. The performance of the relevant methods was measured separately and combined. The highest performance was obtained from the combined dataset with an AUC value of 0.888. This study demonstrates that the proposed gene ontology and NLP approach can successfully predict protein-protein interactions for antiviral drug design with significantly fewer features using the MMPC-DNN model.
Supporting learning difficulties with e-book based on context-based teaching strategy from social perspective: design and experience
Authors: Şanal Seda Özer , Güler Tülay Dargut , Torun Fulya 
Published: January 2025

Abstract
Although the positive effects of the use of technology for students with learning disabilities (LD) have been reported, there is a lack of both theory and practice in terms of integrating technology with the appropriate strategy in accordance with the student, content and purpose. Both teaching materials developed with qualified strategies and the active involvement of the target audience in the process are a need to design effective and sustainable learning materials and processes for LD. This study involved both the production of a material with the common views of stakeholders related to LD (individuals diagnosed with LD, special education teachers, academics working on LD) and the examination of the effectiveness of this material. The focus of the study is primarily on the fact that human being is a social being and learning, language and reading are social phenomena. In this context, within the framework of social constructivist perspective, an e-book design based on context based teaching (CBT) strategy and its effectiveness on reading performance were analysed. The study involved a two-stage process. In the first phase, design-based research was conducted and a CBT-supported e-book (DIJIKIT) was developed. In the second stage, DIJIKIT and an e-book were compared in an adapted alternating implementation design. The participants of the study in the CBT process consisted of researchers, three special education teachers, academicians and two primary school students diagnosed with learning disabilities. In each cycle of the DBR, the participants provided feedback on the material. Semi-structured interviews, focus group discussions, video recordings and a researcher's diary were used as data collection tools in the DBR. As a result of the DBR, DIJIKIT design and content features were determined. In the experimental process, DIJIKIT was used by three primary school students diagnosed with learning disabilities. In the experimental process, efficacy (the informal reading invetory), reliability (treatment integrity, interobserver agreement) and social validity (social validity forms) data were collected. The reading comprehension performances and reading levels of all three students increased. Social validity data supported the experimental process data and clearly demonstrated the need for effective instructional technologies for both parents and teachers. The study offers practical implications and recommendations for future researchers in terms of exemplary design features and greater visibility of the relationship between instructional technologies and special education.
Teacher Satisfaction in Online Education: A Two-Factor Model of Extrinsic and Intrinsic Factors Across Diverse Contexts
Authors: Yan Ning , Batako Andre , Czanner Gabriela , Zhang Aiping 
Published: January 2025

Abstract
Blended learning modes are becoming the norm in educational institutions. This research investigates the factors affecting teacher satisfaction with online lesson delivery. This study was undertaken in primary, secondary, and higher education institutions across ten countries worldwide. A total of 247 teachers responded to the survey. This work innovatively validates a two-factor model of teacher satisfaction with online teaching, grounded in Herzberg’s two-factor theory. A first-order confirmatory factor analysis (CFA) was used to validate the constructs, followed by a second-order exploratory factor analysis (EFA) to identify key drivers of teacher satisfaction. The results showed that there were two key hidden drivers of teacher satisfaction i.e., intrinsic factor and extrinsic factor. The two factors explained 75% of the variance in teacher satisfaction. Teachers reported higher satisfaction due to the flexibility and work-life balance that online teaching affords, while dissatisfaction stemmed from institutional policies and insufficient incentives. It was observed that STEM teachers and older educators had lower satisfaction and higher technology anxiety. These insights have potential applications beyond online teaching, extending to teacher satisfaction in physical classroom settings.