bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gastroprotective Effects of Biological Macromolecule: Polysaccharides
Authors: Pathak Rashmi , Pandey S. P. , Chandra Phool 
Published: February 2024

AbstractThe large molecular weight of the macromolecules sets them apart from all other components. This may range from 10 000 to over a million. While the molecular weight of other plant metabolites is seldom beyond 1000. Chemically, macromolecules are made up of long chains and little “building pieces,” which are joined covalently in a variety of ways. Biological macromolecules are large, naturally occurring cellular building blocks that play a range of crucial roles in the development and existence of living organisms. Biomacromolecules are essential in the biomedical field and other related professions. They feature a variety of beneficial properties, including excellent biodegradability, suitable mechanical strength, enhanced bioavailability, etc. They also have significant biocompatibility. They display a variety of biological characteristics, such as antimalignant, antidiabetic, antibacterial, antioxidant, and immunomodulatory. The use of essential carbohydrates including alginate, chitosan, pectin, starches, carrageenan, fucoidan, etc. is common in commercial applications. Natural substance‐based pharmacotherapy is now considered to be a highly promising future alternative to conventional medicine. Along with proteins and polynucleotides, polysaccharide is a vital biomacromolecule that has a crucial function in the growth and expansion of living things. A crucial element of higher plants, cell membranes of animal, and cell walls of microbes is polysaccharide. It is intimately tied to physiological processes as well. The importance of polysaccharides as a significant class of bioactive natural compounds has received more attention recently. Numerous studies have shown that natural polysaccharides contain bioactivities, which have led to the use of polysaccharides in the treatment of illness. The many parts of the research findings on the bioactivities of polysaccharides in gastro‐protection are included in this paper.
Studying the adsorption of emerging organic contaminants in zeolites with dispersion‐corrected density functional theory calculations: From numbers to recommendations
Authors: Fischer Michael , Brauer Jakob 
Published: February 2024

AbstractAdsorption energies obtained from dispersion‐corrected density functional theory (DFT) calculations show a considerable dependence on the choice of exchange‐correlation functional and dispersion correction. A number of investigations have employed different approaches to compute adsorption energies of small molecules in zeolites, using reference values from high‐level calculations and/or experiments. Such comparative studies are lacking for larger functional organic molecules such as pharmaceuticals or personal care products, despite their potential relevance for applications, e. g., in contaminant removal or drug delivery. The present study aims to fill this gap by comparing adsorption energies and, for selected cases, equilibrium structures of emerging organic contaminants adsorbed in MOR‐ and FAU‐type all‐silica zeolites. A total of 13 dispersion‐corrected DFT approaches are compared, including methods using a pairwise dispersion correction as well as non‐local van der Waals density functionals. While absolute values of adsorption energies vary widely, qualitative trends across the set of zeolite‐guest combinations are not strongly dependent on the choice of functional. For selected cluster models, DFT adsorption energies are compared to reference values from coupled cluster (DLPNO‐CCSD(T)) calculations. Although all DFT approaches deliver systematically more negative adsorption energies than the coupled cluster reference, this tendency is least pronounced for the rev‐vdW‐DF2 functional.
Prevalence of panoramic radiographs findings of cementum‐osseous dysplasia with calcification in the department of oral surgery at hospital: A retrospective study
Authors: Sato Kazumichi , Kurihara Kinue , Tanaka Aya , Yoshida Masayasu , Sekiya Kazumi , Ishizaki Ken 
Published: February 2024

AbstractAimCemento‐osseous dysplasia (COD) is a benign, non‐neoplastic, fibro‐osseous lesion that is often asymptomatic and discovered incidentally on dental radiographs. Radiological examination, rather than biopsy, is recommended for diagnosis because of the risk of infection. The dentist working at a hospital sometimes requests a family dentist to follow up on the progress of COD discovered accidentally during panoramic radiography at a hospital. However, there are few reports on the prevalence of COD on panoramic radiographs obtained at the oral surgery department of a hospital; therefore, we investigated this in this study.Materials and methodsWe examined the prevalence of COD with calcification (classification, presence of infection, and complications with simple bone cysts) in 1194 patients for whom panoramic radiographs were obtained.ResultsTwelve patients (1.0%) had findings of COD with calcification: four cases of periapical COD (three females and one male), five cases of focal COD (four females and one male), and three cases of florid COD (three females and zero males). Infection was seen in one case of Florid COD and one case of Focal COD. A simple bone cyst was observed in one patient with Florid COD.ConclusionCOD with calcification was observed in a certain percentage of patients, and many were asymptomatic. Dental healthcare workers need to understand that COD carries a risk of infection and that there are diseases that need to be differentiated. Patients should also be advised about the need for long‐term follow‐up.
Late‐life mood disorder as the initial presentation of progressive supranuclear palsy: A case series
Authors: Ichijo Kei , Takahata Keisuke , Kurose Shin , Watanabe Takemi , Nagase Yukihiro , Endo Hironobu , Tagai Kenji , Ishitobi Makoto , Higuchi Makoto 
Published: February 2024

AbstractAimProgressive supranuclear palsy (PSP) is a rapidly progressive neurodegenerative disorder characterized by Parkinsonism, supranuclear ophthalmoplegia, postural instability, and cognitive impairment.PatientsThis case series describes three patients initially diagnosed with late‐life mood disorders (depression and bipolar disorder) who were later diagnosed with PSP because of the development of typical neurological symptoms.ResultThe diagnostic challenge of PSP is highlighted in this case report, particularly in the early stages, when characteristic symptoms may not be present. The importance of considering PSP in the differential diagnosis of late‐life mood disorders, especially in the absence of response to standard antidepressant therapy, is also emphasized. The heterogeneity of PSP is described, with various subtypes and atypical variants presenting with different clinical features. The psychiatric symptoms of PSP include apathy, disinhibition, depression, and anxiety, whereas hallucinations and delusions are less frequent. Tau positron emission tomography imaging is discussed as a potential biomarker for atypical PSP.ConclusionEarly diagnosis and intervention are crucial for improved outcomes in PSP, necessitating further research to enhance the diagnostic and treatment strategies for PSP and other neurodegenerative diseases.
Evaluating the effect of individual characteristics and COVID‐19 pandemic on people living under extreme poverty conditions in Greece
Authors: Mimarakis Dimitrios , Alegakis Athanasios , Koukouli Sofia 
Published: February 2024

AbstractPoverty dimensions and COVID‐19 are risk factors for a low quality of life and anxiety. The aims of this study are to: (a) examine the quality of life and anxiety levels of citizens living under poverty and (b) explore whether this population's characteristics shaped the way the pandemic affected their anxiety and quality of life. Data were collected from two groups, each one consisting of 50 citizens of the Attica region of Greece who live in poverty. The first wave was collected in 2019 and the second in 2021. The same information was collected in both waves, including individual characteristics, quality of life dimensions, and anxiety traits. Both sample groups were receivers of “TEBA,” a social solidarity allowance for people living in poverty in Greece. Specific individual characteristics were more strongly associated with better quality of life and different anxiety levels during the COVID‐19 pandemic. The results offer potential insights into specific sociodemographic characteristics which are strongly associated with quality of life and/or anxiety, factors which are being constantly affected by the poverty phenomenon.
Who do learners of English as a lingua franca want to sound like? English speaker role models and envisioned selves in imagined communities
Authors: Savova Lilia , Azarnoosh Maryam 
Published: February 2024

AbstractTo examine speaker role models (SRMs) in second language acquisition (SLA), this study explored the sociolinguistic English as a lingua franca (ELF) perspective targeting international intelligibility. Thus, it examined the way ELF learners align with self‐selected English speakers and anonymous (audio‐recorded) English speakers as potential SRMs. The researchers asked the participants, 57 Iranian English majors, “Who do you want to sound like in English and why?” and analyzed their answers in the context of their SRMs and envisioned selves in imagined communities (ICs). The study's findings revealed that most participants had SRMs with three sets of characteristics—high English language proficiency and intelligibility, likeable personal attributes, and notable professional accomplishments. To explore anonymous English speakers in particular as potential SRMs, the researchers asked participants to rate anonymous speakers' intelligibility and likeability. The findings established that, based on the sound of their voices alone, anonymous audiorecorded speakers were less likely to be selected as SRMs. Further, the study found no significant difference between anonymous native and nonnative speakers, celebrities and others, for whom accent intelligibility and likeability increased but did not determine the likelihood of selecting them as their SRMs. Based on its findings, this study draws implications for language and teacher education and offers classroom applications with specific activities.
Military Spending and the Burden Sharing Debate
Authors: Du Bois Cind 
Published: February 2024

AbstractIn 2022 worldwide military spending peaked largely due to the invasion of Russia in Ukraine. This paper provides an overview of the world’s military spending over the last three decades where we clearly see an influence of the geopolitical situation on the budgets. On a more theoretical note, models on military spending have traditionally focused on the aspect of burden sharing between allies. By giving a synopsis of this academic literature on burden sharing, we set the frame for a discussion on the current differences in worldwide military spending. We highlight the caveats one should keep in mind when comparing military expenditure data and debating defense burdens.
Multilingual Stories for Immigrants and Refugees: A Language-as-Resource Approach
Authors: Stranger-Johannessen Espen , Damiani Valeria 
Published: February 2024

AbstractA major challenge for many immigrants and refugees, particularly those with little or no formal schooling, is learning the language of the host country. Open educational resources (OER), including courses and websites with multilingual stories, are ways to address this challenge. This chapter will present and analyse the OER developed in three projects funded by the Erasmus+ Programme: Advenus and Regap, which integrated language and content learning in courses, and LIDA Stories (https://lidastories.net), a collection of country-specific websites with multilingual stories for youth and adult immigrants and refugees who are learning the language of the host country. The chapter concludes by highlighting the key role of stories for the acquisition of the host country language by migrants and refugees.
The Relationship Between Negative Self-Concept, Trauma, and Maltreatment in Children and Adolescents: A Meta-Analysis
Authors: Melamed Daniela M. , Botting Jessica , Lofthouse Katie , Pass Laura , Meiser-Stedman Richard 
Published: February 2024

AbstractExperiencing trauma in childhood is a global public health issue linked to worse physical and mental health outcomes, including Post-Traumatic Stress Disorder (PTSD). Self-concept is a transdiagnostic concept linked to various psychopathologies and understanding its unique relationship to trauma is important. This meta-analysis aimed to understand the size of the effect between trauma and maltreatment and self-concept in children and adolescents. The current meta-analysis searched PubMed, PILOTS, PsycINFO, and Web of Science databases. Inclusion criteria involved studies with defined trauma exposure, valid measures of self-concept, and participants’ mean age under 18 years old. One-hundred-and-thirty-four studies were included in the meta-analysis (N = 255,334). A random-effects meta-analysis was performed. A small negative relationship was observed between trauma exposure and self-concept (r = − 0.20, 95% CI − 0.22, − 0.18). This relationship was significantly moderated by some variables (type and nature of trauma exposure) but not others (participant gender, type of self-concept measure, quality of studies, country economic status). A small relationship between trauma exposure and negative self-concept in children and adolescents was detected, with repeated trauma exposure and type of trauma exposure moderating this relationship. This provides important directions for clinical practice around providing support for those exposed or most vulnerable to experiencing trauma.
Using automatic model calibration for 3D morphological simulations: a case study of the Bodendorf reservoir flushing
Authors: Shoarinezhad Vahid , Olsen Nils Reidar Bøe , Wieprecht Silke , Haun Stefan 
Published: February 2024

AbstractReservoir sedimentation poses a significant challenge to water resource management. Improving the lifespan and productivity of reservoirs requires appropriate sediment management strategies, among which flushing operations have become more prevalent in practice. Numerical modeling offers a cost-effective approach to assessing the performance of different flushing operations. However, calibrating highly parametrized morphological models remains a complex task due to inherent uncertainties associated with sediment transport processes and model parameters. Traditional calibration methods require laborious manual adjustments and expert knowledge, hindering calibration accuracy and efficiency and becoming impractical when dealing with several uncertain parameters. A solution is to use optimization techniques that enable an objective evaluation of the model behavior by expediting the calibration procedure and reducing the issue of subjectivity. In this paper, we investigate bed level changes as a result of a flushing event in the Bodendorf reservoir in Austria by using a three-dimensional numerical model coupled with an optimization algorithm for automatic calibration. Three different sediment transport formulae (Meyer-Peter and Müller, van Rijn, and Wu) are employed and modified during the calibration, along with the roughness parameter, active layer thickness, volume fraction of sediments in bed, and the hiding-exposure parameter. The simulated bed levels compared to the measurements are assessed by several statistical metrics in different cross-sections. According to the goodness-of-fit indicators, the models using the formulae of van Rijn and Wu outperform the model calculated by the Meyer-Peter and Müller formula regarding bed patterns and the volume of flushed sediments.
On Optimizing Ensemble Models using Column Generation
Authors: Aziz Vanya , Wu Ouyang , Nowak Ivo , Hendrix Eligius M. T. , Kronqvist Jan 
Published: February 2024

AbstractIn recent years, an interest appeared in integrating various optimization algorithms in machine learning. We study the potential of ensemble learning in classification tasks and how to efficiently decompose the underlying optimization problem. Ensemble learning has become popular for machine learning applications and it is particularly interesting from an optimization perspective due to its resemblance to column generation. The challenge for learning is not only to obtain a good fit for the training data set, but also good generalization, such that the classifier is generally applicable. Deep networks have the drawback that they require a lot of computational effort to get to an accurate classification. Ensemble learning can combine various weak learners, which individually require less computational time. We consider binary classification problems studying a three-phase algorithm. After initializing a set of base learners refined by a bootstrapping approach, base learners are generated using the solution of an linear programming (LP) master problem and then solving a machine learning sub-problem regarding a reduced data set, which can be viewed as a so-called pricing problem. We theoretically show that the algorithm computes an optimal ensemble model in the convex hull of a given model space. The implementation of the algorithm is part of an ensemble learning framework called decolearn. Numerical experiments with CIFAR-10 data set show that the base learners are diverse and that both the training and generalization error are reduced after several iterations.
Quantifying agricultural land-use intensity for spatial biodiversity modelling: implications of different metrics and spatial aggregation methods
Authors: Roilo Stephanie , Paulus Anne , Alarcón-Segura Viviana , Kock Lucas , Beckmann Michael , Klein Nadja , Cord Anna F. 
Published: February 2024

Abstract
Context
Agricultural intensification is a major driver of farmland biodiversity declines. However, the relationship between land-use intensity (LUI) and biodiversity is complex and difficult to characterise, not least because of the difficulties in accurately quantifying LUI across heterogeneous agricultural regions.

Objectives
We investigated how the use of different LUI metrics and spatial aggregation methods can lead to large variations in LUI estimation across space and thus affect biodiversity modelling.

Methods
We used three spatial aggregation methods (square, hexagonal, and voronoi grids) to calculate ten commonly used LUI metrics describing three LUI dimensions: land use, land management and landscape structure. Using a virtual species approach, we compared how LUI values sampled at biodiversity monitoring sites vary across different metrics and grids. We modelled the distribution of three virtual species using Generalised Additive Models to test how omitting certain LUI dimensions from the models affected the model results.

Results
The density distributions of LUI values at the presence points of the virtual species were significantly different across metrics and grids. The predefined species-environment relationships characterising the environmental niches of two out of three virtual species remained undetected in models that omitted certain LUI dimensions.

Conclusions
We encourage researchers to consider the implications of using alternative grid types in biodiversity models, and to account for multiple LUI dimensions, for a more complete representation of LUI. Advances in remote sensing-derived products and increased accessibility to datasets on farm structure, land-use and management can greatly advance our understanding of LUI effects on biodiversity.
An ensemble approach for classification of tympanic membrane conditions using soft voting classifier
Authors: Akyol Kemal , Uçar Emine , Atila Ümit , Uçar Murat 
Published: February 2024

AbstractOtitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of 880 otoscopy images, including different eardrum cases such as normal, earwax plug, myringosclerosis, and chronic otitis media. The prediction results of these models were evaluated with voting approaches to increase the overall prediction accuracy. In this context, the performances of both soft and hard voting ensembles were examined. Soft voting ensemble framework achieved highest performance in experiments with 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. Our proposed model achieved the highest classification performance so far in the current dataset. The results reveal that our voting ensemble-based DL approach showed quite high performance for the diagnosis of middle ear disease. In clinical applications, this approach can provide a preliminary diagnosis of the patient's condition just before field experts make a diagnosis on otoscopic images. Thus, our proposed approach can help field experts to diagnose the disease quickly and accurately. In this way, clinicians can make the final diagnosis by integrating automatic diagnostic prediction with their experience.
What is Your Reference Point? How Price Volatility and Organizational Context Affect the Reference Points of Family and Nonfamily Managers
Authors: Stommel Evelyn , Gottschalck Nicole , Hack Andreas , Eddleston Kimberly A. , Kellermanns Franz , Kraiczy Nils 
Published: February 2024

AbstractDrawing from prospect theory, we use an experimental study design to explore how and why reference points of managers of family and nonfamily firms differ. We contribute to research on the role of economic theories for family businesses by elaborating on decision-making mechanisms in the context of family firms. Furthermore, we investigate whether family and nonfamily managers within family firms vary in their investment decisions. Our study demonstrates the importance of price volatility as a determinant of reference points and shows how the same type of information can lead to different reference points based on whether the manager is from a family or nonfamily firm.