bluuebunny/crossref_metadata_2025_split huggingface dataset:
RGS$$^\oplus $$: RDF graph synchronization for collaborative robotics
Authors: Berger Cyrille , Doherty Patrick , Rudol Piotr , Wzorek Mariusz 
Published: November 2023

AbstractIn the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge is gathered incrementally and in different ways by heterogeneous robots and humans. The purpose of this paper is to describe an RDF Graph Synchronization System called RGS$$^\oplus $$⊕. It is assumed that a dynamic set of agents provide or retrieve knowledge stored in their local RDF Graphs which are continuously synchronized between agents. The RGS$$^\oplus $$⊕System was designed to handle unreliable communication and does not rely on a static centralized infrastructure. It is capable of synchronizing knowledge as timely as possible and allows agents to access knowledge while it is incrementally acquired. A deeper empirical analysis of the RGS$$^\oplus $$⊕System is provided that shows both its efficiency and efficacy.
Comparing Machine Learning and Deep Learning Techniques for Text Analytics: Detecting the Severity of Hate Comments Online
Authors: Marshan Alaa , Nizar Farah Nasreen Mohamed , Ioannou Athina , Spanaki Konstantina 
Published: November 2023

AbstractSocial media platforms have become an increasingly popular tool for individuals to share their thoughts and opinions with other people. However, very often people tend to misuse social media posting abusive comments. Abusive and harassing behaviours can have adverse effects on people's lives. This study takes a novel approach to combat harassment in online platforms by detecting the severity of abusive comments, that has not been investigated before. The study compares the performance of machine learning models such as Naïve Bayes, Random Forest, and Support Vector Machine, with deep learning models such as Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM). Moreover, in this work we investigate the effect of text pre-processing on the performance of the machine and deep learning models, the feature set for the abusive comments was made using unigrams and bigrams for the machine learning models and word embeddings for the deep learning models. The comparison of the models’ performances showed that the Random Forest with bigrams achieved the best overall performance with an accuracy of (0.94), a precision of (0.91), a recall of (0.94), and an F1 score of (0.92). The study develops an efficient model to detect severity of abusive language in online platforms, offering important implications both to theory and practice.
On asymptotic convergence rate of random search
Authors: Tarłowski Dawid 
Published: November 2023

AbstractThis paper presents general theoretical studies on asymptotic convergence rate (ACR) for finite dimensional optimization. Given the continuous problem function and discrete time stochastic optimization process, the ACR is the optimal constant for control of the asymptotic behaviour of the expected approximation errors. Under general assumptions, condition ACR$$<1$$<1implies the linear behaviour of the expected time of hitting the$$\varepsilon $$ε- optimal sublevel set with$$\varepsilon \rightarrow 0^+ $$ε→0+and determines the upper bound for the convergence rate of the trajectories of the process. This paper provides general characterization of ACR and, in particular, shows that some algorithms cannot converge linearly fast for any nontrivial continuous optimization problem. The relation between asymptotic convergence rate in the objective space and asymptotic convergence rate in the search space is provided. Examples and numerical simulations with use of a (1+1) self-adaptive evolution strategy and other algorithms are presented.
Application of Iterative Elastic SH Reverse Time Migration to Synthetic Ultrasonic Echo Data
Authors: Grohmann Maria , Niederleithinger Ernst , Maack Stefan , Buske Stefan 
Published: November 2023

AbstractThe ultrasonic echo technique is widely used in non-destructive testing (NDT) of concrete objects for thickness measurements, geometry determinations and localization of built-in components. To improve ultrasonic imaging of complex concrete structures, we transferred a seismic imaging technique, the Reverse Time Migration (RTM), to NDT in civil engineering. RTM, in contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, considers all wavefield types and thus, can handle complex wave propagations in any direction with no limit on velocity variations and reflector dip. In this paper, we focused on the development, application and evaluation of a two-dimensional elastic RTM algorithm considering horizontally polarized shear (SH) waves only. We applied the elastic SH RTM routine to synthetic ultrasonic echo SH-wave data generated with a concrete model incorporating several steps and circular cavities. As these features can often be found in real-world NDT use cases, their imaging is extremely important. By using elastic SH RTM, we were able to clearly reproduce almost all reflectors inside the concrete model including the vertical step edges and the cross sections of the cavities. We were also capable to show that more features could be mapped compared to SAFT, and that imaging of complex reflectors could be sharpened compared to elastic P-SV (compressional-vertically polarized shear) RTM. Our promising results illustrate that elastic SH RTM has the potential to significantly enhance the reconstruction of challenging concrete structures, representing an important step forward for precise, high-quality ultrasonic NDT in civil engineering.
Generation of patient-derived pediatric pilocytic astrocytoma in-vitro models using SV40 large T: evaluation of a modeling workflow
Authors: Selt Florian , El Damaty Ahmed , Schuhmann Martin U. , Sigaud Romain , Ecker Jonas , Sievers Philipp , Kocher Daniela , Herold-Mende Christel , Oehme Ina , von Deimling Andreas , Pfister Stefan M. , Sahm Felix , Jones David T. W. , Witt Olaf , Milde Till 
Published: November 2023

Abstract
Purpose
Although pediatric low-grade gliomas (pLGG) are the most common pediatric brain tumors, patient-derived cell lines reflecting pLGG biology in culture are scarce. This also applies to the most common pLGG subtype pilocytic astrocytoma (PA). Conventional cell culture approaches adapted from higher-grade tumors fail in PA due to oncogene-induced senescence (OIS) driving tumor cells into arrest. Here, we describe a PA modeling workflow using the Simian Virus large T antigen (SV40-TAg) to circumvent OIS.

Methods
18 pLGG tissue samples (17 (94%) histological and/or molecular diagnosis PA) were mechanically dissociated. Tumor cell positive-selection using A2B5 was perfomed in 8/18 (44%) cases. All primary cell suspensions were seeded in Neural Stem Cell Medium (NSM) and Astrocyte Basal Medium (ABM). Resulting short-term cultures were infected with SV40-TAg lentivirus. Detection of tumor specific alterations (BRAF-duplication and BRAF V600E-mutation) by digital droplet PCR (ddPCR) at defined time points allowed for determination of tumor cell fraction (TCF) and evaluation of the workflow. DNA-methylation profiling and gene-panel sequencing were used for molecular profiling of primary samples.

Results
Primary cell suspensions had a mean TCF of 55% (+/− 23% (SD)). No sample in NSM (0/18) and ten samples in ABM (10/18) were successfully transduced. Three of these ten (30%) converted into long-term pLGG cell lines (TCF 100%), while TCF declined to 0% (outgrowth of microenvironmental cells) in 7/10 (70%) cultures. Young patient age was associated with successful model establishment.

Conclusion
A subset of primary PA cultures can be converted into long-term cell lines using SV40-TAg depending on sample intrinsic (patient age) and extrinsic workflow-related (e.g. type of medium, successful transduction) parameters. Careful monitoring of sample-intrinsic and extrinsic factors optimizes the process.
Specifying the role of religion in entrepreneurial action: a cognitive perspective
Authors: Dubard Barbosa Saulo , Smith Brett R. 
Published: November 2023

AbstractResearch on the relationship between religion and entrepreneurship has produced mixed findings. We argue such equivocal findings are partly the result of under-specification of the role of religion in entrepreneurial action. To address this issue, we build on the process perspective of entrepreneurial cognition by simultaneously incorporating mental representations and cognitive resources. Specifically, we theorize a cognitive process that incorporates both framing effects of opportunity cues and religious belief integration based on sanctification into the assessment of feasibility and desirability of entrepreneurial action. Through two within-subject experiments, we find (i) positively framed opportunity cues yield more favorable assessments of entrepreneurial action than negatively framed opportunity cues, and (ii) religious belief integration moderates the relationship between framing and assessments of entrepreneurial action, enhancing perceived feasibility and desirability when information framing is negative. We discuss the implications of our model to research the theological turn of entrepreneurship and a cognitive perspective of entrepreneurial action.
A review of spectrum sensing in modern cognitive radio networks
Authors: Muzaffar Muhammad Umair , Sharqi Rula 
Published: November 2023

AbstractCognitive radio network (CRN) is a pioneering technology that was developed to improve efficiency in spectrum utilization. It provides the secondary users with the privilege to transmit on the licensed parts of the spectrum if the licensed user is not utilizing it. The cognitive radio must, however, relinquish the spectrum when the primary user decides to reoccupy it. By exploiting the unused portion of the spectrum, a cognitive radio helps in making the use of the radio spectrum more efficient. Furthermore, the most important capability that a cognitive radio (CR) must possess is spectrum sensing. A CR must be able to correctly determine the status of the target spectrum with the help of spectrum sensing. This is a very challenging task and several methods have been investigated over the years. In this work, the state of the art of different spectrum sensing techniques for a variety of CRNs is presented. Both conventional and modern spectrum sensing techniques for different types of primary user signals are discussed in this work for Narrowband and Wideband signals. Legacy techniques such as energy detection are most commonly used due to their simplicity in implementation. However, this comes at the cost of poor performance at low SNR (signal-to-noise ratio) values. This issue is countered by methods that use statistical information of the primary signal to make a more informed decision on spectrum occupancy. Several techniques that make use of the power of machine learning algorithms are also discussed which show clear improvement in performance. The primary challenge in such techniques is selection of the best features. The most commonly used features are also discussed. Furthermore, spectrum sensing techniques that consider the 5G signal as the primary user signal of the network are discussed. It is observed that there is a significant need for research in additional spectrum sensing techniques for 5G cognitive radio networks.
Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics
Authors: Shuttleworth Joseph G. , Lei Chon Lok , Whittaker Dominic G. , Windley Monique J. , Hill Adam P. , Preston Simon P. , Mirams Gary R. 
Published: November 2023

AbstractWhen using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises—models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, ‘information-rich’ protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict—highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems.
Atorvastatin Promotes Pro/anti-inflammatory Phenotypic Transformation of Microglia via Wnt/β-catenin Pathway in Hypoxic-Ischemic Neonatal Rats
Authors: Yu Luting , Huang Lingyi , Zhao Yuanyuan , Liu Shixi , Zhou Ruixi , Yue Yan , Sun Hao , Su Xiaojuan , Liu Qian , Li Shiping , Ying Junjie , Zhao Fengyan , Qu Yi 
Published: November 2023

AbstractInflammatory reaction plays a key role in the pathogenesis of hypoxic-ischemic encephalopathy (HIE) in neonates. Microglia are resident innate immune cells in the central nervous system and are profoundly involved in neuroinflammation. Studies have revealed that atorvastatin exerts a neuroprotective effect by regulating neuroinflammation in adult animal models of brain stroke and traumatic brain injury, but its role regarding damage to the developing brain remains unclear. This study aimed to clarify the effect and mechanism of atorvastatin on the regulation of microglia function in neonatal hypoxic-ischemic brain damage (HIBD). The oxygen glucose deprivation (OGD) of microglia and neonatal rat HIBD model was established. Atorvastatin, recombinant sclerostin protein (SOST), and XAV939 (degradation of β-catenin) were administered to OGD microglia and HIBD rats. The pathological changes of brain tissue, cerebral infarction volume, learning and memory ability of rats, pro-inflammatory (CD16+/Iba1+) and anti-inflammatory (CD206+/Iba1+) microglia markers, inflammation-related indicators (Inos, Tnfα, Il6, Arg1, Tgfb, and Mrc1), and Wnt/β-catenin signaling molecules were examined. Atorvastatin reduced OGD-induced pro-inflammatory microglia and pro-inflammatory factors, while increasing anti-inflammatory microglia and anti-inflammatory factors. In vivo, atorvastatin attenuated hypoxia-ischemia (HI)-induced neuroinflammation and brain damage. Mechanistically, atorvastatin decreased SOST expression and activated the Wnt/β-catenin signaling pathway, and the administration of recombinant SOST protein or XAV939 inhibited Wnt/β-catenin signaling and attenuated the anti-inflammatory effect of atorvastatin. Atorvastatin promotes the pro/anti-inflammatory phenotypic transformation of microglia via the Wnt/β-catenin pathway in HI neonatal rats. Atorvastatin may be developed as a potent agent for the treatment of HIE in neonates.
Economic Benefits of Blended Teaching Mode in the Knowledge Economy Era: A Case Study of College English Course
Authors: Wang Ruishu 
Published: November 2023

AbstractThe era of the knowledge economy is defined by the pivotal role of knowledge and information in economic production, dissemination, and utilization. Information technology stands as a cornerstone in propelling the advancement of the knowledge economy. The transformative impact of information technology has been a driving force in ushering in the knowledge economy era. Amid this backdrop, the blended teaching mode emerges as a novel pedagogical approach infused with information technology, demanding an evaluation of its economic merits as it gains traction in educational practice and adoption. The implementation of blended teaching modes within university settings necessitates considerable investments from educators, encompassing teaching efforts, human resources, and technological infrastructure. The linchpin in introducing blended teaching modes is whether they yield a superior economic benefit that justifies the resources expended. To scrutinize whether the blended teaching mode possesses the potential for heightened economic benefits, this study embarks on an empirical journey, investigating its influence on student learning outcomes through rigorous experimentation. This investigation formulates twelve research hypotheses designed to explore intricate relationships between latent constructs through multiple linear regression tests. The study’s findings confirm that the blended teaching mode can generate heightened economic benefits. These outcomes underscore the pedagogical and economic promise inherent in the integration of technology and innovative teaching methods within higher education, aligning with the imperatives of the knowledge economy.
New advances in management and treatment of cardiac implantable electronic devices infections
Authors: Russo Alessandro , Serraino Riccardo , Serapide Francesca , Trecarichi Enrico Maria , Torti Carlo 
Published: November 2023

AbstractCardiac implantable electronic devices (CIED) are increasingly used worldwide, and infection of these devices remains one of the most feared complications.CIED infections (CDIs) represent a challenge for physicians and the healthcare system in general as they require prolonged hospitalization and antibiotic treatment and are burdened by high mortality and high costs, so management of CDIs must be multidisciplinary.The exact incidence of CDIs is difficult to define, considering that it is influenced by various factors mainly represented by the implanted device and the type of procedure. Risk factors for CDIs could be divided into three categories: device related, patient related, and procedural related and the etiology is mainly sustained by Gram-positive bacteria; however, other etiologies cannot be underestimated. As a matter of fact, the two cornerstones in the treatment of these infections are device removal and antimicrobial treatment. Finally, therapeutic drug monitoring and PK/PD correlations should be encouraged in all patients with CDIs receiving antibiotic therapy and may result in a better clinical outcome and a reduction in antibiotic resistance and economic costs.In this narrative review, we look at what is new in the management of these difficult-to-treat infections.
Biodiesel production from transesterified yellow grease by ZSM-5 zeolite-supported BaO catalyst: process optimization by Taguchi’s experimental design approach
Authors: Yusuff Adeyinka Sikiru , Onibonoje Moses Oluwafemi 
Published: November 2023

AbstractMethanolysis of yellow grease (YG) was performed to synthesize its corresponding methyl ester (YGME) using BaO loaded on ZSM-5 (BaO/ZSM-5) as a heterogeneous base catalyst that was prepared via metallic solution hydrolysis method and characterized using N2 adsorption–desorption (BET), surface basicity, XRD, TGA/DTA, SEM, FTIR and Raman techniques.### The Taguchi design approach was utilized to optimize the transesterification process factors, and among the parameters studied, calcination temperature was found to have a significant influence on YGME yield. At 70 ℃ for 3 h, a YGME yield of 95.9 $$\pm 0.94$$

±
0.94

% was obtained using a methanol/YG molar ratio of 15:1 and 1 g (2 wt.% of YG used) of BaO/ZSM-5 sample calcined at 700 ℃. The BaO/ZSM-5 catalyst was reused six times with only a 15% decrease in activity.
Effects of endocrine disorders on maxillary and mandibular growth in Colombian children and adolescents: a cross-sectional study
Authors: Alfaro J. M. , Manrique R. , Santamaría A. , Álvarez E. , Manes C. , Jiménez M. 
Published: November 2023

Abstract
Objective
To establish the influence of overweight/obesity, medicated hypothyroidism, and medicated non-syndromic hypogrowth on maxillary and mandibular growth.

Materials and methods
The relation between 10 craniofacial anthropometric measurements and hypothyroidism (n = 216), overweight/obesity (n = 108), and non-syndromic hypogrowth (n = 250) were evaluated in patients aged 1–19 years and a control group of healthy patients (n = 587). A subgroup analysis was performed at the peak growth in all groups.

Results
Patients with overweight/obesity and hypothyroidism showed increased craniofacial growth, while hypogrowth patients showed differences in zygomatic width and nasal base growth. Females with hypothyroidism and non-syndromic hypogrowth showed decreased head circumference at peak growth. Several anthropometric measurements were increased in patients with overweight/obesity, including head circumference. When all age groups were analyzed, overweight/obese and hypothyroidism patients showed increased zygomatic width while decreased hypogrowth. Overall, most craniofacial anthropometric measurements in overweight/obese patients were increased. Finally, the peak growth in males with hypothyroidism and subjects with non-syndromic hypogrowth was delayed compared to the control group (p < 0.05).

Conclusions
Children and adolescents with overweight/obesity and endocrine disorders showed alterations in craniofacial growth. Clinicians must be aware that the growth peak in these patients may be delayed when planning maxillary and mandibular orthopedic treatment.
Recommendations for Assessing and Managing Sleep Problems in Children with Neurodevelopmental Conditions
Authors: Hamilton Anna , Joyce Anna , Spiller Jayne 
Published: November 2023

Abstract
Purpose of Review
This review draws upon the authors’ practical experience of assessing sleep in children with neurodevelopmental conditions alongside empirical evidence of recommendations for clinicians and researchers to support assessment of sleep problems and strategies to promote healthy sleep in children with specific neurodevelopmental conditions. These include communication about sleep, mental health/behavioural considerations, pain, sensory profile, epilepsy, melatonin secretion profiles, sleep-disordered breathing and restless leg syndrome.

Recent Findings
This review has a particular focus on children with autism spectrum disorder, attention-deficit and hyperactivity disorder, Down syndrome, Smith-Magenis syndrome, Angelman syndrome, William’s syndrome and cerebral palsy.

Summary
Sleep disturbance varies in severity between neurodevelopmental conditions and the need for individualised assessment is emphasised. The impact of children’s poor sleep on parents is highlighted as a particular concern. A checklist of recommendations and example resources for clinicians to enquire about sleep in children with neurodevelopmental conditions has been included in a summary table.