bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rapid expansion of Eurasian beavers in Hungary: thirty-year history of the species’ return
Authors: Czabán Dávid , Juhász Erika 
Published: July 2024

AbstractThe Eurasian beaver is an ecosystem engineer species capable of altering the riparian vegetation and the hydrology of freshwater habitats. The study of its distribution process is therefore important for both nature conservation and conflict management. Thanks to protection efforts and natural expansion, the beaver has already inhabited most of its former range, starting from the brink of extinction. The changes in the Eurasian distribution and population size have been continuously documented in the scientific literature. In these studies, however, Hungary, as a country with an important position along the beavers’ colonisation routes provided by the Danube drainage basin, has hitherto been underrepresented due to a severe lack of data. In this paper, we summarise all the available information about the thirty-year history, colonisation process, current distribution, as well as the management of the Hungarian population. Based on the newest available data, the beaver is present along all of the large rivers and along most permanent watercourses in the country. Despite the continuing growth trend of the population, a beaver management strategy has not yet been developed. Its establishment could be significantly supported by further monitoring surveys and scientific studies focusing on the region- and site-specific ecological effects of the species.
Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network
Authors: Abdullah Saleem , Ullah Ihsan , Ghani Fazal 
Published: July 2024

AbstractNetwork selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user preferences. This is because the various wireless access technologies have varying capabilities and limitations, and the best network for a voice, video, and data service depends on a variety of factors. For selecting the optimal network in HWNs, varying factors such as the user’s position, accessible network resources, service quality requirements, and user preferences must be considered. The classical decision making procedure is very difficult and uncertain to select the desirable HWNs for voice, video, and data. Therefore, we develop a novel decision making model based on feed-forward neural networks under the double hierarchy linguistic information for the selection of the best HWNs for voice, video, and data. In this article, we introduce a novel feed-forward double hierarchy linguistic neural network using the Hamacher t-norm and t-conorm. Further, the feed-forward double hierarchy linguistic neural network applies to the decision making model for the selection of the best HWNs for voice, video, and data. In this novel decision making model, we first take the given data about HWNs and use the converting function to convert the given data into a double hierarchy linguistic term set. We calculate the hidden layer and output layer information by using Hamacher aggregation operations. Finally, we use the sigmoid activation function on the output layer information to decide on the best HWNs for voice, video, and data according to ranking. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model.
High Expression of miR-218-5p in the Peripheral Blood Stream and Tumor Tissues of Pediatric Patients with Sarcomas
Authors: Özdenoğlu Fazilet Yıldız , Ödemiş Demet Akdeniz , Erciyas Seda Kılıç , Tunçer Şeref Buğra , Gültaşlar Büşra Kurt , Salduz Ahmet , Büyükkapu Sema , Olgaç Necat Vakur , Kebudi Rejin , Yazıcı Hülya 
Published: July 2024

AbstractSarcomas are malignant tumors that may metastasize and the course of the disease is highly aggressive in children and young adults. Because of the rare incidence of sarcomas and the heterogeneity of tumors, there is a need for non-invasive diagnostic and prognostic biomarkers in sarcomas. The aim of the study was to investigate the level of miR-218-5p in peripheral blood and tumor tissue samples of Ewing’s sarcoma, osteosarcoma, spindle cell sarcoma patients, and healthy controls, and assessed whether the corresponding molecule was a diagnostic and prognostic biomarker. The study was performed patients (n = 22) diagnosed and treated with Ewing’s sarcoma and osteosarcoma and in a control group of 22 healthy children who were matched for age, gender, and ethnicity with the patient group. The expression level of miR-218-5p in RNA samples from peripheral blood and tissue samples were analyzed using the RT-PCR and the expression level of miR-218-5p was evaluated by comparison with the levels in patients and healthy controls. The expression level of miR-218-5p was found to be statistically higher (3.33-fold, p = 0.006) in pediatric patients with sarcomas and when the target genes of miR-218-5p were investigated using the bioinformatics tools, the miR-218-5p was found as an important miRNA in cancer. In this study, the miR-218-5p was shown for the first time to have been highly expressed in the peripheral blood and tumor tissue of sarcoma patients. The results suggest that miR-218-5p can be used as a diagnostic and prognostic biomarker in sarcomas and will be evaluated as an important therapeutic target.
Experience of Marginalization in Noncooperative Spaces: The Case of Undocumented Migrant Workers in Italy
Authors: Derakhshan Roya , Chowdhury Rashedur 
Published: July 2024

AbstractUndocumented migrant workers are among a group of marginalized stakeholders who are severely exploited at their workplace and across broader society. Despite recent scholarly discussions in marginalized stakeholder theory and migration studies, our understanding of how undocumented workers experience marginalization in noncooperative spaces remains very limited. In noncooperative spaces, uncooperative powerful actors deliberately thwart cooperation with local marginalized stakeholders and fail to develop supportive institutional frameworks, such as regulative and transparent governance principles. To address these issues, we conducted interviews with 47 undocumented workers and civil society workers in Italy. Our findings reveal that the marginalization experienced by undocumented workers encompasses socio-economic immobility, systemic incapability, and a sense of meaninglessness. Further, our research challenges the principles of stakeholder capitalism inherent in traditional stakeholder theory, revealing the inadequacy of conventional notions in noncooperative spaces where marginalized stakeholders deal with disempowerment and immobility. We delve into the silent and tacit collusion among uncooperative firms in these spaces, shedding light on the ways in which this problematic cooperation leads to the creation of normative harm. Moreover, we introduce the experience of meaninglessness as an internal barrier hindering migrant inclusion, underscoring the imperative need for widespread immigration reforms and normative changes to foster an environment conducive to meaningful transformations for migrants.
Genetically predicted immune cells mediate the association between gut microbiota and neuropathy pain
Authors: Lan Zhixuan , Wei Yi , Yue Kan , He Ruilin , Jiang Zongbin 
Published: July 2024

Abstract
Background
Previous observational studies have indicated a complex association between gut microbiota (GM) and neuropathic pain (NP). Nonetheless, the precise biological mechanisms underlying this association remain unclear. Therefore, we adopted a Mendelian randomization (MR) approach to investigate the causal relationship between GM and neuropathic pain including post-herpetic neuralgia (PHN), painful diabetic peripheral neuropathy (PDPN), and trigeminal neuralgia (TN), as well as to explore the potential mediation effects of immune cells.

Methods
We performed a two-step, two-sample Mendelian randomization study with an inverse variance-weighted (IVW) approach to investigate the causal role of GM on three major kinds of NP and the mediation effect of immune cells between the association of GM and NP. In addition, we determine the strongest causal associations using Bayesian weighted Mendelian randomization (BWMR) analysis. Furthermore, we will investigate the mediating role of immune cells through a two-step Mendelian randomization design.

Results
We identified 53 taxonomies and pathways of gut microbiota that had significant causal associations with NP. In addition, we also discovered 120 immune cells that exhibited significant causal associations with NP. According to the BWMR and two-step Mendelian randomization analysis, we identified the following results CD4 on CM CD4 + (maturation stages of T cell) mediated 6.7% of the risk reduction for PHN through the pathway of fucose degradation (FUCCAT.PWY). CD28 + DN (CD4-CD8-) AC (Treg) mediated 12.5% of the risk reduction for PHN through the influence on Roseburia inulinivorans. CD45 on lymphocyte (Myeloid cell) mediated 11.9% of the risk increase for TN through the superpathway of acetyl-CoA biosynthesis (PWY.5173). HLA DR + CD8br %T cell (TBNK) mediated 3.2% of the risk reduction for TN through the superpathway of GDP-mannose-derived O-antigen building blocks biosynthesis (PWY.7323). IgD-CD38-AC (B cell) mediated 7.5% of the risk reduction for DPN through the pathway of thiazole biosynthesis I in E. coli (PWY.6892).

Discussion
These findings provided evidence supporting the causal effect of GM with NP, with immune cells playing a mediating role. These findings may inform prevention strategies and interventions directed toward NP. Future studies should explore other plausible biological mechanisms.
Optimizing seaweed biomass production - a two kelp solution
Authors: Stekoll Michael , Pryor Alf , Meyer Alexandra , Kite-Powell Hauke L. , Bailey David , Barbery Kendall , Goudey Clifford A. , Lindell Scott , Roberson Loretta , Yarish Charles 
Published: July 2024

AbstractInterest in farming kelps has grown beyond using kelp for food, feed or biofuels. There is considerable interest in generating biomass from seaweed for use in bioplastics and other products that would substitute for petroleum-derived products. For these uses to be viable, large amounts of biomass are needed. Very large kelp farms can be expensive to build and maintain, leading to the need to optimize the biomass per unit area. Although close spacing of growlines can lead to poor growth, a viable approach may be to grow two species of kelps together: one that hangs down and one that is buoyant, growing up. This system would increase the spacing in three dimensions. In Alaska, Saccharina latissima is commonly grown hanging down from longlines. One of the buoyant Alaskan kelps is Nereocystis luetkeana. Because there are commercial uses for wild-harvested Nereocystis in Alaska, we undertook a preliminary trial in Kodiak, Alaska, that grew both Saccharina and Nereocystis in the same longline array. Closely spaced lines were seeded the first week of February 2023 and set at 3 m below the surface. The arrays were harvested in late June 2023. Total yields were greatest on the combined arrays, followed by the Nereocystis only and Saccharina only arrays. Despite having 45% fewer grow-lines, the total yield of the Nereocystis on the combined arrays was statistically similar to the Nereocystis only arrays. These results may have significance for large scale macroalgal production.
Development of prospective elementary teachers’ knowledge to teach early algebra through case discussions
Authors: Öztürk-Tavşan Nejla , İşler-Baykal Işıl 
Published: July 2024

AbstractThis study investigated the development of prospective elementary teachers (PETs)’ subject matter and pedagogical content knowledge for teaching early algebra through participation in case discussions. PETs attended five weeks of intervention as part of a method course, which included case discussions. During the intervention, the participants were presented with big ideas of equivalence and equations, generalized arithmetic, and functional thinking as the content of early algebra through text-based classroom cases. Data were collected through one-hour individual interviews before and after the early algebra lessons. The analysis of the pre-interviews indicated that prospective elementary teachers may not be ready to foster algebraic thinking in elementary grades in terms of the required knowledge for teaching. We found that PETs needed more development of required subject matter knowledge to guide algebraic thinking in elementary grades, such as the relational meaning of the equal sign, generalizing, representing, or justifying arithmetic or functional relationships, and reasoning with them. Likewise, PETs were found to lack in pedagogical content knowledge related to students’ conceptions and misconceptions and effective teaching strategies to promote early algebraic thinking. However, we found that specifically designed method courses could help develop PETs’ knowledge of teaching early algebra. Following early algebra lessons centered around case discussions, PETs showed progress in multiple aspects of knowledge across all big ideas, though their improvement in generalized arithmetic was somewhat less pronounced. This progress was observed in their subject matter and pedagogical content knowledge.
Improving Amplification Bandwidth by Combining Josephson Parametric Amplifiers for Active Axion Search Experiments at IBS/CAPP
Authors: Uchaikin Sergey V. , Kim Jinmyeong , Ivanov Boris I. , van Loo Arjan F. , Nakamura Yasunobu , Ahn Saebyeok , Oh Seonjeong , Park Seongtae , Matlashov Andrei , Chung Woohyun , Semertzidis Yannis K. 
Published: July 2024

AbstractThe Center for Axion and Precision Physics Research at the Institute for Basic Science in Republic of Korea is home to multiple active axion search experiments using cavity haloscopes that operate within the frequency range of 1–6 GHz. The haloscopes convert axions to photons, resulting in an output power of about 10$${^{-24}}$$

-
24

–10$${^{-22}}$$

-
22

W. To detect such a small signal amidst noise, quantum-limited noise amplifiers and ultra-low-temperature environment (a few tenths of mK) are required for all critical readout components to minimize noise from all active and passive lossy components. Our primary objective is to achieve the highest possible scanning-frequency speed, which includes the time for maintenance and system calibration. This paper presents the development and operation of low-noise amplifiers for haloscope experiments targeting different frequency ranges and provides design, operational, and performance details of the amplifiers.
Volatile matter characterization of birch biochar produced under pressurized conditions
Authors: Babinszki Bence , Czirok István Sándor , Johnson Robert , Sebestyén Zoltán , Jakab Emma , Wang Liang , Turn Scott , Skreiberg Øyvind , Czégény Zsuzsanna 
Published: July 2024

AbstractThe volatile matter (VM) content and composition of birch biochars produced at 320 °C under elevated pressure (0.1–11 MPa) and constant pressure or constant volume reactor conditions were characterized by thermogravimetry/mass spectrometry (TG/MS) and pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS). Some of the thermal properties of the biochars and the composition of the VMs varied as a function of the maximal pressure applied during carbonization. The samples prepared at higher pressures released more volatiles up to 320 °C, while the maximal rate of thermal decomposition at around 440 °C showed decreasing tendency with the carbonization pressure. In terms of VM composition, the most apparent effect was the significant increase of the amounts of apoallobetulins from biochars prepared at elevated pressures, which were formed by dehydration, ring closure and rearrangement from the betulin content of birch. The change in the ratio of the evolved guaiacol and 4-methylguaiacol as well as that of syringol and 4-methylsyringol as a function of the maximal pressure of carbonization indicated a modification of the lignin decomposition mechanism.
Brain magnetic resonance image (MRI) segmentation using multimodal optimization
Authors: Akan Taymaz , Oskouei Amin Golzari , Alp Sait , Bhuiyan Mohammad Alfrad Nobel 
Published: July 2024

AbstractOne of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters.
Likelihood-of-harm/help of microsurgery compared to radiosurgery in large vestibular schwannoma
Authors: Wang Sophie Shih-Yüng , Horstmann Gerhard , van Eck Albertus , Tatagiba Marcos , Naros Georgios 
Published: July 2024

Abstract
Purpose
It has been shown that in large vestibular schwannomas (VS), radiosurgery (SRS) is inferior with respect to tumor control compared to microsurgical resection (SURGERY). However, SURGERY poses a significantly higher risk of facial-function deterioration (FFD). The aim of this study was to illustrate the effectiveness in terms of number-needed-to-treat/operate (NNO), number-needed-to-harm (NNH), and likelihood-of-harm/help (LHH) by comparing both treatment modalities in large VS.

Methods
This was a retrospective, dual-center cohort study. Tumor size was classified by Hannover Classification. Absolute risk reduction and risk increase were used to derive additional estimates of treatment effectiveness, namely NNO and NNH. LHH was then calculated by a quotient of NNH/NNO to illustrate the risk–benefit-ratio of SURGERY.

Results
Four hundred and forty–nine patients treated met the inclusion criteria. The incidence of tumor recurrence was significantly higher in SRS (14%), compared to SURGERY (3%) resulting in ARR of 11% and NNO of 10. At the same time, SURGERY was related to a significant risk of FFD resulting in an NNH of 12. Overall, the LHH calculated at 1.20 was favored SURGERY, especially in patients under the age of 40 years (LHH = 2.40), cystic VS (LHH = 4.33), and Hannover T3a (LHH = 1.83) and T3b (LHH = 1.80).

Conclusions
Due to a poorer response of large VS to SRS, SURGERY is superior with respect to tumor control. One tumor recurrence can be prevented, when 10 patients are treated by SURGERY instead of SRS. Thus, LHH portrays the benefit of SURGERY in large VS even when taking raised FFD into account.
Fine Representation of Hessian of Convex Functions and Ricci Tensor on RCD Spaces
Authors: Brena Camillo , Gigli Nicola 
Published: July 2024

AbstractIt is known that on RCD spaces one can define a distributional Ricci tensor $$\textbf{Ric}$$
Ric
. Here we give a fine description of this object by showing that it admits the polar decomposition $$\begin{aligned} \textbf{Ric}=\omega \,|\textbf{Ric}| \end{aligned}$$

Ric
=
ω

|
Ric
|

for a suitable non-negative measure $$|\textbf{Ric}|$$

|
Ric
|

and unitary tensor field $$\omega $$
ω
. The regularity of both the mass measure and of the polar vector are also described. The representation provided here allows to answer some open problems about the structure of the Ricci tensor in such singular setting. Our discussion also covers the case of Hessians of convex functions and, under suitable assumptions on the base space, of the Sectional curvature operator.
The effect of prepaid incentives on panelists’ response across survey modes in a sequential mixed-mode design
Authors: Becker Rolf 
Published: July 2024

AbstractWhile the use of prepaid incentives and data collection in a sequential mixed-mode survey design is standard in cross-sectional and longitudinal studies, little is known about the interaction of these strategies and how this affects target persons’ survey participation. Therefore, based on a panel study, this study investigates whether such unconditional prepayments—monetary or non-monetary incentives—increase the efficiency of a sequential mixed-mode survey design in regard to boosted response rates and speed of return after receipt of an invitation to participate in a survey. While the survey design is always identical, the different incentives vary across the waves considered. Different prepayments, such as cash or in-kind incentives, have different effects on invitees’ participation in different survey modes. In particular, non-monetary incentives seem to work only in a sequential mixed-mode design, while cash, as a universal medium, always works in the expected way, across different survey modes. In sum, the study finds that when a single sequential mixed-mode design is used across panel waves the overall response rates are rather similar, even when different incentives are provided.
Investor emotions and market bubbles
Authors: Agarwal Vineet , Taffler Richard J. , Wang Chenyang 
Published: July 2024

Abstract
Asset pricing bubbles are highly emotional market episodes. Despite this, investor emotions are not part of traditional bubble models. We measure the powerful affects influencing investor decisions during speculative market bubbles directly employing textual analysis of media narratives and domain-specific emotion keyword dictionaries and show how understanding investor emotional dynamics helps explain market behavior. Specifically, we focus on the two Chinese stock market bubbles of 2005–2008 and 2014–2016; there is no evidence of investor learning from experience. Despite Chinese media being censored we show it still has strong explanatory power although the independent English language media can provide an additional perspective. Deeper emotions dominate more superficial feelings in information content.