bluuebunny/crossref_metadata_2025_split huggingface dataset:
Habitat segregation by sympatric juvenile Arctic charr and brown trout in shallow lake areas: a consequence of interspecific differences in predator avoidance?
Authors: Svenning Martin-A. , Stien Audun , Borgstrøm Reidar 
Published: May 2024

AbstractIn sympatry, juvenile brown trout (Salmo trutta) usually occupy the shallow and most productive areas (littoral zone) of lakes, while juveniles of Arctic charr (Salvelinus alpinus) are found in deeper, less productive parts. In contrast, when Arctic charr juveniles occur in allopatry, they often occupy shallow littoral areas as well. Habitat segregation has traditionally been interpreted as a trade-off between predation risk and energy gain, while the segregation of these two species has been explained by brown trout being more aggressive and competitively superior to Arctic charr. We hypothesize, however, that the marked habitat segregation between the two species may also be due to differences in predator avoidance. Accordingly, we conducted several laboratory tests, using Arctic charr and brown trout as potential predators. Live fish of the same species were offered as prey, either as small charr only, small trout only, or both small charr and small trout together. Artificial shelters were then introduced to examine the avoidance ability of prey fish against predatory fish. Our results showed that under these circumstances, access to shelters strongly decreased mortality in juvenile brown trout, but had no effect on juvenile Arctic charr mortality. Thus, the habitat segregation shown by sympatric juvenile Arctic charr and brown trout in lakes may be a consequence of interspecific differences in predator avoidance.
A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations
Authors: Lo Iacono F , Maragna R. , Pontone G. , Corino V. D. A. 
Published: May 2024

AbstractRadiomics extracts hundreds of features from medical images to quantitively characterize a region of interest (ROI). When applying radiomics, imbalanced or small dataset issues are commonly addressed using under or over-sampling, the latter being applied directly to the extracted features. Aim of this study is to propose a novel balancing and data augmentation technique by applying perturbations (erosion, dilation, contour randomization) to the ROI in cardiac computed tomography images. From the perturbed ROIs, radiomic features are extracted, thus creating additional samples. This approach was tested addressing the clinical problem of distinguishing cardiac amyloidosis (CA) from aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM). Twenty-one CA, thirty-two AS and twenty-one HCM patients were included in the study. From each original and perturbed ROI, 107 radiomic features were extracted. The CA-AS dataset was balanced using the perturbation-based method along with random over-sampling, adaptive synthetic (ADASYN) and the synthetic minority oversampling technique (SMOTE). The same methods were tested to perform data augmentation dealing with CA and HCM. Features were submitted to robustness, redundancy, and relevance analysis testing five feature selection methods (p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA). Support vector machine performed the classification tasks, and its performance were evaluated by means of a 10-fold cross-validation. The perturbation-based approach provided the best performances in terms of f1 score and balanced accuracy in both CA-AS (f1 score: 80%, AUC: 0.91) and CA-HCM (f1 score: 86%, AUC: 0.92) classifications. These results suggest that ROI perturbations represent a powerful approach to address both data balancing and augmentation issues.
Can the phase of SNR oscillations in GNSS-IR be used to estimate sea-level height?
Authors: Wei Zhenkui , Ren Chao , Liang Yueji , Liu Yintao , Liang Jieyu , Yin Anchao , Yue Weiting , Zhang Xudong , Lin Xiaoqi 
Published: May 2024

AbstractIn existing global navigation satellite system-interference reflectometry (GNSS-IR) research, only the frequency of signal-to-noise ratio (SNR) oscillations has been used to estimate sea-level height. However, the characteristic parameters of SNR oscillations are not isolated from each other, and a single feature cannot accurately and comprehensively capture the environmental changes of reflecting surface. Our simulation results show that for the nonlinear least squares (NLS), when there is a certain difference between the fitting frequency and the actual frequency of SNR oscillations, the deviation of the phase solution obtained is approximately linear with the frequency difference. Consequently, a linear phase correction GNSS-IR sea-level estimation method is constructed in this study. This method integrates the Lomb–Scargle periodogram (LSP) and NLS to process SNR oscillations, using the phase obtained from NLS to correct the retrieval error of LSP. Through processing SNR data from four sites for nearly half a year, we verified the stability of the relationship between phase and frequency-based retrieval error at different sites in continuous monitoring, and established the relationship model between the two. Then, utilizing the relationship model acquired at different sites, we estimated the sea-level variations for the next 6 months at each site through joint frequency and phase versus reflector height relationships. Experimental results show that the phases acquired from NLS can effectively correct the retrieval error of LSP. Compared with the traditional method using only frequency, the root mean square error and mean absolute error of the retrieval results obtained from the linear phase correction GNSS-IR sea-level estimation method based on LSP-NLS are both reduced by about 60%. This multi-feature fusion technique introduces a new perspective and technical approach for GNSS-IR sea-level estimations.
Fill-ins with scalar curvature lower bounds and applications to positive mass theorems
Authors: McCormick Stephen 
Published: May 2024

AbstractGiven a constantCand a smooth closed$$(n-1)$$(n-1)-dimensional Riemannian manifold$$(\Sigma , g)$$(Σ,g)equipped with a positive functionH, a natural question to ask is whether this manifold can be realised as the boundary of a smoothn-dimensional Riemannian manifold with scalar curvature bounded below byCand boundary mean curvatureH. That is, does there exist afill-inof$$(\Sigma ,g,H)$$(Σ,g,H)with scalar curvature bounded below byC? We use variations of an argument due to Miao and the author (Int Math Res Not 7:2019, 2019) to explicitly construct fill-ins with different scalar curvature lower bounds, where we permit the fill-in to contain another boundary component provided it is a minimal surface. Our main focus is to illustrate the applications of such fill-ins to geometric inequalities in the context of general relativity. By filling in a manifold beyond a boundary, one is able to obtain lower bounds on the mass in terms of the boundary geometry through positive mass theorems and Penrose inequalities. We consider fill-ins with both positive and negative scalar curvature lower bounds, which from the perspective of general relativity corresponds to the sign of the cosmological constant, as well as a fill-in suitable for the inclusion of electric charge.
A semi-parametric claims reserving model with monotone splines
Authors: Chang Le , Gao Guangyuan , Shi Yanlin 
Published: May 2024

AbstractStochastic reserving models used in the insurance industry are usually based on an assumed distribution of claim amounts. Despite their popularity, such models may unavoidably be affected by the misspecification issue given that it is likely that the underlying distribution will be different from that assumed. In this paper, we incorporate monotone splines to ensure the expected monotonically increasing pattern of cumulative development factors (CDFs) to develop a new semi-parametric reserving model that does not require a density assumption. To allow the maximum utilization of available information, we also propose an enhanced sampling approach that greatly increases the size of unbiased CDFs, particularly in later development periods. Based on the enhanced samples, a bootstrap technique is employed in the estimation of monotone splines, from which incurred-but-not-reported (IBNR) reserves and prediction errors can be obtained. Associated technical features, such as the consistency of estimator, are discussed and demonstrated. Our simulation studies suggest that the new model improves the accuracy of IBNR reserving, compared with a range of classic competing models. A real data analysis produces many consistent findings, thus supporting the usefulness of the monotone spline model in actuarial and insurance practice.
Effects of supplementary and mainstream education on the secondary–tertiary transitional challenges in English medium higher education
Authors: Yau Alice Hoi Ying , Fung D. , Tsang A. 
Published: May 2024

AbstractDespite the rapid growth of English medium instruction (EMI) in higher education, little research has been conducted to pinpoint the challenges faced by students. This quantitative study explored the challenges posed by the secondary–tertiary transition to first-year students with different English proficiency levels in EMI higher education institutions. In addition, we also investigated students’ perception of the usefulness of supplementary and mainstream education in preparing them for those challenges. 91 participants studying at two tertiary institutions in Hong Kong completed a 52-item questionnaire. The questionnaire included items in relation to three major aspects of challenges: ‘academic studies and skills’, ‘socialization’, and ‘college/university life adaptation’. It was found that students with low proficiency regarded all three aspects as significantly more challenging than the high proficiency ones in their first-year studies. However, despite the different proficiency levels, students similarly perceived mainstream education to be significantly more useful than supplementary education in preparing them for those transitional challenges. The findings together suggest that more English support is needed for low-proficiency students at the tertiary level and argue that mainstream education plays an essential role in assisting students in this secondary–tertiary transition. Implications are discussed in terms of the benefits of having a general English course as perquisite for low English proficiency students before they enroll in English for Academic Purposes courses, and the development of a variety of tasks with more emphasis on communication and collaboration at the secondary level to support them in this transition.
Temporal validity reassessment: commonsense reasoning about information obsoleteness
Authors: Hosokawa Taishi , Jatowt Adam , Sugiyama Kazunari 
Published: May 2024

AbstractIt is useful for machines to know whether text information remains valid or not for various applications including text comprehension, story understanding, temporal information retrieval, and user state tracking on microblogs as well as via chatbot conversations. This kind of inference is still difficult for current models, including also large language models, as it requires temporal commonsense knowledge and reasoning. We approach in this paper the task of Temporal Validity Reassessment, inspired by traditional natural language reasoning to determine the updates of the temporal validity of text content. The task requires judgment whether actions expressed in a sentence are still ongoing or rather completed, hence, whether the sentence still remains valid or has become obsolete, given the presence of context in the form of a supplementary content such as a follow-up sentence. We first construct our own dataset for this task and train several machine learning models. Then we propose an effective method for learning information from an external knowledge base that gives information regarding temporal commonsense knowledge. Using our prepared dataset, we introduce a machine learning model that incorporates the information from the knowledge base and demonstrate that incorporating external knowledge generally improves the results. We also experiment with different embedding types to represent temporal commonsense knowledge as well as with data augmentation methods to increase the size of our dataset.
Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTM
Authors: Poongadan Sameer , Lineesh M. C. 
Published: May 2024

AbstractThis study recommends a new time series forecasting model, namely ICEEMDAN - SVD - LSTM model, which coalesces Improved Complete Ensemble EMD with Adaptive Noise, Singular Value Decomposition and Long Short Term Memory network. It can be applied to analyse Non-linear and non-stationary data. The framework of this model is comprised of three levels, namely ICEEMDAN level, SVD level and LSTM level. The first level utilized ICEEMDAN to break up the series into some IMF components along with a residue. The SVD in the second level accounts for de-noising of every IMF component and residue. LSTM forecasts all the resultant IMF components and residue in third level. To obtain the forecasted values of the original data, the predictions of all IMF components and residue are added. The proposed model is contrasted with other extant ones, namely LSTM model, EMD - LSTM model, EEMD - LSTM model, CEEMDAN - LSTM model, EEMD - SVD - LSTM model, ICEEMDAN - LSTM model and CEEMDAN - SVD - LSTM model. The comparison bears witness to the potential of the recommended model over the traditional models.
Dimensions of teachers’ data literacy: A systematic review of literature from 1990 to 2021
Authors: Lee Jihyun , Alonzo Dennis , Beswick Kim , Abril Jan Michael Vincent , Chew Adrian W. , Oo Cherry Zin 
Published: May 2024

AbstractThe current study presents a systematic review of teachers’ data literacy, arising from a synthesis of 83 empirical studies published between 1990 to 2021. Our review identified 95 distinct indicators across five dimensions: (a) knowledge about data, (b) skills in using data, (c) dispositions towards data use, (d) data application for various purposes, and (e) data-related behaviors. Our findings indicate that teachers' data literacy goes beyond addressing the needs of supporting student learning and includes elements such as teacher reflection, collaboration, communication, and participation in professional development. Considering these findings, future policies should acknowledge the significance of teacher dispositions and behaviors in relation to data, recognizing that they are as important as knowledge and skills acquisition. Additionally, prioritizing the provision of system-level support to foster teacher collaboration within in-school professional development programs may prove useful in enhancing teachers’ data literacy.
Framing the predictive mind: why we should think again about Dreyfus
Authors: Reynolds Jack 
Published: May 2024

AbstractIn this paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for PP. Indeed, some of the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without admitting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning whether it can both explain all cognition and intelligent behavior as minimizing prediction error with just the core formal elements of the PP toolbox, and also adequately comprehend (or explain away) some of the apparent cognitive differences between biological and prediction-based artificial intelligence, notably in regard to establishing relevance and flexible context-switching, precisely the features of interest to Dreyfus’ work on embodied indexicality, habits/skills, and abductive inference. I address several influential philosophical versions of PP, including the work of Jakob Hohwy and Andy Clark, as well as more enactive-oriented interpretations of active inference coming from a broadly Fristonian perspective.
Unfulfilled habits: on the affective consequences of turning down affordances for social interaction
Authors: Vara Sánchez Carlos 
Published: May 2024

Abstract
Many pragmatist and non-representational approaches to cognition, such as the enactivist, have focused on the relations between actions, affectivity, and habits from an intersubjective perspective. For those adopting such approaches, all these aspects are inextricably connected; however, many questions remain open regarding the dynamics by which they unfold and shape each other over time. This paper addresses a specific topic that has not received much attention: the impact on future behavior of not fulfilling possibilities for social interaction even though their fulfillment is desirable within a given context. Inspired by Gibson’s theory of affordances and Dewey’s account of habits, these situations will be characterized as events where an agent does not act upon an inviting affordance for social interaction due to a conflict which he or she experiences between given concerns, needs, and social norms. This conflict leads to a sense of unfulfillment that may eventually bring about a crisis and revision of habits. Through specific examples, this work presents the potential impact of the connection between affordances, habits, and affectivity on everyday situations. It therefore represents an exploration of the common ground between pragmatism and enactivism and an attempt to contribute to a process-based approach to social interaction.
Advanced high-resolution chromatographic strategies for efficient isolation of natural products from complex biological matrices: from metabolite profiling to pure chemical entities
Authors: Queiroz Emerson Ferreira , Guillarme Davy , Wolfender Jean-Luc 
Published: May 2024

AbstractThe isolation of pure compounds from extracts represents a key step common to all investigations of natural product (NP) research. Isolation methods have gone through a remarkable evolution. Current approaches combine powerful metabolite profiling methods for compounds annotation with omics mining results and/or bioassay for bioactive NPs/biomarkers priorisation. Targeted isolation of prioritized NPs is performed using high-resolution chromatographic methods that closely match those used for analytical profiling. Considerable progress has been made by the introduction of innovative stationary phases providing remarkable selectivity for efficient NPs isolation. Today, efficient separation conditions determined at the analytical scale using high- or ultra-high-performance liquid chromatography can be optimized via HPLC modelling software and efficiently transferred to the semi-preparative scale by chromatographic calculation. This ensures similar selectivity at both the analytical and preparative scales and provides a precise separation prediction. High-resolution conditions at the preparative scale can notably be granted using optimized sample preparation and dry load sample introduction. Monitoring by ultraviolet, mass spectrometry, and or universal systems such as evaporative light scattering detectors and nuclear magnetic resonance allows to precisely guide the isolation or trigger the collection of specific NPs with different structural scaffolds. Such approaches can be applied at different scales depending on the amounts of NPs to be isolated. This review will showcase recent research to highlight both the potential and constraints of using these cutting-edge technologies for the isolation of plant and microorganism metabolites. Several strategies involving their application will be examined and critically discussed.
Graphical abstract
Reduced belowground allocation of freshly assimilated C contributes to negative plant-soil feedback in successive winter wheat rotations
Authors: Kaloterakis Nikolaos , Kummer Sirgit , Le Gall Samuel , Rothfuss Youri , Reichel Rüdiger , Brüggemann Nicolas 
Published: May 2024

Abstract
Aims
Successive winter wheat (WW) rotations are associated with yield reduction, often attributed to the unfavorable soil microbes that persist in the soil through plant residues. How rotational positions of WW affect the allocation of freshly assimilated carbon (C), an energy source for soil microbes, above and belowground remains largely unknown.

Methods
A 13CO2 pulse labeling rhizotron experiment was conducted in the greenhouse to study freshly fixed C allocation patterns. WW was grown in soil after oilseed rape (W1), after one season of WW (W2), and after three successive seasons of WW (W4). We used an automatic manifold system to measure excess 13C of soil respiration at six depths and five different dates. Excess 13C was also measured in dissolved organic C (DOC), microbial and plant biomass pools.

Results
There was a strong yield decline in successive WW rotations accompanied by distinct changes in root growth. Higher excess 13C of soil respiration was measured in W1 compared to W4, especially in the topsoil during at later growth stages. Higher excess 13C of the DOC and the microbial biomass was also traced in W1 and W4 compared to W2. Less 13C was taken up by successive WW rotations.

Conclusions
Our study demonstrates a mechanism through which the rotational position of WW affects the allocation of freshly assimilated C above and belowground. WW after oilseed rape sustains belowground allocation of freshly assimilated C for a longer time than successively grown WW and incorporates more of this C to its biomass.
On Fundamental Solutions and Gaussian Bounds for Degenerate Parabolic Equations with Time-dependent Coefficients
Authors: Ataei Alireza , Nyström Kaj 
Published: May 2024

AbstractWe consider second order degenerate parabolic equations with real, measurable, and time-dependent coefficients. We allow for degenerate ellipticity dictated by a spatial $$A_2$$

A
2

-weight. We prove the existence of a fundamental solution and derive Gaussian bounds. Our construction is based on the original work of Kato (Nagoya Math. J. 19, 93–125 1961).
Characterization and Quantification of the Major Bioactive Compounds in Mexican Purple Tomatoes
Authors: Torres Andrea , Pérez-Flores Laura J. , Lobato-Ortíz Ricardo , Navarro-Ocana Arturo 
Published: May 2024

AbstractThe present work carries out a quantitative analysis of the major bioactive compounds found in the native Mexican purple tomatoes. Total phenolic content ranged from 7.54 to 57.79 mg TPC/g DM, total flavonoid content ranged from 1.89 to 16.93 mg TFC/g DM, total anthocyanin content ranged from 0.29 to 2.56 mg TAC/g DM, and total carotenoid content ranged from 0.11 to 0.75 mg TCC/ g DM. In addition, 14 phenolic acids were identified, among which caffeoylquinic acid derivatives were the most abundant compounds with chlorogenic acid concentration up to 9.680 mg/g DM, together with flavonoids, such as rutin and quercetin-hexoxide. The qualitative analysis also showed the presence of 9 acylated anthocyanins and 2 carotenoids with significant functional features. As for anthocyanins, their chemical structures disclosed special structural features: glycosylated anthocyanins exhibited cis-trans hydroxycinnamic moieties and petunidin-3-(trans-p-coumaroyl)-rutinoside-5-glucoside was reported to be the main anthocyanin, whitin the range of concentrations between 0.160 and 1.143 mg/g DM.
Graphical Abstract