bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lite approaches for long-range multi-step water quality prediction
Authors: Islam Md Khaled Ben , Newton M. A. Hakim , Trevathan Jarrod , Sattar Abdul 
Published: August 2024

AbstractForecasting accurate water quality is very important in aquaculture, environment monitoring, and many other applications. Many internal and external factors influence water quality. Therefore, water quality parameters exhibit complex time series characteristics. Consequently, long-range accurate prediction of water quality parameters suffers from poor propagation of information from past timepoints to further future timepoints. Moreover, to synchronise the prediction model with the changes in the time series characteristics, periodic retraining of the prediction model is required and such retraining is to be done on resource-restricted computation devices. In this work, we present a low-cost training approach to improve long-range multi-step water quality prediction. We train a short-range predictor to save training effort. Then, we strive to achieve and/or improve long-range prediction using multi-step iterative ensembling during inference. Experimental results on 9 water quality datasets demonstrate that the proposed method achieves significantly lower error than the existing state-of-the-art approaches. Our approach significantly outperforms the existing approaches in several standard metrics, even in the case of future timepoints at long distances.
Singularities of the hyperbolic elastic flow: convergence, quantization and blow-ups
Authors: Schlierf Manuel 
Published: August 2024

AbstractWe study the elastic flow of closed curves and of open curves with clamped boundary conditions in the hyperbolic plane. While global existence and convergence toward critical points for initial data with sufficiently small energy is already known, this study pioneers an investigation into the flow’s singular behavior. We prove a convergence theorem without assuming smallness of the initial energy, coupled with a quantification of potential singularities: Each singularity carries an energy cost of at least 8. Moreover, the blow-ups of the singularities are explicitly classified. A further contribution is an explicit understanding of the singular limit of the elastic flow of$$\lambda $$λ-figure-eights, a class of curves that previously served in showing sharpness of the energy threshold 16 for the smooth convergence of the elastic flow of closed curves.
In-vitro study on type I collagen synthesis in low-level laser therapy on the early ligament fibroblasts’ healing process
Authors: Cárdenas-Sandoval R. P. , Bernal-Bernal L. D. , Cabrera-Salazar S. , Gómez-Ramírez D. M. , González-Ballesteros L. M. , Hooker-Mendoza K. M. , Ospina-Piedrahíta L. N. , Hernández-Charry C. X. , Ardila-Rojas G. , Velásquez-Durán A. M. , Cucarián-Hurtado J. D. , Ondo-Méndez A. O. , Barbosa-Santibañez J. , Carvajal-Calderón L.L. , Navarrete-Jimenez M. L. 
Published: August 2024

Abstract

Background
Low-level Laser Therapy (LLLT) has demonstrated its potential in promoting fiber matrix maturation, collagen synthesis, and fibroblast proliferation, contributing to tissue regeneration. Our study aimed to investigate the impact of LLLT on collagen type I synthesis, cell proliferation, and viability in human ligament fibroblasts derived from the Anterior Cruciate Ligament (ACL).

Methods
Tissue samples were obtained from individuals undergoing arthroscopic ACL reconstruction surgery. Primary human fibroblasts were isolated, and immunohistochemical assays confirmed their characteristics. LLLT at 850 nm was administered in three groups: Low dose (1.0 J/cm²), High dose (5.0 J/cm²), and Control (0.0 J/cm²). Cell viability was calculated using a membrane integrity assay, proliferation was determined by automated counting, and collagen type I concentration in cell culture was measured using an immunoassay.

Results
Fibroblasts showed decreased viability after low and high doses of LLLT, increased proliferation at the low dose, and increased collagen synthesis at the high dose on day 10 for both sexes after treatment.

Conclusion
Our study demonstrated that LLLT may improve the early ligament healing process by increasing cell proliferation at the low dose and enhancing collagen type I synthesis at the high dose in human ligament fibroblasts.
Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning
Authors: Zijlstra Frank , While Peter Thomas 
Published: August 2024

Abstract
Object
Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic data generation to complement small datasets and improve reconstruction quality.

Materials and methods
An adversarial auto-encoder was trained to generate phase and coil sensitivity maps from magnitude images, which were combined into synthetic raw data.
On a fourfold accelerated MR reconstruction task, deep-learning-based reconstruction networks were trained with varying amounts of training data (20 to 160 scans). Test set performance was compared between baseline experiments and experiments that incorporated synthetic training data.

Results
Training with synthetic raw data showed decreasing reconstruction errors with increasing amounts of training data, but importantly this was magnitude-only data, rather than real raw data. For small training sets, training with synthetic data decreased the mean absolute error (MAE) by up to 7.5%, whereas for larger training sets the MAE increased by up to 2.6%.

Discussion
Synthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.
SAD: semi-supervised automatic detection of BOLD activations in high temporal resolution fMRI data
Authors: Schmidt Tim , Nagy Zoltán 
Published: August 2024

Abstract
Objective
Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To overcome the necessity of presuming a specific model for the hemodynamic response, we introduce a semi-supervised automatic detection (SAD) method.

Materials and methods
The proposed SAD method employs a Bi-LSTM neural network to classify high temporal resolution fMRI data. Network training utilized an fMRI dataset with 75-ms temporal resolution in an iterative scheme. Classification performance was evaluated on a second fMRI dataset from the same participant, collected on a different day. Comparative analysis with the standard GLM approach was conducted to evaluate the cooperative effectiveness of the SAD method.

Results
The SAD method performed well based on the classification scores: true-positive rate = 0.961, area under the receiver operating curve = 0.998, true-negative rate = 0.99, F1-score = 0.979, False-negative rate = 0.038, false-discovery rate = 0.002, false-positive rate = 0.002 at 75-ms temporal resolution.

Conclusion
SAD can detect hemodynamic responses at 75-ms temporal resolution without relying on a specific shape of an HRF. Future work could expand the use cases to include more participants and different fMRI paradigms.
First year-round movement tracking of the Mediterranean Gull (Ichthyaetus melanocephalus) in Europe: insights and conservation recommendations for declining population
Authors: Jankowiak Łukasz , Sidelnik Marcin , Siekiera Joachim , Szymczak Jakub , Zieliński Piotr , Marchowski Dominik 
Published: August 2024

Abstract
This study examines the Mediterranean Gull (Ichthyaetus melanocephalus), utilizing GPS/GSM transmitters year-round for the first time on this species. The research covers habitat use, migration patterns, and breeding behaviors, tracking 19 birds captured at breeding colonies in Poland. To capture the birds and fit transmitters, we selected representative locations: islands in dam reservoirs, gravel pits, rivers, and lakes. Mediterranean Gulls nested mainly in colonies of Black-headed Gulls (Chroicocephalus ridibundus). By tracking birds with transmitters, we identified nine additional breeding sites within Poland, the Czechia, and Germany. We determined the mean length of phenological periods: breeding (76, April 10–June 26), autumn migration (11, June 26–July 7), winter (261, July 5–March 23), and spring migration (18, March 23–April 10). According to residence sites analysis, cropland was the most frequently used habitat during breeding (52.9%), while the open sea was predominant in winter (25.8%). We identified key wintering areas: the English Channel, the Utrecht–Antwerpen area, the North Sea coast, and the Irish Sea coast. One individual wintered along the coast of Portugal and near Malaga in Spain. An interesting discovery was the longer spring migration than autumn what involved visits to multiple potential breeding sites, leading to the mapping of a network of potential breeding areas. This underscores the need for proactive habitat protection and comprehensive conservation strategies. Given the Mediterranean Gull’s migratory behavior and lifecycle, the study advocates for it as a model for conserving migratory waterbirds.
Probability of entering an orthant by correlated fractional Brownian motion with drift: exact asymptotics
Authors: Dȩbicki Krzysztof , Ji Lanpeng , Novikov Svyatoslav 
Published: August 2024

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Lived Experiences of Migrant Fathers in the Perinatal Period: A Systematic Review and Analysis
Authors: Vo Huy N. , McKenzie-McHarg Kirstie , Bennett Pauleen C. , Mai Dac L. 
Published: August 2024

AbstractThe worldwide population of migrant families is on the rise, and there is growing acknowledgement of the significance of supporting parental mental health within these families. However, understanding of the experiences of migrant fathers during the perinatal period remains incomplete. The objective of this review is to provide an overview of existing research on perinatal migrant fathers’ experiences in different cultures. Multiple searches were conducted in April 2023 for quantitative, qualitative, and mixed-methods studies across six electronic databases: Medline, CINAHL, Embase, PsycINFO, Web of Science, and Scopus. Fourteen eligible articles were identified, including nine qualitative studies, five quantitative studies, and no mixed-methods studies. The Mixed-methods Appraisal Tool was used to assess the quality of these studies. The quantitative findings were transformed into narrative summaries to be analysed thematically along with the qualitative data. Three themes were identified: (1) Cultural competence (dealing with cultural differences, needs related to original country); (2) Parenthood in a new country (challenges and adaptation to fatherhood, challenging traditional gender norms, lack of extended family and building new support networks, being the main supporter for the family); (3) Needs of the fathers and their personal difficulties. The findings of this review suggest a direction for future research in perinatal psychology. The review also provides insights into the need for social and community support for migrant fathers and how healthcare services can support this group during the perinatal period.
Epistemic Logics for Relevant Reasoners
Authors: Sedlár Igor , Vigiani Pietro 
Published: August 2024

AbstractWe present a neighbourhood-style semantic framework for modal epistemic logic modelling agents who process information using relevant logic. The distinguishing feature of the framework in comparison to relevant modal logic is that the environment the agent is situated in is assumed to be a classical possible world. This framework generates two-layered logics combining classical logic on the propositional level with relevant logic in the scope of modal operators. Our main technical result is a general soundness and completeness theorem.
Modelling spatiotemporal patterns of wildfire risk in the Garden Route District biodiversity hotspots using analytic hierarchy process in South Africa
Authors: Shinga Phindile Siyasanga , Tesfamichael Solomon G. , Sibandze Phila , Kalumba Ahmed Mukalazi , Afuye Gbenga Abayomi 
Published: August 2024

Abstract
The increasing frequency and intensity of wildfires necessitate effective risk management in biodiversity hotspots to mitigate the potential impacts of wildfire hazards. The study utilised a multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model to analyse wildfire risk patterns in the Garden Route District (GRD), focusing on biodiversity hotspots in the Western Cape, South Africa. The study used weight assignment and overlay analysis to evaluate wildfire risk factors, including human, topographic, and climatic factors, using data from Landsat and WorldClim from 1991 to 2021. The wildfire risk model was validated using MODIS historical fire data from the Global Forest Watch database and Confusion Matrix, with the burned area extent identified using differenced Normalized Burn Ratio (dNBR). The results show that despite 53% of the most burned area, only 12% was burned, with the high-risk zone accounting for only 11%, indicating a higher likelihood of wildfires spreading and intensifying. The results reveal a weak positive correlation (r = 0.28) between historical fire occurrences and burned areas and a negative correlation (r = − 0.27) between historical fire occurrences and fire seasons. Human and climatic factors significantly impact wildfire propagation in high-risk zones, while topographic factors have less influence, indicating a lower risk of ignition. The findings show that 26% of high-risk zones in the southwestern region dominated GRD biodiversity hotspots, while 27% were in the low-moderate-risk zone in the northwestern parts. The results of this study can aid in assigning fire risk-based criterion weights to support decision-makers in regional and global wildfire prevention and management.
The Bergen–Yale Sexual Addiction Scale (BYSAS): Longitudinal Measurement Invariance Across a Two-Year Interval
Authors: Gomez Rapson , Brown Taylor , Stavropoulos Vasileios 
Published: August 2024

AbstractThe Bergen–Yale Sexual Addiction Scale (BYSAS; [1]) is arguably the most popular questionnaire at present for assessing sex addiction. Employing Confirmatory Factor Analysis (CFA) and treating item scores as ordered categorical, we applied Weighted Least Square Mean and Variance Adjusted Chi-Square (WLSMV) extraction to investigate the longitudinal measurement and structural invariance of ratings on the BYSAS among 276 adults (mean = 31.86 years; SD = 9.94 years; 71% male) over a two-year period, with ratings at three yearly intervals. Overall, there was support for configural invariance, full loading, full threshold, the full unique factor invariance; and all structural (latent variances and covariances) components. Additionally, there was no difference in latent mean scores across the three-time points. The psychometric and practical implications of the findings are discussed.
He/She/They - gender inclusivity in developing and using health-related questionnaires: a scoping review
Authors: Scott Desiree , Derrett Sarah , Rupel Valentina Prevolnik , Jelsma Jennifer , Gurung Gagan , Oduro Georgina Yaa , Withey-Rila Cassie 
Published: August 2024

Abstract

Purpose
To ensure the recognition and participation of all potential respondents in health research, surveys and care, including LGBTQIA + broadly, and trans people, specifically, the use of inclusive language should be considered. This scoping review aimed to identify and describe strategies considered for gender inclusivity in development and use of health questionnaires and Patient Reported Outcomes Measures (PROMs).

Methods
A systematic search of peer reviewed publications between January 2000 and September 2022 was conducted in Scopus, ProQuest Central, Ovid Medline (PubMed and EBSCO). Two reviewers independently screened identified publications titles and abstracts, followed by full text screening and data extraction from eligible articles.

Results
The search of over 5000 publications, retained 18; most acknowledged gaps in representation and advocated for gender-inclusive language. Eight articles discussed exclusion from health care and health research for gender minority groups due to the use of gendered language. Improved reliability, validity and response rates were associated with the use of gender-neutral language in seven articles. Only one article reported finding irritation among cisgender males when non-binary gender response options were used. One paper, focussing on instruments for Rheumatoid Arthritis, discussed gaps in representation if diverse gender identities were not considered when developing PROMs.

Conclusion
This scoping review points to the importance of adopting gender-inclusive language in health questionnaires and surveys to reduce the risk of excluding gender minority groups. Despite finding very few specific examples of how others have used gender-inclusive language in health questionnaires, many strengths of gender-inclusive language usage were identified.
First impressions and the built environment: exploring zero acquaintance judgments in socio-spatial contexts
Authors: Tooley Jack 
Published: August 2024

AbstractThe well-researched sociological concept known as Zero Acquaintance Judgment frames first impression scenarios and highlights their prevalence and importance to our everyday lives, yet sociology so far overlooks how these might be affected by the built environment where first impressions are typically situated. Broadly, spatial discriminatory discourse investigates how spaces can affect social judgments, yet no research has investigated how this dynamic might unfold within a first impression scenario. Using the Zero Acquaintance Judgment concept as a lens of inquiry, a comprehensive review of feminist, queer, racial, and disability frameworks (which comprise spatial discriminatory discourse) was undertaken. From this investigation, three broad theoretical themes were articulated and rationalized as to how the built environment can potentially affect first impressions between strangers. The discussion bears relevance for researchers and designers of spaces where first impressions are paramount to function as well as highlights how the settings of research into first impressions may affect outcomes.
A hybrid approach for efficient feature selection in anomaly intrusion detection for IoT networks
Authors: Ayad Aya G. , Sakr Nehal A. , Hikal Noha A. 
Published: August 2024

AbstractThe exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times.
An optimized intelligent open-source MLaaS framework for user-friendly clustering and anomaly detection
Authors: ElDahshan Kamal A. , Abutaleb Gaber E. , Elemary Berihan R. , Ebeid Ebeid A. , AlHabshy AbdAllah A. 
Published: August 2024

AbstractAs data grow exponentially, the demand for advanced intelligent solutions has become increasingly urgent. Unfortunately, not all businesses have the expertise to utilize machine learning algorithms effectively. To bridge this gap, the present paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. The proposed framework includes default algorithms for clustering and outlier detection. In the clustering category, it features three algorithms: k-means, hierarchical clustering, and DBScan clustering. For outlier detection, it includes local outlier factor, K-nearest neighbors, and Gaussian mixture model. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a representational state transfer application programming interface, enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With the innovative MLaaS framework, companies may harness the full potential of business analysis.