bluuebunny/crossref_metadata_2025_split huggingface dataset:
A characterization study of the anterior segment morphology of patients with and without long-term silicone oil following vitrectomy
Authors: Singh Gurfarmaan , Lee Yong Min , Sun Michelle , Selva Dinesh , Chan WengOnn 
Published: January 2025

Abstract

Purpose
To characterize the anterior segment (AS) morphology of patients with long-term silicone oil (SiO) in situ (> 12 months) following pars plana vitrectomy (PPV).

Methods
This prospective, comparative characterization study was conducted between January 2022 and July 2023. Patients were included and sorted based on if they had undergone PPV without long-term SiO or had SiO in situ for at least 12 months at the time of review and image collection. The Zeiss Cirrus HD-OCT (Carl Zeiss, Dublin, California, USA) was employed to image the ASs of study patients. Software within the OCT was used to collect the central corneal thickness (CCT), anterior chamber diameter (ACd), anterior chamber depth (ACD), chamber area, temporal irideocorneal angle and nasal irideocorneal angle.

Results
A total of 78 eyes from 42 total patients were included, their mean age was 63.74 ± 19.13 years and 64.29% (27/42) of participants were males. Patients who underwent PPV with and without long-term SiO were found to have no significant difference in CCT, ACd, ACD, chamber area and irideocorneal angles compared to their fellow unoperated eyes. The multivariate analysis of variance of the pooled data noted a statistically significant difference in the ACd of PPV with long-term SiO eyes compared to PPV without long-term SiO and unoperated eyes (p = < 0.001).

Conclusion
These findings suggest PPV with long-term SiO may alter the AS morphology of patients, its influence on the development of OAG remains unclear and warrants further investigation.
Promoting student voice by involving vocational education students and staff in citizenship education development: a participatory action research study
Authors: Geurts Esther M. A. , Reijs Rianne P. , Leenders Hélène H. M. , Jansen Maria W. J. , Hoebe Christian J. P. A. 
Published: January 2025

Abstract
Introduction: Despite decades of school improvement efforts, maintaining lasting change in schools remains challenging. So far, traditional interventions have been unsuccessful in recognising schools’ unique and complex contexts, which is why a shift towards a more reciprocal, emergent, and contextualised approach is necessary. Objective: Our aim is to develop citizenship education with students and staff in vocational education and training (VET), which increases student voice and fits the complex school system. Methods: We involved students and staff in citizenship education development and identified relevant factors that influenced this process. This participatory action research (PAR) study used a retrospective design assessing logbook entries, observations, interviews, and focus group discussions collected between September 2020 and September 2023 in four study programmes from one VET institution in the Netherlands. Thematic analysis was used to code the datasources. Results: Collaboration between students and staff flourished while using the curriculum negotiation tool. Relevant factors influencing the process were: alignment between project and participants’ priorities and goals, receptivity to student and teacher voices, leadership engagement, and tensions and intentions about roles and responsibilities between participants. Conclusions: This study emphasises the promise of involving students and staff in educational development. When teachers startedworking collaboratively with students, many of their initial doubts decreased, which led to renewed motivation for becoming student voice advocates. PAR has the potential of starkly disrupting the existing status quo and breaking through ingrained patterns within complex systems to ensure influence for students from all levels of education.
Cost-effectiveness of data driven personalised antibiotic dosing in critically ill patients with sepsis or septic shock
Authors: Broulikova Hana M. , Wallage Jacqueline , Roggeveen Luca , Fleuren Lucas , Guo Tingjie , Elbers Paul W.G. , Bosmans Judith E. 
Published: January 2025

Abstract

Purpose
This study provides an economic evaluation of bedside, data-driven, and model-informed precision dosing of antibiotics in comparison with usual care among critically ill patients with sepsis or septic shock.

Methods
This economic evaluation was conducted alongside an AutoKinetics randomized controlled trial. Effect measures included quality-adjusted life years (QALYs), mortality and pharmacokinetic target attainment. Costs were measured from a societal perspective. Missing data was multiply imputed, and bootstrapping was used to estimate statistical uncertainty. Differences in effects and costs were estimated using bivariate regression and used to calculate incremental cost-effectiveness ratios.

Results
Patients in the intervention group had higher costs (€42,684 vs. 39,475), lower mortality (42% vs. 49%), more QALYs (0.184 vs. 0.153), and higher pharmacokinetic target attainment (69% vs. 48%). Only the difference for target attainment was found statistically significant. An additional €18,129, €55,576, and €123,493 needs to be invested to attain the targeted plasma levels for one more patient, to save one life and gain one QALY, respectively. The probability of cost-effectiveness for all effect outcomes is below 60% for most acceptable willingness-to-pay thresholds.

Conclusions
Data-driven personalised antibiotic dosing in critically ill patients as implemented in the AutoKinetics trial cannot be recommended for implementation as a cost-effective intervention.

Trial registration
The trial was prospectively registered at Netherlands Trial Register (NTR), NL6501/NTR6689 on 25 August 2017 and at the European Clinical Trials Database (EudraCT), 2017-002478-37 on 6 November 2017.
The multifaceted benefits of Morus nigra L.: a pharmacological powerhouse
Authors: Özgür Mustafa , Uçar Aslı , Yılmaz Serkan 
Published: January 2025

Abstract
The black mulberry (Morus nigra L.), a member of the Moraceae family, is a resilient plant known for thriving in diverse climates. It is valued for its rich composition of both primary and secondary metabolites, which contribute to a wide range of health benefits. Among the most significant of these are anthocyanins, which are responsible for the plant's antioxidant, anti-inflammatory, and antibacterial properties. Traditionally, Morus nigra has been widely used in Chinese and Indian medicine to treat a variety of conditions, including diabetes, hypertension, and neurodegenerative diseases. Recent scientific studies have supported these traditional uses, highlighting the plant's potential in modern medicine. Specifically, Morus nigra has demonstrated positive effects on cardiovascular health, blood sugar regulation, and tissue protection. The fruit of the mulberry is rich in primary metabolites such as carbohydrates, proteins, and essential fatty acids, enhancing its nutritional value. Secondary metabolites, including flavonoids and phenolic compounds, are particularly important for their role in reducing oxidative stress and inflammation. Additionally, Morus nigra has shown promising antidiabetic and antihyperlipidemic effects, influencing metabolic pathways related to insulin resistance and lipid regulation. Despite its many health benefits, caution is needed when using Morus nigra in supplement form, particularly in high doses. While the fruit is generally safe for consumption, studies on leaf extracts have revealed potential toxicity at elevated doses, emphasizing the need for careful dosage. Further research is essential to determine optimal intake levels and fully explore the plant's pharmacological potential.
An investigation of phonological predictors in Chinese developmental dyslexia using a machine learning approach
Authors: Ding Ning , Peng Peng , Tang Jiuqing , Ding Yiran , Zhao Jingjing 
Published: January 2025

Abstract
Phonological awareness (PA), rapid automatized naming (RAN), and verbal short-term memory (VSTM) deficits have been identified as risk factors for developmental dyslexia (DD) of alphabetic orthographies. Yet, these three facets of phonological skills in DD of Chinese, a logographic writing system, have rarely been investigated together. The present study aimed to investigate PA, RAN, and VSTM deficits in Chinese children with DD. We compared 128 third and fourth grade Mandarin-speaking Chinese children with DD aged between 8 and 11 years (mean age = 9.4 years, 73 male) and 135 age-matched controls on 7 phonological tasks, including 2 PA tasks (phoneme deletion and onset/rime deletion), 3 RAN tasks (digit, object, and color), and 2 VSTM task (spoonerism and digit span). A combination of logistic regression analysis and a machine learning approach with SHapley Additive exPlanations (SHAP) methods was employed for data analyses. Logistic regression analysis showed that similar to the results from DD in six European languages by Landerl and Ramus et al. (2013), phoneme deletion and RAN digits played major roles in predicting Chinese DD, while digit span played a minor role. SHAP analysis revealed that the most effective predictor tasks for Chinese DD are RAN pictures, phoneme deletion, and spoonerism. The results suggest important roles of RAN and PA and a minor role of VSTM in predicting Chinese DD. Findings have important implications for the diagnosis and remediation of Chinese DD.
Contextualization of Diabetes: A Review of Reviews from Organisation for Economic Co-operation and Development (OECD) Countries
Authors: Plebon-Huff Sieara , Haji-Mohamed Hubi , Gardiner Helene , Ghanem Samantha , Koh Jessica , LeBlanc Allana G. 
Published: January 2025

Abstract

Purpose of Review
The prevalence of diabetes is rising around the world and represents an important public health concern. Unlike individual-level risk and protective factors related to the etiology of diabetes, contextual risk factors have been much less studied. Identification of contextual factors related to the risk of type 1 and type 2 diabetes in Organisation for Economic Co-operation and Development (OECD) countries may help health professionals, researchers, and policymakers to improve surveillance, develop policies and programs, and allocate funding.

Recent Findings
Among 4,470 potential articles, 48 were included in this review. All reviews were published in English between 2005 and 2023 and were conducted in over 20 different countries. This review identified ten upstream contextual risk factors related to type 1 and type 2 diabetes risk, including income, employment, education, immigration, race/ethnicity, geography, rural/urban status, built environment, environmental pollution, and food security/environment.

Summary
The ten upstream contextual risk factors identified this review may be integrated into diabetes research, surveillance and prevention activities to help promote better outcomes for people at risk or living with diabetes in OECD countries. Additional research is needed to better quantify the measures of associations between emerging key contextual factors and diabetes outcomes.
Dynamic behaviour of an eco-epidemiological model of fractional-order with a fear effect
Authors: Mani Deepak Nallasamy Prabhu , Shanmugam Muthukumar , Yavuz Mehmet , Muthuradhinam Sivabalan 
Published: January 2025

Abstract
In this paper, we propose a fractional-order prey-predator model with fear effects on the dynamic behavior of the populations. The model is used as a functional response of Holling type II in a non-delayed model. First, we prove several important results such as the existence, uniqueness, and boundedness of the solutions to the fractional-order dynamical system. Next, we discuss both the local and global stabilities of the fractional-order prey-predator model. The occurrence of Hopf bifurcation for fractional order is examined. Finally, the analytical solutions are confirmed through numerical simulations.
A Novel Hyperparameter Optimization Approach for Supervised Classification: Phase Prediction of Multi-Principal Element Alloys
Authors: Fatimi Syed Hassan , Wang Zidong , Chang Isaac T. H. , Liu Weibo , Liu Xiaohui 
Published: January 2025

Abstract
In this paper, a hyperparameter optimization approach is proposed for the phase prediction of multi-principal element alloys (MPEAs) through the introduction of two novel hyperparameters: outlier detection and feature subset selection. To gain a deeper understanding of the connection between alloy phases and their elemental properties, an artificial neural network is employed, with hyperparameter optimization performed using a genetic algorithm to select the optimum hyperparameters. The two novel hyperparameters, outlier detection and feature subset selection, are introduced within the optimization framework, along with new crossover and mutation operators for handling single and multi-valued genes simultaneously. Ablation studies are conducted, illustrating an improvement in prediction accuracy with the inclusion of these new hyperparameters. A comparison with five existing algorithms in multi-class classification is made, demonstrating an improvement in the performance of phase prediction, thereby providing a better perception of the alloy phase space for high-throughput MPEA design.
A deep learning model to identify the initial parameters of dissimilar welded joints under impact fracture
Authors: Avilés-Cruz Carlos , Aguilar-Sánchez Miriam , Vargas-Arista Benjamin , Escudero-Celis Gabriel 
Published: January 2025

Abstract
A weldment is the joining of two elements; therefore, they function as a single unit. However, a “defective weldment” refers to a welded structure that is deficient and weak, resulting in failure at the joint. The aim of the study is to find the initial shielded metal arc welding parameters under which two different steels, AISI 4140 and AISI 1018, were welded from fractures obtained by Charpy impact test. The three initial welding parameters are the type of filler material (AWS E8018 and AWS E9018 electrodes), the application or not of heat treatment, and the number of welding passes (2 or 3). To achieve the above, circular specimens have been designed for testing in the impact machine, allowing for the capture of fracture images from which attributes are extracted through a deep learning model. One novel aspect is the proposed methodology for designing circular specimens with a welded joint in the center with a machined notch to induce fracture under impact loading, allowing the weld penetration to be measured with the naked eye. The other innovative aspect is the deep learning model designed from scratch, consisting of 6 feature extraction layers, a flattening layer, and 4 fully connected classification layers; the model provides a

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identification of the weld parameters, similar to how a fingerprint identifies its owner; it is also independent of subjective expert opinion, and it is efficient, effective, and low cost.
A Quantum Framework for Combinatorial Optimization Problem over Graphs
Authors: Shi Meng , Wu Sai , Li Ying , Yuan Gongsheng , Yao Chang , Chen Gang 
Published: January 2025

Abstract
Combinatorial optimization problems over graphs, such as the traveling salesman problem, longest path problem, and maximum independent set problem, are well-known for being computationally costly, some even NP-hard problems. In this paper, we propose a general quantum algorithm framework searching for approximate solutions to combinatorial optimization problems with linear objective functions. Our framework provides APIs (application programming interfaces) that enable developers to encode weighted graph structures onto quantum circuits and utilize variational algorithms to generate approximate solutions. One key advantage of our framework is that it allows developers to design new graph algorithms for the graph problem represented as linear combinations of edge weights without requiring expertise in quantum programming. Besides, it only uses a logarithmic level of quantum bit scale, making our framework work on quantum computers with limited physical resources. Our experimental results demonstrate that our framework can provide good approximations for the traveling salesman problem compared to current quantum algorithm.
A Dual Approach to Detecting Iron Ions and Analyzing Water Quality
Authors: Paramparambath Sreedevi , Oflaz Kamil , Geetha Mithra , El-Azazy Marwa , El-Shafie Ahmed S. , Sadasivuni Kishor Kumar 
Published: January 2025

Abstract
This research establishes the foundation for developing an IoT (internet of things)-enabled sensor developed for the rapid and precise detection of Fe ions (Fe+ 3 and Fe+ 2) in drinking water. The investigation incorporates the utilization of Phenol red (PR), Universal indicator (UI), and Eriochrome black T (EBT) dyes, revealing their effectiveness in Fe ion detection. UV-visible tests and calibration graphs determined remarkably low average detection limits and exhibited stability at varying temperatures. Their exceptional sensitivity and selectivity across all Fe ionic states are crucial for continuous monitoring and on-site testing, making them well-suited for practical applications. Moreover, the limit of detection (LOD) of Fe ions in dyes are found to be 0.0189 mM for PR dye, 0.2 mM for UI, and 0.192 mM for EBT dye solution. Validation of the procedure was achieved right through fabrication of a 3D-printed tool. The integration of RGB (Red Green Blue) analysis with the 3D-printed prototype enabled the assessment and detection of iron ions (Fe+ 3 and Fe+ 2), visually represented in a unique RGB chart. This technical progress involves notable implications for Fe detection, demonstrating its ability for using in evaluating environmental and health conditions. Additionally, this novel approach can replace well established sophisticated instruments due to their rapidity and selectivity.
Assessing carbon emissions of facility agriculture involving photovoltaic greenhouses – insights from table grapes cultivation
Authors: Li Yichun , Li Shuyue , Luo Yaru , Wang Xuecai , Zhang Hengcheng , Zhang Yuquan W. , Song Shiren 
Published: January 2025

Abstract
Taking climate actions is of increasing importance. The agricultural sector is exploring its carbon neutrality transition pathway. Current relevant studies paid limited attention to agricultural products such as table grapes. This study takes table grapes cultivation in Shanghai as a case study, employing the life cycle assessment (LCA) to account for the carbon emissions and environmental impacts of five different conditions, including open field, simple greenhouse, continuous greenhouse, continuous greenhouse with photovoltaic power, and improved continuous greenhouse with photovoltaic power. Utilizing both observation and simulation data, the SimaPro software is applied to analyze the LCA carbon and environmental footprints of table grapes cultivation. The results show that simple greenhouse grapes exhibit the lowest level of carbon emissions (452 kgCO2-eq/mu-year), and meanwhile the most economical. Moreover, improved greenhouse utilizing photovoltaic power (1.26 × 103kgCO2-eq/mu-year) exhibits higher carbon emissions, however with lower levels of environmental toxicity due to utilizing grapevines for organic fertilizers. Thus, simple greenhouse grapes would score best if considering carbon emissions only, while the improved greenhouse model holds greater potential as a form of modernized facility-based agriculture. Furthermore, this study suggests that for the broader facility agriculture, using low carbon structure materials would serve as the level to reduce carbon footprints.
Method for extracting elemental sulfur in environmental water and its application to blue tide samples from Tokyo Bay, Japan
Authors: Sugahara Shogo , Higa Hiroto , Iwama Mitsuki , Nakamura Yoshiyuki , Inoue Tetsunori , Senga Yukiko , Egawa Michiko , Seike Yasushi 
Published: January 2025

Abstract
A simple method for determining elemental sulfur in environmental water was developed and applied to seawater samples collected immediately after the occurrence of blue tides in Tokyo Bay. To investigate the concentration and extraction methods, artificial elemental sulfur was quantitatively produced by oxidizing a sulfide solution with an iodine solution, then used as a standard reagent in the experiments. To concentrate the elemental sulfur in the water sample, glass filter paper (GF/F) was used to filter and collect the elemental sulfur. The elemental sulfur was then extracted using n-hexane, the main component of petroleum ether; however, the recovery of elemental sulfur from the wet glass filter paper was low, and remained so even when the glass filter paper was dried. We, therefore, used a mixture of n-hexane and an acetone solvent, which is a hydrophilic organic solvent, for extraction and succeeded in recovering more than 90% of the elemental sulfur from the wet glass filter paper. Using this solvent mixture, we extracted and quantified elemental sulfur from seawater samples collected after the occurrence of blue tide, and detected 0.36–0.38 mgS L–1 of elemental sulfur in the near-surface layer. We also found that the elemental sulfur concentrations were higher in the surface layer than in the bottom layer. Therefore, we demonstrated that the quantification of elemental sulfur is important to better understand the blue tide phenomenon.

Graphical abstract
Aircraft resilience to GNSS jamming and impact on ADS-B quality indicators
Authors: Pleninger S. , Hospodka J. , Steiner J. , Lukeš P. , Topková T. , Pilmannová T. , Kraus J. 
Published: January 2025

Abstract
The presented research investigates the impact of interference on the performance of aircraft Global Navigation Satellite System (GNSS) receivers with a specific focus on the behaviour of Automatic Dependent Surveillance-Broadcast (ADS-B) position quality indicators. Several experiments were performed with different aircraft types, such as Airbus, Boeing, Beechcraft King Air B350 or Tecnam, and using various intensities of GNSS jamming. The behaviour of various quality indicators, such as the Navigation Integrity Category, Navigation Accuracy Category, Source Integrity Level and System Design Assurance transmitted in different types of ADS-B messages, is analysed. We investigate not only situations where the quality indicators drop to zero, but also the complete evolution of the changes in the indicators as a function of the increasing power of the jamming signal. Based on the analysis of changes in the ADS-B quality indicators, the estimation of the most likely interference signal power required to discontinue the tracking of an already acquired GPS L1 Coarse/Acquisition signal is made. Additionally, the interference signal power to prevent re-acquisition is also estimated. The findings improve the understanding of interference effects and can support the development of robust interference mitigation techniques in aviation applications.