bluuebunny/crossref_metadata_2025_split huggingface dataset:
Common Mental health issues among non-refugee migrants in Australia: a scoping review
Authors: Das Pritimoy , Browning Colette , Rahman Muhammad Aziz 
Published: February 2025

Abstract

Purpose
Mental health issues were the fourth leading cause of disease burden in Australia in 2022. About 30% of Australia’s population are migrants, whose mental health is poorly understood. We aimed to report the prevalence and risk factors of common mental health issues among non-refugee migrants in Australia.

Methods
We reviewed studies published between 2000 and 2024 on mental health issues amongst migrants in Australia following the Arksey and O’Malley’s methodological framework and PRISMA-ScR guidelines.

Results
Out of 3122 titles retrieved on mental health issues among migrants in Australia, 30 papers were selected. Migrants from Greece reported the highest prevalence (43.1%) of anxiety disorders than Australian-born (15.8%). The highest prevalence of psychological distress and depression were found amongst migrants from Lebanon (33%) and China (19%), respectively. Migrants from North-Africa, the Middle East, Italy, Greece, and Europe experienced a higher prevalence of psychological distress (18.2–21.9%) than Australian-born (12.4%). Prevalence of depression was higher among migrants from non-English-speaking backgrounds (19.7% vs. 10%), Sub-Saharan Africa (18.8% vs. 9.3%), Italy (18% vs. 10%), Greece (17.1% vs. 4.1%), and China (10% vs. 3%), compared to Australian-born people, respectively. The way that risk factors were reported differed across studies. Anxiety disorders were associated with higher stress (p < 0.05), unemployment (OR 1.8, 95%CI:1.4–2.4), female gender (OR 2.13, 95%CI:1.64–2.76) unmarried status (p < 0.01) and poor physical health status (OR 7.35, 95%CI:3.86–14.01). Psychological distress was associated with being a single woman (OR 6.54, 95%CI:1.18–35.3), holding a temporary visa (p < 0.01), being economically inactive (p < 0.01) and having rare contact with friends (AOR 2.083, p < 0.001). Depression was associated with migrants who were never married (OR 4.11, 95%CI:1.59–10.65), younger or older (p < 0.001), female (OR 2.3, 95%CI:1.9–2.7), from non-English speaking countries (OR 2.41, 95%CI:1.14–5.10) and reported poor physical health (OR 3.55, 95%CI:1.60–7.88).

Conclusion
The high prevalence of mental health issues among non-refugee migrants necessitates revisiting strategies to tailor interventions appropriate for their mental health needs.
The subjective well-being of immigrants and natives during Covid-19
Authors: Aftab Fari , Scheffel Juliane , Spencer David 
Published: February 2025

Abstract
This study investigates the subjective well-being of immigrants and natives in the United Kingdom (UK) during the Covid-19 pandemic. A novel aspect of this research is that we exploit the quasi-experimental nature of the pandemic to analyse the potential causal impact of neighbourhood embeddedness in mitigating the adverse shock on subjective well-being. We proxy subjective well-being by life satisfaction and consider neighbourly support and psychological sense of community as indicators of neighbourhood embeddedness. The findings show that the pandemic negatively impacted the life satisfaction of immigrants more than that of natives. The analysis demonstrates that high neighbourhood embeddedness had a significant protective impact on the well-being of immigrants, whereas the opposite was observed for natives. Further analysis reveals that the adverse impact for natives can be attributed to their tendency to comply with Covid-19 social distancing rules, while the results for immigrants remain qualitatively consistent irrespective of their compliance or non-compliance behaviour. The overall findings in this research imply that community-based interventions should be aimed at balancing the promotion of social networking with adherence to health guidelines in a way that minimises well-being trade-offs during a health crisis.
A relativistic approach towards macro-scale modelling: implementation on laser re-melting and powder bed fusion (PBF-LB/M) AM processes
Authors: Pastras Georgios , Tzimanis Konstantinos , Stavropoulos Panagiotis 
Published: February 2025

Abstract
Simulation of laser-based processes is a computational intensive task; its efficiency strongly depends on the determination of the process phenomena and mechanisms that are of key importance. A major challenge in the macro-scale simulation of these processes is the large separation between the workpiece and laser beam dimensions resulting in a need for enormous lattices. In this work, this problem is bypassed via the introduction of an inhomogeneous lattice that is attached to the laser head, which combined with the enthalpy method enables a finite differences fast-running algorithm. Since the lattice moves relatively to the workpiece, the method requires appropriate adaptation of the physical governing equations. To validate the capabilities of this model, the laser re-melting and powder bed fusion (PBF-LB/M) processes are simulated and the results are compared with an experimentally verified modelling approach, indicating the adaptability of the workflow irrespective of the type of feedstock. Additionally, this approach facilitates the beam shaping investigation leveraging the stationary head relatively to the grid.
Expression of SSTR2a, FAP, HER2 and HER3 as potential radionuclide therapy targets in higher-grade meningioma
Authors: Mair Maximilian J. , Hartenbach Sabrina , Tomasich Erwin , Maas Sybren L. N. , Bosch Sarah A. , Widhalm Georg , Eckert Franziska , Sahm Felix , Hainfellner Johannes A. , Hartenbach Markus , Berghoff Anna S. , Preusser Matthias , Albert Nathalie L. 
Published: February 2025

Abstract

Purpose
High-grade meningiomas have high recurrence rates and limited prognosis. Radioligand therapies are approved in extracranial malignancies, but their value in brain tumours including meningiomas is unclear, as data on target expression is scarce.

Methods
CNS WHO grade 2 and 3 meningioma samples were immunohistochemically stained for somatostatin receptor 2a (SSTR2a), fibroblast activation protein (FAP), and human epidermal growth factor receptors 2/3 (HER2/HER3). Target expression was correlated with (epi-)genetic tumour subtyping by DNA methylation analysis, genetic alterations, and survival.

Results
Meningioma samples of 58 patients were included. SSTR2a expression (membranous/cytoplasmic) was observed in 43/55 (78.2%), and FAP expression in 15/58 (25.9%) evaluable samples, with HER2 and HER3 expression in one specimen each (1.7%). Membranous SSTR2a expression was strong in 18 (32.7%), intermediate in 12 (21.8%), and weak in 11 (20.0%) samples. While SSTR2a expression was more homogenous and mainly seen in regions with higher cellularity, FAP immunoreactivity was predominantly seen in tumour stroma and regions of lower cellularity. SSTR2a immunoreactivity was associated with TRAF7 wildtype status (p = 0.034). FAP expression was more frequent in meningiomas of CNS WHO grade 3 (vs. CNS WHO 2; p < 0.001), and samples with NF2 mutations (p = 0.032) or CDKN2A/B deletions (p = 0.013) compared to wildtype. FAP and SSTR2a expression (present vs. absent) were not associated with overall survival (p > 0.05).

Conclusion
SSTR2a and FAP are expressed in high-grade meningioma samples to a variable extent, and differences across meningioma subtypes underscore the need for biomarkers to improve patient selection. Spatial heterogeneity of target expression should be considered in radioligand therapy design.
Denoising neural network for low-light imaging of acoustically coupled combustion
Authors: Hayrapetyan Arin , Vargas Andres , Karagozian Ann R. 
Published: February 2025

Abstract
This study investigates the use of a trained neural network (NN) to enable more efficient noise reduction in processing images associated with acoustically coupled combustion phenomena, as compared with more commonly used image processing techniques. The approach is applied to experiments involving high-speed imaging of a single and coaxial methane–air jet diffusion flames exposed to various acoustically resonant environments. Proper orthogonal decomposition (POD) analysis applied to the flame imaging may be used to capture characteristic signatures in the flame dynamics and in verification of the proposed approach in this investigation. The NN trains on low-exposure input images and high-exposure response images for a steadily burning fuel jet with no coaxial flow, yet is remarkably successful when applied to a range of coaxial flow and acoustic excitation conditions. The proposed neural network approach demonstrates a significant decrease in the preprocess time required in analyzing flame images, typically by over a factor of 5, and preserves image quality. The approach replicates POD-based flame dynamics very well, for both low-amplitude and high-amplitude flame responses, the latter involving transitions in the dynamics due to the introduction of multiple timescales. The relative simplicity and success of this NN approach thus show the potential for improved image processing for complex dynamical flows and their transitional features.
Patterns and outcomes of current antitumor therapy for high-grade neuroendocrine neoplasms: perspective of a tertiary referral center
Authors: Melhorn Philipp , Spitzer Julia , Adel Thomas , Wolff Ladislaia , Mazal Peter , Raderer Markus , Kiesewetter Barbara 
Published: February 2025

Abstract

Purpose
Patients with metastatic high-grade neuroendocrine neoplasms (NEN) have an unfavorable prognosis. Treatment patterns and therapy outcome are scarcely evidenced, especially considering the WHO classification updates since 2017, and were thus investigated in this study.

Methods
This retrospective single-center analysis evaluated patients with neuroendocrine tumors grade 3 (NET G3) or neuroendocrine carcinomas (NEC) treated at the Medical University of Vienna since 2010. The primary endpoints were progression-free survival (PFS) and overall survival (OS) following first-line treatment.

Results
A total of 80 patients were included, 53 (66%) had NEC and 27 (34%) NET G3. Thirty patients had pancreatic NEN (38%), 29 gastrointestinal NEN (36%), 20 an unknown primary (25%), and one gall bladder NEC. All patients had metastatic disease, and all but four received systemic therapy. Platinum/etoposide was the most frequent palliative first-line treatment in NEC (41/47, 87%) and capecitabine/temozolomide (CAPTEM) in NET G3 (14/27, 52%). Overall, the median PFS and OS from first line start were 16.1 and 43.9 months for NET G3 and 6.1 and 12.7 months for NEC, respectively. Median PFS for platin/etoposide in NEC was 6.1 months (overall response rate [ORR] 56%) and for CAPTEM in NET G3 16.9 months (ORR 46%). Irrespective of the limited sample size (n = 4–11), second-line median PFS was short in NEC (FOLFIRI 2.8, FOLFOX 2.6, CAPTEM 5.4, other 2.6 months) and longer in NET G3 (8.2–11.1 months).

Conclusions
The present data from a large European NET center show that multiple treatment strategies are used in NEN and highlight the varying outcomes between NET G3 and NEC.
Investigation of the influence of different hairpin winding patterns on the common mode
Authors: Behrendt Cara-Nastasja , Eschenmann Leander Philipp , Ponick Bernd 
Published: February 2025

Abstract
With the increasing use of hairpin windings and fast-switching inverters, high-frequency effects occur in electric machines. These include parasitic common mode currents and resulting bearing currents. As countermeasures, additional components such as chokes and hybrid bearings are often used. This paper, in contrast, focuses on the winding design to reduce the common mode currents. In this context, not only well-known winding patterns are investigated in this paper, but new winding patterns are also derived for investigation. For this purpose, a search algorithm is introduced to find all feasible winding patterns considering both the manufacturing process and a desired electromagnetic winding design. All feasible windings are evaluated with respect to their common mode peak current. Furthermore, a statement is made as to whether or not the consideration of additional winding patterns offers optimization potential.
Advanced machine learning techniques for predicting power generation and fault detection in solar photovoltaic systems
Authors: Abdelsattar Montaser , AbdelMoety Ahmed , Emad-Eldeen Ahmed 
Published: February 2025

Abstract
This study investigated the application of advanced Machine Learning techniques to predict power generation and detect abnormalities in solar Photovoltaic systems. The study conducted a comprehensive assessment of various sophisticated models, including Random Trees, Random Forest, eXtreme Gradient Boosting, Linear Regression, Gradient Boosting (GB), and Categorical Boosting (CatBoost), utilizing a substantial dataset of 97,333 sets. The analysis focused on two fundamental objectives: power prediction and fault identification, both of which are crucial for enhancing the effectiveness and dependability of PV systems. CatBoost and GB models exhibited exceptional performance in power prediction, with the maximum R-squared value of 0.994. Demonstrating a strong correlation with the data and the ability to account for a substantial amount of the variation in power generation. These models outperformed others by providing more accurate and reliable projections, which are crucial for effective solar energy management and planning. CatBoost demonstrated superior performance compared to other approaches in the flaw detection test, attaining the highest performance metrics. The model achieved an accuracy of 0.999743, precision of 0.997171, recall of 0.999291, and an F1 score of 0.998230. The measures illustrated CatBoost’s exceptional ability to precisely identify problems with little errors, confirming it as the most successful model among those evaluated. The exceptional precision and dependability of the CatBoost model in identifying faults highlighted its capacity for continuously monitoring and maintaining solar systems in real-time, consequently augmenting operational efficiency. The study emphasized the significance of choosing suitable models to achieve the highest level of accuracy in predicting and detecting faults, thereby enabling the development of more sustainable and efficient solar energy systems. Subsequent research should prioritize the validation of these models using varied datasets, integration of up-to-date meteorological data, and creation of defect detection methods in real-time to enhance system efficiency.
An Efficient Damage-Plasticity DEM Contact Model for Highly Porous Rocks
Authors: Zheng Jinhui , Ciantia Matteo Oryem 
Published: February 2025

Abstract
A novel discrete element method (DEM) model is proposed to better reproduce the behaviour of porous soft rocks. With the final goal of simulating pile penetration problems efficiency and scalability are two underlining features. The contact model is based on the macro-element theory and employs damage laws to govern the plastic deformations developing at the microscale. To attain (i) high porosity states, (ii) represent irregular shaped grains and (iii) incorporate the physical presence of bond fragments, the model is cast within a far-field interaction framework allowing for non-overlapping particles to transmit forces. After presenting a calibration procedure, the model is used to replicate the behaviour of Maastricht calcarenite. In particular, the mechanical response of this calcarenite is explored within the critical state theory framework. Finally, the efficiency, performance and scalability of the model is tested by simulating physical model experiments of cone-ended penetration tests in Maastricht calcarenite from the literature. To boost efficiency of the 3D numerical simulations, a coupled DEM-FDM (Finite Differential Method) framework is used. The good fit between the experimental and numerical results suggest that the new model can be used to unveil microscopic mechanism controlling the macroscopic response of soft-rock/structure interaction problems.
Efficient dynamic simulations of planetary journal bearings in wind turbine gearboxes
Authors: Lucassen Mattheüs , Jacobs Georg , Lehmann Benjamin 
Published: February 2025

Abstract
The use of journal bearings as planetary gear bearings in wind turbines instead of roller bearings is becoming common, as journal bearings are advantageous in terms of the torque density of the gearbox. The transition from roller bearings to journal bearings results in different bearing stiffness and damping characteristics, thereby altering the dynamics of the wind turbine gearbox and the load-bearing behavior of the gears. The dynamic behavior of journal bearings is highly dependent on operating conditions, as fluid film heights and hydrodynamic pressures vary with speed. To conduct a detailed analysis of these bearings and their effect on the gearbox dynamics, elasto-hydrodynamic simulations (EHD) coupled with multibody simulations (MBS) are generally performed. These simulations typically require significant computational resources. In order to reduce the calculation times for optimizing the gearbox with regard to Noise-Vibration-Harshness and load-bearing capacity, a simplified representation of the bearing in multi-body simulations is desired, ideally as a simple stiffness and damping matrix.
While standard approaches exist for deriving stiffness and damping matrices for journal bearings, these methods do not adequately capture the complex deformations and load conditions present in planetary journal bearings. As a result, these bearings exhibit stiffness and damping behavior that depends on local conditions and operating points. Therefore, this paper presents an approach to extract these stiffness and damping values from EHD simulations across the operating range of a wind turbine. These extracted values are applied to a multi-body simulation model of a wind turbine gearbox. This paper introduces a novel approach for modelling planetary journal bearings. By dividing the bearing into small slices, a multi-slice bearing is created that can capture the complex stiffness and damping characteristics present in planetary journal bearings. This sliced bearing modelling method is compared with a coupled EHD-MBS model of the same wind turbine gearbox to analyze the effects on system dynamics. The novel planetary journal bearing modelling will enable faster design of wind turbine gearbox components and mitigate vibrations efficiently.
Influence of lubricant particle contamination on discharge events in electrical loaded radial cylindrical roller bearings
Authors: Harling Jörn Christian , Rößler Peter , Jacobs Georg , Lehmann Benjamin 
Published: February 2025

Abstract
The economic efficiency of wind turbines is considerably affected by the occurrence of premature bearing failures in gearboxes. Due to their proximity to the generator, gearbox bearings experience additional electrical stress, leading to voltage buildup across the bearing. When this voltage exceeds a certain threshold (breakdown voltage), it discharges as an arc. It is well established that electrical discharge across a bearing can induce surface- and structural-damage to the bearing material. The effect of particles on the electrical properties of oil, and consequently on potential current conduction, is well documented in other technical fields. The existing design standards for wind turbine gearboxes and bearings only consider the mechanical effects of particles. Therefore, only particles with a diameter ≥ 6 µm are considered. This study experimentally investigates the influence of particles < 6 µm on the breakdown voltage and frequency in electrically loaded cylindrical roller bearings. These particles, which are not considered in current design standards for wind turbine gearboxes, constitute the majority of contaminants. The results show that higher particle concentrations lead to a reduction in breakdown voltage. This effect is also evident within the typical particle contamination levels found in wind turbine gearboxes. Additionally, higher particle concentrations correlate with increased breakdown frequency and reduced energy per breakdown, suggesting a possible shift from surface- to structure-induced damage mechanisms.
Comparative analysis of the structures and properties of cellulose hydrogels prepared using different solvent systems
Authors: Zhang Yangyang , Kobayashi Kayoko , Wada Masahisa 
Published: February 2025

Abstract
Numerous studies have investigated the use of cellulose hydrogels produced via the dissolution–regeneration method. However, using different cellulose solvents, a comprehensive comparison of their structures and properties has yet to be reported. In this study, we prepared cellulose hydrogels using six different solvents: LiCl/N,N-dimethylacetamide (DMAc), 1-butyl-3-methylimidazolium chloride, NaOH/urea, ZnCl2/AlCl3, LiBr, and Ca(SCN)2 solutions with the same cellulose concentration and evaluated their structure, transparency, and mechanical properties. Depending on the cellulose solvent used, significant differences in volume shrinkage were observed during regeneration and washing with water. The cellulose hydrogels prepared from LiCl/DMAc and NaOH/urea solutions showed the most significant volume shrinkage during regeneration and washing. Greater volume shrinkage resulted in a higher solid cellulose content in the hydrogel. A positive correlation exists between solid content and both elastic modulus and strength. The cellulose hydrogel prepared from LiCl/DMAc showed excellent mechanical properties: compressive modulus of 332 kPa, tensile modulus of almost 1000 kPa, and ultimate tensile strength of 523 kPa. The cellulose hydrogels prepared from LiBr and Ca(SCN)2 solutions showed negligible volume shrinkage and lower solid content. However, the elastic modulus and strength of the hydrogels were relatively high despite their solid content due to the three-dimensional network structure composed of nanofibers. Moreover, the transparency was higher for the hydrogels prepared from LiCl/DMAc with amorphous cellulose and a uniform internal structure. These findings could assist in customizing the material properties of cellulose hydrogels.