bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Numerical Algorithm for Calculating Critical Points and Its Application to Predictive Mixture Models and Binary CO$$_2$$ Mixtures
Authors: Rath Sebastian , Gampe Uwe , Jäger Andreas 
Published: November 2023

AbstractThe use of supercritical fluids in technical applications requires an accurate knowledge of their critical points. For mixtures, these can deviate significantly and without a linear dependency from the critical points of its individual pure components. Since even small amounts of admixture can have noticeable effects, this not only concerns blends of targeted compositions, but also unintentional mixtures for example caused by impurities. Within this work, a method for the calculation of critical points is presented which focuses on numerical robustness promoting a fast and reliable generation of results. Implemented into the thermodynamic property software TREND, with its mixture modeling capabilities, the method allows a flexible combination of different equations of state and mixture models which also includes predictive approaches. Against the background of an increasing relevance of mixtures based on supercritical CO$$_2$$

2

(sCO$$_2$$

2

) for energy applications, critical lines are calculated and compared against experimental results for selected sCO$$_2$$

2

-based mixtures recently considered for power plant applications. Herein, several combinations of equations of state (EoS) and mixture models are compared. Critical lines are calculated for the first time in this work with the combination of the multi-fluid mixture model with excess Gibbs energy ($$g^\text {E}$$

g
E

) models. It was found that the critical lines calculated with the combination of the multi-fluid mixture model with the $$g^\text {E}$$

g
E

-model COSMO-SAC yields good predictive results for the investigated CO$$_2$$

2

mixtures.
Attraction Versus Repulsion Between Doublons or Holons in Mott-Hubbard Systems
Authors: Queisser Friedemann , Schaller Gernot , Schützhold Ralf 
Published: November 2023

AbstractFor the Mott insulator state of the Fermi-Hubbard model in the strong-coupling limit, we study the interaction between quasi-particles in the form of doublons and holons. Comparing different methods – the hierarchy of correlations, strong-coupling perturbation theory, and exact analytic solutions for the Hubbard tetramer – we find an effective interaction between doublons and/or holons to linear order in the hopping strength which can display attractive as well as repulsive contributions, depending on the involved momenta. Finally, we speculate about the implications of our findings for high-temperature superconductivity.
Impact of Prescription Medicines on Work-Related Outcomes in Workers with Musculoskeletal Disorders or Injuries: A Systematic Scoping Review
Authors: Tefera Yonas Getaye , Gray Shannon , Nielsen Suzanne , Gelaw Asmare , Collie Alex 
Published: November 2023

Abstract
Purpose
Medicines are often prescribed to workers with musculoskeletal disorders (MSDs) and injuries to relieve pain and facilitate their recovery and return to work. However, there is a growing concern that prescription medicines may have adverse effects on work function. This scoping review aimed to summarize the existing empirical evidence on prescription medicine use by workers with MSD or injury and its relationship with work-related outcomes.

Methods
We identified studies through structured searching of MEDLINE, EMBASE, PsycINFO, CINAHL Plus, Scopus, Web of Science and Cochrane library databases, and via searching of dissertations, theses, and grey literature databases. Studies that examined the association between prescription medicine and work-related outcomes in working age people with injury or MSDs, and were published in English after the year 2000 were eligible.

Results
From the 4884 records identified, 65 studies were included for review. Back disorders and opioids were the most commonly studied musculoskeletal conditions and prescription medicines, respectively. Most studies showed a negative relationship between prescription medicines and work outcomes. Opioids, psychotropics and their combination were the most common medicines associated with adverse work outcomes. Opioid prescriptions with early initiation, long-term use, strong and/or high dose and extended pre- and post-operative use in workers’ compensation setting were consistently associated with adverse work function. We found emerging but inconsistent evidence that skeletal muscle relaxants and non-steroidal anti-inflammatory drugs were associated with unfavorable work outcomes.

Conclusion
Opioids and other prescription medicines might be associated with adverse work outcomes. However, the evidence is conflicting and there were relatively fewer studies on non-opioid medicines. Further studies with more robust design are required to enable more definitive exploration of causal relationships and settle inconsistent evidence.
Which Way Does Time Go? Differences in Expert and Novice Representations of Temporal Information at Extreme Scales Interferes with Novice Understanding of Graphs
Authors: Resnick Ilyse , Chapman Elizabeth Louise , Shipley Thomas F. 
Published: November 2023

AbstractVisual representations of data are widely used for communication and understanding, particularly in science, technology, engineering, and mathematics (STEM). However, despite their importance, many people have difficulty understanding data-based visualizations. This work presents a series of three studies that examine how understanding time-based Earth-science data visualizations are influenced by scale and the different directions time can be represented (e.g., the Geologic Time Scale represents time moving from bottom-to-top, whereas many calendars represent time moving left-to-right). In Study 1, 316 visualizations from two top scholarly geoscience journals were analyzed for how time was represented. These expert-made graphs represented time in a range of ways, with smaller timescales more likely to be represented as moving left-to-right and larger scales more likely to be represented in other directions. In Study 2, 47 STEM novices were recruited from an undergraduate psychology experiment pool and asked to construct four separate graphs representing change over two scales of time (Earth’s history or a single day) and two phenomena (temperature or sea level). Novices overwhelmingly represented time moving from left-to-right, regardless of scale. In Study 3, 40 STEM novices were shown expert-made graphs where the direction of time varied. Novices had difficulty interpreting the expert-made graphs when time was represented moving in directions other than left-to-right. The study highlights the importance of considering representations of time and scale in STEM education and offers insights into how experts and novices approach visualizations. The findings inform the development of educational resources and strategies to improve students’ understanding of scientific concepts where time and space are intrinsically related.
Radioluminescence mapping of 241Am-doped environmental samples and nuclear materials
Authors: Luchkov Maksym , Olaru Claudia , Lalau Ioana , Zadehrafi Mastaneh , Nikolényi István Róbert , Gémesi Zoltán , Ioan Mihail-Razvan , Krasniqi Faton 
Published: November 2023

AbstractIn the framework of the project RemoteALPHA, several radioluminescence scanning systems were developed to establish the metrological basis for the optical detection of alpha-emitting radionuclides. This work demonstrates an application of the ultraviolet imaging system for the characterization of low-activity alpha sources: contaminated environmental samples, and nuclear materials featuring depleted uranium. The measurements were performed at a reference distance of 2 m from the source. The weakest samples were measured in a chamber with an N2 + NO gas atmosphere. Using the scanning method, the total surface activity was calculated for analyzed samples, with the lowest reported value of just 51 Bq.
Identification of stably expressed microRNAs in plasma from high-grade serous ovarian carcinoma and benign tumor patients
Authors: Petersen Patrick H.D. , Lopacinska-Jørgensen Joanna , Høgdall Claus K. , Høgdall Estrid V. 
Published: November 2023

Abstract
Background
Ovarian cancer is a lethal gynecological cancer and no reliable minimally invasive early diagnosis tools exist. High grade serous ovarian carcinoma (HGSOC) is often diagnosed at advanced stages, resulting in poorer outcome than those diagnosed in early stage. Circulating microRNAs have been investigated for their biomarker potential. However, due to lack of standardization methods for microRNA detection, there is no consensus, which microRNAs should be used as stable endogenous controls. We aimed to identify microRNAs that are stably expressed in plasma of HGSOC and benign ovarian tumor patients.

Methods and results
We isolated RNA from plasma samples of 60 HGSOC and 48 benign patients. RT-qPCR was accomplished with a custom panel covering 40 microRNAs and 8 controls. Stability analysis was performed using five algorithms: Normfinder, geNorm, Delta-Ct, BestKeeper and RefFinder using an R-package; RefSeeker developed by our study group [1]. Among 41 analyzed RNAs, 13 were present in all samples and eligible for stability analysis. Differences between stability rankings were observed across algorithms. In HGSOC samples, hsa-miR-126-3p and hsa-miR-23a-3p were identified as the two most stable miRNAs. In benign samples, hsa-miR-191-5p and hsa-miR-27a-3p were most stable. In the combined HGSOC and benign group, hsa-miR-23a-3p and hsa-miR-27a-3p were identified by both the RefFinder and Normfinder analysis as the most stable miRNAs.

Conclusions
Consensus regarding normalization approaches in microRNA studies is needed. The choice of endogenous microRNAs used for normalization depends on the histological content of the cohort. Furthermore, normalization also depends on the algorithms used for stability analysis.
Pioglitazone attenuates tamoxifen-induced liver damage in rats via modulating Keap1/Nrf2/HO-1 and SIRT1/Notch1 signaling pathways: In-vivo investigations, and molecular docking analysis
Authors: Kamel Gellan Alaa Mohamed , Elariny Hemat A. 
Published: November 2023

Abstract
Background
Tamoxifen (TAM) is a chemotherapeutic drug widely utilized to treat breast cancer. On the other hand, it exerts deleterious cellular effects in clinical applications as an antineoplastic agent, such as liver damage and cirrhosis. TAM-induced hepatic toxicity is mainly attributed to oxidative stress and inflammation. Pioglitazone (PIO), a peroxisome proliferator-activated receptor-gamma (PPAR-γ) agonist, is utilized to treat diabetes mellitus type-2. PIO has been reported to exert anti-inflammatory and antioxidant effects in different tissues. This research assessed the impact of PIO against TAM-induced hepatic intoxication.

Methods
Rats received PIO (10 mg/kg) and TAM (45 mg/kg) orally for 10 days.

Results
TAM increased aspartate aminotransferase (AST) and alanine aminotransferase (ALT), triggered several histopathological alterations, NF-κB p65, increased hepatic oxidative stress, and pro-inflammatory cytokines. PIO protects against TAM-induced liver dysfunction, reduced malondialdehyde (MDA), and pro-inflammatory markers along with improved hepatic antioxidants. Moreover, PIO, increased hepatic Bcl-2 expression while reducing Bax expression and caspase-3 levels. In addition, PIO decreased Keap-1, Notch1, and Hes-1 while upregulated HO-1, Nrf2, and SIRT1. Molecular docking showed the binding affinity of PIO for Keap-1, NF-κB, and SIRT1.

Conclusion
PIO mitigated TAM hepatotoxicity by decreasing apoptosis, inflammation, and oxidative stress. The protecting ability of PIO was accompanied by reducing Keap-1 and NF-κB and regulating Keap1/Nrf2/HO-1 and Sirt1/Notch1 signaling.

Graphical abstract
A schematic diagram illustrating the protective effect of PIO against TAM hepatotoxicity. PIO prevented TAM-induced liver injury by regulating Nrf2/HO-1 and SIRT1/Notch1 signaling and mitigating oxidative stress, inflammation, and apoptosis.
In a nutshell: almond hull and shell organic matter amendments increase soil and tree potassium status
Authors: Andrews Ellie M. , Rivers Daniel J. , Gaudin Amélie C. M. , Geisseler Daniel , Brown Patrick H. , Khalsa Sat Darshan S. 
Published: November 2023

Abstract
Background
Crop residues used as organic matter amendments have been shown to release potassium (K) into the soil, promoting K cycling in agronomic systems. Orchard field trials are needed to evaluate K dynamics under almond hull and shell amendments, which contain high K concentrations.

Methods
Three field trials in commercial almond orchards were conducted to assess the effects of surface-applied almond hull and shell amendments on K cycling within plant and soil systems. Amendment K concentrations over time, soil exchangeable K, and tree K status were measured as well as decomposition rate and crop yield.

Results
Hulls and shells released K rapidly under irrigation and rainfall, significantly increasing soil exchangeable K in the upper 0–10 cm soil within 2–7 weeks. Amendments increased tree leaf K status within the first 1–3 years to varying degrees depending on site. Initial amendment K concentrations decreased by at least half by dry weight within the first 25.4 cm (10 inches) of water (irrigation and precipitation) within the irrigated zone.

Conclusions
Almond hulls and shells can increase soil and plant K status when used as amendments on the soil surface. This practice can address byproduct utilization issues, recycle potassium (K), and reduce orchard K fertilizer demand by replacing the majority of tree K demand. Growers can tailor application rates to meet orchard-specific K management goals. Off-ground harvest preserved the hull/shell organic layer over time and maximized K cycling. Hull/shell amendments applied on the soil surface cover more soil area within the irrigated wetted zone compared to banded K fertilizer. This practice can reduce reliance on K fertilizers and reduce associated costs while providing a convenient outlet for hulls and shells.
Defiant conformists: gender and resistance against genocide
Authors: Stallone Kiran , Braun Robert 
Published: November 2023

AbstractThis article argues that college-educated women play a crucial part in successful resistance against genocide because they are more likely to forge secure interregional networks and, consequently, better able to shelter victims of mass-persecution than their male peers. We develop our argument through a study of Jewish rescue networks in the Netherlands during the Holocaust. College-educated women were especially valuable during rescue efforts due to their ability to operate as defiant conformists. These women – a small minority who were anything but traditional – could more fully exploit their biographical availability and university networks by concealing interregional resistance work through the strategic performance of traditional feminine roles. Statistical analyses of geocoded rescue networks reveal that rescue networks involving college-educated women were more successful because they funneled Jews across the country without getting exposed. More in-depth exploration of distinct networks identifies three dramaturgical strategies that college-educated women deployed to facilitate clandestine and geographically expansive rescue work: 1) strategic coquetry; 2) strategic self-devaluation; 3) strategic motherhood and wedlock. Taken together, our findings suggest we should focus on how gender and other forms of social status interact to produce the relational and dramaturgical underpinnings of civilian agency in times of emergency.
Evolution of number of citations per article in Materials Science: possible causes and effect on the impact factor of journals
Authors: Ariza-Guerrero Ana M. , Blázquez J. Sebastián 
Published: November 2023

AbstractAn overall rise in the citation parameters used in the metrics of scientific publications (i.e. journal impact factor, JIF) has taken place since the last decade of the previous century, coinciding with the electronic distribution of (and access to) scientific literature. This inflation like tendency is herein analyzed in the area of Materials Science and also affects the number of publications. Considering average JIF values, its growth is proportional to the number of publications in the area and to its JIF value, leading to an inhomogeneous boost that preferentially benefits those journals with high JIF. An elevation in the number of publications per year alone cannot explain this behavior but it occurs due to a continuous and widespread increment in the number of citations per article, which only remains limited when restrictions are applied by journals to the maximum number of pages per article. In this work we observe this positive correlation between the increase in the number of references per article and the overall increase in JIF but, in our analysis, a kink point is observed in consistency with the appearance of online databases, particularly those free available in 2004. Online databases along with the widespread of open access publishing option made the research content easily available to the scientific community contributing to an increasing trend (without apparent saturation) in the number of articles used to contextualize the new scientific contributions.
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models
Authors: Daniel Schalk , Bernd Bischl , David Rügamer 
Published: November 2023

AbstractVarious privacy-preserving frameworks that respect the individual’s privacy in the analysis of data have been developed in recent years. However, available model classes such as simple statistics or generalized linear models lack the flexibility required for a good approximation of the underlying data-generating process in practice. In this paper, we propose an algorithm for a distributed, privacy-preserving, and lossless estimation of generalized additive mixed models (GAMM) using component-wise gradient boosting (CWB). Making use of CWB allows us to reframe the GAMM estimation as a distributed fitting of base learners using the$$L_2$$L2-loss. In order to account for the heterogeneity of different data location sites, we propose a distributed version of a row-wise tensor product that allows the computation of site-specific (smooth) effects. Our adaption of CWB preserves all the important properties of the original algorithm, such as an unbiased feature selection and the feasibility to fit models in high-dimensional feature spaces, and yields equivalent model estimates as CWB on pooled data. Next to a derivation of the equivalence of both algorithms, we also showcase the efficacy of our algorithm on a distributed heart disease data set and compare it with state-of-the-art methods.
Point process simulation of generalised hyperbolic Lévy processes
Authors: Kındap Yaman , Godsill Simon 
Published: November 2023

AbstractGeneralised hyperbolic (GH) processes are a class of stochastic processes that are used to model the dynamics of a wide range of complex systems that exhibit heavy-tailed behavior, including systems in finance, economics, biology, and physics. In this paper, we present novel simulation methods based on subordination with a generalised inverse Gaussian (GIG) process and using a generalised shot-noise representation that involves random thinning of infinite series of decreasing jump sizes. Compared with our previous work on GIG processes, we provide tighter bounds for the construction of rejection sampling ratios, leading to improved acceptance probabilities in simulation. Furthermore, we derive methods for the adaptive determination of the number of points required in the associated random series using concentration inequalities. Residual small jumps are then approximated using an appropriately scaled Brownian motion term with drift. Finally the rejection sampling steps are made significantly more computationally efficient through the use of squeezing functions based on lower and upper bounds on the Lévy density. Experimental results are presented illustrating the strong performance under various parameter settings and comparing the marginal distribution of the GH paths with exact simulations of GH random variates. The new simulation methodology is made available to researchers through the publication of a Python code repository.
Developing the Additive Manufacturing Chain of AlSi7Mg with Laser Powder Bed Fusion and Tailored Heat Treatments for Railway Spare Parts
Authors: Tassa Oriana , Colaneri Alessandro , Fransesini Leonardo , Sammartino Federica , Demir Ali Gökhan 
Published: November 2023

AbstractThe use of metal additive manufacturing technologies in railways sector can provide increased flexibility in terms of spare part logistics. Combined with lightweight metals such as Al-alloys, the product performance can also be enhanced in terms of weight reduction, vibration and noise control. The railway sector is more likely to exploit large and bulky parts produced by laser powder bed fusion (LPBF), which should have appropriate mechanical properties. Therefore, the whole production chain should be analyzed considering the heat treatment steps suited for a distributed and resource efficient manufacturing scheme. Accordingly, this work analyzes the additive manufacturing of AlSi7Mg alloy by LPBF and its consecutive heat treatment steps. In particular, the impact of LPBF process productivity, heat treatment type and atmosphere composition were considered to analyze the most appropriate route for the needs of the railways applications. The results show that with an appropriate direct aging treatment in air, mechanical characteristics of the alloy could be improved, despite that the high layer thickness used in LPBF could increase process productivity. The absence of a quenching step was found to be favorable for large parts, while the absence of an inert gas such as Ar in the heat treatment did not generate any reduction in the mechanical properties.