bluuebunny/crossref_metadata_2025_split huggingface dataset:
Country-level, mandatory, self-financeable pension replacement rates in OECD countries
Authors: Castro Rubén 
Published: October 2023

AbstractThe replacement rate (RR) is a quintessential property of pension systems. Yet, current measures of the RR are plagued with problems. We argue that the concept of RR should be based on the replacement of lifetime permanent income rather than pre-retirement income, and we show that the self-financeable RR with respect to the permanent income has the advantage of being independent of labor income (wages and density of contributions). We define an RR measure, called CRR, as the country-level RR of the permanent labor income that the working-age population could buy with their mandatory pension deposits if they stay constant over time. Pension deposits refer to national mandatory contributions plus the fraction of non-contributory pensions whose financing could be attributed to the working-age population, all as a percentage of the gross domestic product. The CRR is easy to compute and interpret, is nationally representative, and provides an international ranking because it is independent of pension rules, GDP, intertemporal and intergenerational redistributions, and sustainability. The application of the CRR to most OECD countries using the available data shows a 65% average across them, with several countries achieving a 100% RR, all mostly due to their high mandatory contributions as a percentage of GDP.
3D Simulations of the Archean Earth Including Photochemical Haze Profiles
Authors: Mak M. T. , Mayne N. J. , Sergeev D. E. , Manners J. , Eager‐Nash J. K. , Arney G. , Hébrard E. , Kohary K. 
Published: October 2023

AbstractWe present results from 3D simulations of the Archean Earth including a prescribed (non‐interactive) spherical haze generated through a 1D photochemical model. Our simulations suggest that a thin haze layer, formed when CH4/CO2 = 0.1, leads to global warming of ∼10.6 K due to the change of water vapor and cloud feedback, compared to the simulation without any haze. However, a thicker haze layer, formed when CH4/CO2 > 0.1, leads to global cooling of up to ∼65 K as the scattering and absorption of shortwave radiation from the haze reduces the radiation from reaching the planetary surface. A thermal inversion is formed with a lower tropopause as the CH4/CO2 ratio increases. The haze reaches an optical threshold thickness when CH4/CO2 ∼ 0.175 beyond which the atmospheric structure and the global surface temperature do not vary much.
Enhanced Radiation Levels at Aviation Altitudes and Their Relationship to Plasma Waves in the Inner Magnetosphere
Authors: Aryan Homayon , Bortnik Jacob , Tobiska W. Kent , Mehta Piyush , Siddalingappa Rashmi 
Published: October 2023

AbstractIt is believed that galactic cosmic rays and solar energetic particles are the two major sources of ionizing radiation. However, the radiation source may also be due to relativistic electrons that are associated with precipitation from the Van Allen radiation belts. In this study, we use Automated Radiation Measurements for Aerospace Safety (ARMAS) measurements to investigate the precipitation mechanism of energetic radiation belt electrons. ARMAS instruments are flown on agency‐sponsored (NASA, National Oceanic and Atmospheric Administration, National Science Foundation, Federal Aviation Administration, DOE) flights, commercial space transportation companies and airliners (>9 km) in automated radiation collection mode. We identified magnetic conjunction events between ARMAS and NASA's Van Allen Probes to study the highly variable, dynamic mesoscale radiation events observed by ARMAS instruments at aviation altitudes and their relationship to various plasma waves in the inner magnetosphere measured by the Van Allen Probes. The results show that there is a strong correlation between dose rates observed by ARMAS and plasmaspheric hiss wave power measured by the Van Allen Probes, but no such relationship with electromagnetic ion cyclotron waves and only a modest correlation with whistler mode chorus waves. These results suggest that the space environment could have a potentially significant effect on passenger safety.
Association of mineral and bone biomarkers with adverse cardiovascular outcomes and mortality in the German Chronic Kidney Disease (GCKD) cohort
Authors: Reimer Katharina Charlotte , Nadal Jennifer , Meiselbach Heike , Schmid Matthias , Schultheiss Ulla T. , Kotsis Fruzsina , Stockmann Helena , Friedrich Nele , Nauck Matthias , Krane Vera , Eckardt Kai-Uwe , Schneider Markus P. , Kramann Rafael , Floege Jürgen , Saritas Turgay ,   on behalf of the GCKD study investigators, Schiffer Mario , Prokosch Hans-Ulrich , Bärthlein Barbara , Beck Andreas , Reis André , Ekici Arif B. , Becker Susanne , Alberth-Schmidt Ulrike , Weigel Anke , Marschall Sabine , Schefler Eugenia , Walz Gerd , Köttgen Anna , Kotsis Fruzsina , Meder Simone , Mitsch Erna , Reinhard Ursula , Schaeffner Elke , Baid-Agrawal Seema , Theisen Kerstin , Schmidt-Ott Kai , Zeier Martin , Sommerer Claudia , Aykac Mehtap , Wolf Gunter , Paul Rainer , Börner-Klein Antje , Bauer Britta , Raschenberger Julia , Kollerits Barbara , Forer Lukas , Schönherr Sebastian , Weissensteiner Hansi , Oefner Peter , Gronwald Wolfram 
Published: October 2023

AbstractMineral and bone disorder (MBD) in chronic kidney disease (CKD) is tightly linked to cardiovascular disease (CVD). In this study, we aimed to compare the prognostic value of nine MBD biomarkers to determine those associated best with adverse cardiovascular (CV) outcomes and mortality. In 5 217 participants of the German CKD (GCKD) study enrolled with an estimated glomerular filtration rate (eGFR) between 30–60 mL·min−1 per 1.73 m2 or overt proteinuria, serum osteoprotegerin (OPG), C-terminal fibroblast growth factor-23 (FGF23), intact parathyroid hormone (iPTH), bone alkaline phosphatase (BAP), cross-linked C-telopeptide of type 1 collagen (CTX1), procollagen 1 intact N-terminal propeptide (P1NP), phosphate, calcium, and 25-OH vitamin D were measured at baseline. Participants with missing values among these parameters (n = 971) were excluded, leaving a total of 4 246 participants for analysis. During a median follow-up of 6.5 years, 387 non-CV deaths, 173 CV deaths, 645 nonfatal major adverse CV events (MACEs) and 368 hospitalizations for congestive heart failure (CHF) were observed. OPG and FGF23 were associated with all outcomes, with the highest hazard ratios (HRs) for OPG. In the final Cox regression model, adjusted for CV risk factors, including kidney function and all other investigated biomarkers, each standard deviation increase in OPG was associated with non-CV death (HR 1.76, 95% CI: 1.35–2.30), CV death (HR 2.18, 95% CI: 1.50–3.16), MACE (HR 1.38, 95% CI: 1.12–1.71) and hospitalization for CHF (HR 2.05, 95% CI: 1.56–2.69). Out of the nine biomarkers examined, stratification based on serum OPG best identified the CKD patients who were at the highest risk for any adverse CV outcome and mortality.
SONAR, a nursing activity dataset with inertial sensors
Authors: Konak Orhan , Döring Valentin , Fiedler Tobias , Liebe Lucas , Masopust Leander , Postnov Kirill , Sauerwald Franz , Treykorn Felix , Wischmann Alexander , Kalabakov Stefan , Gjoreski Hristijan , Luštrek Mitja , Arnrich Bert 
Published: October 2023

AbstractAccurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorded by 14 caregivers using five sensors for 61.7 hours. The caregivers wore the sensors as they performed their daily tasks, allowing for continuous monitoring of their activities. We additionally provide machine learning models that recognize the nursing activities given the sensor data. In particular, we present benchmarks for three deep learning model architectures and evaluate their performance using different metrics and sensor locations. Our dataset, which can be used for research on sensor-based human activity recognition in real-world settings, has the potential to improve nursing care by providing valuable insights that can identify areas for improvement, facilitate accurate documentation, and tailor care to specific patient conditions.
Deep learning personalized recommendation-based construction method of hybrid blockchain model
Authors: Yu Xiaomo , Li Wenjing , Zhou Xiaomeng , Tang Ling , Sharma Rohit 
Published: October 2023

AbstractThis study aims to explore the construction of a personalized recommendation system (PRS) based on deep learning under the hybrid blockchain model to further improve the performance of the PRS. Blockchain technology is introduced and further improved to address security problems such as information leakage in PRS. A Delegated Proof of Stake-Byzantine Algorand-Directed Acyclic Graph consensus algorithm, namely PBDAG consensus algorithm, is designed for public chains. Finally, a personalized recommendation model based on the hybrid blockchain PBDAG consensus algorithm combined with an optimized back propagation algorithm is constructed. Through simulation, the performance of this model is compared with practical Byzantine Fault Tolerance, Byzantine Fault Tolerance, Hybrid Parallel Byzantine Fault Tolerance, Redundant Byzantine Fault Tolerance, and Delegated Byzantine Fault Tolerance. The results show that the model algorithm adopted here has a lower average delay time, a data message delivery rate that is stable at 80%, a data message leakage rate that is stable at about 10%, and a system classification prediction error that does not exceed 10%. Therefore, the constructed model not only ensures low delay performance but also has high network security performance, enabling more efficient and accurate interaction of information. This solution provides an experimental basis for the information security and development trend of different types of data PRSs in various fields.
Thermodynamic model of the oxidation of Ln-doped UO2
Authors: Vinograd V. L. , Bukaemskiy A. A. , Deissmann G. , Modolo G. 
Published: October 2023

AbstractLn-doped UO2 is often considered as a model system of spent nuclear fuel (SNF) helping to reveal effects of fission and activation products on its chemical stability. Comparing thermodynamics of UO2-UO3 and LnO1.5-UO2-UO3 systems provides a means to understand the phenomenon of an increased resistivity of Ln-doped UO2 to oxidation in air relative to pure UO2. Here a thermodynamic model is developed and is applied to investigate detailed phase changes occurring along the oxidation of Ln-doped fluorite to U3O8. The study proposes that an enhanced resistivity to oxidation of Ln-doped UO2 is likely caused by a thermodynamically driven partitioning of Ln between a fluorite-type phase and a U3O8 polymorph, which at ambient temperatures becomes hindered by slow diffusion.
CFD-based design optimization of ducted hydrokinetic turbines
Authors: Park Jeongbin , Knight Bradford G. , Liao Yingqian , Mangano Marco , Pacini Bernardo , Maki Kevin J. , Martins Joaquim R. R. A. , Sun Jing , Pan Yulin 
Published: October 2023

AbstractHydrokinetic turbines extract kinetic energy from moving water to generate renewable electricity, thus contributing to sustainable energy production and reducing reliance on fossil fuels. It has been hypothesized that a duct can accelerate and condition the fluid flow passing the turbine blades, improving the overall energy extraction efficiency. However, no substantial evidence has been provided so far for hydrokinetic turbines. To investigate this problem, we perform a CFD-based optimization study with a blade-resolved Reynolds-averaged Navier–Stokes (RANS) solver to explore the design of a ducted hydrokinetic turbine that maximizes the efficiency of energy extraction. A gradient-based optimization approach is utilized to effectively deal with the high-dimensional design space of the blade and duct geometry, with gradients being calculated through the adjoint method. The final design is re-evaluated through higher-fidelity unsteady RANS (URANS) simulations. Our optimized ducted turbine achieves an efficiency of about 54% over a range of operating conditions, higher than the typical 46% efficiency of unducted turbines.
Inhibition of Pseudomonas aeruginosa quorum sensing by methyl gallate from Mangifera indica
Authors: Naga Nourhan G. , Zaki Ahmed A. , El-Badan Dalia E. , Rateb Heba S. , Ghanem Khaled M. , Shaaban Mona I. 
Published: October 2023

AbstractAntipathogenic drugs are a potential source of therapeutics, particularly following the emergence of multiple drug-resistant pathogenic microorganisms in the last decade. The inhibition of quorum sensing (QS) is an advanced antipathogenic approach for suppression of bacterial virulence and dissemination. This study aimed to investigate the inhibitory effect of some Egyptian medicinal plants on the QS signaling system of Pseudomonas aeruginosa. Among the tested plants, Mangifera indica exhibited the highest quorum sensing inhibition (QSI) activity against Chromobacterium violaceum ATCC 12472. Four pure compounds were extracted and identified; of these, methyl gallate (MG) showed the most potent QSI. MG had a minimum inhibitory concentration (MIC) of 512 g/mL against P. aeruginosa strains PAO1, PA14, Pa21, Pa22, Pa23, Pa24, and PAO-JP2. The virulence factors of PAO1, PA14, Pa21, Pa22, Pa23, and Pa24 were significantly inhibited by MG at 1/4 and 1/2 sub-MICs without affecting bacterial viability. Computational insights were performed by docking the MG compound on the LasR receptor, and the QSI behavior of MG was found to be mediated by three hydrogen bonds: Trp60, Arg61, and Thr75. This study indicates the importance of M. indica and MG in the inhibition and modulation of QS and QS-related virulence factors in P. aeruginosa.
Characterizing the profiles of patients with acute concussion versus prolonged post-concussion symptoms in Ontario
Authors: Scott Olivia F. T. , Bubna Mikaela , Boyko Emily , Hunt Cindy , Kristman Vicki L. , Gargaro Judith , Khodadadi Mozhgan , Chandra Tharshini , Kabir Umme Saika , Kenrick-Rochon Shannon , Cowle Stephanie , Burke Matthew J. , Zabjek Karl F. , Dosaj Anil , Mushtaque Asma , Baker Andrew J. , Bayley Mark T. ,   CONNECT, Matheson Flora , Wilcock Ruth , Hardie Billie-Jo , Cusimano Michael , Marshall Shawn , Green Robin , Hoshizaki Thomas , Hutchison James , Schweizier Tom , Hutchison Michael , Zych Justina , Murty David , Tartaglia Maria Carmela 
Published: October 2023

AbstractIdentifying vulnerability factors for developing persisting concussion symptoms is imperative for determining which patients may require specialized treatment. Using cross-sectional questionnaire data from an Ontario-wide observational concussion study, we compared patients with acute concussion (≤ 14 days) and prolonged post-concussion symptoms (PPCS) (≥ 90 days) on four factors of interest: sex, history of mental health disorders, history of headaches/migraines, and past concussions. Differences in profile between the two groups were also explored. 110 patients with acute concussion and 96 patients with PPCS were included in our study. The groups did not differ on the four factors of interest. Interestingly, both groups had greater proportions of females (acute concussion: 61.1% F; PPCS: 66.3% F). Patient profiles, however, differed wherein patients with PPCS were significantly older, more symptomatic, more likely to have been injured in a transportation-related incident, and more likely to live outside a Metropolitan city. These novel risk factors for persisting concussion symptoms require replication and highlight the need to re-evaluate previously identified risk factors as more and more concussions occur in non-athletes and different risk factors may be at play.
Glucocorticoid receptor-NECAB1 axis can negatively regulate insulin secretion in pancreatic β-cells
Authors: Udagawa Haruhide , Funahashi Nobuaki , Nishimura Wataru , Uebanso Takashi , Kawaguchi Miho , Asahi Riku , Nakajima Shigeru , Nammo Takao , Hiramoto Masaki , Yasuda Kazuki 
Published: October 2023

AbstractThe mechanisms of impaired glucose-induced insulin secretion from the pancreatic β-cells in obesity have not yet been completely elucidated. Here, we aimed to assess the effects of adipocyte-derived factors on the functioning of pancreatic β-cells. We prepared a conditioned medium using 3T3-L1 cell culture supernatant collected at day eight (D8CM) and then exposed the rat pancreatic β-cell line, INS-1D. We found that D8CM suppressed insulin secretion in INS-1D cells due to reduced intracellular calcium levels. This was mediated by the induction of a negative regulator of insulin secretion—NECAB1. LC–MS/MS analysis results revealed that D8CM possessed steroid hormones (cortisol, corticosterone, and cortisone). INS-1D cell exposure to cortisol or corticosterone increased Necab1 mRNA expression and significantly reduced insulin secretion. The increased expression of Necab1 and reduced insulin secretion effects from exposure to these hormones were completely abolished by inhibition of the glucocorticoid receptor (GR). NECAB1 expression was also increased in the pancreatic islets of db/db mice. We demonstrated that the upregulation of NECAB1 was dependent on GR activation, and that binding of the GR to the upstream regions of Necab1 was essential for this effect. NECAB1 may play a novel role in the adipoinsular axis and could be potentially involved in the pathophysiology of obesity-related diabetes mellitus.
Comparative study on free vibration analysis of rotating bi-directional functionally graded beams using multiple beam theories with uncertainty considerations
Authors: Taima Moustafa S. , Shehab Mohamed B. , El-Sayed Tamer A. , Friswell Michael I. 
Published: October 2023

AbstractThe present study investigates the free vibration behavior of rotating beams made of functionally graded materials (FGMs) with a tapered geometry. The material properties of the beams are characterized by an exponential distribution model. The stiffness and mass matrices of the beams are derived using the principle of virtual energy. These matrices are then evaluated using three different beam theories: Bernoulli–Euler (BE) or Classical Beam Theory (CBT), Timoshenko (T) or First-order Shear Deformation Theory (FSDT), and Reddy (R) or Third-order Shear Deformation Theory (TSDT). Additionally, the study incorporates uncertainties in the model parameters, including rotational velocity, beam material properties, and material distribution. The mean-centered second-order perturbation method is employed to account for the randomness of these properties. To ensure the robustness and accuracy of the probabilistic framework, numerical examples are presented, and the results are compared with those obtained through the Monte Carlo simulation technique. The investigation explores the impact of critical parameters, including material distribution, taper ratios, aspect ratio, hub radius, and rotational speed, on the natural frequencies of the beams is explored within the scope of this investigation. The outcomes are compared not only with previously published research findings but also with the results of 3-Dimensional Finite Element (3D-FE) simulations conducted using ANSYS to validate the model’s effectiveness. The comparisons demonstrate a strong agreement across all evaluations. Specifically, it is observed that for thick beams, the results obtained from FSDT and TSDT exhibit a greater agreement with the 3D-FE simulations compared to CBT. It is shown that the coefficient of variation (C.O.V.) of first mode eigenvalue of TSDT, FSDT and CBT are approximately identical for random rotational velocity and discernible deviations are noted in CBT compared to FSDT and TSDT in the case of random material properties. The findings suggest that TSDT outperforms FSDT by eliminating the need for a shear correction coefficient, thereby establishing its superiority in accurately predicting the natural frequencies of rotating, tapered beams composed of FGMs.
The innovative technology of dough preparation for bread by the accelerated ion–ozone cavitation method
Authors: Iztayev Auyelbek , Kulazhanov Talgat , Iskakova Galiya , Alimardanova Mariam , Zhienbaeva Saule , Iztayev Baurzhan , Tursunbayeva Sholpan , Yakiyayeva Madina 
Published: October 2023

AbstractDue to the fact that bakery, pasta and flour confectionery products are produced mainly from premium or first-grade flour, which is poor in the content of nutrients and fiber, the issue of developing technology for new types of flour products based on whole-ground flour of different fineness is very relevant and in demand. In the production of wholemeal flour, all parts of the whole grain are used—germ, grain shells, and endosperm. Also, recently the shortage of quality wheat has been growing. Therefore, the use of whole-milled flour from low-class wheat varieties will solve the problem of meeting the needs of the population. Using ion–ozone technology for preparing bread, high-quality bakery products from third-class flour with high nutritional and biological value were obtained. Using the obtained system of equations and constraints, the optimal modes of ion–ozone cavitation processing of dough were determined by a nonlinear programming method, which, subject to all the constraints (limitations) on the dough quality, provided the maximum dough strength of y2 = 181.0% and the dough parameter values of C × 10–4 = 25 units/mg, P = 1 atm, and τ = 5 min, which, in compliance with all constraints (restrictions) on the bread quality, provided a maximum volume of z11 = 232.1 cm3. A new innovative technology was created to increase productivity, efficiency and shorten the preparation time of bread. The method of making bread with the effect of ion–ozone cavitation of dough is very important for the bread industry, which affects the effectiveness of whole wheat flour obtained from the lower class of wheat, increases the quality of bread, shortens the technological processes of production, and increases labor productivity indicators. This method increases the economic efficiency of bread-making industries and the productivity of bread.
Stingy bots can improve human welfare in experimental sharing networks
Authors: Shirado Hirokazu , Hou Yoyo Tsung-Yu , Jung Malte F. 
Published: October 2023

AbstractMachines powered by artificial intelligence increasingly permeate social networks with control over resources. However, machine allocation behavior might offer little benefit to human welfare over networks when it ignores the specific network mechanism of social exchange. Here, we perform an online experiment involving simple networks of humans (496 participants in 120 networks) playing a resource-sharing game to which we sometimes add artificial agents (bots). The experiment examines two opposite policies of machine allocation behavior: reciprocal bots, which share all resources reciprocally; and stingy bots, which share no resources at all. We also manipulate the bot’s network position. We show that reciprocal bots make little changes in unequal resource distribution among people. On the other hand, stingy bots balance structural power and improve collective welfare in human groups when placed in a specific network position, although they bestow no wealth on people. Our findings highlight the need to incorporate the human nature of reciprocity and relational interdependence in designing machine behavior in sharing networks. Conscientious machines do not always work for human welfare, depending on the network structure where they interact.