bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bubble Point Measurements of cis-1,1,1,4,4,4-Hexafluorobutene [R-1336mzz(Z)] + trans-1,2-Dichloroethene [R-1130(E)] mixtures
Authors: Rowane Aaron J. , Outcalt Stephanie L. 
Published: June 2024

AbstractSaturation pressures of pure R-1336mzz(Z) and R-1130(E) and bubble point pressures of three R-1336mzz(Z)/1130(E) blends were measured from 265 K to 360 K. For each pure refrigerant or refrigerant blend, a total of twenty unique saturation pressures or bubble points were measured. In total 100 unique state points were obtained. Presently, no Helmholtz-energy-explicit type equation of state (EoS) is available for R-1130(E). While an extended corresponding states EoS for R-1130(E) is available to estimate the properties of the R-1336mzz(Z)/1130(E) blend, this model does not resolve the azeotropic behavior of the mixture. Therefore, the perturbed-chain statistical-associating fluid theory (PC-SAFT) EoS is used to model the vapor–liquid equilibria of the R-1336mzz(Z)/1130(E) blend. PC-SAFT model parameters are reported, and the overall performance of the model is characterized by deviations from the experimental data.
Value of Original and Generated Ultrasound Data Towards Training Robust Classifiers for Breast Cancer Identification
Authors: Munteanu Bianca-Ştefania , Murariu Alexandra , Nichitean Mǎrioara , Pitac Luminiţa-Gabriela , Dioşan Laura 
Published: June 2024

AbstractBreast cancer represents one of the leading causes of death among women, with 1 in 39 (around 2.5%) of them losing their lives annually, at the global level. According to the American Cancer Society, it is the second most lethal type of cancer in females, preceded only by lung cancer. Early diagnosis is crucial in increasing the chances of survival. In recent years, the incidence rate has increased by 0.5% per year, with 1 in 8 women at increased risk of developing a tumor during their life. Despite technological advances, there are still difficulties in identifying, characterizing, and accurately monitoring malignant tumors. The main focus of this article is on the computerized diagnosis of breast cancer. The main objective is to solve this problem using intelligent algorithms, that are built with artificial neural networks and involve 3 important steps: augmentation, segmentation, and classification. The experiment was made using a publicly available dataset that contains medical ultrasound images, collected from approximately 600 female patients (it is considered a benchmark). The results of the experiment are close to the goal set by our team. The final accuracy obtained is 86%.
On the structure of vertex stabilizers of arc-transitive locally quasiprimitive graphs
Authors: van Bon J. 
Published: June 2024

AbstractLet $$\Delta $$
Δ
be a connected arc-transitive G-graph which is locally finite and locally quasiprimitive. Let $$\{x,y\}$$

{
x
,
y
}

be an edge of $$\Delta $$
Δ
. A relation between $$G_x^{[1]}/O_p(G_x^{[1]})$$

G
x

[
1
]

/

O
p

(

G
x

[
1
]

)

and the existence of certain normal subgroups of $$G_x^{\Delta (x)}$$

G
x

Δ
(
x
)

and $$G_{x,y}^{\Delta (x)}$$

G

x
,
y

Δ
(
x
)

is established. This is then used to determine the vertex stabilizers of a class of 2-arc-transitive graphs with trivial edge kernel.
Novel promising aqueous electrolytes for manganese dioxide/stainless steel pseudocapacitor electrode
Authors: Hassan Sameh , Khafagy A. H. , Usama Dalia 
Published: June 2024

AbstractManganese dioxide (MnO2) films are developed by potentiostatic electrodeposition on a stainless steel sheet as a current collector using manganese acetate tetrahydrate solution at a concentration of 0.25 mol L−1. The deposited sample layer is found to be amorphous as confirmed by the scanning electron microscope (SEM) and X-ray diffraction (XRD) analyses. The capacitive property of the prepared electrodes is characterized by charging/discharging, cyclic voltammetry, and electrochemical impedance spectroscopy techniques in four different aqueous solutions; sodium sulfate, magnesium acetate, nickel acetate, and a mixture of sodium chloride + sodium bicarbonate + borax. Charge–discharge curves reveal higher specific capacitance (Cs) of 1580 F g−1 at 0.5 mA cm−2 for the investigated MnO2 film which is characterized in Ni acetate aqueous solution. The structural, morphological, and electrochemical properties of the electrodeposited manganese oxide films are also studied. The results indicate that the specific capacitance of the deposited samples showed a strong dependency on the electrolyte aqueous solutions, resulting in developing new electrolytes which is a top priority effort in comparison to seeking new electrode materials. It is found that the matching between the pore size structure of the electrode and the ion size of the electrolyte is of great importance for the improved capacity of prepared capacitors.
Self-Testing for Dementia: A Phenomenological Analysis of Fear
Authors: Kapeller Alexandra , de Boer Marjolein 
Published: June 2024

AbstractFollowing the growing economic relevance of mobile health (mHealth) and the increasing global prevalence of dementia, self-testing apps for dementia and mild neurocognitive disorder (MCD) have been developed and advertised. The apps’ promise of a quick and easy tool has been criticized in the literature from a variety of angles, but as we argue in this article, the celebratory characterization of self-testing also stands in disbalance to the various kinds of fears that may be connected to taking the test. By drawing on Sara Ahmed’s phenomenological theory on emotions and by referring to illustrative experiences from two users with a particular dementia self-testing app, we explore four dimensions of fear derived from phenomenology: performative, ontological, embodied, and temporal dimensions. We argue that fear (1) motivates one to take the self-test and to try to take control over one’s health; (2) is shaped by and shapes the ways in which we make sense of ourselves and others as cognitively deficient; (3) constructs and is constructed by our differently embodied presence in the world; and that (4) testing makes a fearful future self as cognitively deficient more tangible. In outlining these different dimensions of fear, this article expands the understanding of the meaning of experiencing self-testing in comparison to the mostly quantitative literature on this topic.
Enhanced optical and antimicrobial activities of mono Zn and bimetallic (Zn, Co), (Zn, Pd) ions modified MCM-41: structural and morphological investigation
Authors: Wahba Mohammed Ahmed , Khaled Rabab K. , Dawy Magdah , Moharam Maysa E. 
Published: June 2024

AbstractMono and bimetallic modified MCM-41(Mobil Composition of Matter No. 41): Zn-MCM (ZM), Zn/Co-MCM41 (ZCM), and Zn/Pd-MCM-41 (ZPM) molecular sieves were produced by a surfactant-assisted technique. The structural and textural features were examined through spectroscopic and analytical techniques. The XRD analysis indicated broadening of diffraction peaks and a shift towards higher 2-theta values in the metal-incorporated (M-MCM-41) samples, confirming the successful integration of metal atoms into the MCM-41 framework; it also highlighted the preservation of a hexagonal structure with reasonable regularity, emphasizing the influence of metal incorporation on the mesoporous architecture of MCM-41. N2 adsorption–desorption isotherms revealed type IV isotherms for all samples; the BET specific surface area decreased to 672.48, 667.90, and 562.50 m2/g in ZM, ZCM, and ZPM, respectively comparing to the unincorporated MCM-41 sample (1200 m2/g), indicating partial filling of mesopores by metal centers, as confirmed by TEM images. The diffuse reflectance spectra exhibited a noteworthy optical band gap reduction of MCM-41 (5.98 eV) upon the incorporation of Zn and Co/Zn ions, resulting in values of 5.86 and 5.24 eV, respectively, with refractive index values close to 2. Additional absorption bands energies are observed at 3.14, 3.18, and 1.70 eV in ZM, ZPM, and ZCM samples, respectively suggesting the suitability of the metal incorporated samples for the photocatalytic applications. The M-incorporated samples exhibited a decline in the transmission intensity accompanied by small shifts. The enhanced antimicrobial activity of the metal-incorporated samples, surpassing that of the pure MCM-41 against a variety of tested microorganisms, is attributed to the presence of incorporated metal species, which create a more acidic environment and substantially contribute to the heightened antimicrobial effectiveness. The ZM compound demonstrated potent inhibition against Bacillus cereus and Pseudomonas aeruginosa bacteria, displaying comparable efficacy to Ampicillin, as a reference antibiotic. Additionally, ZPM exhibited considerable inhibitory activity against Escherichia coli, surpassing the reference antibiotic and showing similar effectiveness against Bacillus cereus, Pseudomonas aeruginosa, and Salmonella typhimurium.
Sustainability Report of Portuguese Municipalities: The Three Pillars of Sustainable Development
Authors: Calazans Nara , Marques Tânia , Eugénio Teresa , Gomes Jorge F. S. 
Published: June 2024

AbstractStudies regarding the economic, social, and environmental impacts of sustainable policies and practices in the public sector are scarce. This is an important gap, as the public sector is oftentimes one of the largest, if not the largest, employer in a region or a country. The current study characterizes the sustainability reporting of the Portuguese municipalities in terms of their most recent sustainability report published online, aiming to better understand the pillars of sustainability. The research is based on secondary data publicly available. Results show that only 28 municipalities (out of 308) published a sustainability report, and that most information are focused on showing a balance between environmental and economic data; the results also show that there is a lack of social information, and that only a few reports show an alignment with the Sustainable Development Goals.
Once upon a time in superspace: the diegetic ideal for the interpretation of physical theories
Authors: Rivers Imogen Lucy Grace 
Published: June 2024

AbstractThis paper offers a novel argument for superspace substantivalism. Superspace is a modified spacetime represented formally through combining ordinary spatial dimensions with anticommuting dimensions whose coordinates are labelled in Grassmann numbers rather than real numbers. At supersymmetric worlds, physical laws exhibit supersymmetry—viz., a symmetry that transforms bosons into fermions and vice versa. Superspace substantivalism is the thesis that, at supersymmetric worlds, among the most fundamental structures is superspace. Initially, the focus will be on a prevalent doctrine in the philosophy of physics literature which I call the mimetic ideal. On the mimetic ideal, interpreting physical theories aims primarily at specifying their ontology, namely at achieving accurate reference (in natural-language accounts of those theories) or representation (in model-theoretic portrayals of those theories) with respect to aspects of physical reality. However, I show that the mimetic ideal doesn’t seem able to account for important aspects of physics practice (Sect. 2). In Sect. 3, therefore, I articulate and defend a new, diegetic ideal, according to which the interpretation of physical theories should aim at perspectival coordination between interpreters and practising physicists. Perspectival coordination, in the context of interpreting physical theories, means that interpreters and practising physicists share a perspective or a point of view on some aspect of physical reality described by that theory. In Sect. 4, I apply this analysis to the study of supersymmetric quantum field theories (QFTs): reframing the realist framework which underlies Baker’s (2020) agnosticism, I examine the exciting upshot that superspace substantivalism is true. I conclude with some reflections on what perspectival coordination means for realism (Sect. 5).
Melamine-Maleic Acid Polyamide Adduct / Polyacrylonitrile Nanofibers as a Novel Adsorbent for Removal of Methyelene Blue Dye from Aqueous Solutions
Authors: Alprol Ahmed. E. , Abu-Saied Mohamed , Thabet Walaa M. , Abdelwahab Ola , El-Ghaffar Mahmoud A. Abd 
Published: June 2024

AbstractEfficient removal of methylene blue (MB) dye from aqueous solutions is crucial for addressing environmental pollution. This study investigates the potential of Melamine-maleic acid polyamide adduct/polyacrylonitrile (ME-MA amide polymer/PAN) nanofibers as a novel adsorbent for MB dye removal. Characterization via scanning electron microscopy (SEM) revealed surface morphology changes, with fiber diameters ranging from 1 to 3 μm. Thermal stability analysis demonstrated enhanced stability for ME-MA/PAN compared to pure PAN fibers. Fourier transform infrared (FT-IR) analysis confirmed the presence of hydroxyl, amide, and thiol groups on the nanoparticle surface, covalently attached to the nanofiber surface. Through electrospinning, ME-MA/PAN composites were synthesized, showing promising capabilities for dye removal. Optimal conditions were observed at pH 12 and a ME-MA PAN dose of 0.02 g. MB removal efficiency increased with higher initial dye concentrations, peaking at 30 mg/L, and higher temperatures up to 40°C. Adsorption isotherm models indicated Langmuir adsorption capacity (Qm) of 111.10 mg g−1 and a separation factor (RL) of 0.297, with Freundlich constants (1/n) and KF values of 0.418 and 22.9, respectively. Kinetic and thermodynamic studies revealed favorable conditions for MB dye removal, validating ME-MA amide polymer/PAN nanofiber composites as environmentally friendly and effective materials for eliminating toxic MB dye from aqueous solutions. This research highlights the potential of ME-MA/PAN nanofibers in addressing dye pollution, suggesting their application in wastewater treatment for sustainable environmental management. Further investigation into regeneration methods and scalability is recommended for practical implementation in real-world scenarios.
Agreement of point of care ultrasound and final clinical diagnosis in patients with acute heart failure, acute coronary syndrome, and shock: POCUS not missing the target
Authors: Núñez-Ramos José Atilio , Duarte-Misol Dagoberto , Petro María Andrea Burgos , Pérez Keren Jemima Sarmiento , Echeverry Vanessa Paola Gutiérrez , Malagón Sergio Velasco 
Published: June 2024

AbstractPoint-of-care ultrasound (POCUS) is an important tool for clinical diagnosis and decision-making in critical and non-critical scenarios. Dyspnea, chest pain, and shock are conditions susceptible to evaluation with ultrasound considering diagnostic accuracy and clinical impact already proven. There is scarce evidence in diagnosis agreement using ultrasound as an extension of physical examination. We aimed to evaluate ED patients in whom POCUS was performed, to analyze agreement between clinical initial diagnosis using ultrasound images and final diagnosis. Furthermore, we analyze failed diagnosis, inconclusive POCUS exams, and discuss details. A cross-sectional analytical study was conducted on adults who visited the emergency department with any of these three chief complaints: dyspnea, chest pain, and shock. All were evaluated with ultrasound at admission. Agreement between initial diagnosis using POCUS and final definite diagnosis was calculated. Failed diagnosis and inconclusive exams were analyzed. A total of 209 patients were analyzed. Populations: mostly males, mean age 64 years old, hypertensive. Agreement on patients with dyspnea and suspicion of acute decompensated heart failure was 0.98; agreement on chest pain suspicion of non-ST acute coronary syndrome was 0.96; agreement on type of shock was 0.90. Among the population, 12 patients had an inconclusive POCUS exam, and 16 patients had a failed diagnosis. The use of POCUS in the emergency department shows almost perfect agreement when compared with the final diagnosis in individuals experiencing acutely decompensated heart failure, acute coronary syndrome, and shock. Prospective studies are needed to evaluate the impact of this tool on mortality and prognosis when there are diagnostic errors.
The AI Act, gender equality and non-discrimination: what role for the AI office?
Authors: Lütz Fabian 
Published: June 2024

AbstractThis article assesses whether the Artificial Intelligence Act sufficiently addresses issues of gender equality and non-discrimination law. To this end, the substantive provisions of the AI Act are analysed through the lens of gender equality and non-discrimination law, highlighting the proposed tools of fundamental rights impact assessments and bias audits to reduce gender biases and discriminatory risk. Furthermore, the role of the AI Office and its cooperation with national, European, and international bodies for gender equality enforcement are discussed and positioned within the global landscape of AI regulation.
Velocity-Vorticity Geometric Constraints for the Energy Conservation of 3D Ideal Incompressible Fluids
Authors: Berselli Luigi C. , Sannipoli Rossano 
Published: June 2024

AbstractIn this paper we consider the 3D Euler equations and we first prove a criterion for energy conservation for weak solutions, where the velocity satisfies additional assumptions in fractional Sobolev spaces with respect to the space variables, balanced by proper integrability with respect to time. Next, we apply the criterion to study the energy conservation of solution of the Beltrami type, carefully applying properties of products in (fractional and possibly negative) Sobolev spaces and employing a suitable bootstrap argument.
A Lactobacillus Combination Ameliorates Lung Inflammation in an Elastase/LPS—induced Mouse Model of Chronic Obstructive Pulmonary Disease
Authors: Shen Huan-Ting , Fang Yi-Ting , Tsai Wan-Hua , Chou Chia-Hsuan , Huang Ming-Shyan , Yeh Yao-Tsung , Wu Jiun-Ting , Huang Cheng-Hsieh , Wang Bing-Yen , Chang Wen-Wei 
Published: June 2024

AbstractChronic obstructive pulmonary disease (COPD) is the world's leading lung disease and lacks effective and specific clinical strategies. Probiotics are increasingly used to support the improvement of the course of inflammatory diseases. In this study, we evaluated the potential of a lactic acid bacteria (LAB) combination containing Limosilactobacillus reuteri GMNL-89 and Lacticaseibacillus paracasei GMNL-133 to decrease lung inflammation and emphysema in a COPD mouse model. This model was induced by intranasal stimulation with elastase and LPS for 4 weeks, followed by 2 weeks of oral LAB administration. The results showed that the LAB combination decreased lung emphysema and reduced inflammatory cytokines (IL-1β, IL-6, TNF-α) in the lung tissue of COPD mice. Microbiome analysis revealed that Bifidobacterium and Akkermansia muciniphila, reduced in the gut of COPD mice, could be restored after LAB treatment. Microbial α-diversity in the lungs decreased in COPD mice but was reversed after LAB administration, which also increased the relative abundance of Candidatus arthromitus in the gut and decreased Burkholderia in the lungs. Furthermore, LAB-treated COPD mice exhibited increased levels of short-chain fatty acids, specifically acetic acid and propionic acid, in the cecum. Additionally, pulmonary emphysema and inflammation negatively correlated with C. arthromitus and Adlercreutzia levels. In conclusion, the combination of L. reuteri GMNL-89 and L. paracasei GMNL-133 demonstrates beneficial effects on pulmonary emphysema and inflammation in experimental COPD mice, correlating with changes in gut and lung microbiota, and providing a potential strategy for future adjuvant therapy.
Study on automatic lithology identification based on convolutional neural network and deep transfer learning
Authors: Li Shiliang , Dong Yuelong , Zhang Zhanrong , Lin Chengyuan , Liu Huaji , Wang Yafei , Bian Youyan , Xiong Feng , Zhang Guohua 
Published: June 2024

AbstractAutomatic and fast rock classification identification is an important part of geotechnical intelligent survey system. Image based supervised deep learning analysis, especially for convolutional neural networks (CNN), has potential in optimizing lithologic classification and interpretation using borehole core images. However, the accuracy and efficiency of lithology identification models are low at present. In this work, a systematic and enormous rock data framework based on the geological rock classification system is firstly established to provide rock learning datasets. The dataset is composed of approximately 150,000 images of rock samples, which covers igneous rocks, sedimentary rocks, and metamorphic rocks. Secondly, based on CNN-deep transfer learning algorithm, an end-to-end, image-to-label rock lithology identification is established. Finally, the generalization of the proposed model and the field drilling core verification test show that the constructed intelligent rock recognition model has an ability to identify rocks quickly and accurately, and the recognition accuracy of 12 kinds of common engineering rocks is more than 95%. The proposed rock intelligent classification model provides a convenient and fast tool for field geologists and scientific researchers.
Improving credit risk assessment in P2P lending with explainable machine learning survival analysis
Authors: Bone-Winkel Gero Friedrich , Reichenbach Felix 
Published: June 2024

AbstractRecent research using explainable machine learning survival analysis demonstrated its ability to identify new risk factors in the medical field. In this study, we adapted this methodology to credit risk assessment. We used a comprehensive dataset from the Estonian P2P lending platform Bondora, consisting of over 350,000 loans and 112 features with a loan volume of 915 million euros. First, we applied classical (linear) and machine learning (extreme gradient-boosted) Cox models to estimate the risk of these loans and then risk-rated them using risk stratification. For each rating category we calculated default rates, rates of return, and plotted Kaplan–Meier curves. These performance criteria revealed that the boosted Cox model outperformed both the classical Cox model and the platform’s rating. For instance, the boosted model’s highest rating category had an annual excess return of 18% and a lower default rate compared to the platform’s best rating. Second, we explained the machine learning model’s output using Shapley Additive Explanations. This analysis revealed novel nonlinear relationships (e.g., higher risk for borrowers over age 55) and interaction effects (e.g., between age and housing situation) that provide promising avenues for future research. The machine-learning model also found feature contributions aligning with existing research, such as lower default risk associated with older borrowers, females, individuals with mortgages, or those with higher education. Overall, our results reveal that explainable machine learning survival analysis excels at risk rating, profit scoring, and risk factor analysis, facilitating more precise and transparent credit risk assessments.