bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of high-temperature environment on the rheology and filtration properties of Rhizophora spp. tannin-lignosulfonate as bio-based additive in water-based drilling fluid
Authors: Ghazali Nurul Aimi , Naganawa Shigemi , Masuda Yoshihiro 
Published: January 2024

AbstractBentonite suspension in water-based drilling fluid is susceptible to deterioration in high-temperature environments, hence requiring a deflocculant to stabilize the solid particles. Considering the use of highly toxic chrome-based deflocculant in the industry, Rhizophora spp. tannin-lignosulfonate (RTLS) was synthesized in this study as an alternative deflocculant. A viscometer was used to study the rheological properties, and the filtration performance was evaluated using low-pressure low-temperature and high-pressure high-temperature filter press in accordance with the American Petroleum Institute standard procedure. The addition of 0.5 wt% RTLS to water-based drilling fluid (WBDF) was effective in a significant reduction of the plastic viscosity (PV) and yield point (YP) of WBDF at elevated temperatures. As the amount of RTLS added to the suspension exceeds 0.5 wt%, the effect on PV and YP becomes negligible. A higher fluid loss of 13 mL was observed in the WBDF without RTLS aged at 177 °C. The addition of 2.0 wt% RTLS reduced the fluid loss to 10.7 mL. This suggests that RTLS is an effective deflocculant that can be used to improve the filtration properties of WBDF at high temperatures. The morphology of RTLS filter cakes was examined using field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FESEM-EDX). The interlayer between clay particles was identified as RTLS, a natural additive that plays a vital role in enhancing filtration while minimizing fluid loss. The outcomes of this research are promising, and this non-toxic deflocculant has the potential to replace chrome-based deflocculants that are still in use for borehole drilling.
A productivity equation of horizontal wells in the bottom water drive reservoir with an interlayer
Authors: Cao Jie , Sun Siwei , Liu Xuesong , Wang Yuezhi 
Published: January 2024

AbstractThe interlayer can effectively prevent the ridge of bottom water and has a non-negligible influence on the flow dynamics of oil and gas in the reservoir. In view of the above problems, this paper studies the influence of a bottom water reservoir with an interlayer on the critical productivity of horizontal wells and establishes a coupling model of seepage and wellbore flow in a bottom water reservoir with an interlayer. The critical productivity of horizontal wells in a bottom water reservoir with an interlayer can be evaluated more accurately. Firstly, the three-dimensional seepage field is approximately decomposed into internal and external seepage fields, and the critical productivity formulas of internal and external seepage fields are solved, respectively. Using the equivalent seepage resistance, the critical productivity formula of horizontal wells in interlayer bottom water reservoirs is obtained. The results are compared with the calculation results of the traditional productivity formula and the simulation results of the commercial software ECLIPSE. Second, based on the mass conservation principle and momentum conservation principle, the wellbore-reservoir coupling mathematical model of a horizontal well is established by the discrete method. Finally, the sensitivity analysis of the selected parameters is discussed in detail. The results show that: (1) compared with the traditional productivity algorithm, the algorithm considers the influence of interlayers on productivity and gives a detailed theory, a specific calculation process, and the importance of accurate prediction of wellbore flow. (2) The model is calculated by an example, and the calculation results are compared with the traditional formula and the commercial simulator ECLIPSE. The results show that the horizontal well productivity formula considering the influence of interlayer is closer to the simulation results of commercial software ECLIPSE, and the relative error is 8.56%. (3) The established coupling model considers the influence of interlayer radius length and horizontal well length. The general trend is that productivity gradually increases with an increase in interlayer radius length or horizontal well length, but the growth rate gradually decreases.
Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms
Authors: Meding Kristof , Hagendorff Thilo 
Published: January 2024

AbstractFairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of literature develops new approaches to quantify fairness. Here, we investigate how one can divert the quantification of fairness by describing a practice we call “fairness hacking” for the purpose of shrouding unfairness in algorithms. This impacts end-users who rely on learning algorithms, as well as the broader community interested in fair AI practices. We introduce two different categories of fairness hacking in reference to the established concept of p-hacking. The first category, intra-metric fairness hacking, describes the misuse of a particular metric by adding or removing sensitive attributes from the analysis. In this context, countermeasures that have been developed to prevent or reduce p-hacking can be applied to similarly prevent or reduce fairness hacking. The second category of fairness hacking is inter-metric fairness hacking. Inter-metric fairness hacking is the search for a specific fair metric with given attributes. We argue that countermeasures to prevent or reduce inter-metric fairness hacking are still in their infancy. Finally, we demonstrate both types of fairness hacking using real datasets. Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications.
The anti-cancer potential of active compounds extracted from Millettia griffoniana on pancreatic and colorectal cancer cells
Authors: Hoosen Fatima , Lembe Jordan Tonga , Ndinteh Derek , Maurice Tagatsing Fotsing , Emmanuel Yankep , Motadi Lesetja Raymond , Choene Mpho Susan 
Published: January 2024

AbstractPancreatic and colorectal cancer are two of the most lethal cancers; this stems from poor prognosis. Current treatments may lack effectiveness and produce dangerous side effects, hence a need for alternatives. Medicinal plants like Millettia griffoniana may possess anti-cancer properties. This study aimed to isolate and identify compounds from M. griffoniana to test for potential anti-cancer activity on pancreatic and colorectal cancer. Seeds and root bark of M. griffoniana underwent solvent extraction and separation using column chromatography. Isolated compounds were screened using AlamarBlue assays and cytotoxic compounds were identified with 2D NMR. Compounds were subject to cellular viability assays using AlamarBlue and xCELLigence analysis. The Caspase Glo®-3/7 kit measured caspase activity and Real-Time PCR analysis measured apoptosis-related gene expression. 7 compounds were isolated and screened. Compounds 5 and 7 were chosen; identified as durmillone and isojamaicin. Both showed varying concentration-dependent cytotoxic activity, in AlamarBlue and xCELLigence assays, for both cell lines. Caspases 3 and 7 were up-regulated and both compounds up-regulated BAX and down-regulated BCL-2 and p53 in both cell lines. Durmillone and isojamaicin displayed cytotoxic activity on pancreatic and colorectal cancer. Apoptotic activity induced by the compounds was verified by the up-regulation of caspase activity and BAX and down-regulation of BCL-2 and p53. Further studies need to be done towards understanding the mechanisms by which these compounds bring about their cytotoxic activity.
In silico analysis of virulence factors of Streptococcus uberis for a chimeric vaccine design
Authors: Yılmaz Çolak Çiğdem 
Published: January 2024

AbstractStreptococcus uberis is one of the causative agents of bovine mastitis, which has detrimental effects on animal health and the dairy industry. Despite decades of research, the requirement for effective vaccines against the disease remains unmet. The goal of this study was to create a multi-epitope vaccine using five virulence factors of S. uberis through the reverse vaccinology approach, which has been employed due to its high efficiency and applicability. Plasminogen activator A (PauA), glyceraldehyde-3-phosphate dehydrogenase C (GapC), C5a peptidase, S. uberis adhesion molecule (SUAM), and sortase A (SrtA) were selected for the T cytotoxic (CTL) and B cell epitope analyses as they were extensively studied in S. uberis or other pathogens. Eighteen CTL and ten B cell epitopes that were antigenic, non-toxic, and non-allergenic were selected in order to design a chimeric vaccine candidate that in silico analysis revealed to be potentially immunogenic, non-allergenic, and stable. Molecular docking analysis of the vaccine candidate with Toll-like receptor (TLR) 2 and TLR 4 revealed stable interactions between the candidate and the immune receptors. Meanwhile, the stability of the docked complexes was confirmed using normal mode analysis. Additionally, in silico immune simulation of the vaccine candidate demonstrated the stimulation of primary immune responses, indicating that the chimeric protein can hold promise as a viable vaccine candidate for preventing S. uberis mastitis. Moreover, the current study can provide a background for designing epitope-based vaccines based on the explored epitopes.
On the spectra and spectral radii of token graphs
Authors: Reyes M. A. , Dalfó C. , Fiol M. A. 
Published: January 2024

AbstractLet G be a graph on n vertices. The k-token graph (or symmetric k-th power) of G, denoted by $$F_k(G)$$

F
k

(
G
)

, has as vertices the $${n\atopwithdelims ()k}$$

n
k

k-subsets of vertices from G, and two vertices are adjacent when their symmetric difference is a pair of adjacent vertices in G. In particular, $$F_k(K_n)$$

F
k

(

K
n

)

is the Johnson graph J(n, k), which is a distance-regular graph used in coding theory. In this paper, we present some results concerning the (adjacency and Laplacian) spectrum of $$F_k(G)$$

F
k

(
G
)

in terms of the spectrum of G. For instance, when G is walk-regular, an exact value for the spectral radius $$\rho $$
ρ
(or maximum eigenvalue) of $$F_k(G)$$

F
k

(
G
)

is obtained. When G is distance-regular, other eigenvalues of its 2-token graph are derived using the theory of equitable partitions. A generalization of Aldous’ spectral gap conjecture (which is now a theorem) is proposed.
Artificial intelligence (AI) learning tools in K-12 education: A scoping review
Authors: Yim Iris Heung Yue , Su Jiahong 
Published: January 2024

AbstractArtificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students’ learning outcomes. The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades. This highlights that intelligent agents, including Google’s Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts. Kindergarten students can benefit from learning tools such as PopBots, while software devices, such as Scratch and Python, which help to develop the computational thinking of AI algorithms, can be introduced to both primary and secondary schools. The research shows that project-based, human–computer collaborative learning and play- and game-based approaches, with constructivist methodologies, have been applied frequently in AI literacy education. Cognitive, affective, and behavioral learning outcomes, course satisfaction and soft skills acquisition have been reported. The paper informs educators of appropriate learning tools, pedagogical strategies, assessment methodologies in AI literacy education, and students’ learning outcomes. Research implications and future research directions within the K-12 context are also discussed.
The Experience of PERMA, Hope and Adaptability for UK Higher Education Students During the COVID-19 Pandemic
Authors: Hoelterhoff Mark , Hinds Roz , Andersson Lorna , Lennestål Ellen , Stewart Mahri , Cobourne David 
Published: January 2024

AbstractThis study explores the experiences of hope during the pandemic for higher education students using a qualitative study consisting of semi-structured interviews with nine participants. Thematic analysis identified three core themes; hope, adaptability and wellbeing. Findings revealed that elements of wellbeing, reflected in the PERMA framework, were reported to cultivate hope and adaptability during the pandemic, for university students.
Sustainable application of industrial side streams as alternative fine aggregates for cement mortar
Authors: Perumal Priyadharshini , Gouriou Corentin , Adesanya Elijah , Mistri Abhijit , Illikainen Mirja 
Published: January 2024

AbstractIncrease in industrialization has led to the production of huge volume of side-stream materials that need safe disposal solutions. The present study proposes the use of local industrial side streams such as ferrochrome slag, phyllite dust and mine tailings as secondary raw materials for construction, mainly as fine aggregates. Four different cement mortar mixtures, with a combination of selected side streams as a sole, binary, and ternary blends were investigated. Workability, strength, and durability properties of the derived mortar mixtures were compared with mortar produced using standard sand as reference. Mortar mixtures with a ternary blend of side-stream fine aggregates resulted in a compressive strength of 68–72 MPa at 28 days, which is 30–40% higher than that of control mix. The addition of industrial side streams resulted in a denser microstructure and enhanced the mechanical properties. The durability performance of the mortar with alternative fine aggregates is comparable with those of standard sand.
Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches
Authors: Al-Kindi Khalifa M. , Alabri Zahra 
Published: January 2024

AbstractThis study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets of topographical, geological, and environmental parameters; the goal was to investigate the intricacies of flood susceptibility within the arid riverbeds of Wilayat As-Suwayq, which is situated in the Sultanate of Oman. The results underscored the exceptional discrimination prowess of XGB and CB, boasting impressive area under curve (AUC) scores of 0.98 and 0.91, respectively, during the testing phase. RF, a stalwart contender, performed commendably with an AUC of 0.90. Notably, the investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness index (TWI), topographic roughness index (TRI), and normalised difference vegetation index (NDVI), were critical in achieving an accurate delineation of flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity to transportation networks, soil composition, and geological attributes, though non-negligible, exerted a relatively lesser influence on flood susceptibility. This empirical validation was further corroborated by the robust consensus of the XGB, RF and CB models. By amalgamating advanced deep learning techniques with the precision of geographical information systems (GIS) and rich troves of remote-sensing data, the study can be seen as a pioneering endeavour in the realm of flood analysis and cartographic representation within semiarid fluvial landscapes. The findings advance our comprehension of flood vulnerability dynamics and provide indispensable insights for the development of proactive mitigation strategies in regions that are susceptible to hydrological perils.
Review of Electrical and Electronic Architectures for Autonomous Vehicles: Topologies, Networking and Simulators
Authors: Wang Wenwei , Guo Kaidi , Cao Wanke , Zhu Hailong , Nan Jinrui , Yu Lei 
Published: January 2024

AbstractWith the rapid development of autonomous vehicles, more and more functions and computing requirements have led to the continuous centralization in the topology of electrical and electronic (E/E) architectures. While certain Tier1 suppliers, such as BOSCH, have previously proposed a serial roadmap for E/E architecture development, implemented since 2015 with significant contributions to the automotive industry, lingering misconceptions and queries persist in actual engineering processes. Notably, there are concerns regarding the perspective of zone-oriented E/E architectures, characterized by zonal concentration, as successors to domain-oriented E/E architectures, known for functional concentration. Addressing these misconceptions and queries, this study introduces a novel parallel roadmap for E/E architecture development, concurrently evaluating domain-oriented and zone-oriented schemes. Furthermore, the study explores hybrid E/E architectures, amalgamating features from both paradigms. To align with the evolution of E/E architectures, networking technologies must adapt correspondingly. The networking mechanisms pivotal in E/E architecture design are comprehensively discussed. Additionally, the study delves into modeling and verification tools pertinent to E/E architecture topologies. In conclusion, the paper outlines existing challenges and unresolved queries in this domain.
Literature Review on the Smart City Resources Analysis with Big Data Methodologies
Authors: Gubareva Regina , Lopes Rui Pedro 
Published: January 2024

AbstractThis article provides a systematic literature review on applying different algorithms to municipal data processing, aiming to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification, correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types, ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long Short-Term Memory (LSTM) for timeseries prediction and classification.
Pathological changes in various organs in HLA-B*57:01 transgenic mice with abacavir-induced skin eruption
Authors: Kazaoka Akira , Kumagai Kazuyoshi , Matsushita Junya , Aida Tetsuo , Kuwahara Saki , Aoki Shigeki , Ito Kousei 
Published: January 2024

AbstractSeveral patients with cutaneous adverse drug reactions exhibit extracutaneous organ damages, and it becomes severe in a few patients resulting in death due to multiorgan failure. Understanding the sequential changes in various organs in patients with cutaneous eruption following drug administration will help understand disease onset and progression, aiding the development of prevention strategies and interventions. Therefore, we aimed to understand the effects of abacavir (ABC) on various organs in patients with ABC-induced eruptions by evaluating its effects in a mouse model. We found pathological changes in various organs of HLA-B*57:01 transgenic mice (B*57:01-Tg) following oral administration of ABC (20 mg/body/day). B*57:01-Tg exhibited a significant body weight decrease from day 1 of ABC administration, and reddening of the auricle was observed from day 5, and approximately 2/3 mice died by day 7. Histopathological examination revealed severe thymic atrophy after day 3, infiltration of inflammatory cells, predominantly lymphocytes with neutrophils, not only in the skin but also in the liver, kidney, and lung after day 5, and an increased number of lymphocytes with enlarged nuclei and granulocytic hematopoiesis were observed in the spleen after day 5. Blood chemistry revealed that albumin/globulin ratio was below 1.0 on day 5, reflecting a systemic inflammatory response, and the aspartate aminotransferase concentration rose to 193 ± 93.0 U/L on day 7, suggesting that cell damage may have occurred in various organs including liver accompanying inflammatory cell infiltration. These examinations of a mouse model of ABC-induced skin eruption show that disorders in various organs other than the skin should be considered and provide insights into the unexpected early systemic responses dependent on HLA-B*57:01.
Cosmological gravitational waves from isocurvature fluctuations
Authors: Domènech Guillem 
Published: January 2024

AbstractGravitational waves induced by large primordial curvature fluctuations may result in a sizable stochastic gravitational wave background. Interestingly, curvature fluctuations are gradually generated by initial isocurvature fluctuations, which in turn induce gravitational waves. Initial isocurvature fluctuations commonly appear in multi-field models of inflation as well as in the formation of scattered compact objects in the very early universe, such as primordial black holes and solitons like oscillons and cosmic strings. Here, we provide a review on isocurvature induced gravitational waves and its applications to dark matter and the primordial black hole dominated early universe.
Updating glacier inventories on the periphery of Antarctica and Greenland using multi-source data
Authors: Liu Xingchen , An Lu , Hai Gang , Xie Huan , Li Rongxing 
Published: January 2024

AbstractMelting and calving of glaciers and ice caps in Antarctica and Greenland could potentially contribute significantly to global sea level rise. Updates to existing outlines that provide critical glacier baseline information in both regions could help in the analysis of particular changes in glacier parameters such as area and volume from time-series inventories. Here we synthesize previously established techniques and apply new multi-source datasets to update glacier outlines in selected test areas of Antarctica and Greenland, as well as to reduce uncertainties and errors during the mapping process. The workflow includes mapping glacier boundaries, subdividing glaciers by watersheds and assigning glacier attributes. Complicated glacier scenarios and updating challenges in polar regions are discussed and demonstrated by representative case studies. For the first time in Antarctica, we analyze the effect of terminus types on mapped glacier areas, and in Greenland we compare the differences with glacier mapping results using Landsat OLI and ETM+. With new data sources, the methods described in this study might help to create glacier outlines on a larger scale in Antarctica and Greenland. Although data sources can be substituted, the enormous amount of manual labor required to update glacier inventories remains a significant challenge.