bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pre-bulging hydromechanical deep-drawing forming performance of a hydraulic punch for stepped parts
Authors: Dong Chenchen , Ma Jianping , Han Haimei , Zhang Wenze , Liu Fei , Yang Lianfa 
Published: November 2024

Abstract

With the development of lightweight materials in various fields, there is a need to research and improve the application of aluminum alloys in sheet metal forming, including body covering parts and complex curved aircraft skins. Specifically, the forming quality and formability of complex stepped parts, such as automobile motor shells and oil bottom boxes, should be improved. Therefore, this study is based on the principle of pre-bulging hydromechanical deep-drawing (PHDD), conducting a forming study of 1060-O aluminum plate with stepped parts. First, based on the analysis of stepped parts, a forming device consisting of a hydraulic punch and rigid die is designed, and a PHDD test platform is built to conduct experiments. Second, the effects of pre-bulging parameters on the forming height, wall thickness distribution, and hardness distribution of stepped parts are analyzed. Finally, to obtain parts with good formability under an optimal parameter combination, a four-factor and four-level orthogonal test is designed, and simulation analysis is conducted using DYNAFORM software. The results show that the wall thickness distribution of the stepped parts is more uniform under the PHDD process, and the movable die designed by the forming device can effectively improve the forming height of stepped parts, reaching 28.9 mm. This study has engineering applications for improving the formability and forming quality of stepped parts.
The impacts of Anion and Cation Modification on the Physicochemical Properties of Tri-(2-hydroxyethyl) Ammonium-Based Protic and Aprotic Ionic Liquids
Authors: ABDELMAGID AHMED MOHAMED , Ziyada ABOBAKR Khidir , Elkarsani KHALID Saad Mohamed 
Published: November 2024

Abstract

The main distinguishing feature of protic ionic liquids (PILs) is that their cations have at least one accessible proton, which allows them to form hydrogen bonds with anions. Furthermore, their cost-effectiveness and straightforward synthesis technique make them suitable for large-scale applications. In the present work, a series of ammonium based protic (PILs) and aprotic ionic liquids (AILs) which are tris(2-hydroxyethyl)ammonium acetate ([HEA][Ac]), tris(2-hydroxyethyl)ammonium lactate ([HEA][La]), tris(2-hydroxyethyl)ammonium butyrate ([HEA][BU]), Tris(2-hydroxyethyl)ammonium ascorbate ([HEA][AS]), allyl tris(2-hydroxyethyl)ammonium chloride ([AyHEA][Cl]), and benzyl tris(2-hydroxyethyl)ammonium chloride ([BzHEA][Cl]), were synthesized and characterized using NMR, IR, and elemental analysis. The densities, viscosities, and refractive indices of these ILs were determined at temperatures ranging from 273.15 to 353.15 K. In addition, various thermodynamic properties, including the thermal expansion coefficient, molar refraction, standard molar entropy, and lattice energy, were estimated for these ILs. The results demonstrate that these ILs exhibit lower densities, comparable refractive indices and viscosities, and a lower decomposition temperature than their analogs based on tri-(2-hroxyethyl) ammonium. In addition, the ILs exhibited a slight dependence on temperature for the thermal expansion coefficients, αp, which ranged from 6.27 × 10−4 to 7.07 × 10−4 K-1. These findings offer valuable information on the properties and potential uses of tris(2-hydroxyethyl)ammonium -based protic and aprotic ionic liquids containing carboxylate and chloride anions.
Learning Constraint Orderings for Direct Diagnosis
Authors: Uta Mathias , Le Viet-Man , Felfernig Alexander , Helic Denis 
Published: November 2024

Abstract

Given an inconsistent set of constraints, it is important to efficiently resolve the underly-ing conflicts. Conflict resolution can be regarded as a specific type of explanation, oftendenoted as diagnosis. Efficiency is of high importance in interactive constraint-based appli-cations. In this article, we utilize diagnosis knowledge from the past to improve diagnosisefficiency while also maintaining user-defined preference criteria. Our approach integratesmodel-based collaborative filtering with model-based diagnosis. This allows us to increasethe efficiency of diagnostic reasoning for determining preference-preserving diagnoses.With our experiments on real-world configuration knowledge bases (B2C, BUSYBOX,EA and LINUX KERNEL), we demonstrate an improved runtime performance comparedto state-of-the-art diagnosis approaches. At the same time, we are able to achieve a highaccuracy level in diagnosis prediction. With this, we also contribute to the growing bodyof literature on combining machine learning and constraint-based reasoning.
Molecular Cartography of the Human and Mouse Down Syndrome Brain
Authors: Kalish Brian , Feng Min Yi , Cao Wuxinhao , Li Gen , Kukreja Bharti , Tahmasian Nareh , Rusu Bianca , Youn Ji-Young 
Published: November 2024

Abstract

Down syndrome (DS, or Trisomy 21) is one of the most common genetic causes of intellectual disability. DS results in both abnormal neurodevelopment and accelerated neurodegeneration, but the molecular mechanisms underlying abnormal cortical construction and aging are incompletely understood. To gain molecular insight into the prenatal neurobiology of DS, we performed single-nucleus sequencing, spatial transcriptomics, and proteomics on mid-gestational prenatal human brain tissue. We captured altered expression dynamics of lineage commitment genes and pronounced de-repression of transposable elements in DS neural progenitor cells, which suggest changes to the fate and functionality of neuronal and glial cells. Given the importance of linking human and model system pathobiology, we also performed highly multiplexed RNA in situ spatial transcriptomics on a well-established trisomic mouse model (Ts65Dn) to study the cellular landscape of the trisomic brain during early life and aging. We profiled the spatial transcriptome of > 240,000 cells in the mouse brain and identified trisomy-associated gene expression patterns in the molecular control of neurogenesis and gliogenesis. Together, our study provides a comprehensive cross-species understanding of the complex multicellular processes underlying DS neurodevelopment.
Urine and serum L-type fatty acid-binding protein levels in preeclampsia: A prospective cohort study
Authors: Katsura Daisuke , Tsuji Shunichiro , Tokoro Shinsuke , Inatomi Ayako , Kita Nobuyuki , Murakami Takashi 
Published: November 2024

Abstract

Background
We aimed to examine the relationship of maternal urine and serum L-type fatty acid-binding protein (L-FABP) levels with the severity of preeclampsia (PE) and the effectiveness of these biomarkers as predictors of maternal and fetal deterioration in PE.
Methods
This study included women with singleton pregnancies. Urine and blood samples were collected at the diagnosis of PE. The cases were classified as 1) PE cases that worsened and required delivery within 1 week from sample collection (PE-delivery group) and 2) PE cases that did not result in delivery within 1 week from sample collection (PE-control group). We analyzed the relationship between L-FABP levels and the severity of PE and compared the data between the two groups.
Results
In total, 53 singleton pregnancies were analyzed and classified into the PE-delivery (32 pregnancies) and PE-control groups (21 pregnancies). No significant differences were observed in urine (median 12.3 µg/gCr vs. 27.4, p = 0.449) and serum L-FABP levels (median 2.73 ng/mL vs. 1.67, p = 0.279) between severe [43 (71.7%)] cases and non-severe [17 (28.3%)] PE cases. Urine (median 79.7 µg/gCr, p = 0.044) and serum L-FABP levels (median 9.52 ng/mL, p < 0.001) were significantly higher in cases of severe PE due to maternal factors than in the non-severe PE cases. In the PE-delivery group, 26 (70.3%) pregnancies had maternal indications and 11 (29.7%) had fetal indications. Urine and serum L-FABP levels were significantly higher in cases with delivery owing to maternal indications in the PE-delivery group than in the PE-control group (median 57.4 vs. 2.83 µg/gCr; p < 0.001; median 3.47 vs. 1.18 ng/mL; p < 0.001, respectively); no significant difference was found in cases with delivery owing to fetal indications in the PE-delivery group compared to such cases in the PE-control group (median 1.93 µg/gCr; p = 0.555; median 1.18 ng/mL; p = 0.68, respectively). The areas under the concentration-time curve and optimal cutoff values for urine and serum L-FABP levels for delivery within 1 week owing to maternal indications were 0.901 (12.3 µg/gCr) and 0.819 (1.64 ng/mL), respectively.
Conclusions
Urine and serum L-FABP levels were significantly higher in cases of PE, reflecting maternal conditions, and were highly accurate predictive factors of maternal deterioration in PE.
The Impact of Artificial Intelligence on Fintech Innovation and Financial Inclusion: A Global Perspective
Authors: Kouam Arthur William Fodouop 
Published: November 2024

Abstract

This study investigates the impact of Artificial Intelligence (AI) on Fintech innovation and financial inclusion from a global perspective, addressing significant gaps in the literature where regional and sector-specific analyses have predominated. Utilizing a robust empirical framework grounded in Technology Adoption Theory and Innovation Diffusion Theory, the research analyzes a diverse sample of 300 Fintech companies across various geographical contexts. Through rigorous data collection from industry reports, corporate disclosures, and financial databases, we employed Ordinary Least Squares (OLS) regression and moderation analyses to examine the relationships between AI integration, innovation levels, and financial inclusion outcomes. Our findings reveal a significant positive correlation between AI integration and both Fintech innovation (β = 0.42, p < 0.01) and financial inclusion (β = 0.35, p < 0.01), indicating that effective AI adoption enhances operational efficiency and accessibility to financial services, particularly for underserved populations. Notably, while perceived challenges associated with AI implementation significantly impacted innovation and financial inclusion, they did not moderate these relationships, suggesting that the transformative potential of AI remains viable despite existing obstacles. This research contributes to the existing literature by enriching theoretical understanding and offering practical implications for stakeholders aiming to harness AI for sustainable financial ecosystems. Our results advocate for developing tailored regulatory frameworks that foster innovation and address the ethical and operational concerns posed by AI in Fintech.
Utilization of Postpartum Family Planning and Associated Factors among Postpartum Women Attending 5 Healthcare Facilities in a Rural District in Northern Uganda
Authors: Emmanuel Madira , Grace Auma Anna , Kabunga Amir , Asiimwe Mary Goretti , Acobi Andrew , Namukwana Beth , Izaruku Ronald , Acayo Vicky Caroline , Opio Peter Paul , Dokotum Opio Okaka 
Published: November 2024

Abstract

Background: Postpartum family planning (PPFP) uptake in Sub-Saharan Africa remains low, with Uganda reporting a 35% utilization rate. Despite the availability of both short-term and long-acting methods, the use of PPFP in Alebtong a rural district in Northern Uganda, East Africa, is particularly low. This low utilization persists despite the district's high fertility rate and being the country’s lowest number of registered family planning users. Family planning is a crucial public health program that significantly improves maternal and child health, reduces mortality rates, and enhances community well-being
Objective: To assess the level of utilization of PPFP and the contributing factors among postpartum women attending health facilities in a rural district of Northern Uganda.
Methods: A descriptive cross-sectional study was conducted among 483 women within 12 months postpartum, recruited from five randomly selected health facilities between October and November 2022. Quantitative data was collected using interviewer-administered questionnaires. Data was analyzed using STATA version 17.0, with descriptive statistics and multivariate logistic regression performed at a 95% confidence interval to identify predictors of PPFP utilization.
Results: The utilization of postpartum family planning was 41.61% (95% CI: 37.17 – 46.15). Independent predictors or contributing factors to PPFP utilization included education level (primary: APR: 0.753; 95% CI: 0.641-0.883; p=<0.001), partner’s age (APR: 1.203; 95% CI: 1.004-1.443; p=0.05), parity (APR: 0.755; 95% CI: 0.635-0.897; p=0.001), uncertainty about timing for next pregnancy (APR: 1.994; 95% CI: 1.09-3.646; p=0.03), and advice from friends about PPFP methods (APR: 1.358; 95% CI: 1.171-1.575; p=<0.001).
Conclusion: Low utilization of PPFP among postpartum women is influenced by various factors. The Ministry of Health should prioritize improving access to PPFP services through both health facilities and community outreach. A multisectoral approach is essential to raise awareness, and enhance knowledge among women and the population, and contribute to the achievement of Sustainable Development Goals.
Ransomware Detection Using Multi-Vector Anomaly Profiling for Maximum Security
Authors: Chadler Sheldon , Davis Sullivan , Delacroix Tobias , Carrington William 
Published: November 2024

Abstract

The every increasing sophistication of cyber threats requires innovative detection methodologies to safeguard digital infrastructures. Multi-Vector Anomaly Profiling (MVAP) emerges as a groundbreaking approach, offering a comprehensive analysis of system behaviors across multiple dimensions to identify subtle anomalies indicative of ransomware activity. MVAP integrates analyses of file system interactions, network traffic patterns, process execution sequences, and resource utilization metrics, constructing detailed anomaly profiles that enhance detection accuracy. Empirical evaluations demonstrate MVAP's efficacy in identifying diverse ransomware types, achieving high accuracy rates while maintaining low false positive and false negative occurrences. Comparative analyses underscore MVAP's superiority over traditional detection methods, highlighting its adaptability and robustness in addressing the evolving landscape of ransomware threats. The findings affirm MVAP's potential as a significant advancement in cybersecurity, providing a proactive and effective solution for mitigating the impact of ransomware attacks.
Development Of Bio-Based Pva/Gelatin Film Extracted From Hilsa Ilisha Fish
Authors: Singh Anurag 
Published: November 2024

Abstract

Fish consumption has steadily increased during the last few decades, accompanied by an increase in aquaculture production at the expense of wild capture. In line with this trend, the preferences of an increasingly wealthier and urbanised population are turning towards convenience foods, driving the fish industry to transition away from marketing whole fish and towards filleted products. Gelatin is created by partially denaturing collagen, the primary structural protein in connective tissue, resulting in a more soluble material with a larger range of applications. Gelatin properties vary based on animal species, age, source tissue, and extraction circumstances. In fish, the skin yields more and higher-quality gelatin than the bone. Fish skin waste can be widely used for biopolymer extraction. Gelatin extraction using saline and alkaline solutions. FTIR was also used to examine the chemical structure and thermal degradation. The current study focuses on the extraction and characterisation of gelatin from fish skin, as well as the preparation of a gelatin/PVA/ZnO based biodegradable blend film prepared solvent casting method. The effect of several parameters, such as mechanical, thermal, optical, and morphological qualities, on different blend film compositions was investigated. Differential scanning calorimetry and scanning electron microscope examinations of gelatin/PVA/ZnO blend films revealed miscibility, interaction, and micro phase separation of chitosan, PVA, and ZnO molecules. The created mix film can be used for biodegradable packaging
Learning Operators with NEAT for Boolean Composition in Reinforcement Learning
Authors: Esterhuysen Amir , James Steven , Tasse Geraud Nangue , Rosman Benjamin , Shock Jonathan 
Published: November 2024

Abstract

Skill composition is a growing area of interest within reinforcement learningresearch. This approach promotes efficient use of knowledge and represents arealistic, human-like style of learning. Existing work has demonstrated how simple skills can be composed using Boolean operators to solve new, unseen taskswithout further learning. However, this approach assumes that the learned value functions for each atomic skill are optimal, an assumption which is violated in most practical cases. We propose a method that instead learns operators forcomposition using evolutionary strategies. We empirically verify our approach in tabular and high-dimensional environments. Results demonstrate that our approach outperforms existing composition methods when faced with learned,suboptimal behaviours, while also promoting robust agents and allowing for transfer between domains.
The Effect of Dexamethasone Sodium Phosphate on Surgical Third Molar Extraction using Pain, Trismus, Oedema, and Quality of Life as Parameters: A Comparative Study
Authors: Aljohani Marwan H 
Published: November 2024

Introduction
Lower third molar extractions are a common surgical procedure that can lead to post-operative complications such as trismus, discomfort, and swelling. One of the several corticosteroids frequently used to treat these issues is dexamethasone. This study aimed to assess the effects of submucosal and intramuscular injections of dexamethasone sodium phosphate on postoperative outcomes after surgical lower third molar extraction.

Materials and Methods
A total of 90 people were randomly allocated to one of three groups: submucosal dexamethasone (8 mg/2ml), intramuscular dexamethasone (8 mg/2ml), and a control group. Participants were evaluated for mouth opening, pain (Visual Analog Scale), oedema, and oral health-related quality of life (OHIP-14 Arabic) on the day of surgery and the third and seventh post-operative days.

Results
In comparison to the control group, the intramuscular dexamethasone groups showed noticeably improved results in terms of mouth opening, pain thresholds, and oedema measurements. On the seventh post-operative day, the intramuscular dexamethasone group demonstrated higher gains in mouth opening and reduced pain scores and oedema measurements.

Conclusion
Regardless of the method, administering dexamethasone effectively lowers post-operative problems after the lower third molar extraction, but an overall decrease in oral health and quality of life was observed. Submucosal delivery of dexamethasone can be a good alternative, even though intramuscular delivery may be somewhat more effective in some cases. These data support the use of dexamethasone as an effective adjuvant therapy in oral and maxillofacial surgery.
Estimation of Biofilm Components and Prevalence of SDR Genes among Staphylococcus Aureus Isolated from Anbar Hospitals
Authors: Almaadhidy Rafal Abdul Khaliq Juma , Lateef Al Meani Safaa Abed 
Published: November 2024

Background
Nosocomial infections, a major health problem, are due at 80% to biofilm‐associated infection. Staphylococcus aureus, a prominent biofilm producer, has both MRSA and MSSA biofilm-forming capabilities that highlight its persistence in hospital environments. In previous studies, less focus was given to the components of the biofilms. Therefore, understanding biofilm composition has become crucial.

Objective
In this study, the components in the biofilm matrix in MRSA and MSSA isolates were estimated, and the prevalence of sdr genes (sdrC,sdrD,sdrE) among S. aureus strains was investigated.

Methods
Between September, 2023 and January, 2024, a total of 200 specimens were collected from patients with suspected Staphylococcus aureus infections. Eighty-two isolates were identified as S. aureus from various sources, including wounds, nasal swabs, ear swabs, and blood, based on macroscopic, microscopic, and molecular properties. Biofilms were subjected to DNase and proteinase K treatment in order to identify their biochemical composition. Multiplex PCR was used to determine the distribution of sdr genes among isolates.

Results
The average reduction after treatment with proteinase K in MSSA biofilms (29.67±16.62) was significantly lower than in MRSA biofilms (46.21±26.33, p=0.032). Also, when treated with DNase, MRSA biofilms showed a greater average reduction (49.65±25.30) compared to MSSA biofilms (28.71±20.98, p=0.006). MRSA and MSSA biofilms also had different polysaccharide concentrations, with MSSA biofilms having significantly higher levels (206.86±82.92 μM/OD595) than MRSA biofilms (85.80±32.52 μM/OD595, p=0.005). The sdrC gene was most common in the gene profiles of MRSA (100%) and MSSA (50%). There was a strong association between the presence of the sdrD gene and methicillin-resistant S. aureus isolates (p < 0.001).

Conclusion
These findings indicate that the biofilms of MRSA isolates are largely composed of proteins and eDNA, while MSSA isolates rely on polysaccharides in their biofilms. The results indicate the importance of proteins encoded by SDR genes in the development of biofilm structure.
Travel Time Prediction Using Machine Learning Algorithms: Focusing on k-NN, LSTM, and Transformer
Authors: Jang Jinhwan 
Published: November 2024

Background
Travel time prediction is fundamentally important in advanced traveler information systems. Particularly, in the Dedicated Short-Range Communications (DSRC) traffic information system targeted by this study, travel times are obtained after vehicles terminate trips on the route, indicating that a time-lag phenomenon is inevitable. Therefore, travel time prediction is even more emphasized in DSRC systems. With advances in artificial intelligence technologies, many new machine learning algorithms have recently been introduced.

Methods
In this study, three machine learning algorithms—k-Nearest Neighbor (k-NN), Long Short-Term Memory (LSTM), and Transformer—were applied to predict travel times collected on a DSRC-equipped national highway. Travel time outliers caused by entry and exit maneuvers at intersections were filtered using a robust outlier treatment algorithm.

Results
From applying the prediction algorithms, errors of 12.4%, 11.7%, and 10.3% were revealed for k-NN, LSTM, and Transformer, respectively. To identify the statistical significance of the differences in prediction performance, paired t-tests were performed. Consequently, all the differences between the algorithms were proven to be statistically significant. The differences in performance were found to be generally more significant under congested conditions compared to uncongested conditions.

Conclusion
The enhanced reliability of real-time travel time information can increase the effectiveness of DSRC traffic information systems by encouraging drivers to divert from congested routes or adjust their trip schedules to less congested periods.