bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of rare earth element cerium on electrocrystallization and microstructure of nickel deposits
Authors: Xu Yang-tao , Li Yan-hong , Peng Yin , Zhong Zhi-qiang , Xue Yu 
Published: May 2024

Abstract

To meet the demand for high-quality, precisely controllable organizational structure of high-end nickel foil, this study employed cerium rare earth as an additive to improve the quality of the nickel deposition layer in industrial electrolytes. The effects of adding different concentrations of CeCl3 (ranging from 0 g·L-1 to 1.0 g·L-1) on the electrocrystallization behavior of nickel deposition were investigated using linear sweep voltammetry (LSV), cyclic voltammetry (CV), and chronoamperometry (CA). Additionally, the impact of CeCl3 on the surface morphology and crystal structure of nickel deposits was analyzed using scanning electron microscopy (SEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS). The results indicated that adding 0.8 g L-1 CeCl3 not only promoted the electro-deposition process of nickel, accelerating the nucleation rate and shortening the nucleation relaxation time, thereby refining the grain size of the deposited layer, but also improved the structure of the coating, facilitating grain refinement. Surface morphology and micro-area composition analysis revealed that adding 0.8 g L-1 CeCl3 enhanced the preferred orientation of the nickel deposition layer, resulting in a more uniform distribution of crystal-face preference on the surface of the deposition layer and multi-directional growth. The apparent quality of the nickel deposition layer was improved, with a uniform and smooth surface and finer grains. Detection found that the cerium content in the deposition layer was extremely low, which did not affect the purity of the nickel deposition layer.
The Glucose Transporter 5 Enhances CAR-T Cell Metabolic Function and Anti-tumour Durability
Authors: O'Connor Roddy , Valentić Bakir , Kelly Andre , Shestov Alexander , Gan Zhiyang , Shen Feng , Chatoff Adam , Jaccard Alison , Crispim Claudia , Scholler John , Heeke Simon , Snyder Nathaniel , Ghassemi Saba , Jones Nicholas , Gill Saar 
Published: May 2024

Abstract

Activated T cells undergo a metabolic shift to aerobic glycolysis to support the energetic demands of proliferation, differentiation, and cytolytic function. Transmembrane glucose flux is facilitated by glucose transporters (GLUT) that play a vital role in T cell metabolic reprogramming and anti-tumour function. GLUT isoforms are regulated at the level of expression and subcellular distribution. GLUTs also display preferential selectivity for carbohydrate macronutrients including glucose, galactose, and fructose. GLUT5, which selectively transports fructose over glucose, has never been explored as a genetic engineering strategy to enhance CAR-T cells in fructose-rich tumour environments. Fructose levels are significantly elevated in the bone marrow and the plasma of acute myeloid leukaemia (AML) patients. Here, we demonstrate that the expression of wild-type GLUT5 restores T cell metabolic fitness in glucose-free, high fructose conditions. We find that fructose supports maximal glycolytic capacity and ATP replenishment rates in GLUT5-expressing T cells. Using steady state tracer technology, we show that 13C6 fructose supports glycolytic reprogramming and TCA anaplerosis in CAR-T cells undergoing log phase expansion. In cytotoxicity assays, GLUT5 rescues T cell cytolytic function in glucose-free medium. The fructose/GLUT5 metabolic axis also supports maximal migratory velocity, which provides mechanistic insight into why GLUT5-expressing CAR-Ts have superior effector function as they undergo “hit-and-run” serial killing. These findings translate to superior anti-tumour function in a xenograft model of AML. In fact, we found that GLUT5 enhances CAR-T cell anti-tumour function in vivo without any need for fructose intervention. Accordingly, we hypothesize that GLUT5 is sufficient to enhance CAR-T resilience by increasing the cells’ competitiveness for glucose at physiologic metabolite levels. Our findings have immediate translational relevance by providing the first evidence that GLUT5 confers a competitive edge in a fructose-enriched milieu, and is a novel approach to overcome glucose depletion in hostile tumour microenvironments (TMEs).
Biome type and availability of natural vegetation dictate whether farmland intensification or expansion is worse for biodiversity
Authors: Ceausu Silvia , Leclère David , Newbold Tim 
Published: May 2024

Abstract

To reduce the biodiversity impact of agriculture, increasing yields on existing farmland has been proposed as an alternative to farmland expansion. However, the relative effects of yield increases versus agricultural expansion have mostly been examined regionally, and measured in terms of species persistence—a metric relevant to extinction risk but limited in describing ecological communities and their support for ecosystem services. Without a thorough analysis, the lower biodiversity impacts of agricultural intensification remain largely speculative. This study provides a global assessment of biodiversity responses to land conversion and yield increases, including closing yield gaps. We also compare the biodiversity impacts of expanding farmland versus intensifying yields in agricultural landscapes to achieve a 1% increase in total production. Utilizing a large biodiversity database, natural vegetation data, and agricultural yield estimates at the landscape scale, we assess three biodiversity metrics: species richness, total abundance, and relative community abundance-weighted average range-size (RCAR), which provides a proxy for biotic homogenisation. Our models highlight that land conversion is associated with significant biodiversity loss at both local and landscape scales, emphasizing the importance of avoiding farmland expansion into new landscapes. However, yield also lead to significant biodiversity loss; closing yield gaps is associated with a median species loss of nearly 11%, and median abundance loss of almost 13%, with some agricultural landscapes losing almost 90% of species and more than 90% in abundance. Additionally, 30% of global agricultural landscapes, predominantly in the tropics, are likely to experience increased biotic homogenization. Neither expansion nor intensification is consistently better for biodiversity, with biome type, crop, biodiversity metric, and percentage of natural vegetation influencing which approach is less harmful. Our results suggest that minimising the biodiversity cost of agriculture requires a context-dependent balance between intensification and expansion in agricultural landscapes.
Lithofacies architecture of Middle-Upper Jurassic succession of the Ler Dome, Kachchh, western India: implications for depositional environments and palaeogeography
Authors: Ghaznavi Asma A. , Singh B. P , Alam Mohammad Masroor 
Published: May 2024

Abstract

Facies associations of the Middle-Upper Jurassic (Callovian-Oxfordian) sequences of the Ler Dome, Kachchh, western India are studied to determine suitable depositional environments and fluctuations in the sealevel during their deposition. In the present study, nine lithofacies are recognized that are grouped into four facies associations. The facies associations are tide-dominated estuarine facies, foreshore-offshore facies, onshore-offshore facies and lagoonal-tidal flat facies. These facies associations in ascending order suggest at least two transgressive and two regressive phases in a cyclic manner. During Callovian- Oxfordian, India belonged to the Indo-East African Province where shallow water of the Indo-Malagasy or Indo-East African Gulf was present, including the Kachchh Basin.
Tribological Analysis of Ti6Al4V Using Machine Learning to Determine Coefficient of Friction and Wear Rate
Authors: Parlak İsmail Enes , Kaykılarlı Cantekin , Eken Taha Yasin 
Published: May 2024

Abstract

Random Forest and AdaBoost, machine learning (ML) models are applied in this paper to predict friction coefficient (COF) and wear rate based on experimental datasets collected from the literature. Suboptimal classification performance for some models is one challenge faced by this study due to limitation in the number of experiments available in the literature. Therefore, this research considered the problem as a binary classification task through employment of threshold value instead of regression problem. Even with these limitations, both Random Forest and AdaBoost demonstrated significant success in classifying data sets with or without reduction dimensionality using Principal Component Analysis (PCA). These models were useful for binary classification, specifically when considering pre-defined thresholds for COF and wear rate. The conclusion suggests that future work could improve ML model performance by comparing their regression capabilities in augmented datasets from real lab experiments. Additionally, it advocates for an inverted application of ML approaches to obtain input parameters corresponding to desired COF and wear rate values.
Enhancement of W nanoparticles synthesis by injecting H 2 in a magnetron sputtering gas aggregation cluster source operated in Ar
Authors: Acsente Tomy , Stoica Silviu Daniel , Craciun Cristina , Mitu Bogdana , Dinescu Gheorghe 
Published: May 2024

Abstract

Synthesis of W nanoparticles by magnetron sputtering combined with gas aggregation operated in Ar suffers from a continuous decrease of the synthesis rate, ceasing in a finite time interval, in the range of minutes to tens of minutes. Experimentally, we noticed that the addition of small amounts of H2 to Ar (5–20%) increases the synthesis rate, which remains constant over time, at a value dependent on the amount of injected hydrogen. Mass spectrometry investigations revealed, in the hydrogen presence, a dominance of the ArH+ ions over the Ar+ ones, associated also with an increased number of W+ and WH+ species in plasma, sustaining a substantial increase in the nucleation rate.
Uncalibrated camera approach for vehicle speed estimation using detection and tracking
Authors: MOUAFO Donald , BIAOU Ulrich 
Published: May 2024

Abstract

The availability of cheap and high-quality digital cameras has fostered their deployment on the roads for traffic monitoring as part of Intelligent Transport System. Implementing accurate vehicle speed estimation solution exploiting traffic surveillance footage from such widespread camera represents a credible and cost-effective alternative to traditional methods. In this work, we present an uncalibrated camera approach for vehicle speed estimation based on detection and tracking using YoloV8 and ByteTrack. The proposed method considers an average speed over a given distance by means of intrusion lines and the implementation of vector product to monitor the arrival of the traveling vehicle at expected reference locations. The experimental results demonstrated average Root Mean Squared Error and Mean Absolute Error of respectively 1.78 km/h and 1.41 km/h for vehicles traveling in the range 10 km/h – 60 km/h. These are well below the legal tolerance values for actual speed estimation systems and demonstrate the potentiality of the presented method that paves the way toward future improvement for deployment as a simple and robust solution of vehicle speed estimation.
From Allegations to Actions: Examining the Impact of Fraud Reporting Mechanisms in Healthcare
Authors: Amponsah Isaac Asamoah 
Published: May 2024

Abstract

Introduction:
The U.S. healthcare sector is characterized by a diverse mix of public and private funding and provision, resulting in a lack of a singular governing philosophy. Both the public and private sectors are known for providing high-quality medical services. However, since 1980, healthcare spending in the U.S. has substantially increased. The immense size and financial resources of the healthcare sector make it a prominent target for fraudulent activities.
Aim
This study investigated the impact of collaborative efforts between managed care organizations (MCOs) and oversight agencies on the detection and reduction of healthcare fraud within the Illinois Medicaid program.
Method
This study examines how collaboration between MCOs and oversight agencies impacts healthcare fraud in Illinois Medicaid. A FOIA request for complaint data was sent to the Illinois Department of Healthcare and Family Services. The Illinois Medicaid Office of the Inspector General oversees program integrity and maintains e database to prevent the enrollment of excluded providers.
Results
As of September 20, 2023, 2,741 providers were sanctioned, including 857 physicians, 679 waiver service providers, 159 pharmacies, and 157 Medicare providers. Over the period from September 2022 to August 2023, 1,059 fraud referrals were recorded, with internal data mining contributing the most (448) and County Care being the top-referring MCO (260 cases). The collaborative efforts between MCOs and the Medicaid – Office of Inspector General led to the identification of $23,830,110 in questioned costs, resulting in a recovery of $13,441,726 in fiscal year 2021.
Conclusion/Recommendation:
The findings emphasize the significance of data sharing and transparency in the fight against fraud. The imposition of sanctions on errant healthcare providers has emerged as a key deterrent against fraudulent activities. To combat fraud and information gaps, a comprehensive strategy is needed. This includes empowering patients, improving communication, using advanced analytics, and enforcing regulations. User-friendly digital platforms provide reliable information, enabling informed decisions and reducing disparities. Strengthened collaboration and advanced analytics are crucial for early fraud detection, preserving healthcare integrity, and preventing financial losses.
Multi-tier Fog Network Planning in Internet of Things Infrastructure for Delay Sensitive Applications
Authors: Maiti Prasenjit , Sahoo Bibhudatta , Turuk Ashok Kumar 
Published: May 2024

Abstract

Fog computing is a promising approach for enhancing processing and storage capabilities in end devices and edge networks. This paper aims to address the challenges of planning fog nodes, allocating resources, and implementing offloading techniques in this networking paradigm. The authors propose a mathematical framework that optimizes the trade-off between capital expenditure and network delay, considering constraints such as device capacity, workload offloading, and range. To achieve this optimization, the authors present an integer programming model that formulates the deployment of gateways, fog nodes, and fog controllers. They employ a weighted sum approach and two hybrid meta-heuristic algorithms (Particle Swarm Optimized Non-dominated Sorting Genetic Algorithm and Discrete Monkey Genetic Algorithms) to solve the multi-objective optimization problem and obtain benchmark values. Additionally, they suggest a hybrid Non-dominated Sorting Sine-Cosine Genetic Algorithm as an effective method to tackle the problem. The simulations conducted in this paper demonstrate that the proposed model and methods can aid network designers in deploying fog networks to complement their existing computing and storage infrastructure. The methodology also takes into account traffic prioritization and considers the delay sensitivity of priority traffic. The suggested approach is thoroughly discussed in the paper, offering a comprehensive solution for handling priority traffic while optimizing network performance.
In Vitro Evaluation of the Virulence Attributes of Oropharyngeal Candida Species Isolated from People Living with Human Immunodeficiency Virus with Oropharyngeal Candidiasis on Antiretroviral Therapy
Authors: Musinguzi Benson , Akampurira Andrew , Derick Hope , Mwesigwa Alex , Mwebesa Edson , Mwesigye Vicent , Kabajulizi Immaculate , Sekulima Tahalu , Ocheng Francis , Itabangi Herbert , Mboowa Gerald , Sande Obondo James , Achan Beatrice 
Published: May 2024

Abstract

Background
Oropharyngeal Candida species are part commensal microflora in the the oral cavity of health individuals. Commensal Candida species can become opportunist and transition to pathogenic causes of oropharyngeal candidiasis (OPC) in individuals with impaired immunity through ecological cues and expression of virulence factors. Limited studies have evaluated virulence attributes of oropharyngeal Candida species among people living with human immunodeficiency virus (PLHIV) with OPC on antiretroviral therapy (ART) in Uganda.
Objective
Evaluation of the Virulence Attributes of Oropharyngeal Candida Species Isolated from People Living with Human Immunodeficiency Virus with Oropharyngeal Candidiasis on Antiretroviral Therapy
Methods
Thirty-five (35) Candida isolates from PLHIV with OPC on ART were retrieved from sample repository and evaluated for phospholipase activity using the egg yolk agar method, proteinase activity using the bovine serum albumin agar method, hemolysin activity using the blood agar plate method, esterase activity using the Tween 80 opacity test medium method, coagulase activity using the classical tube method and biofilm formation using the microtiter plate assay method in vitro.
Results
Phospholipase and proteinase activities were detected in 33/35 (94.3%) and 31/35 (88.6%) of the strains, respectively. Up to 25/35 (71.4%) of the strains exhibited biofilm formation while esterase activity was demonstrated in 23/35 (65.7%) of the strains. Fewer isolates 21/35 (60%) of the strains produced hemolysin and coagulase production was the least virulence activity detected in 18/35 (51.4%).
Conclusion
Phospholipase and proteinase activities were the strongest virulence attributes of oropharyngeal Candida species.
Assessing the Impact of Family Practice Nursing on Enhancing the Health Status of Acute Care Patients
Authors: monfared zeinab 
Published: May 2024

Abstract

Introduction:
Family practice nursing plays a crucial role in the healthcare system, particularly in acute care settings, where patients require comprehensive and continuous care. This review aims to assess the impact of family practice nursing interventions on enhancing the health status of acute care patients.
Methods:
A systematic review of relevant literature was conducted, utilizing databases such as PubMed, CINAHL, and Cochrane Library. Studies focusing on family practice nursing interventions in acute care settings were identified and analyzed to evaluate their impact on patient health outcomes.
Results:
The review findings highlight the significant positive impact of family practice nursing interventions on the health status of acute care patients. These interventions encompass a wide range of activities, including patient assessment, care coordination, health education, and psychosocial support.Improved patient outcomes observed include reduced hospital readmissions, enhanced medication adherence, better symptom management, and overall satisfaction with care received.
Conclusion:
Family practice nursing interventions have shown promising results in enhancing the health status of acute care patients. These findings underscore the importance of integrating family practice nursing into acute care settings to optimize patient outcomes and promote holistic healthcare delivery. Further research is warranted to explore the effectiveness of specific nursing interventions and their long-term impact on patient health status.
Sustainable Development Goals (SDGs) Associated with Groundwater Over-Exploitation Vulnerability: Geographic Information System-Based Multi-criteria Decision Analysis
Authors: Saqr Ahmed M. 
Published: May 2024

Abstract

Groundwater overexploitation is related to various environmental, economic, and social aspects that should be comprehensively addressed to support sustainable water supply globally. This is the first study to delineate the sustainable development goals (SDGs) associated with the groundwater management practices for minimizing the overexploitation episodes. Ten thematic maps of the hydrologic, geologic, and topographic layers for the Wadi El-Natrun basin, Egypt, were weighted by multi-criteria decision analysis (MCDA). The layers were integrated by geographic information system (GIS) to obtain an overall vulnerability map, showing a high validation accuracy with field measurements of piezometric water levels (R2: 0.87). Based on the sensitivity analysis, groundwater overexploitation was highly influenced by distance from the feeding fault (DFF), followed by the soil’s hydraulic conductivity (HC) and land surface elevation (ELE). The western and southern zones of the basin were “Highly” vulnerable to groundwater overexploitation, assigning to the far DFF, low HC, and the dominance of upland plains, steep slopes, and elevated surface with an increased groundwater depth. The interaction between all thematic criteria and vulnerability indices showed strong correlations with SDGs and associated targets, regarding end poverty and hunger, provide safe and affordable water utilization, support economic growth, and protect land and water ecosystems. Defining the SDGs interlinkages would assist in sustainable water resource utilization, planning, and management worldwide, particularly in water-stressed areas.
A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation
Authors: Wang Shuli , Wang Wei , Zhao Guizhang 
Published: May 2024

ABSTRACT

Precise long-term runoff prediction holds crucial significance in water resource management. Although the long short-term memory (LSTM) model is widely adopted for long-term runoff prediction, it encounters challenges such as error accumulation and low computational efficiency. To address these challenges, we utilized a novel method to predict runoff based on a Transformer and the base flow separation approach (BS-Former) in the Ningxia section of the Yellow River Basin. To evaluate the effectiveness of the Transformer model and its responsiveness to the base flow separation technique, we constructed LSTM and artificial neural network (ANN) models as benchmarks for comparison. The results show that Transformer outperforms the other models in terms of predictive performance and that base flow separation significantly improves the performance of the Transformer model. Specifically, the performance of BS-Former in predicting runoff 7 days in advance is comparable to that of the BS-LSTM and BS-ANN models with lead times of 4 and 2 days, respectively. In general, the BS-Former model is a promising tool for long-term runoff prediction.