bluuebunny/crossref_metadata_2025_split huggingface dataset:
Triglyceride-glucose index for the detection of subclinical heart failure with preserved ejection fraction in patients with type 2 diabetes
Authors: Wang Tingting , Xu Jiani , Zhang Hong , Tao Lichan , Huang Xiaolin 
Published: October 2022

Abstract
Objectives
The triglyceride-glucose (TyG) index has been identified as a reliable and simple surrogate of insulin resistance. In this study, we sought to determine the association between TyG index and cardiac function among asymptomatic individuals with type 2 diabetes (T2DM) without history of any cardiovascular disease.
Materials and Methods
The cross-sectional study enrolled 180 T2DM patients without cardiac symptoms. Heart failure with preserved ejection fraction (HFpEF) was defined as Heart Failure Association (HFA)-PEFF score ≥ 5 points.
Results
A total of 38 (21.1%) diabetic patients were identified with HFpEF. Compared with the low-TyG group (TyG index < 9.47), patients in high-TyG group (TyG index ≥ 9.47) showed increased risk of metabolic syndrome and diastolic dysfunction (P < 0.05 for each). Furthermore, after adjustment of confounding variables, the TyG index showed positive correlation with risk factors of metabolic syndrome (including BMI, HbA1c, TG, TC, non-HDL-C, and fasting blood glucose, P < 0.05 for each) and parameters of diastolic odysfunction (E/e’ ratio, P < 0.0001) in patients with T2DM. Moreover, receive operating characteristic analysis showed that the TyG index could be better to predict the risk of suspected HFpEF than other indicators (AUC: 0.706, 95% CI: 0.612–0.801). According, on multiple regression analysis, TyG index was independently correlated with the incidence of HFpEF (odds ratio: 0.883, P = 0.0003), indicating that TyG index could be a reliable biomarker to predict the risk of HFpEF.
Conclusions
The TyG index showed a positive correlation with the risk of subclinical HFpEF in patients with T2DM, providing a new marker to predict and treat HFpEF in diabetes.
Study on microstructure and cyclic oxidation behavior of WC-CoCr cermet based Plasma sprayed coatings on austenite steel
Authors: Alam Md Sarfaraz , Das Anil Kumar 
Published: October 2022

Abstract
This article studies the cyclic oxidation behavior of WC-10Co-4Cr powder coatings obtained by plasma spraying on AISI316L austenitic stainless steel in an ambient air at 850°C for 50 cycles. Weight change observations of coated samples have been used to establish the oxidation kinetics, and the outcomes for the uncoated and coated specimens were compared. Utilizing scanning electron microscopy (SEM) and X-ray diffraction (XRD) the coating's microstructure has been identified before and after oxidation. XRD, SEM and energy dispersive spectroscopy (EDS) were utilized to analyse the scales surface-morphology of coated austenite steel samples following the oxidation cycle. The uncoated AISI 316L Steel had severe spalling, peeling-off, and sputtering from its oxide scale. Additionally, the scale is rich in Fe2O3, which contributed to the large weight gain. The findings showed that coatings outperform uncoated materials in terms of oxidation resistance. The coating lowered the weight gain of austenite 316 steel by 84.34%. It was discovered that the protective WO3 and Cr2O3 phase generated on the oxide scale provided higher oxidation resistance.
A real-time fire and flame detection method for electric vehicle charging station based on Machine Vision
Authors: Gao Dexin , Zhang Shiyu , Ju Yifan , Yang Qing 
Published: October 2022

Abstract
In the charging process of electric vehicles (EVs), high voltage and high current charging methods are widely used to reduce charging time, resulting in severe battery heating and an increased risk of fire. To improve fire detection efficiency, this paper proposes a real-time fire and flame detection method for electric vehicle charging station based on Machine Vision. The algorithm introduces the Kmeans + + algorithm in the GhostNet-YOLOv4 model to rescreen anchor boxes for flame smoke targets to optimize the classification quality for the complex and variable features of flame smoke targets; and introduces the coordinate attention (CA) module after the lightweight backbone network GhostNet to improve the classification quality. In this paper, we use EV charging station monitoring video as a model detection input source to achieve real-time detection of multiple pairs of sites. The experimental results demonstrate that the improved algorithm has a model parameter number of 11.436M, a mAP value of 87.70 percent, and a video detection FPS value of 75, which has a good continuous target tracking capability and satisfies the demand for real-time monitoring and is crucial for the safe operation of electric vehicle charging stations and the emergency extinguishing of fires.
Digital twin based dynamic prediction and simulation model of carbon efficiency in gear hobbing process
Authors: Hu Chunhui , Yi Qian , Li Congbo , Luo Yusong , Yi Shuping 
Published: October 2022

Abstract
The transformation of manufacturing industry to green manufacturing is one of the important tasks to achieve the carbon peaking and carbon neutrality goals, which needs to improve the use efficiency of unit carbon emission. Aiming at the defect of static model in predicting accuracy and reflecting real-time machining state, a dynamic prediction and simulation model of carbon efficiency in hobbing process based on digital twin is proposed. After analyzing the dynamic characteristics of carbon emission during hobbing, three carbon efficiency targets are defined. Then a dynamic prediction and simulation model is constructed based on convolutional neural network and dynamic discrete event system specification. The framework of the carbon efficiency digital twin (CEDT) of the hobbing process is built, and the dynamic prediction and simulation models are integrated into CEDT as virtual models. Finally, the model is applied to a case study to verify the effectiveness of the method, and the influence of dynamic parameters is discussed. This model can realize the dynamic analysis of carbon emission in hobbing process, provide support for the real-time dynamic optimization of carbon efficiency in hobbing process, and help enterprises to realize green manufacturing.
“Evaluation of an Electromagnetically Actuated Drum Brake Concept”
Authors: Guckes Lennart , Hoffmann Jens , Schrimpf Malte , Winner Hermann 
Published: October 2022

Abstract
In publications and conferences on the subject of wheel brakes, different concepts of electromechanically actuated wheel brakes can be found, as well as investigations into their suitability for the use in passenger cars. The vast majority of these brakes are disc or drum brakes, which are actuated by an electric motor. In the present publication, a brake concept is considered, that combines an electromagnetically actuated full-pad disc brake with a 10” duo-duplex drum brake. The brake concept is researched in a project regarding brakes for autonomous shuttles and thus dimensioned using vehicle data of an example shuttle. The electromagnet was designed using finite elements methods and the overall brake prototypically realized. The validation of the system design is carried out in component and system tests. The results show a suitability of the concept for the selected vehicle in terms of dynamics, installation space and energy requirements. However, there is a strong dependence of the braking torque output on the frictional sliding speed. Using hypothesis-based testing, electromagnetic effects like eddy currents are ruled out as a possible cause and the friction coefficient within the full-pad disc brake is identified as the main cause for the loss in torque. Consequently, the associated development conflict is identified and lies in the double function of the flux-carrying material in the electromagnet, which also acts as a friction partner for the braking disc.
Adaptation of a model for integration of interventions for alcohol use disorder in primary health care in Tanzania
Authors: Mushi Dorothy , Hanlon Charlotte , Francis Joel M , Candida Moshiro , Demissie Mekdes , Teferra Solomon 
Published: October 2022

Abstract
Integrating evidence-based interventions for people with alcohol use disorder (AUD) into primary healthcare (PHC) can increase access to care and reduce morbidity, mortality, and population burden. However, for the integration to be feasible, acceptable, and sustainable, there is a need to contextualize the approach and involve stakeholders. Therefore, this study aimed to use participatory methods to adapt a model for integrating AUD interventions in Tanzania’s PHC system at the community, facility, and organizational levels. Methods A mixed-methods study was used. Participants include key mental health stakeholders, experts, and PHC providers. We conducted a situational analysis to investigate opportunities and constraints in the existing systems of care, utilizing data available from the routine collection and/or in the public domain; individual semi-structured interviews (n = 11), and focus group discussions (3 groups; total n = 26 participants) and a series of theory of change (ToC) workshops (n = 32). Data from the three methods were triangulated to develop the adapted model for integrating interventions for AUD in PHC. Results The consensus long-term outcome of the Theory of Change map is to improve the physical, mental health, and social well-being of people treated for AUD to increase productivity in the community. Necessary interventions for the community were identified as awareness-raising, improving access to care, community engagement, and resource mobilization. Facility interventions included capacity building to strengthen staff competencies and supportive supervision. Organizational level interventions included strengthening infrastructure for the mental health service delivery system and financing. Identified facilitators for implementing the intervention packages were leveraging existing health system resources and recognizing that mental health was part of PHC. Potential barriers included an under-resourced health system, reflected in an inadequate number of HCPs in the community and facility and inadequate prioritization of mental health care. Conclusions The intervention packages for an adapted model for integrating AUD interventions in PHC in Tanzania have been identified, as well as their potential barriers and facilitators to the feasibility of implementation. There is a need to pilot the adapted model to inform plans for wider implementation or scaling up.
The effect of addition Eriobotrya japonica L. on probiotic yogurts physicochemical, microbiological, and sensory properties
Authors: turgut tamer , diler abdulkerim 
Published: October 2022

AbstractThis study aimed to investigate the effects of loquat (Eriobotrya japonicaL.) marmalade (LM) supplementation in probiotic yogurt and the viability ofBifidobacteriassp and its effect on the quality characteristics of yogurt were also investigated. For this purpose, four types of yogurt, including plain yogurt, yogurt with %5 LM, 10% LM, and 15% LM, were prepared. physicochemical properties, microbial growth, and textural and sensory properties were investigated on days 1, 7, 14, and 21 during storage. The addition of LM to yogurt significantly affected total dry matter, fat, pH, titratable acidity, syneresis, water-holding capacity values, and color parameters (L*, a*, b*) during storage time. The addition of LM caused an increase in L* values, while an increase in a* value. Yogurts containing % 10 and % 15 LM demonstrated lower syneresis than the control sample during storage. The viability of bifidobacteria in yogurt was not affected by the LM addition. The highest overall acceptance sensory score was observed in the yogurts containing 15% LM.
Integrated use of ligand and structure based virtual screening, molecular dynamics, free energy calculation and ADME prediction for the identification of potential PTP1B inhibitors
Authors: Devi Bharti , Vasishta Sumukh Satyanarayana , Das Bhanuranjan , Baidya Anurag TK , Rampa Rahul Salmon , Mahapatra Manoj Kumar , Kumar Rajnish 
Published: October 2022

Abstract
Protein tyrosine phosphatases (PTPs) are the group of enzymes that control both cellular activity and the dephosphorylation of tyrosine (Tyr)-phosphorylated proteins. Dysregulation of PTP1B has contributed to numerous diseases including Diabetes Mellitus, Alzheimer’s disease, and obesity rendering PTP1B as a legitimate target for therapeutic applications. However, it is highly challenging to target this enzyme because of its highly conserved and positively charged active-site pocket motivating researchers to find novel lead compounds against it. The present work makes use of an integrated approach combining ligand based and structure-based virtual screening to find hit compounds targeting PTP1B. Initially, pharmacophore modelling was performed to find common features like two hydrogen bond acceptors, an aromatic ring and one hydrogen bond donor from the ligands with reported inhibition activity against PTP1B. The dataset of compounds matching with the common pharmacophoric features was filtered to remove Pan-Assay Interference substructure and to match the Lipinski criteria. Then, compounds were further prioritized using molecular docking and top fifty compounds with good binding affinity were selected for ADME predictions. The top five compounds with high solubility, absorption and permeability holding score of -10 to -9.3 kcal/mol along with Ertiprotafib were submitted to all-atom molecular dynamic (MD) studies. The MD studies and binding free energy calculations showed that compound M4, M5 and M8 were having better binding affinity for PTP1B enzyme with ∆Gtotal score of -24.25, -31.47 and -33.81 kcal/mol respectively than other compounds indicating that compound M8 could be a suitable lead compound for PTP1B enzyme inhibition.
Cost-effectiveness of COVID-19 vaccination in Latin America and the Caribbean: an analysis in Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru.
Authors: Federico Augustovski , Ariel Bardach , Adrián Santoro , Federico Rodriguez Cairoli , Alejandro Lopez Osornio , Fernando Argento , Maissa Havela , Alejandro Blumenfeld , Jamile Ballivian , Germán Solioz , Analía Capula , Analía López , Cintia Cejas , William Savedoff , Alfredo Palacios , Rubinstein Adolfo , Riviere Andres Pichon 
Published: October 2022

Abstract
Objective. Our study analyzes the cost-effectiveness of the COVID-19 vaccination campaign in Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, and Peru.
Methods. Using a previously published SVEIR model, we analyzed the impact of a vaccination campaign (2021) from a national healthcare perspective. The primary outcomes were quality adjusted life years (QALYs) lost and total costs. Other outcomes included COVID-19 cases, hospitalizations, deaths, and life years. We applied a discount rate of 3% for health outcomes. We modeled a realistic vaccination campaign in each country (the country-specific campaign implemented). Additionally, we assessed a standard campaign (similar, “typical“ for all countries), and an optimized campaign (similar in all countries with higher but plausible population coverage). One-way deterministic sensitivity analyses were performed.
Findings. Vaccination was health improving as well as cost-saving in almost all countries and scenarios. Our analysis shows that vaccination in this group of countries prevented 573,141 deaths (508,826 standard; 685,442 optimized) and gained 5.07 million QALYs (4.53 standard; 6.03 optimized). Despite the incremental costs of vaccination campaigns, they had a total net cost saving to the health system of US$16.29 billion (US$16.47 standard; US$18.58 optimized). The realistic vaccination campaign in Chile was the only scenario, which was not cost saving, but it was still highly cost-effective with an ICER of US$22 per QALY gained. Main findings were robust in the sensitivity analyses.
Interpretation. The COVID-19 vaccination campaign in seven Latin American and Caribbean countries -that comprise nearly 80% of the region- was beneficial for population health and was also cost-saving or highly cost-effective.
Shumkonia mesophila gen. nov., sp. nov., a novel representative of the order Rhodospirillales and proposal of Shumkoniaceae fam. nov
Authors: Yang Min , Zhang Xue , Li Jiang , Fan Hui , Ma Shichun , Dai Lirong , Cheng Lei 
Published: October 2022

Abstract
A facultatively anaerobic, mesophilic, chemoorganoheterotrophic bacterium, designated YP2T, was isolated from oil sludge enrichment in China. Cells of the strain were Gram-stain-negative, non-motile, rod-shaped or slightly curved with 0.8–3.0 µm in length and 0.4–0.6 µm in diameter. Spore formation was not observed. The strain YP2T grew optimally at 25 oC (range from 15 to 30 oC) and pH 7.1 (range from pH 7.1 to 8.3) without NaCl. The major cellular fatty acids were C16:0, sum in feature 3 (C16:1 ω7c and/or C16:1 ω6c), sum in feature 8 (C18:1 ω7c and/or C18:1 ω6c), and the main polar lipids were aminolipid, phosphatidylethanolamine, phosphatidylglycerol, phospholipid and unidentified lipids. The respiratory quinone was Q-10. The DNA G + C content was 66.0%. Strain YP2T shared 90.2% 16S rRNA gene sequence similarity with the most related Oceanibaculum nanhaiense L54-1-50T belonging to the order Rhodospirillales of the class Alphaproteobacteria. Phylogenetic analyses based on 16S rRNA gene sequences showed that strain YP2T formed a distinct evolutionary lineage within the order Rhodospirillales. On the basis of phenotypic, phylogenetic and phylogenomic data, we propose that strain YP2T represents a novel species in a novel genus, for which Shumkonia mesophila gen. nov., sp. nov., within a new family Shumkoniaceae fam. nov.. The type strain is YP2T (= CCAM 826T = JCM 34766T).
Meet MASKS: A novel Multi-Classifier's verification approach
Authors: Dehkordi Amirhoshang Hoseinpour , Alizadeh Majid , Movaghar Ali 
Published: October 2022

Abstract
In this study, a new ensemble approach for classifiers is introduced. A verification method for better error elimination is developed through the integration of multiple classifiers. A multi-agent system comprised of multiple classifiers is designed to verify the satisfaction of the safety property. In order to examine the reasoning concerning the aggregation of the distributed knowledge, a logical model has been proposed. To verify predefined properties, a Multi-Agent Systems' Knowledge-Sharing algorithm (MASKS) has been formulated and developed. As a rigorous evaluation, we applied this model to the Fashion-MNIST, MNIST, and Fruit-360 datasets, where it reduced the error rate to approximately one-tenth of the individual classifiers.
Lipo-MGN Nanoparticle Hypoxia Attenuation Mediated Single-dose Radiotherapy and pH/ROS Responsive T1 Contrast Magnetic Resonance Imaging in Hepatocellular Carcinoma
Authors: Thomas Reju George , Kim Subin , Nagareddy Raveena , Vijayan Veena , Pullickal Ansuja Mathew , Yoon Mee Sun , Park In Kyu , Jeong Yong Yeon 
Published: October 2022

Abstract
Tumour hypoxia is an important factor for developing resistance to radiation therapy (RT) and present a bleak prognosis in cancer patients undergoing treatment for RT resistant hepatocellular carcinoma. Here, we present the synthesis of liposome-coated Mn3O4 (MGN) nanoparticles (Lipo-MGN) and investigation of their therapeutic potential with RT utilizing a HepG2 cancer model. According to in-vitro research, Lipo-MGN effectively produced oxygen in the presence of H2O2 and significantly reduced the expression of HIF-1 in human HepG2 cells that were under hypoxic conditions. Lipo-MGN reversed the radio-resistance brought on by hypoxia and increased cell damage. When Lipo-MGN and RT were administered together in a HepG2 xenograft mice model, the tumor growth was delayed more than with RT alone. As determined by histochemistry, liposome-MGN also inhibited tumor angiogenesis. According to these findings, Lipo-MGNs may increase the impact of RT by simultaneously focusing on angiogenesis and tumor hypoxia. Hypoxic, radioresistant HepG2 cancer may be treated with Lipo-MGN in clinical studies.
Proactive resource management for cloud of services environments
Authors: Marques Gonçalo , Senna Carlos , Sargento Susana , Carvalho Luís , Pereira Luís , Matos Ricardo 
Published: October 2022

Abstract

Microservices offer advantages such as better fault isolation, smaller and faster deployments, scalability in addition to streamlining the development of new applications through composition of services. However, their large-scale usage, specific purposes and requirements increase the challenges of management systems that require a monitoring capable of accurately detecting the specific resources that are lacking for a service, and providing them according to the requirements.
To meet these new management challenges, we propose a monitoring and management system for microservices, containers and container clusters that autonomously predicts load variations and resource scarcity, which is capable of making new resources available in order to ensure the continuity of the process without interruptions.Our solution's architecture allows for customizable service-specific metrics that are used by the load predictor to anticipate resource consumption peaks and proactively allocate them. In addition, our management system, by identifying/predicting low demand, frees up resources making the system more efficient.
We evaluated our solution in the Amazon Web Services environment, where different services were installed for users of a vehicular network, an environment that is characterized by high mobility, dynamic topologies caused by disconnection, dropped packets, and delay problems.Our results show that our solution improves the efficiency of escalation policies, and reduces response time by improving the QoS/QoE of the system.