bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of CO 2 mole fraction via CO 2 CPU process using different machine learning algorithms
Authors: Mansouri Somayeh , Shahraki Farhad , Sadeghi Jafar , Koohestanian Esmaeil , Birjandi Mohammad Reza Sardashti 
Published: March 2023

Abstract
There is a general industrial procedure called compression and refining unit to catch CO2 from the flue gases produced during oxyfuel combustion. This research discusses the application of decision trees, Adaboosting, random forests, machines that support vectors, and k-nearest neighbor classifiers and gradient boosting in predicting CO2’s mole fracion from flue gases of oxyfuel’s combustion emitted from the power plant. First of all, a training and test dataset was developed using the different variables. Then, a total of 491 simulations were performed and the mole fraction of CO2 was examined. The anticipated outcomes suggested that six machine learning algorithms that rank performance from excellent to poor, RF, GB, AB, DT, KNN, and SVM can be picked to forecast the mole fraction of CO2. Important features were detected by SHAP and the best algorithm was chosen by cross-validation. Results were shown that The RF algorithm enjoyed a great CO2 mole fraction ability to predict and displayed the very best ability for generalization and most reliable prediction precision among all four with an accuracy of 97%. After that LIME was used to explain the results of the RF algorithm. Out of the various variables studied, the pressure of the multistage compressor had the highest effect on the CO2 mole fraction. These results show that machine learning can be used as a reliable predictor of CO2 performance capture within the CPU process.
Antagonistic activity of a novel chitosan-selenium nanoflower against common aquaculture pathogen Aeromonas caviae
Authors: Sowmiya Prasad , Dhas Tharmathass Stalin , Inbakandan Dhinakarasamy , Mani Ravi , Natarajan Anandakumar , Dharani Gopal , Govindaraju Kasivelu , Kannan Malaichamy , Velu Karthick , Kumar Chirayil Meethalepurayil Vineeth 
Published: March 2023

AbstractAquatic pathogens contribute the most severe economic loss in fishes. Nanoparticles are being developed as potent antimicrobial agents against various pathogens. This study involved the synthesis and characterization of a novel chitosan‐selenium nanoflower employing multiple spectroscopic and microscopic approach. The UV‐vis spectra obtained at 265 nm indicated the formation of the Chitosan‐selenium nanoflower. The Particle size analysis revealed the size of the nanoflowers to be 186.3 nm. The Transmission Electron Micrographs revealed a unique nanoflower like morphology. XRD spectrum revealed amorphous nature and the Raman spectrum showed characteristic resonance peak at 254 cm−1was a characteristic absorption band for monoclinic Se and α‐Se. Cytotoxicity analysis of the synthesized nanoflowers against isolated fish pathogenA. caviaeshowed increasing toxicity in a dose dependent manner. The DCFDA assay was conducted for estimating increase in ROS production and the highest percentage increase in ROS was observed at 1000 µg/mL. The lipid peroxidation assay was performed by quantification of lipid oxidation product Malondialdehyde (MDA). The highest percentage lipid peroxidation was found to occur at a dose of 500 µg/mL. As a result, the synthesised chitosan‐selenium nanoflowers can be exploited as a promising antibacterial treatment against the fish pathogenA. caviae.
Investigation of HPW/TiO 2 -SnO 2 -ZrO 2 catalytic performance for epoxidation of soybean oil under hydrodynamic cavitation
Authors: Mo Simin , Cheng Qianwei , Wei Xiaoli , Chen Tong , Meng Luli , Ming Gao , Yu Kena 
Published: March 2023

Abstract

BACKGROUND

Epoxidized vegetable oils are widely used as plasticizers, lubricants and reaction intermediates. In this work, a new solid acid catalyst was developed to obtain epoxidized soybean oil in high yield and combined with hydrodynamic cavitation technology to improve the efficiency of epoxidation. The structure and morphology characteristics of the catalyst were studied by XRD, FT-IR, Py-FTIR, NH3-TPD, SEM, N2-adsorption and desorption analysis and TG techniques. It was applied to the epoxidation of soybean oil, and the influence of various parameters including catalyst dosage, formic acid dosage, hydrogen peroxide dosage and reaction temperature on the relative conversion rate of oxirane of soybean oil, as well as the recyclability of catalyst was studied.

RESULTS

The catalyst has both Lewis and Brönsted acid sites and is stable at high temperatures. Under the optimum reaction conditions, the relative conversion rate of oxirane was 85.08% at 2h. The catalytic activity did not decrease significantly after 5 cycles of the reaction.

CONCLUSION

The results show that the catalyst is easy to prepare, has good catalytic activity in catalyzing the epoxidation reaction of soybean oil, and is easy to recover and highly reusable.
Analysis of codon usage bias in Ziziphi Spinosae Semen based on transcriptome data
Authors: Zhao Mengwei , Wang Jiemin , Liu Yuting , Yan Yuping , Li Si , Wang Wei , Sun HuiGai , Ma DongLai 
Published: March 2023

Abstract
The characteristics of codon usage and the influencing factors of the transcriptomic coding sequence in Ziziphi Spinosae Semen were investigated. The optimal codon was determined by analyzing 5,045 CDS samples using the Perl program and Codon W 1.4.2 software. The average content of GC, GC1, GC2 and GC3 of CDS ranged from 38.53–48.82%. The ENC-plot, bias, and neutral analyses showed that the codon preference is affected by mutation under selection. A total of 28 optimal codons were obtained, including UCC, UGC, UCG, etc. These were identified by the gene high expression screening codon methods, most of which end with C and G, and only CGU ends with U. Selection pressure has a great impact on the codon usage preference in Ziziphi Spinosae Semen CDS, and the mutation pressure also has some effects. The results of this study will provide a theoretical basis for the carrier selection for saponins and flavonoid synthesis and the evolution and contribution to the increased production of this medicinal plant species.
The risk may not be limited to the flooding – Polluted high flood sediments pose a health threat to the unaware public
Authors: Weber Alexandra , Wolf Stefanie , Becker Nadine , Märker-Neuhaus Leonie , Bellanova Piero , Brüll Catrina , Hollert Henner , Klopries Elena-Maria , Schüttrumpf Holger , Lehmkuhl Frank 
Published: March 2023

Abstract
Background
Because of global climate change, extreme flood events will increase in quantity and intensity in the upcoming decades. In catchments affected by ore mining, flooding leads to the deposition of fine sediments enriched in trace metal(loid)s. Depending on their concentration, trace metal(loid)s can be a health hazard. Therefore, the exposure of the local population to flood sediments, either by ingestion (covering direct ingestion and consuming food grown on these sediments) or via inhalation of dried sediments contributing to atmospheric particulate matter, is of concern.
Results
The extreme flood of July 2021 deposited enormous amounts of sediment across the town of Eschweiler (western Germany), with the inundation area exceeding previously mapped extreme flood limits (HQextreme). These sediments are rich in fine material (with the < 63 µm fraction making up 32% to 96%), which either can stick to the skin and be ingested or be inhaled. They are moderately to heavily enriched in Zn>Cu>Pb>Cd>Sn compared to local background concentrations. The concentrations of Zn, Pb, Cd, Cu, and As in flood sediments exceed international trigger action values. A simple assessment of uptake reveals that the tolerable daily intake is exceeded for Pb. Despite the enrichment of other trace elements like Zn, Cu, Cd, and Sn, they presumably do not pose a risk to human well-being. However, exposure to high dust concentrations may be a health risk.
Conclusions
In conclusion, flood sediments, especially in catchments impacted by mining, may pose a risk to the affected public. Hence, we propose to (I) improve the flood mapping by incorporating potential pollution sources, (II) extend warning messages to incorporate specific guidance, (III) use appropriate clean-up strategies in the aftermath of such flooding events, (IV) provide medical support, and (V) clue the public and medical professionals in on this topic accordingly.
Early proximal tubular injury in COVID-19 related ARDS: the URICOV study
Authors: Bobot Mickaël , Heim Xavier , Max Howard , Boucraut José , Simeone Pierre , Stein Claire , Velly Lionel , Bruder Nicolas , Forel Jean-Marie , Hraiech Sami , Guervilly Christophe , Carvelli Julien , Gainnier Marc , Mège Jean-Louis , Chopinet Sophie , Jourde-Chiche Noémie , Papazian Laurent , Burtey Stéphane 
Published: March 2023

Abstract
Background: During COVID-19, renal impairment is the most frequent after lung impairment and is associated with a poor prognosis particularly in intensive care unit (ICU). In this work we aimed to assess the existence and incidence of early renal dysfunction and its prognostic value in patients with COVID-19-related acute respiratory distress syndrome (ARDS) and to compare them with patients with non-COVID-19-related ARDS.
Methods: This prospective multicenter study was conducted in 3 ICUs. Patients aged 18 years and older with invasive mechanical ventilation for ARDS were enrolled. Precise evaluation of renal dysfunction markers including urinary proteins electrophoresis (UPE) and quantification, was performed within 24 hours after mechanical ventilation onset.
Results: From March 2020 to December 2021, 135 patients in ICU for ARDS were enrolled: 100 COVID-19 ARDS and 35 non-COVID-19 ARDS. UPE found more tubular dysfunction in COVID-19 patients (68% vs. 21.4%, p<0.0001) and more normal profiles in non-COVID-19 patients (65.0% vs. 11.2%, p=0.0003). COVID-19 patients significantly displayed early urinary leakage of tubular proteins like beta-2-microglobulin and free-light chains, tended to display more frequently acute kidney injury (AKI) (51.0% vs 34.3%, p=0.088), and had longer mechanical ventilation (20 vs. 9 days, p<0.0001) and longer ICU length of stay (26 vs. 15 days, p<0.0001). In COVID-19 ARDS, leakage of free lambda light chain was significantly associated with the onset of KDIGO ≥2 AKI (OR: 1.014, 95%CI [1.003-1.025], p=0.011).
Conclusion: Patients admitted to the ICU for COVID-19-related ARDS display a proximal tubular dysfunction, prior to the onset of AKI, which predicts AKI. Proximal tubular damage seems an important mechanism of COVID-19-induced nephropathy. Analysis of urinary proteins is a reliable and non-invasive tool to assess proximal tubular dysfunction in the ICU.
Trial Registration: Registered retrospectively with www.clinicaltrials.gov (NCT05699889) 26 January 2023.
ANN supported study on the performance and slurry erosion resistance of thermal sprayed WC20Cr 3 C 2 7Ni coatings
Authors: Bhosale Digvijay G. , Bhosale Poonam , Patil Yashraj , Patil Lalit , Jadhav Swapnil , Vasudev Hitesh , Prabhu T. Ram 
Published: March 2023

Abstract
The thermal spray coatings are commonly employed in slurry pump components and hydrodynamic turbine blades, where wear progression is an intricate phenomenon. In this research work, the performance analysis of HVOF and APS sprayed WC20Cr3C27Ni coatings for slurry erosion wear is carried out by using artificial neural networks (ANN). The influence of time, particle size, impact angle, speed, and slurry concentration on wear performance of coatings and turbine steel substrate are evaluated. Under the experimental settings, slurry erosion wear rates and mass loss for both coatings and substrate were determined. When ASTM A743 steel was coated with thermal sprayed WC20Cr3C27Ni coatings, the slurry erosion wear resistance of the steel was enhanced by 2 and 3.5 times for APS and HVOF coatings, respectively. The design of ANN made it possible to examine the interactions between the seven input variables. A robust model was formed by the two outputs that followed. This model enables the prediction of slurry erosion wear rate and mass loss of WC20Cr3C27Ni coatings and substrate.
Enhancing Privacy and Security in Cloud Computing Using the Covenant Code Exchange
Authors: Ghann Patricia , Zou Yousuo , Huang Jun Steed , Yang Hong 
Published: March 2023

Abstract
This paper proposes a model for enhancing privacy and security in cloud computing known as the Covenant Code Exchange (CCE). The model is proposed as part of the Service Level Agreement (SLA) for cloud providers and users as well as a third party (a witness). In particular, the model we propose allows a cloud provider, user and a witness to exchange covenant codes during transactions to ensure privacy, security and accountability. The covenant code exchange (CCE) requires the code of all parties that are involved for a successful transaction. A key feature of the proposed model is that a transaction cannot be completed with the codes of all parties concerned. Our model is very efficient and effective; when compared to other models; certification, accreditation, authorization and authentication, it involves a transaction cannot be completed until all parties have exchange of covenant codes. Additionally, when there is a breach in security, all parties will be held accountable. This ensures that each party is critical and cautions before, during and after a transaction is successfully executed.
Assessment of low-density polyethylene and poly (ethylene-co-vinyl acetate) capability for the uptake Persistent Organic Pollutants (POPs) in atmospheric passive sampling devices
Authors: Sánchez-Andica Rubén Albeiro , Troches-Mafla Estefanía , Páez-Melo Martha Isabel , Martínez-Lara Jina 
Published: March 2023

AbstractIn this study, the ability of Poly (ethylene vinyl acetate) (EVA) and low-density polyethylene (LDPE) to uptake persistent organic pollutants (POPs) (13 organochlorine pesticides (OCPs) and 19 polychlorinated biphenyls (PCBs)) was evaluated in passive environmental monitoring samplers. The compounds adsorbed on the polymers surface, were extracted with n-hexane and methanol. Afterward, they were quantified by gas chromatography coupled to a mass spectrometry detector (GC-MS2). In the process of uptaking, a fast accumulation kinetics for both polymeric materials were observed. Generally, the compounds with lower molecular weight (186-291.9 g/mol) and lower KOA(6.17-6.82) reached the equilibrium region in less than 1 day, while the compounds with high molecular weight (318-464 g/mol), the equilibrium region was reached after 10 days. Finally, the GFF-EVA was used in a pilot sampling test in the city of Santiago de Cali (Colombia) in four sampling campaigns that were carried out between March and May 2019 in an exposure time of 15 days. Ten (10) PCBs and thirteen (13) OCPs were detected. The highest concentrations of these POPs were detected at the following stations:Transitoria, ERA-Obrero, La Flora and Base Aérea. The evaluated polymers proved to be suitable and promising materials for monitoring POPs due to their low cost, easy implementation, and deployment.
A generalization of M-weakly compact operators
Authors: Hazrati Somayeh , Azar Kazem Haghnejad , Zare Sajjad Ghanizadeh 
Published: March 2023

Abstract
In this paper, we introduce and study new classifications of operators as name unbounded L-weakly and generalized M-weakly compact operators. We study some lattice properties of these classifications of operators and we investigate their relationships with others known operators such as M-weakly compact operators. We show that each M-weakly compact operator is generalized M-weakly compact operator, but the converse, in general, not holds.
Mathematics Subject Classification (2010) 47B65 · 46B40 · 46B42
Validation of a Musculoskeletal Model for Simulating Muscle Mechanics and Energetics During Diverse Human Hopping Tasks
Authors: Jessup Luke N. , Kelly Luke A. , Cresswell Andrew G. , Lichtwark Glen A. 
Published: March 2023

Abstract
Computational musculoskeletal modelling has emerged as an alternative technique to indirect calorimetry for estimating energy expenditure. In comparison, musculoskeletal modelling is less constrained – removing the need for repetitive motion, often at steady-state, over prolonged time periods. However, predictions from modelling tools depend on many assumptions around muscle architecture and function and motor control. Therefore, these tools need to continue to be validated if we are to eventually develop subject-specific simulations that can accurately and reliably model rates of energy consumption for any given task. In this study, we used OpenSim software and experimental motion capture data to simulate muscle activations, muscle fascicle dynamics, and whole-body metabolic power across mechanically and energetically disparate hopping tasks, and then evaluated these outputs at a group- and individual-level against experimental electromyography, ultrasound, and indirect colorimetry data. We found weak to strong correlations for peak muscle activations, moderate to strong correlations for absolute fascicle shortening and mean shortening velocity, and strong correlations for gross metabolic power. These correlations tended to be stronger on a group-level rather than individual-level. Therefore, current modelling approaches may be sufficient for predicting relative differences in metabolic power across movement conditions on a group-level, but caution is required for interpretation of simulation outputs for individuals. We also urge that appropriate validation be performed before running analyses of simulated muscle mechanics, especially of muscle activations. We encourage others to use our publicly available dataset and develop others alike to refine the modelling of muscle mechanics and, more globally, the subject-specificity of simulations.
De-noising of Well log signal using Multilayer Decomposition for Lithology Identification in the Jharia coalfield region
Authors: Sinha Sugata Kumar , Gautam Priyanka , Lutte Sudhakar , Mundhe Newton 
Published: March 2023

Abstract
In the present study, the use of the multi-layer decomposition using wavelet for denoising of the non-stationary signal, i.e., the well log signal based on three wavelets such as Symlet wavelet (Sym8), biorthogonal wavelet (bior6.8) and Daubechies wavelet (db8) are used to identify lithology in the Jharia coalfield region. This work mainly focuses on the development of unconventional methods for signal denoising using wavelets. The well log data of the Jharia coalfield region includes gamma ray log (GR), resistivity log (shallow resistivity log, medium resistivity log), density log, sonic log, and neutron log (NPHI) which are used as the signal to which de-noising has been applied. This study describes the lithology of the Jharia coalfield region using the wavelet denoising effect, power spectrum analysis of the denoised signal, and lithology identification using denoised data. Lithology identification using de-noised signals is used to delineate three lithologies such as sand, shale, and coal to understand the performance of each wavelet decomposition method. Different parameters like the ‘heursure’ soft thresholding and 8-level decomposition are used for denoising the signal. Lithology obtained from the denoised signal using the 'sym8' wavelet gives lithofacies differences compared to other wavelets, information obtained from the 'sym8' waveform is more applicable to identify the reservoir properties, stratigraphic sequence, and sedimentary facies.
Research of CNN-SCN for Early Diagnosis of Alzheimer's Disease
Authors: Tong Yinsheng , Li Zuoyong , Huang Hui , Gao Libin , Xu Minghai , Hu Zhongyi 
Published: March 2023

Abstract
The early and effective diagnosis of Alzheimer's disease(AD) and mild cognitive impairment(MCI) has received increasing attention in recent years, but most of the existing studies do not pay enough attention to the spatial structure information in structural magnetic resonance images(MRI), and do not go deeper to explore the potential connection between spatial slices and slices, thus making the network models less accurate, poor generalization ability. To explore deeper the connection between spatial context slices, this research is designed to develop a new deep learning method to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability.
Population structure of Aequiyoldia eightsii (Jay, 1839) (Protobranchia, Sareptidae) in the shallow water zone of an Antarctic Specially Managed Area (Admiralty Bay, King George Island, Antarctic Peninsula)
Authors: Alcântara¹ Isabella B. , Dalto¹ Adriana G. , Lavrado¹ Helena P. 
Published: March 2023

Abstract

The protobranch Aequiyoldia eightsii is one of the most common bivalves in Antarctica and for the first time, its population structure was studied in Admiralty Bay, an Antarctic Specially Managed Area (ASMA), to establish a baseline for future monitoring. Density, size structure, length-weight relationship, condition index, and shell morphometry were evaluated at the shallow coastal zone of the bay. High abundance values of A. eightsii were related to sites with greater contributions of mud particles and total organic carbon contents in the sediment, while the condition indexes were higher in Martel Inlet, possibly due to higher food availability. Individuals obtained from gravel-rich sediments were significantly larger than compressed, while rounded forms were observed in sandy and muddy bottoms of the bay. Positive allometric growth was reported in most sites, except Botany Point, where specimens presented an isometric growth. The results demonstrated that the sediment composition and its organic content influence the population structure of A. eightsi as well as biological factors such as predation and recruitment rates. It is also worth noting that the phenotypic shell plasticity observed for this protobranch species in geographically close sites (hundreds of meters) highlights the importance of local-scale heterogeneity to be considered in future monitoring programmes for Antarctic marine ecosystems.
What does it mean to be compliant with sustainability standards and certification? Projecting confidence
Authors: Pennington Derric 
Published: March 2023

Abstract
What can consumers expect from compliance with four iconic voluntary market-based sustainability standards for global sugarcane, cotton, oil palm, coffee, tea, and cacao: Rainforest Alliance, Better Cotton Initiative, Roundtable on Sustainable Palm Oil, and Bonsucro? These Voluntary Sustainability Standards (VSS) represent some of the most popular solutions for improving the sustainability outcomes of global value chains. I grouped 244 compliance-indicators into one of four compliance categories: policy, practice, measurement or performance-based. I hypothesize how these could provide a priori information to stakeholders on the expected outcomes. I found VSS are largely comprised of policy and practice-based standards: 36% policy, 48% practice, 6% measurement, 10% performance. Only 11% of performance-based standards were core requirements. Most policy and practice-based indicators were qualitative while performance-standards were only quantitative. I found that compliance with measurement and performance-based standards is more likely to translate directly to SDG indicators used by governments to report progress. Incorporating performance-based standards can increase the likelihood that compliance is achieving desired results.