bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of biochar on enzyme activities during composting of cattle manure
Authors: Zhang Jiming , Li Xue , Qi Xinxin , Zhou Rongxin , Zhang Hongda , Liu Hang , du xiao , Sun Zhentao , Liu Ning , Yang Jinfeng
Published: April 2023

With the rapid growth of livestock, they produce more and more waste, causing severe environmental damage. Biochar as a composting additive has several advantages, including improved performance and humification processes, increased enzyme activity, and reduced nitrogen (N) losses. To investigate the effect of biochar addition on compost quality and enzyme activity, we composted cattle manure with different biochar measures (2%, 5%, and 10%) and different types (rice husk and rice straw) of biochar. The results showed that adding biochar to the aerobic compost of cow manure effectively accelerated the temperature rise of the compost, decreased the total carbon (C) content of the compost, and the total N content increased from the mesophilic phase to the decaying period. The C-to-N compost showed a decreasing trend. The 5% rice husk biochar and 2% rice straw biochar treatments more effectively reduced the N loss during composting and played a better role in N fixation. The addition of rice husk biochar more effectively increased the urease activities compared with the addition of rice straw biochar. Adding 5% rice husk biochar increased the peroxidase and polyphenol oxidase activities by 78% and 38%, respectively. Therefore, adding 5% rice husk biochar effectively improved compost quality and enzyme activities and reduced N loss in the aerobic composting process of cattle manure for optimal compost production.
Acceptability and Evaluation of APSIM-Qryza for Promoting Water and Nitrogen Productivity in Paddy Fields
Authors: Hosseinpour Shayan , Pirdashti Hemmatollah , Kaveh Mohammad , Dokoohaki Hamze
Published: April 2023

Aims
Crop models are designed to handle the various agronomic complexities that often arise from water and nutrient management in agroecosystems. In paddy fields, irrigation, and nitrogen (N) application are critical factors for achieving adequate yield, but unsuitable strategies can make the environment vulnerable.
Methods
we parametrized and validated the APSIM-Oryza model using two years experiment under various irrigation techniques (continuous flood (CF) and alternate wetting and drying (AWD) and nitrogen (N) application rates. In addition, two scenarios were run based on a decade of historical weather data.
Results
The model demonstrated a fit match in calculating aboveground biomass, leaf area index (LAI), and total N uptake over both years. The calculated grain yield from APSIM-Oryza was indicated to closely attach the measured data over parametrization (R2 = 0.92, RMSE = 203 kg ha− 1) and validation R2 = 0.89, RMSE = 308 kg ha− 1). Scenario analysis was recorded the lowest yield, fertilizer recoveries (RE), partial factor productivity (PFP), and agronomic efficiency (AE) was obtained with the basal N application, while the highest physiological efficiency (PE) was achieved. Simulation results showed that under both water management strategies, the highest yield, total N uptake, AE, RE, and partial PFP were observed with 69 (kg N ha− 1) at a split ratio of 5:5.
Conclusion
The suitable water and N management strategies are able to enhance crop productivity, reduce environmental degradation and increase the efficiency and profitability of food production by addressing water shortages and N loss in farming systems.
Development of personalized mobile information service platform of university library based on multi-core processor and cloud computing platform
Authors: Li Yingxin
Published: April 2023

In recent years, depending on information technology, knowledge and skills, university libraries have established a new "user centered" data service system, which is called personalized information service. Although this service has made some progress in recent years, these systems have only achieved a certain amount of personalized information services. Multi-core processor is the basic process unit of high-performance cloud computing node, which is an important basis to achieve system security, high performance, low power consumption and other high-performance requirements. Cloud computing node, as one of the core components in the high-performance computer system architecture, in addition to the acceleration node, master control unit, communication system, etc., its efficiency has constituted an important basic condition for the realization of the "high efficiency" goal of the system. The emergence of multi-core processors and cloud computing platforms has created conditions for the development of personalized mobile information service platforms in university libraries. Under the technical support of multi-core processors and cloud computing platforms, it is also an important goal of mobile libraries in the future to meet the needs of users to search library data information at any time and provide users with diversified data services. At present, the knowledge service platform of university library can not only actively select and recommend relevant materials for users according to their knowledge preferences, data utilization habits, and academic backgrounds, but also enable service users to quickly obtain the borrowing expiration prompt, independent consultation and retrieval and other personalized information functions, and carry out more in-depth personalized intelligent knowledge consulting services. Based on multi-core processor and cloud computing platform, this paper develops personalized mobile information service platform of university library.
Research on Internet Financial Risk Control Based on Deep Learning Algorithm
Authors: Wu Ziai , Zhou Qiao , Wang Lijuan , Zhao Di
Published: April 2023

With the rapid development of Internet technology, Internet finance has entered thousands of households, bringing a lot of convenience to people's lives. As a financial model based on Internet technology, compared with traditional bank loans, online loans have lower operating costs and faster returns, so they are developing rapidly. Providing users with better and faster services while standardizing operations has always been the development goal of various Internet finance companies. At present, many domestic Internet financial enterprises are facing many problems such as difficulty in risk control. Therefore, this paper makes full use of deep learning algorithms to build an Internet financial risk control system. After in-depth analysis and research on the deep learning algorithm, the Internet financial risk control system is divided into several modules. The project mainly includes: model management module, user behavior analysis module, alarm management module, monitoring module, product management module, etc., and then by analyzing the test results of each test scenario, it is concluded that the performance test of the system design meets the actual needs of users, and simulates the test. It is carried out in accordance with the constraints and regulations of the test plan, and the performance test meets the standard. The system not only ensures the safety of the company's funds, but also helps the company to form a smooth and effective financial risk control process. This paper designs a class of effective management systems by applying deep learning algorithms to the field of Internet financial risk control.
Diagnosis of Crohn's Disease and Ulcerative Colitis Using the Microbiome
Authors: Kang Da-Yeon , Park Jong-Lyul , Yeo Min-Kyung , Kang Sang-Bum , Kim Jin-Man , Kim Ju Seok , Kim Seon-Young
Published: April 2023

Background: Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with disease severity. This study aimed to differentiate and diagnose these two diseases using the microbiome and identify specific biomarkers associated with disease activity.
Results: We observed differences in the abundance and composition of the microbiome between patients with IBD and healthy controls (HC). Compared to HC, the diversity of the gut microbiome in patients with IBD decreased; the diversity of the gut microbiome in patients with CD was significantly lower. We identified 68 members of the microbiota (28 for CD and 40 for UC) associated with these diseases. Additionally, as the disease progressed through different stages, the diversity of the bacteria decreased. The abundances of Alistipes shahii and Pseudodesulfovibrio aespoeensis were negatively correlated with the severity of CD, whereas the abundance of Polynucleobacter wianus was positively correlated. The severity of UC was negatively correlated with the abundance of A. shahii, Porphyromonas asaccharolytica and Akkermansia muciniphilla, while it was positively correlated with the abundance of Pantoea candidatus pantoea carbekii. A regularized logistic regression model was used for the differential diagnosis of the two diseases. The area under the curve(AUC) was used to examine the model performance. The model discriminated between UC and CD at an AUC of 0.886 (training set) and 0.826 (test set) and an area under the precision-recall curve (AUCPR) of 0.871 (test set).
Conclusions: Based on fecal whole-metagenome shotgun (WMS) sequencing, CD and UC were diagnosed using a machine-learning predictive model. Additionally, microbiome biomarkers associated with disease activity (UC and CD) have been proposed.
Simulation of Book selection planning based on deep learning and its application
Authors: Bian Kun
Published: April 2023

Due to the complexity of network information, it presents an unbalanced feature. For the current book publishers, the publishing theme selection and planning methods of the newsroom can no longer meet the development speed of the Internet background. As the publishing house can not accurately analyze the needs of users, it is difficult to obtain the specific standards of the book publishing market. Therefore, the demand of consumers for books has decreased, and further generated practical problems such as inventory accumulation and revenue impairment. Based on the demand of extracting and analyzing target information for books, it can be realized by using deep learning methods. Therefore, this study establishes a book selection planning system. If the information of the book itself and the corresponding evaluation information are required, the system first uses Anaconda to crawl the website to obtain the book information data, and then uses the proposed KIEM algorithm to supplement the information of the data crawled by Anaconda. After completing this step, use the first layer of CRF model to separate the evaluation sentence from the opinion sentence, and split the separated target sentence to get the attribute and emotion words. Finally, ARIA algorithm is combined with the improved recommendation algorithm to improve the recommendation performance and achieve the accuracy and personalization of the system. It can be seen from the actual measurement that the MAE value calculated by the application of the system proposed in this paper is relatively small, which shows that the system has certain practicability in prediction accuracy. This paper designs an effective system application by introducing deep learning technology into the field of book topic selection planning.
3D sculpture technology innovation based on image segmentation algorithm
Authors: Zhang Xincheng , Ge Chunyu
Published: April 2023

At present, 3D modeling technology is simplified due to the improvement of image segmentation algorithm and feature point detection and other related technologies, so it is widely used in major museums. For example, science and technology museums can display emerging scientific and technological models by using 3D modeling, while museums and art galleries can display works of art and antiques by using 3D modeling. By using 3D modeling to display, tourists can effectively enhance the sense of substitution. 3D sculpture is a brand new product under this technical background, which can enhance the vividness of the exhibition of works in the Expo Park, thus bringing tourists a better tour experience. Based on this background, this study introduces the image segmentation algorithm to adjust and optimize 3d sculpture works, and introduces the main optimization process. Then, modeling, modification and model reconstruction are carried out through the collection and calculation of multi angle pictures. In this process, file content import, model data import and 3d object image import are required. The simulation results show that the optimized 3d model can effectively improve the frame rate performance of the model, but also reduce the volume, number of faces and vertex points of the model to a certain extent. It is an effective and innovative research work to optimize 3d sculpture by using image segmentation algorithm. This paper introduces the image segmentation algorithm into the field of 3d sculpture to achieve technological innovation.
Cloud computing English teaching application platform based on machine learning algorithm
Authors: Zhang Peili
Published: April 2023

Due to the progress of computer technology at this stage, English teaching has gradually achieved reforms with the help of the Internet. This paper builds an English teaching application platform based on machine learning algorithms, and realizes personalized online teaching with the help of cloud computing technology. In view of the complex characteristics of learners' online learning behavior data, this paper uses Pearson's correlation coefficient to quantify the correlation between various learning behavior variables. In order to further improve the accuracy of system data analysis, regression analysis is used to detect abnormal data points, and data with a data prediction deviation greater than 1 is determined as abnormal data. In order to reduce the loss of a large amount of data loss caused by the regression analysis to judge some anomalies, this paper uses the local outlier detection method again to detect local outliers, and compares the data repeatedly detected in the two identification algorithms, and judges it as abnormal data. special handling. In order to further verify the practicability of the system, network simulation is used to increase the number of concurrent operators in the system. The experiment proves that the click delay time of the English teaching video of the system is maintained below 1.8 seconds under 5000 concurrent operations, and the running speed of the system is significantly improved when the dynamic scheduling function is enabled, which proves that the system can meet the needs of multiple users for concurrent operations. Finally, a survey is conducted on the personalized education of the English teaching platform, and the relevant experimental and survey data are analyzed in detail, and a series of constructive strategies for the development of personalized English teaching in the cloud education environment are proposed.
Pathologic complete response of ductal carcinoma in situ to neoadjuvant systemic therapy in HER2-positive invasive breast cancer patients: a nationwide analysis
Authors: Ploumen Roxanne , Claassens Eva , Kooreman Loes , Keymeulen Kristien , Kats Maartje van , Gommers Suzanne , Siesling Sabine , Nijnatten Thiemo van , Smidt Marjolein
Published: April 2023

Purpose
Ductal carcinoma in situ (DCIS) is present in more than half of HER2-positive invasive breast cancer (IBC). Recent studies show that DCIS accompanying HER2-positive IBC can be completely eradicated by neoadjuvant systemic therapy (NST). Our aim was to determine the percentage of pathologic complete response of the DCIS component in a nationwide cohort and to assess associated clinicopathologic variables. Furthermore, the impact on surgical treatment after NST was investigated.
Methods
Women diagnosed with HER2-positive IBC, treated with NST and surgery, between 2010-2020, were selected from the Netherlands Cancer Registry. Pre-NST biopsy and postoperative specimen pathology reports were obtained from the Dutch Nationwide Pathology Databank, and assessed for presence of DCIS. Clinicopathologic factors associated with DCIS response were assessed using logistic regression analyses.
Results
A DCIS component was present in the pre-NST biopsy in 1443 of the 5834 included patients (24.7%). Pathologic complete response of the DCIS component was achieved in 743 (51.5%) of these patients. Complete response of DCIS occurred more frequently in case of complete response of IBC (63.4% versus 33.8%, p<0.001). ER-negative IBC (OR 1.79; 95%CI 1.33-2.42) and treatment with HER2-targeted therapy (OR 5.97; 95%CI 1.82-19.55) were associated with complete response of DCIS. Mastectomy rates were higher in IBC+DCIS compared to IBC (53.6% versus 41.0%, p<0.001).
Conclusion
Pathologic complete response of DCIS occurred in 51.5% of HER2-positive IBC patients and was associated with ER-negative IBC and complete response of IBC. Future studies should investigate imaging evaluation of DCIS response to improve surgical decision making.
Investigating the effects of Brachiaria (Syn. Urochloa) varieties on soil properties and microbiome
Authors: Merloti Luis Fernando , Bossolani João William Bossolani William , Mendes Lucas William , Rocha Gabriel Silvestre , Rodriguez Mayara , Asselta Fernanda Ometto , Crusciol Carlos Alexandre Costa , Tsai Siu Mui
Published: April 2023

Background and Aims

The Brachiaria sp. (synonymous with Urochloa) is one of Brazil’s main grass species used in livestock production and has become the focus of breeding genetic programs to enhance its resistance to drought, flooding, and pests, as well as improving its palatability to animals. However, there is a limited understanding of how genetic breeding can affect the soil microbiome and its potential functions. Thus, this study aimed to investigate the impact of four different Brachiaria varieties on the soil prokaryotic and fungal communities, particularly emphasizing their potential functions related to the N-cycle.

Methods

We combined molecular techniques, such as quantitative PCR and amplicon sequencing, to target prokaryotic and fungi communities and traditional soil and plant chemical analyses.

Results

Our findings revealed that all varieties improved soil porosity, P content, and organic carbon. Soil acidity, nutrient availability, and porosity were the main drivers of the microbial communities. The Nitrososphaeraceae, Gaiellales, Conexibacter, Sphingomonas, Hydnophlebia meloi, Conocybe, and Cladosporium were the main taxa associated with the dissimilarities between the Brachiaria varieties and the Control. In addition, the presence of the plants increased potential microbial functions such as Chemoheterotroph, Aerobic-Chemoheterotroph, and Pathotroph-Saprotroph groups. The study also identified the ability of each variety to recruit nitrogen-fixing and bacterial and archaeal ammonia-oxidizing communities.

Conclusion

Our findings suggest that selecting an efficient Brachiaria variety could positively impact soil quality, improving agricultural systems and increasing food production.
Identification of anterior cruciate ligament fibroblasts and their contribution for knee osteoarthritis progression by single-cell analyses
Authors: Zhang Ziji , Li Zhiwen , Zhang Shiyong , Xu Yiyang , Mao Guping , Kang Yunze , Zheng Linli , Long Dianbo , Chen Weishen , Gu Minghui , Zhang Zhiqi , Kang Yan , Sheng Puyi
Published: April 2023

Objective
A better Understanding of the key regulatory cells in the anterior cruciate ligament (ACL), and their role and regulatory mechanisms in knee osteoarthritis (KOA) progression can facilitate the development of targeted treatment strategies for KOA.
Methods
The relationship between ACL degeneration and KOA was first explored using human ACL specimens and mouse models. Next, single-cell RNA sequencing (scRNA-seq) and single-cell detection of transposase accessible and chromatin sequencing (scATAC-seq) data were integrated to reveal the transcriptional and epigenomic landscape of ACL in normal and osteoarthritis (OA) states.
Results
Six cell populations were identified in the human ACL, among which were inflammation-associated fibroblasts (IAFs). Degeneration of the ACL during OA mechanically alters the knee joint homeostasis and influences the microenvironment by regulating inflammatory- and osteogenic-related factors, thereby contributing to the progression of KOA. Specifically, a IAF subpopulation identified in OA ACL was found to enhance the transcription and secretion of EGER via SOX5 upregulation, with consequent activation of the EGER–EGFR signaling pathway. These molecular events led to the upregulation of downstream inflammatory and osteogenic factors, and the downregulation of the extracellular matrix-associated factor, thereby leading to knee osteoid formation, cartilage degeneration, and OA progression.
Conclusions
In summary, this study identifies a novel subpopulation of fibroblasts in the ACL, which confirms the importance of the ACL in knee joint homeostasis and disease. Additionally, the specific mechanism by which these IAFs regulate KOA progression was uncovered, which provides new foundation for the development of targeted treatments for KOA.
Aging gene signature of IL-7 receptor alpha low effector memory CD8+ T cells is associated with neurocognitive functioning in Alzheimer’s disease
Authors: Young Juan , Park Hong-Jai , Kim Minhyung , Par-Young Jennefer , Bartlett Hugh , Kim Hye Sun , Unlu Serhan , Osmani Lais , Shin Min Sun , Bucala Richard , Dyck Christopher van , Allore Heather , Mecca Adam , You Sungyong , Kang Insoo
Published: April 2023

CD45RA+ effector memory (EM) CD8+ T cell expansion was reported in Alzheimer’s disease (AD). Such cells are IL-7 receptor alpha (IL-7Rα)low EM CD8+ T cells, which expand with age and have a unique aging gene signature (i.e., IL-7Rαlow aging genes). Here we investigated whether IL-7Rαlow aging genes and previously reported AD and memory (ADM) genes overlapped with clinical significance in AD patients. RT-qPCR analysis of 40 genes, including 29 ADM, 9 top IL-7Ralow aging and 2 control genes, showed 8 differentially expressed genes between AD and cognitively normal groups; five (62.5%) of which were top IL-7Rαlow aging genes. Over-representation analysis revealed that these genes were highly present in molecular and biological pathways associated with AD. Distinct expression levels of these genes were associated with neuropsychological testing performance in 3 subgroups of dementia participants. Our findings support the possible implication of the IL-7Rαlow aging gene signature with AD.
Graphene Nanoplatelets Reinforced Al-Cu-Mg Composite Fabricated using Laser Powder Bed Fusion: Microstructure, Mechanical Properties, and Wear Behaviour
Authors: Pekok Mulla Ahmet , Setchi Rossitza , Ryan Michael , Brousseau Emmanuel , Han Quanquan , Gu Dongdong
Published: April 2023

Aluminium-based metal matrix composites reinforced with Graphene (Gr) and its derivatives have been reported as promising composites due to their superior properties such as strength, damage tolerance, fatigue resistance, and density. However, the crack and porosity susceptibility of Aluminium 2024 Alloy (AA2024) with added Gr when fabricated using additive manufacturing techniques is not sufficiently well understood. The present work addresses this knowledge gap by focusing on the effect of Graphene Nanoplatelets (GNPs) and scanning speed on the AA2024 composites’ wear performance, microstructural and mechanical properties of fabricated specimens using Laser Powder Bed Fusion (LPBF). The experimental findings demonstrate that the Gr presence in the composite up to 0.5% improves its crystallite size and microhardness by up to 37.6% and 45%, respectively; however, it increases the porosity and crack formation due to the high laser power requirement. Moreover, the composites’ macroscale scratch and nanoscale wear performances showed improvements by up to 50% and 56% with higher Gr concentration (0.5%), suggesting that Gr is distributed uniformly in the structure. The improved understanding of the relationship between microstructure and mechanical characteristics of the GNPs/Al2024 composites fabricated using LPBF in terms of cracking and porosity formation is another significant contribution of this work.
Early prediction of wheat yield using seasonal weather forecasts and the static stress drought index
Authors: García-Gamero Vanesa , Peñuela Andrés , Peña Adolfo , Vanwalleghem Tom
Published: April 2023

Crop yield prediction considering soil moisture as a proxy for water supply remains crucial for global food security. This study evaluates the potential of using seasonal weather forecasts combined with a drought index, Static Stress, based on both precipitation and soil moisture conditions to predict winter wheat yield 7 to 1 month in advance in Córdoba (South Spain). First, using observed climate and crop yield data we evaluate the use of Static Stress, as a potential crop yield predictor and compare it to a more traditionally used index, the SPEI, which is only based on precipitation conditions. Then we evaluate the performance of simple linear regression models to predict crop yields from forecasted Static Stress values calculated using weather forecast data from the ECMWF seasonal forecasting system (SEAS5). We find that Static Stress is better correlated to crop yield than SPEI and that Static Stress derived from seasonal forecasts has a good performance (R2 > 0.5; p-value < 0.05) for crop yield predictions of 4 or fewer months before harvest, i.e., from March to July. In this case study, these results indicate that drought indicators that consider soil moisture conditions are better predictors of crop yields than indicators that only consider precipitation. Furthermore, this study demonstrates the potential of using simple regression models together with mid-term forecasts of the Static Stress index to maximize cereal yields and mitigate drought impacts.
Induction of anti-tumor immunity by human telomerase reverse transcriptase (hTERT) and viral-derived peptides in breast cancer mice models
Authors: Kardar Gholam Ali , Songwe Fanuel , Colagar Abasalt Hosseinzadeh , Ghorbani Mehdi , Mohammadi Amir
Published: April 2023

Human telomerase reverse transcriptase (hTERT) is the most common tumor-associated antigen (TAA) and its activity is upregulated in more than 90% of cancers. Therefore, its targeting could be used in cancer immunotherapy. In continuing with our previous study, we evaluated hTERT derived peptides conjugated to KLH in mice breast cancer models. To induce tumors in Balb/C mice, about 5 × 105 mice breast cancer 4T1 cell were washed and suspended in 100 μl sterile PBS and subcutaneously (s.c) injected to 4th mammary fat pat of 6-8 weeks old mice. Thereafter, tumor size measurements were taken two times a week. About 100 ug KLH conjugated peptides accompanied by Incomplete Freund′s adjuvant (IFA) were injected s.c on days 10th and 20th after cancer cells inoculation. In spite of their significantly lower immunogenicity and affinities on mice HLA compared to human (approximately 5-30 times), these peptides inhibited tumor growth as well as being able to increase the overall survival of vaccinated Balb/C mice compare untreated group.
Prediction and examination of the impact of the raster angle on the orthotropic elastic response of 3D-printed objects using a novel homogenization strategy based on the real clustering of RVEs
Authors: BENAISSA Hamza AIT , Zaghar Hamid , Moujibi Nabil , Sossey-Alaoui Ismail
Published: April 2023

The distinct process of layer-by-layer 3D printing and the differential cooling of the 3D-printed component generates a low adhesive force between the layers, defects, and gaps interlayers, which results in an anisotropy distribution of mechanical and physical properties. Computational models, such as homogenization via green functions or homogenization based on ideal geometry-material models, are required as a first approximation of the elastic response of 3D-printed components, but have received less attention than experimental methods. This research establishes a novel multi-scale method for accurately predicting the mechanical behavior of 3D-printed components. At the micro and mesoscale, the micro-mechanical analysis of a representative volume element (RVE) corresponding to the real morphologies is acquired by clustering the microscopic observations of the internal structure of the 3D-printed samples using K-means algorithms, then used to generate micro-mechanical models in a function of the raster angle to compute the effective orthotropic constants, and these outputs are used to generate macro-scale numerical models that simulate the mechanical behavior under a tensile stress of the 3D-printed samples Compared to the idealized antecedent models and confirmed by experimental data, this methodology yields results that are consistent with reality. In conclusion, the incorporation of clustering approaches applying K-means algorithms into the homogenization procedure yields accurate prediction results that match the experimental elastic response of 3D-printed components. This study presents reliable prediction laws that enable the designer to conduct a faster iterative analysis and choose the optimal printing process parameters based on FE analysis to produce high-quality 3D FFF-printed components.
Periodic dynamics of predator-prey system with Beddington-DeAngelis functional response and discontinuous harvesting
Authors: Wang Yingying , Xia Zhinan
Published: April 2023

In this paper, a delayed predator-prey model with discontinuous harvesting and Beddington-DeAngelis functional response is investigated. Using the theory of differential inclusion theory, the existence of positive solutions in the sense of Filippov is discussed. Under reasonable assumptions and periodic disturbances, the existence of positive periodic solutions of the model is studied based on the theory of Mawhin coincidence degree. Finally, through numerical simulation, the correctness and feasibility of the conclusions are verified.
Increasing Image Classification Speed in Python Environments Using Convolutional Neural Networks Supported by High-Performance Computing
Authors: ASHRAF MD SADI , HOSSIAN NOBI , FAIZA FAHMEE
Published: April 2023

Convolutional Neural Networks (CNNs) have been found to be extremely effec- tive at categorising photos. However, because CNN models are so large and image processing requires so many calculations, classification time can be significantly decreased. We look at how to leverage High-Performance Computing (HPC) to speed up how CNNs identify images in Python in this work. We create and test alternative CNN architectures on an HPC-enabled cluster by using paral- lel processing and GPU acceleration to speed up image categorization. We also investigate how different data pretreatment methods and hardware configurations influence how effectively categorization works. Our research shows that employing Python environments that are optimised for high-performance computing (HPC) can significantly enhance image classification speed while maintaining excellent accuracy rates. This research is critical for developing better and easier-to-use image classification systems, particularly in computer vision, remote sensing, and biomedical imaging.
Transcriptomic insights into archaeal nitrification in the Amundsen Sea Polynya, Antarctica
Authors: Gwak Joo-Han , Awala Samuel Imisi , Kim So-Jeong , Lee Sang-Hoon , Yang Eun-Jin , Park Jisoo , Jung Jinyoung , Rhee Sung-Keun
Published: April 2023

Background
Ammonia-oxidizing archaea (AOA) are among the most ubiquitous and abundant microorganisms in the ocean, and play a primary role in the global nitrogen cycle. Antarctic polynyas are ecological hotspots with the highest primary productivity in the Southern Ocean during summer, and their production and formation reflect anthropogenic climate change. Strong seasonal variations in AOA abundance from winter to summer may have affected marine nitrification. We utilized metagenomics and metatranscriptomics to gain insights into the physiology and metabolism of AOA in polar oceans, which are associated with ecosystem functioning.
Results
A polar-specific ecotype of AOA, from the “Candidatus Nitrosomarinus”-like group, was observed to be dominant in the Amundsen Sea Polynya (ASP), West Antarctica, during a succession of summer phytoplankton blooms. AOA had the highest transcriptional activity among prokaryotes during the bloom decline phase (DC). Expression rank profiles for transcripts of key genes involved in ammonia oxidation (e.g., genes encoding subunits of ammonia monooxygenase, NirK, ferredoxin, and plastocyanin) and carbon fixation (3-hydroxypropionate/4-hydroxybutyrate cycle) indicated that AOA are actively involved in nitrification in the bloom DC. The highly ranked transcripts of flavodoxin and superoxide dismutase genes imply that AOA are exposed to iron limitation and ROS toxicity, respectively. The expression of high-affinity ammonia transporter (Amt2) gene, together with the absence of urea utilization-related genes, indicates that ammonia is a major source of AOA nitrification. The Phn system may aid AOA in acquiring phosphorus-containing compounds. The expression patterns of cdvA and cdvB also support the active cell division and cellular processes of AOA in the bloom DC.
Conclusions
The polar-specific ecotype AOA of “Candidatus Nitrosomarinus”-like group was found to be the representative AOA in the ASP, West Antarctica. Metatranscriptomic analysis of key genes involved in ammonia oxidation, carbon fixation, transport, and cell division indicated that this polar ecotype AOA was actively involved in nitrification in the bloom DC in the ASP. This study revealed the physiological and metabolic traits of this key polar-type AOA in response to phytoplankton blooms in the ASP and provided insights into AOA functions in polar oceans.
Bi-functional C5 antibody-CR1 fusion for complement inhibition
Authors: Gong Yuemin , Chai Xingxing , Jia Jinsong , Wang Huaquan , Guo Ruowen , Yang Hongzhou , Liu Yunhua , Ma Haili , Liu Heng , He Guangsheng , Li Jianyong
Published: April 2023

The authors have requested that this preprint be removed from Research Square.
On the Existence and Uniqueness Theory: A Generalized Fractional Impulsive p-Laplacian BVP
Authors: Chabane Farid , Benbachir Maamar , Etemad Sina , Rezapour Shahram , Avcı Ibrahim
Published: April 2023

This work investigates the boundary value problem (BVP) of impulsive differential equations (DEs) with a nonlinear ρ−Caputo fractional and p-Laplacian operator. To prove the existence and uniqueness of solutions, we employ Schauder’s and Schaefer’s fixed point theorems, along with the Banach contraction principle. Additionally, we present two examples to show the practical applications and significance of our main results. Our study provides a new perspective on impulsive DEs with fractional and p–Laplacian operators and contributes to the development of the theory of impulsive DEs.
AMS Subject Classi cation: Primary 34A08; 34B15; secondary 34B27
A generalized framework for designing open-source natural hazard parametric insurance
Authors: Steinmann Carmen B. , Guillod Benoît P. , Fairless Christopher , Bresch David N.
Published: April 2023

Insurance plays a crucial role in rebuilding after devastating events caused by natural hazards. However, the insurance protection gap (the difference between economic and insured damages) has been growing over the past 40 years. An innovative solution with the potential to contribute to closing this widening gap is parametric insurance schemes. Payouts are triggered by real-time hydro-/meteorological parameters rather than assessed damages, can be settled swiftly and ensure liquidity. In this work we propose a generalized framework for designing globally consistent parametric insurance schemes. One crucial aspect in the viability of parametric schemes is understanding the basis risk: the difference of the index-based payout and the actual damage. A systematic quantification of basis risk captures uncertainties and allows parametric insurance even in data scarce areas. Furthermore, building on open-source data and methods makes products accessible to low income stakeholders. We implement the framework in the open-source global risk assessment platform CLIMADA and illustrate it with two stylized parametric insurance case studies, targeting tropical cyclones in Mozambique and winter storms in France. Being globally-consistent, open-source, and readily available, the provided code base is easily adaptable to other hazards and exposures worldwide. This improves the accessibility to and the transparency of such innovative insurance schemes.
Designing a multi-epitope vaccine against chickenpox virus using in silico approach
Authors: Samuel Johnson , Borah Madhushruti , Castiglione Filippo , Patil Sharangouda J.
Published: April 2023

Human Herpesvirus 3 (HHV3), commonly known as the Varicella zoster virus (VZV), is a highly infectious α-herpesvirus that causes Varicella (chicken pox) on primary infection, as well as Herpes zoster (shingles) on latent reactivation. Life-threatening sequelae and complications such as post-herpetic neuralgia (PHN), meningoencephalitis, myelopathy, skin necrosis, etc. have also been observed in some immunocompromised individuals. Although effective vaccines have been developed against VZV, they are limited by the scope of vaccination and in some cases by the vaccine efficacy. Addressing the need for a much safer and more efficient vaccine, an in-silico approach-based study was carried out to design a multi-epitope vaccine against VZV. Accounting for their crucial role in the viral entry mechanism, the envelope glycoproteins were analyzed for potent epitopes that can induce cytotoxic T cells, Helper T cells, and B cells. These epitopes were further screened using various computational tools, for constructing a multi-epitope vaccine, via the addition of suitable linkers and an adjuvant. Antigenicity, allergenicity, conservancy, population coverage, and other physiochemical properties of the final vaccine construct were evaluated, post which their structural properties and the interaction patterns with TLR1 and TLR4 were analyzed. Further, the vaccine construct was subjected to molecular dynamics simulation studies, in order to assess its structural stability. Finally, the multi-epitope vaccine was codon optimized and cloned into the expression vector pET-28a+, to have it expressed efficiently in the K12 strain of E.Coli bacteria. The immune reactions triggered by the vaccine candidate were validated using immune simulation studies. Laboratory validation of the findings for its immunogenicity and safety can help in assessing the in vivo efficiency of the vaccine to prevent VZV infection.
Securing the Perceptual Layer of the Internet of Things (Iot) Devices Using Elliptic Curve Cryptography
Authors: Okediran T. M , Vincent O. R , Abayomi-Alli A. A , Adeniran O. J
Published: April 2023

The security of IOT layers has become very important considering the various attacks faced by IOT devices especially, the perceptual layer. Reported attacks in recent times include direct Denial of Service (DDoS), Botnet attack and Falsification attacks. Several security techniques in place can not be directly implemented on IOT devices; hence, there is a need to improve the research.This work has proposed an ECC based on binary field solution, there were three stages of implementation which includes device registration where each devices are recognised and locked against all forms of communication, device authentication for key agreement and unlocking, communication phase for exchange of information. It was implemented on a contactless ATM card and a mobile phone as an IOT devices used for electronic payment. The evaluation result for storage capacity showed that the mobile phone and the ATM card has the capacity to run ECC security protocol directly, the Jitter result obtained reveals that the Jitter value did not deviate from normal for both devices while, the computational time showed that it is computationally efficient to use ECC over binary field for the security of the perceptual layer.
The Meaning of Noise in The Wind in the Willows
Authors:   British And American Language And Literature Association Of Korea, Kang Min - Jeong
Published: April 2023

The purpose of this study is to explore the meaning of noise in Kenneth Grahame’s The Wind in the Willows, a representative work of the Edwardian era. From the perspective of the social context, noise in this novel can be divided into two aspects: collision noise caused by class conflict and mechanical noise created by technological civilization. During the Edwardian period, the conflict between the upper class and the working class was deepening while the advent of automobiles was ushering in a new era of technological civilization. At times, the class conflicts would erupt into noisy public demonstrations, even rebellion. Simultaneously, new technologies such as automobiles created noises never heard before in rural environments. All of these new noises disrupted the peace and contributed to a break - down in the stable class hierarchy and the idyllic tranquility of nature. Symbolically, these noises reflect Grahame’s fear of the destruction of the established ideology and the terror of industrialization. Grahame maintains a critical stance on society by depicting the issues of class conflict and industrialization as a cacophony that he wishes to eliminate.
The influence of metaverse platform Gather Town and video conference platform Zoom on the class satisfaction and achievement of the students taking an online real - time TOEIC class
Authors:   British And American Language And Literature Association Of Korea, Park Sungbae
Published: April 2023

This study investigated which platform had a more positive effect on the learners' class achievement when the metaverse platform Gather Town and the video conference platform Zoom were used for online real - time lectures, and whether there was a difference in the degree of the learners' satisfaction with each platform. One hundred and thirty eight college students took a five - session online real - time class during the fall semester of 2021. The students were divided into the Gather Town group and the Zoom group. To investigate the class achievement, each group took a vocabulary test and a comprehension quiz during the class. The scores of the Gather Town group were significantly higher than those of the Zoom group in the test and quiz. In addition, a survey was conducted to investigate the learners' satisfaction with each platform. The Gather Town group cited promoting students' interest in class and interaction as the merit of the platform, and connection problems as the demerit. The Zoom group cited convenience as the merit of the platform, and a decrease in class concentration as the demerit.
William Thackeray's Vanity Fair: the Possibility as the Public Sphere
Authors:   British And American Language And Literature Association Of Korea, Lim Hyeon - ah
Published: April 2023

This article examines the public sphere embodied in Vanity Fair. This article is based on Habermas's idea upon the public sphere in terms of the historical consideration. In his Structural Transformations of the Public Sphere, Habermas, by sketching the political, economical and historical context surrounding the public sphere, shows how the bourgeois public sphere was formed and collapsed. Especially, the readers of the novels, newspapers and journals are called the reading public. Based on the public sphere, the public sphere in the world of letters made up of the reading public was organized. In Thackeray's Vanity Fair, the narrator uses the double - discourse or the double voice. While one of the double narration is considered commonly predominant notion those days, the other can be regarded as suggesting a new point of view about the social, political and moral issues. By reference to those facts, this article tries to reveal the possibility to the formation of the public sphere in the text of Vanity Fair.
Storytelling in Development: universal technologies and current contexts
Authors: Chumikov A. N. , Chumikova S. Yu.
Published: April 2023

The article is devoted to storytelling technology, which involves the production and promotion of optimized stories in the interests of companies and their products. The authors explore the dynamic development of this area of science and practice from structuring universal technologies to focusing on certain models of construction and actual application of these technologies in various socio-economic contexts. As a special perspective, the authors consider the interrelated concepts of “storytelling” and “conflict”, which perform the task of conflict management in order to obtain a positive effect. It is shown how the conflict component of the story simultaneously serves to make it more attractive and to promote the conflict towards its constructive solution. The practice of creating immersive stories that arise spontaneously or in an organized manner among consumers and the media, as well as the reaction of companies to them in the form of creating counterstories, is demonstrated. The experience of storytelling of three major corporations is analyzed: Procter & Gamble, Philip Morris and McDonald’s.
The Russian Civilization Code in the Context of Understanding the Pillars of Designing the Landscape of Cultural Policy
Authors: Stetsenko V. V.
Published: April 2023

The relevance of the article is predetermined by the need to protect and translate the Russian civilizational code in the new geopolitical reality. The methodological basis of the study was the methods of content analysis of the works of leading researchers, as well as discourse analysis of publications in the media and on information resources. The author focuses on culture as one of the foundations for the preservation and transmission of the Russian civilizational code. In this regard, the researcher refers to such categories as cultural sovereignty and cultural immunity. The article analyzes the legal sources, the priority of which is the preservation of the Russian civilizational code. Based on the analysis of the space of social communications, the author draws attention to public support for legislative initiatives aimed at strengthening Russian cultural immunity, as well as protecting Russian cultural sovereignty. The article positions the importance of updating the ideological foundations of interaction between the state and society in the context of preserving the Russian civilizational code. Besides, the attention is drawn to the issues of the value-semantic foundation of the activities of the modern Russian political elite, which hinder the effectiveness of social communications. The researcher puts forward the thesis about the expediency of developing the educational direction of educational and upbringing activities, focused on the translation of the Russian civilizational code in the youth information environment.