bluuebunny/crossref_metadata_2025 huggingface dataset:
Energy Efficient Utilization toward Rural Biomass Waste by Straw Biogas Engineering
Authors: Wang Xiao , Zhao Dejie
Published: June 2022

In this article, we summarize and analyze the constraints and technical difficulties of biogas plants in clusters, storage and transportation yards, pretreatment yards, and anaerobic fields. Combined with Tyre’s own experience, several major techniques have been proposed. To guide and recommend the suitable technicians to decision makers, to acquire operation courses suitable for the general requirements of our biogas, and to promote the development of booth biogas seedlings in China, the use of environmental slam objects in a small and sufficient nonscale biogas program is formulated by the use of biogas funding alone. Current rating measures leverage the World Age Assessment (LCA) for the Integrated Booth Biogas Swing Type (typically 40,000 kWh) and Bio-Natural Gasoline Program (daily gasoline workload of 12,000 cubic curves). This is built upon the Environmental Impact Assessment and Analogy for Warships Both environmental stroke and sensitivity component analysis. These techniques include termination outlines, register analysis, dynamism drops for each performance stagecoach, and environmental issue calculations. According to the above summarization, when biogas dominates the family, the highest environmental emission becomes the biogas fermentation layer of the booth. Meanwhile, the most sympathetic substitute is the work cost of physical and chemical farting (mL g-1TS). The above two indicators denote the essence of biogas purification, and the choice of biogas purification technology. By evaluating the change in the impact of suppressing environmental emissions, the biogas substitute of scorpion charcoal has a remarkable improvement in the biogas equivalent of the charcoal-bright swing family. Comprehensive experimental results have demonstrated this conclusion.
Influence of Degree of Chinese Cultural Identity and Performance Expectation on Consumption Attitude for Health Care of Traditional Chinese Medicine
Authors: Lin Shengliang , Yan Yan , Hu Chenxi
Published: June 2022

There are some advantages of traditional Chinese medicine in anti-COVID-19, and more and more Chinese people are beginning to accept health care of Traditional Chinese Medicine (TCM). By selecting two important independent variables, degree of Chinese cultural identity and performance expectation, their influence mechanism on the consumption attitude of health care for TCM is studied. The results show that the two dimensions of degree of Chinese cultural identity significantly affect the consumption attitude of health care for TCM, and the surface recognition dimension negatively affects the behavioral attitude of health care for TCM. The deep behavioral dimension has a positive impact on the consumption attitude of health care for TCM. Performance expectation has a positive impact on the consumption attitude of health care for TCM. Chinese cultural identity and performance expectation have a positive interaction on the consumption attitude of health care for TCM.
Garden Landscape Design of Sponge City Residential Area Based on Digital Technology
Authors: Sun YunJuan , Jiang Fang
Published: June 2022

In order to improve the effect of garden landscape design in the residential area of sponge city, this paper combines digital technology to carry out the garden landscape design of the residential area of sponge city, and conducts the research on it through the intelligent system. Moreover, this paper studies the digital color processing algorithm and proposes a digital technology-based color processing algorithm for the garden landscape design of the residential area of the sponge city. In addition, this paper builds a garden landscape design system for sponge city residential areas on the basis of algorithms. Through the simulation test research, we can see that the garden landscape design of sponge city residential area based on the digital technology proposed in this paper can meet the needs of the sponge city garden landscape design.
Meta-Analysis of the Correlation between Postoperative Cognitive Dysfunction and Intraoperative Cerebral Oxygen Saturation
Authors: Chen Na , Lu Jing
Published: June 2022

Objective. This study is aimed at performing a meta-analysis for discussing the association between postoperative cognitive dysfunction (POCD) and cerebral cortical oxygen saturation after surgery (rSO2). Method. Search common English databases such as Cochrane Library, PubMed, and Embase databases to evaluate the quality of all references. According to the normalized mean difference (SMD) and 95% confidence interval calculated by the revman5 software, the correlation between reported POCD and rSO2 was evaluated. The retrieval time is up to February 1, 2021. Results. A total of 7 randomized controlled trials and 564 POCD patients were included in the study, with follow-up duration of 1-12 months. All patients were divided into control and operation subgroups. In the subgroup analysis of elderly patients, abdominal surgery, and orthopedic surgery, the mean intraoperative cerebral oxygen saturation of patients with POCD was significantly lower than those of patients without POCD (

I

2

=
55
%

,

SMD
=
−
0.57

). Conclusion. Lower intraoperativersO2 was associated with reduced incidence of neurological complications and renal alure as well as the length of stay in the intensive care unit and the total hospital stay.
Application of Machine Learning in Enterprise Risk Management
Authors: Jia Dian , Wu Zhaoyang
Published: June 2022

With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.
Based on Data Mining and Big Data Intelligent System in Enterprise Cost Accounting Optimization Application
Authors: Wang Wenyan , Guo Jie
Published: June 2022

With the continuous development of science and technology, we have completely entered the information age. The amount of data around us increases linearly, and everyone lives in a pile of data. Enterprises also need to use big data technology in the process of operation and management to mine data and realize various data management functions. For the current enterprise, the operation of data will not only affect the management cost of the enterprise but also affect the future development of the enterprise. Based on the problem of enterprise management cost, this paper proposes to effectively use big data technology to solve the problem of enterprise cost management. The article also uses big data technology to optimize the management cost of the enterprise and uses big data technology to innovate and apply the management method of the enterprise. This paper constructs a cost management model based on data mining and expounds on the objects, sources, and calculation methods of data mining. Under this model, the company’s data was mined, a specific plan was proposed, and an improved association algorithm was proposed to test the completion and consolidation of tasks. In the case of a large number of tasks, in order to efficiently realize the selection and merging of data, this paper proposes a new cost forecasting scheme based on a fuzzy model. Use time series for cost forecasting, and the accuracy is relatively high. In the process of forecasting, our forecasting method is suitable for the human reasoning process and can have better adaptability. Finally, we applied the research results of the thesis to actual business management to meet the management needs of the business, thus verifying the feasibility of the method proposed in this article.
Association between Genetic Polymorphisms in Methylenetetrahydrofolate Reductase and Risk of Autoimmune Diseases: A Systematic Review and Meta-Analysis
Authors: Lu Mao , Peng Ke , Song Li , Luo Li , Liang Peng , Liang Yundan
Published: June 2022

Methylenetetrahydrofolate reductase (MTHFR) is a critical rate-limiting enzyme in the homocysteine/methionine metabolism pathway that is implicated in the pathogenesis and progression of autoimmune diseases. Previous association studies have been performed to investigate the effect of polymorphisms in MTHFR on the risk of autoimmune diseases with inconsistent results. Therefore, this meta-analysis was designed to assess the association between the MTHFR 677 C/T and 1298 A/C polymorphisms and the susceptibility to autoimmune diseases. We identified reports by a literature search in the following electronic databases: PubMed, Ovid, Web of science, and China National Knowledge Infrastructure. Statistical analyses of the summary odds ratios (ORs) and 95% confidence intervals (CIs) were done using STATA software. In a recessive genetic model, the MTHFR 677 C/T polymorphism was associated with an increased risk of Behcet’s disease (

OR
=
1.97

, 95% CI, 1.31-2.97), multiple sclerosis (

OR
=
1.57

, 95% CI, 1.03-2.38), and ankylosing spondylitis (

OR
=
2.90

, 95% CI, 1.92-4.38). The MTHFR 1298 A/C polymorphism was associated an increased risk of multiple sclerosis in a heterozygote comparison (

OR
=
2.36

, 95% CI, 1.29-4.30) and in a dominant model (

OR
=
2.31

, 95% CI, 1.24-4.29). This meta-analysis demonstrated that the MTHFR 677 C/T was a risk factor for Behcet’s disease, multiple sclerosis, and ankylosing spondylitis, and the 1298 A/C was a risk factor for multiple sclerosis.
Crosslinking vs. Observation in Fellow Eyes of Keratoconus Patients
Authors: Li Gavin , Di Meglio Laura , Wang Jiangxia , Woreta Fasika A. , Bower Kraig S. , Jhanji Vishal , Srikumaran Divya , Soiberman Uri S.
Published: June 2022

Purpose. To evaluate whether unilateral crosslinking (CXL) and conservative follow-up of the fellow eye is an acceptable management strategy in patients with keratoconus (KC). Methods. Seventy-nine fellow eyes of KC subjects that initially underwent unilateral CXL were included. Thirty fellow eyes ultimately received CXL (group 1) whereas 49 fellow eyes were followed (group 2). Best spectacle corrected visual acuity (BSCVA) and corneal tomographic parameters were collected in all eyes preoperatively and at the last follow-up. Results. Subjects who received CXL in the fellow eye (group 1) were younger than subjects who did not (group 2,

p
=
0.026

). Group 1 eyes had higher baseline K1 (

p
=
0.026

), K2 (

p
=
0.006

), Km (

p
=
0.01

), and Kmax (

p
=
0.002

) compared to group 2 eyes. Amongst the 49 naïve fellow eyes (group 2), 19 eyes showed evidence of progression. Progressing naïve eyes had higher baseline K1, K2, Km, and Kmax (

p
<
0.01

); progressors also had thinner pachymetry at the pupil, apex, and thinnest point (

p
<
0.01

). Baseline values of K1 ≥ 43.5 Diopter (D), K2 > 45.1D, Km > 44.3D, Kmax > 47.9D, astigmatism > 1.4D, pachymetry at the pupil <475 μm, and thinnest pachymetry <478 μm were tentative predictors of progression in the naïve fellow eye. Conclusions. Unilateral CXL with vigilant follow up of the fellow eye may be an acceptable management strategy in a subset of KC eyes.
Metabolomic Analysis of Key Regulatory Metabolites in the Urine of Flavivirus-Infected Mice
Authors: Zheng Xiaoyan , Wang Ran
Published: June 2022

Objective. Dengue virus (DENV), Japanese encephalitis virus (JEV), and Zika virus (ZIKV) are several important flaviviruses, and infections caused by these flaviviruses remain worldwide health problems. Different flaviviruses exhibit different biological characteristics and pathogenicity. Metabolomics is an emerging research perspective to uncover and observe the pathogenesis of certain infections. Methods. To improve the understanding of the specific metabolic changes that occur during infection with different flaviviruses, considering the principle of noninvasive sampling, this article describes our comprehensive analysis of metabolites in urine samples from the three kinds of flavivirus-infected mice using a liquid chromatography tandem mass spectrometry method to better understand their infection mechanisms. Results. The urine of DENV-, JEV-, and ZIKV-infected mice had 68, 64, and 47 different differential metabolites, respectively, compared with the urine of control mice. Among the metabolic pathways designed by these metabolites, ABC transporters, arginine and proline metabolism, and regulation of lipolysis play an important role. Furthermore, we predicted and fitted potential relationships between metabolites and pathways. Conclusions. These virus-specific altered metabolites may be associated with their unique biological properties and pathogenicity. The metabolomic analysis of urine is very important for the analysis of flavivirus infection.
Models and Molecular Markers of Spermatogonial Stem Cells in Vertebrates: To Find Models in Nonmammals
Authors: Song Hyuk , Park Hyun-Jung , Lee Won-Young , Lee Kyung Hoon
Published: June 2022

Spermatogonial stem cells (SSCs) are the germline stem cells that are essential for the maintenance of spermatogenesis in the testis. However, it has not been sufficiently understood in amphibians, reptiles, and fish because numerous studies have been focused mainly on mammals. The aim of this review is to discuss scientific ways to elucidate SSC models of nonmammals in the context of the evolution of testicular organization since rodent SSC models. To further understand the SSC models in nonmammals, we point out common markers of an SSC pool (undifferentiated spermatogonia) in various types of testes where the kinetics of the SSC pool appears. This review includes the knowledge of (1) common molecular markers of vertebrate type A spermatogonia including putative SSC markers, (2) localization of the markers on the spermatogonia that have been reported in previous studies, (3) highlighting the most common markers in vertebrates, and (4) suggesting ways of finding SSC models in nonmammals.
Correlation-Based Anomaly Detection Method for Multi-sensor System
Authors: Li Han , Wang Xinyu , Yang Zhongguo , Ali Sikandar , Tong Ning , Baseer Samad
Published: June 2022

In industry, sensor-based monitoring of equipment or environment has become a necessity. Instead of using a single sensor, multi-sensor system is used to fully detect abnormalities in complex scenarios. Recently, physical models, signal processing technology, and various machine learning models have improved the performance. However, these methods either do not consider the potential correlation between features or do not take advantage of the sequential changes of correlation while constructing an anomaly detection model. This paper firstly analyzes the correlation characteristic of a multi-sensor system, which shows a lot of clues to the anomaly/fault propagation. Then, a multi-sensor anomaly detection method, which finds and uses the correlation between features contained in the multidimensional time-series data, is proposed. The method converts the multidimensional time-series data into temporal correlation graphs according to time window. By transforming time-series data into graph structure, the task of anomaly detection is considered as a graph classification problem. Moreover, based on the stability and dynamics of the correlation between features, a structure-sensitive graph neural network is used to establish the anomaly detection model, which is used to discover anomalies from multi-sensor system. Experiments on three real-world industrial multi-sensor systems with anomalies indicate that the method obtained better performance than baseline methods, with the mean value of F1 score reaching more than 0.90 and the mean value of AUC score reaching more than 0.95. That is, the method can effectively detect anomalies of multidimensional time series.
Exploring the Effect and Mechanism of Si-Miao-Yong-An Decoction on Abdominal Aortic Aneurysm Based on Mice Experiment and Bioinformatics Analysis
Authors: Xu Zhenyu , Zhang Lulu , Huangfu Ning , Jiang Fengchun , Ji Kangting , Wang Shenghuang
Published: June 2022

Background. Abdominal aortic aneurysm (AAA) is a fatal disease characterized by high morbidity and mortality in old population. Globally, effective drugs for AAA are still limited. Si-Miao-Yong-An decoction (SMYAD), a traditional Chinese medicine (TCM) formula with a high medical value, was reported to be successfully used in an old AAA patient. Thus, we reason that SMYAD may serve as a potential anti-AAA regime. Objective. The exact effects and detailed mechanisms of SMYAD on AAA were explored by using the experimental study and bioinformatics analysis. Methods. Firstly, C57BL/6N mice induced by Bap and Ang II were utilized to reproduce the AAA model, and the effects of SMYAD were systematically assessed according to histology, immunohistochemistry, and enzyme-linked immunosorbent assay (ELISA). Then, network pharmacology was applied to identify the biological processes, pathways, and hub targets of SMYAD against AAA; moreover, molecular docking was utilized to identify the binding ability and action targets. Results. In an animal experiment, SMYAD was found to effectively alleviate the degree of pathological expansion of abdominal aorta and reduce the incidence of Bap/Ang II-induced AAA, along with reducing the damage to elastic lamella, attenuating infiltration of macrophage, and lowering the circulating IL-6 level corresponding to the animal study, and network pharmacology revealed the detailed mechanisms of SMYAD on AAA that were related to pathways of inflammatory response, defense response, apoptotic, cell migration and adhesion, and reactive oxygen species metabolic process. Then, seven targets, IL-6, TNF, HSP90AA1, RELA, PTGS2, ESR1, and MMP9, were identified as hub targets of SMYAD against AAA. Furthermore, molecular docking verification revealed that the active compounds of SMYAD had good binding ability and clear binding site with core targets related to AAA formation. Conclusion. SMYAD can suppress AAA development through multicompound, multitarget, and multipathway, which provides a research direction for further study.
Application of Scanning Magnetic Resonance Imaging in the Diagnosis of Prenatal Placental Implantation and Related Care
Authors: Lin Qiuping , Li Bizhu , Chen Shuyun , Lin Cairu , Lin Zhixia , Zhang Fengjiao , Luo Xiaojiao , Chen Yulin , Wu Biyu
Published: June 2022

In order to solve the problem of scanning magnetic resonance imaging in prenatal diagnosis, the application and research of placental implantation have been proposed. Placental implantation is a serious obstetric emergency, which refers to the abnormal attachment of placental villi caused by the dysplasia of decidual basal layer. A study from the United States showed that the incidence of placental implantation in pregnant women during delivery increased from 9.9/30000 to 11.6/20000 from 2006 to 2019, which increased the risk of prenatal or postpartum hemorrhage, hysterectomy, stillbirth, abdominal organ injury, and so on. Clinically, patients can show severe prenatal or postpartum hemorrhage, postpartum placental retention, uterine perforation, and secondary infection, which may seriously endanger the lives of pregnant mothers and fetuses. Placental implantation can also have no obvious symptoms before delivery, which leads to insufficient prenatal diagnosis. Gielchinsky retrospectively studied 410 patients with placental implantation and found that only 9 patients were detected by prenatal ultrasound or magnetic resonance imaging (MRI), and the detection rate was only 6.6%. It can be seen that if the accurate diagnosis of placental implantation can be made before prenatal or symptoms appear, clinical intervention treatment can be carried out in time to reduce the probability of hysterectomy and improve the examination means of patients’ prerecovery. At present, studies at home and abroad suggest that it has good clinical application value and research prospect in the clinical diagnosis of placental implantation.
Integrative Analysis of Pyroptosis‐Related Prognostic Signature and Immunological Infiltration in Lung Squamous Cell Carcinoma
Authors: Luo Pengfei , Jiang Yongjun , Ding Sijuan , Jiang Shaohui , Tang Ruoting , Tang Zhaohui
Published: June 2022

Background. Lung cancer is one of leading causes of human health threatening with approximately 2.09 million initially diagnosed cases and 1.76 million deaths worldwide annually. Pyroptosis is a programmed cell death mediated by Gasdermin family proteins. Pyroptosis could suppress the tumor oncogenesis and progression; nevertheless, pyroptosis could promote tumor growth by forming a suitable microenvironment. Methods. LASSO Cox regression analysis was performed to construct prognostic pyroptosis‐related gene (PRG) signature. A ceRNA was constructed to explore the potential lncRNA‐miRNA‐mRNA regulatory axis in LUSC. Results. The expression of 26 PRGs were increased or decreased in LUSC. We also summarized simple nucleotide variation and copy number variation landscape of PRGs in LUSC. Prognosis analysis suggested a poor overall survival rate in LUSC patients with high expression of IL6, IL1B, ELANE, and CASP6. A pyroptosis‐related prognostic signature was developed based on four prognostic PRGs. High‐risk score LUSC patients had a poor overall survival rate versus low‐risk score patients with an AUC of 0.565, 0.641, and 0.619 in 1‐year, 3‐year, and 5‐year ROC curves, respectively. Moreover, the risk score was correlated with immune infiltration in LUSC. Further analysis revealed that pyroptosis‐related prognostic signature was correlated with immune cell infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also constructed a ceRNA network and identified a lncRNA KCNQ1OT1/miR‐328‐3p/IL1B regulatory axis for LUSC. Conclusion. A bioinformatics method was performed to develop a pyroptosis‐related prognostic signature containing four genes (IL6, IL1B, ELANE, and CASP4) in LUSC. We also constructed a ceRNA network and identified a lncRNA KCNQ1OT1/miR‐328‐3p/IL1B regulatory axis for LUSC. Further in vivo and in vitro studies should be conducted to verify these results.
An Optimized Digital Image Processing Algorithm for Digital Oil Painting
Authors: Tang Jinyu
Published: June 2022

Human civilization’s accomplishments have grown with the passage of time and the advancement of society. With the fast growth of computer networks and information technology, the conventional method of information transmission based on words cannot fulfill the demands of people in the current era. As a result, in this age of extensive information and image processing techniques, images as a means of information sharing are becoming increasingly popular. As we know, digital image processing knowledge has a far-reaching impact in the field of artistic creation, among which the creation of oil painting is facing severe challenges. Aiming at the problem that the effect of digital oil painting is not ideal, this paper aims to study digital oil painting by using digital image processing technology. This paper first uses the image edge recognition based on the improved Canny algorithm to detect the edge of the oil painting image, then uses the nonlinear image enhancement algorithm to enhance the effect of the oil painting image, then uses the improved genetic algorithm to segment the image, and finally enlarges the oil painting image to calibrate the color of the oil painting image. Experiments reveal that the proposed approach outperforms existing algorithms in terms of edge detection data integrity, high-quality coefficient index of image enhancement, picture segmentation running time, and the ability to successfully increase the visual effect of oil painting.
Molecular Detection and Glycoprotein B (UL55) Genotyping of Cytomegalovirus among Sudanese Renal Transplant Recipients
Authors: Ahmed Hind Haidar , Altyab Hisham N. , Saeed Samar M. , Abdalla Wafaa Mohammed , Omer Alfadil Elobeid
Published: June 2022

Background. Cytomegalovirus (CMV) is the most common opportunistic pathogen among renal transplants with significant morbidity and mortality. This study was designed to detect CMV DNA and to determine the frequency of different glycoprotein B (UL55) genotypes among Sudanese renal transplant recipients. Methods. One hundred and four renal transplant recipients were included in this study. A blood specimen was collected from each recipient. DNA was extracted from plasma using the QIAamp DNA mini kit. CMV amplification and quantification were performed using CMV Real‐RT Quant kits. Genotyping of human CMV gB was carried out by nested PCR and sequencing of the highly diverse region of gB. Results. CMV DNA was detected in 40/104 (38.5%) of renal transplant recipients. The average of the CMV DNA viral load was 358 × 104 copies/ml (6.5 log10) ranging from 62 copies/ml (1.8 log10) to 1.43 × 108 copies/ml (9 log10). CMV viremia was detected in 60% of recipients of less than 1–12 months, 17% of 13–24, 10% of 25–36, 5% of 37–48, and 8% in more than 48 months posttransplantation with no association (p = 0.296) between CMV viremia and postrenal transplantation time. The association between the type of immunosuppressive drugs and high viral loads (>1000 copies/ml) showed a significant difference (p = 0.05). The association between CMV loads of >1000 copies/ml and symptoms of CMV disease was highly significant (p ≤ 0.001). Fever 7 (41%), fever and leucopenia 6 (35%), and gastrointestinal disease 4 (24%) were the most common symptoms of CMV disease. CMV genotyping revealed 8 cases (80%) for gB3 and 2 cases (20%) for gB4 genotypes. The most frequent genotype among Sudanese renal transplant recipients was gB3. Conclusions. The frequency of CMV DNA is high among Sudanese renal transplant recipients. CMV gB3 is the most predominant glycoprotein B genotype in Sudanese renal transplant recipients.
A New Method for Inverter Diagnosis of Electric Locomotive Using Adversarial Neural Networks
Authors: Shi Yingchun , Chen Chunyang , Luo Yu
Published: June 2022

In order to improve the fault diagnosis accuracy of the electric locomotive inverter, this article combines the adversarial neural network to construct the electric locomotive inverter diagnosis system. Moreover, at the data level, this article compares and analyzes three methods of data expansion based on single-sample processing, data expansion based on image front and background separation, and data expansion based on an adversarial neural network. In addition, this article adopts a new feature extractor and increases the penalty cost of small samples being misclassified. Finally, this article uses the LBP operator to extract the image texture features to distinguish and detect the different shapes of the rotor windings and build an intelligent system to verify the effect of the proposed system model. The experimental research shows that the inverter diagnosis system for electric locomotives based on the proposed adversarial neural network has a good practical effect.
Imaging Adipose Tissue Browning using Mitochondrial Complex‐I Tracer [18F]BCPP‐EF
Authors: Goggi Julian L. , Hartimath Siddesh , Khanapur Shivashankar , Ramasamy Boominathan , Tang Jun Rong , Cheng Peter , Barron Anna M. , Tsukada Hideo , Robins Edward G.
Published: June 2022

Browning of white adipose tissue (WAT) into beige adipocytes has been proposed as a strategy to tackle the ongoing obesity epidemic. Thermogenic stimuli have been investigated with the aim of converting existing white adipose tissue, primarily used for energy storage, into beige adipocytes capable of dissipating energy; however, evaluation is complicated by the dearth of noninvasive methodologies to quantify de novo beige adipocytes in WAT. Imaging with [18F]FDG is commonly used to measure brown adipose tissue (BAT) and beige adipocytes but the relationship between beige adipocytes, thermogenesis and [18F]FDG uptake is unclear. [18F]BCPP‐EF, a tracer for mitochondrial complex‐I (MC‐I), acts as a marker of oxidative metabolism and may be useful for the detection of newly formed beige adipocytes. Mice received doses of the β3‐adrenergic agonist CL‐316,243 subchronically for 7 days to induce formation of beige adipocytes in inguinal white fat. PET imaging was performed longitudinally with both [18F]FDG (a marker of glycolysis) and [18F]BCPP‐EF (an MC‐I marker) to assess the effect of thermogenic stimulation on uptake in browning inguinal WAT and interscapular BAT. Treatment with CL‐316,243 led to significant increases in both [18F]FDG and [18F]BCPP‐EF in inguinal WAT. The uptake of [18F]BCPP‐EF in inguinal WAT was significantly increased above control levels after 3 days of stimulation, whereas [18F]FDG only showed a significant increase after 7 days. The uptake of [18F]BCPP‐EF in newly formed beige adipocytes was blocked by pretreatment with an adrenoceptor antagonist suggesting that beige adipocyte formation may be associated with the activation of MC‐I. However, in BAT, uptake of [18F]BCPP‐EF was unaffected by β3‐adrenergic stimulation, potentially due to the high expression of MC‐I. [18F]BCPP‐EF can detect newly formed beige adipocytes in WAT generated after subchronic treatment with the β3‐adrenergic agonist CL‐316,243 and displays both higher inguinal WAT uptake and earlier detection than [18F]FDG. The MC‐I tracer may be a useful tool in the evaluation of new therapeutic strategies targeting metabolic adipose tissues to tackle obesity and metabolic diseases.
Deep Learning Model on Energy Management in Grid-Connected Solar Systems
Authors: Nayagam V. Senthil , Jyothi A. P. , Abirami P. , Femila Roseline J. , Sudhakar M. , Al-Ammar Essam A. , Wabaidur Saikh Mohammad , Hoda N. , Sisay Asefa
Published: June 2022

Because of increased electricity consumption and the inherent limitations of fossil fuel ability to replenish themselves in the future, a shift to renewable energy sources is unavoidable. Although renewable energy sources are afflicted by intermittency, this problem can be alleviated by combining them with other sources of electricity. As a result of the above situation, the secondary source will take over if the primary source is unable to match the load demand. In this paper, we develop a hybrid renewable source that is connected with grids in an optimal way for the prediction of energy using an energy management system (EMS). The study is aimed at optimal handling of energy production, grid interaction, and the storage system, all of which must be accomplished simultaneously. The current state information from the battery, as well as control objectives, is used in this study to design control actions that maximise the amount of electricity injected into the grid. During the prediction window, it is assumed that the control inputs received at the start of the window will remain consistent throughout the duration of the window. The results of RMSE show errors lesser than 0.3% that shows improved rate of accuracy using EMS.
Small Object Detection Network Based on Feature Information Enhancement
Authors: Luo Huilan , Wang Pei , Chen Hongkun , Kowelo Vladimir Peter
Published: June 2022

Due to the small size and weak characteristics of small objects, the performance of existing object detection algorithms for small objects is not ideal. In this paper, we propose a small object detection network based on feature information enhancement to improve the detection effect of small objects. In our method, two key modules, information enhancement module and dense atrous convolution module, are proposed to enhance the expression and discrimination ability of feature information. The detection accuracy of this method on PASCAL VOC, MS COCO, and UCAS-AOD data sets is 81.3%, 34.8%, and 87.0%, respectively. In addition, the detection results of this paper in detecting small objects are slightly (0.2% and 0.1%) higher than the current advanced algorithms (YOLOv4 and DETR, respectively). Moreover, when these two modules are integrated into other algorithms, such as RFBNet, it can also bring considerable improvement.
Inadvertent Transfer of Murine VL30 Retrotransposons to CAR-T Cells
Authors: Lee Sung Hyun , Hao Yajing , Gui Tong , Dotti Gianpietro , Savoldo Barbara , Zou Fei , Kafri Tal
Published: June 2022

For more than a decade, genetically engineered autologous T-cells have been successfully employed as immunotherapy drugs for patients with incurable blood cancers. The active components in some of these game-changing medicines are autologous T-cells that express viral vector-delivered chimeric antigen receptors (CARs), which specifically target proteins that are preferentially expressed on cancer cells. Some of these therapeutic CAR expressing T-cells (CAR-Ts) are engineered via transduction with

γ

-retroviral vectors (

γ

-RVVs) produced in a stable producer cell line that was derived from murine PG13 packaging cells (ATCC CRL-10686). Earlier studies reported on the copackaging of murine virus-like 30S RNA (VL30) genomes with

γ

-retroviral vectors generated in murine stable packaging cells. In an earlier study, VL30 mRNA was found to enhance the metastatic potential of human melanoma cells. These findings raise biosafety concerns regarding the possibility that therapeutic CAR-Ts have been inadvertently contaminated with potentially oncogenic VL30 retrotransposons. In this study, we demonstrated the presence of infectious VL30 particles in PG13 cell-conditioned media and observed the ability of these particles to deliver transcriptionally active VL30 genomes to human cells. Notably, VL30 genomes packaged by HIV-1-based vector particles transduced naïve human cells in culture. Furthermore, we detected the transfer and expression of VL30 genomes in clinical-grade CAR-T cells generated by transduction with PG13 cell-derived

γ

-retroviral vectors. Our findings raise biosafety concerns regarding the use of murine packaging cell lines in ongoing clinical applications.
Image Enhancement Method Based on Deep Learning
Authors: Zhang Peipei
Published: June 2022

Image enhancement and reconstruction are the basic processing steps of many real vision systems. Their purpose is to improve the visual quality of images and provide reliable information for subsequent visual decision-making. In this paper, convolution neural network, residual neural network, and generative countermeasure network are studied. A rain fog image enhancement generative countermeasure network model structure including a scalable auxiliary generation network is proposed. The objective loss function is defined, and the periodic consistency loss and periodic perceptual consistency loss analysis are introduced. The core problem of image layering is discussed, and a layering solution framework with a deep expansion structure is proposed. This method realizes multitasking through adaptive feature learning, which has a good theoretical guarantee. This paper can not only bring a pleasant visual experience to viewers but also help to improve the performance of computer vision applications. Through image enhancement technology, the quality of low illumination image can be effectively improved, so that the image has better definition, richer texture details, and lower image noise.
Effect of Traditional Chinese Medicine on Allergic Rhinitis in Children under Data Mining
Authors: Shen Min , Liu Jianghai , Wang Kuanhong
Published: June 2022

The data mining analysis of the medication rule and the curative effect of traditional Chinese medicine in treating allergic rhinitis in children was performed by using the association rule Apriori algorithm. The model of interest degree was introduced to improve the Apriori algorithm, and the performance difference of the algorithm before and after improvement was analyzed. Traditional Chinese medicine prescriptions for the treatment of allergic rhinitis in children were selected from the dictionary of Chinese medicine formulations. The frequency, frequent itemsets, and the improved Apriori algorithm of each prescription were analyzed comprehensively. The results showed that both the execution time of the improved Apriori algorithm and the number of mining association rules were signally lower. 102 Chinese herbal compounds were selected, in which the occurrence frequency of Flos magnoliae was the highest (67 times, 5.33%). The occurrence frequency of diaphoretic drugs was the highest (412 times, 32.78%) in drug types. The occurrence frequency of Yu Ping Feng powder was the highest (21 times, 20.59%) in the Chinese herbal compound. After the association rule analysis of the improved Apriori algorithm, Perilla frutescens, Saposhnikovia divaricata, ginseng, Notopterygium root, and Astragalus propinquus Schischkin were often mixed with liquorice, and Flos magnoliae were usually mixed with Fructus xanthii and black plum. Compared with the conditions before treatment, the sign scores of children with allergic rhinitis were remarkably decreased after treatment with traditional Chinese medicine compounds (P<0.05). The mining performance of the Apriori algorithm was improved by introducing an interest-based model. The treatment of traditional Chinese medicine on allergic rhinitis in children was combined with children’s physiological and pathological characteristics of children, which used mild medicines.
A Microservice Resilience Deployment Mechanism Based on Diversity
Authors: Yu Hang , Wang Xiulei , Xing Changyou , Xu Bo
Published: June 2022

The microservice architecture has many advantages, such as technology heterogeneity, isolation, scalability, simple deployment, and convenient optimization. These advantages are important as we can use diversity and redundancy to improve the resilience of software system. It is necessary to study the method of improving the resilience of software system by diversity implementation and redundant deployment of software core components based on microservice framework. How to optimize the diversity deployment of microservices is a key problem to maximize system resilience and make full use of resources. To solve this problem, an efficient microservice diversity deployment mechanism is proposed in this paper. Firstly, we creatively defined a load balancing indicator and a diversity indicator. Based on this, a diversified microservices deployment model is established to maximize the resilience and the resource utilization of the system. Secondly, combined with load balancing, a microservice deployment algorithm based on load balance and diversity is proposed, which reduces the service’s dependence on the underlying mirror by enriching diversity and avoids the overreliance of microservices on a single node through decentralized deployment, while taking into account load balancing. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. The results demonstrate that the proposed algorithm outperforms other algorithms.
DDoS Attack Detection by Hybrid Deep Learning Methodologies
Authors: Xinlong Li , Zhibin Chen
Published: June 2022

A Distributed Denial of Service (DDoS) attack occurs when large amounts of traffic from hundreds, thousands, or even millions of other computers are routed to a network or server to crash the system and disrupt its function. These attacks are commonly used to shut down websites or applications temporarily. Such problems often need to be addressed with models that can manage the time information contained in network traffic flows. In this work, we apply a Hybrid Deep Learning method to detect malicious web traffic in the form of DDoS attacks, controlling the web flow of information reaching a server, using any dependencies between the different elements of a data stream. An original and cutting-edge Hierarchical Temporal Memory (HTM) hybrid model has been proposed. The operation of this model is predicated primarily on the portion of the cerebral cortex known as the neocortex. The neocortex is in charge of various fundamental brain functions, including the perception of senses, the comprehension of language, and the control of movement. For the hybrid implementation to be capable of encoding time sequences that incorporate incoming data, a Long Short-Term Memory (LSTM) shell is added.
Building Information Modeling and Internet of Things Integration in the Construction Industry: A Scoping Study
Authors: Hashim Mohammed Baydaa , Sallehuddin Hasimi , Safie Nurhizam , Husairi Afifuddin , Abu Bakar Nur Azaliah , Yahya Farashazillah , Ali Ihsan , AbdelGhany Mohamed Shaymaa
Published: June 2022

Building Information Modeling (BIM) has emerged as a prospective technology used to advance the practices of construction projects. Also, Internet of Things (IoT), as a technology that connects sensing devices to share information across platforms, has become essential in building and construction environment. The integration of BIM‐IoT in the construction industry, a high‐risk industry, might increase overall performance and reduce related hazards. However, there is a dearth of studies on the integration of BIM and IoT in the construction industry. Scoping review of literature was performed using various databases such as IEEE Xplore, Science Direct, ACM, Emerald Insight, and Taylors & Francis databases to explore the study demographics, research direction, category, adoption, and performance of the BIM‐IoT integration for the construction industry. Out of 2270 articles identified, a total of 81 key and vital articles were found and collected in scoping review to formulate the research questions. The study results revealed that the literature related to BIM‐IoT integration and adoption is moderately steady, with constant output in the last four years. Twelve of the contributions were identified, and five were identified to be proposed more and conducted by researchers: investigation, evaluation, model, framework, and system. Also, fifteen (18.51%) studies were identified from the selected works that were evaluated using performance measurement. The findings shed light on some of the most significant difficulties in research related to BIM‐IoT integration in the construction industries as well as potential future initiatives.
Carbon Stock Potential across Different Land Covers in Tropical Ecosystems of Damota Natural Vegetation, Eastern Ethiopia
Authors: Hussein Abdulbasit
Published: June 2022

The research was carried out in Damota kebele, in the Oromia regional state of Ethiopia, to examine the carbon sequestration potentials under three land covers (i.e., farmland (FL); bushland (BL), and woodland (WL)). In the three land covers, 60 squares of 20 m × 20 m, 5 m × 5 m, and 1 m × 1 m with six horizontal transect lines were employed to gather data on tree, shrub, herbaceous, and soil, respectively. To estimate organic carbon percentage, soil parameters were collected from three soil profiles (i.e., 0–10 cm, 10–20 cm, and 20–30 cm). The results showed that MA had significantly higher above-ground carbon (AGC) with 67.9 ± 11.4 Mg ha−1, whereas BL had significantly higher below-ground carbon (BGC) stocks with 16.32 ± 5.5 Mg ha−1, compared to other gradients. However, FL had the lowest AGC (53.2 ± 4.5 Mg ha−1) and BGC (8.04 ± 2.9 Mg ha−1). FL exhibited a significantly higher SOC value than the other two land covers followed by WL. The BL had the lowest SOC value. SOC across the three soil profiles follows a reduction trend from topsoil depth to lower soil depth with significant variation. WL had relatively higher TC than the other gradients. But FL had the lowest TC stock. Due to a high amount of human and animal interference in FL, weak security, and law enforcement measures, it has low TC. In conclusion, FL should embrace the better ecological, policy, and socioeconomic considerations than the other land covers.
Design of a Wireless Sensor Network-Based Risk Assessment Algorithm for Cave Collapse
Authors: Pei Xiaoyang
Published: June 2022

Accidents frequently happen during tunnel and subway construction. This study defines the cave collapse types based on the investigation results of a cave collapse in Yanchang County, the precise scenario of the cave collapse, paired with Yan’an City. Flac3D software is used to simulate the slope before and after the excavation of the cave. The findings of comparing the stress and strain characteristics of the soil slope lead to the cave’s destruction. A comprehensive risk probability estimation method based on interval probability level of accident tree and Bayesian network, weight confidence index, and confidence interval is proposed to solve the problems of subjectivity and inaccurate results in tunnel risk assessment, and it is applied to the risk assessment of cave collapse. First, the Bayesian network was constructed by using accident tree, and the conditional probability (joint probability) of sensor nodes is obtained by using the dependence relationship between the case factors. Then, the piJ estimated value of expert J for the occurrence probability of the fundamental event XI was calculated using the interval probability grade division and weight confidence index approach. The sample space U and its statistics were constructed from the estimated values of all experts, and the probability range of the occurrence probability of the basic event Xi was obtained by introducing the confidence interval method. For risk inference, the probability range of all occurrences is derived and integrated with the conditional probability of a Bayesian network, ensuring the scientific correctness and precision of risk assessment. At the same time, the model can be used to determine the cause of an accident. According to our experimental results, cave collapse causes were investigated soon after an accident occurred, allowing for the development of risk management strategies to prevent future occurrences.