bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Influencing Factors of Sentinel Lymph Node Metastasis in Breast Cancer
Authors: Lyu Wei , Guo Yujuan , Peng Haiyan , Xie Nanyu , Gao Hongyi
Published: August 2022

Objective. To analyze and discuss the influencing factors of sentinel lymph node metastasis in breast cancer. Methods. A total of 469 breast cancer patients admitted in the Department of Pathology of Guangdong Women and Children Hospital from October 2016 to December 2021 were retrospectively analyzed. The general information, immunohistochemical expression, tumor molecular subtype, tumor size, histological grade, pathological type, and tumor location were collected and the relationship with sentinel lymph node metastasis was analyzed. Results. For patients with different age, Ki-67 and Human epidermal growth factor receptor-2 (HER-2) immunohistochemical expression level (invasive cancer), molecular subtype (invasive cancer), tumor size, histological grade (invasive cancer) and pathological type. The results of multivariate logistic regression analysis showed that the age was less than or equal to 40 years; the molecular subtype was Luminal B and HER-2 overexpression (invasive cancer); tumor was larger; the histological grade (invasive cancer) was higher; the pathological type was invasive carcinoma, there were independent risk factors for sentinel lymph node metastasis in breast cancer. The sentinel lymph node metastasis rates of invasive lobular carcinoma, invasive micropapillary carcinoma, and metaplastic carcinoma (all met the criteria for squamous cell carcinoma and histological grade III) were higher than 50% in special invasive carcinomas. Conclusion. Age, expression level of Ki 67 and HER-2, molecular typing, tumor volume and histological grade are all high-risk factors related to sentinel lymph node metastasis of breast cancer. When one or more of the above factors are involved in an examination, pathologists should be more cautious in making a sentinel lymph node frozen diagnosis. By standardizing the sampling and increasing the number of frozen sections (slicing more frozen tissue layers), the section quality can be improved. This may be conducive to reducing the false negative rate and reducing the pain and risk of secondary surgery.
Characterization of Boswellia rivae Engl Resin as a Potential Use for Pharmaceutical Excipient
Authors: Gashe Fanta , Assefa Desta , Tesema Shibiru , Zeleke Gemechu , Tatiparthi Ramanjireddy , Kebebe Dereje , Suleman Sultan
Published: August 2022

Pharmaceutical excipients derived from natural sources like resins are nowadays meritoriously used in the formulation of drugs. Resins of natural origin have many advantages over chemically synthesized substances; they are safer, nontoxic, less expensive, biodegradable, and widely available. To our knowledge, resins from plants have been not sufficiently explored for application in pharmaceutical formulations. Thus, in the present study, a resin isolated from Boswellia rivae Engl was characterized for its potential use as a pharmaceutical excipient. Method. The resin was extracted from the oleo gum resin of Boswellia rivae Engl, which involved the removal of volatile oils, gum, and Boswellic acid contents. The dried resin powder was then characterized for its micromeritic properties, heavy metal contents, moisture content, moisture absorption power, pH, solubility, swelling property, and acute toxicity profile. Moreover, the crystal nature and the chemical functionality of the resin were evaluated by using X‐ray diffraction and Fourier transform infrared spectrometry, respectively. Results. The yield of the neutral resin was 13.17%, and the powder was pale yellow and had irregular surfaces. The resin was freely soluble in organic solvents but almost insoluble in water. The moisture content of the dried extract was 2.5% while its moisture absorption capacity was 2.5%, 4%, and 5.47% at 40%, 60%, and 75% RH, respectively. Besides, the maximum swelling capacities of the resin observed were 40%, 37%, and 30% at 350C, 300C, and 250C, respectively. The bulk powder exhibited a 1.21 Hausner ratio, 36.497 angles of repose, and 17.03% Carr’s index, indicating the fair flowability of the powder. Heavy metals such as zinc, chromium, and cobalt were detected at a low level while elements like copper, manganese, lead, and cadmium were absent. The X‐ray diffraction study revealed that the crystallinity index of the powder was 42.7% with a crystal size of 994.5A. The Boswellia resin could be safe in mice up to 3 g/kg of their body weight. In conclusion, the physicochemical properties of the resin powder investigated reveal its potential application as pharmaceutical additives in the formulation of modified release solid dosages forms like tablets and microcapsules.
Big Data Analysis on the Effect of Cost Stickiness on Digital Transformation
Authors: Chen Diandian
Published: August 2022

The A-share listed companies in Shanghai and Shenzhen from 2012 to 2020 were taken as the research sample to study the impact of digital transformation on enterprise cost stickiness. Overall, digital transformation has a restraining effect on enterprise cost stickiness. This kind of inhibitory effect is more significant in the samples with strong asset specificity, high-environmental uncertainty, and high-management cost rate, indicating that enterprises have reduced adjustment costs through digital transformation, weakened the optimistic expectations of management, and reduced agency costs, thus curbing cost stickiness. Our research results provided a new understanding of how the implementation of digital transformation affects the cost stickiness of enterprises and can help to provide policy suggestions for promoting the supply side structural reform from the perspective of “cost reduction.”
Serological Evidence of Infectious Laryngotracheitis Infection and Associated Risk Factors in Chickens in Northwestern Ethiopia
Authors: Birhan Mastewal , Syoum Ashenafi , Ibrahim Saddam Mohammed , Fentahun Tewodros , Mohammed Addisu , Berhane Nega , Bitew Molalegne , Gelaye Esayas , Atanaw Malede Birhan , Getachew Belayneh , Dessalegn Bereket , Abat Anmaw Shite , Berrie Kassahun , Adamu Kassaye , Abayneh Takele
Published: August 2022

Infectious laryngotracheitis (ILT) is a disease of high economic consequence to the poultry sector. Gallid herpesvirus 1 (GaHV-1), a.k.a infectious laryngotracheitis virus (ILTV), under the genus Iltovirus, and the family Herpesviridae, is the agent responsible for the disease. Despite the clinical signs on the field suggestive of ILT, it has long been considered nonexistent and a disease of no concern in Ethiopia. A cross-sectional study was conducted from November 2020 to June 2021 in three selected zones of the Amhara region (Central Gondar, South Gondar, and West Gojjam zones), Ethiopia, with the objective of estimating the seroprevalence of ILTV in chickens and identifying and quantifying associated risk factors. A total of 768 serum samples were collected using multistage cluster sampling and assayed for anti-ILTV antibodies using indirect ELISA. A questionnaire survey was used to identify the potential risk factors. Of the 768 samples, 454 (59.1%, 95% CI: 0.56–0.63) tested positive for anti-ILTV antibodies. Mixed-effect logistic regression analysis of potential risk factors showed that local breeds of chicken were less likely to be seropositive than exotic breeds (OR: 0.38, 95% CI: 0.24–0.61). In addition, factors such as using local feed source (OR: 6.53, 95% CI: 1.77–24.04), rearing chickens extensively (OR: 1.97, 95% CI: 0.78–5.02), mixing of different batches of chicken (OR: 14.51, 95% CI: 3.35–62.77), careless disposal of litter (OR: 1.62, 95% CI: 0.49–4.37), lack of house disinfection (OR: 11.05, 95% CI: 4.09–47.95), lack of farm protective footwear and clothing (OR: 20.85, 95% CI: 5.40–80.45), and careless disposal of dead chicken bodies had all been associated with increased seropositivity to ILTV. Therefore, implementation of biosecurity measures is highly recommended to control and prevent the spread of ILTV. Furthermore, molecular confirmation and characterization of the virus from ILT suggestive cases should be considered to justify the use of ILT vaccines.
Impact of Carbon Information on Enterprise Value: Analysis Model Design Based on Big Data
Authors: Kong Cong , Zhao Yu
Published: August 2022

Investors pay more and more attention to the issue of climate change information disclosure of listed companies. Whether the company can effectively deal with the impact of climate change on its own operations and make reasonable information disclosure will undoubtedly affect the value of the enterprise. Aiming at this, an analysis model based on big data is designed for the impact of carbon information on enterprise value. This study selects the sample stocks of the Shanghai Stock Exchange responsibility index every year as the research sample, counts the carbon information through the social responsibility reports of listed companies, and uses the index method to score the carbon information of each company to measure the quality of carbon information disclosure. Firstly, it analyzes the situation of carbon information disclosure in China through descriptive statistics and then constructs relevant models to conduct an empirical analysis on the impact of carbon information disclosure on enterprise value.
Big Data Privacy Preservation Using Principal Component Analysis and Random Projection in Healthcare
Authors: Ratra Ritu , Gulia Preeti , Gill Nasib Singh , Chatterjee Jyotir Moy
Published: August 2022

With the rising usage of technology, a tremendous volume of data is being produced in the current scenario. This data contains a lot of personal data and may be given to third parties throughout the data mining process. Individual privacy is extremely difficult for the data owner to protect. Privacy-Preservation in Data Mining (PPDM) offers a solution to this problem. Encryption or anonymization have been recommended to preserve privacy in existing research. But encryption has high computing costs, and anonymization may drastically decrease the utility of data. This paper proposed a privacy-preserving strategy based on dimensionality reduction and feature selection. The proposed strategy is based on dimensionality reduction and feature selection that is difficult to reverse. The objective of this paper is to propose a perturbation-based privacy-preserving technique. Here, random projection and principal component analysis are utilized to alter the data. The main reason for this is that the dimension reduction combined with feature selection would cause the records to be perturbed more efficiently. The hybrid approach picks relevant features, decreases data dimensionality, and reduces training time, resulting in improved classification performance as measured by accuracy, kappa statistics, mean absolute error and other metrics. The proposed technique outperforms all other approaches in terms of classification accuracy increasing from 63.13 percent to 68.34 percent, proving its effectiveness in detecting cardiovascular illness. Even in its reduced form, the approach proposed here ensures that the dataset's classification accuracy is improved.
A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI
Authors: Haq Ejaz Ul , Jianjun Huang , Huarong Xu , Li Kang , Weng Lifen
Published: August 2022

Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model’s performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.
Value Assessment for a Theory-Oriented Flipped Classroom of Physical Education Based on Multi-Source Data Analysis
Authors: Li Na
Published: August 2022

In order to overcome the problem of students’ boredom in physical education theory classes in general, and to give full play to the role of assessment in guiding, diagnosing, motivating and proving, this paper analyses the multi-source data generated during the teaching process, which can predict students’ subsequent learning status. Based on the collection and processing of data from multiple sources, this study takes the PE course as an example and predicts the course performance based on multiple dimensions; and carries out empirical analysis with the actual course performance through numerical correlation, ranking correlation and the bottom band. The analysis was compared with actual course grades through numerical correlation, ranking correlation and bottom band warning coverage of students. The results of the study show that the earliest grade prediction using taught courses has the highest student warning coverage; the highest grade prediction based on unit tests has the highest numerical relevance and ranking relevance; and the highest grade prediction based on unit tests has the highest student warning coverage.
Assessment of Condylar Shape through Digital Panoramic Radiograph among Nepalese Population: A Proposal for Classification
Authors: Gupta Abhishek , Acharya Gaurav , Singh Harender , Poudyal Sijan , Redhu Anju , Shivhare Peeyush
Published: August 2022

Background. Panoramic radiograph is the first and most commonly advised radiograph for screening of temporomandibular joints/condyles. Different shapes of the mandible have been discussed by various authors with no consensus for a definite classification for condyle shape. This study was conducted with the objective to observe various shapes of condyles, symmetry of bilateral condyles, and variations of condyle shapes among males and females. Materials and Methods. This cross‐sectional study was conducted on digital panoramic radiographs available at a tertiary center of Lalitpur from 25.12.2020 to 20.06.2021 after ethical approval from the institutional review board (reference no. 077/078/27). Panoramic radiographs were selected on the basis of inclusion and exclusion criteria, and various shapes of condyles were noted on HP 15 inch flat LED monitor (1280 × 1024). The collected data was analyzed using SPSS (chi‐square test: a p value of < 0.05 was considered significant). Intra‐ and interobserver agreement was observed for condylar shapes. Results. Among the selected 850 panoramic radiographs (1700 condyles), most of them, i.e., 1343 (79%), were round/oval, followed by flattened, i.e., 149 (8.76%), diamond/angled, i.e., 93 (5.47%), crooked finger shaped, i.e., 28 (1.6%), and mixed, i.e., 46 (2.7%), and the least common shape observed was bifid, i.e., 40 (2.3%) (18 (2.1%) left condyle and 22 (2.6%) right condyle). Conclusions. Six different types of condyle shapes were noted: type I, oval; type II, flat; type III, diamond; type IV, mixed; type V, bifid; and type VI, crooked finger among the study population of Lalitpur.
Diminution of Smart Grid with Renewable Sources Using Support Vector Machines for Identification of Regression Losses in Large-Scale Systems
Authors: Teekaraman Yuvaraja , Kirpichnikova Irina , Manoharan Hariprasath , Kuppusamy Ramya , Angadi Ravi V. , Thelkar Amruth Ramesh
Published: August 2022

This article examines the effect of smart grid systems by implementing artificial intelligence (AI) technique with application of renewable energy sources (RES). The current state generation smart grid system follows a high demand on supply of equal energy load to all grid states. However, in conventional techniques, high demand is observed as manual operation is preformed and load problems are not solved within the stipulated time period due to lack of technological advancements. However, applications of AI in smart grid process reduces risk of operation as manual adjustments are converted to highly automated procedures. This type of automatic process identifies the fault location at stage 1 and diagnosis of identified faults will be processed at stage 2. The abovementioned two stage processes will be incorporated with two constant parameters as dummy load is produced to overcome high- to low-power flows. Additionally, a scrap model has been designed to reduce the wastage of power as 100 percent effective progress can be achieved for low- to high-power supplies. To detect the corresponding regression losses in the grid systems, support vector machine (SVM) which completely identifies the previous state loss in the system is integrated. Hence, to analyze the effectiveness of the SVM model, four different scenarios are evaluated and compared with heuristic algorithms, long short-term memory (LSTM), autoregressive indicated moving average (ARIMA), adaptive ARIMA, and linear regression models with distinct performance analysis that includes error in percentage values where a total efficiency of 81% is achieved for projected SVM in all power lines including large-scale systems as compared to existing approaches.
Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis
Authors: Bangare Sunil L. , Virmani Deepali , Karetla Girija Rani , Chaudhary Pankaj , Kaur Harveen , Bukhari Syed Nisar Hussain , Miah Shahajan
Published: August 2022

The term heart-related disease is stated as the range of condition that impacts an individual heart negatively. In the current scenario, cardiovascular diseases are causing more deaths when compared with other ailments, it has been estimated that there are nearly 18 million deaths annually as per the recent report released by World Health Organization (WHO). It has been stated that unhealthy habits and other related aspects adopted by individuals are considered as the primary reasons for an increase in the risk of heart diseases. High cholesterol, eating more junk foods, hypertension, etc., created the issue related to heart diseases. Hence, addressing food quality and suggesting better eating habits enable individuals to enhance their living and support better health. The application of new technologies like machine learning, deep learning, and other models support doctors, nurses, and radiologists to predict heart disease effectively. Studies have stated that the various models are used mainly for the classification and forecasting of the diagnosis of heart-related diseases. The researchers have identified that critical algorithms like CART support the predictability of the disease by 93.3% whereas the conventional models possess vert less specificity. Furthermore, deep neural networks can be applied for analyzing and detecting heart failures effectively and supporting medical practitioners in making better and more critical clinical decisions making. The researchers focus on using a descriptive research study for performing the study; moreover, the researcher collates the data using the questionnaire method, which enables sourcing the critical information from the medical practitioners and supports in making critical data analysis effectively. The researchers also use secondary data modes for sourcing the information related to past studies on the related topic. The researchers use the frequency analysis, correlation analysis, and structural equation model analysis for performing the study, and the results are stated in detail in the respective sections.
Effect of Cholesterol to Vitamin D3 and Span 60 to Tween 60 Ratios on the Characteristics of Niosomes: Variable Optimization Using Response Surface Methodology (RSM)
Authors: Mowlaeifar Mohammad Hossein , Niakousari Mehrdad , Hosseini Seyed Mohammad Hashem , Eskandari Mohammad Hadi
Published: August 2022

The main objective of this research was to evaluate the partial replacement of cholesterol (CSL) with vitamin D3 (VD3) on the niosome structure. The effects of different molar ratios of Span 60 (SP60) : Tween 60 (TW60) and CSL : VD3 were investigated on the physicochemical characteristics of niosomes, including particle size, span (distribution width), stability, and encapsulation efficiency of VD3. The data were then optimized using response surface methodology (RSM). Larger particles were obtained as the ratios of SP60 : TW60 and CSL : VD3 were increased. The smallest particles were obtained at SP60 : TW60 and CSL : VD3 ratios of 40.46 : 59.54 and 38.06 : 61.94, respectively. Increasing the ratio of SP60 : TW60 led to higher values of span. As CSL : VD3 ratio was increased from 0 : 100 to 67.67 : 32.32, value of the span was decreased; however, increasing this ratio further led to the increased value of the span. The lowest values of the span were observed at SP60 : TW60 and CSL : VD3 ratios of 67.90 : 32.10 and 72.41 : 27.59, respectively. The increase in the SP60 : TW60 ratio led to lower values of encapsulation efficiency. The highest values for encapsulation efficiency were observed at ratios of 31.76 : 68.24 and 40.02 : 59.98 for SP60 : TW60 and CSL : VD3, respectively. The highest stability was observed at SP60 : TW60 and CSL : VD3 ratios of 31.72 : 68.28 and 14.65 : 85.35, respectively. The optimum conditions were achieved at ratios of 31.72 : 68.28 and 49.37 : 50.63 for SP60 : TW60 and CSL : VD3, respectively. Finally, it can be concluded that VD3 is a suitable replacement for CSL in terms of stability and encapsulation efficiency of niosome.
Spatial and Temporal Variations in the Ecological Vulnerability of Northern China
Authors: Song Chunwei , Du Huishi
Published: August 2022

Ecological vulnerability is the focus of research on global environmental impact, regional sustainable development, ecological civilization, and green development. There are eight deserts and four sandy lands in northern China. The ecological environment is sensitive to climate change and human activities. It is of great significance to carry out long-term sequential ecological vulnerability assessments. Therefore, taking northern China as the research area, this paper selects 13 data indicators such as climate, topography, and soil based on the ecological sensitivity-ecological recovery-ecological pressure model (SPR) and uses the spatial principal component analysis method (SPCA) to quantitatively evaluate the spatial and temporal differentiation characteristics and driving forces of ecological vulnerability in this area from 1980 to 2020. The results showed that areas with extreme, severe, and moderate vulnerability dominated northern China, accounting for 74.58% of the total area. The analysis revealed a decrease in ecological vulnerability from west to east and north to south. Meanwhile, from the perspective of timing, the overall level of ecological vulnerability showed an upward trend before 2000, and the overall level of ecological vulnerability continued to decline after 2000, and the quality of the ecological environment improved. During the study period, areas in northern China with severe vulnerability and slight vulnerability showed a change of 15.53% and -14.01%, respectively. The main reason for the change in ecological vulnerability is the frequent transformation between forest land, grassland, water, and cultivated land. In addition, the study found a spatial autocorrelation of ecological vulnerability of northern China and a significantly positive correlation. After 2000, the spatial aggregation of vulnerability was high-high cluster, which was mainly distributed in northwest China. The study’s findings will provide a robust scientific basis for ecosystem management and sustainable development.
Performance Enhancement of Cluster-Based Ad Hoc On-Demand Distance Vector Routing in Vehicular Ad Hoc Networks
Authors: Phull Nitika , Singh Parminder , Shabaz Mohammad , Sammy F.
Published: August 2022

Recently, extensive research has focused on the design of clustering algorithms for grouping vehicles in a set of clusters in Vehicular Ad Hoc Networks (VANETs). However, due to the dynamic nature of VANETS, frequently connected or detached nodes threaten the stability of the network. When these nodes are clustered heads (CHs), the impact of these disruptions on network performance is even worse. Therefore, the stability of clusters is an important issue that should be maintained to enhance the performance of the network with minimum data loss. In this article, a novel clustering approach is proposed, which is based on the average weight concept of three main attributes, namely, the geostationary coordinates (x, y) and drop density. The average of the above-mentioned attributes is calculated and compared with the values of other vehicles’ attributes. Those vehicles having minimum average values are dropped and not to be considered in the cluster formation. The cluster members having minimum Euclidean distance are considered as Cluster Head (CH) until a certain checkpoint is not attained. The checkpoints are created in order to generate the social score of the vehicles. Once the social score is attained, a multiobjective framework is developed, which considers minimizing the Euclidian distance and maximizing the social score. The social score is generated based on the Quality of Service (QoS) attained attributes, and the data transmission is performed using Ad Hoc On-Demand Distance Vector (ADOV) routing protocol. A comparison of the proposed work and existing work has also been undertaken to demonstrate the improvement in the proposed work. As a result, throughput, PDR, network longevity, and energy consumption have been improved by 28.27%, 20.9%, 23.12%, and 27.48 percent, respectively. Extensive simulations show that the proposed scheme, unlike other popular clustering algorithms, can significantly increase the stability of the network by extending the life of the cluster head.
Rice Plants (Oryza sativa L.) under Cd Stress in Fe Deficiency Conditions
Authors: Atabayeva Saule D. , Rakhymgozhina Agilan B. , Nurmahanova Akmaral S. , Kenzhebayeva Saule S. , Usenbekov Bakdaulet N. , Alybayeva Ravilya A. , Asrandina Saltanat Sh. , Tynybekov Bekzat M. , Amirova Aigul K.
Published: August 2022

Due to the environment pollution by cadmium (Cd) near industrial metallurgic factories and the widespread use of phosphorus fertilizers, the problem of toxic Cd effect on plants is well discussed by many authors, but the phytotoxicity of Cd under iron (Fe) deficiency stress has not been sufficiently studied. The aim of the work was to study comprehensively the effect of Cd under Fe deficiency conditions on physiological, biochemical, and anatomical parameters of rice varieties, to identify varietal differences in plant response to the effect of double stress. Relative resistance and sensitivity to the joint effect of Cd and Fe deficiency stress rice varieties have been identified. Double stress decreased a linear growth and biomass accumulation of roots and shoots (by 36‐50% and 33‐46% and 32‐56% and 32‐48%, accordingly), content of photosynthetic pigments (Chla, Chlb, and carotenoids by 36‐51%, 32‐47%, and 64‐78%, accordingly), and relative water content (by 18‐26%). Proline content increased by 28‐103% in all rice varieties, but to a lesser extent in sensitive varieties. The thickness of the lower and upper epidermis and the diameter of vascular bundles of leaves decreased by 18‐50%, 46‐60%, and 13‐48%, accordingly. The thickness of the root endodermis and exodermis and diameter of the central cylinder mainly decreased. The thickness of the exodermis increased slightly by 7%, and the diameter of the central cylinder remained at the control level in resistant Madina variety while in sensitive Chapsari variety, these indicators decreased significantly by 50 and 45%, accordingly. Thus, the aggravation of adverse effect of Cd under Fe deficiency conditions and the varietal specificity of plants’ response to double stress were shown. It creates the need for further study of these rice varieties using Fe to identify mechanisms for reducing the toxic effect of Cd on plants as well as the study of Fe and Cd transporter genes at the molecular level.
A Fused Multidimensional EEG Classification Method Based on an Extreme Tree Feature Selection
Authors: Lin Ruijing , Dong Chaoyi , Ma Pengfei , Ma Shuang , Chen Xiaoyan , Liu Huanzi
Published: August 2022

When a brain-computer interface (BCI) is designed, high classification accuracy is difficult to obtain for motor imagery (MI) electroencephalogram (EEG) signals in view of their relatively low signal-to-noise ratio. In this paper, a fused multidimensional classification method based on extreme tree feature selection (FMCM-ETFS) is proposed for discerning motor imagery EEG tasks. First, the EEG signal was filtered by a Butterworth filter for preprocessing. Second, C3, C4, and CZ channels were selected to extract time-frequency domain and spatial domain features using autoregressive (AR), common spatial pattern (CSP), and discrete wavelet transform (DWT). The extracted features were fused for a further feature elimination. Then, the features were selected using three feature selection methods: recursive feature elimination (RFE), principal component analysis method (PCA), and extreme trees (ET). The selected feature vectors were classified using support vector machines (SVM). Finally, a total of twelve subjects’ EEG data from Inner Mongolia University of Technology (IMUT data), the 2nd BCI competition in 2003, and the 4th BCI competition in 2008 were employed to show the effectiveness of this proposed FMCM-ETFS method. The results show that the classification accuracy using the multidimensional fused feature extraction (AR + CSP + DWT) is 3%–20% higher than those using the aforementioned three single feature extractions (AR, CSP, and DWT). Extreme trees (ET), which is a sort of tree-based model method, outperforms RFE and PCA by 1%–9% in term of classification accuracies, when these three methods were applied to the procedure of feature extraction, respectively.
Estimation of Current Water Use over the Complex Topography of the Nile Basin Headwaters: The Case of Ghba Subbasin, Ethiopia
Authors: Hiben Mehari Gebreyohannes , Gebeyehu Awoke Admasu , Adugna Ashenafi Abraha
Published: August 2022

Quantifying the available and useable water is critical work in any water resource study and design project. However, it is challenging to provide a robust and accurate estimation of water use and distribution for better water resource management and planning. This study aims to estimate the water use by different sectors, including water supply and irrigation sectors, by adopting estimated demand and supply water quantity. The current total population of the subbasin has been estimated to be 1.21 million. Thus, in the subbasin, current water use is estimated as follows: domestic and nondomestic water use in the rural area is 3.5 Mm3/year and 0.174 Mm3/year, respectively. The domestic water use of the towns is 12.77 Mm3/year. The industrial water use of the urban areas is 21.2 Mm3/year, whereas the commercial, public, and institutional water use are 1.87 Mm3/year. The real loss for all the water supply uses is 7.8 Mm3/year. Thus, the total current water supply uses are about 47.225 Mm3/year. From the existing irrigation schemes, about 10,254.8 ha areas are irrigated by both smallholders and different investors, growing vegetables, cereals, and fruit trees. The annual irrigation water requirements of these schemes are computed to be 151.55 Mm3. Livestock water demand of the subbasin was assessed and estimated based on the population and consumption rates of the species. Currently, the subbasin has a total livestock population of 1,527,835, and the water demand of which is estimated at 5.3 Mm3 per annum. Hence, the total current water use estimate of the subbasin is 204.1 Mm3.
Possibility for Visualizing the Muscle Microstructure by q-Space Imaging Technique
Authors: Sera Yasushi , Nakashima Daisuke , Hata Junichi , Okano Hirotaka James , Sato Kazuki , Nakamura Masaya , Nagura Takeo
Published: August 2022

In the human body, skeletal muscle microstructures have been evaluated only by biopsy. Noninvasive examination of the microstructure of muscles would be useful for research and clinical practice in sports and musculoskeletal areas. The study is aimed at determining if q-space imaging (QSI) can reveal the microstructure of muscles in humans. Forty-three Japanese subjects (controls, distance runners, powerlifting athletes, and teenage runners) were included in this cross-sectional study. Magnetic resonance imaging of the lower leg was performed. On each leg muscle, full width at half maximum (FWHM) which indicated the muscle cell diameters and pennation angle (PA) were measured and compared. FWHM showed significant positive correlations with PA, which is related to muscle strength. In addition, FWHM was higher for powerlifting, control, distance running, and teenager, in that order, suggesting that it may be directing the diameter of each muscle cell. Type 1 and type 2 fibers are enlarged by growth, so the fact that the FWHM of the control group was larger than that of the teenagers in this study may indicate that the muscle fibers were enlarged by growth. Also, FWHM has the possibility to increase with increased muscle fibers caused by training. We showed that QSI had the possibility to depict noninvasively the microstructure like muscle fiber type and subtle changes caused by growth and sports characteristics, which previously could only be assessed by biopsy.
Grading Evaluation of Cardiotoxicity in Patients with Breast Cancer Treated with Adjuvant Paclitaxel Anthracycline/Cyclophosphamide Chemotherapy: A Meta-Analysis
Authors: Hu Weibao , Song Mengli , Li Linfeng
Published: August 2022

Objective. The cardiac safety of concurrent treatment with anthracycline (A), cyclophosphamide (C), and paclitaxel (T) in an adjuvant BC treatment regimen is still under debate. In this study, we aimed to determine cardiotoxicity events following ACT chemotherapy among operable breast cancer patients without HER2-positive. Methods. We searched PubMed and the Cochrane Library for RCTs prior to July 2019 evaluating the cardiac impairment of ACT chemotherapy regimens in BC patients. The search terms were “BC,” “chemotherapy,” “docetaxel or “doxorubicin,” “paclitaxel,” and “cyclophosphamide.” Cardiotoxic events included LVEF

decline
≥
10

points, congestive heart failure (CHF), and cardiac death. Results. In total, 12 studies with 4032 subjects were included in this meta-analysis, and all patients received ACT regimen. The analysis results indicated that LVEF

decrease
≥
10

points was the most common cardiotoxic event (16%; (95% CI (8%–24%)) with

χ

2

=
95.75

,

P
<
0.001

,

I

2

=
95.8
%

). CHF showed the lowest rate (1%; (95% CI (0%–1%)) with

χ

2

=
8.00

,

P
=
0.433

,

I

2

=
0.0
%

). Subgroup analysis demonstrated that the incidence of CHF due to A → C → T chemotherapy regimen was lower than that of other events, however, without significance. No significant difference was observed in the occurrence of cardiac death. Conclusion. The ACT regimen in patients with HER2-negative BC was associated with an increased risk of adverse cardiactoxic events.
Analysis of Competition Relationship of Multiairport Regional Airport Market Based on Deep Learning
Authors: Wei Wei , Jing He
Published: August 2022

At present, China’s civil aviation industry is in a period of rapid development. The passenger traffic is increasing year by year. The contradiction between the demand and supply of flight resources is becoming more and more prominent, and the competition between airports is becoming more and more fierce. Driven by local development and the country’s overall planning, there are more and more multiairport areas, so how to analyze the airport market competition in multiairport areas needs to be further realized. This paper constructs a two-dimensional Hotelling model based on flight frequency and passenger travel cost to analyze the internal mechanism of market competition among multiple airports. It is assumed that, in a multiairport area, two airports are distributed at both ends of a linear straight line, and the other airport is in between, and each airport provides displacement services connecting the same destination airport. The research shows that, (1) with the increase of the flight frequency of the airports at both ends, its equilibrium profit shows a trend of decreasing first and then increasing; the difference of the flight frequencies of the airports at both ends affects the equilibrium profit of the middle airport. (2) With the gradual increase of flight frequency, the reduction of passenger travel cost will make the equilibrium market share and equilibrium profit show a trend of first decreasing and then increasing. (3) Taking the Yangtze River Delta regional airport as an example, it is found after verification that increasing flight frequency or reducing passenger travel costs can increase the balanced profit and market share of Hangzhou Xiaoshan Airport and Nanjing Lukou Airport; increasing flight frequency can improve the balanced profit and balanced market share of Sunan Shuofang Airport.
A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices
Authors: Sethi Monika , Rani Shalli , Singh Aman , Mazón Juan Luis Vidal
Published: August 2022

Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer’s disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN’s receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN’s capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.
A Data-Characteristic-Driven Decomposition Ensemble Forecasting Research on the Demand of Space Science Payload Components
Authors: Kang Zhijuan , Dang Wei , Feng Yewei
Published: August 2022

In order to solve the problem of long product lead time, accurate demand forecasting for space science payload components is of great significance to the development of China’s space science industry. In view of the unsteady, nonlinear, and small sample characteristics of space science payload component demand, this paper proposes the EEMD-CC&CV-MPSO-SVR model to predict the future demand of space science payload components. First, this paper effectively adopts EEMD to decompose the normalized demand sequence and analyze the stationarity of each subsequence. The sequence complexity is distinguished by sample entropy, and the optimum kernel function CC-MPSO-SVR and CV-MPSO-SVR prediction models are established for high-complexity and low-complexity sequences, respectively. Finally, the prediction results of each subsequence are ensemble to form a total prediction. Experimental results shows that the model proposed in this paper performs better than single benchmark models and other hybrid models in terms of prediction performance and robustness. It can effectively predict the quantity and trend of the demand for China’s space science payload components, which provide decision-making basis for the government to formulate policies, demand-side procurement, and supply-side inventory control.
Structural Evolution of Regional Firm Network System under the Influence of Industrial transfer: A Case Study of the Refrigeration Industrial Cluster of Minquan County
Authors: Gao Shang , Ran Zhao , Li Yating , Pan Shaoqi
Published: August 2022

The transplanted firm is an important force to promote the network evolution and cluster transformation and upgrading of the undertaking firm. From the micro‐analytic perspective of firm network, this paper puts forward a theoretical framework with “relationship‐network‐evolution” as the main line. Taking the refrigeration industry cluster in Minquan County of China as a case study and keeping the firm networks of economic relation, technical cooperation, and social communication firm network in 2009, 2013, and 2017 as the research objects, this paper analyzes the structure and evolution characteristics of regional firm network system, proposes the degree and effect of the local embeddedness of transplanted firms, and discusses their differences between international and interregional transplanted firms. The results revealed that: (1) the local embeddedness of transplanted firms significantly promotes the development of refrigeration industry network. (2) The network power of large‐scale transplanted firms in the cluster is increasing day by day, and the network presents a multi‐core trend. (3) The local embeddedness of some transplanted firms is not high, and the overall connectivity of the network is not strong. (4) Network intermediary nodes have strong heterogeneity, and the intermediary role of some large transplanted firms in the network needs to be improved. (5) The three network systems have similar structural characteristics, and social capital plays an important role in the local embeddedness of transplanted firms and the development of regional firm network. (6) Compared with international transplanted firms, interregional transplanted firms are more adaptable in terms of local embeddedness. The research results provide a reference for the construction of similar industrial clusters in China and other developing countries.
Eco-Friendly Detoxification of Congo Red Dye from Water by Citric Acid Activated Bioadsorbents Consisting of Watermelon and Water Chestnuts Peels Collected from Indigenous Resources
Authors: Hussain Muhammad Sadiq , Rehman Rabia , Imran Muhammad , Dar Amara , Akram Mehwish , Al-Abbad Eman A.
Published: August 2022

The native peels of two cheap, locally available adsorbents, watermelon (PWM) and water chestnuts (PWC), were chemically processed with different chemicals as modifying agents for the determination and assessment of their adsorption ability for the removal and clearance of harmful, venomous, and pernicious Congo red (CGR), as an acidic nature anionic dye, from the aqueous system. In successive batch experiments, the citric acid-treated peels CPWM and CPWC have shown more promising adsorption performance than their raw and untreated peel counterparts due to the availability of additional adsorption active binding sites evidenced through FT-IR and SEM characterizations. In the Langmuir and Temkin models, the correlation coefficients ([Formula: see text]) for the adsorptive removal of CGR on CPWM, PWM, CPWC, and PWC are very close to unity, 0.99 for each case of adsorption performance. Furthermore, the [Formula: see text] nonlinear statistical results for the elimination of CGR on citric acid-treated adsorbents (CPWM and CPWC) are 8.3 and 7.95 mg/g whereas for their unmodified forms (PWM and PWC) are 2.23 and 4.32 mg/g, respectively, reflecting homogenous and monolayer adsorption mechanism. The greater values of [Formula: see text] 1.4 and 1.3 J/mole, for adsorptive removal of dye on CPWM and CPWC, respectively, as compared to their unmodified forms PWM and PWC which are 0.53 and 0.55 J/mole, respectively, indicate the stronger adsorbate-adsorbent associations. The mechanism follows the pseudo second order in the better mode, while thermodynamic statics for ΔH0, ΔG0, ΔS0, and ΔE0, indicate spontaneous and exothermic behavior of adsorption. This study tends to suggest that citric acid-modified adsorbents CPWM and CPWC may indeed be exploited efficiently to eliminate Congo red dye from wastewater.
Influence of Digital Information Management on the Audit Path of Financial Special Poverty Alleviation Funds
Authors: Ma Lianfeng
Published: August 2022

In order to fully implement the Party’s tough fight against poverty, conscientiously implement the policy of targeted poverty alleviation, and accelerate the development of the socialist market economy and the national economy, it is particularly important to strengthen digital information management in the audit of special fiscal poverty alleviation funds. With the continuous advancement of science and technology, the informatization of audit work has become an inevitable trend. However, in the past audit work of special financial funds, it was found that there were a series of problems such as imperfect audit system, weakened audit work, poor audit projects, imperfect supporting facilities, inadequate poverty alleviation funds, slow project progress, and failure to timely check the completion of projects. In order to better realize the timeliness and practicability of audit information of special fiscal poverty alleviation funds and solve the loopholes and deficiencies found in the past, based on the original digital information submanagement system, this paper adopts the J2EE system framework, MVC mode, and B/S security detection system to build a more stable, efficient, and secure financial special poverty alleviation fund management system. After a series of tests, under the premise of effective use of poverty alleviation funds, the system data shows that, from 2012 to 2018, the per capita disposable income of residents in poverty-stricken areas in China increased by 12.1% in nominal terms, an increase of 8.9% compared with 2012, and the gap between the national rural averages has narrowed again.