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PloS One 2024In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing...
In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy.
Topics: Marketing; Commerce; Forecasting; Learning; Machine Learning
PubMed: 38206947
DOI: 10.1371/journal.pone.0294759 -
Plants (Basel, Switzerland) Dec 2023The legacy effects of invasive plant species can hinder the recovery of native communities, especially under nitrogen deposition conditions, where invasive species show...
The legacy effects of invasive plant species can hinder the recovery of native communities, especially under nitrogen deposition conditions, where invasive species show growth advantages and trigger secondary invasions in controlled areas. Therefore, it is crucial to thoroughly investigate the effects of nitrogen deposition on the legacy effects of plant invasions and their mechanisms. The hypotheses of this study are as follows: (1) Nitrogen deposition amplifies the legacy effects of plant invasion. This phenomenon was investigated by analysing four potential mechanisms covering community system structure, nitrogen metabolism, geochemical cycles, and microbial mechanisms. The results suggest that microorganisms drive plant-soil feedback processes, even regulating or limiting other factors. (2) The impact of nitrogen deposition on the legacy effects of plant invasions may be intensified primarily through enhanced nitrogen metabolism via microbial anaerobes bacteria. Essential insights into invasion ecology and ecological management have been provided by analysing how nitrogen-fixing bacteria improve nitrogen metabolism and establish sustainable methods for controlling invasive plant species. This in-depth study contributes to our better understanding of the lasting effects of plant invasions on ecosystems and provides valuable guidance for future ecological management.
PubMed: 38202380
DOI: 10.3390/plants13010072 -
American Journal of Cancer Research 2023This study aimed to summarize the current developments and hub genes in the ferroptosis field using bibliometrics and bioinformatics and provide guidance for future...
This study aimed to summarize the current developments and hub genes in the ferroptosis field using bibliometrics and bioinformatics and provide guidance for future developments. The publications on ferroptosis from 2012 to 2021 were extracted from the Web of Science database. VOSviewer software and CiteSpace software were used to visualize and predict the trend of ferroptosis research. The key genes related to ferroptosis were selected from the Web of Genecards, and Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO) analysis was performed. Cytoscape software and online survival curve analysis platform were also used to screen hub genes and analyze their roles. Chinese researchers published the highest number of publications in this field, while American publications exhibited higher quality. In terms of institutions, Central South University and Zhejiang University have the highest number of publications. published more studies than other journals. The application of ferroptosis is a major research area, and, importantly, "RCD", "FTH1", and "nomogram" are the keywords. We also found tumor-related pathways of interest in the field of ferroptosis. genes were of significance for the prognosis of tumors. The number of publications on ferroptosis may increase in the future. Cooperation among countries and disciplines is particularly important in this regard. Also, the applications of ferroptosis, especially in chemotherapy and immunotherapy for tumors, will be the focus of future research. Keywords "RCD", "FTH1", and "nomogram" is receiving high attention, and in-depth studies on tumor-related genes , , and may provide new therapeutic targets.
PubMed: 38187041
DOI: No ID Found -
BMC Plant Biology Jan 2024As a vital type of noncoding RNAs, circular RNAs (circRNAs) play important roles in plant growth and development and stress response. However, little is known about the...
BACKGROUND
As a vital type of noncoding RNAs, circular RNAs (circRNAs) play important roles in plant growth and development and stress response. However, little is known about the biological roles of circRNAs in regulating the stability of male fertility restoration for cytoplasmic male sterility (CMS) conditioned by Gossypium harknessii cytoplasm (CMS-D2) cotton under high-temperature (HT) stress.
RESULTS
In this study, RNA-sequencing and bioinformatics analysis were performed on pollen grains of isonuclear alloplasmic near-isogenic restorer lines NH [N(Rfrf)] and SH [S(Rfrf)] with obvious differences in fertility stability under HT stress at two environments. A total of 967 circRNAs were identified, with 250 differentially expressed under HT stress. We confirmed the back-splicing sites of eight selected circRNAs using divergent primers and Sanger sequencing. Tissue-specific expression patterns of five differentially expressed circRNAs (DECs) were also verified by RT-PCR and qRT-PCR. Functional enrichment and metabolic pathway analysis revealed that the parental genes of DECs were significantly enriched in fertility-related biological processes such as pollen tube guidance and cell wall organization, as well as the Pentose and glucuronate interconversions, Steroid biosynthesis, and N-Glycan biosynthesis pathways. Moreover, we also constructed a putative circRNA-mediated competing endogenous RNA (ceRNA) network consisting of 21 DECs, eight predicted circRNA-binding miRNAs, and their corresponding 22 mRNA targets, especially the two ceRNA modules circRNA346-miR159a-MYB33 and circRNA484-miR319e-MYB33, which might play important biological roles in regulating pollen fertility stability of cotton CMS-D2 restorer line under HT stress.
CONCLUSIONS
Through systematic analysis of the abundance, characteristics and expression patterns of circRNAs, as well as the potential functions of their parent genes, our findings suggested that circRNAs and their mediated ceRNA networks acted vital biological roles in cotton pollen development, and might be also essential regulators for fertility stability of CMS-D2 restorer line under heat stress. This study will open a new door for further unlocking complex regulatory mechanisms underpinning the fertility restoration stability for CMS-D2 in cotton.
Topics: Gossypium; RNA, Circular; Cytoplasm; Fertility; RNA; Heat-Shock Response
PubMed: 38183049
DOI: 10.1186/s12870-023-04706-w -
Frontiers in Psychiatry 2023Depression affects the development of adolescents and makes it difficult for them to adapt to future life. The purpose of this study was to elucidate the population...
BACKGROUND
Depression affects the development of adolescents and makes it difficult for them to adapt to future life. The purpose of this study was to elucidate the population characteristics of adolescent depression.
METHODS
This study measured depression based on the Patient Health Questionnaire-9 items and sociodemographic questionnaire. A total of 8,235 valid questionnaires were collected from six schools in Haikou and Qionghai, Hainan Province, covering the ages of 13 to 18. The questionnaires included high schools with multiple levels, including general high schools, key high schools, and vocational high schools. Latent category analysis (LCA) was used to identify potential categories of depressive symptoms among adolescents. Latent Class Analysis (LCA) was used for determining depressive symptom latent categories and their proportional distribution among adolescents.
RESULTS
LCA analysis divided the data into 3 categories, namely no depression, low depression, and high depression groups. The percentage of the high depression group was 10.1%, and that of the low depression group was 48.4%. The Jorden index was greatest for a PHQ-9 score of 14.5. The 1 grade of junior middle school students entered the high and low depression groups 1.72 and 1.33 times more often than seniors. The number of the 1 grade of high school students included in the high and low depression groups was 1.55 and 1.42 times of the 3 grade of high school students group. The detection rate of the high depression group of vocational school adolescents was 13.5%, which was significantly higher than that of key high schools (9.6%) and general high schools (9.0%).
CONCLUSION
This study found that 1 grade of junior middle school students and the 1 grade of high school students were more likely to fall into depressive conditions. Moreover, Adolescent girls require more attention than boys. Vocational school students need more psychological guidance.
PubMed: 38152357
DOI: 10.3389/fpsyt.2023.1182024 -
Orphanet Journal of Rare Diseases Dec 2023Despite the increasing incidence of aplastic anemia in China, few studies have explored its effect on the patients' quality of life from the perspective of these...
BACKGROUND
Despite the increasing incidence of aplastic anemia in China, few studies have explored its effect on the patients' quality of life from the perspective of these patients. In fact, patients with aplastic disorder live with the disease for a long time, and need to face a variety of difficult realities, including multiple disease symptoms and drug side effects, heavy burden of medical costs, difficulties in social reintegration, and negative emotional distress. Therefore, this study used descriptive qualitative research to explore the direct and rich quality-of-life experiences of patients with aplastic anemia.
METHODS
A total of 19 patients with aplastic anemia were recruited in this study using purposive sampling combined with maximum variation strategy. 5 of the patients with AA were from northern China, and the others were from southern China. Data were collected using semi-structured interviews and analyzed using the conventional content analysis method.
RESULTS
This study yielded important information about the experiences of patients with aplastic anemia in China. The content analysis method finally identified 3 themes and 9 sub-themes, including: physical symptoms (declining physical capacity, treatment-related symptoms, changes in body image), psychological symptoms (mood changes related to the stage of the disease, change in self-image, growth resulting from the disease experience), social burden (decline in career development, perceived burden to the family, social stigma). Patients with AA from different regions didn't show much difference in quality of life.
CONCLUSIONS
Aplastic anemia affects the physical, psychological, and social aspects of patients' lives. Therefore, health care providers need to consider the patients' physical response and psychological feelings to provide relevant medical guidance and multi-channel social support that would improve their confidence and quality of life.
CLINICAL TRIAL REGISTRATION
Name: Development and preliminary application of Quality of Life Scale for Patients with Aplastic Anemia. Number: ChiCTR2100047575. URL: http://www.chictr.org.cn/login.aspx?referurl=%2flistbycreater.aspx .
Topics: Humans; Anemia, Aplastic; Quality of Life; Qualitative Research; Emotions; China
PubMed: 38129869
DOI: 10.1186/s13023-023-02993-y -
Scientific Reports Nov 2023The conventional star-shaped honeycomb (CSSH) structure is inherently rich in mechanical properties. Based on the CSSH structure, the Poisson's ratio and Young's modulus...
The conventional star-shaped honeycomb (CSSH) structure is inherently rich in mechanical properties. Based on the CSSH structure, the Poisson's ratio and Young's modulus can be improved by adding the tip re-entrant angle (ISSH). In this paper, a new concave four-arc honeycomb (CFAH) structure is proposed by designing the straight rod as a curved rod and retaining the tip re-entrant angle from the ISSH structure. The Young's modulus, specific stiffness and Poisson's ratio of CFAH structures are derived from Castigliano's second theorem and Moore's theorem. The theoretical results show good agreement with the numerical and experimental results. The results show that the normalized effective specific stiffness and normalized effective Young's modulus of the CFAH structure are further improved by about 12.95% and 16.86%, respectively, compared with the ISSH structure, and more significant auxiliary effects are obtained. CFAH structures show good promise in aerospace, construction and other applications due to their enhanced mechanical property. Meanwhile, the present work provides guidance for the study of concave four-arc honeycomb structures.
PubMed: 38036740
DOI: 10.1038/s41598-023-48570-y -
Heliyon Nov 2023This research is of great importance because it applies artificial intelligence methods, more specifically the Random Forest algorithm and the Anfis method to research...
This research is of great importance because it applies artificial intelligence methods, more specifically the Random Forest algorithm and the Anfis method to research the key factors that influence the success of students in vocational schools. Identifying these influencing factors is not only useful for improving curriculum and practice but also provides valuable guidance to help students master the material more effectively. The main goal of this research is to penetrate deeply into the core of the factors that influence the success of students in vocational schools, using two different methods. Each of the factors represented as input is mutually independent and does not affect each other, but each of them affects the output variable. The parameters considered as input variables are prior programming knowledge and pretest requirements. Then, by finding one factor that has the greatest influence, the factor of pre-exam obligation was investigated in more detail, using the Anfis method, which was broken down into several input parameters. These results emphasize the importance of the combination of the Random Forest algorithm and the ANFIS method in the statistical evaluation and assessment of student achievement in vocational schools. This study provides useful guidelines for improving education and practice in vocational schools to optimize educational outcomes.
PubMed: 38027614
DOI: 10.1016/j.heliyon.2023.e21768 -
PloS One 2023The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of...
The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of 'slow employment' increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of 'slow employment' of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.
Topics: Humans; Support Vector Machine; Algorithms; Machine Learning; Employment
PubMed: 37943766
DOI: 10.1371/journal.pone.0294114 -
Turkish Journal of Pharmaceutical... Nov 2023Chemical neurotransmission, managed by neurotransmitters, has a crucial role in brain processes such as fear, memory, learning, and pain, or neuropathology such as...
OBJECTIVES
Chemical neurotransmission, managed by neurotransmitters, has a crucial role in brain processes such as fear, memory, learning, and pain, or neuropathology such as schizophrenia, epilepsy, anxiety/depression, and Parkinson's disease. The measurement of these compounds is used to elucidate the disease mechanisms and evaluate the outcomes of therapeutic interventions. However, this can be quite difficult because of various matrix effects and the problems of chromatographic separation of analysts. In the current study; for the first time, an optimized and fully validated high-performance liquid chromatography-electrochemical detection (HPLC-EC) method according to Food and Drug Administration and European Medicines Agency Bioanalytical Validation Guidance was developed for the simultaneous analysis of nine neurotransmitter compounds, namely dopamine, homovanilic acid, vanilmandelic acid, serotonin (SER), 5-hydroxyindole-3-acetic acid, 4-hydroxy-3-methoxyphenylglycol, norepinephrine, 3,4 dihydroxyphenylacetic acid, and 3-methoxytyramine and simultaneously determined in rat brain samples.
MATERIALS AND METHODS
Separation was achieved with 150 mm x 4.6 mm, 2.6 μm Kinetex F5 (Phenomenex, USA) column isocratically, and analysis was carried out by HPLC equipped with a DECADE II EC detector.
RESULTS
The method exhibited good selectivity, and the correlation coefficient values for each analyte's calibration curves were > 0.99. The detection and quantification limits ranged from 0.01 to 0.03 ng/mL and 3.04 to 9.13 ng/mL, respectively. The stability of the analyses and method robustness were also examined in detail in the study, and the obtained results are presented statistically.
CONCLUSION
The developed and fully validated method has been successfully applied to actual rat brain samples, and important results have been obtained. In the rat brain sample analysis, the lowest number of SER and the highest amount of noradrenaline were found.
PubMed: 37933822
DOI: 10.4274/tjps.galenos.2022.06606