Postpartum depression and partner support during the period of lactation:Correlation research and its influencing factors

BACKGROUND Postpartum depression(PPD)not only affects the psychological and physiological aspects of maternal health but can also affect neonatal growth and development.Partners who are in close contact with parturient women play a key role in communication and emotional support.This study explores the PPD support relationship with partners and its influencing factors,which is believed to establish psychological well-being and improve maternal partner support.AIM To explore the correlation between PPD and partner support during breastfeeding and its influencing factors.METHODS Convenience sampling was used to select lactating women(200 women)who underwent postpartum examinations at the Huzhou Maternity and Child Health Care Hospital from July 2022 to December 2022.A cross-sectional survey was conducted on the basic information(general information questionnaire),depression level[edinburgh postnatal depression scale(EPDS)],and partner support score[dyadic coping inventory(DCI)]of the selected subjects.Pearson’s correlation analysis was used to analyze the correlation between PPD and DCI in lactating women.Factors affecting PPD levels during lactation were analyzed using multiple linear regression.RESULTS The total average score of EPDS in 200 lactating women was(9.52±1.53),and the total average score of DCI was(115.78±14.90).Dividing the EPDS,the dimension scores were:emotional loss(1.91±0.52),anxiety(3.84±1.05),and depression(3.76±0.96).Each dimension of the DCI was subdivided into:Pressure communication(26.79±6.71),mutual support(39.76±9.63),negative support(24.97±6.68),agent support(6.87±1.92),and joint support(17.39±4.19).Pearson’s correlation analysis demonstrated that the total mean score and individual dimension scores of EPDS during breastfeeding were inversely correlated with the total score of partner support,stress communication,mutual support,and cosupport(P<0.05).The total mean score of the EPDS and its dimensions were positively correlated with negative support(P<0.05).Multiple linear regression analysis showed that the main factors affecting PPD during breastfeeding were marital harmony,newborn health,stress communication,mutual support,negative support,cosupport,and the total score of partner support(P<0.05).CONCLUSION PPD during breastfeeding was associated with marital harmony,newborn health,stress communication,mutual support,negative support,joint support,and the total DCI score.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

Research on the Correlation between Meteorological Factors and Flue-cured Tobacco Chemical Components in Different Altitudes of Lincang Region

In this study, the relationship between major meteorological factors and tobacco chemical components in different altitudes of Yongde County, Lincang Region of Yunnan Province was studied using correlation analysis and path analysis methods.The results showed that there was a difference on meteorological factors in different altitudes, causing different impacts on chemical components of flue-cured tobacco; contents of nicotine and total nitrogen decreased with the rising of altitude, conversely, total sugar and reducing sugar contents increased with the rising trend of altitude, and the differences on the contents of potassium （K） and chlorine（CI）, and the ratio of K to CI in different altitudes were not significant, at the altitude of 1 250-1 450 m, each chemical component indicator was better. The correlation between main meteorological factors and conventional chemical components of tobacco leaves was as follows： contents of reducing sugar and total sugar increased with the increase of rainfall and relative humidity, and decreased with the increase of sunshine duration; nicotine content increased with the increase of accumulated temperature and sunshine duration; total nitrogen （N） content increased with the increase of daily mean temperature and accumulated temperature; sugar-alkali ratio increased with the increase of rainfall. Path analysis showed that the direct effect of sunshine duration on reducing sugar and total sugar of tobacco leaves was faint, but the indirect effect was obvious; meanwhile, the direct and indirect effects of it on nicotine and total N were the greatest.

Preliminary research on the relationship between long-range correlations and predictability

By establishing the Markov model for a long-range correlated time series （LRCS） and analysing its evolutionary characteristics, this paper defines a physical effective correlation length （ECL） T, which reflects the predictability of the LRCS. It also finds that the ECL has a better power law relation with the long-range correlated exponent γ of the LRCS： T = Kexp（-γ/0.3） ＋ Y, （0 〈 γ〈 1） the predictability of the LRCS decays exponentially with the increase of γ It is then applied to a daily maximum temperature series （DMTS） recorded at 740 stations in China between the years 1960-2005 and calculates the ECL of the DMTS. The results show the remarkable regional distributive feature that the ECL is about 10-14 days in west, northwest and northern China, and about 5-10 days in east, southeast and southern China. Namely, the predictability of the DMTS is higher in central-west China than in east and southeast China. In addition, the ECL is reduced by 1-8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS; however, it is only slightly altered when the decadal linear trend is removed from the original DMTS. Therefore, it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures. Seasonal oscillation is also significant for guiding general weather predictions, as well as seasonal weather predictions.

Research on the effect of Reynolds correlation in natural convection film condensation

Film condensation is a vital phenomenon in the nuclear engineering applications,such as the gas-steam pressurizer design,and heat removing on containment in the case of postulated accident.Reynolds number in film condensation can be calculated from either the mass relation or the energy relation,but few researches have distinguished the difference between them at present.This paper studies the effect of Reynolds correlation in the natural convection film condensation on the outer tube.The general forms of the heat transfer coefficient correlation of film condensation are developed in different flow regimes.By simultaneously solving a set of the heat transfer coefficient correlations with Re_(mass) and Re_(energy),the general expressions for Re_(mass) and Re_(energy) and the relation between the corresponding heat transfer coefficients are obtained.In the laminar and wavefree flow regime,Re_(mass) and Re_(energy) are equivalent,while in the laminar and wavy flow regime,Re_(mass) is much smaller than Re_(energy),and the deviation of the corresponding average heat transfer coefficients is about 30% at the maximum.In the turbulent flow regime,the relation of Re_(mass) and Re_(energy)is greatly influenced by Prandtl number.The relative deviation of their average heat transfer coefficients is the nonlinear function of Reynolds number and Prandtl number.Compared with experimental results,the heat transfer coefficient calculated from Re_(energy) is more accurate.

Research on Correlation between TCM Syndrome Distribution Characteristics and Prognosis of Hypertensive Intracerebral Hemorrhage Operation

Objective: This study is to analyze correlation between traditional Chinese medicine (TCM) syndrome distribution characteristics and prognosis of hypertensive intracerebral hemorrhage (HICH) operation. Methods: In this study, a total of 150 patients who had received HICH operation from April, 2017 to December, 2020 in our hospital and conformed to inclusion standards were selected. According to classification of TCM syndromes, amount of bleeding of patients was recorded through multiple radiological technologies, baseline information was collected, and prognosis was investigated. The final event was long-term follow-up visit of all-cause mortalities. Moreover, correlation between prognosis and TCM syndromes was analyzed. Results: It found through investigations that there are no statistically significant differences in composition ratio of TCM syndromes among patients with different genders and different age ranges (P > 0.05). Among so many TCM syndromes, bleeding amount of patients with declining vitality and distraction is the highest, while bleeding amount of patients with stirring wind due to yin deficiency is the lowest. With respect NIHSS scores, the patients with declining vitality and distraction show the highest NIHSS scores at admission and 10 d of the course of the disease, followed by patients with upward disturbance of wind-fire. There are statistically significant differences among these two groups (P < 0.05). In this study, follow-up visits are performed to all 150 patients and the average follow-up visit time ranges within 2 - 15 months. A total of 13 deaths are reported. Number of all-cause deaths has statistically significant differences among different syndromes (P < 0.05). Conclusions: Evolutionary characteristics of TCM syndromes of HICH are manifested by development from evidence-based symptoms to deficiency syndromes. Different syndromes are correlated with prognosis of patients. On the one hand, this can be used as an objective index for TCM syndrome classification. On the other hand, this is conducive to judge prognosis recovery of patients and to apply the corresponding symptomatic treatment.

Research progress on the correlation between gestational vaginal microecology and vaginal infectious diseases

The health status of the vaginal microenvironment, a complicated system, is an important indicator of female reproductive health. The vaginal flora is in a state of balance, and the microorganisms coexist and are interdependent to maintain the vaginal micro-ecological balance, which is a kind of dynamic balance influenced by endogenous and exogenous factors. Vaginal infections are traditionally treated by killing microbes in the vagina. Given the extensive study of the internal vaginal environment, people become gradually aware of the significance of maintaining the vaginal micro-ecological balance rather than blindly using antimicrobial agents. The balance in the vaginal internal environment is disrupted during the gestation period as the secretion of progesterone increases. The imbalanced vaginal micro-ecological environment may lead to vaginal infectious diseases. This paper provides a review of the relationship between the vaginal microecology and infectious diseases during gestation period.

The correlation and relationship of obesity and cancer: a possible research perspective

Recently, obesity is a well-recognized risk factor of type 2 diabetes and cardiovascular disease. There is more and more sufficient evidence that excess body weight is an avoidable cause of excess cancers including gastrointestinal, endometrial, esophageal adenocarcinoma, colorectal, postmenopausal breast, prostate, and renal cancers. The mechanism that obesity association with cancer is remains not well understood. There be some most studied hypothesized mechanisms such as, high levels of insulin and free levels of insulin-like growth factors （IGFs）, sex hormones, adipocytokines, intlammatory cytokines, c-Myc （or Myc） oncogenic transcription factor, obesity-induced hypoxia and Warburg effect, and so on. In the future, the potential mechanisms and conclusions in obesity associated with increased risk for developing cancer, and the underlying cellular and molecular mechanisms will be studied.

Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.

Canonical Correlation Analysis and climate research

Correlation analysis as used by meteorologists and oceanographers is a tool for the analysisof the spacial or temporal variability of physical fields. In his notes, Dr. Hasselmann pro-posed to combine correlation analysis and linear regression analysis in climate prediction re-search. The main idea is to decompose the physical field into its principal oscillation patterns.

Research on the Relationship between Corporate Social Responsibility and Financial Performance Based on Grey Correlation Analysis: Taking the Smartphone Company as an Example

In order to explore the relationship between the corporate social responsibility (CSR) and financial performance in different CSR cultures, this paper respectively takes four representative smartphone companies in China, the United States, Japan and Korea as research objects, that is Huawei, Apple, Sony and Samsung smartphone companies. Then, dividing the CSR into five dimensions i.e. shareholder, customer, supplier, technology innovation and government and establishing the grey correlation model to empirically analyze the relationship between CSR and financial performance. The results indicate that for HUAWEI, the CSR performance in shareholder, customer and technology innovation have greater impact on its financial performance;for Apple, the CSR performance in shareholder, technology innovation and customer has greater impact on its financial performance;for SONY, the CSR performance in technology innovation, customer and shareholder has greater impact on its financial performance;for Samsung, the CSR performance in technology innovation, government and shareholder has greater impact on its financial performance.

Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network

Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural network.Methods The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned,classified and sorted,on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset.Subsequently,the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions.The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set.Results Of all selected samples,the highest accuracy rates were 80% for the blood lipid index-TCM constitution model;100% for the renal function index-TCM constitution model;100% for the blood routine(male)index-TCM constitution model;88.8% for the blood routine(female)index-TCM constitution model;84.1%for the urine routine index-TCM constitution model;and 100% for the blood transfusion index-TCM constitution model.Conclusions The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions,making it feasible to apply the established correlation models to TCM constitution identification.

Research on a Diagnosis Method Based on a Correlation Integral

For the first time, the diagnosis idea based on a correlation integral isproposed, which regard's the correlation integral as a feature set. The correlation dimension iscontained in the double-log curve of the correlation integral to scale, so extracting featuresdirectly from the correlation integral can avoid the bottleneck problem of determining the range ofnon-scale length. Several features extracted from the correlation integral are better than thesingle feature of the correlation dimension when describing the signal. It is obvious that thismethod utilizes more information of the signal than does the correlation dimension. The diagnosisexamples verify that this method is more accurate and more effective.

Research funding and citations in papers of Nobel Laureates in Physics, Chemistry and Medicine, 2019-2020

Purpose:The goal of this study is a comparative analysis of the relation between funding(a main driver for scientific research)and citations in papers of Nobel Laureates in physics,chemistry and medicine over 2019-2020 and the same relation in these research fields as a whole.Design/methodology/approach:This study utilizes a power law model to explore the relationship between research funding and citations of related papers.The study here analyzes 3,539 recorded documents by Nobel Laureates in physics,chemistry and medicine and a broader dataset of 183,016 documents related to the fields of physics,medicine,and chemistry recorded in the Web of Science database.Findings:Results reveal that in chemistry and medicine,funded researches published in papers of Nobel Laureates have higher citations than unfunded studies published in articles;vice versa high citations of Nobel Laureates in physics are for unfunded studies published in papers.Instead,when overall data of publications and citations in physics,chemistry and medicine are analyzed,all papers based on funded researches show higher citations than unfunded ones.Originality/value:Results clarify the driving role of research funding for science diffusion that are systematized in general properties:a)articles concerning funded researches receive more citations than(un)funded studies published in papers of physics,chemistry and medicine sciences,generating a high Matthew effect(a higher growth of citations with the increase in the number of papers);b)research funding increases the citations of articles in fields oriented to applied research(e.g.,chemistry and medicine)more than fields oriented towards basic research(e.g.,physics).Practical implications:The results here explain some characteristics of scientific development and diffusion,highlighting the critical role of research funding in fostering citations and the expansion of scientific knowledge.This finding can support decision-making of policymakers and R&D managers to improve the effectiveness in allocating financial resources in science policies to generate a higher positive scientific and societal impact.

Influence of Sows Farrowing Performance on Growth of 21-Day-Old Weaned Piglets and Their Correlation Research

[ Objective ] To study the influence of sows farrowing performance on the growth of 21-day-old weaned piglets and their correlation. [ Methods ] A total of 1 350 Duroc x Landrace Yorkshire piglets produced by 2-6 fetal Landrace x Yorkshire sows from 118 litters within a month were selected to be the test objects. All the newborn piglets were lactated by their parent sows. The test groups were divided according to different numbers （6 - 17） of live birth. The variance analysis were conducted on birth weight, litter weight at birth, number of 21-day-old weaning piglets, weaning weight, weaninl2g weight of litter, weaning survival rate and relative individual weight gain among different weeks. [ Results] Sows farrowing performance had significant influence on the growth performance of 21- day-old piglets. However, the influences among different traits varied, some were even on the contrary. With the increase of the number of live birth, the birth weight, weaning weight of litter and weaning survival rate showed a decline trend. And when the number of live birth was equal or more than 14, the weaning sur- vival rate dropped to below 90% （P 〈 0.01 ） ; when the number of live birth was 13, the litter weight at birth and weaning weight of litter reached the peak. What- ever the number of live birth increased or decreased, the weaning weight showed a regular decline trend. [ Conclusions] When the number of live birth changed from 11 to 13, the maximum values of the economic indicator （weaning weight of litter） and the technical specifications （weaning survival rate） were achieved.

Research on the correlation between North China earthquake and global regional strong earthquakes

Active belts of global strong earthquakes were divided into 17 seismic zones based on the principles of global plate tectonics.The seismicity index number A(b) was used to denote the yearly seismicity level.Using the stepwise regression method,an empirical formula was derived to predict the North China seismic risk in the next year by the aid of strong earthquakes in the world.

General laws of funding for scientific citations:how citations change in funded and unfunded research between basic and applied sciences

Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.Design/methodology/approach:A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.Findings:The original results reveal general characteristics of the diffusion of science in research fields:a)Funded articles receive higher citations compared to unfunded papers in journals;b)Funded articles exhibit a super-linear growth in citations,surpassing the increase seen in unfunded articles.This finding reveals a higher diffusion of scientific knowledge in funded articles.Moreover,c)funded articles in both basic and applied sciences demonstrate a similar expected change in citations,equivalent to about 1.23%,when the number of funded papers increases by 1%in journals.This result suggests,for the first time,that funding effect of scientific research is an invariant driver,irrespective of the nature of the basic or applied sciences.Originality/value:This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society.These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.Practical implications:This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.