Increased number of symptoms during the acute phase of SARS-CoV-2 infection in athletes is associated with prolonged time to return to full sports performance—AWAREⅧ

Purpose The aim of the study was to identify factors associated with prolonged time to return to full performance(RTFP)in athletes with recent severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.Methods Prospective cohort study with cross sectional analysis.A total of 84 athletes with confirmed SARS-CoV-2 infection assessed at a coronavirus disease 2019 recovery clinic gave a history of age,sex,type/level of sport,co-morbidities,pre-infection training hours,and 26 acute SARS-CoV-2 symptoms from 3 categories(“nose and throat”,“chest and neck”,and“whole body”/systemic).Data on days to RTFP were obtained by structured interviews.Factors associated with RTFP were demographics,sport participation,history of co-morbidities,pre-infection training history,and acute symptoms(type,number).Outcomes were:(a)days to RTFP(median,interquartile range(IQR))in asymptomatic(n=7)and symptomatic athletes(n=77),and(b)hazard ratios(HRs;95%confidence interval)for symptomatic athletes with vs.without a factor(univariate,multiple models).HR<1 was predictive of higher percentage chance of prolonged RTFP.Significance was p<0.05.Results Days to RTFP were 30 days(IQR:23–40)for asymptomatic and 64 days(IQR:42–91)for symptomatic participants(p>0.05).Factors associated with prolonged RTFP(univariate models)were:females(HR=0.57;p=0.014),endurance athletes(HR=0.41;p<0.0001),co-morbidity number(HR=0.75;p=0.001),and respiratory disease history(HR=0.54;p=0.026).In symptomatic athletes,prolonged RTFP(multiple models)was significantly associated with increased“chest and neck”(HR=0.85;p=0.017)and“nose and throat”(HR=0.84;p=0.013)symptoms,but the association was more profound between prolonged RFTP and increased total number of“all symptoms”(HR=0.91;p=0.001)and“whole body”/systemic(HR=0.82;p=0.007)symptoms.Conclusion A larger number of total symptoms and specifically“whole body”/systemic symptoms during the acute phase of SARS-CoV-2 infection in athletes is associated with prolonged RTFP.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

Gut microbiota affects the estrus return of sows by regulating the metabolism of sex steroid hormones

Background Sex hormones play important roles in the estrus return of post-weaning sows.Previous studies have demonstrated a complex and bi-directional regulation between sex hormones and gut microbiota.However,the extent to which the gut microbiota affects estrus return of post-weaning sows is largely unknown.Results In this study,we first screened 207 fecal samples from well-phenotyped sows by 16S rRNA gene sequencing and identified significant associations between microbes and estrus return of post-weaning sows.Using metagenomic sequencing data from 85 fecal samples,we identified 37 bacterial species that were significantly associated with estrus return.Normally returning sows were characterized by increased abundances of L.reuteri and P.copri and decreased abundances of B.fragilis,S.suis,and B.pseudolongum.The changes in gut microbial composition significantly altered the functional capacity of steroid hormone biosynthesis in the gut microbiome.The results were confirmed in a validation cohort.Significant changes in sex steroid hormones and related compounds were found between normal and non-return sows via metabolome analysis.An integrated analysis of differential bacterial species,metagenome,and fecal metabolome provided evidence that normal return-associated bacterial species L.reuteri and Prevotella spp.participated in the degradation of pregnenolone,progesterone,and testosterone,thereby promoting estrogen biosynthesis.Furthermore,the microbial metabolites related to sow energy and nutrient supply or metabolic disorders also showed relationships with sow estrus return.Conclusions An integrated analysis of differentially abundant bacterial species,metagenome,and fecal metabolome revealed the involvement of L.reuteri and Prevotella spp.in sow estrus return.These findings provide deep insight into the role of gut microbiota in the estrus return of post-weaning sows and the complex cross-talk between gut microbiota and sex hormones,suggesting that the manipulation of the gut microbiota could be an effective strategy to improve sow estrus return after weaning.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

Symptom presentation and evolution in the first 48 hours after injury are associated with return to play after concussion in elite Rugby Union

Background:Return to play(RTP)in elite rugby is managed using a 6-stage graduated RTP protocol,which can result in clearance to play within 1 week of injury.We aimed to explore how symptom,cognitive,and balance presentation and evolution during concussion screens 2 h(head injury assessment(HIA2)and 48 h(HIA3)after injury were associated with time to RTP)to identify whether a more conservative graduated RTP may be appropriate.Methods:A retrospective cohort study was conducted in 380 concussed rugby players from elite men’s rugby over 3 consecutive seasons.Players were classified as shorter or longer returns,depending on whether RTP occurred within 7 days(allowing them to be considered to play the match 1 week after injury)or longer than 8 days,respectively.Symptom,cognitive,and balance performance during screens was assessed relative to baseline(normal or abnormal)and to the preceding screen(improving or worsening).Associations between sub-test abnormalities and RTP time were explored using odds ratios(OR,longer vs.shorter).Median day absence was compared between players with abnormal or worsening results and those whose results were normal or improving.Results:Abnormal symptom results during screens 2 h and 48 h after concussion were associated with longer return time(HIA2:OR=2.21,95%confidence interval(95%CI):1.39-3.50;HIA3:OR=3.30,95%CI:1.89-5.75).Worsening symptom number or severity from the time of injury to 2 h and 48 h post-injury was associated with longer return(HIA2:OR=2.49,95%CI:1.36-4.58;HIA3:OR=3.34,95%CI:1.10-10.15).Median days absence was greater in players with abnormal symptom results at both HIA2 and HIA3.Cognitive and balance performance were not associated with longer return and did not affect median days absence.Conclusion:Symptom presentation and evolution within 48 h of concussion were associated with longer RTP times.This may guide a more conservative approach to RTP,while still adhering to individualized concussion management principles.

Exploring the Factors Associated with 12-Month Non-Return to Work among Motorcyclists Involved in Road Accidents

Introduction: Motorcyclists bear a disproportionate burden of morbidity and mortality from road accidents. In addition, the consequences of these accidents affect the ability of victims to return to work. This study aimed to determine the prevalence and factors associated with non-return to work among surviving motorcyclists involved in road accidents 12 months after the event. Materials and Methods: It was a cross-sectional study conducted using data from a cohort of motorcyclists involved in accidents and recruited in five hospitals in Benin from July 2019 to January 2020. The dependent variable was non-return to work 12 months after the accident (yes vs no). The independent variables were categorized into two groups: baseline and 12-month follow-up variables. Logistic regression was used to determine the factors associated with non-return to work at 12 months among the participants. Results: Among the 362 participants, 55 (15.19%, 95% CI = 11.84 - 19.29) had not returned to work 12 months after the accident. Risk factors for non-return to work identified were: smoking (aOR = 4.41, 95% CI = 1.44 - 13.56, p = 0.010), hospitalization (aOR = 2.87, 95% CI = 1.14 - 7.24, p Conclusion: The prevalence of non-return to work at 12 months was high among surviving motorcyclists involved in road accidents in Benin. Integrated support for patients based on identified risk factors should effectively improve their return to work.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function

BACKGROUND Hepatectomy is the first choice for treating liver cancer.However,inflammatory factors,released in response to pain stimulation,may suppress perioperative immune function and affect the prognosis of patients undergoing hepatectomies.AIM To determine the short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function.METHODS Clinical data from patients with liver cancer admitted to Suzhou Ninth People’s Hospital from January 2020 to December 2023 were retrospectively analyzed.Thirty-five patients underwent laparoscopic hepatectomy for liver cancer(liver cancer resection group)and 35 patients underwent medical image-guided microwave ablation(liver cancer ablation group).The short-term efficacy,complications,liver function,and immune function indices before and after treatment were compared between the two groups.RESULTS One month after treatment,19 patients experienced complete remission(CR),8 patients experienced partial remission(PR),6 patients experienced stable disease(SD),and 2 patients experienced disease progression(PD)in the liver cancer resection group.In the liver cancer ablation group,21 patients experienced CR,9 patients experienced PR,3 patients experienced SD,and 2 patients experienced PD.No significant differences in efficacy and complications were detected between the liver cancer ablation and liver cancer resection groups(P>0.05).After treatment,total bilirubin(41.24±7.35 vs 49.18±8.64μmol/L,P<0.001),alanine aminotransferase(30.85±6.23 vs 42.32±7.56 U/L,P<0.001),CD4+(43.95±5.72 vs 35.27±5.56,P<0.001),CD8+(20.38±3.91 vs 22.75±4.62,P<0.001),and CD4+/CD8+(2.16±0.39 vs 1.55±0.32,P<0.001)were significantly different between the liver cancer ablation and liver cancer resection groups.CONCLUSION The short-term efficacy and safety of microwave ablation and laparoscopic surgery for the treatment of liver cancer are similar,but liver function recovers quickly after microwave ablation,and microwave ablation may enhance immune function.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

A novel predictor of unsustained return of spontaneous circulation in cardiac arrest patients through a combination of capnography and pulse oximetry: a multicenter observational study

BACKGROUND:Unsustained return of spontaneous circulation(ROSC)is a critical barrier to survival in cardiac arrest patients.This study examined whether end-tidal carbon dioxide(ETCO_(2))and pulse oximetry photoplethysmogram(POP)parameters can be used to identify unsustained ROSC.METHODS:We conducted a multicenter observational prospective cohort study of consecutive patients with cardiac arrest from 2013 to 2014.Patients’general information,ETCO_(2),and POP parameters were collected and statistically analyzed.RESULTS:The included 105 ROSC episodes(from 80 cardiac arrest patients)comprised 51 sustained ROSC episodes and 54 unsustained ROSC episodes.The 24-hour survival rate was significantly higher in the sustained ROSC group than in the unsustained ROSC group(29.2%vs.9.4%,P<0.05).The logistic regression analysis showed that the difference between after and before ROSC in ETCO_(2)(ΔETCO_(2))and the difference between after and before ROCS in area under the curve of POP(ΔAUCp)were independently associated with sustained ROSC(odds ratio[OR]=0.931,95%confi dence interval[95%CI]0.881-0.984,P=0.011 and OR=0.998,95%CI 0.997-0.999,P<0.001).The area under the receiver operating characteristic curve ofΔETCO_(2),ΔAUCp,and the combination of both to predict unsustained ROSC were 0.752(95%CI 0.660-0.844),0.883(95%CI 0.818-0.948),and 0.902(95%CI 0.842-0.962),respectively.CONCLUSION:Patients with unsustained ROSC have a poor prognosis.The combination ofΔETCO_(2) andΔAUCp showed signifi cant predictive value for unsustained ROSC.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

Exploring influences and risk of bias of studies on return to sport and work after lateral ankle sprain:A systematic review and metaanalysis

BACKGROUND Lateral ankle sprains are the most common traumatic musculoskeletal injuries of the lower extremity,with an incidence rate of 15%-20%.The high incidence and prevalence highlights the economic impact of this injury.Ankle sprains lead to a high socioeconomic burden due to the combination of the high injury incidence and high medical expenses.Up to 40%of patients who suffer from an ankle sprain develop chronic ankle instability.Chronic instability can lead to prolonged periods of pain,immobility and injury recurrence.Identification of factors that influence return to work(RTW)and return to sports(RTS)after a lateral ankle sprain(LAS)may help seriously reduce healthcare costs.AIM To explore which factors may potentially affect RTW and RTS after sustaining an LAS.METHODS EMBASE and PubMed were systematically searched for relevant studies published until June 2023.Inclusion criteria were as follows:(1)Injury including LAS or chronic ankle instability;(2)Described any form of treatment;(3)Assessment of RTW or RTS;(4)Studies published in English;and(5)Study designs including randomized controlled clinical trials,clinical trials or cohort studies.Exclusion criteria were:(1)Studies involving children(age<16 year);or(2)Patients with concomitant ankle injury besides lateral ankle ligament damage.A quality assessment was performed for each of the included studies using established risk of bias tools.Additionally quality of evidence was assessed using the GRADEpro tool in cases where outcomes were included in the quantitative analysis.A best evidence synthesis was performed in cases of qualitative outcome analysis.For all studied outcomes suitable for quantitative analysis a forest plot was created to calculate the effect on RTW and RTS.RESULTS A total of 8904 patients were included in 21 studies,10 randomized controlled trials,7 retrospective cohort studies and 4 prospective cohort studies.Fifteen studies were eligible for meta-analysis.The overall RTS rate ranged were 80%and 83%in the all treatments pool and surgical treatments pool,respectively.The pooled mean days to RTS ranged from 23-93 d.The overall RTW rate was 89%.The pooled mean time to RTW ranged from 5.8-8.1 d.For patients with chronic ankle instability,higher preoperative motivation was the sole factor significantly and independently(P=0.001)associated with the rate of and time to RTS following ligament repair or reconstruction.Higher body mass index was identified as a significant factor(P=0.04)linked to not resuming sports or returning at a lower level(median 24,range 20-37),compared to those who resumed at the same or higher level(median 23,range 17-38).Patients with a history of psychological illness or brain injury,experienced a delay in their rehabilitation process for sprains with fractures and unspecified sprains.The extent of the delayed rehabilitation was directly proportional to the increased likelihood of experiencing a recurrence of the ankle sprain and the number of ankle-related medical visits.We also observed that 10%of athletes who did return to sport after lateral ankle sprain without fractures described non-ankle-related reasons for not returning.CONCLUSION All treatments yielded comparable results,with each treatment potentially offering unique advantages or benefits.Preoperative motivation may influence rehabilitation after LAS.Grading which factor had a greater impact was not possible due to the lack of comparability among the included patients.

Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.

Litter Productivity and Nutrient Return Characteristics of Three Typical Forest Stands in Golden Mountain

Objective: The paper aims to analyze the dynamic characteristics of litter production and nutrient return of the forest ecosystems in subtropical areas, and provide a theoretical basis for the nutrient cycling study in southwest Hubei Province and carbon sink function of the whole forest ecosystem. Methods: Three typical forest stands (Chinese fir plantation, Cryptomeria fortunei plantation and evergreen and deciduous broad-leaved mixed forest) in Golden Mountain Forest Farm in southwest Hubei Province were investigated and monitored continuously for the litter types and productivity and nutrient return. Results: The annual litter productivity of the three forest stands ranged from 161.77 to 396.26 kg·hm-2;Litters of branches, leaves and reproductive organs accounted for 14.14% - 20.85%, 33.26% - 78.33%, 7.52% - 42.18% of the total, respectively;The litter productivity and total litter productivity of each composition in the three forest stands show unimodal or bimodal changes over months, and the total litter productivity reached the highest value in January, April and October respectively. For different nutrient contents of the three forest stands, the common feature is C > N. The order of nutrient return amount from greatest to least is evergreen and deciduous broad-leaved mixed forest, Cryptomeria fortunei plantation and Chinese fir plantation. For different nutrient return amounts, the common feature is C > N, and the nutrient return amounts are 76.51-180.69 kg·hm-2 and 2.3 - 5.71 kg·hm-2 respectively. Conclusion: The annual litter productivity and nutrient return amount of the evergreen and deciduous broad-leaved mixed forest are the highest among the three forest stands. Therefore, protecting the evergreen and deciduous broad-leaved mixed forest and studying the litter changes of Chinese fir plantation and Cryptomeria fortunei plantation are of far-reaching significance for the development of sustainable forest management in this region and the further improvement of the carbon sequestration function of the whole forest ecosystem.

Using Return and Risk Model for Choosing Perfect Portfolio Applied Study in Cairo Stock Exchange

Modern financial theory, commonly known as portfolio theory, provides an analytical framework for the investment decision to be made under uncertainty. It is a well-established proposition in portfolio theory that whenever there is an imperfect correlation between returns risk is reduced by maintaining only a portion of wealth in any asset, or by selecting a portfolio according to expected returns and correlations between returns. The major improvement of the portfolio approaches over prior received theory is the incorporation of 1) the riskiness of an asset and 2) the addition from investing in any asset. The theme of this paper is to discuss how to propose a new mathematical model like that provided by Markowitz, which helps in choosing a nearly perfect portfolio and an efficient input/output. Besides applying this model to reality, the researcher uses game theory, stochastic and linear programming to provide the model proposed and then uses this model to select a perfect portfolio in the Cairo Stock Exchange. The results are fruitful and the researcher considers this model a new contribution to previous models.

The Misdirection and Return of Graduate Course Teaching

Graduate course teaching plays an irreplaceable role in knowledge mastery,skill enhancement,and quality cultivation of graduate students.This paper addresses current issues in graduate course teaching,such as ambiguous objectives,formulaic content,formalistic operation,and oversimplified evaluation.It proposes corresponding solutions with the hope of steering graduate course teaching back to its original purpose and realizing its true significance.

Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region,China

China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity.The country has entered a new development stage designed to achieve its second centenary goal.However,ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty.In this paper,the Liupan Mountain Region of China was used as a case study,and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area.The indicator data for returning to ecological poverty from 2011-2020 were collected and summarized in three dimensions:ecological,economic and social.The autoregressive integrated moving average model(ARIMA)time series and exponential smoothing method(ES)were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties(districts)in the Liupan Mountain Region for 2021-2030.The back propagation neural network(BPNN)and geographic information system(GIS)were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty(RREP index).The results show that 1)ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region.2)The RREP index for the 61 counties(districts)exhibited a downward trend from 2021-2030.The RREP index declined more in medium-and high-risk areas than in low-risk areas.From 2021 to 2025,the RREP index exhibited a slight downward trend.From 2026 to2030,the RREP index was expected to decline faster,especially from 2029-2030.3)Based on the RREP index,it can be roughly divided into three types,namely,the high-risk areas,the medium-risk areas,and the low-risk areas.The natural resource conditions in lowrisk areas of returning to ecological poverty,were better than those in medium-and high-risk areas.