Paleostress Reconstruction from 3D Seismic Data and Slip Tendency in the Northern Slope Area of the Bongor Basin, Southwestern Chad

Paleostress plays a significant role in controlling the formation, accumulation, and distribution of reservoirs, and this could be an important factor in controlling the production of hydrocarbons from the unconventional reservoirs. In this study, we will use 3D seismic reflection data to perform the slip-tendency-based stress inversion to determine the stress field in the basement of the northern slope area in the Bongor Basin. The dataset for this technique is easily available in the oil and gas companies. The stress inversion results from the basement of the northern slope area of Bongor basin show that the maximum principal stress axis (σ1) is oriented vertically, the intermediate principal stress axis (σ2) is oriented N18° and the minimum principal stress axis (σ3) is oriented N105°, and σ2/σ1 = 0.60 and σ3/σ1 = 0.29. The findings of this paper provide significant information to understand the fault reactivation at the critical stage of hydrocarbon accumulation and the regional tectonic evolution.

The Effect of Self-Investment on Hoarding Tendency of Chinese College Students:Role of Psychological Connections

Because of factors such as energy and time one invests in an object,the stronger the connection,value,and reluc-tance to lose said object individual will have.Hoarding behavior arises when individuals incorporate a strong attachment with themselves to an object.The purpose of this study is to examine the effect of self-investment on hoarding tendency and the roles of possession-self link and liking level in this connection.A hypothetical model of the relationship between self-investment,possession-self link,liking level,and hoarding tendency was tested.A convenience sampling method was used to survey 450 college students in Yunnan Province on either a paper-based or online self-report scale.The data were collected using self-investment,possession-self link,and liking level questionnaires,as well as the Saving Inventory Revised.Results showed positive relationship between the study variables,ranging from 0.37 to 0.87.College students’self-investment had a direct positive pre-dictive effect on hoarding tendency;self-investment,in turn,indirectly predicted hoarding tendency through the mediating effect of possession-self link;and individual liking level of items had a moderating effect for self-invest-ment on the possession-self link.This study shows how self-investment affects the hoarding tendency of college students,and the results of this study also help demonstrate the value of self-investment and possession-self link in optimizing students’hoarding tendency and thus promoting good psychological status.

Construction of Psychological Adjustment Function Model of Music Education Based on Emotional Tendency Analysis

In the face offierce competition in the social environment,mental health problems gradually get the attention of the public,in order to achieve accurate mental health data analysis,the construction of music education is based on emotional tendency analysis of psychological adjustment function model.Design emotional tendency analysis of music education psychological adjustment function architecture,music teaching goal as psychological adjust-ment function architecture building orientation,music teaching content as a foundation for psychological adjust-ment function architecture and music teaching process as a psychological adjustment function architecture building,music teaching evaluation as the key of building key regulating function architecture,Establish a core literacy oriented evaluation system.Different evaluation methods were used to obtain the evaluation results.Four levels of psychological adjustment function model of music education are designed,and the psychological adjust-ment function of music education is put forward,thus completing the construction of psychological adjustment function model of music education.The experimental results show that the absolute value of the data acquisition error of the designed model is minimum,which is not more than 0.2.It is less affected by a bad coefficient and has good performance.It can quickly converge to the best state in the actual prediction process and has a strong con-vergence ability.

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.

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.

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 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.

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.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

The Feminist Tendency in Hotel du Lac

It is true that some important feminist literary theories apply to Hotel du Lac,which illustrates clearly Brookner’s deep concern over modern women’s predicament in favor of their interests,and thus proves her strong tendency to feminism.And then it is pointed out that the reason for Brookner’s perverse attitude toward feminism mainly lies in her unwillingness to confine herself to the category of feminism and actually Brookner has transcended the more stereotypical feminism.

A Study of Romantic Tendency in Jane Austen's works——A Comparison between Sense and Sensibility and Persuasion

Sense and Sensibility and Persuasion is Jane Austen's first and last published works respectively, which are read widely all over the world. Austen proclaims that feeling should be governed by reason in Sense and Sensibility and that reason should be compatible with feeling in Persuasion. This paper attempts to explore romantic tendency in Austen's works according to the comparison and contrast between her two novels. Through comparison and contrast, we can see the transformation of Marianne from a lady of sensibility to a sensible lady and the growth of Anne into a lady with romance.

Development Tendency in Main Fields of Reading Researches

The last few decades have witnessed the publication of a considerable amount of empirical evidence supporting what Krashen has called "the power of reading"(Krashen,1993).There is a widespread approval that reading itself leads to better reading,better vocabulary,better writing,and better control of grammar in both first and second languages.In this article,the following research areas will be discussed,which can be mainly divided into the following 20 categories.

Symmetry and tendency judgment of Ms≥8. 0 strong earthquakes in Chile

The Ms ≥ 8.0 strong earthquakes occurring in Chile since 1800 were analyzed using the ternary, quaternary, and quinary commensurability methods and the butterfly structure diagram, and it was believed that the earthquake signal in Chile in 2014 is relatively strong, a large earthquake is likely to occur in Chile in 2014. An analysis of spatial epicenter migrations showed that the longitudinal and latitudinal epicenter migra- tions have symmetry and synchronism, and there were five obvious northward migrations and four southward migrations. The symmetry axis of the longitudinal migrations is at about 71.7°W and that of the latitudinal mi- grations is at about 30°S; these spatial symmetry axes are located at the subduction zone on the western margin of South America, where two major plates （the Nazca Plate and the South American Plate） converge.

Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF

The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution.

Lowering ash slagging and fouling tendency of high-alkali coal by hydrothermal pretreatment

High-alkali species in coal are notorious for causing ash slagging and fouling incidents.In this paper,four high-alkali coals were individually subject to hydrothermal pretreatment(HTP),within a batch-type autoclave at 300 -C for 1 h,and the treated coals were analyzed,along with the oxygen-containing functional groups determined by Fourier transform infrared spectrometer(FT-IR).Then the alkali species and other components in the coal ash were quantified by X-ray fluorescence(XRF)for evaluating the ash slagging and fouling tendency.Apart from this,FactSage was adopted to simulate the occurrence and transformation of alkali species during coal thermal conversion ending at various temperatures.The findings indicate that the treated coals are superior to the parent ones in terms of certain remarkable changes via HTP.The moisture,oxygen and sulfur of the hydrothermally treated coals decline obviously,while the calorific value rises sharply.HTP could reduce the alkali species to less than 2%(%,by weight,equivalent to Na2O in dry ash),with a maximum removal ratio of 88.9%,lowering the ash slagging and fouling tendency.The proposed mechanism of HTP was that the alkali species in coal matrix became released due to the breakage of the coal functional groups and micropores during HTP.

Effect of Zn/Mg/Cu Additions on Hot Cracking Tendency and Performances of Al-Cu-Mg-Zn Alloys for Liquid Forging

During the process of liquid forging, a host of hot cracking defects were found in the Al-CuMg-Zn aluminum alloy. Therefore, mechanical tests and analyses by optical microscope, scanning electron microscope, and X-ray diffraction were performed to research the influences of zinc, magnesium, and copper(three main alloying elements) on hot cracking tendency and mechanical properties. It was concluded that all the three alloying elements exerted different effects on the performances of newly designed alloys. And the impact of microstructures on properties of alloys was stronger than that of solution strengthening. Among new alloys, Al-5 Cu-4.5 Mg-2.5 Zn alloy shows better properties as follows: σb=327 MPa, δ=2.7%, HB=107 N/mm^2, and HCS=40.

Evaluation of spontaneous combustion tendency of sulfide ore heap based on nonlinear parameters

To explore a new evaluation method for spontaneous combustion tendency of different areas in sulfide ore heap, ore samples from a pyrite mine in China were taken as experimental materials, and the temperature variations of the measuring points of simulated ore heap were measured. Combined with wavelet transform and nonlinear parameters extraction, a new method for spontaneous combustion tendency of different areas in sulfide ore heap based on nonlinear parameters was proposed and its reliability was verified by field test. The results indicate that temperature field evolution of the simulated ore heap presents significant spatial difference during self-heating process. Area with the maximum increasing extent of temperature in sulfide ore heap changes notably with the proceeding of self-heating reaction. Self-heating of sulfide ore heap is a chaotic evolution process, which means that it is feasible to evaluate spontaneous combustion tendency of different areas by nonlinear analysis method. There is a relatively strong correlation between the maximum Lyapunov exponent and spontaneous combustion tendency with the correlation coefficient of 0.9792. Furthermore, the sort of the maximum Lyapunov exponent is consistent with that of spontaneous combustion tendency. Therefore, spontaneous combustion tendency of different areas in sulfide ore heap can be evaluated by means of the maximum Lyapunov exponent method.