Volar locking distal radius plates show better short-term results than other treatment options: A prospective randomised controlled trial

AIM To compare the outcomes of displaced distal radius fractures treated with volar locking plates and with immediate postoperative mobilisation with the outcomes of these fractures treated with modalities that necessitate 6 wk wrist immobilisation.METHODS A prospective, randomised controlled single-centre trial was conducted with 56 patients who had a displaced radius fracture were randomised to treatment either with a volar locking plate(n = 29), or another treatment modality(n = 27; cast immobilisation with or without wires or external fixator). Outcomes were measured at 12 wk. Functional outcome scores measured were the Patient-Rated Wrist Evaluation(PRWE) Score; Disabilities of the Arm, Shoulder and Hand and activities of daily living(ADLs). Clinical outcomes were wrist range of motion and grip strength. Radiographic parameters were volar inclination and ulnar variance.RESULTS Patients in the volar locking plate group had significantly better PRWE scores, ADL scores, grip strength and range of extension at three months compared with the control group. All radiological parameters were significantly better in the volar locking plate group at 3 mo. CONCLUSION The present study suggests that volar locking plates produced significantly better functional and clinical outcomes at 3 mo compared with other treatment modalities. Anatomical reduction was significantly more likely to be preserved in the plating group. Level of evidence: Ⅱ.

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.

Vulvectomy: Indications and Results in the General Surgery Department of the Ignace Deen Chu Hospital in Conakry

Introduction: The aim of this study was to report the indications and evaluate the results of vulvectomy in the general surgery department of Ignace Deen Hospital, CHU Conakry. Materials and Methods: This was a retrospective descriptive study, conducted over a period of five (05) years from January 1, 2018 to December 31, 2022, in the general surgery department of Ignace Deen Hospital, CHU Conakry. We included in our study all patient records in whom vulvectomy was performed. Results: We recorded 15 cases of vulvectomy out of 453 perineal surgeries, i.e. 3.31%, with a mean age of 43.56 years and extremes of 35 and 69 years. Vulvar cancer was the most common diagnosis (46.67%), followed by Buschke-Lowenstein (33.33%) and anal canal cancer extending to the vulva (20%). Six patients had undergone biopsy (40%). Vulvectomy with lymph node dissection was performed in only 9 patients (60%), and all surgical specimens were sent to anatomical pathology (100%). Conclusion: Vulvectomy is a surgical technique most often indicated for the treatment of vulvar cancer.

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.

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.

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.

Clinical implication of naive and memory T cells in locally advanced cervical cancer:A proxy for tumor biology and short-term response prediction

Background:This study was designed to investigate the feasibility of tumor-infiltrating immune cells with different phenotypic characteristics for predicting short-term clinical responses in patients with locally advanced cervical cancer(LACC).Methods:Thirty-four patients who received concurrent chemoradiotherapy and twenty-one patients who merely underwent radiotherapy were enrolled in this study.We retrospectively analyzed the T cell markers(i.e.,CD3,CD4,CD8),memory markers(i.e.,CD45,CCR7),and differentiation markers(i.e.,CD27)in the peripheral blood and tumor tissues of patients with LACC before treatment based on flow cytometry.We also analyzed the relationship of T cell subsets between peripheral blood and tumor tissues,and their correlation with complete response or partial response.Results:The percentage of central memory CD8^(+)TCM(CD8^(+)CD45RA^(−)CD27^(+)CCR7^(+))cells in LACC patients was significantly lower than that of the control group.The percentage of CD8^(+)TN in the peripheral blood of LACC patients was significantly higher than that of tumor tissues.CD8^(+)TEM in the peripheral blood was significantly lower than that of tumor tissues.The percentage of CD8^(+)TN and CD8^(+)TCM in human papillomavirus(HPV)positive samples was significantly higher than that of HPV-negative samples.Similarly,the percentage of CD8^(+)TCM in tumor tissues was significantly higher in cancer tissue samples with lymph nodes compared with those without.Conclusion:A higher proportion of CD4^(+)TCM and a lower proportion of CD8^(+)TN in the tumor microenvironment of LACC may contribute to the therapy response prediction.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Short-term load forecasting (STLF) is part and parcel of theefficient working of power grid stations. Accurate forecasts help to detect thefault and enhance grid reliability for organizing sufficient energy transactions.STLF ranges from an hour ahead prediction to a day ahead prediction. Variouselectric load forecasting methods have been used in literature for electricitygeneration planning to meet future load demand. A perfect balance regardinggeneration and utilization is still lacking to avoid extra generation and misusageof electric load. Therefore, this paper utilizes Levenberg–Marquardt(LM) based Artificial Neural Network (ANN) technique to forecast theshort-term electricity load for smart grids in a much better, more precise,and more accurate manner. For proper load forecasting, we take the mostcritical weather parameters along with historical load data in the form of timeseries grouped into seasons, i.e., winter and summer. Further, the presentedmodel deals with each season’s load data by splitting it into weekdays andweekends. The historical load data of three years have been used to forecastweek-ahead and day-ahead load demand after every thirty minutes makingload forecast for a very short period. The proposed model is optimized usingthe Levenberg-Marquardt backpropagation algorithm to achieve results withcomparable statistics. Mean Absolute Percent Error (MAPE), Root MeanSquared Error (RMSE), R2, and R are used to evaluate the model. Comparedwith other recent machine learning-based mechanisms, our model presentsthe best experimental results with MAPE and R2 scores of 1.3 and 0.99,respectively. The results prove that the proposed LM-based ANN modelperforms much better in accuracy and has the lowest error rates as comparedto existing work.

Relationship between body mass index and short-term postoperative prognosis in patients undergoing colorectal cancer surgery

BACKGROUND Obesity is a state in which excess heat is converted into excess fat,which accumulates in the body and may cause damage to multiple organs of the circulatory,endocrine,and digestive systems.Studies have shown that the accumulation of abdominal fat and mesenteric fat hypertrophy in patients with obesity makes laparoscopic surgery highly difficult,which is not conducive to operation and affects patient prognosis.However,there is still controversy regarding these conclusions.AIM To explore the relationship between body mass index(BMI)and short-term prognosis after surgery for colorectal cancer.METHODS PubMed,Embase,Ovid,Web of Science,CNKI,and China Biology Medicine Disc databases were searched to obtain relevant articles on this topic.After the articles were screened according to the inclusion and exclusion criteria and the risk of literature bias was assessed using the Newcastle-Ottawa Scale,the prognostic indicators were combined and analyzed.RESULTS A total of 16 articles were included for quantitative analysis,and 15588 patients undergoing colorectal cancer surgery were included in the study,including 3775 patients with obesity and 11813 patients without obesity.Among them,12 articles used BMI≥30 kg/m^(2)and 4 articles used BMI≥25 kg/m^(2)for the definition of obesity.Four patients underwent robotic colorectal surgery,whereas 12 underwent conventional laparoscopic colorectal resection.The quality of the literature was good.Meta-combined analysis showed that the overall complication rate of patients with obesity after surgery was higher than that of patients without obesity[OR=1.35,95%CI:1.23-1.48,Z=6.25,P<0.0001].The incidence of anastomotic leak after surgery in patients with obesity was not significantly different from that in patients without obesity[OR=0.99,95%CI:0.70-1.41),Z=-0.06,P=0.956].The incidence of surgical site infection(SSI)after surgery in patients with obesity was higher than that in patients without obesity[OR=1.43,95%CI:1.16-1.78,Z=3.31,P<0.001].The incidence of reoperation in patients with obesity after surgery was higher than that in patients without obesity;however,the difference was not statistically significant[OR=1.15,95%CI:0.92-1.45,Z=1.23,P=0.23];Patients with obesity had lower mortality after surgery than patients without obesity;however,the difference was not statistically significant[OR=0.61,95%CI:0.35-1.06,Z=-1.75,P=0.08].Subgroup analysis revealed that the geographical location of the institute was one of the sources of heterogeneity.Robot-assisted surgery was not significantly different from traditional laparoscopic resection in terms of the incidence of complications.CONCLUSION Obesity increases the overall complication and SSI rates of patients undergoing colorectal cancer surgery but has no influence on the incidence of anastomotic leak,reoperation rate,and short-term mortality rate.

Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.

Short-term night lighting disrupts lipid and glucose metabolism in Zebra Finches:Implication for urban stopover birds

Night lighting has been shown to affect wild animals.To date,the effects of night lighting on the metabolic homeostasis of birds that spend short time in urban environments remain unclear.Using model bird species Zebra Finch(Taeniopygia guttata),we investigated the effects of short-term night lighting on liver transcriptome,blood glucose,triglyceride,and thyroxine(T4 and T3)levels in birds exposed to two different night lighting duration periods(three days and six days).After three days of night lighting exposure,the expression of genes involved in fat synthesis in the liver was upregulated while the expression of genes involved in fatty acid oxidation and triglyceride decomposition was downregulated.There was also a reduction in blood triglyceride,glucose,and T3 concentrations.However,after six days of night lighting,the expression of genes associated with fatty acid decomposition and hyperglycemia in the liver was upregulated,while the expression of genes involved in fat synthesis was downregulated.Simultaneously,blood glucose levels and T3 concentration increased.These findings indicate that short-term exposure to night lighting can disrupt the lipid and glucose metabolism of small passerine birds,and longer stopovers in urban area with intense night lighting may cause birds to consume more lipid energy.

Parameter calculation and result storage for phase-field simulation in α-Mg dendrite growth of Mg-5-wt% Zn alloy

Parameter calculation and result storage, as two necessary steps in phase-field simulation play an important role in ensuring the accuracy of simulation results. A strategy of parameter calculation and result storage is presented for phase-field simulation in α-Mg dendrite growth of Mg-5-wt% Zn alloy under isothermal solidification. Based on the phase diagram and empirical formulas, key parameters of the phase-field model, such as equilibrium partition coefficient k, liquidus slope m, solutal diffusion coefficient in liquid Dl, and solutal diffusion coefficient in solid Ds, can be obtained.Both structured grid method and structured point method can be used to store simulation results, but using the latter method will reduce about 60% storage space and 37.5% storage time compared with the former. Finally, convergent simulation results of α-Mg dendrite growth are obtained and they are in good agreement with the experimental results about optical micrograph, which verify the accuracy of parameters and stability of storage method.

Centralized-local PV voltage control considering opportunity constraint of short-term fluctuation

This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.

Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

P&T-Inf: A Result Inference Method for Context-Sensitive Tasks in Crowdsourcing

Context-Sensitive Task(CST)is a complex task type in crowdsourc-ing,such as handwriting recognition,route plan,and audio transcription.The current result inference algorithms can perform well in simple crowd-sourcing tasks,but cannot obtain high-quality inference results for CSTs.The conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing,but this method ignores the context correlation among subtasks and reduces the quality of result inference.To solve this problem,we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer(P&T-Inf)for CSTs.Firstly,we screen the candidate results of context-sensitive tasks based on the partially ordered set.If there are parallel candidate sets,the conditional mutual information among subtasks containing context infor-mation in external knowledge(such as Google n-gram corpus,American Contemporary English corpus,etc.)will be calculated.Combined with the tree augmented naive(TAN)Bayes model,the maximum weighted spanning tree is used to model the dependencies among subtasks in each CST.We collect two crowdsourcing datasets of handwriting recognition tasks and audio transcription tasks from the real crowdsourcing platform.The experimental results show that our approach improves the quality of result inference in CSTs and reduces the time cost compared with the latest methods.