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.

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.

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.

Probability Theory Predicts That Chunking into Groups of Three or Four Items Increases the Short-Term Memory Capacity

Short-term memory allows individuals to recall stimuli, such as numbers or words, for several seconds to several minutes without rehearsal. Although the capacity of short-term memory is considered to be 7 &#177?2 items, this can be increased through a process called chunking. For example, in Japan, 11-digit cellular phone numbers and 10-digit toll free numbers are chunked into three groups of three or four digits: 090-XXXX-XXXX and 0120-XXX-XXX, respectively. We use probability theory to predict that the most effective chunking involves groups of three or four items, such as in phone numbers. However, a 16-digit credit card number exceeds the capacity of short-term memory, even when chunked into groups of four digits, such as XXXX-XXXX-XXXX-XXXX. Based on these data, 16-digit credit card numbers should be sufficient for security purposes.

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.

Impacts of Passive Smoking on Learning and Memory Ability of Mouse Offsprings and Intervention by Antioxidants

Objective To determine the impact of passive smoking and the protective effect of antioxidants such as vitamin E and quercetin on learning and memory ability of mouse offsprings. Methods A passive smoking model of pregnant mice was established. Learning and memory ability was evaluated by the water maze test and long term potentiation （LTP）. Nitric oxide （NO）, content, nitric oxide synthase （NOS）, acetylcholinesteras （Ache） activity in brain, vitamin E concentration, and reactive oxygen species （ROS） in serum were determined. The latency period （the time during which the mice swim from the starting position to the ending position） and errors （the number of mice entering the blind end） in control and antioxidant intervention groups were compared with those in the smoke exposure group after 6 days. Results The latency period as well as errors in the air, control diet, tobacco smoke （TS）, and vitamin E diet groups were decreased significantly as compared with the TS and control diet groups （P〈O.05）. LTP was restrained in the TS and control diet groups. LTP in all the antioxidant diet groups was significantly increased compared with the TS and control diet groups. In addition, NOS and acetylcholinesteras （Ache） activitiy was significantly higher in the TS and control diet groups than in the air and control diet group. NO content was not significantly different among the different groups, and significantly lower in the TS and vitamin E diet groups than in the TS group, control diet group, quercetin diet group, and mixture diet group （P〈0.05）. Vitamin E concentration and ROS activity in serum were correlated with the outcome of water maze and LTP. Conclusion Passive smoking reduces LTP formation by disturbing the hippocampus function of mice, by decreasing NOS （especially vitamin E） partially improve the learning and memory smoke during pregnancy. and Ache activity and increasing NO content. Antioxidants ability of offsprings whose mothers are exposed to tobacco

Kongsheng Zhenzhong pill's effect on the learning and memory ability and its neuroprotective effects in vascular dementia rats

Clinical reports have demonstrated that the Kongsheng Zhenzhong pill （KSZZP）, a classical prescription deriving from Valuable Prescription for Emergencies, has good therapeutic effects on vascular dementia. However, the mechanisms that mediate its effects remain unclear. In this study, the expression of N-methyI-D-aspartate receptor 1 mRNA, the content of nitric oxide, and the concentration of calcium in neurons was determined with in situ hybridization, spectrophotometry and flow cytometry, respectively. In addition, the expressions of N-methyI-D-aspartate receptor 1, nerve growth factor protein, and glial cell line-derived neurotrophic factor protein were detected with immunohistochemistry. We found that KSZZP could significantly decrease the expression of N-methyI-D-aspartate receptor 1 mRNA and protein, the content of nitric oxide, and the concentration of calcium in neurons. KSZZP also increased the expression of nerve growth factor and glial cell line-derived neurotrophic factor protein in the hippocampus CA1 region and in the cerebral cortex. Morris water maze and passive avoidance tests verified that KSZZP ameliorated the cognitive impairments of vascular dementia rats. Moreover, the KSZZP-induced improvements in the cognitive functions of vascular dementia rats were correlated with both inhibition of N-methyl-D-aspartate-induced excitable neurotoxicity and elevation of neurotrophic factor expression.

Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

In this study,an optimized long short-term memory(LSTM)network is proposed to predict the reliability and remaining useful life(RUL)of rolling bearings based on an improved whale-optimized algorithm(IWOA).The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing.To provide covariates for reliability assessment,a kernel principal component analysis is used to reduce the dimensionality of the features.A Weibull distribution proportional hazard model(WPHM)is used for the reliability assessment of rolling bearing,and a beluga whale optimization(BWO)algorithm is combined with maximum likelihood estimation(MLE)to improve the estimation accuracy of the model parameters of the WPHM,which provides the data basis for predicting reliability.Considering the possible gradient explosion by training the rolling bearing lifetime data and the difficulties in selecting the key network parameters,an optimized LSTM network called the improved whale optimization algorithm-based long short-term memory(IWOA-LSTM)network is proposed.As IWOA better jumps out of the local optimization,the fitting and prediction accuracies of the network are correspondingly improved.The experimental results show that compared with the whale optimization algorithm-based long short-term memory(WOA-LSTM)network,the reliability prediction and RUL prediction accuracies of the rolling bearing are improved by the proposed IWOA-LSTM network.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.

Recognition of mortar pumpability via computer vision and deep learning

The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material waste.This paper proposes an effective method by combining a 3-dimensional convolutional neural network(3D CNN)with a 2-dimensional convolutional long short-term memory network(ConvLSTM2D)to automatically classify the mortar pumpability.Experiment results show that the proposed model has an accuracy rate of 100%with a fast convergence speed,based on the dataset organized by collecting the corresponding mortar image sequences.This work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification.

Study of the Influence of the Chunking on Improving Speaking Ability Among Senior High Students

English speaking skill is one of the most important skills that senior high students need to obtain in learning English.However,there are still many problems existing in students’speaking practice.As a teaching and learning strategy,Chunking is now gradually used in English classroom and has received a positive feedback.Therefore,in this paper,the influence of Chunking on improving English speaking skill among senior high school students will be investigated and analyzed through the methods of questionnaire and the follow-up interview to answer four questions:(1)What effect does Chunking have on the oral fluency of high school students?(2)What effect does Chunking have on the oral accuracy of high school students?(3)What effect does Chunking have on the vocabulary?And(4)Does the English speaking performance relate to genders?After analyzing the results of questionnaire by the SPSS and summing up the interview record,we found that most of them agree the fact that the strategy of Chunking does benefit their oral fluency,oral accuracy,and vocabulary.Also,female students have higher scores than male students.

Short-Term Memory Capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages. To model it, we consider the number of words between any two contiguous interpunctions Ip, because this parameter can model how the human mind memorizes “chunks” of information. Since IP can be calculated for any alphabetical text, we can perform experiments—otherwise impossible— with ancient readers by studying the literary works they used to read. The “experiments” compare the IP of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an “overlap index”. We also define the population of universal readers, people who can read any New Testament text in any language. Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

Changes of learning and memory ability associated with neuronal nitric oxide synthase in brain tissues of rats with acute alcoholism

BACKGROUD： Ethanol can influence neural development and the ability of leaming and memory, but its mechanism of the neural toxicity is not clear till now. Endogenous nitric oxide （NO） as a gaseous messenger is proved to play an important role in the formation of synaptic plasticity, transference of neuronal information and the neural development, but excessive nitro oxide can result in neurotoxicity. OBJECTIVE ： To observe the effects of acute alcoholism on the learning and memory ability and the content of neuronal nitric oxide synthase （nNOS） in brain tissue of rats. DESIGN ： A randomized controlled animal experiment. SETTING ： Department of Physiology, Xinxiang Medical College MATERIALS： Eighteen male clean-degree SD rats of 18-22 weeks were raised adaptively for 2 days, and then randomly divided into control group （n = 8） and experimental group （n = 10）. The nNOS immunohistochemical reagent was provided by Beijing Zhongshan Golden Bridge Biotechnology Co.,Ltd. Y-maze was produced by Suixi Zhenghua Apparatus Plant. METHODS ： The experiment was carded out in the laboratory of the Department of Physiology, Xinxiang Medical College from June to October in 2005. ① Rats in the experimental group were intraperitoneally injected with ethanol （2.5 g/kg） which was dissolved in normal saline （20%）. The loss of righting reflex and ataxia within 5 minutes indicated the successful model. Whereas rats in the control group were given saline of the same volume. ② Examinations of learning and memory ability： The Y-maze tests for learning and memory ability were performed at 6 hours after the models establishment. The rats were put into the Y-maze separately. The test was performed in a quiet and dark room. There was a lamp at the end of each of three pathways in Y-maze and the base of maze had electric net. All the lamps of the three pathways were turned on for 3 minutes and then turned off. One lamp was turned on randomly, and the other two delayed automatically. In 5 seconds after alternation, pulsating electric current presented in the base of unsafe area to stimulate rat＇s feet to run to the safe area. The lighting lasted for 15 seconds as one test. Running from unsafe area to safe area at one time in 10 seconds was justified as successful. Such test was repeated for 10 times for each rat and the successful frequency was recorded. The qualified standard of maze test was that the rat ardved in the safe area g times during 10 experiments. The number of trainings for the qualified standard was used to represent the result of spatial learning. ③ Determination of the content of nNOS in brain tissue： After the Y-maze test, the rats were anaesthetized, and blood was let from the incision on right auricle, transcardially perfused via the left ventricle with about 200 mL saline, then fixed by perfusion of 40 g/L paraformaldehyde. Hippocampal CA1 region, corpus striatum and cerebellum were taken to prepare serial freezing coronal sections. The nNOS contents in the brain regions were determined with the immunohistochemical methods to reflect the changes of nitdc oxide in brain tissue. MAIN OUTCOME MEASURES ： The changes of learning and memory ability and the changes of the nNOS contents in the brain tissue of rats with acute alcoholism were observed. RESULTS ： One rat in the experimental group was excluded due to its slow reaction to electdc stimulation in the Y-maze test, and the other 17 rats were involved in the analysis of results. ① The training times to reach qualifying standards of Y-maze in the expedmental group was more than that in the control group [（34.33 ±13.04）, （27.50±8.79） times, P〈 0.05]. ② Forms and numbers of nNOS positive neurons in brain tissue： It could be observed under light microscope that in the hippocampal CA1 region, there were fewer nNOS positive neurons, which were lightly stained, and the processes were not clear enough; But the numbers of the positive neurons which were deeply stained as huffy were obviously increased in the experimental group, the cell body and cyloplasm of process were evenly stained, but the nucleus was not stained. The nNOS positive neurons in corpus stdatum had similar forms and size in the experimental group and control group. The form of the nNOS positive neurons in cerebellum were similar between the two groups. The numbers of nNOS positive neurons in hippocampal CA1 region and corpus striatum in the expedmental group [（18.22±7.47）, （11.38±5.00） cells/high power field] were obviously higher than those in the control group [（10.15±4.24）, （6.15±3.69） cells/high power field. The number of nNOS positive neurons in cerebellum had no significant difference between the two groups [（49.56±18.84）, （44.43±15.42） cells/high power field, P〉 0.05]. CONCLUSION ： Acute alcoholism may impair learning and memory ability, and nitric oxide may be involved in mediating the neurotoxic role of ethanol.

Effects of ginsenoside of stem and leaf combined with choline on learning and memory ability of rat models with Alzheimer diseases

BACKGROUND： Central adrenergic nerve and 5-serotonergic nerve can influence central cholinergic nerve on learning and memory and make easy for study; however, ginsenoside of stem and leaf （GSL） can improve functions of central adrenergic nerve; moreover, 5-serotonergic nerve and the combination with choline can produce synergistic effect and enhance learning and memory ability so as to improve learning and memory disorder of patients with Alzheimer disease （AD）. OBJECTIVE ： To observe the effects of GSL combining with choline on learning and memory of AD model rats DESIGN ： Randomized grouping design and controlled animal study SETIING ： Department of Pharmacology, Taishan Medical College MATERIALS ： The experiment was carried out in the Pharmacological Department of Medical College of Jilin University from October 1996 to January 1997. Forty healthy male Wistar rats of clean grade were randomly divided into 5 groups, including sham-injury group, model group, GSL group, choline group and combination group, with 8 rats in each group. Main medications： GSL with the volume more than 92.8% was provided by Department of Chemistry, Norman Bethune Medical College of Jilin University. Panaxatriol, the main component, was detected with thin layer scanning technique and regarded as the index of GSL quality [（55±1）%, CV= 2%, n = 5]. Choline was provided by the Third Shanghai Laboratory Factory. METHODS ： 150 nmol quinolinic acid was used to damage bilateral Meynert basal nuclei of adult rats so as to establish AD models. Rats in GSL, choline and combination groups were intragastric administrated with 400 mg/kg GSL, 200 mg/kg choline （20 mL/kg）, and both respectively last for 17 days starting from two days before operation. Rats in sham-injury group and model group were perfused with the same volume of distilled water once in each morning for the same days. （1） Passive avoidance step-down test： Five minutes later, rats jumped up safe platform when they were shocked with 36 V alternating current. If rats jumped down from the platform and the feet touched railings, the response was wrong. Numbers of wrong response were recorded within 3 minutes, and then the test was redone after 24 hours. （2） Morris water-maze spatial localization task： Swimming from jumping-off to platform directly was regarded as right response. Additionally, 4 successively right responses were regarded as the standard. Each rat was trained 10 times a day with 120 s per time for 3 successive days. The interval was 30 s. Three days later, numbers of right response were recorded. The training times were increased to 30 for unlearned rats. （3） Measurement of activity of choline acetylase in cerebral cortex： Rats were sacrificed at 17 days after operation to obtain cerebral cortex to measure activity of choline acetylase with radiochemistry technique. （4） Synergistic effect： It was expressed as Q value： Q value = factual incorporative effect/anticipant incorporative effect; Q ≥ 1 was regarded as synergistic effect. Anticipant incorporative effect = （EA＋EB-EA·EB）, EA and EB were single timing effect, respectively in GSL group and choline group. E（step-down test and Morris water maze test） = （x in model group - factual value in medicine groups）/x in model group; E （activity of choline acetylase） = （factual value in medicine groups -xin model group）/xin model group. MAIN OUTCOME MEASURES ： （1） Passive avoidance step-down test and Morris water-maze spatial localization task in the study of learning and memory; （2） activity of choline acetylase. RESULTS ： All 40 rats were involved in the final analysis. （1） Passive avoidance response： At learning phase on first day and retesting phase on the next day, numbers of wrong responses within 3 minutes were more in model group than sham operation group, and there was significant difference [（5.88±1.46）, （2.25±0.87） times; （2.63±1.06）, （0.50±0.53） times; P 〈 0.01]; numbers of wrong responses within 3 minutes were less in combination group than model group, and there was significant difference [learning phase： （1.12±0.83）, （5.88±1.46） times; retesting phase： （0.38±0.74）, （2.63±1.06）times, P 〈 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and 1.59, respectively and it showed synergistic effect. Spatial localization task： Training times were more in model group than sham operation group, and there was significant difference [（2.9±2.5）, （12.6±3.5） times; P 〈 0.01]. Training times were less in combination group than model group, and there was significant difference [（11.8±2.4）, （27.9±2.5） times, P 〈 0.01]; moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.07 and it showed synergistic effect. （3） Activity of choline acetylase： Activity was lower in model group than sham operation group, and there was significant difference [（30.56±8.33）, （61.11 ±8.33） nkat/g; P 〈 0.01]. Activity was higher in combination group than model group and there was significant difference [（50.00±8.33）, （30.56±8.33） nkat/g, P 〈 0.01];moreover, effect was stronger than that in GSL group and choline group. The Q value was 1.5 and it showed synergistic effect. CONCLUSZON： GSL in combination with choline can synergically improve the disorder of learning and memory of AD model rats. Its mechanism may be involved in enhancing the function of central cholinergic system.

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.

Short-term train arrival delay prediction:a data-driven approach

Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performance.

Embryonic skeleton development and neonatal learning and memory ability of rats anesthetized with pentobarbital sodium: Differences of administration occasion and time

BACKGROUND: Generally speaking, anesthesia is often used in gravid body and it has been already proved that many kind of medicine can result in malformation. OBJECTIVE: To explore embryonic skeleton development and neonatal learning and memory of rats anesthetized with pentobarbital sodium in gravid rats. DESIGN: A randomized control trial. SETTING: Laboratory Animal Center of Xuzhou Medical College. MATERIALS: A total of 80 adult female SD rats, of clean grade and weighing 220-240 g, were selected in this study. The main reagents were detailed as follows: pentobarbital sodium (Shanghai Xingzhi Chemical Plant, batch number: 921019); MG-2 maze test apparatus (Zhangjiagang Biomedical Instrument Factory); somatotype microscope (Beijing Taike Instrument Co., Ltd.). METHODS: ① A total of 160 SD rats of half males and females were selected in this study. All rats were copulated. The day that the plug was checked out in the vagina next day was looked as the first day of pregnancy. Gravid rats were divided randomly into four groups, including early anesthesia group, second anesthesia group, late anesthesia group and control group with 20 in each group. Rats in the early anesthesia group were injected with 25 mg/kg soluble pentobarbitone on the 7th day of pregnancy for once; rats in the second anesthesia group were anesthetized with 25 mg/kg soluble pentobarbitone on the 7th and the 14th days of pregnancy for once; rats in the late anesthesia group were anesthetized with 25 mg/kg soluble pentobarbitone on the 14th day of pregnancy for once; rats in the control group did not treat with anything. The time of anesthetizing was controlled in 3 to 4 hours and ether was absorbed while the time was not enough. ② Half of each group was sacrificed on day 20th of pregnancy and the fetus was taken out to be stained with alizarin red S. After stained, the fetal skeleton was examined. The learning and memorizing of one-month rats that were given birth by the rest gravid rats were tested through electric mare method. Determine their study ability according to their correct rate of 90% or above of arrival at the safe area in 20 s. After they finally learned to arrive at the safe area correctly, test them once more in 24 hours and record the correct rate of 15 times. MAIN OUTCOME MEASURES: The rate of malformation in fetus and ability of learning and memory in one-month rats. RESULTS: A total of 80 female rats were anesthetized in this experiment. Totally 490 immature rats were tested with maze testing machine and 196 fetuses were stained with alizarin red S to observe the development of their skeleton. However, one of the 80 female rats was led to death because of overdose. ① Malformation experiment: Learning ability of second anesthesia group was evidently different from the control group while the other two groups were not in the electric mare method. The fetal skeleton malformation rate of three experimental groups was 87.0%, 60.9% and 17.9%, respectively, while it was 5.6% in the control group. ② Electric mare method: Times of rats which arrived at the safe regions were respectively 49.0±31.0, 68.0±35.0, 47.0±31.0 and 44.0±21.0 in early anesthesia group, second anesthesia group, late anesthesia group and control group; and then, there was significant difference between the second anesthesia group and the control group (P < 0.05). Exact rates of memory of rats were respectively (64.36±14.35)%, (62.15±18.33)%, (54.19±12.28)% and (68.24±15.91)% in early anesthesia group, second anesthesia group, late anesthesia group and control group; and then, there were no significant differences as compared with the control group (P > 0.05). CONCLUSION: The influence of anesthesia with pentobarbital sodium is obvious in fetal skeleton development and learning and memory ability.

一种基于long short-term memory的唇语识别方法

唇动视觉信息是说话内容的重要载体。受嘴唇外观、背景信息和说话习惯等影响,即使说话者说相同的内容,唇动视觉信息也会相差很大。为解决唇语视觉信息多样性的问题,提出一种基于long short-term memory(LSTM)的新的唇语识别方法。以往大多数的方法从嘴唇外表信息入手。本方法用嘴唇关键点坐标描述嘴唇形变信息作为唇语视频的特征,它具有类内一致性和类间区分性的特点。然后利用LSTM对特征进行时序编码,它能学习具有区分性和泛化性的空间-时序特征。在公开的唇语数据集GRID、MIRACL-VC和Oulu VS上对本方法做了针对分割的单词或短语的说话者独立的唇语识别评估。在GRID和MIRACL-VC上,本方法的准确率比传统方法至少高30%;在Oulu VS上,本方法的准确率接近于最优结果。以上实验结果表明,本文提出的基于LSTM的唇语识别方法有效地解决了唇语视觉信息多样性的问题。