Comparative study on the performance of ConvLSTM and ConvGRU in classification problems-taking early warning of short-duration heavy rainfall as an example

卷积长短期记忆单元ConvLSTM和卷积门控循环单元ConvGRU是两种广泛应用的深度学习单元,通过将循环机制与卷积运算相结合,常常用于时空序列的预测.为了明确上述两种模型的收敛速度和分类能力,需要使用相同的模型架构对相同的分类问题进行预测.本研究将北京短时强降水区级预警问题看作深度学习中的二分类问题,使用京津冀雷达网的组合反射率数据和北京区域内的自动气象站降雨数据进行深度学习模型的训练和评估.结果表明,ConvGRU的收敛速度比ConvLSTM快约25%.ConvLSTM和ConvGRU的预警性能随地区,时间,降雨强度的变化趋势相似,但大部分ConvLSTM的得分较高,少数情况下ConvGRU的得分较高.

Predicting and Curing Depression Using Long Short Term Memory and Global Vector

In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.

Trends of Inequalities in the Use of Long-Term Reversible Contraceptives in Burkina Faso between 2010 and 2015

Background: Efforts have been made in Burkina Faso, a French-speaking country, since 2010 to improve healthcare access and provide affordable contraceptive methods to women. With the increasing prevalence of modern contraceptives in Burkina Faso, it is important to examine the socio-demographic factors that contribute to this new pattern of contraceptive use. This study aims to analyze the changes in socio-demographic factors associated with long-term contraceptive use and provide scientific evidence to guide policy development and action planning in family planning. Data and Methods: We utilized data from the 2010 Demographic and Health Survey, which included 17,087 women aged 15 - 49 years, and the 2015 Demographic and Health Module, which included 11,504 women in the same age group. For the analysis of contraceptive use, we focused on women who were in need of contraception (either met or unmet), of reproductive age, non-pregnant, and either married or sexually active but not married. We included users of modern reversible methods and excluded non-users, as well as users of traditional or permanent methods. Results: Our findings revealed a high prevalence of long-term contraceptive use across all categories;however, certain challenges were identified, such as lower levels of information about contraceptive methods among users and the persistence of inequalities. Family planning discussions and partner approval did not influence long-term contraceptive choice. Additionally, some providers selectively offered specific methods based on women’s life course characteristics, such as parity and marital status, despite evidence suggesting that young and nulliparous women can effectively use long-term methods. Conclusion: Given the high effectiveness of long-term contraceptive methods, it is crucial to address barriers that hinder their utilization among young and nulliparous women, as well as those who desire to delay pregnancy. Efforts should focus on improving knowledge and dispelling misconceptions surrounding long-term methods. Providers play a pivotal role in this process by adopting counseling strategies that enhance users’ understanding and facilitate informed decision-making regarding contraceptive options.

Short-and long-term behaviors of drifts in the Callovo-Oxfordian claystone at the Meuse/Haute-Marne Underground Research Laboratory

Since 2000, the French National Radioactive Waste Management Agency （ANDRA） has been constructing an Underground Research Laboratory （URL） at Bure （east of the Paris Basin） to perform experiments in order to obtain in situ data necessary to demonstrate the feasibility of geological repository in the Callovo- Oxfordian claystone. An important experimental program is planned to characterize the response of the rock to different drift construction methods, Before 2008, at the main level of the laboratory, most of the drifts were excavated using pneumatic hammer and supported with rock bolts, sliding steel arches and fiber shotcrete. Other techniques, such as road header techniques, stiff and flexible supports, have also been used to characterize their impacts. The drift network is developed following the in situ major stresses. The parallel drifts are separated enough so as they can be considered independently when their hydromechanical （HM） behaviors are compared. Mine-by experiments have been performed to measure the HM response of the rock and the mechanical loading applied to the support system due to the digging and after excavation. Drifts exhibit extensional （mode I） and shear fractures （modes II and III） induced by excavation works. The extent of the induced fracture networks depends on the drift orientation versus the in situ stress field. This paper describes the drift convergence and deformation in the surrounding rock walls as function of time and the impact of different support methods on the rock mass behavior. An observation based method is finally applied to distinguish the instantaneous and time-dependent parts of the rock mass deformation around the drifts.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market.

Long-term effects of early antibiotic intervention on blood parameters,apparent nutrient digestibility, and fecal microbial fermentation profile in pigs with different dietary protein levels

Backgroud： This study aimed to determine the effects of early antibiotic intervention（EAI） on subsequent blood parameters, apparent nutrient digestibility, and fecal fermentation profile in pigs with different dietary crude protein（CP） levels. Eighteen litters of piglets（total 212） were randomly allocated to 2 groups and were fed a creep feed diet with or without in-feed antibiotics（olaquindox, oxytetracycline calcium and kitasamycin） from postnatal d 7 to d 42. On d 42, the piglets within the control or antibiotic group were mixed, respectively, and then further randomly assigned to a normal-（20%, 18%, and 14% CP from d 42 to d 77, d 77 to d 120, and d 120 to d 185,respectively） or a low-CP diet（16%, 14%, and 10% CP from d 42 to d 77, d 77 to d 120, and d 120 to d 185,respectively）, generating 4 groups. On d 77（short-term） and d 185（long-term）, serum and fecal samples were obtained for blood parameters, microbial composition and microbial metabolism analysis.Results： EAI increased（P 〈 0.05） albumin and glucose concentrations in low-CP diet on d 77, and increased（P 〈 0.05） urea concentration in normal-CP diet. On d 185, EAI increased（P 〈 0.05） globulin concentration in normal-CP diets, but decreased glucose concentration. For nutrient digestibility, EAI increased（P 〈 0.05）digestibility of CP on d 77. For fecal microbiota, the EAI as well as low-CP diet decreased（P 〈 0.05） E. coli count on d 77. For fecal metabolites, on d 77, EAI decreased（P 〈 0.05） total amines concentration but increased skatole concentration in low-CP diet. On d 185, the EAI increased（P 〈 0.05） putrescine and total amines concentrations in low-CP diets but reduced（P 〈 0.05） in the normal-CP diets. The low-CP diet decreased the concentrations of these compounds.Conclusions： Collectively, these results indicate that EAI has short-term effects on the blood parameters and fecal microbial fermentation profile. The effects of EAI varied between CP levels, which was characterized by the significant alteration of glucose and putrescine concentration.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

Conditional Random Field Tracking Model Based on a Visual Long Short Term Memory Network

In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.

The Impact of Meteorological Factors on the Short-term Cumulative Effect of Air Particulate in Tianjin

In order to reveal the rapid increase mechanism of particulate concentration in short time,a notion of short-term cumulative effect of air particulate is defined as the significant increase of pollutant concentration in a short time under the condition of breeze,stable weather and constant emission caused by human being's activities. Subsequently,an index of short-term cumulative effect is established with air diffusive equation,and then the macro-scale meteorological situation and micro-scale factors of forming the short-term cumulative effect are discussed with the observation data. The macro-scale meteorological variables contain upper-level weather situations and surface weather situations. The micro-scale factors mainly include the boundary-layer height and boundary-layer stability. The analyses show that boundary-layer factors and weather variables have a significant influence on the short-term cumulative effect. The notion of short-term cumulative effect will play an important role in interpreting the severe pollution weather.

Thermomechanical analysis of long-term global modal and local deformation measurements of the Kishwaukee Bridge using the bootstrap

In this paper we present a comparative analysis of global frequency and local deformation data for a large concrete bridge. The asymptotic probability distributions of the central statistics are presented, and compared with empirical bootstrap estimates. Bootstrapped distributions are calculated from reference data obtained during 1999–2000 and used to develop change-point alarm criteria for the structure, using reasonable sensitivity measures developed from FEM simulations and structural analysis. The implications of the frequency data are discussed in conjunction with the strain and displacement measurements in order to discern if the load carrying capacity of the bridge has been affected. The critical need for more advanced temperature compensation models for large structures continually in thermal disequilibrium is discussed.

Short-Term Relay Quality Prediction Algorithm Based on Long and Short-Term Memory

The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.

Long Term Application of a Vehicle-Based Health Monitoring System to Short and Medium Span Bridges and Damage Detection Sensitivity

Largest portion of the bridge stock in almost any country and bridge owning organisation consists on ordinary bridges that has short or medium spans and are now deteriorating due to aging, etc. Therefore, it is becoming an important social concern to develop and put to practical use simple and efficient health monitoring systems for existing short and medium span (10 - 30 m) bridges. In this paper, one practical solution to the problem for condition assessment of short and medium span bridges was discussed. A vehicle-based measurement with a public bus as part of a public transit system (called “Bus monitoring system”) has been developed to be capable of detecting damage that may affect the structural safety of a bridge from long term vibration measurement data collected while the vehicle (bus) crossed the target bridges. This paper systematically describes how the system has been developed. The bus monitoring system aims to detect the transition from the damage acceleration period, in which the structural safety of an aged bridge declines sharply, to the deterioration period by continually monitoring the bridge of interest. To evaluate the practicality of the newly developed bus monitoring system, it has been field-tested over a period of about four years by using an in-service fixed-route bus operating on a bus route in the city of Ube, Yamaguchi Prefecture, Japan. The verification results thus obtained are also described in this paper. This study also evaluates the sensitivity of “characteristic deflection”, which is a bridge (health) condition indicator used by the bus monitoring system, in damage detection. Sensitivity of “characteristic deflection” is verified by introducing artificial damage into a bridge that has ended its service life and is awaiting removal. As the results, it will be able to make a rational long-term health monitoring system for existing short and mediumspan bridges, and then the system helps bridge administrators to establish the rational maintenance strategies.

Study on the operation effect of long term care insurance system in Qingdao

Population aging has become a global social problem, the problem in our country has become the core of the aging problem. With the gradual aging of the elderly, loss of energy, loss of energy has become the norm for the elderly or sick elderly. According to the Ministry of Civil Affairs statistics, as of the end of 2014, the number of disabled elderly in China reached 35 million, about 90% of the disabled elderly need professional care services and professional treatment to ease the pain. In order to deal with the social problems brought about by the aging of the population, in July 1, 2012, Qingdao City, the first in the country to establish a long-term health care insurance system, has taken a key step to crack the problem of aging. In this paper, the situation of Qingdao long term care insurance system as the research object, taking Qingdao city as study sites, according to statistical data analysis of the implementation effect of long-term local health care policy, and through questionnaires and interviews in the form of local residents to participate in the investigation of awareness, and perception. At the same time, the results of the survey on long-term health care insurance system in Qingdao, the operation effect of the overall analysis.

Clinical Observation on the Improved Short-Term Effects of Anti-Tumor Purified Chinese Medicine Injection Ai-Jing for 138 Cases with Malignant Tumors

The subjects of this study were to observe the improved short-term effects of anti-tumor purified Chinese medicine injection Ai-Jing for 138 cases with malignant tumors. This was a multi-center clinical study, and a total of 138 patients included 96 males and 42 females, with the age from 60 to 76 years. Ai-Jing has obtained the Chinese invention patent since 2008 (ZL991-03528.3). A novel technique acupoint injection method was adopted. Except for lung cancer, patients with other malignant tumors were injected once every other day, 2 ml each time. A course of treatment needs 6 Ai-Jing injections, lung cancer patients need 10 - 20 Ai-Jing injections. At the same time, patients were treated with traditional medicine decoction. Due to the limited number of cases, cure rate, recovery rate and remission rate were calculated as a whole, respectively 18.12%, 44.20% and 32.15%. All the invalid patients were lung cancer, accounting for 16.67% of the patients treated with lung cancer, and the total treatment inefficiency was 6.52%. Conclusion: Chinese Medicine Injection Ai-Jing extracts the effective components of anti-cancer and tumorigenesis, and has the effects of anti-cancer and tumorigenesis, anti-inflam-matory and analgesic, and repairing body damage, with small toxic and side effects and low cost.

Analyses of fear memory in Arc/Arg3.1-deficient mice: intact short-term memory and impaired long-term and remote memory

Activity-regulated cytoskeleton-associated protein (Arc/Arg3.1) was originally identified in patients with seizures. It is densely distributed in the hip-pocampus and amygdala in particular. Because the expression of Arc/Arg3.1 is regulated by nerve in-puts, it is thought to be an immediate early gene. As shown both in vitro and in vivo, Arc/Arg3.1 is in-volved in synaptic consolidation and regulates some forms of learning and memory in rats and mice [1,2]. Furthermore, a recent study suggests that Arc/Arg3.1 may play a significant role in signal transmission via AMPA-type glutamate receptors [3-5]. Therefore, we conducted a detailed analysis of fear memory in Arc/Arg3.1-deficient mice. As previously reported, the knockout animals exhib-ited impaired fear memory in both contextual and cued test situations. Although Arc/Arg3.1-deficient mice showed almost the same performance as wild-type littermates 4 hr after a conditioning trial, their performance was impaired in the retention test after 24 hr or longer, either with or without reconsolidation. Immunohistochemical analyses showed an abnormal density of GluR1 in the hip-pocampus of Arc/Arg3.1-deficient mice;however, an application of AMPA potentiator did not improve memory performance in the mutant mice. Memory impairment in Arc/Arg3.1-deficient mice is so ro-bust that the mice provide a useful tool for devel-oping treatments for memory impairment.

A LONG TERM EFFECT EVALUATION OF TOTAL HIP PROSTHETIC REPLACEMENT

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State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory

Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model.

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。

Evolution of interfacial dislocation networks during long term thermal aging in Ni-based single crystal superalloy DD5

Interfacial dislocations found in single crystal superalloys after long term thermal aging have an important effect on mechanical properties. Long term thermal aging tests for DD5 single crystal superalloy were carried out at 1,100 ℃ for 20, 100, 200, 500 and 1000 h, and then cooled by air. The effect of long term thermal aging on the dislocation networks at the γ/γ' interfaces was investigated by FE-SEM. Results showed that during the long term thermal aging at 1,100 ℃, misfit dislocations formed firstly and then reorientation in the(001) interfacial planes occurred. Different types of square or rectangular dislocation network form by dislocation reaction. Square dislocation networks consisting of four groups of dislocations can transform into octagonal dislocation networks, and then form another square dislocation network by dislocation reaction. Rectangular dislocation networks can also transform into hexagonal dislocation networks. The interfacial dislocation networks promote the γ' phase rafting process. The dislocation networks spacings become smaller and smaller, leading to the effective lattice misfit increasing from-0.10% to-0.32%.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.