The Impact of Sleep Determination on Procrastination before Bedtime:The Role of Anxiety

Objective:The importance of good sleep for energy recovery and overall physical and mental health cannot be overstated.However,the increasing competitiveness of society,diversifying lifestyles,and the rapid spread of the internet and electronic devices have significantly impacted people’s sleep patterns,particularly through bedtime procrastination.Therefore,this study aims to investigate the relationship and underlying mechanisms between sleep determination,anxiety,and bedtime procrastination among the Chinese population.Method:The study utilized data from a national survey—the China Residents’Sleep Condition Survey(November 2021,with 6,037 participants).By constructing mediation and moderation models,it analyzed the relationship and mechanisms between sleep determination and bedtime procrastination,highlighting the dual role of anxiety:as a mediator between sleep determination and bedtime procrastination,and as a moderator of their relationship.Results:There was a negative correlation between sleep determination and bedtime procrastination;anxiety was related to an increase in bedtime procrastination behaviors;In the interplay between sleep determination and pre-bedtime procrastination,the regulatory effect of anxiety is found to be more pronounced than its intermediary role,with anxiety significantly diminishing the inverse relationship between sleep determination and procrastination before bedtime.Conclusion:Bedtime procrastination can lead to sleep issues,thus reducing sleep quality.Enhancing sleep determination or alleviating pre-sleep anxiety can help inhibit bedtime procrastination behaviors.Intervening and reducing bedtime procrastination is one of the effective measures to improve sleep quality.

SLEEP-MAD模式护理策略在结直肠癌病人术后护理中的应用

目的:探讨SLEEP-MAD模式护理策略在结直肠癌病人术后护理中的应用价值。方法:2022年5月—2023年5月选取医院收治的86例结直肠癌病人为研究对象,按照随机数字表法将病人分为观察组、对照组各43例,对照组行常规护理,观察组实施SLEEP-MAD模式护理策略,干预前后采用汉密尔顿焦虑量表(HAMA)、汉密尔顿抑郁量表(HAMD)、视觉模拟疼痛评分表(VAS)、匹兹堡睡眠质量指数量表(PSQI)、癌因性疲乏评分及欧洲癌症研究与治疗组织生命质量量表(EORTC QLQ-C30)评价病人的不良情绪、疼痛感、癌因性疲乏、睡眠质量及生活质量。结果:干预后观察组病人HAMA、HAMD、VAS、PSQI及癌因性疲乏评分低于对照组(P<0.05);干预后观察组病人EORTC QLQ-C30评分高于对照组(P<0.05)。结论:SLEEP-MAD模式护理策略可有效减轻结直肠癌病人焦虑及抑郁情绪,减轻病人术后疼痛感及癌因性疲乏,改善病人睡眠质量及生活质量。

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

ICON/MIGHTI与TIMED/SABER探测温度数据的对比

ICON卫星为临近空间环境特性研究、建模和预报提供了新数据.通过对ICON/MIGHTI与TIMED/SABER在90~105 km高度探测温度数据的比较,计算两者的年平均温度偏差和均方根误差,同时分析月平均温度偏差在不同月份中随高度和纬度的分布情况,为MIGHTI和SABER温度探测数据在临近空间大气建模和预报应用提供参考依据.结果表明,MIGHTI和SABER的温度垂直廓线变化趋势基本吻合,数值上有所差异.在12°S-42°N范围内,MIGHTI探测温度与SABER相比,在90~93 km时偏低,偏差最大值约2.5 K,在93~105 km偏高,偏差的绝对值最大约10 K.在不同季节,白天的温度偏差通常高于夜晚.SABER和MIGHTI的月平均温度偏差随季节和纬度的变化显著,夏季时的月平均温度偏差最大,且温度的均方根误差最大.

Associations of Time Spent on Study and Sleep with Anxiety and Depressive Symptoms in Junior High School Students:Report from the Large-Scale Monitoring of Basic Education Data in China

In the context of the COVID-19 pandemic and under the pressure of high competitiveness for higher education in China,junior high school students’mental health is facing greater challenges.Understanding the time allocation of study and sleep is necessary for developing effective prevention and treatment programs.Based on a survey of 31,057 junior high school students in 47 junior high schools in Gansu Province,China's Mainland,the study analyzed the associations of time spent on study and sleep with anxiety and depressive symptoms among junior high school students through chi-square test,ANOVA,logistic regression model and threshold regression model.It was found that 18.4%–21.1%of junior high school students had mild and above anxiety and depressive symptoms.Female junior high school students were more likely to have anxiety and depressive symptoms.In addition,time spent on study was significantly and positively associated with anxiety and depressive symptoms and time spent on sleep was significantly and negatively associated with anxiety and depressive symptoms.Late sleepers were more likely to suffer from anxiety and depressive tendencies.Reducing after-school study time appropriately and getting more sleep are beneficial in reducing anxiety and depression,but it is more important to ensure time of sleep.For the reduction of anxiety and depressive symptoms,the maximum time spent on study after school should not exceed 1.92 h,the minimum time spent on sleep at night should be more than 7 h and a 40-min lunch break should be guaranteed at noon.

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

Association of accelerometer-measured sleep duration and different intensities of physical activity with incident type 2 diabetes in a population-based cohort study

Purpose:The aim of the current study was to investigate the association of accelerometer-measured sleep duration and different intensities of physical activity(PA)with the risk of incident type 2 diabetes in a population-based prospective cohort study.Methods:Altogether,88,000 participants(mean age=62.2±7.9 years,mean±SD)were included from the UK Biobank.Sleep duration(short:<6 h/day;normal:6-8 h/day;long:>8 h/day)and PA of different intensities were measured using a wrist-won accelerometer over a 7-day period between 2013 and 2015.PA was classified according to the median or World Health Organization-recommendation:total volume of PA(high,low),moderate-to-vigorous PA(MVPA)(recommended,not recommended),and light-intensity PA(high,low).Incidence of type 2diabetes was ascertained using hospital records or death registries.Results:During a median follow-up of 7.0 years,1615 incident type 2 diabetes cases were documented.Compared with normal sleep duration,short(hazard ratio(HR)=1.21,95%confidence interval(95%CI):1.03-1.41)but not long sleep duration(HR=1.01,95%CI:0.89-1.15)was associated with excessive type 2 diabetes risk.This increased risk among short sleepers seems to be protected against by PA.Compared with normal sleepers with high or recommended PA,short sleepers with low volume of PA(HR=1.81,95%CI:1.46-2.25),not recommended(below the World Health Organization-recommended level of)MVPA(HR=1.92,95%CI:1.55-2.36),or low light-intensity PA(HR=1.49,95%CI:1.13-1.90)had a higher risk of type 2 diabetes,while short sleepers with a high volume of PA(HR=1.14,95%CI:0.88-1.49),recommended MVPA(HR=1.02,95%CI:0.71-1.48),or high light-intensity PA(HR=1.14,95%CI:0.92-1.41)did not.Conclusion:Accelerometer-measured short but not long sleep duration was associated with a higher risk of incident type 2 diabetes.A higher level of PA,regardless of intensity,potentially ameliorates this excessive risk.

Clinical efficacy and mechanism study of mid-frequency anti-snoring device in treating moderate obstructive sleep apnea-hypopnea syndrome

BACKGROUND Obstructive sleep apnea-hypopnea syndrome(OSAHS)is primarily caused by airway obstruction due to narrowing and blockage in the nasal and nasopha-ryngeal,oropharyngeal,soft palate,and tongue base areas.The mid-frequency anti-snoring device is a new technology based on sublingual nerve stimulation.Its principle is to improve the degree of oropharyngeal airway stenosis in OSAHS patients under mid-frequency wave stimulation.Nevertheless,there is a lack of clinical application and imaging evidence.METHODS We selected 50 patients diagnosed with moderate OSAHS in our hospital between July 2022 and August 2023.They underwent a 4-wk treatment regimen involving the mid-frequency anti-snoring device during nighttime sleep.Following the treatment,we monitored and assessed the sleep apnea quality of life index and Epworth Sleepiness Scale scores.Additionally,we performed computed tomo-graphy scans of the oropharynx in the awake state,during snoring,and while using the mid-frequency anti-snoring device.Cross-sectional area measurements in different states were taken at the narrowest airway point in the soft palate posterior and retrolingual areas.RESULTS Compared to pretreatment measurements,patients exhibited a significant reduction in the apnea-hypopnea index,the percentage of time with oxygen saturation below 90%,snoring frequency,and the duration of the most prolonged apnea event.The lowest oxygen saturation showed a notable increase,and both sleep apnea quality of life index and Epworth Sleepiness Scale scores improved.Oropharyngeal computed tomography scans revealed that in OSAHS patients cross-sectional areas of the oropharyngeal airway in the soft palate posterior area and retrolingual area decreased during snoring compared to the awake state.Conversely,during mid-frequency anti-snoring device treatment,these areas increased compared to snoring.CONCLUSION The mid-frequency anti-snoring device demonstrates the potential to enhance various sleep parameters in patients with moderate OSAHS,thereby improving their quality of life and reducing daytime sleepiness.These therapeutic effects are attributed to the device’s ability to ameliorate the narrowing of the oropharynx in OSAHS patients.

基于TimeGAN数据增强的复杂过程故障分类方法

针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题,提出基于TimeGAN数据增强的复杂过程故障分类方法.针对小子样故障,使用TimeGAN对历史故障数据进行数据增强,生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量,剔除不可信样本,构造平衡的故障样本集.将故障样本映射到高维核空间,并在核空间中提取故障子空间.设计故障分类策略并定义4种故障分类性能评估指标以定量衡量算法的分类性能.Tennessee Eastman应用结果表明,所提数据增强方法可以有效扩充故障样本,进而提高故障重构率.与WGAN-GP和SMOTE方法进行对比,发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能.

Effects of hormone replacement therapy on mood and sleep quality in menopausal women

BACKGROUND Hormone replacement therapy is an effective treatment strategy for the management of symptoms in naturally menopausal women.However,some patients report experiencing adverse effects.AIM To analyze the effects of hormone replacement therapy in menopausal female patients.METHODS A total of 152 menopausal female patients admitted to the Gynecology Department of the Ganzhou Maternal and Child Health Hospital between January 2021 and December 2023 were divided into the observation group(n=76,conventional treatment+hormone replacement therapy)and the control group(n=76,conventional treatment only)via random casting.The improvement observed in the following items were compared between the groups:Kupperman menopausal index(KMI),emotional state[The Positive and Negative Affect Scale(PANAS)],sleep quality[Self-Rating Scale of Sleep(SRSS)],treatment effectiveness,and treatment safety.RESULTS The modified KMI and SRSS scores of the observation group were lower than those of the control group after three rounds of treatment.The improvement in the PANAS score observed in the observation group was greater than that observed in the control group(P<0.05).The total treatment effectivity rate in the observation group was higher than that in the control group(86.84%vs 96.05%,χ2=4.121,P=0.042).The incidence rate of adverse reactions in the two groups was comparable(6.58%vs 9.21%,χ2=0.361,P=0.547).CONCLUSION Hormone replacement therapy effectively improved the clinical symptoms,actively channeled negative emotions,and improved the quality of sleep in menopausal patients,indicating its effectiveness and safety.

Midday Napping,Nighttime Sleep,and Mortality:Prospective Cohort Evidence in China

Objective In developed countries,midday napping and nighttime sleep duration have been linked to long-term survival;however,little is known about such effects in less developed regions.Therefore,this study aimed to assess the associations of midday napping and nocturnal sleep with mortality in middle-aged and older Chinese adults.Methods A nationwide cohort of 15,524 adults aged≥45 years was enrolled from 28 provincial regions across China's Mainland and followed up from 2011 to 2018,using data from the Chinese Health and Retirement Longitudinal Study.Midday napping and nighttime sleep duration were assessed using standardized questionnaires.Cox proportional hazards models with random intercepts for the surveyed provinces were used to estimate hazard ratios(HRs)of all-cause mortality,adjusting for sociodemographic characteristics,behavioral factors,and health status.Results A total of 1,745 deaths occurred during a median follow-up of 7.1 years,and the mean(standard deviation)age was 59(10.1)years at baseline.Compared with non-nappers,over 60 min nappers had a higher risk of all-cause mortality[HR:1.35,95%confidence interval(CI):1.17–1.56],while no significant associations were observed among<30 min nappers.Compared with sleep duration of 6–8 h/night,both short(<6 h)and long(≥8 h)sleep duration were significantly associated with increased mortality,with corresponding HR(95%CI)estimates of 1.21(1.05–1.38)and 1.26(1.10–1.44),respectively.We observed significant patterns for greater risks associated with longer nap duration,with a Ptrend value<0.001 for all-cause mortality.No significant evidence of an additive interaction was identified between midday napping and nighttime sleep.Conclusion Long midday napping and inappropriate nighttime sleep were independently associated with an increased risk of all-cause mortality in middle-aged and older Chinese populations.Biological studies are needed to validate our findings and clarify the mechanisms underlying this association.

Exercise-with-melatonin therapy improves sleep disorder and motor dysfunction in a rat model of ischemic stroke

Exercise-with-melatonin therapy has complementary and synergistic effects on spinal cord injury and Alzheimer's disease,but its effect on stroke is still poorly understood.In this study,we established a rat model of ischemic stroke by occluding the middle cerebral artery for 60 minutes.We treated the rats with exercise and melatonin therapy for 7 consecutive days.Results showed that exercise-with-melatonin therapy significantly prolonged sleep duration in the model rats,increased delta power values,and regularized delta power rhythm.Additionally,exercise-with-melatonin therapy improved coordination,endurance,and grip strength,as well as learning and memory abilities.At the same time,it led to higher hippocampal CA1 neuron activity and postsynaptic density thickness and lower expression of glutamate receptor 2 than did exercise or melatonin therapy alone.These findings suggest that exercise-withmelatonin therapy can alleviate sleep disorder and motor dysfunction by increasing glutamate receptor 2 protein expression and regulating hippocampal CA1 synaptic plasticity.

NLRP3-mediated autophagy dysfunction links gut microbiota dysbiosis to tau pathology in chronic sleep deprivation

Emerging evidence indicates that sleep deprivation(SD)can lead to Alzheimer’s disease(AD)-related pathological changes and cognitive decline.However,the underlying mechanisms remain obscure.In the present study,we identified the existence of a microbiota-gut-brain axis in cognitive deficits resulting from chronic SD and revealed a potential pathway by which gut microbiota affects cognitive functioning in chronic SD.Our findings demonstrated that chronic SD in mice not only led to cognitive decline but also induced gut microbiota dysbiosis,elevated NLRP3 inflammasome expression,GSK-3βactivation,autophagy dysfunction,and tau hyperphosphorylation in the hippocampus.Colonization with the“SD microbiota”replicated the pathological and behavioral abnormalities observed in chronic sleep-deprived mice.Remarkably,both the deletion of NLRP3 in NLRP3-/-mice and specific knockdown of NLRP3 in the hippocampus restored autophagic flux,suppressed tau hyperphosphorylation,and ameliorated cognitive deficits induced by chronic SD,while GSK-3βactivity was not regulated by the NLRP3 inflammasome in chronic SD.Notably,deletion of NLRP3 reversed NLRP3 inflammasome activation,autophagy deficits,and tau hyperphosphorylation induced by GSK-3βactivation in primary hippocampal neurons,suggesting that GSK-3β,as a regulator of NLRP3-mediated autophagy dysfunction,plays a significant role in promoting tau hyperphosphorylation.Thus,gut microbiota dysbiosis was identified as a contributor to chronic SD-induced tau pathology via NLRP3-mediated autophagy dysfunction,ultimately leading to cognitive deficits.Overall,these findings highlight GSK-3βas a regulator of NLRP3-mediated autophagy dysfunction,playing a critical role in promoting tau hyperphosphorylation.

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Causal role of immune cells in obstructive sleep apnea hypopnea syndrome:Mendelian randomization study

BACKGROUND Despite being one of the most prevalent sleep disorders,obstructive sleep apnea hypoventilation syndrome(OSAHS)has limited information on its immunologic foundation.The immunological underpinnings of certain major psychiatric diseases have been uncovered in recent years thanks to the extensive use of genome-wide association studies(GWAS)and genotyping techniques using highdensity genetic markers(e.g.,SNP or CNVs).But this tactic hasn't yet been applied to OSAHS.Using a Mendelian randomization analysis,we analyzed the causal link between immune cells and the illness in order to comprehend the immunological bases of OSAHS.AIM To investigate the immune cells'association with OSAHS via genetic methods,guiding future clinical research.METHODS A comprehensive two-sample mendelian randomization study was conducted to investigate the causal relationship between immune cell characteristics and OSAHS.Summary statistics for each immune cell feature were obtained from the GWAS catalog.Information on 731 immune cell properties,such as morphologic parameters,median fluorescence intensity,absolute cellular,and relative cellular,was compiled using publicly available genetic databases.The results'robustness,heterogeneity,and horizontal pleiotropy were confirmed using extensive sensitivity examination.RESULTS Following false discovery rate(FDR)correction,no statistically significant effect of OSAHS on immunophenotypes was observed.However,two lymphocyte subsets were found to have a significant association with the risk of OSAHS:Basophil%CD33dim HLA DR-CD66b-(OR=1.03,95%CI=1.01-1.03,P<0.001);CD38 on IgD+CD24-B cell(OR=1.04,95%CI=1.02-1.04,P=0.019).CONCLUSION This study shows a strong link between immune cells and OSAHS through a gene approach,thus offering direction for potential future medical research.

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.