The impact of the provincial reimbursement scheme on the use of targeted anticancer medications in Zhejiang,China:a controlled interrupted time-series analysis

To alleviate problems with access and affordability,six targeted anticancer medications(TAMs)were listed in the Provincial Reimbursement Drug List(PRDL)for the first time in Zhejiang,China in February 2015.In the present study,we aimed to evaluate the implementation of the PRDL policy on TAMs use.Using the pharmaceutical procurement data of these six listed TAMs(study group)and four unlisted TAMs(control group)from 22 tertiary hospitals in Zhejiang,China dated between January 2014 and February 2017,interrupted time-series analysis was adopted to examine differences in the average hospital purchasing volume(HPV)and the average hospital purchasing spending(HPS)between the two groups.The average daily cost of listed TAMs in the study group was decreased after April 2015.After enlistment,the average HPV per month was significantly increased by 34.6 defined daily doses(DDDs)(P<0.001),and the average HPS per month was significantly increased by USD 6614.9(P<0.001)for the listed TAMs in the study group(n=6).Neither the average HPV nor the average HPS changed significantly for the unlisted TAMs in the control group(n=4).The PRDL policy showed positive effects on improving patients’affordability and promoting access to TAMs in Zhejiang.The government should conduct further price negotiations and include more TAMs with clinical benefits into reimbursement schemes to relieve patients’financial burden and promote access.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Influence of Interrupted Childhood Biographies on Health Development: Evaluation of the Scientific Literature on the Example of Being Taken into Care*

Background: An interrupted family history, as is the case after taking someone into care, can complicate collecting family anamnesis data. In addition, the interrupted family history itself could be considered part of a person’s risk profile. Aim and methods: Literature analysis was conducted to examine whether there are scientific studies on health development after placement in out-of-home-care in order to recognise any existing medical characteristics that may be relevant for internal medical care. Results: There are few scientific publications on the health development of people after being placed in out-of-home-care. Direct reactions to the stress of being taken into custody include nausea and fever. However, effects that go beyond the acute situation and last into adulthood have also been described, such as AD(H)D, asthma, diabetes, cancer, hypertension and cardiovascular diseases (myocardial infarction, stroke), epilepsy and increased overall mortality in adulthood. Studies show that not only previous experience but also the stress of being taken into care is triggers for this. Conclusion: Information about a previous institutionalisation can hence be important for internal medical practice. The available scientific literature shows heterogeneous study methodology and no group of people with experience of out-of-home-placement has yet been scientifically accompanied for a long time period. Further studies on this could help to better weigh up the consequences of omitting and conducting an intervention for child/youth protection as well as to improve the medical care for this group of people.

Effect of interrupted aging on mechanical properties and corrosion resistance of 7A75 aluminum alloy

The effects of interrupted aging on mechanical properties and corrosion resistance of 7A75 aluminum alloy extruded bar were investigated through various analyses,including electrical conductivity,mechanical properties,local corrosion properties,and slow strain rate tensile stress corrosion tests.Microstructure characterization techniques such as metallographic microscopy,scanning electron microscopy(SEM),and transmission electron microscopy(TEM)were also employed.The results indicate that the tensile strength of the alloy produced by T6I6 aging is similar to that produced by T6I4 aging,and it even exceeds 700 MPa.Furthermore,the yield strength increases by 52.7 MPa,reaching 654.8 MPa after T6I6 aging treatment.The maximum depths of intergranular corrosion(IGC)and exfoliation corrosion(EXCO)decrease from 116.3 and 468.5μm to 89.5 and 324.3μm,respectively.The stress corrosion factor also decreases from 2.1%to 1.6%.These findings suggest that the alloy treated with T6I6 aging exhibits both high strength and excellent stress corrosion cracking resistance.Similarly,when the alloy is treated with T6I4,T6I6 and T6I7 aging,the sizes of grain boundary precipitates(GBPs)are found to be 5.2,18.4,and 32.8 nm,respectively.The sizes of matrix precipitates are 4.8,5.7 and 15.7 nm,respectively.The atomic fractions of Zn in GBPs are 9.92 at.%,8.23 at.%and 6.87 at.%,respectively,while the atomic fractions of Mg are 12.66 at.%,8.43 at.%and 7.00 at.%,respectively.Additionally,the atomic fractions of Cu are 1.83 at.%,2.47 at.%and 3.41 at.%,respectively.

Feasibility and clinical benefits of the double-ProGlide technique for hemostasis after cryoballoon atrial fibrillation ablation with uninterrupted oral anticoagulants

OBJECTIVE To access the efficacy and safety of the double-ProGlide technique for the femoral vein access-site closure in cryoballoon ablation with uninterrupted oral anticoagulants(OAC),and its impact on the electrophysiology laboratory time as well as hospital stay after the procedure in this observational study.METHODS Patients with atrial fibrillation undergoing cryoballoon ablation with uninterrupted OAC at Department of Cardiology,Beijing Anzhen Hospital,Capital Medical University,Beijing,China from May 2019 to May 2021 were enrolled in this study.From October 2020,double-ProGlide technique was consistently used for hemostasis(ProGlide group),and before that conventional manual compression was utilized(manual compression group).The occurrence of vascular and groin complications was accessed during the hospital stay and until the three-month follow-up.RESULTS A total of 140 participants(69.30%of male,mean age:59.21±10.29 years)were evaluated,70 participants being in each group.Immediate hemostasis was achieved in all the patients with ProGlide closure.No major vascular complications were found in the ProGlide group while two major vascular complications were occurred in the manual compression group.The incidence of any groin complication was obviously higher in subjects with manual compression than patients with ProGlide devices(15.71%vs.2.86%,P=0.009).In addition,compared with the manual compression group,the ProGlide group was associated with significantly shorter total time in the electrophysiology laboratory[112.0(93.3–128.8)min vs.123.5(107.3–158.3)min,P=0.006],time from sheath removal until venous site hemostasis[3.8(3.4–4.2)min vs.8.0(7.6–8.5)min,P<0.001],bed rest time[8.0(7.6–8.0)h vs.14.1(12.0–17.6)h,P<0.001]and hospital stay after the procedure[13.8(12.5–17.8)h vs.38.0(21.5–41.0)h,P<0.001].CONCLUSIONS Utilization of the double-ProGlide technique for hemostasis after cryoballoon ablation with uninterrupted OAC is feasible and safe,which has the clinical benefit in reducing the total electrophysiology laboratory time and the hospital stay length after the procedure.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.

Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Sentinel-1 In SAR observations and time-series analysis of co-and postseismic deformation mechanisms of the 2021 Mw 5.8 Bandar Ganaveh Earthquake,Southern Iran

In the past two decades,because of the significant increase in the availability of differential interferometry from synthetic aperture radar and GPS data,spaceborne geodesy has been widely employed to determine the co-seismic displacement field of earthquakes.On April 18,2021,a moderate earthquake(Mw 5.8)occurred east of Bandar Ganaveh,southern Iran,followed by intensive seismic activity and aftershocks of various magnitudes.We use two-pass D-InSAR and Small Baseline Inversion techniques via the LiCSBAS suite to study the coseismic displacement and monitor the four-month post-seismic deformation of the Bandar Ganaveh earthquake,as well as constrain the fault geometry of the co-seismic faulting mechanism during the seismic sequence.Analyses show that the co-and postseismic deformation are distributed in relatively shallow depths along with an NW-SE striking and NE dipping complex reverse/thrust fault branches of the Zagros Mountain Front Fault,complying with the main trend of the Zagros structures.The average cumulative displacements were obtained from-137.5 to+113.3 mm/yr in the SW and NE blocks of the Mountain Front Fault,respectively.The received maximum uplift amount is approximately consistent with the overall orogen-normal shortening component of the Arabian-Eurasian convergence in the Zagros region.No surface ruptures were associated with the seismic source;therefore,we propose a shallow blind thrust/reverse fault(depth~10 km)connected to the deeper basal decollement fault within a complex tectonic zone,emphasizing the thin-skinned tectonics.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy.

Pulsatile gonadotropin-releasing hormone therapy induces spermatogenesis in pituitary stalk interruption syndrome:A case report and review of the literature

BACKGROUND Pituitary stalk interruption syndrome(PSIS)is a rare anatomical defect of the pituitary gland falling under the spectrum of holoprosencephaly phenotypes.It is characterized by a deficiency in anterior pituitary hormones,such as growth hormone,gonadotropins,and thyroid hormones.Due to the syndrome's rarity and nonspecific manifestations,there is a lack of standardized treatment strategies.Consequently,early diagnosis through imaging and on-time intervention are crucial for improving patients’outcomes.CASE SUMMARY A 30-year-old man presented with absent secondary sexual characteristics and azoospermia.Laboratory evaluation revealed a deficiency in gonadotropins,while thyroid function was mostly within normal ranges.Magnetic resonance imaging of the pituitary gland showed pituitary stalk agenesis,hypoplasia of the anterior pituitary,and ectopic posterior pituitary,leading to the diagnosis of PSIS.Initially,the patient underwent 6 mo of gonadotropin therapy without significant changes in hormone levels and secondary sexual characteristics.Pulsatile gonadotropin-releasing hormone therapy was then administered,resulting in the detection of sperm in the semen analysis within 3 mo.After 6 mo,routine semen tests showed normal semen quality.The couple faced challenges in conceiving due to abstinence and underwent three cycles of artificial insemination,which was unsuccessful.They also attempted in vitro fertilization,but unfortunately,the woman experienced a miscarriage 10 wk after the embryo transfer.CONCLUSION Early detection,accurate diagnosis,and timely treatment are crucial in improving the quality of life and fertility of PSIS patients.

Pituitary stalk interruption syndrome complicated with liver cirrhosis:A case report

BACKGROUND Pituitary stalk interruption syndrome(PSIS)is a rare disorder,often characterized by delayed growth and development,short stature,and hypogonadism as the main clinical manifestations.It is not clear whether PSIS can lead to liver cirrhosis.CASE SUMMARY This paper reported a case of liver cirrhosis of unknown origin.The patient was admitted to Beijing Ditan Hospital Affiliated to Capital Medical University in November 2023.The diagnosis of PSIS complicated with liver cirrhosis was established after a series of blood tests and pituitary magnetic resonance imaging examination.CONCLUSION We also reviewed the literature from both domestic and international sources to deepen the clinical understanding of PSIS in conjunction with liver cirrhosis among medical practitioners.

Analyses of unified congestion measures for interrupted traffic flow on urban roads

To study the congestion of interrupted flow on urban roads, a comprehensive evaluation method is proposed. First, based on the results of correlation analysis between different parameters of interrupted flow, the traffic parameters of interrupted traffic flow are divided into two categories： the basic parameters and the operation parameters. Polynomial regression is used to formulize the nonlinear relationships between the basic parameters and the operation parameters. Then, the congestion model incorporating both operational and volume characteristics of traffic flow is proposed. The inputs of the model are the basic parameters, while the output is a dimensionless index value between 0 and 1. Finally, the proposed methods are compared with existing evaluation measures of congestion. Results show that the proposed indices can capture the variation of both the basic parameters and the operation parameters, which is more balanced compared with the existing evaluation measures.

Using Interrupted Time Series Design to Analyze Changes in Hand, Foot, and Mouth Disease Incidence during the Declining Incidence Periods of 2008-2010 in China

Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.

ECCM scheme against interrupted sampling repeater jammer based on time-frequency analysis

The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure（ECCM） scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency（TF） analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ.

Temperature and Daily Mortality in Shanghai:A Time-series Study

To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to monitor large-scale deformation with millimeter accuracy,the SBAS method has been widely used in various geodetic fields,such as ground subsidence,landslides,and seismic activity.The obtained long-term time-series cumulative deformation is vital for studying the deformation mecha-nism.This article reviews the algorithms,applications,and challenges of the SBAS method.First,we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm,which provides a basic framework for the following improved time series methods.Second,we classify the current improved SBAS techniques from different perspectives:solving the ill-posed equation,increasing the density of high-coherence points,improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation.Third,we summarize the application of the SBAS method in monitoring ground subsidence,permafrost degradation,glacier movement,volcanic activity,landslides,and seismic activity.Finally,we discuss the difficulties faced by the SBAS method and explore its future development direction.