Adjacent Time Periods Research Articles

The calculation method of blood flow pressure based on four-dimensional flow magnetic resonance imaging and deep learning

Utilizing artificial intelligence methods for blood flow pressure estimation can significantly enhance the computational speed of blood flow pressure. However, current related research can only calculate the blood flow pressure parameters of vessels with different geometric shapes under fixed boundary conditions, thus fail to achieve transient flow field calculation and consider the hemodynamic differences formed by patients' varying physiological and pathological conditions. In view of this, this study proposes a method for relative pressure estimation based on four-dimensional flow magnetic resonance imaging (4D flow MRI) of patient blood flow and deep learning. 4D flow MRI was used to obtain the patient's blood flow velocity gradient data, and feature engineering processing is performed on the sampled data. Then, a novel neural network was proposed to acquire the characteristic relationship between velocity gradient and pressure gradient in the vicinity of the point to be measured and within adjacent sampling time periods, thereby achieving the calculation of the relative pressure in the vicinity of the point to be measured. Statistical analysis was performed to evaluate the efficacy of the method, comparing it with computational fluid dynamics methods and catheter pressure measurement techniques. The accuracy of the proposed method exceeded 96%, while computational efficiency was improved by several tens of times, and no manual setting of physiological parameters was required. Furthermore, the results were compared with clinical catheter-measured pressure results, r2 = 0.9053, indicating a significant consistency between the two methods. Compared to previous research, the method proposed in this study can take the blood flow velocity conditions of different patients at different times as input features via 4D flow MRI, thus enabling the calculation of pressure in transient flow fields, which significantly improved computational efficiency and reduced costs while maintaining a high level of calculation accuracy. This provides new direction for future research on machine learning prediction of blood flow pressure.

Task persistence as a competency capable of reducing delinquency in early to mid-adolescence

In an effort to understand the segment of the juvenile population that seemingly ceases engaging in delinquency during adolescence, the relationship between a performance competency (task persistence) and offending was explored in 3,928 youth (2,005 boys and 1,923 girls) from the Longitudinal Study of Australian Children. Three waves of data, with adjacent time periods separated by two years, were used to test the hypothesis that a change in task persistence would correlate with a change in future delinquency. Given that the two dependent variables in this study (delinquency at Time 2 and delinquency at Time 3) followed a negative binomial distribution, negative binomial regression and binomial logistic regression analyses were performed. Results from both analyses confirmed the hypothesis that a rise in task persistence from Time 1 to Time 2 would predict a decrease in delinquency from Time 1 to Time 2 to Time 3 and that a static measure of task persistence at Time 1 would predict a change in delinquency from Time 1 to Time 2. These results suggest that task persistence may be a competency capable of suppressing delinquency during a developmental period in which delinquency ordinarily rises.

Socioeconomic Positions and Midlife Health Trajectories in a Changing Social Context: Evidence from China, 1991-2006.

Cumulative (dis)advantage theory posits that socioeconomic disparities in health may increase with age. This study examines individuals' midlife health trajectories, taking account of how their life courses are embedded within changing social contexts. Using the China Health and Nutrition Survey (1991-2006), it examines the health gap between Chinese rural peasants and urban nonpeasants in three adjacent time periods, during which a rapid process of social change increased the inequalities between rural and urban areas. Findings show that the health gap increases more rapidly in the more recent time periods, with higher levels of inequality, indicating that health inequalities between the two groups are contingent upon the social contexts in which individuals' lives unfold. To better understand the differences observed over these time periods, further analysis will examine the roles of two structural factors: income inequality and differential access to medical care.

Space-Time Resource Integrated Optimization Method for Time-of-Day Division at Intersection Based on Multidimensional Traffic Flows

Based on the change trends of traffic flow in different controlled directions at an intersection, the space-time resource integrated optimization method for TOD (time-of-day) division based on multidimensional traffic-flow data is proposed in this paper. By analyzing the traffic-flow data of 8, 4, 2, and 1 dimensions commonly used at the intersection, the dynamic Fisher algorithm is used to complete the time segment division of the traffic-flow sequence of different dimensions. On this basis, the preliminary TOD division is completed, and the phase timing and lane-use assignment corresponding to the preliminary time periods are optimized. Then, the adjacent time periods are merged and tested to complete the final result of the TOD division. In order to verify the effectiveness of the proposed method, the schemes based on traffic-flow data of different dimensions are carried out by using the data at an actual intersection in Wuhu City, and the total and average vehicle delays of different schemes throughout the day are evaluated by VISSIM. The results show that the more dimensions of traffic flow data are adopted, the more refined the TOD division scheme is, and the less the total delay and average delay at intersections throughout the day are. In particular, the TOD division scheme after further optimizing the lane-use assignment can further reduce the total and average vehicle delay throughout the day. It shows that the method using multidimensional traffic-flow data at an intersection to carry out the integrated optimization of TOD division, lane-use assignment, and phase timing has good applicability.

CLSTM-AR-Based Multi-Dimensional Feature Fusion for Multi-Energy Load Forecasting

Integrated Energy Systems (IES) are an important way to improve the efficiency of energy, promote closer connections between various energy systems, and reduce carbon emissions. The transformation between electricity, heating, and cooling loads into each other makes the dynamic characteristics of multiple loads more complex and brings challenges to the accurate forecasting of multi-energy loads. In order to further improve the accuracy of IES short-term load forecasting, we propose the Convolutional Neural Network, the Long Short-Term Memory Network, and Auto-Regression (CLSTM-AR) combined with the multi-dimensional feature fusion (MFFCLA). In detail, CLSTM can extract the coupling and periodic characteristics implied in IES load data from multiple time dimensions. AR takes load data as the input to extract features of sequential auto-correlation over adjacent time periods. Then, the diverse and effective features extracted by CLSTM, LSTM, and AR can be fused using the multi-dimensional feature fusion technique. Ultimately, the model achieves the accurate prediction of multiple loads. In conclusion, compared with other forecasting models, the case study results show that MFFCLA has higher forecasting precision compared with the comparable model in the short-term multi-energy load forecasting performance of electricity, heating, and cooling. The accuracy of MFFCLA can help to optimize and dispatch IES to make better use of renewable energy.

Seventy years of food-web change in South Bay, Lake Huron

Though aquatic ecosystems (and the Laurentian Great Lakes in particular) have faced many stressors over the past century, including fisheries collapses and species invasions, rarely are data available to evaluate the long-term impacts of these stressors on food web structure. Stable isotopes of fish scales from the 1940s to the 2010s in South Bay, Lake Huron were used to quantify trophic position and resource utilization for fishes from offshore (alewife, cisco, lake trout, lake whitefish, rainbow smelt) and nearshore (rock bass, smallmouth bass, white sucker, yellow perch) habitats, providing one of the longest continuous characterizations of food webs in the Laurentian Great Lakes. Mean δ15N and δ13C values for each species were compared across twenty-year time periods. Using directional statistics, no significant community-wide changes were detected between time periods from 1947 to 1999. In contrast, a significant change was detected between 1980-1999 and 2000–2017, with all species showing increased reliance on nearshore resources. The increase in nearshore resource reliance for lake whitefish between these time periods was the greatest in magnitude compared with any other species between any two adjacent time periods. Besides lake whitefish, the increased reliance on nearshore resources was more pronounced for nearshore compared to offshore species. The timing of these shifts coincided with the invasion of dreissenid mussels and round goby, and declines in offshore productivity and prey densities. These results show the unprecedented magnitude of recent food-web change in Lake Huron after 50 years of relative stability.

Use of information criteria for selecting a correlation structure for longitudinal cluster randomised trials.

When designing and analysing longitudinal cluster randomised trials, such as the stepped wedge, the similarity of outcomes from the same cluster must be accounted for through the choice of a form for the within-cluster correlation structure. Several choices for this structure are commonly considered for application within the linear mixed model paradigm. The first assumes a constant intra-cluster correlation for all pairs of outcomes from the same cluster (the exchangeable/Hussey and Hughes model); the second assumes that correlations of outcomes measured in the same period are higher than outcomes measured in different periods (the block exchangeable model) and the third is the discrete-time decay model, which allows the correlation between pairs of outcomes to decay over time. Currently, there is limited guidance on how to select the most appropriate within-cluster correlation structure. We simulated continuous outcomes under each of the three considered within-cluster correlation structures for a range of design and parameter choices, and, using the ASReml-R package, fit each linear mixed model to each simulated dataset. We evaluated the performance of the Akaike and Bayesian information criteria for selecting the correct within-cluster correlation structure for each dataset. For smaller total sample sizes, neither criteria performs particularly well in selecting the correct within-cluster correlation structure, with the simpler exchangeable model being favoured. Furthermore, in general, the Bayesian information criterion favours the exchangeable model. When the cluster auto-correlation (which defines the degree of dependence between observations in adjacent time periods) is large and number of periods is small, neither criteria is able to distinguish between the block exchangeable and discrete time decay models. However, for increasing numbers of clusters, periods, and subjects per cluster period, both the Akaike and Bayesian information criteria perform increasingly well in the detection of the correct within-cluster correlation structure. With increasing amounts of data, be they number of clusters, periods or subjects per cluster period, both the Akaike and Bayesian information criteria are increasingly likely to select the correct correlation structure. We recommend that if there are sufficient data available when planning a trial, that the Akaike or Bayesian information criterion is used to guide the choice of within-cluster correlation structure in the absence of other compelling justifications for a specific correlation structure. We also suggest that researchers conduct supplementary analyses under alternate correlation structures to gauge sensitivity to the initial choice.

Distributionally robust optimal dispatch in the power system with high penetration of wind power based on net load fluctuation data

The power system with high penetration of wind power faces a great challenge for system dispatch due to the high volatility and intermittency of the wind power. This work proposes a day-ahead optimal dispatch model which is formulated for a power system with thermal power, hydropower, and controllable load as dispatchable resources. According to the anti-peak regulation, the system dynamic power regulation margin model considering adjacent time periods is established to address the uncertainty in the fluctuation rate of net load power, and to sufficiently use the dispatchable resources to reduce wind curtailment. However, in some circumstances, curtailing wind has to be considered to ensure secure operation of the system and maintain the economic goal from the total cost point of view. The risk of curtailing wind is formulated using conditional value at risk (CVaR), and is minimized as part of the total operating cost. Another objective function of the proposed dispatch model is to maximize the power regulation margin. Uncertainty in the fluctuation rate of net load power is modelled for different weather conditions using moment-based ambiguity set. The moment information is obtained from large amount of historical data using clustering methodologies. The proposed optimal dispatch model with uncertainty and CVaR formulation is reformulated and solved under the distributionally robust conditional value at risk (DRCVaR) framework. The model is transformed into a semi-definite programming problem through the duality theory and can be solved efficiently by commercial solvers. Simulation results show that the proposed dispatch model can effectively strengthen wind power absorption, ensure secure operation, and improve the robustness of the dispatch strategy facing the uncertainty from the wind power.

Design Wave Height Parameter Estimation Model Reflecting the Influence of Typhoon Time and Space

Typhoon storm surge disasters are one of the main restrictive factors of sustainable development in coastal areas. They are one of several important tasks in disaster prevention and reduction in coastal areas and require reasonable and accurate calculations of wave height in typhoon-affected sea areas to predict and resist typhoon storm surge disasters. In this paper, the design wave height estimation method based on the stochastic process and the principle of maximum entropy are theoretically advanced, and it can provide a new idea as well as a new method for the estimation of the return level for marine environmental elements under the influence of extreme weather. The model uses a family of random variables to reflect the influence of a typhoon on wave height at different times and then displays the statistical characteristics of wave height in time and space. At the same time, under the constraints of the given observations, the maximum uncertainty of the unobtainable data is maintained. The new model covers the compound extreme value distribution model that has been widely used and overcomes the subjective interference of the artificially selected distribution function—to a certain extent. Taking the typhoon wave height data of Naozhou Observatory as an example, this paper analyzes the probability of typhoon occurrence frequency at different times and the characteristics of typhoon intensity in different time periods. We then calculate the wave height return level and compare it with traditional calculation models. The calculation results show that the new model takes into account the time factor and the interaction between adjacent time periods. Furthermore, it reduces the subjective human interference, so the calculated results of the typhoon’s influence on wave height return level are more stable and accurate.

Lack of observed tolerance to diazepam nasal spray (Valtoco®) after long-term rescue therapy in patients with epilepsy: Interim results from a phase 3, open-label, repeat-dose safety study

ObjectiveTolerance is a known consideration for maintenance use of benzodiazepines and other antiseizure drugs; however, clinical experience suggests that tolerance may not be anticipated with long-term intermittent use of benzodiazepines as rescue therapy. Diazepam nasal spray (Valtoco®) is a proprietary intranasal formulation approved for the acute treatment of intermittent, stereotypic episodes of frequent seizure activity (ie, seizure clusters, acute repetitive seizures) in patients with epilepsy aged ≥6 years. Reported here are exploratory analyses investigating whether there was evidence of development of tolerance in an interim analysis of a long-term, phase 3, open-label safety study of diazepam nasal spray. MethodsPatients and care partners were trained to administer 5, 10, 15, or 20 mg of diazepam nasal spray (age- and weight-based dosing), with a second dose administered 4–12 hours later if needed. A series of analyses were performed to assess evidence of tolerance using 2 equal, adjacent time periods and data for each patient to compare the proportion of events for which second doses of diazepam nasal spray (as a proxy for effectiveness) were administered in period 1 compared with period 2. ResultsA total of 175 patients were enrolled at interim cutoff, and 158 were treated with diazepam nasal spray for 3370 seizure-cluster events. For 73.4% of patients, duration of exposure to diazepam nasal spray was ≥12 months. A total of 191 analyses were conducted; the proportion of analyses in which second doses in period 2 were lower than in period 1 was 72.8%. Only 5 analyses showed nominally statistically significant changes (P < 0.05); this is fewer than expected by chance, and these differences were not directionally consistent. There was no safety signal with continued use. ConclusionsThese analyses found no statistical evidence of tolerance with the use of diazepam nasal spray over time based on use of a second dose in an initial period of the study compared with a subsequent period for each patient. These results are in agreement with prior studies of benzodiazepine rescue therapy.

Variations in the Antithyroid Antibody Titre During Pregnancy and After Delivery.

BackgroundImmunosuppression occurs during pregnancy, and the antithyroid antibody titre drops, rebounding after delivery. We aimed to determine variations in antithyroid antibody titres during pregnancy and after delivery.MethodsThis retrospective study was conducted in a single centre. Antibody titres of 142 patients were measured to assess variations in the levels of thyroid-stimulating hormone receptor antibodies (TRAbs), thyroid peroxidase antibodies (TPOAbs), and thyroid globulin antibodies (TgAbs). We compared the titres of each antibody between adjacent time periods (eg, first trimester (T1) vs second trimester (T2), T2 vs third trimester (T3), T3 vs the postpartum period (PP)) by paired t-test or the Wilcoxon test. Then, we analysed data from patients with complete laboratory examination results in all four periods with the Friedman test, performing comparisons among groups.ResultsIn the TgAb group, significant differences existed between T1 and T2 and between T2 and T3 in the LT4 subgroup and between T1 and T2 in the no-medication subgroup. In the TRAb group, significant differences existed between T1 and T2 in the LT4 subgroup. In the TPOAb group, significant differences existed among each group in the LT4 subgroup, and there were significant differences between T1 and T2 and between T2 and T3 in the no-medication subgroup. The Friedman test showed that the P-values were 0.013 and 0.004 in the LT4 and no-medication subgroups of the TgAb group, respectively; 0.122 in the LT4 subgroup of the TRAb group; and <0.001 and 0.272 in the LT4 and no-medication subgroups of the TPOAb group, respectively. In the LT4 subgroup of the TgAb group, the P-values for comparisons of time periods were 0.602 between T1 and T2, 0.602 between T2 and T3, 0.006 between T1 and T3, and 0.602 between T3 and PP. In the no-medication subgroup of the TgAb group, the P-values were 0.078 between T1 and T2, 1.000 between T2 and T3, 0.011 between T1 and T3, and 0.078 between T3 and PP. In the LT4 subgroup of the TPOAb group, the P-values were 0.09 between T1 and T2, 0.014 between T2 and T3, <0.001 between T1 and T3, and 0.772 between T3 and PP.ConclusionWe can conclude that the TgAb and TPOAb titres dropped during pregnancy.

Changes in HIRS Detection of Cloud over Australia from 1985 to 2001

A long-term archive of cloud properties (cloud top pressure, CTP; and cloud effective emissivity, ε) determined from High-resolution Infrared Radiation Sounder (HIRS) data is investigated for evidence of regional cloud cover change. In the 17 years between 1985 and 2001, different cloud types are analysed over the Australian region (10° S–45° S, 105° E–160° E) and areas of change in total cloud frequency examined. Total cloud frequency change over the Australian region between two adjacent eight-year time periods (1994 to 2001 minus 1985 to 1992) shows the largest increases (ranges between 6% and 12%) of average HIRS total cloud cover occurring over the offshore regions to the northwest and northeast of the continent. Over land, the largest reduction of average HIRS total cloud frequency is in the southwestern region of Australia (between 2% and 8%). Through central Australia, there is a 2% to 7% increase in average HIRS total cloud frequency when comparing these eight-year periods. This paper examines the regional cloud changes in 17 years over Australia that are embedded in global cloud statistics. Examining total HIRS cloud cover frequency over Australia and comparing two different eight-year time periods, has highlighted notable areas of average change. Preliminary reporting of satellite-derived HIRS cloud products and Global Precipitation Climatology Project (GPCP) rainfall products during La Niña seasons between 1985 and 2001 has also been undertaken.

Removal of Dust Microelectric Signal Based on Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis.

Microcharge induction has recently been applied as a dust detection method. However, in complex environments, the detection device can be seriously polluted by noise. To improve the quality of the measured signal, the characteristics of both the signal and the noise should be analyzed so as to determine an effective noise removal method. Traditional removal methods mostly deal with specific noise signals, and it is difficult to consider the correlation of measured signals between adjacent time periods. To overcome this shortcoming, we describe a method in which wavelet decomposition is applied to the measured signal to obtain sub-band components in different frequency ranges. A time-lapse Pearson method is then used to analyze the correlation of the sub-band components and the noise signal. This allows the sub-band component of the measurement signal that has the strongest correlation with the noise to be determined. Based on multifractal detrended fluctuation analysis combined with empirical mode decomposition, the similarity between the signal sub-band components and the noise sub-band components is analyzed and three indices are employed to determine the multifractal characteristics of the sub-band components. The consistency between noise components and signal components is obtained and the main signal components are verified. Finally, the sub-band components are used to reconstruct the signal, giving the noise-free measured (microcharge induction) signal. The filtered signal presents smoother, multifractal features.

Girjas Reindeer Herding Community v. Sweden: Analysing the Merits of the Girjas Case

For the first time in the Swedish Supreme Court, a small Sami reindeer herding community has won an important victory affirming the community’s small game hunting and fishing rights. Because of protracted use and the concept of immemorial prescription, the Court recognised the community’s exclusive hunting and fishing rights, including the right to lease these rights to others. Such leases have long been prohibited by legislation and the State has retained its powers to administer such leases. This case signifies a considerable development in the area of Sami law. In its decision, the Supreme Court made some adjustments to the age-old doctrine of immemorial prescription, and provided insights into how historic evidence should be evaluated when the claimant is an Indigenous people. A common motivator for these adjustments is an enhanced awareness of international standards protecting Indigenous peoples and minorities. Even ILO Convention No. 169 – the only legally binding convention concerning Indigenous rights, but which Sweden has not yet ratified – is relevant when it comes to evaluating Sami customary uses. The Court addressed the problem of gaps in the historical material and used evidence from other parts of Swedish Lapland and adjacent time-periods, making reasonable assumptions to fill in these gaps. The Court imposes on the State the burden of proof regarding the extinguishment of already established Sami rights, as well as proof that extinguishment by legislation or expropriation, is “clear and definitive”. These conditions were not met in this case.

Integrated pricing framework for optimal power and semi‐dynamic traffic flow problem

Large-scale fast charging of electric vehicles inextricably entwines the operation of power and transportation systems. This study examines the integrated pricing of electricity and roads to maximise the social welfare associated with the coupled systems, i.e. minimising the total travel time and energy generation costs. While electric demand and transportation network conditions vary significantly over the day, most of the work, however, considers only static network flows for a single time period. This study proposes a multi-period integrated pricing model that can describe the variations of electricity and travel demands. Specifically, the distribution of traffic flows is characterised by a semi-dynamic traffic assignment model to consider flow propagation between adjacent time periods. To determine the optimal prices of electricity and road tolls, a marginal-cost pricing scheme based on the first-best tolls and locational marginal prices is developed. Mathematically, the multi-period pricing model is formulated as a convex optimisation problem. Moreover, a decentralised collaborative pricing framework is developed for independent power and transportation system operators to attain the optimal pricing strategies. Numerical experiments are conducted to demonstrate the effectiveness of the pricing method.

A Comparative Analysis of the Wind and Significant Wave Height on the Extreme Weather Events (TC Cempaka and TC Dahlia) in the Southern Sea of Java, Indonesia

The tropical cyclones (TCs) are a serious danger to marine and coastal areas in the tropics because they cause strong winds and extreme sea wave height. The combination of these two parameters will be dangerous for shipping safety. The purpose of this study was to investigate the effect of Tropical cyclone on significant wave heights and surface winds in the southern sea of Java with case studies of TC Cempaka (21-29 November 2017) and TC Dahlia (26 November-4 December 2017). The relationship between two parameters was also discussed in this paper. Both of these tropical cyclone events are interesting to study because they occured in the southern sea of Java and in adjacent time periods. Special attention was paid to the southern sea of Java because it had high shipping traffic where 8 locations were used as case studies. Significant wave height and wind conditions were evaluated using data obtained from the European Mid-Term Weather Forecast Center (ECMWF) with 6 hourly temporal resolution (time UTC 00.00, 06.00, 12.00, 18.00) and 0.5 x 0.5 degrees spatial resolution. The obtained results showed that the influence of TC Dahlia on significant wave height and surface wind appears stronger than the influence of TC Cempaka. During TC Dahlia, the strongest relationship between wind and significant wave height was in the southern sea of Central Java 01 and Yogyakarta 01 with correlation coefficients of 0.84 and 0.81, respectively. While the frequency of extreme wave height of 55.56% appeared to occur in the southern seas of West Java 01 and Central Java 01.

Comparative Transcriptomic Analysis of Two Rice (Oryza sativa L.) Male Sterile Line Seed Embryos Under Accelerated Aging

Seed aging is a complex and irreversible process during seed development and storage. The quality of parental seeds affects yield and quality of hybrid rice seeds directly. However, little is known about the mechanism of the accelerated aging seeds in hybrid rice photoperiod-thermo-sensitive genic male sterile (PTGMS) lines. RNA-Seq technique was performed by using seed embryos of two PTGMS lines, Y58S (YS) and Zhun S (ZS), under the restrictive conditions (95% relative humidity and 45 °C) for 0 day, 5 days, 7 days, and 10 days. In total, 1198, 487, 1308, 396, and 669 differentially expressed genes (DEGs) were identified between adjacent time periods of accelerated aging treatment in the ZS and YS. Gene Ontology (GO) analysis revealed the DEGs enriched in the starch catabolic process and sucrose catabolic process. It also showed that many genes were enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways such as starch and sucrose metabolism, plant-pathogen interaction, and plant hormone signal transduction. In addition, 34 candidate genes were selected to the following phylogenetic analysis and different gene expression profiles. Finally, several key candidate genes of seed aging were identified, containing a rice sucrose synthase Os07g0616800 and an unknown gene Os12g0161500, by quantitative real-time PCR. Together, these results established a good foundation for studying the molecular mechanism of rice seed storability and also provide new insights into the PTMGS line seed storage.

A Method for Detecting Abnormal Changes in the Temperature Field of Grain Bulk Based on HSV Features of Cloud Maps

HighlightsAbnormal grain temperature changes were detected by calculating the similarity of HSV features in cloud maps.The F-measures were higher for the improved method than for methods based on HSV and LBP feature similarity.The improved method can detect abnormal heating of grain due to mold activity or spoilage and the fluctuation in grain temperature caused by aeration.The temperature field of the grain bulk in adjacent time periods has high similarity during normal storage.Abstract. Analyses of grain temperature data are time-consuming and labor-intensive, and thorough analyses are difficult to perform. This article proposes an improved method based on the hue, saturation, and value (HSV) color feature similarity of temperature field cloud maps to detect abnormal changes in grain temperature. Historical grain temperature data are preprocessed to generate temperature field cloud maps. The improved method based on HSV feature similarity is used to calculate the similarity of temperature field cloud maps for two successive days during normal storage, and a similarity threshold is set. Five types of grain bulk temperature anomalies are then simulated. Additionally, a comparative experiment is carried out that considers traditional methods based on HSV feature similarity and local binary pattern (LBP) feature similarity. The results show that the average recall rates of the F-measures of the improved method, the traditional method based on HSV feature similarity, and the method based on LBP feature similarity are 96.2%, 89.3%, and 95.4%, respectively, and the processing speeds are 340, 300, and 690 ms per group, respectively. Finally, an abnormal grain temperature experiment is carried out. The experimental results show that the improved method can detect sudden changes in the temperature field due to mold activity or spoilage and the fluctuations in grain temperature caused by aeration. Keywords: Grain storage, HSV feature, LBP feature, Similarity, Temperature.

Time Window Selection Algorithm for ISAR Ship Imaging Based on Instantaneous Doppler Frequency Estimations of Multiple Scatterers

Selecting a suitable time window is an important aspect of the ship imaging process for inverse synthetic aperture radar (ISAR) systems. One type of existing algorithm involves finding the time period during which a ship's rotation is relatively stable by extracting the instantaneous Doppler frequency variation caused by the ship's rotation. However, existing algorithms extract either the instantaneous Doppler frequency of only a single scatterer or the average Doppler frequency of the entire ship. A novel time window selection algorithm for ISAR ship imaging is proposed through extraction of the instantaneous Doppler frequencies of multiple scatterers. The instantaneous Doppler frequency of a scatterer caused by a ship's rotation is verified to behave approximately as a linear frequency modulation signal over a short period. The formula for calculating the length of such a short period is derived. A parameter estimation method based on compressed sensing after data extrapolation is presented to extract the centroid and frequency modulation rate for short-time chirp signals. The instantaneous Doppler frequency variations in the multiple scatterers over the entire acquisition period are obtained by associating the data from adjacent short time periods. The suitable time window during which the instantaneous Doppler frequency changes are all relatively stable in the multiple scatterers is then selected. The results of the performed simulation and real data processing verify the effectiveness of the proposed algorithm. The proposed algorithm achieves better image quality than the image-contrast-based algorithm and algorithms based on instantaneous Doppler frequency estimation of a single scatterer.

Variability trends in the daily air temperatures series&lt;span style="color: #484646"&gt;Running head: Variability trends prague&lt;/span&gt;

An abstract is a brief of the paper; the abstract should not contain references, the text of the abstract section should be in 12 point normal Times New Roman. Temperature variability was investigated for the near surface air temperature time series measured at Sporilov station (Prague, the Czech Republic) between 2003–2016. The interpretation of the Sporilov daily air temperature averages was completed with the results of the long-term observation at the Prague Klementinum station (daily averages, daily maximum and daily minimum temperatures). Variability was detected by the method of absolute difference of temperature anomalies between two adjacent discrete time periods. The results demonstrated increasing warming trends for all investigated temperature time series and a general reduction of the diurnal temperature range. The range of temperature oscillations reduces also on the annual scale. The temperature variability has shown general decline, however the velocity of the detected variability decreasing trend is significantly minor in comparison with the previously investigated period 1994–2001. The analysis also revealed that the occurrence of both hottest and coldest extremes have been increasing during the investigated period at both stations.