Rescaled range analysis and conditional probability-based probe into the intrinsic pattern of rainfall over North Mountainous India

In work reported here, we have explored rainfall over North Mountainous India for pre-monsoon (MAM), Indian summer monsoon (JJAS), post-monsoon (OND) and Annual. The dependence of JJAS on MAM and OND on JJAS has been explored through conditional probabilities utilizing frequency distribution. An autocorrelation structure has shown that a low lag-1 autocorrelation coefficient characterizes all the time series. We have implemented rescaled range analysis. Through Hurst's exponent and fractal dimension, we have observed that the MAM time series of rainfall over North Mountainous India has a smooth trend and low volatility. We have further observed that for MAM and JJAS, we have , and D is closer to 1 than to 2. However, we have further observed that for OND and Annual rainfall over North Mountainous India and . Therefore, these two time series have been characterized by high volatility and randomness.

Characteristics of the Near-Ground Typhoon Morakot

Two WJ-3 anemometers placed at the same height on the top of an architectural engineering building in Wenzhou University are used to determine the wind speed of Typhoon Morakot during its landing in real time. This study aims to explore Typhoon Morakot’s wind field characteristics, including mean wind speed, probability density distribution of fluctuating wind speed, power spectral density, correlation analysis, and coherence, on the basis of data measured by the two anemometers. Results show that the probability density distribution of the fluctuating wind speed of the typhoon follows the Gaussian distribution, and the measured cross-power spectrum of fluctuating wind speed is in good agreement with the modified Karman spectrum. The autocorrelation decreases with the increase in time interval (τ). The longitudinal autocorrelation coefficient decays rapidly with the increase in τ, and the lateral autocorrelation coefficient decays at an unchanged rate. The exponential attenuation coefficients of the longitudinal and transverse fluctuating wind speeds increase with the increase in the mean wind speed, and their mean values are 10.86 and 15.33, respectively. The change trends of the coherence coefficients of the two wind speed components with the mean wind speed are the same. The measured coherence coefficients of the two wind speed components are in good agreement with the exponential function.

The diversity and spatial polarisation of own incomes of the Polish gminas

Own incomes are considered one of the most important sources of financing for local governments in Poland. Although own incomes have been the subject of numerous analyses, extensive research focusing on their spatial aspect is rarely conducted. This article aims to identify the changes in the spatial diversification and polarisation of gminas (communes’) own incomes. Data from the Local Data Bank of Statistics Poland, the National Bank of Poland and the World Bank were used. The analysis covered the years 1995–2019. The study used the global spatial autocorrelation coefficient and the LISA method to identify the process of spatial dependence and to determine the degree of spatial polarisation. The Gini coefficient was applied to assess the level of diversity. The results of the analysis confirmed that an increasing spatial autocorrelation occurred in the studied period, leading to the spatial polarisation of the Polish gminas in terms of their own incomes. Gminas with a high level of own income formed spatial clusters within large urban agglomerations, in regions where natural resources were exploited, along the western border and the coastal belt. The findings show that the area of these clusters was expanding. On the other hand, low-own-income gminas were located in eastern and south-eastern Poland. The analysis has not confirmed that the dynamics of the gross domestic product or the level of inequality in gminas’ own income per capita had any effect on the changes in the spatial autocorrelation coefficient, nor, consequently, on the process of spatial polarisation.

Early-Warning Signals of Drought-Flood State Transition over the Dongting Lake Basin Based on the Critical Slowing Down Theory

In this study, the standardized precipitation index (SPI) data in Hunan Province from 1961 to 2020 is adopted. Based on the critical slowing down theory, the moving t-test is firstly used to determine the time of drought-flood state transition in the Dongting Lake basin. Afterwards, by means of the variance and autocorrelation coefficient that characterize the phenomenon of critical slowing down, the early-warning signals indicating the drought-flood state in the Dongting Lake basin are explored. The results show that an obvious drought-to-flood (flood-to-drought) event occurred around 1993 (2003) in the Dongting Lake basin in recent 60 years. The critical slowing down phenomena of the increases in the variance and autocorrelation coefficient, which are detected 5–10 years in advance, can be considered as early-warning signals indicating the drought-flood state transition. Through the studies on the drought-flood state and related early-warning signals for the Dongting Lake basin, the reliabilities of the variance and autocorrelation coefficient-based early-warning signals for abrupt changes are demonstrated. It is expected that the wide application of this method could provide important scientific and technological support for disaster prevention and mitigation in the Dongting Lake basin, and even in the middle and lower reaches of the Yangtze River.

Behavior Estimation Based on Multiple Vibration Sensors for Elderly Monitoring Systems

Aging has become a global social issue nowadays. We want to provide an elderly care system for older people who live alone. Based on the perspective of an informationally structured space (ISS), we have developed a monitoring system by using high-precision vibration sensors. In preliminary experiments, we observed that the autocorrelation coefficient reflected periodic human activities to a certain extent. Therefore, we propose a time delay neural network (TDNN) with autocorrelation as the input to analyze the vibration data. The system can estimate the current state of the elderly. When the system observes any abnormal situation of the elderly, the system can confirm by voice or notify the caregiver, if necessary. In the experiments, we compared the proposed method with traditional TDNNs using raw data as the input. The results demonstrated that proposed methods had performed well when using vibration sensors to measure user behaviors in the bathroom and living room.

Substantiation of informative amplitudes during registration of acoustic emission signals from the friction zone of tribosystems

In this work, the dependence of the change in the probability density of the distribution of the number of pulses and amplitudes of acoustic emission (AE) signals from the friction zone at the steady-state operation of the tribosystem is obtained. Acoustic vibrations that the tribosystem generates during operation are due to the impact interaction of the roughness of the friction surfaces of their elastoplastic deformation, processes of formation and destruction of frictional links, structural and phase rearrangement of materials, the formation and development of microcracks in the surface layers of contacting bodies, separation of wear particles. The dependence allows you to determine a sufficient number of pulses in the signal frame and their amplitude values for diagnosing tribosystems during their operation. The values of the informative amplitudes of the clusters are experimentally substantiated К2, К3, К4 in relation to the base cluster К1. It is shown that an increase in the informative frequency fAE(fix) from 250 to 500 kHz, increases the value of the informative amplitude to 17,6…43,75%. Based on the results obtained, it was concluded that this fact must be taken into account when developing methods, which will increase the accuracy of diagnosing tribosystems. The autocorrelation coefficient characterizes the closeness of the linear relationship of the current and previous frames of the series for each of the analyzed clusters. By the value of the autocorrelation coefficient, one can judge the presence of a linear relationship between the values of the recorded amplitudes, their reproducibility in terms of recording time in the steady-state operation of the tribosystem. To confirm the sufficiency of the selected number of pulses in the clusters of the AE signal frame, as well as the reproducibility of the results of the analysis of frames when they shift in time of registration, an expression is obtained for calculating the autocorrelation function, which reflects the relationship between successive levels of the time series. Based on the results of the experimental data, the values of the autocorrelation coefficients were calculated, equal to 0,82…0,92, which indicates the robustness of the chosen diagnostic technique.

Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran’s I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord $$G_{i}^{*}$$ G i ∗ statistic. The Moran’s I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran’s I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of $$G_{i}^{*}$$ G i ∗ showed varying rates of infections, with a large spatial variability between the different wilayats (district). The epidemic situation in some wilayats, such as Mutrah, As-Seeb, and Bowsher in the Muscat Governorate, was more severe, with Z score higher than 5, and the current transmission still presents an increasing trend. This study indicated that the directional pattern of COVID-19 cases has moved from northeast to northwest and southwest, with the total impacted region increasing over time. Also, the results indicate that the rate of COVID-19 infections is higher in the most populated areas. The findings of this paper provide a solid basis for future study by investigating the most resolute hotspots in more detail and may help decision-makers identify targeted zones for alleviation plans.

Precursory Indicator for Mode I Fracture in Brittle Rock through Critical Slowing Down Analysis

The abrupt rock-related hazards, such as landslide, rock burst, and collapse, seriously threaten the safety and service life of engineering works. Precursory information on critical transitions preceding sudden fracture is of great significance in rock mechanics and engineering. This study investigates the critical slowing down feature of acoustic emission (AE) signals and precursory indicators during the mode I fracture process of brittle rock. Cracked chevron notched Brazilian disc (CCNBD) specimens were utilized, accompanied by acoustic emission monitoring. The principle of critical slowing down was introduced to study AE count sequences, and the variance and autocorrelation coefficient versus loading time curves were analyzed. The results show critical slowing down phenomenon exists during mode I rock fracture. The variance and autocorrelation coefficient of AE counts grow significantly prior to rock fracture, and thus, the significant growth of variance and autocorrelation coefficient of AE signals can act as the precursory indicator of rock fracture. Compared to the autocorrelation coefficient, the precursors determined by the variance are more remarkable. The time interval between the precursory indicator using the critical slowing down theory and fracture moment ranges from 2% to 15% of the entire loading time. The findings in this study could facilitate better understandings on the rock fracture process and early-warning technique for rock fracture-related geological disasters.

Territorial Clusters of Farmers’ Interest in Diversification in Poland: Geospatial Location and Characteristics

With a gradual shift towards sustainable rural development, farm diversification has recently gained importance in EU policy. To increase the efficiency of policies aiming to support farm diversification, it is of crucial importance to know the factors motivating farmers to diversify. The purpose of this paper was to research spatial determinants of farm diversification in Poland by identifying and describing territorial clusters of rural areas (municipalities), in which farmers’ interest in diversification is above or below the national average. The Moran’s global spatial autocorrelation coefficient was used to test for spatial autocorrelation, while the local Moran’s statistic served to group together municipalities which exhibited a level of the frequency of applying for diversification support above/below the average value for the entire territory covered by the analysis. Furthermore, the clusters were described with the use of synthetic characteristics of the Polish agriculture and rural areas. The existence and characteristics of clusters suggest that the policy toward diversification in Poland favors areas of better developed agricultural structures. In clusters with structural disadvantages where diversification is most needed, the program’s performance has been very modest. However, our analysis also revealed the existence of outlier municipalities which demonstrated outstanding performance in applying for diversification funds despite structural disadvantages. These observations suggest that the farmers’ interest in diversification may be driven by a number of additional factors beyond a structural disadvantage alone.