Search

A spectral criterion involving the model parameters is given for the existence and uniqueness of a periodically correlated and seasonal non‐negative integer‐valued autoregressive process. The structure of the mean and covariance functions of the periodically stationary distribution of the model is derived using its implicit state‐space representation. Two infinite series representations for the process, the moving average, and the immigrant generation, are established. Based on the latter representation, a novel and parallelizable simulation method is proposed to generate the process. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to study the existence and uniqueness solutions for the nonlinear Hilfer pantograph fractional differential equations. This paper initiates with the persistence of the nonlinear Hilfer pantograph fractional differential equation. Also, it extended to the fractional integrodifferential equation. The premises are attained by using the fixed-point theorem. Ultimately, numerical examples are furnished to demonstrate our outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Clinical Epileptology / Zertifizierte Fortbildung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volcanic tremor is a crucial indicator for assessing the state and hazard potential of volcanic systems. At Whakaari (White Island volcano, Aotearoa New Zealand), a pulsed tremor signal emerged after a hydrothermal explosion in August 2012. The tremor accompanied the extrusion of a lava dome, before gradually disappearing prior to the onset of renewed hydrothermal activity in January 2013. We interpret this seismic signal to represent discrete gas transfers from a magmatic intrusion toward a permeable cap—possibly a hydrothermal seal—in the upper layers of Whakaari's hydrothermal system. Such tremor may thus be associated with heightened potential for hazardous explosive activity but is difficult to detect using conventional seismic monitoring parameters. To highlight the emergence of subtle periodic signals, we experiment with Lomb‐Scargle periodograms (LS). LS detect the tremor 5 days before it becomes visible in seismograms, thus facilitating the recognition of such elusive seismic patterns. Plain Language Summary: A periodic seismic signal was observed at Whakaari (White Island volcano, Aotearoa New Zealand) in 2012, a few weeks after an explosion took place at the volcano. This signal persisted over more than 3 months and consists of slowly emerging, then slowly disappearing and regularly spaced "pulses." Similar signals have been observed at other volcanic systems, where they are usually linked to rising magma, which then releases gases. Subsequently, gas slugs forming from the magma rise toward the surface and interact with and excite more solid layers of the system, which is visible in the seismic records. Therefore, such seismic signals could indicate increased potential for dangerous volcanic activity. Common techniques applied to track volcano activity struggled to highlight the emergence of this very subtle pattern. Instead, a new visualization approach allowed us to track the emergence and decline of the signal over time. This shows that such subtle patterns can be discovered days before they become recognizable in seismograms. We therefore suggest ongoing testing and validating this approach before adding it to current monitoring techniques. This work helps to identify and better understand the physical processes behind the patterns hidden in the seismic signals of volcanoes. Key Points: An unusually long‐lasting episode of pulsed tremor was recorded at Whakaari volcano during its 2012/13 eruptive unrest episodeThe tremor may reflect discrete gas transfers from a magmatic intrusion toward a permeable cap or sulfuric seal in a hydrothermal systemSubtle, periodic seismic patterns can efficiently be visualized via Lomb‐Scargle periodograms [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis on the variations in the tropical cyclone (TC) frequencies during 1980–2021, including the linear trends, periodicities, and their variabilities on both global and basin-wise scales. An increasing trend in the annual number of global TCs is identified, with a significant rising trend in the numbers of tropical storms (maximum sustained wind 35   k t s ≤ U m a x < 64   k t s ) and intense typhoons ( U m a x ≥ 96   k t s ) and a deceasing trend for weak typhoons ( 64   k t s ≤ U m a x < 96   k t s ). There is no statistically significant trend shown in the global Accumulated Cyclone Energy (ACE). On a regional scale, the Western North Pacific (WNP) and Eastern North Pacific (ENP) are the regions of the first- and second-largest numbers of TCs, respectively, while the increased TC activity in the North Atlantic (NA) contributes the most to the global increase in TCs. It is revealed in the wavelet transformation for periodicity analysis that the variations in the annual number of TCs with different intensities mostly show an inter-annual period of 3–7 years and an inter-decadal one of 10–13 years. The inter-annual and inter-decadal periods are consistent with those in the ENSO-related ocean drivers (via the Niño 3.4 index), Southern Oscillation Index (SOI), and Inter-decadal Pacific Oscillation (IPO) index. The inter-decadal variation in 10–13 years is also observed in the North Atlantic Oscillation (NAO) index. The Tropical North Atlantic (TNA) index and Atlantic Multi-decadal Oscillation (AMO) index, on the other hand, present the same inter-annual period of 7–10 years as that in the frequencies of all the named TCs in the NA. Further, the correlations between TC frequencies and ocean drivers are also quantified using the Pearson correlation coefficient. These findings contribute to an enhanced understanding of TC activity, thereby facilitating efforts to predict particular TC activity and mitigate the inflicted damage. [ABSTRACT FROM AUTHOR]

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present the Gaussian Mixture Periodicity Detection Algorithm (GMPDA), a novel method for detecting periodicity in the binary time series of event onsets. The GMPDA addresses the periodicity detection problem by inferring parameters of a generative model. We introduce two models, the Clock Model and the Random Walk Model, which describe distinct periodic phenomena and provide a comprehensive generative framework. The GMPDA demonstrates robust performance in test cases involving single and multiple periodicities, as well as varying noise levels. Additionally, we evaluate the GMPDA on real-world data from recorded leg movements during sleep, where it successfully identifies expected periodicities despite high noise levels. The primary contributions of this paper include the development of two new models for generating periodic event behavior and the GMPDA, which exhibits high accuracy in detecting multiple periodicities even in noisy environments. [ABSTRACT FROM AUTHOR]

This paper discusses impulsive effects on fractional differential equations. Two approaches are taken to obtain our results: either with fixed or changing lower limits in Caputo fractional derivatives. First, we derive an existence result for periodic solutions of fractional differential equations with periodically changing lower limits. Then, the impulsive effects are modeled for fractional differential equations regarding the nonlinearities rather than the initial value conditions. The proposed impulsive model differs from common discontinuous and nonsmooth dynamical systems. [ABSTRACT FROM AUTHOR]

Accurate traffic forecasting is critical to improving the safety, stability, and efficiency of intelligent transportation systems. Although many spatiotemporal analysis methods have been proposed, accurate traffic prediction still faces many challenges, for example, it is difficult for long-term predictions to model the dynamics of traffic data in temporal and spatial to capture the periodicity and spatial heterogeneity of traffic data. Most existing studies relieve this problem by discovering hidden spatiotemporal dependencies with graph neural networks and attention mechanisms. However, the period-related information between spatiotemporal sequences is not sufficiently considered in these models. Therefore, we propose a local global spatiotemporal attention network (LGA) to solve the above challenge. Specifically, we present a local spatial attention module to extract the spatial correlation of hourly, daily, and weekly periodic information. We propose a weight attention mechanism to assign different weights of the periodic feature extracted on local spatial attention. The local periodic temporal features are extracted through the local temporal attention we proposed. And we develop the global spatiotemporal attention module to extract the global spatiotemporal information of the entire time slice, which is more conducive to learning the periodic features of traffic data. The extensive experiments on four real-world datasets demonstrate the effectiveness to our proposed model.The code is publicly available on github 1 (github: https://github.com/lyc2580/LGAmodel.). [ABSTRACT FROM AUTHOR]

Discrete-time systems are sometimes used to explain natural phenomena that happen in nonlinear sciences. We study the periodicity, boundedness, oscillation, stability, and certain exact solutions of nonlinear difference equations in this paper. Using the standard iteration method, exact solutions are obtained. Some well-known theorems are used to test the stability of the equilibrium points. Some numerical examples are also provided to confirm the theoretical work's validity. The numerical component is implemented with Wolfram Mathematica. The method presented may be simply applied to other rational recursive issues. In this paper, we explore the dynamics of adhering to a rational difference formula -xn+1 = xn􀀀29 -1-xn􀀀5xn􀀀11xn􀀀17xn􀀀23xn􀀀29, where the initials are arbitrary nonzero real numbers. [ABSTRACT FROM AUTHOR]

Copyright of Adab Al-Kufa is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Like Kanban or classic inventory management, Demand Driven Material Requirements Planning (DDMRP) assumes that an order is created when the stock level drops below a given level. The assessment of this level can be continuous or periodic. A periodic evaluation can force better repartition of the load over time. DDMRP characterizes the demand for a product by its average daily demand. This aggregation specifies neither the average size of the elementary demands nor the average duration between two elementary demands. Based on a discrete event simulation model, this study suggests that, at least for a flow-shop organization, the DDMRP method, with continuous order generation, is suitable for large mean time between elementary orders, while periodic observation improves the method for small values of it. [ABSTRACT FROM AUTHOR]

BACKGROUND: Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales. METHODS: Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments. RESULTS: Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales. CONCLUSIONS: These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them.

The main scope of this work is to study the effect of embedding a periodic pattern inside a porous material, in order to passively improving its acoustic performance in terms of sound transmission loss. A contemplated application is the improvement of classical aeronautical soundproofing packages. In order to reach this goal, numerical models of an acoustic package including periodic patterns are implemented using the finite element method and the Transfer Matrix Method. Firstly, some of the proposed configurations are experimentally tested, providing a comparison and validation of the obtained numerical results. Afterwards, several configurations of inclusions are numerically studied, and incorporate hollow cylindrical inclusions, half-cut hollow cylindrical inclusions and cylindrical Helmholtz resonators. The improvements in terms of transmission loss, essentially brought by a periodicity peak, are evaluated under plane wave excitation with various incidence angles. The main novelties of the present work are represented by an experimental validation of the proposed acoustic meta-materials that were only numerically studied in previous works. The effect of the inclusion of a periodic pattern of Helmholtz resonators inside the foam core is also considered. The presented numerical results are also evaluated for different incidence angles of an exciting acoustic plane wave. [ABSTRACT FROM AUTHOR]

We present the Gaussian Mixture Periodicity Detection Algorithm (GMPDA), a novel method for detecting periodicity in the binary time series of event onsets. The GMPDA addresses the periodicity detection problem by inferring parameters of a generative model. We introduce two models, the Clock Model and the Random Walk Model, which describe distinct periodic phenomena and provide a comprehensive generative framework. The GMPDA demonstrates robust performance in test cases involving single and multiple periodicities, as well as varying noise levels. Additionally, we evaluate the GMPDA on real-world data from recorded leg movements during sleep, where it successfully identifies expected periodicities despite high noise levels. The primary contributions of this paper include the development of two new models for generating periodic event behavior and the GMPDA, which exhibits high accuracy in detecting multiple periodicities even in noisy environments.

In this study, we explored an asymmetric multivariate stochastic difference volatility model that extends various probabilistic and statistical properties previously discussed in the literature. We rigorously established that the model exhibits periodic stationarity and periodic ergodicity. Additionally, we delved into the robust consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator (QMLE), providing a comprehensive analysis of its theoretical underpinnings. Finally, we demonstrated the practical applicability of our major findings through a series of pertinent applications. This work not only contributes to the existing body of knowledge on stochastic volatility modeling, but also opens new avenues for further research in this domain.

When a rhythm makes an event predictable, that event is perceived faster, and typically more accurately. However, the experiments showing this used simple tasks, and most manipulated temporal expectancy by using periodic or aperiodic precursors unrelated to stimulus and task. Three experiments tested the generality of these observations in a complex task in which rhythm was intrinsic to, rather than a precursor of, the information needed to respond: listeners averaged the laterality of a stream of noise bursts. We varied presentation rate, degree of periodicity, and average lateralisation. Decisions following a probe tone were fastest after periodic stimuli, and slowest after the most aperiodic stimuli. Without a probe tone, listeners responded sooner during periodic sequences, thus hearing less information. Periodicity did not benefit accuracy overall. This gain in speed but not accuracy for less information is not reported for simpler tasks. Neural entrainment supplemented by cognitive factors provide a tentative explanation. When the task is inherently complex and demands high attention over long durations, both expected-periodic and unexpected-aperiodic stimuli can increase response amplitude, enhancing stimulus representation, but periodicity increases confidence to respond early. Drift diffusion modelling supports this proposal: aperiodicity modulated the decision threshold, but not the drift rate or non-decision time. Together, these new data and the literature point towards task-dependent effects of temporal expectation on decision-making, showing interactions between rhythmic variance, task complexity, and sources of expectation about stimuli. We suggest the implications are worth exploring to extend understanding of rhythmicity on decision-making to everyday situations. [ABSTRACT FROM AUTHOR]

There are 2 main problems with the current periodic table: artificial breaks from a given noble gas to the next alkali metal (along with the common protrusion of the "f" block) and hydrogen placed in the alkali group, although this gas also exhibits halogen properties. This paper proposes arranging chemical elements in a square spiral with hydrogen at the centre. This element is also above lithium but passes above fluorine to connect with helium, representing its dual alkali and halogen nature effectively. Then the spiral moves outwards in a counter-clockwise direction, avoiding artificial breaks and following the natural direction of reading for the "s" and "p" blocks elements placed at the bottom of the spiral. Furthermore, this proposed square spiral improves upon previous Janet´s and Benfey´s representations with a more regular shape to draw, an effective depiction of the dual nature of hydrogen, and easily identifiable orbital blocks without the need for protrusions. [ABSTRACT FROM AUTHOR]

Recently a new class of continued fraction algorithms, the (N , α )-expansions, was introduced in Kraaikamp and Langeveld (J Math Anal Appl 454(1):106–126, 2017) for each N ∈ N , N ≥ 2 and α ∈ (0 , N - 1 ] . Each of these continued fraction algorithms has only finitely many possible digits. These (N , α) -expansions 'behave' very different from many other (classical) continued fraction algorithms; see also Chen and Kraaikamp (Matching of orbits of certain n-expansions with a finite set of digits (2022). To appear in Tohoku Math. J arXiv:2209.08882), de Jonge and Kraaikamp (Integers 23:17, 2023), de Jonge et al. (Monatsh Math 198(1):79–119, 2022), Nakada (Tokyo J Math 4(2):399–426, 1981) for examples and results. In this paper we will show that when all digits in the digit set are co-prime with N, which occurs in specified intervals of the parameter space, something extraordinary happens. Rational numbers and certain quadratic irrationals will not have a periodic expansion. Furthermore, there are no matching intervals in these regions. This contrasts sharply with the regular continued fraction and more classical parameterised continued fraction algorithms, for which often matching is shown to hold for almost every parameter. On the other hand, for α small enough, all rationals have an eventually periodic expansion with period 1. This happens for all α when N = 2 . We also find infinitely many matching intervals for N = 2 , as well as rationals that are not contained in any matching interval. [ABSTRACT FROM AUTHOR]

Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets. [ABSTRACT FROM AUTHOR]

Groundwater flow systems are influenced by the changes in surface waters as well as climatic factors. These teleconnections significantly increase in cases of extreme weather conditions. To prepare and mitigate the effect of such phenomena, the background factors that create and influence natural processes must be recognized. In the present study, 94 shallow groundwater (SGW) wells' water level time series were analyzed in the inner delta of the River Danube (Europe) the Szigetköz region to explore which factors contribute to the development of diurnal periodicity of SGW and what its drivers are. The relationship between surface meteorological processes and SGW dynamics in the Szigetköz region was investigated using hourly data from monitoring wells. Hourly water temperature data exhibited weak correlations with meteorological parameters. However, daily averaged data revealed stronger correlations, particularly between SGW levels and air temperature and potential evapotranspiration. Diurnal periodicity in SGW fluctuations correlated strongly with potential evapotranspiration. The study also demonstrated the role of capillary fringe dynamics in linking surface evapotranspiration with SGW fluctuations. Changes in groundwater levels, even small, can significantly affect soil moisture, vegetation, and ecosystem functioning, highlighting the sensitivity of the unsaturated zone to SGW fluctuations driven by surface processes. [ABSTRACT FROM AUTHOR]

We show that the pure mapping class group \mathcal {N}_{g}^{k} of a non-orientable closed surface of genus g\geqslant 2 with k\geqslant 1 marked points has p-periodic cohomology for each odd prime p for which \mathcal {N}_{g}^{k} has p-torsion. Using the Yagita invariant and cohomology classes obtained from some representations of subgroups of order p, we obtain that the p-period is less or equal than 4 when g\geqslant 3 and k\geqslant 1. Moreover, combining the Nielsen realization theorem and a characterization of the p-period given in terms of normalizers and centralizers of cyclic subgroups of order p, we show that the p-period of \mathcal {N}_{g}^{k} is bounded below by 4, whenever \mathcal {N}_{g}^{k} has p-periodic cohomology, g\geqslant 3 and k\geqslant 0. These results provide partial answers to questions proposed by G. Hope and U. Tillmann. [ABSTRACT FROM AUTHOR]

Periodicity in chemistry is often regarded as a challenging or difficult subject owing to its seemingly abstractness and factors such as ineffective pedagogies, leading to poor achievement and low retention of knowledge. Also, the knowledge of periodicity is fundamental to students' understanding of a substantial range of chemistry concepts. Therefore, there is a need for the adoption of activity-oriented teaching methods, which have the capacity to not only foster achievement, but also encourage retention. Therefore, this study determined the effects of puzzle game and video-based puzzle strategies on secondary school students' achievement and retention in periodicity concept in chemistry. Pre-/post-test, delayed post-test control group quasi experimental research design was adopted in the study. Multi-stage sampling procedure was used in the sampling of participants. A total of 183 senior secondary school chemistry students were drawn from six purposively selected public secondary schools in Irewole Local Government Area of Osun State, Nigeria. Two categories of research instruments (stimulus and response) were developed, validated and used to collect data, which were analyzed using analysis of covariance and Bonferroni's post-hoc test. The findings of the study revealed improved achievement and retention among students exposed to experimental groups (puzzle game and video-based puzzle) than the control group (conventional teaching). Puzzle game appeared to be the most efficacious. It was therefore suggested that teachers should adopt activity-based learning approaches such as puzzle games for teaching periodicity and other chemistry concepts as an attempt to reduce the abstractness perceived by learners and consequently enhance learning outcomes including retention. [ABSTRACT FROM AUTHOR]

Introduction: The goal of the current study was to determine how the leukocyte count varied on a monthly basis during the various stages of the menstrual cycle. Periodic vaginal bleeding is a cyclical phenomenon associated with the menstrual cycle, which impacts all bodily systems. It takes place in the course of a woman's reproductive life and is governed by a planned progression of structural, functional, and hormonal systems. A regular menstrual cycle typically lasts 28 days, with an average blood loss of 20 to 60 millilitres and a flow that lasts 2 to 6 days. The menstrual cycle is regulated by the pituitary-ovarian system. Material and Methods: 38 healthy females in the 16-28 age range with regular cycles lasting 28-28 days were examined.The current study was conducted at the Obstetrics & Gynaecology Department of MKCG MCH, Berhampur, between August 2014 and October 2015. 38 healthy female subjects, ages 16 to 28, with average menstrual cycles lasting 28P2 days participated in the study. Women with irregular periods, gastrointestinal disorders, anaemia, a history of drug usage that interfered with their menstrual cycle, a history of chronic disease, or a fever at the time of sample collection were not included in the research. Each female participant informedly consented to the research after learning about its protocols. The tests were carried out in accordance with ethical standards. Result: During the secretory phase, the total leukocyte count significantly increased. When it came to the differential leukocyte count, the neutrophil count increased significantly during the secretory phase, the lymphocyte count increased during both the menstrual and secretory phases, the monocyte count increased during the proliferative phase but did not significantly change, and the eosinophil count did not significantly change throughout the three phases. Conclusion: In order to better understand a variety of illnesses, this study sought to ascertain the typical fluctuation in the leucocyte count during the various phases of the menstrual cycle.This study shows variations in the total leukocyte count and differential leukocyte count during the various stages of the menstrual cycle and offers information on a variety of disorders that affect women and are linked to monthly swings. [ABSTRACT FROM AUTHOR]

T he researchers investigate some phenomena by continuously observing physical variables, i.e., time series. Nowadays, the Least-Squares Spectral Analysis (LSSA) technique has been preferred for the analysis of time series to conduct more reliable analysis. This technique uses the least-squares principle to estimate the hidden periodicities in the time series. Based on the previous investigations, LSSA gives more reasonable results in the experimental time series that have disturbing effects such as the datum shifts, linear trend, unequally spaced data and etc. The LSSA method is a unique method that can overcome these problems without preprocessing the original series. However, a practical and user-friendly software package in C programming language is not available for scientific purposes to implement the LSSA method. In this paper, we review the computational scheme of the LSSA method, then a software (LSSASOFT) package in the C programming language is developed in the view of the simplicity of the method and compatibility of all types of data. Finally, LSSASOFT is applied in two sample studies for the determining hidden periods in the synthetic data and sea level observations. Consequently, the numerical results indicate that LSSASOFT is a useful tool that can efficiently predicting hidden periodicity for the experimental time series that have disturbing effects. [ABSTRACT FROM AUTHOR]

In this study, we explored an asymmetric multivariate stochastic difference volatility model that extends various probabilistic and statistical properties previously discussed in the literature. We rigorously established that the model exhibits periodic stationarity and periodic ergodicity. Additionally, we delved into the robust consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator (QMLE), providing a comprehensive analysis of its theoretical underpinnings. Finally, we demonstrated the practical applicability of our major findings through a series of pertinent applications. This work not only contributes to the existing body of knowledge on stochastic volatility modeling, but also opens new avenues for further research in this domain. [ABSTRACT FROM AUTHOR]

While rhythm can facilitate and enhance many aspects of behavior, its evolutionary trajectory in vocal communication systems remains enigmatic. We can trace evolutionary processes by investigating rhythmic abilities in different species, but research to date has largely focused on songbirds and primates. We present evidence that cetaceans--whales, dolphins, and porpoises--are a missing piece of the puzzle for understanding why rhythm evolved in vocal communication systems. Cetaceans not only produce rhythmic vocalizations but also exhibit behaviors known or thought to play a role in the evolution of different features of rhythm. These behaviors include vocal learning abilities, advanced breathing control, sexually selected vocal displays, prolonged mother-infant bonds, and behavioral synchronization. The untapped comparative potential of cetaceans is further enhanced by high interspecific diversity, which generates natural ranges of vocal and social complexity for investigating various evolutionary hypotheses. We show that rhythm (particularly isochronous rhythm, when sounds are equally spaced in time) is prevalent in cetacean vocalizations but is used in different contexts by baleen and toothed whales. We also highlight key questions and research areas that will enhance understanding of vocal rhythms across taxa. By coupling an infraorder-level taxonomic assessment of vocal rhythm production with comparisons to other species, we illustrate how broadly comparative research can contribute to a more nuanced understanding of the prevalence, evolution, and possible functions of rhythm in animal communication. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of two-dimensional nonlinear difference equations system of second order, which is a considerably extension of some recent results in the literature. Our main results show that class of system of difference equations is solvable in closed form theoretically. It is noteworthy that the solutions of aforementioned system are associated with generalized Mersenne numbers. The asymptotic behavior of solution to aforementioned system of difference equations when a = b and p = 0 are also given. Finally, numerical examples are given to support the theoretical results presented in this paper. [ABSTRACT FROM AUTHOR]

Studying the temporal characteristics of earthquake activity contributes to enhancing earthquake prediction capabilities. The seismic b-value is a key indicator describing the relationship between seismic frequency and magnitude. This study investigates the correlation between the occurrence of major earthquakes and seismic b-values using earthquake activity records in Japan from 1990 to 2023. Local singularity analysis and wavelet analysis of earthquake frequency and b-value time series reveal significant 5-year periodic features in seismic activity in Japan. Furthermore, our research identifies that this periodicity is also prominent in major earthquakes with magnitudes of 7 and above. Additionally, through a detailed analysis of the cross-correlation between seismic b-values and the occurrence time of major earthquakes, we uncover a notable pattern: major earthquakes often occur approximately two years after the peak of seismic b-values. This discovery offers a new perspective on earthquake prediction and may play a crucial role in future earthquake early warning systems. [ABSTRACT FROM AUTHOR]

In this paper, we delve into the dynamics of two and three coupled SRRs models, exploring their nonlinear properties such as stability, periodicity, or chaos. Additionally, we examine the energy function Hamilton within the context of these models. Numerical examples are provided to illustrate the obtained results and demonstrate the applicability of our findings. [ABSTRACT FROM AUTHOR]

We investigate the structure of rank-to-rank elementary embeddings at successor rank, working in ZF set theory without the Axiom of Choice. Recall that the set-theoretic universe is naturally stratified by the cumulative hierarchy, whose levels V α are defined via iterated application of the power set operation, starting from V 0 =∅, setting V α+1 =P(V α), and taking unions at limit stages. Assuming that j:V α+1 →V α+1 is a (non-trivial) elementary embedding, we show that V α is fundamentally different from V α+1: we show that j is definable from parameters over V α+1 iff α+1 is an odd ordinal. The definability is uniform in odd α+1 and j. We also give a characterization of elementary j:Vα+2 → Vα+2 in terms of ultrapower maps via certain ultrafilters. For limit ordinals λ, we prove that if j:Vλ→Vλ is Σ1-elementary, then j is not definable over Vλ from parameters, and if β < λ and j:Vβ → Vλ is fully elementary and ∈-cofinal, then j is likewise not definable. If there is a Reinhardt cardinal, then for all sufficiently large ordinals α, there is indeed an elementary j: Vα → Vα, and therefore the cumulative hierarchy is eventually periodic (with period 2). [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

Accurate prediction of traffic flow is essential for optimizing transportation resource allocation and enhancing urban mobility efficiency. However, traffic data generated daily are vast and complex, involving dynamic and intricate changes in the traffic road network and traffic flow. Therefore, real-time and accurate prediction of traffic flow is a challenging task that requires modeling the intricate spatial–temporal dynamics of traffic data. In this paper, we propose a novel approach for traffic flow prediction, based on a Multi-Scale Residual Graph Convolution Network with hierarchical attention. First, we design a novel encoder–decoder with multi-independent channels to capture traffic flow information from different time scales and diverse temporal dependencies. Second, we employ a coupled graph convolution network with residual graph attention to dynamically learn the varying spatial features among and within traffic stations. Third, we utilize channel attention to fuse the multi-scale spatial–temporal dependencies and accurately predict traffic flow. We evaluate the proposed approach on multiple benchmark datasets, and the experimental results demonstrate its superior performance compared to state-of-the-art approaches in terms of various metrics. [ABSTRACT FROM AUTHOR]

Using the Rescaled Adjusted Partial Sums (RAPS) and spectral analysis methods, the study aimed to extract subperiods (a break point analysis) based on data from 18 meteorological stations in Montenegro and determine the elementary periodicity using the Lomb periodogram of mean seasonal and annual air temperatures and precipitation totals for the period 1961–2020. Prior to this, trend calculations were performed, which showed a significant increase in mean seasonal and annual temperatures, with the most intense warming occurring in the summer (from 0.30 to 0.75 °C/decade). Trends in seasonal and annual precipitation totals are insignificant. According to RAPS values, two subperiods are clearly distinguished for temperatures, and a sudden increase in mean annual temperatures at 11 stations (61.1%) occurred only in 1997 and 1998. The year 1991 was a break point in 22.1% of cases. For seasonal and annual precipitation totals, a minimum of 4 subseries can be distinguished at all stations. According to the RAPS method, the years 2002 and 2003 most frequently appear as the last pivotal points leading to an increase in annual precipitation totals. In terms of precipitation, the most prominent periodicity observed is at 2.1 and 3.6 years, suggesting a quasi-biennial oscillation. Additionally, a 14.7-year periodicity is noted in winter precipitation, and a 3-year periodicity signals ENSO. Regarding average annual and seasonal temperatures, short-term (2–3 years), medium-term (4–5 years) and long-term (6, 8 and over 20 years) periodicities are distinguished. Only at two stations, was the Markowitz wave (MW) signal. [ABSTRACT FROM AUTHOR]

We define forward entrainment as that part of behavioral or neural entrainment that outlasts the entraining stimulus. In this review, we examine conditions under which one may optimally observe forward entrainment. In Part 1, we review and evaluate studies that have observed forward entrainment using a variety of psychophysical methods (detection, discrimination, and reaction times), different target stimuli (tones, noise, and gaps), different entraining sequences (sinusoidal, rectangular, or sawtooth waveforms), a variety of physiological measures (MEG, EEG, ECoG, CSD), in different modalities (auditory and visual), across modalities (audiovisual and auditory-motor), and in different species. In Part 2, we describe those experimental conditions that place constraints on the magnitude of forward entrainment, including an evaluation of the effects of signal uncertainty and attention, temporal envelope complexity, signal-to-noise ratio (SNR), rhythmic rate, prior experience, and intersubject variability. In Part 3 we theorize on potential mechanisms and propose that forward entrainment may instantiate a dynamic auditory afterimage that lasts a fraction of a second to minimize prediction error in signal processing.

BackgroundUnderstanding of possible periodicity of cytokines, chemokines and growth factors is of great interest and provide valuable information for research into pathophysiological mechanism of inflammatory disease and chronic pain. Significant efforts have been made to identify different analytes in saliva. For precision and accuracy in measurement and interpretation of results, it is crucial to know the source of variability, especially the circadian variation for the analytes.ObjectiveThe study aimed to analyze circadian variation in 71 inflammatory markers in both unstimulated and stimulated saliva, as well as plasma, from a sample of healthy individuals.MethodsTen young adults participated. Unstimulated and stimulated whole saliva were collected at 3-h intervals between between 7:30 am and 7:30 pm. Blood samples were drawn in connection with the first and last saliva collection. All samples were analyzed using the U-PLEX 71-Plex assay.ResultsThe analysis showed distinct clustering of the 71 inflammatory mediators between plasma and saliva. Furthermore, differences were also observed between stimulated and unstimulated saliva. The proteins were clustered into three groups that expressed different circadian rhythms. These clusters were stable over time in stimulated saliva but showed significant variability in unstimulated saliva (P

Abstract Volcanic tremor is a crucial indicator for assessing the state and hazard potential of volcanic systems. At Whakaari (White Island volcano, Aotearoa New Zealand), a pulsed tremor signal emerged after a hydrothermal explosion in August 2012. The tremor accompanied the extrusion of a lava dome, before gradually disappearing prior to the onset of renewed hydrothermal activity in January 2013. We interpret this seismic signal to represent discrete gas transfers from a magmatic intrusion toward a permeable cap—possibly a hydrothermal seal—in the upper layers of Whakaari's hydrothermal system. Such tremor may thus be associated with heightened potential for hazardous explosive activity but is difficult to detect using conventional seismic monitoring parameters. To highlight the emergence of subtle periodic signals, we experiment with Lomb‐Scargle periodograms (LS). LS detect the tremor 5 days before it becomes visible in seismograms, thus facilitating the recognition of such elusive seismic patterns.

Abstract Background Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. Methods We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the “rising trend line”; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. Results Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. Conclusion Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Background: Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves. Methods: We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave. Results: Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy. Conclusion: Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity. [ABSTRACT FROM AUTHOR]

Context: Racing structure and focus for racehorses differs across jurisdictions and is poorly described. In New Zealand and Australia, jumps racing accounts for a small proportion of total Thoroughbred races each year, as opposed to the larger jumps-racing (National Hunt) industry with purpose-bred horses found in the UK and Ireland. Aims: This study aimed to describe the career profile and pattern of racing for Thoroughbred jumps-racing horses in New Zealand. Methods: Metrics from every horse with a full jumps-racing career (n = 1276, 89% geldings) between the 2005/06 and 2021/22 racing seasons (n = 40,302 starts) were examined. Descriptive statistics, box plots and heat maps were used to describe the data, by using horse age as a proxy for career progression. Key results: Jumps-racing horses had a median of 17 flat starts (IQR 9–30) before their first jumps-racing start. The median career of a jumps-racing horse was 3.7 years (IQR 2.3–5.2) and 29 starts (IQR 16–44). Older horses had a decreasing number of flat-racing starts and an increasing proportion of steeplechase-racing starts. However, the majority of horses still had at least one flat-race start per calendar year, often prior to the first jumps race. Most horses had one spell each season (length 194 days, IQR 124–259), and a condensed preparation length over the winter months, with a median of 14 days (IQR 10–21) between starts. Conclusion: The career pattern for jumps-racing horses in New Zealand reflects the industry retention of horses with a prior career in flat racing into a secondary jumps-racing career. Successful horses have increased focus on preparation for the winter jumps-racing season. Implications: Jumps racing provides a secondary racing career for some flat-racing horses, particularly geldings. This strategy reduces wastage of horses from the industry. Jumps racing in the southern hemisphere is unique and poorly described. This study aimed to describe the career profile and pattern of racing for jumps-racing horses in New Zealand by using industry race data. All jumps-racing horses in New Zealand had a primary career in flat racing, before repurposing in a jumps-racing career for a further 2 years, increasing specialisation as the horse aged. This strategy results in a decrease in wastage from the racing industry. This article belongs to the Collection Equine Science Part II. [ABSTRACT FROM AUTHOR]

In this paper, we study discrete-time quantum walks on Cayley graphs corresponding to Dihedral groups, which are graphs with both directed and undirected edges. We consider the walks with coins that are (real) linear combinations of permutation matrices of order three. We show that the walks are periodic only for coins that are permutation or negative of a permutation matrix. Finally, we investigate the localization property of the walks through numerical simulations and observe that the walks localize for a wide range of coins for different sizes of the graphs. [ABSTRACT FROM AUTHOR]

Meteorological factors are important factors driving the evolution of runoff. Cross-wavelet method was used to analyze the resonance cycle law between runoff and precipitation, maximum temperature and minimum temperature of four typical tributaries in the upper reaches of Shiyang River, which reveals the degree changes and anomalies of correlation in different time domains and frequency domains, supplementing the existing correlation research. The results show that the runoff at Xidaxihe Reservoir station and Dajingxia Reservoir station has an increasing trend while the runoff at Nanying Reservoir station and Gulang station decreases. The cycle pattern and correlation between the runoff and precipitation are the most significant, followed by the maximum temperature and the minimum temperature, and the resonance period ranges from 2 a to 8 a. The analysis shows that this method plays an important role in guiding the exploration of the mutation of meteorological factors, the correlation of hydrometeorological elements and their time-frequency changes. When implementing targeted water replenishment in the Shiyang River Basin, priority should be given to artificial rainfall from the precipitation link. [ABSTRACT FROM AUTHOR]

In this paper, we first discuss a class of meromorphic functions satisfying a certain property on characteristic functions of Nevanlinna theory and then give the applications of the class to Yang's conjecture and its variants. [ABSTRACT FROM AUTHOR]