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Motivated by the One Health paradigm, we found the expected changes in temperature and UV radiation (UVR) to be a common trigger for enhancing the risk that viruses, vectors, and diseases pose to human and animal health. We compared data from the mosquito field collections and medical studies with regional climate model projections to examine the impact of climate change on the spreading of one malaria vector, the circulation of West Nile virus (WNV), and the incidence of melanoma. We analysed data obtained from ten selected years of standardised mosquito vector sampling with 219 unique location-year combinations, and 10 years of melanoma incidence. Trends in the observed data were compared to the climatic variables obtained by the coupled regional Eta Belgrade University and Princeton Ocean Model for the period 1961-2015 using the A1B scenario, and the expected changes up to 2030 were presented. Spreading and relative abundance of Anopheles hyrcanus was positively correlated with the trend of the mean annual temperature. We anticipated a nearly twofold increase in the number of invaded sites up to 2030. The frequency of WNV detections in Culex pipiens was significantly correlated to overwintering temperature averages and seasonal relative humidity at the sampling sites. Regression model projects a twofold increase in the incidence of WNV positive Cx. pipiens for a rise of 0.5°C in overwintering TOctober-April temperatures. The projected increase of 56% in the number of days with Tmax ≥ 30°C (Hot Days-HD) and UVR doses (up to 1.2%) corresponds to an increasing trend in melanoma incidence. Simulations of the Pannonian countries climate anticipate warmer and drier conditions with possible dominance of temperature and number of HD over other ecological factors. These signal the importance of monitoring the changes to the preparedness of mitigating the risk of vector-borne diseases and melanoma.

Solar irradiance represents one of the principal phenomena of interest in geophysics and recent research, especially which concerned with renewable energy, suggests that the complexity of solar irradiance time series offers important insights into the dynamics of different geophysical systems. We examined the complexity of the daily cumulative global horizontal irradiance (kWh/m2; dGHI in further text) recorded by satellite for 32 stations on the island of La Réunion over a 35-month period (2004–2006) using Kolmogorov complexity (KC) and a recently introduced measure—Aksentijevic–Gibson complexity (AG) which is capable of quantifying the complexity of both long and short strings. Previous examinations of physical data suggest that AG could represent a useful addition to the geophysical analysis toolkit. Our results demonstrate for the first time that running KC is capable of capturing periodic patterns in data and that AG is sensitive to both global/long-scale spatial and temporal structure and local/short-range complexity fluctuations. Importantly, we report a putative weekly periodicity which might be related to environmental factors and human activity. In conclusion, we suggest that AG could represent a useful tool in the study of solar irradiation time series but also with other types of geophysical data.

Given that complexity is critical for psychological processing, it is somewhat surprising that the field was dominated for a long time by probabilistic methods that focus on the quantitative aspects of the source/output. Although the more recent approaches based on the Minimum Description Length principle have produced interesting and useful models of psychological complexity, they have not directly defined the meaning and quantitative unit of complexity measurement. Contrasted to these mathematical approaches are various ad hoc measures based on different aspects of structure, which can work well but suffer from the same problem. The present manuscript is composed of two self-sufficient, yet related sections. In Section 1, we describe a complexity measure for binary strings which satisfies both these conditions (Aksentijevic–Gibson complexity; AG). We test the measure on a number of classic studies employing both short and long strings and draw attention to an important feature—a complexity profile—that could be of interest in modelling the psychological processing of structure as well as analysis of strings of any length. In Section 2 we discuss different factors affecting the complexity of visual form and showcase a 2D generalization of AG complexity. In addition, we provide algorithms in R that compute the AG complexity for binary strings and matrices and demonstrate their effectiveness on examples involving complexity judgments, symmetry perception, perceptual grouping, entropy, and elementary cellular automata. Finally, we enclose a repository of codes, data and stimuli for our example in order to facilitate experimentation and application of the measure in sciences outside psychology.

Alluvial channels with sinuosity follow an altered flow behavior, contradictory to straight flows. At the interface of surface water and groundwater, seepage is a significant phenomenon occurring at the boundary of alluvial channels. The study of turbulence in seepage affected sinuous alluvial channels would thus provide us with a better insight into their hydro-morphological behavior. To address the nature of turbulence in sinuous channel with downward seepage an experimental framework was design. The paper reports the structure of turbulence in the sinuous channel for no seepage and seepage flow. With downward seepage, there is a noticeable shift of Reynolds shear stress at near-bed, which reports more momentum transport. The average streamwise and transverse turbulence intensity increased by 3.8–18.5% and 4–10.6%, respectively with downward seepage. Calculation of Kolmogorov complexity and the Kolmogorov complexity spectrum suggests higher randomness in the outer region, which can be associated with excess momentum transport. In the lower flow depth z/h=0.2, the randomness in the transverse velocities is higher in the outer region of the bend for about 25% and 38% compared to the central and inner region of the bend, respectively. With downward seepage, randomness increased especially in the outer region. This increase in randomness may report the erosive action in the outer part of the bend. Permutation entropy provided an informative measure to study the complex behavior of the transverse velocity time-series, which we found to be higher in the outer flow zone. For downward seepage, mean of entropy increased across the bend. The turbulent flow alterations and increase in randomness with seepage may be helpful to understand the flow in seepage affected sinuous alluvial channels.

Aksentijevic–Gibson complexity is an original complexity measure based on the amount of change in a string or 2D array that has been successfully implemented on data from psychology to physics. The key ingredient to computing the measure is a change symmetry (CS)—a novel form of structure (also known as generalised palindrome) which represents a central or mirror symmetry based on the redundant arrangement not of symbols but of changes. This results in patterns that although globally symmetrical do not appear as such when inspected locally. We used this property to (a) affect the registration of a target, (b) prime the symmetry judgment of 2D arrays and (c) faces using 1D patterns possessing change symmetry. In Experiment 2, we applied the lock and key principle to complete the prime without showing its structure at once. In Experiments 3 and 4, we presented subjects with fast sequences of CSs such that the configuration of an individual pattern was masked by the subsequent pattern leaving only the structural “essence” of the prime symmetry. The results strongly support the contention that higher-level hidden structure of change symmetry successfully primes the symmetry perception of 2D arrays as well as facial attractiveness.

We have used the Kolmogorov complexity (KC) and three suggested complexity measures to describe the complexity of spatial distribution of precipitation in Bosnia and Herzegovina, for the period 1960–1984. In particular, we have examined the monthly precipitation amount time series from 23 stations and then calculated the KC using the Lempel–Ziv Algorithm (LZA), Kolmogorov complexity spectrum (KCS), Kolmogorov complexity spectrum highest value (KCM) and overall Kolmogorov complexity (OKC) values for each time series. Our results indicate that the difference in complexity of spatial distribution of precipitation may be attributed to influence of Adriatic Sea, relief and Pannonian Basin.

One effective method for analyzing complexity involves applying information measures to time series derived from observational data. Permutation entropy (PE) is one such measure designed to quantify the degree of disorder or complexity within a time series by examining the order relations among its values. PE is distinguished by its simplicity, robustness, and exceptionally low computational cost, making it a benchmark tool for complexity analysis. This text reviews the advantages and limitations of PE while exploring its diverse applications in hydrology from 2002 to 2025. Specifically, it categorizes the uses of PE across various subfields, including runoff prediction, streamflow analysis, water level forecasting, assessment of hydrological changes, and evaluating the impact of infrastructure on hydrological systems. By leveraging PE’s ability to capture the intricate dynamics of hydrological processes, researchers can enhance predictive models and improve our understanding of water-related phenomena.

One of the most challenging tasks in studying streamflow is quantifying how the complexities of environmental and dynamic parameters contribute to the overall system complexity. To address this, we employed Kolmogorov complexity (KC) metrics, specifically the Kolmogorov complexity spectrum (KC spectrum) and the Kolmogorov complexity plane (KC plane). These measures were applied to monthly streamflow time series averaged across 1879 gauge stations on U.S. rivers over the period 1950–2015. The variables analyzed included streamflow as a complex physical system, along with its key components: temperature, precipitation, and the Lyapunov exponent (LEX), which represents river dynamics. Using these metrics, we calculated normalized KC spectra for each position within the KC plane, visualizing interactive master amplitudes alongside individual amplitudes on overlapping two-dimensional planes. We further computed the relative change in complexities (RCC) of the normalized master and individual components within the KC plane, ranging from 0 to 1 in defined intervals. Based on these results, we analyzed and discussed the complexity patterns of U.S. rivers corresponding to each interval of normalized amplitudes.

One of the most challenging tasks in studying precipitation is quantifying how the complexities of individual components contribute to the overall system complexity. To address this, we employed information measures based on Kolmogorov complexity (KC), specifically the Kolmogorov complexity spectrum (KC spectrum) and the Kolmogorov complexity plane (KC plane). We applied these measures to monthly time series data, both measured and simulated by the EBU POM regional climate model, spanning the period from 1982 to 2005 for Sombor (45.78° N, 19.12° E) in Serbia. The variables analyzed included precipitation—a complex physical system—and its individual components: mean temperature, minimum and maximum temperatures, humidity, wind speed, and global radiation. By applying the listed measures to all time series, we calculated normalized KC spectra for each position in the KC plane, displaying interactive master amplitudes against individual amplitudes. We proposed a simplified four-step method to compute the relative change in complexities within the overlapping area beneath the KC spectra. Our results facilitated a discussion on the relationship between the complexity of precipitation and that of its individual components.

A positive surge is associated with a sudden change in flow that increases the water depth and modifies flow structure in a channel. Positive surges are frequently observed in artificial channels, rivers, and estuaries. This paper presents the application of Kolmogorov complexity and its spectrum to the velocity data collected during the laboratory investigation of a positive surge. Two types of surges were considered: a undular surge and a breaking surge. For both surges, the Kolmogorov complexity (KC) and Kolmogorov complexity spectrum (KCS) were calculated during the unsteady flow (US) associated with the passage of the surge as well as in the preceding steady-state (SS) flow condition. The results show that, while in SS, the vertical distribution of KC for Vx is dominated by the distance from the bed, with KC being the largest at the bed and the lowest at the free surface; in US only the passage of the undular surge was able to drastically modify such vertical distribution of KC resulting in a lower and constant randomness throughout the water depth. The analysis of KCS revealed that Vy values were peaking at about zero, while the distribution of Vx values was related both to the elevation from the bed and to the surge type. A comparative analysis of KC and normal Reynold stresses revealed that these metrics provided different information about the changes observed in the flow as it moves from a steady-state to an unsteady-state due to the surge passage. Ultimately, this preliminary application of Kolmogorov complexity measures to a positive surge provides some novel findings about such intricate hydrodynamics processes.