Random Events Research Articles

Faunal Diversity and Biodiversity Conservation within Homagama Mahinda Rajapaksha College, Sri Lanka

A faunal diversity survey was carried out to analyze the faunal diversity in Homagama Mahinda Rajapaksha College in Colombo District, Sri Lanka. The survey site is located in a suburban area that is surrounded by disturbed properties. The aim of the survey was to analyze the faunal diversity and recognize the special habitat types in the school premises for conservation purposes. It could recognize five major zones which have different types of microhabitats and rich in faunal diversity. The survey was done as a visual encounter survey, and smartphones, digital cameras and binoculars were used to collect data of the fauna species for documenting and identifying purposes. Also, pitfalls traps, light traps and beating were used as survey methods. The study was done for three days including three random sampling events during both day and night. Standard guides were used for identifying and photographs, notes on observations were sent to experts on each taxon for further confirmation. A total of 188 fauna species were recorded during the survey including 55 species of vertebrates including five classes (Aves, Amphibia, Mammalia, Reptilia, Actinopterygii) and 133 species of invertebrates including six classes (Insecta, Gastropoda, Diplopoda, Clitellata, Chilopoda, Arachnida). The study could document 30 bird, 14 reptile, 6 mammals, 4 amphibian and 1 ray-finned fish species as vertebrates and 95 insect, 5 mollusks, 3 millipede, 2 clitellate, 2 centipede, 26 arachnid species as invertebrate fauna species in the school premises. Based on the results of the survey, the fauna diversity of the studied site is stable and depends on the microhabitat types in the different zones. As a suburban area, the species richness of the site is in well conditions. To keep stable and increase the fauna diversity of the site, doing regular surveys and recording data, generating microhabitats, and conserving highly diverse habitat types are suggested. Unique species like Indian Hare (Lepus nigricollis), Indian Crested Porcupine (Hystrix indica) were observed and as the only venomous snake species, Merrem‘s hump-nosed viper (Hypnale hypnale) was recorded. The faunal diversity of the studied site helps to keep the unique surroundings there and keeping frequent records and data on the site will help to maintain the biodiversity with constructions in and around the school premises. 
 Keywords: Faunal diversity, Suburban, Citizen Science, Conservation, Visual encounter

The Effects of Construal Level on Predictive Heuristics: Disentangling Representativeness From Availability

Intuitive predictions about the future can expect upcoming events to conform with a pattern - representativeness heuristic - or to simply repeat the most recent and accessible outcome - availability heuristic. The present work proposes that while representativeness predictions require a relatively abstract comparison process, availability-based predictions reflect a concrete and local use of accessible information. Grounded on the construal level theory, this research pits one heuristic against the other in predictions about random events (rolling dice; coin tosses). Three studies suggest that low construal levels increase predictions consistent with the most accessible and recent outcomes while high levels of construal facilitate representativeness-consistent predictions. The findings highlight how construal level may determine reliance on one heuristic or the other.

A full‐probabilistic cloud analysis for structural seismic fragility via decoupled M‐PDEM

AbstractPerformance‐based earthquake engineering (PBEE) is essential for ensuring engineering safety. Conducting seismic fragility analysis within this framework is imperative. Existing methods for seismic fragility analysis often rely heavily on double loop reanalysis and empirical data fitting, leading to challenges in obtaining high‐precision results with a limited number of representative structural analysis instances. In this context, a new methodology for seismic fragility based on a full‐probabilistic cloud analysis is proposed via the decoupled multi‐probability density evolution method (M‐PDEM). In the proposed method, the assumption of a log‐normal distribution is not required. According to the random event description of the principle of preservation of probability, the transient probability density functions (PDFs) of intensity measure (IM) and engineering demand parameter (EDP), as key response quantities of the seismic‐structural system, are governed by one‐dimensional Li‐Chen equations, where the physics‐driven forces are determined by representative analysis data of the stochastic dynamic system. By generating ground motions based on representative points of basic random variables and performing structural dynamic analysis, the decoupled M‐PDEM is employed to solve the one‐dimensional Li‐Chen equations. This yields the joint PDF of IM and EDP, as well as the conditional PDF of EDP given IM, resulting in seismic fragility analysis outcomes. The numerical implementation procedure is elaborated in detail, and validation is performed using a six‐story nonlinear reinforced concrete (RC) frame subjected to non‐stationary stochastic ground motions. Comparative analysis against Monte Carlo simulation (MCS) and traditional cloud analysis based on least squares regression (LSR) reveals that the proposed method achieves higher computational precision at comparable structural analysis costs. By directly solving the physics‐driven Li‐Chen equations, the method provides the full‐probabilistic joint information of IM and EDP required for cloud analysis, surpassing the accuracy achieved by traditional methods based on statistical moment fitting and empirical distribution assumptions.

Illusion of Control: Psychological Characteristics as Moderators in Financial Decision Making

Financial decision making requires a sound handling of chance events. However, various studies have suggested that people are prone to illusion of control, i.e., the belief that prospects of a chancy event are better if they are involved in the randomisation process. This paper reports results from an experiment (N=420) suggesting that psychological characteristics moderate risk-taking behaviour under such circumstances. For example, we find that subjects high in sensation seeking buy more tickets of a risky lottery if they determine the winning numbers themselves and the random event lies in the future. The findings suggest that “illusion of control” effects are at least partly driven by underlying (idiosyncratic) emotions/preferences rather than an actual belief in control. Regarding applications, the results emphasise the importance of individual characteristics for the behaviour of decision makers in a financial context.

Artist V.N. Elizarov and his collection in the collection of the Minusinsk Museum of Local Lore. N.M. Martyanova

The subject of the research is the creative biography of Valery Elizarov, a Tuvan artist who currently lives in Abakan. Valery Nikolaevich Elizarov in 2022 donated 36 his works to the Martyanovsky Museum. The artist works in painting, graphics, arts and crafts. The basis for this study was personal meetings, conversations, interviews with the artist, his autobiography, published in a single copy by his wife, Marina Mikhailovna Elizarova, and works stored in the MBUK MKM. It is concluded that the artist's work is based on the historical and cultural heritage of the region, that his biography is not a chain of random events, but a spiritual path of knowing creativity.

Natural emergency modeling and forecasting

The purpose of this study is to develop forecast and analytical models (FAM) for predicting the most catastrophic natural emergencies caused by floods, earthquakes and forest fires. The article discusses predictive and analytical solutions for natural hazards for urbanized areas based on the Bayesian classifiers. The result is a formalized description of models for predicting forest fires, consequences of earthquakes and floods. The novelty of the models is due to the application of a unified scientific approach - the statistical processing method based on Bayes’ theorem. In contrast to the frequency probability determined by the relative frequency of occurrence of random events over sufficiently long observations, the Bayesian probability is the main forecasting method for constructing and training neural networks. Scientific forecasting of crises and incidents based on the Bayesian method and Bayesian networks requires a large amount of up-to-date data to model natural disasters, which is typical for frequently recurring negative events. Due to the lack of statistical data, the Bayesian method is not applicable to predicting catastrophic natural disasters that occur rarely but cause significant damage.

Prediction for Some Tech Stock Prices of U.S. Stock Market based on ARIMA Model

Predicting the future price of stocks is a common practice in the financial industry. Individuals and organizations engage in stock price forecasting for investment decision-making, risk management, portfolio optimization and asset allocation. However, the predicting stock prices is challenging due to the complexity of financial markets, the influence of numerous variables, and the presence of random and unpredictable events, which all make it harder to predict. This paper uses predicted future stock price trends for technology companies in The United States based on ARIMA and breaks it down using APPLE as a specific example for a more in-depth discussion of ARIMA. The raw data is collected from 2001/5/11 to 2021/4/22. Then it predicted the stock prices for the next two weeks (working days) by ARIMA and made a fitted trend curve, and finally compared it with the real data from 2021/4/23 to 2021/5/12 to see if ARIMA's predictions were accurate. The result shows that prediction model is in the form of ARIMA (1,1,1), and the prediction of APPL from 2021/4/23 to 2021/5/12 is trending down, which matches the real trend.

Compound Conway-Maxwell Poisson Gamma Distribution: Properties and Estimation

The distribution of a random sum of random events is called a compound distribution. It involves a counting (discrete) distribution to model the number of occurrences of the random event in a fixed time period and a continuous distribution to model the outcome of the random event. It has applications in the fields of actuarial sciences, meteorology etc. For example, in modelling insurance loss amounts through compound distributions, the number of claims and the claim amounts are used to calculate the total claim amount of a portfolio. The number of claims is modelled through a discrete distribution and the claim amounts are modelled through continuous distributions. Generally, Poisson distribution is used in compound models as the discrete distribution and such models are known as compound Poisson models. However, the equi-dispersion property of the Poisson distribution hinders its application in scenarios where the underlying count data is either over- or under-dispersed. In this paper, a two-parameter Poisson distribution, namely, Conway-Maxwell Poisson (CMP) distribution, which handles both over- and under-dispersed data, is considered as the counting distribution, and the corresponding compound CMP distribution is developed. Some mathematical properties of the distribution are derived and a methodology to estimate the parameters using the likelihood approach is proposed. A numerical illustration of the proposed methodology is given through a simulation study. An application of the compound CMP model is illustrated through transportation security administration (TSA) insurance claim data. Also, the estimation of the risk measures associated with the TSA claim data is discussed.

Predicting Day-Ahead Electricity Market Prices through the Integration of Macroeconomic Factors and Machine Learning Techniques

Several events in the last years changed to some extent the common understanding of the electricity day-ahead market (DAM). The shape of the electricity price curve has been altered as some factors that underpinned the electricity price forecast (EPF) lost their importance and new influential factors emerged. In this paper, we aim to showcase the changes in EPF, understand the effects of uncertainties and propose a forecasting method using machine learning (ML) algorithms to cope with random events such as COVID-19 pandemic and the conflict in Black Sea region. By adjusting the training period according to the standard deviation that reflects the price volatility, feature engineering and by using two regressors for weighing the results, significant improvements in the performance of the EPF are achieved. One of the contributions of the proposed method consists in adjusting the training period considering the price variation. Thus, we introduce a rule-based approach given an empirical observation that for days with a higher growth in prices the training interval should be shortened, capturing the sharp variations of prices. The results of several cutting-edge ML algorithms represent the input for a predictive meta-model to obtain the best forecasting solution. The input dataset spans from Jan. 2019 to Aug. 2022, testing the proposed EPF method for both stable and more tumultuous intervals and proving its robustness. This analysis provides decision makers with an understanding of the price trends and suggests measures to combat spikes. Numerical findings indicate that on average mean absolute error (MAE) improved by 48% and root mean squared error (RMSE) improved by 44% compared to the baseline model (without feature engineering/adjusting training). When the output of the ML algorithms is weighted using the proposed meta-model, MAE further improved by 2.3% in 2020 and 5.14% in 2022. Less errors are recorded in stable years like 2019 and 2020 (MAE = 6.71, RMSE = 14.67) compared to 2021 and 2022 (MAE = 9.45, RMSE = 20.64).

Analysis of Extreme Random Uncertainty in Energy and Environment Systems for Coal-Dependent City by a Copula-Based Interval Cost–Benefit Stochastic Approach

Extreme random events will interfere with the inversion analysis of energy and environment systems (EES) and make the planning schemes unreliable. A Copula-based interval cost–benefit stochastic programming (CICS) is proposed to deal with extreme random uncertainties. Taking Yulin city as an example, there are nine constraint-violation scenarios and six coal-reduction scenarios are designed. The results disclose that (i) both system cost and pollutant emission would decrease as the industrial energy supply constraint-violation level increases; (ii) when the primary and secondary energy output increases by 9% and 13%, respectively, and industrial coal supply decreases by 40%, the coal-dependent index of the system would be the lowest, and the corresponding system profitability could reach [29.3, 53.0] %; (iii) compared with the traditional chance-constrained programming, Copula-based stochastic programming can reflect more uncertain information and achieve a higher marginal net present value rate. Overall, the CICS-EES model offers a novel approach to gain insight into the tradeoff between system reliability and profitability.

Growth kinetics and morphology characterization of binary polymeric fluid under random photo-illumination.

We present a comprehensive study using dissipative particle dynamics simulations to investigate phase separation kinetics (PSK) in three-dimensional (3d) polymeric fluids under random photo-illumination. We consider two scenarios: polymer blends with active radicals at one end of each immiscible chain and block copolymer (BCP) melts with photosensitive bonds linking incompatible blocks. The phase separation (PS) is induced by temperature quench of the initial homogeneously mixed system. Simultaneously, the system experiences random photo-illumination, simulated by two concurrent random events: (a) the recombination of active radicals in polymer blends and (b) the breaking of photosensitive bonds in BCP chains. Variations in the bond-breaking probability, Pb, mimic the change in light intensity. The length scale follows power law growth, R(t) ∼ tϕ, where ϕ represents the growth exponent. Increasing Pb results in a gradual transition in growth kinetics from micro-PS to macro-PS, accompanied by corresponding transition probabilities for both systems. Micro-PSK dominates the evolution process at low Pb values. The scaling functions exhibit data overlap for most scaled distances, indicating the statistical self-similarity of evolving patterns. Our study enhances the understanding of PSK in polymeric fluids, revealing the impact of photosensitive bonds and active radicals. Furthermore, it suggests the potential for designing novel polymeric materials with desired properties.

Approaches to accounting for random events in the modeling of production processes of production and logistics systems of machine-building enterprises

The paper considers approaches to the analysis and prediction of the state of production and logistics systems of machine-building enterprises. The main groups of random events that occur during the operation of production and logistics systems and can have a significant impact on the implementation of the production program are systematized. Simulation modeling systems are analyzed: AnyLogic, Plant Simulation, PlantTwin. The functional capabilities of simulation modeling systems for setting random events are considered. A comparison of simulation modeling systems is carried out by the method of setting the distribution of a random variable and by the possibilities of accounting for random events.

Dynamic heterogeneity in COVID-19: Insights from a mathematical model.

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load. We find that treatment efficacy and baseline patient or viral features are not the sole determinant of outcome. We found patients with enhanced innate or adaptive immune responses can experience poor viral control, resolution of infection or non-infectious inflammatory injury depending on treatment efficacy and initial viral load. Hypoxemia may result from poor viral control or ongoing inflammation despite effective viral control. Adaptive immune responses may be inhibited by very early effective therapy, resulting in viral load rebound after cessation of therapy. Our model suggests individual disease course may be influenced by the interaction between external and patient-intrinsic factors. These data have implications for the reproducibility of clinical trial cohorts and timing of optimal treatment.

Sequential Clearing of Network-Aware Local Energy and Flexibility Markets in Community-Based Grids

In this paper, network-aware clearing algorithms for local energy markets (LEMs) and local flexibility markets (LFM) are proposed to be sequentially run and coordinate assets and flexible resources of energy communities (ECs) in distribution networks. In the proposed LEM clearing algorithm, EC managers run a two-stage stochastic programming while considering random events by scenario generation and network constraints using linearized DistFlow. As one of outcomes, maximum available up-and down-regulations provided by ECs are estimated in LEM and communicated to LFM. In the distributed LFM clearing algorithm, an iterative auction is designed using a dual-decomposition technique (Augmented Lagrangian) which is solved by consensus alternating direction method of multipliers. The LFM algorithm efficiently dispatches the flexibility provided by ECs in operating time while considering flexibility local marginal price as pricing method. Network constraints are included in the algorithm with an AC distribution optimal power flow for dynamic network topology in which branches and buses are decomposed to solve the problem in distributed fashion. The designed LFM algorithm can respond to exogenous and endogenous signals for flexibility requests. The simulation results in a test case display effectiveness of two proposed LEM and LFM algorithms for an efficient provision of flexibility.

Dynamics of phase-separated microdroplets near the contact line of evaporating all-aqueous drops.

Evaporation of multicomponent drops can induce liquid-liquid phase separation and spatial reconfiguration of phases. Here, we unveil several novel dynamics near the contact line of evaporating multicomponent drops containing polyethylene glycol and dextran. The interplay between background Marangoni flow and self-migration of nucleated microdroplets creates both unstable and stable equilibrium points. This leads to either continuous migration or stepwise advancement of microdroplets, influenced by random coalescence events. Tiny dextran microdroplets nucleating at the contact line can migrate toward the bulk only by growing in size with coalescence events. Our findings offer new insights into the fundamental understanding of evaporating multicomponent drops and factors influencing the spatial segregation of phases in evaporative liquid-liquid phase separation with implications in prebiotic biomolecular reactions to industrial applications.

DECOMPOSITION OF FAILURE STATES OF GRAIN HARVESTING COMBINERS

The article discloses methodological approaches to the formation of the validity of the application of the simulation model of the decomposition of failure-free states of combine harvesters. The authors reproduce the functional decomposition of the reliability of grain harvesters obtained through the analysis of system functions. The authors reveal analytical approaches to the question of what the system does, regardless of how it works. As a basis, the authors formed a division into functional subsystems in the community of functions performed by groups of elements. When conducting the experiment, a set of random events that occur with the module under consideration is first generated - the event of failure of the blocked module, the event of activation of the blocking of the module and the event of the transmission of the module. Then, by assigning the corresponding probabilities, states are defined and logical dependencies between them are found. The experiment consisted in the sequential generation of events and obtaining the final states of the functional module. This experiment was repeated several times, then it was calculated, in several cases the functional module of the grain harvester was in good condition. The ratio of the number of experiment results in which the functional module was found to be working to all results shows the possibility of the functioning of the functional module. By performing a certain number of calculations of each state, summing the results of each calculation, taking the true value as one and the false value as zero, and then dividing the result by the length of the set, we obtain the value of the probability of blocked failure calculated by the Monte Carlo method.When performing the calculations and with the original data, it was equal to 0.885. The similarity of the result to the result accepted as true proves the truth. The method given in the article allows to improve the quality of work of the fail-safe system of grain harvesters, providing more accurate failure-free accounting, expanding the list of analyzed reliability parameters and making fuller use of technical means of monitoring technical condition parameters.

Stopping Methods for Technology-assisted Reviews Based on Point Processes

Technology-assisted Review (TAR), which aims to reduce the effort required to screen collections of documents for relevance, is used to develop systematic reviews of medical evidence and identify documents that must be disclosed in response to legal proceedings. Stopping methods are algorithms that determine when to stop screening documents during the TAR process, helping to ensure that workload is minimised while still achieving a high level of recall. This article proposes a novel stopping method based on point processes, which are statistical models that can be used to represent the occurrence of random events. The approach uses rate functions to model the occurrence of relevant documents in the ranking and compares four candidates, including one that has not previously been used for this purpose (hyperbolic). Evaluation is carried out using standard datasets (CLEF e-Health, TREC Total Recall, TREC Legal), and this work is the first to explore stopping method robustness by reporting performance on a range of rankings of varying effectiveness. Results show that the proposed method achieves the desired level of recall without requiring an excessive number of documents to be examined in the majority of cases and also compares well against multiple alternative approaches.

Morbidity rates of the children population under conditions of aerogenic chemical exposure: risk factors and associations

Introduction. In the the industrialized regions of the Russian Federation one of the hygienic problems is the formation of additional morbidity among the population associated with intense air pollution.
 The purpose of the work is to study the health disorders in children under conditions of aerogenic chemical exposure in terms of primary morbidity.
 Materials and methods. Analysis of the primary morbidity of the children population (data from state statistical reporting and actual seeking medical care in 2017–2021); the content of the main chemical pollutants in the atmospheric air in residential buildings (data from social and hygienic monitoring and the regular observation network of Roshydromet for 2016–2020 according to the content of twenty two impurities); chronic non-carcinogenic risk; connection of diseases with aerogenic chemical exposure according to epidemiological indicators; economic assessment of predicted losses.
 Results. Comparing to the reference area, the level of primary morbidity in children with diseases of the respiratory system, digestion, kidneys, nervous system, osteoarticular apparatus, associated with aerogenic exposure to chemical pollutants, was established to be an increased by up to 3.4 times. In the atmospheric air of residential buildings, the content of a number of chemicals (metals, aromatic and fluorine-containing inorganic compounds) was found to be increased by 6.9 times and 4.0 times relative to the average daily and maximum one-time maximum permissible concentrations, respectively, and within the hygienic standards. The formed exposure causes an unacceptable risk of developing non-carcinogenic effects (HI up to 18.7 times) in relation to target organs. Additional morbidity in the children population (about 1.5 thousand cases per year) has been identified in the event of which the projected economic losses will amount to about 10 million rubles over year.
 Limitations. The study includes the children population under the age of 14 years.
 Conclusion. The primary morbidity of children in regions with developed industrial infrastructure is characterized by special random events associated with aerogenic exposure to risk factors. Projected additional losses require priority regulatory action.

Kinetic model in the agglomeration design: on the example of Lviv

The kinetic components of local settlement systems with implemented processes related to the movement of people, materials, energy, information, etc. are studied. A kinetic model of the organization and controlled development of local settlement systems is proposed, which is built on the knowledge of movement theories and the properties of space associated with them and provides an opportunity to organize connections between its subsystems and elements of various spheres. The nature of kinetics (movement) in urbanized systems is concentrated in the systems' processes, events, and behavior. Their study and modeling are accompanied by the analysis of phenomena that occur from the point of view of time (presented in the form of a function where time is independent), as well as random events, which by their nature are poorly predicted. The provisions of the kinetic model are applied to the analysis, evaluation, and justification of the concept of spatial organization and development of the local settlement system "Great Lviv". The prospects of Lviv as an agglomeration in the system of connections and relations with the environment are revealed. The requirements for development and conceptual proposals for the optimization of movement within the agglomeration are substantiated. It is defined that requirements and proposals should be oriented toward the search for solutions that reveal the uniqueness of the city space and its surroundings, ensure the harmonious integrity of the agglomeration, and the practical "mounting" of communication functions into the structure of the space.

Fractional Criticality Theory and Its Application in Seismology

To understand how the temporal non-locality («memory») properties of a process affect its critical regimes, the power-law compound and time-fractional Poisson process is presented as a universal hereditary model of criticality. Seismicity is considered as an application of the theory of criticality. On the basis of the proposed hereditarian criticality model, the critical regimes of seismicity are investigated. It is shown that the seismic process has the property of «memory» (non-locality over time) and statistical time-dependence of events. With a decrease in the fractional exponent of the Poisson process, the relaxation slows down, which can be associated with the hardening of the medium and the accumulation of elastic energy. Delayed relaxation is accompanied by an abnormal increase in fluctuations, which is caused by the non-local correlations of random events over time. According to the found criticality indices, the seismic process is in subcritical regimes for the zero and first moments and in supercritical regimes for the second statistical moment of events’ reoccurrence frequencies distribution. The supercritical regimes indicate the instability of the deformation changes that can go into a non-stationary regime of a seismic process.