Random Process Research Articles

Virtual Sound Image Reconstruction Method for Multi-objective Optimization of Folk Music Based on Evolutionary Algorithm

At present, the exchanges in various fields such as culture, economy, and politics are becoming more and more close in the world. From the perspective of the relationship between national music and cultural development, the development of national music has also received more and more attention, and it has become an inevitable trend in the development of today's era. The purpose of this paper was to perform virtual reconstruction of the sound image of folk music through multiple objective optimizations, and to model the virtual sound image of folk music as a multi-objective optimization problem. According to the research on sound image positioning, the relevant noise factors were removed, so as to achieve the playback of ethnic music that enhanced the surround effect and visual enjoyment. The evolutionary algorithm in this paper was mainly based on the multi-objective optimized sound image localization technology. For the music virtual sound image, an improved FCM algorithm and an evolutionary multi-objective optimization algorithm combining local and non-local information of the sound image were proposed, respectively. Through the analysis of the traditional sound image algorithm method, the accuracy of the sound image localization on the horizontal plane could be effectively improved. After the conversion, the audience could feel the better stereo image and surround feeling of the folk music. Through experimental analysis, it can be seen that the system can not only perform virtual conversion of audio-visual signals of different frequencies, but also provide data for different audio playback systems. The feature point registration error is low, and the reconstruction effect is good, especially the random delay processing in the range of 0∼20m, and the performance is better than the traditional method. Finally, virtual left and right surround sound image signals were obtained, which effectively improved the three-dimensional surround feeling of folk music.

Error analysis of machine learning methods as the educational background for its use skills formation

Introduction. Machine learning methods and elements of artificial intelligence are used to analyze random data, processes and signals. The study of relevant tools is already included in the various levels curricula. The purpose of the study is to demonstrate, using examples available to students of various specialties, that the error analysis of machine learning methods in solving specific tasks can be the basis in the educational process for the skills formation of using artificial intelligence elements.Materials and Methods. For processing random signals and data, widely available software is used: Microsoft Excel for preparing training and test samples, the Deductor analytical platform for implementing machine learning algorithms. As an example, quasi-harmonic signals with random parameters are processed for technical specialties, and the results of psycho diagnostics are used to process multidimensional random data.Results. As a typical solution of approximation technical problems, direct propagation neural network errors in using to determine random signal parameters are analyzed. As a solution of classification problems, multidimensional random data with different dimensions were processed using neural networks and the "decision tree" method. The advantages of the combined use of these two machine learning methods are analyzed. These examples and their analysis were tested in classes with university students in the disciplines of "Digital Signal Processing" and "Fundamentals of Statistics".Discussion and Conclusions. The statistical features of the obtained results, the possibilities of reducing the training sample and selective analysis of multidimensional random data are discussed. It is shown that an adequate assessment of the machine learning methods errors can significantly expand the possibilities of their application, and can be the basis for the formation of skills for their use.

Spatial distribution and drivers of arbuscular mycorrhizal fungi on the Tibetan Plateau

IntroductionArbuscular mycorrhizal fungi (AMF) are pivotal in plant resource acquisition, mediating plant interactions, and influencing soil carbon dynamics. However, their biogeographical distribution in Tibetan alpine grasslands remains understudied. MethodsIn this research, we examined the distribution pattern of AMF communities and their key determinants along a 2000-km transect across the Tibetan plateau, encompassing 7 alpine meadows and 8 alpine steppes. ResultsOur findings indicate that AMF community diversity and composition exhibit similarities between alpine meadows and alpine steppes, primarily influenced by latitude and evapotranspiration. At the genus level, Glomus predominated in both alpine meadow (36.49%±2.67%) and alpine steppe (41.87%±2.36%) soils, followed by Paraglomus (27.14%±3.69%, 32.34%±3.28%). Furthermore, a significant decay relationship of AMF community was observed over geographical distance. Null model analyses revealed that random processes predominantly (>50%) drove the assembly of AMF communities. DiscussionIn summary, our study elucidates the spatial distribution pattern of AMF in Tibetan plateau grasslands and underscores the significant influence of geographical and climatic factors on AMF community dynamics.

Investigating the impact of initial parameters on autonomous robot swarm movement strategies for enhanced exploration efficiency: a comprehensive study

Swarm robots are frequently preferred for the exploration of harsh environments and search and rescue operations. This study explores the factors that influence the movement strategies of autonomous robot swarms and their impact on swarm distribution in the field, employing simulation-based analysis. The research consists of two parts: initially, robots undergo free-fall as passive entities, followed by a phase where they employ predefined movement strategies from their fall positions. The study aims to investigate how the initial position and related parameters affect movement characteristics and the ultimate swarm distribution. To achieve this objective, four parameters-radius, height, mass, and the Coefficient of Restitution-were identified, each assigned three different values. The study observes the effects of these parameters on robot motion, considering motion strategies such as Random Walk, Levy Walk, Markov Process, and Brownian Motion. Results indicate that increasing parameter values induce changes in the position values of the free-falling swarm in the first part, which is the initial position for the second part, influencing movement strategies in diverse ways. The outcomes are analyzed concerning the radial and angular spread of the robots. Radial spread measures how far swarm elements spread from their initial positions, while angular spread indicates how homogeneously the robots are distributed according to the polar angle. The study comprehensively investigates how the movement strategies of autonomous robot swarms are impacted by parameters and how these effects manifest in the results. The findings are anticipated to enhance the effective utilization of autonomous robot swarms in exploration missions.

Conformational Analysis of Trifluoroacetyl Triflate, CF3C(O)OSO2CF3: Experimental Vibrational and DFT Investigation

The conformations of trifluoroacetyl triflate, CF3C(O)OSO2CF3, were investigated through experimental vibrational methods (gas-phase FTIR, liquid-phase Raman, and Ar matrix FTIR spectroscopy) and density functional theory (DFT) calculations. A potential energy surface was computed using the B3P86/6-31+g(d) approximation as a function of the dihedral angles τ1 = CC−OS and τ2 = CO−SC. The surface reveals three minima, which were further optimized using the B3LYP method with various basis sets (6-31++G(d), 6-311++G(d), tzvp, and cc-pvtz). The global minimum corresponds to a syn–anti conformer (the C=O double-bound syn with respect the O−S single bond and the C−O single bond anti with respect to S−C single bond). The other two minima represent enantiomeric syn–gauche forms. The Ar matrix FTIR spectrum exhibited clear evidence of the presence of two conformers. Furthermore, the randomization process observed following broadband UV–visible irradiation facilitated the identification of the IR absorption of each conformer. Based on the Ar matrix FTIR experiments, the vapour phase of trifluoroacetyl triflate at room temperature was composed of approximately 60–70% of the syn–anti conformer and 30–40% of the syn–gauche form.

Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses

Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann–Whitney U Test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Fox and Lu type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.

Synhospitality of eriophyoid mites (Acariformes, Eriophyoidea): taxonomic analysis of gall-forming mite species complexes on boreal woody dicotyledons

Microscopic mites of the Eriophyoidea superfamily are characterized by an ability to cause the growth of galls on plants. Synhospital complexes of gall-forming Eriophyoidea (SCGE) species are formed by several species of gall mites coexisting on the same host plant. To assess the phenomenon of synhospitality, we investigated the features of the taxonomic composition and diversity of galls formed by eriophyoid mites on boreal woody dicotyledons and carried out a statistical analysis of the host relationships of different gall mite genera from the Eriophyidae family. We found out that phylogenetically closely related hosts within biogeographically integral territories possess the similar SCGE. During colonization of plants, gall mites demonstrate a high ability to cryptic speciation, which leads to genesis of morphologically similar mite species that cause the formation of different types of galls. Host preferences of gall mites belonging to the different supergenera groups play an important role in the formation of the SCGE, which determines the differences in mite taxa spectra associated with hosts from different families. In general, the results of the work demonstrate that the synhospitality of Eriophyoidea is not a random process, but is the result of a long-term coadaptation in the “parasite–host” system. This is one of the phenomena reflecting the stages of the global historical process of the colonization of Embriophyta plants by mites of the Eriophyoidea superfamily.

Parameter learning for Wiener systems with time‐delay state‐space model

AbstractThis paper discusses a novel scheme for learning the Wiener output error nonlinear system with time‐delay state‐space model. In the Wiener system, the dynamic linear block is approximated by time‐delay state‐space model, and the static nonlinear block is established using neural fuzzy network. Combined signals designed including separable signal and random signal are devoted to achieving parameters separation learning of the Wiener system, that is, the two blocks are learned independently. Firstly, using the properties of shift operator and transforming state‐space model with time‐delay into a representation with input and output, then linear dynamic block parameters are learned by the virtue of correlation analysis method in the condition of Gaussian signals. Moreover, a recursive extended least squares estimation is carried out to learn parameters of static nonlinear block and colored noise model under the condition of random signals. The efficiency and accuracy of proposed scheme are confirmed on experiment results of a numerical simulation and a typical practical nonlinear process, and experimental simulation results demonstrate that the learning scheme proposed obtains good learning precision.

Scheduling stochastic distributed flexible job shops using an multi-objective evolutionary algorithm with simulation evaluation

The trend of reverse globalisation prompts manufacturing enterprises to adopt distributed structures with multiple factories for improving production efficiency, meeting customer requirements, and responding disturbance events. This study focuses on scheduling a distributed flexible job shop with random job processing time to achieve minimal makespan and minimal total tardiness. First, a stochastic programming model is established to formulate the concerned problems. Second, in accordance with the natures of two objectives and randomness, an evolutionary algorithm incorporating an evaluation method is designed. In it, population-based and external archive-based search processes are developed for searching candidate solutions, and the evaluation method integrates stochastic simulation and discrete event simulation to calculate objective values of acquired solutions. Finally, a mathematical optimisation solver, CPLEX, is employed to validate the developed model and optimisation approach. A set of cases is solved to verify the performance of the proposed method. The comparisons and discussions show the superiority of the proposed method for handling the problems under study.

Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process

Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process

Sign of slant receiver for skewed alpha-stable noise shift keying-based random communication system

Purpose In contrast to traditional communication systems, slower data rate has always remained a weak link for non-traditional random communication systems (RCSs), which use alpha-stable (a-stable) noise as a carrier. This paper aims to introduce a fast receiver for skewed a-stable noise shift keying (SkaSNSK)-based RCSs. Design/methodology/approach The introduced receiver is based on the sign of slant estimator (SoSE), which provides rapid estimation of the skewed a-stable random noise signals (RNSs) received from the additive white Gaussian noise channel. The SoSE-based receiver minimizes the number of samples required to extract the encoded information from the received RNSs. This is achieved by manipulating the antipodal properties of the slant/skewness parameter of the a-stable carrier. Hence, a high data rate with relatively low complexity is guaranteed. Findings In comparison with the previously introduced sinc, logarithmic and modified extreme value method-based receivers, the proposed SoSE-based receiver also achieves improved bit error rate (BER) along with the better covertness values so that the essence of security provided by SkaSNSK-based RCSs remains intact. Research limitations/implications Because of the selected range of the associated parameters of the a-stable noise as a carrier, the BER vs MSNR results are may lack applicability for the complete range of values. Therefore, further research is required to produce results in different ranges. Practical implications The study includes implications for the hardware development based on the proposed communication scheme. Originality/value It can be seen that the paper fulfils the desired need of a fast receiver design for RCS.

Predictors of parental acceptance to live attenuated influenza vaccine for children

ABSTRACT To determine the influencing factors of Chinese parents' intention and behavior for children to receive live attenuated influenza vaccine during the 2022–2023 influenza season. A theoretical model was developed and included seven constructs, and structural equation modeling was used to test 11 hypotheses. From October 2022 to December 2023, a survey was conducted across 38 medical institutions in four Chinese cities and their subordinate districts, counties, and rural areas. Parents who accompanied their children for vaccinations were selected through a randomization process based on their child’s medical card numbers. Measures were taken to minimize method bias, including a diverse geographical representation and random sampling. The survey resulted in the collection of 936 valid responses, exceeding the recommended sample size for structural equation model analysis and providing robust statistical inferences. During the study period, 936 respondents were included in the study. Perceived ease of use was verified to be a predictor of perceived usefulness and perceived value. Perceived usefulness was verified as a predictor of perceived value and behavioral intention. Knowledge was a significant antecedent of perceived value and risk perception of influenza disease. Risk perception of influenza disease was proved to be a significant predictor of perceived value and self-reported vaccination behavior. Perceived value significantly affected behavioral intention, and behavioral intention significantly affected self-reported vaccination behavior. Six demographic variables significantly moderate the theoretical models. The low vaccination coverage of live attenuated influenza vaccine (LAIV) among children in China suggests a need for a deeper understanding of the factors that influence vaccination rates. Particularly, effective strategies are necessary from policymakers and practitioners to elevate childhood LAIV coverage.

Clinical impact of medication review and deprescribing in older inpatients: A systematic review and meta-analysis.

Polypharmacy is a primary risk factor for the prescription of potentially inappropriate medications (PIMs), drug-drug interactions (DDIs), and ultimately, adverse drug reactions (ADRs). Medication review and deprescribing represent effective strategies to simplify therapeutic regimens, minimize risks, and reduce PIM prescriptions. This systematic review and meta-analysis of experimental and observational studies aimed to evaluate the impact of different medication review and deprescribing interventions in hospitalized older patients. Experimental and observational prospective cohort studies evaluating the clinical effects of medication review and deprescribing strategies in older hospitalized patients were searched in the bibliographic databases, PubMed, Embase, and Scopus, from inception until January 8, 2024. A narrative synthesis of the results was provided, along with a meta-analysis of dichotomous data (i.e., re-hospitalizations and mortality). Overall, 21 randomized controlled trials, 7 non-randomized interventional studies, and 2 prospective cohort studies were included in the systematic review. Of these, 14 (46.7%) assessed medication appropriateness as the primary outcome, while the remaining evaluated clinical outcomes (e.g., length of hospital stay, hospital readmissions, emergency department visits, and incidence of ADRs) and/or quality of life. The meta-analysis revealed a slight but statistically significant 8% reduction in hospital readmissions (HR: 0.92; 95% CI: 0.85-0.99) following medication review and deprescribing, but no significant impact on mortality (HR: 0.98; 95% CI: 0.96-1.00). Of the 30 included studies, 21 were considered at high risk of bias, mostly due to potential deviations from intended interventions and randomization processes. The remaining nine studies had "some concerns" (eight studies) or were considered at "low" risk of bias (one study). Medication review and deprescribing are associated with potential benefits in reducing hospital readmission rates among hospitalized older patients, particularly through the reduction of PIM prescriptions. The integration of thorough medication review and deprescribing protocols in hospital settings may improve post-discharge outcomes and reduce overall healthcare costs.

Reconstructing Unknown Coefficients of Stochastic Differential Equations and Intelligently Predicting Random Processes with Directed Learning

Reconstructing Unknown Coefficients of Stochastic Differential Equations and Intelligently Predicting Random Processes with Directed Learning

A Breakthrough Authentication Protocol for Fortifying IoT-Cloud Security

Abstract: Physical layer key generation, leveraging the reciprocity and randomness of wireless fading channels, has garnered significant research interest in recent years. While theoretical studies have demonstrated its potential to generate informationtheoretically secure keys, challenges remain in translating theory into practice. This paper provides an overview of the physical layer key generation process and discusses its practical challenges. Various passive and active attacks are analyzed and evaluated through numerical studies. A new key generation scheme using random probing signals and combining usergenerated randomness with channel randomness is introduced to counteract active attacks. Numerical results demonstrate that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Secret-key generation relying on wireless channel reciprocity is an intriguing solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS).

Mathematical aspects of digital technologies: The problem of the absent-minded passenger

<p>The example of the absent-minded passenger problem illustrates the fundamental difficulty of estimating practical values of probabilistic parameters on the basis of general theorizing and allows us to give more accurate and subtle estimates of various parameters, as well as their practical computable approximation. This article is important for building systems of artificial intelligence and artificial consciousness, as well as for modern didactics of higher education, especially for the disciplines of “applied mathematics” and “theoretical computer science”. It is shown that a detailed study of a random process provides more significant applied materials than formally correct theoretical calculations.</p>

Research on temperature control of proton exchange membrane electrolysis cell based on MO‐TD3

AbstractTo solve the problem of temperature control in proton exchange membrane electrolytic cell (PEMEC), this paper presents a temperature control method based on multi‐experience pool probability playback and Ornstein‐Uhlenbeck noise‐twin delay depth deterministic strategy gradient. Firstly, considering the influence of water supply, anode and cathode pressure, and natural heat dissipation on temperature, a refined thermal model of PEMEC is established and transformed into a Markov model under the framework of deep reinforcement learning (DRL). Then, to solve the training instability and poor control effect of DRL caused by inertia delay of the PEMEC temperature control system, multi‐empirical pool probability playback and Ornstein‐Uhlenbeck random process noise techniques are introduced on the basis of the traditional DRL method. Finally, the simulation and hardware‐in‐the‐loop experience results show that the proposed method outperforms other advanced methods.

DETECTION OF PSEUDOMONAS AERUGINOSA FROM VARIOUS CLINICAL SPECIMEN AND ANTIMICROBIAL RESISTANCE PATTERN OF HUMAN

In this study, 60 samples were collected from different sources in Al-Zahra Teaching Hospital and Karma Hospital . The random sampling process was conducted to identify the foci of pollution in P.aeruginosa, which was one of the objectives of this study and obtained diagnostic procedures. 45 samples were collected from Al-Zahra Teaching Hospital and collected from 15 samples Karma Hospital . Results of agricultural tests and tests biochemistry 10 isolates of P.aeruginosa bacteria . The pharmacological sensitivity of 10 samples was tested against 10 types of antibiotics in the method of spreading the disk to Kirby_bauer. All isolates were almost resistant . All antibiotics were resistant to severe resistance

Stochastic intervention control of mean-field jump system with noisy observation via L-derivatives with application to finance

In this paper, we investigate stochastic optimal intervention control of mean-field nonlinear random Poisson-jump-system with related noisy process. We derive the necessary conditions of optimality for partially observed optimal intervention control problems of mean-field type. The coefficients depend on the state of the solution process as well as of its probability distribution and the control variable. The proof of our main result is obtained by applying L-derivatives in the sense of Lions. In our control model, there are two models of jumps for the state process, the inaccessible ones which come from the random Poission process and the predictable ones which come from the intervention control. Finally, we apply our result to study conditional mean-variance portfolio selection problem with interventions, where the foreign exchange interventions are intended to contain excessive fluctuations in foreign exchange rates and to stabilize them.

A numerical investigation on the vibration of a two-deck Euler–Bernoulli beam flooded by a potential flow

Abstract This work concerns the structural vibration of a bladeless wind turbine, modelled by a two-deck Euler–Bernoulli beam, due to a surrounding potential flow. The deflection is governed by the Euler–Bernoulli equation which is studied first by a linear theory and then computed numerically by a finite difference method in space with a collocation method over the arc length, and an implicit Euler method in time. The fluid motion in the presence of gravity is governed by the full Euler equations and solved by the time-dependent conformal mapping technique together with a pseudo-spectral method. Numerical experiments of excitation by a moving disturbance on the fluid surface with/without a stochastic noise are carried out. The random process involved in generating the noise on the water surface is driven by a Wiener Process. A Monte Carlo method is used for stochastic computations. The generated surface waves impinge on the beam causing structural vibration which is presented and discussed in detail. By elementary statistical analysis, the structural response subject to the stochastic hydrodynamic disturbance caused by white noise is found to be Gaussian.