Stochastic Approach Research Articles

Emergence of temporal noise hierarchy in co-regulated genes of multi-output feed-forward loop.

Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors. Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression patterns of the two genes (including symmetric and asymmetric expressions), and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the transcription factors influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of transcription factor binding affinities.

A stochastic numerical approach on the diagonal shear behavior of brick masonry wallettes fabricated with traditional lime-sand mortar

A stochastic numerical approach on the diagonal shear behavior of brick masonry wallettes fabricated with traditional lime-sand mortar

Optimal Regulation Strategy of Distribution Network with Photovoltaic-Powered Charging Stations Under Multiple Uncertainties: A Bi-Level Stochastic Optimization Approach

Optimal Regulation Strategy of Distribution Network with Photovoltaic-Powered Charging Stations Under Multiple Uncertainties: A Bi-Level Stochastic Optimization Approach

Stochastic modelling for the effects of micromixing on soot in turbulent non-premixed flames

The impact of micromixing and scalar dissipation rate (SDR) fluctuations on the evolution of soot particle size distribution (PSD) dynamics in non-premixed turbulent flames is investigated within the context of Large Eddy Simulation (LES). To accomplish this, a stochastic approach based on a physicochemical sectional soot model, spatially homogeneous Conditional Moment Closure (CMC) with first-order moment closure, and a stochastic differential equation for the SDR are employed, mimicking the function of an LES sub-grid scale (SGS) combustion model. After linking the magnitude of SDR fluctuations to the LES filter size, it was found that the standard deviation of observables, such as the soot volume fraction and soot number density, decreases rapidly with increasing LES filter size, which can significantly influence the intermittency of soot reproduced by models. Mean conditional expectations are also affected, indicating a closure problem, with the mean soot volume fraction, in particular, showing a decrease of up to 20% in the case considered compared to a direct numerical simulation resolution even when the Pope criterion is satisfied. The influence of SDR fluctuations on the dynamic behaviour of the soot PSD is further discussed under different working assumptions for particle transport, and the role of differential diffusion in soot oxidation- and formation-dominant regions is particularly highlighted. The findings underline the shortcomings of LES-CMC and similar mixture-fraction-based combustion models in capturing the dynamic evolution of soot at the SGS, which has significant implications for modelling the emissions of practical combustion devices. However, this study also offers a framework for better evaluating the effectiveness of these models using numerical experimentation and a stochastic model for the turbulent fluctuations of the micromixing rate.

An Extensive Review of Leaching Models for the Forecasting and Integrated Management of Surface and Groundwater Quality

The present study reviews leachate models useful for proactive and rehab actions to safeguard surface and subsurface soft water, which have become even more scarce. Integrated management plans of water basins are of crucial importance since intensively cultivated areas are adding huge quantities of fertilizers to the soil, affecting surface water basins and groundwater. Aquifers are progressively being nitrified on account of the nitrogen-based fertilizer surplus, rendering water for human consumption not potable. Well-tested solute leaching models, standalone or part of a model package, provide rapid site-specific estimates of the leaching potential of chemical agents, mostly nitrates, below the root zone of crops and the impact of leaching toward groundwater. Most of the models examined were process-based or conceptual approaches. Nonetheless, empirical prediction models, though rather simplistic and therefore not preferrable, demonstrate certain advantages, such as less demanding extensive calibration database information requirements, which in many cases are unavailable, not to mention a stochastic approach and the involvement of artificial intelligence (AI). Models were categorized according to the porous medium and agents to be monitored. Integrated packages of nutrient models are irreplaceable elements for extensive catchments to monitor the terrestrial nitrogen-balanced cycle and to contribute to policy making as regards soft water management.

Disaggregation of climatic variables using a novel stochastic approach and its application in building performance simulation studies

Daily weather data, both observed and synthesized, are available for most locations worldwide but not at the sub-daily temporal scale, which is desirable for building performance analysis. In this context, we introduce a novel stochastic methodology based on pattern mapping for disaggregation of daily data into sub-daily (hourly) time resolution. The methodology has been tested and validated for three weather variables required for energy simulation (temperature, relative humidity and solar radiation). This approach yields better results than existing methodologies for the selected climate variables. Process efficiency is maintained even with the limited input data. Its stochastic nature enables time-invariant pattern mapping to generate future weather files. The annual ‘space conditioning energy’ was simulated to assess building performance accuracy using weather files for the hottest, coldest, average and test reference years (TRY) from disaggregated and observed datasets. Results show that the pattern mapping disaggregation methodology accurately downscales daily to sub-daily data.

Assessment of carbon emission reduction potential in new-type urbanization policy: A perspective on carbon rebound effect

In this research, we utilize a refined model for carbon emissions, known as the Stochastic Frontier Approach (SFA), to measure the extent of the carbon rebound effect across a sample of 231 urban areas in China, spanning the years 2011 through 2019. Additionally, the study explores the impact that policies associated with the advancement of new urbanization have had on the aforementioned carbon rebound phenomenon. The findings of this thesis indicate that this policy can reduce carbon emission volumes effectively; however, there is also a significant carbon rebound effect, making the reduction less efficient than expected. After several robustness tests, the main conclusions remain consistent and reliable. Our investigation into the underlying mechanisms reveals that policies aimed at fostering new urbanization may attenuate the carbon rebound effect by enhancing the efficiency and composition of industrial sectors. However, technological advancements and the degree of energy efficiency remain pivotal in potentially escalating the carbon rebound effect. The analysis of variations across different scenarios demonstrates that the policy’s impact on the carbon rebound effect is subject to variation depending on several factors, including geographical contexts like provincial boundaries, the administrative hierarchy of cities, transportation infrastructure, and the constraints imposed by resource availability. As a multi-target policy, although this policy has a carbon rebound effect, it also has a significant social welfare effect, which coincides with the development goal of China. Ultimately, some suggestions are offered to enhance the role of this policy in promoting carbon emission reduction and to foster a virtuous interaction between this policy and economic growth.

Productivity and growth decomposition: a novel single-index smooth-coefficient stochastic frontier approach

Abstract Our paper investigates productivity, output growth and total factor productivity (TFP) growth using a novel single-index smooth-coefficient stochastic frontier approach and two firm-level datasets respectively from the high technology (high-tech) manufacturing and Knowledge Intensive Business Services (KIBS) sectors in Norway. The approach considers input productivity and technical inefficiency to be flexible functions of production environmental variables indexed with unknown parameters for more precise estimation of marginal effects of these variables on the frontier and inefficiency. Output growth is decomposed into technical change (TC), input-driven component (IDC) and efficiency change (EC), while TFP growth is decomposed into TC, scale component and EC. The primary objective is to (i) maximise output through the frontier and efficiency channels and (ii) enhance productivity growth through such channels as technical progress and efficiency improvement, specifically tailored for the manufacturing and services industries. The empirical results reveal substantial heterogeneity in technology across firms. Overall speaking, geographical industrial concentration, export intensity and urbanisation positively influence output in both sectors. Technical progress contributes to TFP growth in both sectors; however, TC is biased towards capital in the high-tech sector and driven by labour in the KIBS sector. In addition to TC, TFP growth in the high-tech and KIBS sectors also benefits from EC and IDC, respectively.

Demand Response Strategy Considering Industrial Loads and Energy Storage with High Proportion of Wind-Power Integration.

To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources in the power system are analyzed, and a demand response scheduling framework based on energy storage systems and industrial loads is established. Building on this foundation, a multi-scenario stochastic programming approach is employed to develop a day-ahead and intra-day multi-time-scale optimization scheduling model, aimed at maximizing economic benefits. In response to the challenges of wind-power fluctuations with high temporal resolution, a strategy for smoothing intra-day wind-power variability is further proposed. Finally, simulations are conducted to derive optimal demand response strategies for different stages. As verified by the comparison of different scheduling strategies, the demand response strategy proposed in this paper can reduce wind curtailment when there is sufficient wind power and reduce load shedding when there is insufficient wind power, which effectively reduces the system operation cost.

An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems

This research compares SERU manufacturing systems to traditional assembly lines, focusing on the impact of uncertainty in task processing time on production output. The study considers worker skill levels and team identity, using a stochastic mixed integer linear programming approach to model uncertainty and optimize workforce allocation. Discrete event simulation is then integrated to evaluate performance using five key performance indicators (KPIs). Results show that SERU systems outperform traditional lines in terms of throughput when uncertainty is considered. The integrated approach provides more reliable performance data than deterministic optimization alone. The study also highlights the advantages of SERU systems when worker skill levels and team identity are factored in. This research fills a gap in the literature by proposing a stochastic optimization approach that considers uncertainty and worker skill levels, and by integrating stochastic optimization with simulation for comprehensive analysis. This approach provides valuable guidance for production managers in optimizing production systems.

Fertility model evolution: A survey on mathematical models of age-specific fertility with application to Nepalese and Malaysian data

Fertility pattern analysis and modeling to smooth age-specific fertility rates (ASFRs) form a well-established research field that holds particular importance for Asian countries. In developed nations, ASFRs typically display a bimodal skewed fertility curve, whereas, in developing countries, they usually exhibit a unimodal skewed fertility curve that diverges from the normal one. For decades, demographic experts worldwide have been interested in creating models using deterministic and stochastic approaches to represent these fertility curves. In this regard, parametric and non-parametric models have been created, with the latter providing a better fit for ASFR data. This research investigates the evolution of fertility models aimed at smoothing ASFRs. It explores suitable alternative models for countries with fast-declining, unimodal, and skewed fertility curves of ASFRs, such as Nepal and Malaysia. Nepal’s fertility rate is transitioning from a high level toward the replacement rate (2.1) at the year 2021; meanwhile, Malaysia’s fertility rate (1.7) in the year 2021 has dropped below the replacement rate. Given the lack of a universally applicable model for ASFR pattern variation, this study proposes the Kumaraswamy log-logistic distribution as a promising model to represent the ASFRs of Nepal and Malaysia accurately. Various approaches, including the Akaike information criterion, and Bayesian information criterion, are employed to validate the fitting of the proposed model.

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

Computational insights into optimal household portfolio decisions: a stochastic approach with heston model and finite difference scheme

A two-stage stochastic programming approach for generation and transmission maintenance scheduling with risk management

A two-stage stochastic programming approach for generation and transmission maintenance scheduling with risk management

Studies on effective solar photovoltaic integration in distribution network with a blend of Monte Carlo simulation and artificial hummingbird algorithm

AbstractIn this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance‐constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance‐constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.

QSLE-v1.0: A New Software Package for the Calculation of Coupled Quantum-Classical Dynamics in Condensed Phases Based on a Stochastic Approach.

Recently, a stochastic version of the quantum-classical Liouville equation has been proposed [Campeggio, J.; Cortivo, R.; Zerbetto, M. J. Chem. Phys. 2023, 158, 244104], to compute the coupled quantum-classical dynamics of molecules in condensed phases. The approach, called quantum-stochastic Liouville equation (QSLE), is based on coupling the time evolution of electronic states to a stochastic description of the relevant (classical) nuclear coordinates. Natural internal coordinates are used, i.e., bond lengths, bond angles, and dihedral angles. The approach is tailored to directly propagate the populations of the electronic states over time, based on a classical Fokker-Planck equation for the nuclear degrees of freedom coupled to a master equation for the jumps among the electronic states. The QSLE is a multiscale approach requiring many ingredients to be assembled in order to carry out the numerical solution. To make the approach accessible, a software package that handles the main (and most cumbersome) details of the numerical workflow has been implemented into the software package QSLE-v1.0, which is introduced in the present paper. Here, it is considered the case of one torsional nuclear coordinate and two nonadiabatic electronic potential energy surfaces. This is a very basic model for interpreting photoisomerization or charge transfer phenomena, but despite its simplicity, it can be applied even in complex systems where the relevant quantum/classical parts affecting the phenomena under study are highly localized. A sand-box model system for describing photoisomerization processes is reported to demonstrate the usage of the software package. QSLE-v1.0 is open source and distributed under the GPLv2.0 license.

Comparative Study of Seismic Hazard Analysis Using Classical PSHA and PSHA Method in Java Island, Indonesia

In general, seismic hazard analysis is conducted using a probabilistic approach (PSHA), whilst seismic risk analysis is computed using a stochastic approach. To figur out more differences between the two methods, a comparative study of those two approaches needs to be conducted. The study was conducted in Java Island which is the most populated island in Indonesia, and prone to earthquakes, particularly in the southern part due to the subduction of the Australian Plate and many active shallow faults along the island. To find out whether the hazards occurred in the risk analysis were closer to the results of Classical PSHA, it is necessary to examine the comparison of the two methods. The difference between the Event-Based and Classical methods is the use of a synthetic catalog that depends on the input value of SES (Stochastic Event Set). The research began with hazard computation with both methods, where the Event-Based PSHA method was given varying SES values. Determining the SES value in the Event-Based method is an important stage to conduct as a significant input parameter in a risk analysis, particularly the loss analysis. The comparison results conclude that the Event-Based PSHA with SES= 200,000 produced a wellconfirmed hazard map compared to the Classical PSHA result with the smallest standard deviation and variation, i.e. 0.0172 and 0.0003 respectively.

On penalized goal-reaching probability minimization under borrowing and short-selling constraints

Abstract We consider a robust optimal investment–reinsurance problem to minimize the goal-reaching probability that the value of the wealth process reaches a low barrier before a high goal for an ambiguity-averse insurer. The insurer invests its surplus in a constrained financial market, where the proportion of borrowed amount to the current wealth level is no more than a given constant, and short-selling is prohibited. We assume that the insurer purchases per-claim reinsurance to transfer its risk exposure to a reinsurer whose premium is computed according to the mean–variance premium principle. Using the stochastic control approach based on the Hamilton–Jacobi–Bellman equation, we derive robust optimal investment–reinsurance strategies and the corresponding value functions. We conclude that the behavior of borrowing typically occurs with a lower wealth level. Finally, numerical examples are given to illustrate our results.

Three-Dimensional Subsurface Model of Luk-Ulo Melange Complex, Karangsambung, Indonesia: Insights from Gravity Modeling

The Luk-Ulo Melange Complex (LMC) is characterized by a chaotic assemblage of mixed rocks with a block-in-matrix fabric. The exposed blocks consist of various scattered rock types, trending in an ENE-WSW direction. In the case of Mt. Parang, the origin of the diabase remains uncertain, with ongoing debate as to whether it is associated with in situ volcanic activity or represents an exotic block within the melange deposit. Subsurface data obtained through geophysical investigation can aid in modeling the geometry of intrusive bodies using inverse modeling techniques. In this study, we conducted a gravity survey and performed 3D inverse modeling to investigate the subsurface beneath Karangsambung. A total of 818 gravity data points and 28 rock density measurements were integrated with existing geological data to construct an a priori 3D geological model. To ensure the results align with geological concepts, the 3D inversion utilized a stochastic approach, allowing for the incorporation of multiple geological constraints over fifty million iterative procedures. Ultimately, the inversion successfully reduced the misfit between observed and calculated data from 2.71 to 0.55 mGal. Based on the inverted 3D model, the diabase rock in Mt. Parang is identified as having an intrusive origin. The intrusion model exhibited minimal changes in density, volume, and shape during the inversion process. Additionally, the model suggests the presence of a solidified magma reservoir at a depth of approximately 3 km, potentially related to Dakah volcanism. The inverted model also reveals the block-in-matrix structure of the Luk-Ulo Melange Complex in the northern area.

Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures

The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (p>0.05). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures.

Enhancing Flexibility and Reliability in Smart Distribution Networks: A Self-Healing Approach

This paper presents a pioneering approach that integrates a Self-Healing System through a Smart Distribution Network, which employs a two-step implementation of a comprehensive Energy Management algorithm and a multi-agent system with an autonomous distributed regeneration method. The novel method improves network operation through optimized management of local microgrids, which include diverse components comprising renewable energy sources, electric vehicle charging infrastructure, and energy storage systems. The first phase concentrates on minimizing network operation costs by adhering to system utilization restrictions and optimal load distribution, while the second phase addresses the optimization of regeneration processes, which decreases discrepancies between recoverable priority loads and switching operations. The evaluation introduces a stochastic programming approach to model and manage uncertainties, and utilizes the Kantorovich method and the roulette wheel mechanism to improve reliability. ...