Chain Model Research Articles

Design of a one-sided adaptive EWMA X̄ control chart using the truncation method in the presence of measurement errors

In this paper, a one-sided adaptive exponentially weighted moving average X ¯ scheme utilizing the truncation method (called the one-sided TAEWMA X ¯ chart) is proposed for monitoring processes involving measurement errors. Leveraging the linear covariate error model, both the Markov chain model and the Monte Carlo simulation are established to evaluate the run length ( RL ) properties of the proposed chart. In the presence of measurement errors, an optimal search strategy is developed to identify the optimal design parameters of the scheme. Subsequently, the effects of measurement errors on both zero-state and steady-state average run length ( ARL ) performance of the proposed scheme are studied. Comparative studies with two other existing schemes reveal that, despite the significant effects of measurement errors on ARL performance, the proposed chart remains superior to the existing competing schemes in shift detection, especially when multiple measurement operations are involved. Finally, an illustrative example is provided to demonstrate the application of the suggested scheme.

Numerical Simulation on Chain Dynamic of Polymer Solution in Microchannels: A Dissipative Particle Dynamics Study

AbstractThe chain dynamics and the rheological property of the polymer solution in the microscale confined space (e.g., microchannel) are complex and still unclear. In this paper, based on the prior research work, a dissipative particle dynamics (DPD) method is established to systematically simulate the dynamic behavior of polymer chains and the properties of the polymer solution in microchannels by combining the modified FENE chain model and a new boundary condition setting, especially for the gradually contracted microchannel. It is found that the concentration of the polymer chain, the degree of constraint, and the Reynolds number influence the dynamic behavior of the polymer chain by changing the constraint effect or the hydrodynamic effect. In addition, the geometrical structure of the microchannel significantly changes the dynamic behavior of the polymer chain. The chain dynamics in the gradually contracted microchannel are quite different from that in the straight microchannel. Finally, the rheological characteristics of the power‐law fluid and the lateral migration of the solid particle in the viscoelastic fluid in the microchannel are also simulated, and the simulation results are in good agreement with the result reported in the literature, which further verifies the accuracy of the present simulation method.

Understanding Agricultural Water Consumption Trends in Henan Province: A Spatio-Temporal and Determinant Analysis Using Geospatial Models

In the context of water scarcity, understanding the mechanisms influencing and altering agricultural water consumption can offer valuable insights into the scientific management of limited water resources. Using Henan Province as a case study, this research applies the Mann–Kendall test method, the spatial Markov transfer chain model, the optimal parameter geo-detector model, and the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the evolution characteristics of agricultural water consumption in Henan Province and its key influencing factors. The findings revealed the following: (1) Agricultural water consumption has shown a significant decline from 1999 to 2022. (2) According to observations, the stability of agricultural water consumption exceeds the spillover effect, and cross-border grade transfer is challenging. Moreover, this phenomenon is influenced by the neighboring regions. (3) The key influencing factors of added agricultural value are the sown area of food crops, total sown area, irrigated area, and average annual air temperature. (4) Among the decomposition effects on agricultural water consumption, the contribution of each decomposition effect to changes in agricultural water consumption and the role of spatial distribution exhibit notable differences. Overall, these findings provide theoretical references for the efficient use of agricultural water resources and sustainable development in the region.

Estimating urban growth on Mersin, Tarsus and Adana corridor in Türki̇ye by using Cellular Automata and Markov Chain

In many developing countries, urban growth is often unplanned and haphazard, leading to significant threats to the sustainable use of urban land. This study predicts future urban expansion patterns in the Mersin, Tarsus and Adana corridor in Türkiye using Cellular Automata and Markov Chain models. Analysing Landsat satellite images from 1989 to 2019, we simulated land use changes for 2019, 2031, and 2049. The results indicate a projected increase in urban areas from 3.8% in 2019 to 7.4% by 2049, with significant expansion onto fertile agricultural areas. This unregulated growth highlights the urgent need for a well-planned governance approach that balances economic, social, and environmental factors to ensure resilient and sustainable urban development.

Spatial and Temporal Evolution of Regional Energy Efficiency in China and Its Influencing Factors

Finding ways to improve regional energy efficiency is important for the Chinese government to achieve its dual carbon target. This paper aims to explore ways to improve regional energy efficiency by studying the spatial–temporal dynamic evolution of energy efficiency. To scientifically study the evolution trend in regional energy efficiency in China, this study uses convergence analysis, a spatial Gini coefficient decomposition model (no spatial consideration), and a spatial Markov chain model and spatial measurement model (spatial consideration). The results show the following: from 2008 to 2019, the mean value of regional single-factor energy efficiency (RS) showed an obvious trend of continuous increase, while the mean value of regional green total-factor energy efficiency (RT) changed from a trend of continuous decline to a relatively stable trend. The overall Gini coefficient of RS showed a trend of “steady–rising–steady”, and the overall Gini coefficient of RT showed a trend of “steady–small increase–sharp increase–fall”. There was club convergence in the two types of regional energy efficiency, and both of them achieved certain “leapfrog” changes. The factors that had a significant impact on RS include human capital, industrialization, openness, urbanization, financial development, and innovation environment. The significant factors for RT included governance structure, industrialization, openness, policy support, and financial development. The limitation of this paper is that only provincial data were used. In the future, city-level data can be mined and more detailed policy suggestions can be put forward for city-level differences. The research method used in this paper to study regional energy efficiency evolution trends is also applicable to other countries.

Supply Chain Model for Three-Echelon Supply Chain for Perishable Products

This paper proposed three echelon supply chain models. In this study, we have considered that after production, the manufacturer supplies the perishable items to the supplier, and then the retailer receives the product from the supplier for sale to the customers. The retailer assumes the customer’s seasonal demand along with the supplier’s uncertain lead time. In this model, the product expiration date is also considered. The model helps the complete supply stakeholders to optimize their profits by managing optimal inventory.

Coarse-Grained Simulation Study of the Association of Selected Dipeptides.

The association of 55 dipeptides extracted from aggregation-prone regions of selected proteins was studied by means of multiplexed replica-exchange molecular dynamics simulations with the coarse-grained UNRES model of polypeptide chains. Each simulation was carried out with 320 dipeptide molecules in a periodic box at 0.24 mol/dm3 concentration, in the 260-370 K temperature range. The temperature profiles of the degree of association, distributions of dipeptide cluster size, and structures of clusters were examined. It has been found that the dipeptides composed of strongly nonpolar (aromatic or aliphatic) residues associate nearly completely at all temperatures to form tight clusters, while those composed of charged or polar residues exhibited no or residual association. The dipeptides composed of nonpolar and small polar residues and those composed of less hydrophobic residues formed single clusters, gradually dissolving with increasing temperature, while those composed of phenylalanine or tryptophan and polar or charged residues formed multiple irregular clusters with room to accommodate water inside, suggesting the formation of liquid droplets or gels. The logarithms of the average degree of association and the free energy of aggregation per monomer were found to correlate with the dipeptide hydrophobicity.

Identifying Markov Chain Models from Time-to-Event Data: An Algebraic Approach.

Many biological and medical questions can be modeled using time-to-event data in finite-state Markov chains, with the phase-type distribution describing intervals between events. We solve the inverse problem: given a phase-type distribution, can we identify the transition rate parameters of the underlying Markov chain? For a specific class of solvable Markov models, we show this problem has a unique solution up to finite symmetry transformations, and we outline a recursive method for computing symbolic solutions for these models across any number of states. Using the Thomas decomposition technique from computer algebra, we further provide symbolic solutions for any model. Interestingly, different models with the same state count but distinct transition graphs can yield identical phase-type distributions. To distinguish among these, we propose additional properties beyond just the time to the next event. We demonstrate the method's applicability by inferring transcriptional regulation models from single-cell transcription imaging data.

Fixing detailed balance in ancilla-based dissipative state engineering

Dissipative state engineering is a general term for a protocol which prepares the ground state of a complex many-body Hamiltonian using engineered dissipation or engineered environments. Recently, it was shown that a version of this protocol, where the engineered environment consists of one or more dissipative qubit ancillas tuned to be resonant with the low-energy transitions of a many-body system, resulted in the combined system evolving to reasonable approximation to the ground state. This potentially broadens the applicability of the method beyond nonfrustrated systems, to which it was previously restricted. Here we argue that this approach has an intrinsic limitation because the ancillas, seen as an effective bath by the system in the weak-coupling limit, do not give the detailed balance expected for a true zero-temperature environment. Our argument is based on the study of a similar approach employing linear coupling to bosonic ancillas. We explore overcoming this limitation using a recently developed open quantum systems technique called pseudomodes. With a simple example model of a one-dimensional quantum Ising chain, we show that detailed balance can be fixed, and a more accurate estimation of the ground state obtained, at the cost of two additional unphysical dissipative modes and the extrapolation error of implementing those modes in physical systems. Published by the American Physical Society 2024

EVALUATION OF TIMING CHAIN QUALITY AND ITS EFFECT ON TIMING SYSTEM OPERATION

Internal combustion engines are still the main source of propulsion for automotive vehicles. The timing system plays a very important role in the proper functioning of these engines. One of the widely used technical solutions to drive this system is the timing chain. However, in recent years, there has been a clear trend towards more frequent timing system failures. Many research works have focused on wear analysis or finite element method (FEM) modeling of chains. In contrast, it was decided to carry out a comparative study of the mechanical properties of new timing chains that are commercially available as spare parts for automotive workshops. The present study was carried out on timing system components of the popular Multijet 1.3 diesel engine used in Fiat, Opel, and Suzuki vehicles. Force-elongation curves were obtained for each of the tested chains, allowing their elongation under load to be estimated. In addition, breaking forces were measured, and breaking mechanisms were analyzed to compare the quality of the chains. Numerical parameters were proposed to estimate the quality of the tested chains. The chain tensioning forces resulting from the operation of the hydraulic tensioner were also estimated. The effect of the dynamic forces stretching the chain elastically on the timing accuracy during engine operation was also calculated.

Supply chain model for improving material & environment sustainability: an analysis from green supply chain implementation in automobile industry

Abstract Prevention of environmental damage must be considered by all stakeholders in order to reduce the negative effects. As a result, the objective of this inquire about is to set up how a few organizations utilize green supply chain administration and the affect it has on corporate execution. This study uses observation methods to obtain primary data and also uses secondary data from various relevant research sources. According to the findings, 1) Automotive industry has effectively executed green supply chain administration hones. Its application is to employ ecologically friendly raw materials, maximize raw materials, make goods easier to recycle, reduce waste, and encourage environmental activities, 2) Green supply chain management can increase firm performance by increasing efficiency and lowering expenses

Techno-economic and supply chain model of a small-scale LNG for BMPP in Sambelia

Abstract This paper addresses a method for creating the supply chain model of liquefied natural gas (LNG) to a barge-mounted power plant (BMPP) of 30 MW in Sambelia. This paper conducts the supply chain using a landing craft tank (LCT) ship as the LNG carrier, equipped with a specific principal dimension, and an ISO tank as the LNG storage facility. The use of LCT has the advantage of being able to sail in coastal areas and can be converted using existing ships. This comprehensive techno-economic assessment consists of calculating the LNG demands for the BMPP, the arrangement of ISO containerized tank stacking in the LCT, and identifying the LNG terminal facilities. Moreover, the research points for a future LNG economy are highlighted; cost-effective production and utilization solutions for enhancing the feasibility of LNG. The Landing Craft Tank (LCT), a vessel with a load capacity of 32 container tanks (20 ft) and a volume of 20.38 m3, distributes LNG to BMPP Sambelia. The economic analysis of LNG distribution patterns from Benoa LNG Terminal to BMPP 30 MW Sambelia, using LCT ships with FSRU (Floating Storage and Regasification Unit) receiver terminals, reveals that at a margin of $2.2, the most optimal option has an IRR of 11.6%, a payback period of 5.7, and an ROI of 17.41%.

OP62 Economic Evaluations Submitted For Reimbursement To The Brazilian Unified Health System: A Meta-Epidemiological Study

IntroductionAllocating financial resources efficiently poses a significant challenge for health systems worldwide. In this context, economic evaluations (EE) are pivotal for the health technology assessment (HTA) process, particularly in standardizing approaches to enhance decision-making accuracy. This study focuses on delineating methodological nuances of EE submissions within Brazil’s Unified Health System (SUS).MethodsThis meta-epidemiological study analyzed dossiers submitted to the National Committee for Health Technology Incorporation (Conitec) for drug reimbursement from January 2022 to May 2023. We selected dossiers that included complete EE studies such as cost–utility or cost-effectiveness analyses. Our evaluation involved extracting data on the characteristics of the studies, reimbursement decisions, elements of the base case scenario, and parameters used in utility and sensitivity analyses. [Protocol: DOI 10.17605/OSF.IO/JVYEC]ResultsWe included 56 dossiers, leading to 11 favorable reimbursement decisions. EE study methods were cost-effectiveness (17) and cost–utility analyses (17), with some employing both (16). Markov chain models were used in 27 dossiers, primarily utilizing quality-adjusted life years as the health outcome measure. Additional outcomes included life years gained and frequency of avoided events. While utility assessment was reported in 33 dossiers, only six adjusted for age and seven accounted for disutility from adverse events. Thirty-two conducted deterministic or probabilistic sensitivity analyses, but only four presented parameters like credibility intervals, and 17 presented acceptability curves.ConclusionsThis study highlights the need for more standardized and refined methods in the EE submitted to Conitec. The existing EE methodological guidelines, dating from 2014, are currently under revision. This update is crucial to integrate recent advancements in health technology assessment and to better address contemporary requirements, particularly in light of the newly defined willingness-to-pay threshold.

Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.

Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur, Malaysia

Application of Cellular Automata and Markov Chain model for urban green infrastructure in Kuala Lumpur, Malaysia

Strategic Integration of Policy and Logistics: A Case Study of Copenhagen’s Model for Sustainable Urban Supply Chains

This study explores integrating logistics policies and practices in creating efficient and sustainable urban supply chains. The study analyzes how green transport policies, the use of digital technologies, and collaboration between government and the private sector contribute to improving logistics efficiency and reducing emissions through a qualitative approach with thematic interviews with stakeholders and a case study of Copenhagen. Copenhagen has successfully implemented low-emission zones and an intelligent traffic management system that optimizes delivery routes, thereby reducing congestion and increasing sustainability. The findings provide important insights for other cities, including Jakarta, that face similar challenges in the logistics sector. The study suggests that implementing an effective policy integration model can help address environmental and operational issues, as well as improve the quality of life for urban residents. The results of this study contribute to the literature on urban logistics and sustainability policies and offer strategic recommendations for policymakers.

In - Depth Research on User Profiles in Securities Companies Under the Digital Background

Based on systematically grasping the development, integration, and application of relevant technologies such as the industrial value - chain model in the securities industry, the securitization of real - industry assets and their flow patterns, and regulatory and risk - prevention - and - control models, this paper conducts in - depth research on user profiles centering on the main path of securities business and product innovation. Aiming at the modeling problems of business - behavior - level homomorphism consistency and business - network - isomorphy consistency in business network systems, the concept of business - network profiles is originally proposed. That is, taking the description image of user profiles as an index, equivalent classification analysis of the description image of business - network profiles is carried out. According to the behavior equivalence class and behavior - homomorphism consistency, the user - network associations and interaction relationships are identified, and the user - association - action - and - influence - network profile is established, thereby obtaining more accurate and scientific data analysis and business applications. Finally, from the perspectives of algorithms and modeling, the structural model frameworks of the correlation relationships, interaction relationships, and influence relationships between user profiles and business - network profiles are discussed one by one, and the extended user profiles, business - network profiles, and their application results are presented.

An Integrated Multi-Product Biodiesel and Bioethanol Supply Chain Model with Torrefaction Under Uncertainty

This study presents an integrated supply chain network model for biodiesel and bioethanol production, incorporating torrefaction under uncertain conditions related to the establishment of new facilities. The proposed mixed-integer linear programming model aims to minimize the total cost of the supply chain while maximizing social objectives such as reducing unemployment. To solve the bi-objective model, a three-stage approach is employed: first, uncertain parameters are defuzzified; second, the augmented epsilon-constraint method is applied to generate a set of efficient Pareto-optimal solutions; and third, robust optimization is used to handle real-world uncertainties, such as disruptions caused by natural disasters and sanctions, ensuring feasibility under different scenarios. The study considers various stages of the supply chain, from feedstock cultivation to processing, transportation, and distribution. A real-life case study in Iran is used to evaluate the effectiveness of the proposed model, highlighting that biodiesel and bioethanol supply chains are interrelated, particularly at the cultivation stage, where each crop impacts the other. In this regard, Kermanshah, Isfahan, Chahar Mahal & Bakhtiari, Khorasan North, Kohgiluyeh & Boyer-Ahmad, and Lorestan are identified as the most suitable provinces for second-generation plant cultivation. Additionally, Azerbaijan East is identified as the best location for a bioethanol refinery, while Tehran and Markazi are the optimal choices for biodiesel refineries. This integrated approach offers a novel solution that prevents impractical overlaps in land use, providing a comprehensive, sustainable, and socially beneficial framework for bioenergy supply chain management.

Uncertainty Analysis in Ecological Risk Assessment: Sensitivity Analysis of Different Exposure Models

Ecological risk assessments are used for decision making regarding releasing new products to market as well as remedial action required to address contamination at legacy sites. Uncertainty analysis is considered a key part of the process (USEPA 1997) but assessments seldom address uncertainty quantitatively. Key sources of uncertainty include: Sampling and analytical variability (soil/ sediment matrix, lab error), Choice of “Indicator” Species as Target Receptors for Different Exposure Pathways, Sample size. Bioaccumulation and Food Chain Modeling involve assumptions and uncertainty including: Linear assumption of bioaccumulation and selection of bioaccumulation factors; Other model inputs such as: Home range size, Dietary percentages, Body Weights, Ingestion Rate; Literature data on effects, Use of No Observed Adverse Effects Level (NOAEL) or Lowest Observed Adverse Effects Level (LOAEL) as a decision point. Toxicity testing where employed brings its own set of assumptions and interpretation that are subject to uncertainty. These include: Use of single organism, Correlation (or lack thereof) with Contaminants of Concern, Contaminant mixtures, and Reference area selection. Keywords: Ecotoxicology, Risk Assessment, Ecology

Energy transfer efficiency rather than productivity determines the strength of aquatic trophic cascades.

Trophic cascades are important determinants of food web dynamics and functioning, yet mechanisms accounting for variation in trophic cascade strength remain elusive. Here, we used food chain models and a mesocosm experiment (phytoplankton-zooplankton-shrimp) to disentangle the relative importance of two energetic processes driving trophic cascades: primary productivity and energy transfer efficiency. Food chain models predicted that the strength of trophic cascades was increased as the energy transfer efficiency between herbivore and predator (predator efficiency) increased, while its relationship with primary productivity was relatively weak. These model predictions were confirmed by a mesocosm experiment, which showed that the strength of trophic cascade increased with predator efficiency but remained unaffected by nutrient supply rate or primary productivity. Combined, our results indicate that the efficiency of energy transfer along the food chain, rather than the total amount of energy fixed by primary producers, determines the strength of trophic cascades. Our study provides an integrative perspective to reconcile energetic and population dynamics in food webs, which has implications for both ecological research and ecosystem management.