Impact Of Changes In Parameters Research Articles

Analyzing how inflation affects non-instantly decaying goods with demand linked to ads and selling price in a dual-warehouse setup

This study explores inventory management strategies specifically designed for non-instantaneous deteriorating goods under inflationary conditions, utilizing a dual-warehouse system—one owned and the other rented with limited storage capacity. We examine the effects of advertising frequency and product selling price on demand rates, taking into account the gradual decline in customer patience, which leads to partial backlogging of shortages. The primary objective is to determine optimal replenishment policies for retailers that effectively minimize total costs per unit time. In real life, managing oil inventory is crucial for industries where factors like gradual deterioration, demand fluctuations due to advertising and pricing, and sensitivity to inflation require sophisticated inventory models to optimize replenishment policies and minimize costs. To validate our proposed inventory model, we provide a numerical. A sensitivity study using MATLAB R2024a software highlights the impact of parameter changes, providing significant information for decision-makers across various industries.

Dynamic incentive mechanisms in mega project-risk management considering the participation of the insurance company

PurposeThe construction of megaprojects often involves substantial risks. While insurance plays an important role as a traditional risk transfer means, owners and insurance companies may still suffer huge losses during the risk management process. Therefore, considering the strong motivation of insurance companies to participate in the on-site risk management of megaprojects, this study aims to propose a collaborative incentive mechanism involving insurance companies, to optimize the risk management effect and reduce the risk of accidents in megaprojects.Design/methodology/approachBased on principal-agent theory, the research develops the static and dynamic incentive models for risk management in megaprojects, involving both the owner and insurance company. The study examines the primary factors influencing incentive efficiency. The results are numerically simulated with a validation case. Finally, the impact of parameter changes on the stakeholders' benefits is analyzed.FindingsThe results indicate that the dynamic incentive model is available to the achievement of a flexible mechanism to ensure the benefits of contractors while protecting the benefits of the owner and insurance company. Adjusting the incentive coefficients for owners and insurance companies within a specified range promotes the growth of benefits for all parties involved. The management cost and economic benefit allocation coefficients have a positive effect on the adjustment range of the incentive coefficient, which helps implement a more flexible dynamic incentive mechanism to motivate contractors to carry out risk management to reduce risk losses.Originality/valueThis study makes up for the absence of important stakeholders in risk management. Different from traditional megaproject risk management, this model uses insurance companies as bridges to break the island effect of risk management among multiple megaprojects. This study contributes to the body of knowledge by designing appropriate dynamic incentive mechanisms in megaproject risk management through insurance company participation, and provides practical implications to both owner and insurance company on incentive contract making, thus achieving better risk governance of megaprojects.

Equivalent analysis model of a GaN LED used as a receiver

In general, visible light communication (VLC) uses LEDs as transmitters. However, LEDs can serve as receivers to construct a simple duplex VLC system that uses only two LEDs instead of one LED and one photo-diode (PD). There is a lack of effective equivalent analysis models for characterizing and evaluating the inherent behavioral characteristics of LEDs used as receivers. This paper presents an equivalent analysis model for GaN LEDs as receivers. First, based on the proposed receiving equivalent circuit model, a third-order signal transmission mathematical analysis model is established, revealing the transmission relationship between the photocurrent and output voltage. Further research is conducted on the impact of parameter changes on the bandwidth, and the model can be simplified into a first-order low-pass mathematical analysis model under specific conditions, providing theoretical support for improving the bandwidth of LED receiving applications. The experimental results also confirm the theoretical predictions. This research result holds significant importance for revealing the intrinsic mechanisms and the improved optical communication performance of LEDs for effective reception.

Modeling the Bifurcation Dynamics of Rumor Propagation in the Spatial Environment

The harm caused by rumors is immeasurable. Studying the dynamic characteristics of rumors can help control their spread. In this paper, we propose a nonsmooth rumor model with a nonlinear propagation rate. First, we utilize the positive invariant regions to prove the boundedness of solutions. Second, we analyze the conditions for the existence of equilibrium points in both the left and right systems. Additionally, we confirm the occurrence of saddle-node bifurcation in the left system. Next, by considering the influence of spatial diffusion, we establish the conditions for Turing instability. Then we discuss the conditions for spatial homogeneous and inhomogeneous Hopf bifurcations in the left and right systems, respectively. We differentiate between supercritical and subcritical bifurcations using the Lyapunov coefficient. Furthermore, we examine the conditions for the existence of discontinuous Hopf bifurcation at the demarcation point. Finally, in the numerical simulation section, we validate our theorems on Turing patterns. We also investigate the impact of parameter changes on rumor propagation and conclude that an increase in the psychological inhibitory factor significantly reduces the rate of rumor propagation, providing an effective strategy for curbing rumors. To that end, we fit actual data to our system and the results are excellent, confirming the validity of the system.

Vat‐photopolymerization‐based complex ceramic: Optimization of slurry, printing process, and postprocessing parameters

AbstractVat photopolymerization is an effective and economical method for preparing ceramic specimens with complex structures. However, the improper selection of process parameters during the preparation process limits the scope of its application. In this study, the most suitable shear rate was determined based on the solid content and rheological properties of the ceramic slurry. In addition, the specific printing‐process parameters were objectively selected, and the gray correlation method was used to determine the improved weight resolution coefficient through orthogonal experiments combined with the entropy weight method to select the most suitable printing parameters. Finally, the main debinding and sintering parameters that affect the heat treatment performance were considered, and multiple characterization methods were used to detect the specimens and study the impact of parameter changes on specimen performance. The experimental results show that when the laser power is 210 mW, the slice layer thickness is .04 mm, the scanning spacing is .05 mm, and the filling mode is XY, the green bodies have the best forming quality. When the sintering temperature is 1600°C, and the holding time is 4 h, the specimens have the best mechanical properties (the flexural strength reaches 114 MPa).

A Multi-Period Model of Compressor Scheme Optimization for the Shale Gas Gathering and Transportation System

In the process of shale gas production, with the change in gas productive parameters, the pressurization demand for the shale gas gathering and transportation system (SGGTS) also changes, which affects the choice of pressurizing location and timing. Our purpose is to effectively respond to the impact of parameter changes during shale gas production and to better select the pressurization schemes. Therefore, we considered the modularization of the compressors and established a mixed-integer nonlinear programming (MINLP) model to minimize the total cost of the SGGTS. Taking an actual shale gas field as an example, by discretizing the time during a given production period to solve the model under multi-period and single-period conditions, the optimal pressurization scheme for the SGGTS in the specified production period is obtained. It indicates that the results obtained under a multi-period condition are more conducive to actual production. Compared with the results obtained under the single-period condition, the cumulative cost obtained in the multi-period condition is reduced by 17.19%. By deploying the MINLP model in the specified production period, the pressurization demand is met in each time period. This greatly improves the utilization rate of modular compressors, reduces the total cost, and improves the economic benefits of the SGGTS.

Train Rescheduling for Large Transfer Passenger Flow by Adding Cross-Line Backup Train in Urban Rail Transit

The cross-line operation mode, based on interoperability technology, is becoming increasingly common in urban rail transits (URTs). Compared to trains running on a single line, cross-line trains can greatly facilitate transfer passengers. Taking the scenario of emergent large transfer passenger flow as an example, this paper explores the train rescheduling problem for serving transfer passengers by adding a cross-line backup train. To maximize the number of transfer passengers served by the cross-line backup train, a nonlinear optimization model is constructed by taking into account the operation parameters of planned trains on relevant lines, the deviation degree of the planned timetable, the utilization of the cross-line backup train, and the passenger flow calculation as constraints, and some linearization lemmas are proposed to transform it into a mixed integer programming (MIP) model with quadratic terms. A case study is conducted to discuss the impact of parameter changes on the objective function value and the applicability of different solution approaches. The results suggest that the operation trajectory of the cross-line backup train has an effect on the objective function value, which is related to the demand, the deviation tolerance of the planned timetable, and the running efficiency tolerance of the cross-line backup train. The corresponding methods help guide the organization of the cross-line backup train for large transfer passenger flow scenarios.

Modelling the Interaction of Illuminations with a Sensor Matrix Used in Vision Systems

The article discusses a method of modelling the interaction of industrial illuminators with sensor arrays used in industrial vision systems cameras. The research used a model containing a light source and a sensor matrix. As part of the research, the average intensity of electromagnetic radiation in the visible range on the surface of the sensor matrix was measured, as well as its analysis and interpretation using a model of illuminators. The light source is described based on the measurement of an authentic industrial illuminator, for which a photometric solid was determined using a photogoniometer and spectral irradiance with a spectroradiometer. A theoretical model of the matrix was prepared, enabling the selection and control of the parameters of its work. As part of the conducted research, the impact of changing the lighting model parameters, such as the photometric solid, spectral irradiance, and number of rays, and the effect of changing the matrix parameters, such as the dimensions, number of pixels, on the irradiance measurement, were checked. The results of simulation tests are presented for selected sets of models of the light source and for the model of sensors’ matrix. The summary presents the impact of changes in parameters adopted for models on the simulation results.

Blockchain-based tripartite evolutionary game study of manufacturing capacity sharing

In the context of the new round of manufacturing innovation, the sharing economy drives the transformation of manufacturing industry to accelerate the integration and development. However, there are some problems in the process of manufacturing capacity sharing, such as information privacy and security, and difficulty in tracing the sharing process, etc. The application of blockchain technology can effectively solve these problems. To explore the capacity sharing behaviour of manufacturing enterprises from the perspective of blockchain, the article combines evolutionary game theory and constructs a tripartite game model of manufacturing capacity sharing. The replication dynamics and evolutionary stability of the model are analysed using evolutionary game theory, and numerical simulations are carried out using MATLAB software to analyse the impact of parameter changes on the evolutionary outcome. The research results show that the incentive and penalty coefficients under blockchain technology have a facilitating effect on enterprises to carry out sharing, and the enhancement of reputation gain coefficient and loss can promote positive services on the platform.

Optimizing the fuel economy of hydrostatic power-split system in continuously variable tractor transmission

The aim of this study is to enhance the fuel economy of a continuously variable tractor transmission by analyzing its energy and fuel consumption. First, we present the principle of a self-developed tractor transmission based on power splitting and examine its parasitic power characteristics. Next, we construct a mathematical model of the hydraulic system, mechanical system, and entire transmission, calibrating the model to ensure the accuracy of subsequent results. We then perform a systematic analysis of the energy and fuel consumption of the tractor transmission. Finally, we optimize the transmission through design and power matching, investigating the impact of changes in parameters and control strategies on the fuel economy of the transmission. The results indicate that fuel consumption can be reduced by 2%–14% through parameter optimization and by an additional 0%–20% through appropriate power matching.

Modelling the dynamics of firm valuation: An assessment of impact of exchange rate fluctuations on firm value using system dynamics

AbstractExchange rate volatility is a vital issue in international finance due to its effect on the value of the firm, depending on firm's exposure to the exchange rate risk. However, this impact is not clear in literature as evidence is mixed. This article analyses the impact of exchange rate fluctuations on firm value. A system dynamics‐based corporate planning model has been developed that incorporates the modules for financial and physical processes, firm valuation and exchange rate determination. Simulation results reveal that a stronger home currency leads to an increase in market price per share of the firm and vice versa. The results provide counter intuitive evidence on the impact of exchange rate on firm value and provide nonlinear feedback loop‐based explanation. The study fills the gap in literature by incorporating a systemic approach in the strategic planning process to assess the composite impact of parameter changes on key variables.

Evolutionary Game Analysis on Cloud Providers and Enterprises’ Strategies for Migrating to Cloud-Native under Digital Transformation

Cloud-native is an innovative technology and methodology that is necessary to realize the digital transformation of enterprises. Promoting the wide adoption of cloud-native in cloud providers and enterprises has gained popularity in recent years. According to the technological and commercial characteristics of cloud-native, this paper analyzes the game relationship between cloud providers and enterprises on the selection of cloud-native, and combines evolutionary game theory to establish a model. In addition, empirical analysis indicates the impact of parameter changes on the dynamic evolution process. The results show that (1) enterprises are more vulnerable to the impact of direct benefit to adopt cloud-native, and cloud providers are especially affected by the cost of providing cloud-native; (2) enterprises are more likely to be impacted by the invisible benefit than cloud providers, but the impact has a marginal decreasing effect; (3) the low price is one of the reasons to attract enterprises; (4) enterprises are more concerned about the potential loss caused by the supply and demand mismatch. The results of the discussion provide a reference for all stakeholders to promote the implementation of cloud-native and the digital transformation of enterprises.

Disassembly Line Balancing of Electronic Waste Considering the Degree of Task Correlation

With growing concerns about the depletion of rare-earth elements, managing End-of-Life products has become a key sustainability initiative in the supply chains of global corporations. Recycling, the process of dismantling, separating, and recovery of components and raw materials from wastes, is technologically challenging and should be planned in such a way as to ensure operational efficiency as well as safety. This study explores the Disassembly Line Balancing Problem with Correlated Tasks (DLBP-CT), which is prevalent in the recycling of the Waste of Electrical and Electronic Equipment (WEEE). For this purpose, an original Integer Nonlinear Programming (INLP) model is proposed to find the optimal configuration for the disassembly lines. Given the NP-hard nature of this problem, the Adaptive Genetic Algorithm (AGA) is developed to solve the problem, minimizing the number of workstations and maximizing the relationship between the disassembly tasks. A case example from electronic waste is provided to test the practicality of the developed optimization approach. Sensitivity analysis is conducted to explore the impact of parameter changes in the optimization outcomes. Results are supportive of the applicability of the developed approach and show that it can serve as a strong decision aid tool when selecting the best disassembly process, workstations, and task assignments.

A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone

AbstractHard rock aquifers (HRAs) in West Africa (WA) are located within a thick regolith layer. The representation of thick tropical regolith in integrated hydrological models lacks consensus on aquifer geometries and parameter ranges. Our main objective was to determine the knowledge requirements on saturated hydraulic conductivity (Ks) to model the critical zone (CZ) of HRAs in WA. A parametric sensitivity analysis with a focus on the representation of the Ks heterogeneity of the regolith was conducted with a critical zone model (Parflow‐CLM [Community Land Model]) of the Upper Ouémé catchment in Benin (14,000 km2) at a 1‐ × 1‐km2 resolution. The impact of parameter changes in the near subsurface (0.3‐to‐5‐m depth) and in the deeper regolith aquifer (24‐ and 48‐m maximum depth) was assessed in five modeling experiments. Streamflow was largely dependent on Ks and on clay distribution in the near subsurface and less on the properties of the deeper subsurface. Groundwater table depths and amplitudes were controlled by vegetation and topography as observed on instrumented hillslopes and for Ks within the literature range. Experiments with higher Ks suggested a Ks threshold where dynamics become less determined by one‐dimensional vertical and more determined by lateral processes. Such heterogeneity impacts from smaller scales need to be accounted for when hydrological models are upscaled to larger domains (1‐ × 1‐km2 resolution or coarser). Our findings highlight the need for a new conceptual approach to represent clay distribution in order to develop catchment‐scale CZ models of HRAs in WA that capture the observed processes.

Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore

In order to improve production control ability in the gold ore flotation process, the output index in this process was studied. Flotation is an effective gold recovery process. Gold concentrate grade and gold recovery rate are the key output indicators of the flotation process. However, in the existing studies exploring the impact of parameter changes on the output indicators, the control ability of the output indicators is insufficient, and the interaction between variables is inadequately considered. Therefore, a multi-objective optimization model based on response surface methodology and the non-dominated sorting genetic algorithm-II (NSGA-II) is proposed in this paper. Firstly, the experiment was designed based on the Box-Behnken principle. Based on the experimental results, the interaction between variables was analyzed and the response polynomial was fitted. Secondly, a multi-objective optimization model was constructed, and the NSGA-II was used to solve the model. Finally, an example of gold ore flotation was used to verify the effectiveness of the method. The optimal solution was a gold concentrate grade of 75.46 g/t and a gold recovery rate of 85.98%.

Modelling of changes in pavement serviceability of the asphalt road

Parameter of pavement serviceability have a significant impact on road life and safety. For this reason, the research focuses on modeling changes in the parameters of pavement serviceability, which can be considered as one of the current problems of the pavement management system (PMS). Due to the deteriorating condition of roads, it is necessary to monitor the impact of changes in parameters on the resulting effectiveness of the repair. The cost-effectiveness of spending helps road managers decide on road repairs. The paper focuses on modeling changes in the longitudinal unevenness parameter using various degradation functions. The aim of the research is to create an optimal degradation model, which will serve as a basis for sensitivity analysis and determination of the optimal year of repair.

Optimization of electric heating operation based on day-ahead electricity spot market

The emergence of Electricity Spot Market makes it possible that distributed electric heating equipment connect with the grid and participate in market competition as well. However, due to the functional limits, it is impossible for the power supply side to have a thorough understanding of the specific conditions of the load characteristics of the power user, and the user side will not take a global consideration of the real-time security and stability of the power grid. This paper focuses on the research about electricity consumption strategy of thermoelectric coupled load with the background of electricity spot market. The main works are as following: An operation strategy of consumer electric heating equipment based on electric spot market is proposed. Besides, an operation mode which responds to the market is obtained by analysing the time span of clearing nodes in power spot market and start-stop characteristics of distributed heat load equipment. Based on this, the operating cost control function of consumer electric heating load is designed, making it possible that the load follows changes of the clearing price under the premise of ensuring that the user’s heat demand is met within a day. Then we had a discussion about the impact of parameter changes on the operating strategy. With the help of the constraints of existing power grid, the operation strategy model of consumer electric heating equipment based on power spot market is realized.

Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System

In this work, issues related to the application of the D-decomposition technique to selection of the controller parameters for a drive system with flexibility are presented. In the introduction the commonly used control structures dedicated to two-mass drive systems are described. Then the mathematical model as well as control structure are introduced. The considered structure has only basic feedbacks from the motor speed and PI type controller. Due to the order of the closed-loop system, the free location of the system’s poles is not possible. Large oscillations can be expected in responses of the plant. In order to improve the characteristics of the drive, the tuning methodology based on the D-decomposition technique is proposed. The initial working point is selected using an analytical formula. Then the value of controller proportional gain is decreasing, until the required value of overshoot is obtained. In the paper different advantages of the D-decomposition technique are presented, for instance calculation of global stability area for the selected gain and phase margin, the impact of parameter changes, and additional delay evident in the system. Theoretical considerations are confirmed by simulation and experimental results.

Parametrisation of change-permitting extreme value models and its impact on the description of change

The potential for changes in environmental extremes is routinely investigated by fitting change-permitting extreme value models to long-term observations, allowing one or more distribution parameters to change as a function of time or some other covariate. In most extreme value analyses, the main quantity of interest is typically the upper quantiles of the distribution, which are often needed for practical applications such as engineering design. This study focuses on the changes in quantile estimates under different change-permitting models. First, metrics which measure the impact of changes in parameters on changes in quantiles are introduced. The mathematical structure of these change metrics is investigated for several change-permitting models based on the Generalised Extreme Value (GEV) distribution. It is shown that for the most commonly used models, the predicted changes in the quantiles are a non-intuitive function of the distribution parameters, leading to results which are difficult to interpret. Next, it is posited that commonly used change-permitting GEV models do not preserve a constant coefficient of variation, a property that is typically assumed to hold for environmental extremes. To address these shortcomings a new (parsimonious) model is proposed: the model assumes a constant coefficient of variation, allowing the location and scale parameters to change simultaneously. The proposed model results in changes in the quantile function that are easier to interpret. Finally, the consequences of the different modelling choices on quantile estimates are exemplified using a dataset of extreme peak river flow measurements in Massachusetts, USA. It is argued that the decision on which model structure to adopt to describe change in extremes should also take into consideration any requirements on the behaviour of the quantiles of interest.

Parametric Modeling Study of PEM Fuel Cell with Platinum Group Metal -Free Cathode Catalyst Layer

Polymer electrolyte fuel cells offer zero-emissions energy conversion but are commercially limited by cost and durability. Stack cost can be reduced by using platinum group metal-free (PGM-free) catalysts for the oxygen reduction reaction (ORR). PGM-free catalysts have lower volumetric activity relative to PGM catalysts and consequently require thicker catalyst layers. Thicker catalyst layers result in increased oxygen transport resistance and flooding in the cathode, which must be considered to accurately model PGM-free fuel cells and quantify the distinct transport resistance. For this purpose, we have developed a well validated model to elucidate relative contributions to oxygen transport resistance. This two-phase, transient, non-isothermal, channel-to-channel model contains cathode catalyst layer morphology informed by plasma-focused ion beam (P-FIB) SEM. Furthermore, the model includes agglomerate treatment of volumetrically active catalyst primary particles coated by ionomer. The structure of the agglomerate model is well suited for describing the catalyst layers containing metal organic framework (MOF) derived metal-nitrogen-carbon catalysts that are roughly spherical and volumetrically active for ORR. The MOF-derived Fe-N-C catalyst used in this study’s experiments contains atomically dispersed active sites and uniform, tunable primary particle size. In this presentation, we will report on our investigation of the performance sensitivity to a wide variety of operating conditions and MEA design specificiation, including cell temperature, gas pressures, cathode thickness, hydrophobic pore fractions and wettability, catalyst primary particle size, ionomer content, and ionomer equivalent weight. Non-dimensional characteristic parameter analysis on the model results is used to distinguish the transition between proton conductivity limited and oxygen transport limited performance, and indicate critical values that affect the the through plane ultilization of the catalyst. This information and studies on the impact of active site density help us set catalyst-specific targets for meeting fuel cell performance goals. With highly active PGM-free catalysts, we can model the impact of parameter changes on transport with experimental validation.DOE ACKNOWLEDGEMENTThis material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Fuel Cell Technologies Office (FCTO) under award number DE-EE0008076. The authors gratefully acknowledge research support from the Electrocatalysis Consortium (ElectroCat), established as part of the Energy Materials Network under the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office, under contract number DEEE0008076.