Parameters Of Functions Research Articles

Heterogeneous decision-making dynamics of threshold-switching agents on complex networks.

In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures.

ПОКАЗАТЕЛИ ФУНКЦИЙ ГЕПАТОБИЛИАРНОЙ СИСТЕМЫ У БОЛЬНЫХ ХРОНИЧЕСКИМ ОПИСТОРХОЗОМ С ГЕНЕТИЧЕСКОЙ ПРЕДРАСПОЛОЖЕННОСТЬЮ К НАРУШЕНИЯМ ЛИПИДНОГО ОБМЕНА

To assess the influence of genetic features of metabolic processes on the reactivity to opisthorchiasis invasion, biochemical parameters of the hepatobiliary system functions were studied in chronic opisthorchiasis patients with and without mutant alleles of polymorphisms associated with lipid metabolism disorders were studied. It was found that the presence of mutations in the loci rs2854117 APOC3, rs7412 APOE enhances the negative impact of chronic opisthorchiasis invasion on the state and function of hepatocytes. The presence of the rare allele rs11206510 PCSK9 prevents an increase in ALP activity. In subgroups with mutations in individual polymorphisms of the APOE, APOC3I, ABCA1, lipoprotein lipase, and paraoxanase-1 genes, differences were found in the change in parameters after anthelmintic therapy, possibly associated with their role in the response of metabolic processes not only to invasion, but also to the treatment process.

General point load weight function for semi-elliptical surface cracks in round bars

Offshore mooring chains are continuously subjected to cyclic environmental loadings, resulting in fatigue cracks that always develop at the surface and grow across the circular cross-sections. To analyse the fatigue crack growth in a mooring chain based on a fracture mechanics-based approach, the evaluation of stress intensity factors (SIFs) of surface cracks in the chain segments becomes essential. A general point load weight function of SIFs for semi-elliptical surface cracks in straight and curved round bars was derived in this study. Parametric three-dimensional finite element (FE) models were developed to calculate the SIF solutions for determining the unknown parameters in the weight function. The analysis matrix covered wide ranges of aspect ratios 0.1 ≤ a/c ≤ 2.0, relative crack depths 0.03 ≤ a/D ≤ 0.6 and relative bar curvatures 0.0 ≤ D/Ri ≤ 1.5. The developed weight function was verified against the solutions available in the literature and FE results, indicating good accuracy. The proposed weight function can be used in SIF calculation and remaining fatigue life prediction of surface cracked round bar components under complex stress distributions, such as chain links.

Evaluation of Cognitive and Biochemical Brain Status in Patients after Carotid Endarterectomy

INTRODUCTION: In the large studies such as NASCET, ESCT, VACS, ACAS and ACST-1, the role of alteration of the cognitive functions of a patient in the early and long-term postoperative periods have not been determined.
 AIM: To evaluate the biochemical parameters of cerebral functions, the cognitive status of patients, and their interrelation after carotid endarterectomy.
 MATERIALS AND METHODS: The study involved 60 patients divided into two groups. The mean age was 67.3 ± 7.9 years, there were 49 (81%) men and 19 (19%) women. Group A included ‘symptomatic’ patients (n = 30) who underwent carotid endarterectomy and had a history of an acute cerebrovascular event. Group B included ‘asymptomatic’ patients (n = 30) who underwent carotid endarterectomy but had no history of cerebrovascular events. As part of the study, the following data were evaluated within a 6-month period: cognitive status (on MMSE, FAB, MoCA-test, NIHSS scales), biochemical parameters (S100b protein, neuron specific enolase (NSE), brain derived neurotrophic growth factor (BDNF)) and the ultrasound data of the carotid arteries.
 RESULTS: Patients of group A showed improvement of the cognitive status by the 6th month on MMSE scale (p = 0.001) and in MoCA-test (p = 0.09) relative to the initial parameters; on FAB scale relative to the data after 1 month (p = 0.01); a decline of the level of neurological deficit on NIHSS scale (p = 0.01). In group A, an inverse correlation was found between the results on MMSE scale and resistance index (r = -0.675; p = 0.005), and a direct correlation between the results on FAB scale and the end diastolic pressure in the internal carotid artery (r = +0.912; p = 0.005) according to the ultrasound data. The data obtained in group B, reflected improvement of the cognitive status by the 6th month only by 2 points on MoCA test (p = 0. 03) relative to the initial data. Besides, in 6 months after the surgery, there was a 2-fold increase in S100b marker (p = 0.01) and NSE protein (p = 0.02) and a 1.5-fold increase in BDNF protein as compared to the initial values (p = 0.005).
 CONCLUSIONS: Carotid endarterectomy leads to improvement of the cognitive status evaluated on MMSE, FAB, MoCA, NIHSS scales in the long-term postoperative period in symptomatic patients. The extent of improvement of neuropsychological status depends on the level of the end diastolic pressure and resistance index in the internal carotid artery on the operated side.

Peak‐valley period partition and abnormal time correction for time‐of‐use tariffs under daily load curves based on improved fuzzy c‐means

AbstractPeak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min.

Comparison between Hyaluronic Acid and Chlorhexidine in the treatment of Orthodontically induced gingival enlargement: A randomized controlled trial

Introduction: The study aimed to assess and compare the effectiveness of chlorhexidine and hyaluronic acid when used as an adjuvant to professional mechanical plaque removal (PMPR) in the treatment of orthodontically induced gingival enlargement. Methods: The study conducted was a randomized controlled clinical trial involving 45 patients. The patients will be categorized into 3 groups; control group receiving conventional PMPR, study 1 group receiving PMPR and chlorhexidine (CHX), and study 2 group receiving PMPR and hyaluronic acid (HA). Probing depth (PD), Gingival overgrowth index (GOI), gingival bleeding index (GBI), and plaque index (PI), will be recorded at baseline, 1 month, 2 months, and 4 months’ post therapy. A bivariate analysis was conducted to evaluate the parameters in function of the three study groups, and to evaluate the changes in PD and GBI between baseline, month 1, month 2 and month 4 in the three study groups. Results: A significant reduction in probing depth and gingival bleeding was observed in the three groups (p 0.05). The change in PD, PI, GBI and GOI was more in Group 1 and Group 2 than in the control group. Hyaluronic acid demonstrated the same effect of chlorhexidine. Conclusions: HA is just as effective as CHX in treating gingival enlargement. Based on the acceptance of HA by patients generally and the negative effects of CHX, HA may be a potential alternative to CHX.

Left atrial functional assessment a prime clue in solving the puzzle of stable angina symptoms in suspected coronary artery disease patients

Abstract Background Assessing clinically for significant coronary artery disease (CAD) in stable angina patients presents a formidable task. Routinely performed echocardiography is limited in diagnosing CAD with provision for the regional wall motion abnormality (RWMA) for left ventricle (LV) systolic dysfunction and altered mitral inflow velocities for LV diastolic dysfunction. However, the advent of two-dimensional (2D) speckle-tracking technique has established the era of strain imaging, opening new possibilities in echocardiography. It is now possible to have a detailed functional assessment of left atrium (LA). We utilised speckle tracking imaging to obtain strain parameters for LA function. Combining these strain parameters with the doppler myocardial velocities and mitral inflow velocities, may prove useful mode of imaging to diagnose significant CAD in stable angina patients. Purpose To study the parameters of left atrial functions utilising echocardiographic strain imaging and evaluate their correlation with the syntax score-II in patients with symptoms of stable angina. Methods 103 patients with symptoms of stable angina undergoing coronary angiography for suspected coronary artery disease(CAD), were considered for LA function assessment and compared with their syntax-II score. Results The LA reservoir function parameters, peak atrial longitudinal strain (PALS) and peak global systolic strain rate (LAsSR), decreased with increasing syntax-II score (p values 0.011 and 0.001 respectively) and the parameter of LA conduit function, peak global early diastolic strain rate (LAeSR) increased (p value <0.001). The LA filling pressures reflected by E/e`, also increased with more severe syntax-II score (p value <0.001). We, further, configured cut off values of < 23.56 for PALS, < 1.15 for LAsSR, > -1.09 for LAeSR, and E/e` ratio > 10.39 as predictors of significant CAD, with good sensitivity and specificity. Therefore, stable angina patients who had significant CAD on coronary angiogram represented by severe syntax score-II, had higher LA filling pressures and deranged LA function with transition from more of reservoir chamber to merely a conduit. Conclusion Our study shows a significant association between LA strain parameters (PALS, LAsSR, and LAeSR) and LA filling pressures (E/e`), with the presence of significant CAD in these patients. The stable angina patients with more severe CAD as quantified by their severe syntax score-II, have an impaired LA reservoir and LA functions more as a conduit, with elevated filling pressures. This study establishes a significant association between LA deformation strain variables and severity of CAD and hence LA strain imaging can be a useful tool to diagnose significant CAD in stable angina patients.

Robust estimation and bias-corrected empirical likelihood in generalized linear models with right censored data

In this paper, we study the robust estimation and empirical likelihood for the regression parameter in generalized linear models with right censored data. A robust estimating equation is proposed to estimate the regression parameter, and the resulting estimator has consistent and asymptotic normality. A bias-corrected empirical log-likelihood ratio statistic of the regression parameter is constructed, and it is shown that the statistic converges weakly to a standard χ 2 distribution. The result can be directly used to construct the confidence region of regression parameter. We use the bias correction method to directly calibrate the empirical log-likelihood ratio, which does not need to be multiplied by an adjustment factor. We also propose a method for selecting the tuning parameters in the loss function. Simulation studies show that the estimator of the regression parameter is robust and the bias-corrected empirical likelihood is better than the normal approximation method. An example of a real dataset from Alzheimer's disease studies shows that the proposed method can be applied in practical problems.

Robot Arm Control Using Hybrid intelligent

The design of a fuzzy logic control suffers from select parameters of the membership functions, scaling factors, defuzzification action, Inference engine and base rules. speaking generally, such prosecutions are executed by traditionally techniques which do not assure an robust fuzzy control system design. There are various techniques introduced in literatures that used Genetic Algorithms to optimize a fuzzy logic control component. . In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Geneticn Algorithm (GA). The FLC used because it is efficient tools for control of nonlinear and uncertain parameters systems. GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors, defuzzification Method, Inference engine and control base rules. GA with different fitness functions in a form of the cumulative response error which are widely used as an efficient optimization technique. This paper also introduce a new methodology with new multi-objective function to improve fuzzy control parameters based on Genetic Algorithm techniques. The dynamic model of the robot manipulator its done by differential equations, these equations are hardly nonlinear, parameters uncertainty and time varying with multiple input and multiple output (MIMO).The manipulator robot and the fuzzy genetic control are modeled in MATLAB SIMULINK; the manipulator robot model driven nonlinear controller to draw a circle in the space with and without parameters uncertainties. The proposed techniques showed that the proposed fuzzy controller gives superior response in the output performance. When the parameter uncertainties including in the system, given satisfactory response.

BJT Based Translinear Implementation of an Evolutionary Optimised Non-Linear Function for Sensor Linearisation

The linear characteristics of temperature sensors are affected by material properties, aging, self-heating, lead resistance, and external interference. This study aims to improve sensor characteristics by overcoming power consumption and response time limitations. An evolutionary optimised log-ratiometric function is implemented for sensor linearisation using a translinear circuit. The differential evolution optimisation strategy is used to find the optimum values of the linearising parameters in the non-linear function. Further, these parameters are applied to the current mode circuit to linearise the selected sensors as an input current source. Due to higher fidelity, better comprehensive dynamic range, and improved linear areas, implementing the non-linear function using the BJT-based current mode circuit results in a better full-scale error reduction capability than MOSFET-based translinear implementation. Furthermore, the devices such as Gallium Arsenide Field-Effect Transistors (GaAsFET) and Modulation-Doped FET (MODFET) could be used in place of BJT in the future for additional error reduction due to rapid response speed, low power consumption, and reduced internal noise. Thus, the translinear implementation of the non-linear function can enhance the sensor characteristics in all aspects.

Age-Related Shift in Cardiac and Metabolic Phenotyping Linked to Inflammatory Cytokines and Antioxidant Status in Mice.

Age-related alterations in cardiac function, metabolic, inflammatory and antioxidant profiles are associated with an increased risk of cardiovascular mortality and morbidity. Here, we examined cardiac and metabolic phenotypes in relation to inflammatory status and antioxidant capacity in young, middle-aged and old mice. Real-time reverse transcription-polymerase chain reactions were performed on myocardium and immunoassays on plasma. Left ventricular (LV) structure and function were assessed by echocardiography using high-frequency ultrasound. Middle-aged mice exhibited an altered metabolic profile and antioxidant capacity compared to young mice, whereas myocardial expression of inflammatory factors (TNFα, IL1β, IL6 and IL10) remained unchanged. In contrast, old mice exhibited increased expression of inflammatory cytokines and plasma levels of resistin compared to young and middle-aged mice (p < 0.05). The pro-inflammatory signature of aged hearts was associated with alterations in glutathione redox homeostasis and elevated contents of 4-hydroxynonenal (4-HNE), a marker of lipid peroxidation and oxidative stress. Furthermore, echocardiographic parameters of LV systolic and diastolic functions were significantly altered in old mice compared to young mice. Taken together, these findings suggest age-related shifts in cardiac phenotype encompass the spectrum of metabo-inflammatory abnormalities and altered redox homeostasis.

Balanced influence maximization in social networks based on deep reinforcement learning

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users’ historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy.

Estimation of uncertainty distribution function by the principle of least squares

In order to estimate the unknown parameters in an uncertainty distribution function, this article uses the principle of least squares that minimizes the sum of the squared deviations between the uncertainty distribution and the empirical distribution of the observed data. After that, the principle of least squares is applied to determining the uncertain disturbance term of uncertain regression model and uncertain time series model, and estimating the unknown parameters in uncertain differential equation. Finally, in order to illustrate the proposed method, some real-world examples are provided, including PetroChina stock price, electricity price, grain yield, China’s population, and beef price.

Investigation of parameters related to lower extremity muscle strength and proprioception in Patients with Multiple Sclerosis: a cross-sectional study

BackgroundPatients with Multiple Sclerosis (PwMS) may experience a decline in balance, gait, and mobility as well as an increase in fear of falling (FoF) and fatigue due to weaker muscles and proprioceptive loss in their lower limbs. The study aimed to compare lower extremity muscle strength and proprioception, balance, functional mobility, gait, FoF, and fatigue between PwMS and healthy controls and to investigate the relationship between lower extremity muscle strength and proprioception and balance, functional mobility, gait, FoF, and fatigue in PwMS. MethodsThe study was completed with 35 PwMS and 35 healthy controls with matching age and gender. The 5 Repetition Sit-to Stand Test (5-STS-test), knee and ankle reposition tests (using a digital inclinometer), the Berg Balance Scale (BBS), the Timed Up and Go (TUG) test, the Dynamic Gait Index (DGI), the Falls Efficacy-International (FES-I), and the Fatigue Severity Scale (FSS) were used for evaluations. ResultsThe 5-STS test, knee and ankle reposition tests, TUG, FES-I, and FSS Test scores were higher and BBS and DGI scores were lower in PwMS compared to healthy controls (p < 0.05). BBS, DGI, TUG, FES-I, and FSS were associated with 5-STS test and knee and ankle reposition tests except for some nondominant reposition tests in PwMS (r between 0.342 and -0.714; p < 0.05 for all). ConclusionLower extremity muscle strength and proprioception were associated with balance, functional mobility, gait, FoF, and fatigue in PwMS. These results suggested that detailed assessment of neuromuscular parameters in lower extremity function is important in determining the appropriate rehabilitation programs.

EVALUATION AND STUDY OF PATHO-PHYSIOLOGICAL ROLE OF GLUCOCORTICOID RECEPTOR (GR) LIGANDS IN ACETAMINOPHEN-INDUCED LIVER INJURY IN MICE

Stress elicited by various drugs and psychosocial components is extensively prevalent globally. Glucocorticoid receptors control different features of lipid metabolism and lipoproteins in the liver. Acetaminophen or paracetamol is a broadly used antipyretic and analgesic drug. Overdose of acetaminophen causes centrilobular necrosis and fibrosis by generating oxidative stress. This experimental study was conducted to evaluate alone as well as combined systemic effects of glucocorticoid receptor (GR) ligands, Mifepristone (GR antagonist), and Dexamethasone (GR agonist) on oxidative damage and liver injury induced by acetaminophen. Evaluation of parameters of liver functions showed a substantial increase in AST ALT levels accompanied by elevation in TOS and reduction in TAC in the APAP-induced hepatotoxic group. While the groups that received dexamethasone, RU-486, and Dex+RU-486 as pretreatment significantly decreased the ALT, AST, and TOS levels compared to the toxic group (APAP). The protective effects of glucocorticoid receptor ligands in APAP-induced liver injury were further verified by histopathological examination. However, additional studies are necessary to illustrate the hepato-protective efficacy, safety profile, basic underlying process of protection, and therapeutic applications of glucocorticoid receptor ligands.

Association between severity of malocclusion and parameters of oral functions in permanent dentition with various malocclusion: case-control study

ABSTRACT Purpose It has been reported that people with malocclusion have poor masticatory function, including decreased bite force and masticatory efficiency, but the relationship between the severity of malocclusion and oral function remains unclear. The purpose of this study was to clarify the relationship between the severity of malocclusion and various oral function parameters using the Dental Health Component (DHC) of the Index of Orthodontic Treatment Need (IOTN), which quantitatively evaluates the state of malocclusion. Materials and methods A total of 138 permanent dentition patients (39-men, 99-women; aged 15–59 years) who visited Tsurumi University Dental Hospital were included in the study. The severity of malocclusion was determined using IOTN-DHC grading, and patients were classified into three groups. All patients underwent oral function tests before orthodontic treatment, including masticatory efficiency, occlusal force, occlusal contact area, lip pressure and tongue pressure. The relationship between malocclusion severity and oral function was statistically analysed. Results Firstly, we compared the medians of each oral function values among three groups and found statistically significant lower value in group 3, the worst IOTN-DHC grading group, as compared to group 1. Correlation analysis revealed correlations between malocclusion severity and oral functions, such as lip pressure, masticatory efficiency and tongue pressure. In addition, masticatory efficiency was correlated with occlusal contact area, tongue pressure and lip pressure. Conclusions Our results suggest that oral function is lower in patients with severe malocclusion and masticatory efficiency is related not only to dental occlusion but also to the function of surrounding soft tissues, such as the tongue and lips.

Recognition of mineralogical and technological varieties of iron ore on the basis of ultrasound backscatter spectrograms

The research is aimed to the analysis and modeling of the process of propagation of ultrasonic waves in iron ore samples to assess its mineralogical varieties. The paper analyzes domestic and foreign experience in modeling of ultrasonic waves propagation; methods of mathematical and computer modeling were used, as well as methods of mathematical statistics and probability theory for analysis of the results. Scientific novelty consists in developing and substantiating a method for recognizing the mineralogical and technological varieties of iron ore of a developed deposit based on spectrograms of a backscattered ultrasonic probing signal. Practical valueconsists in developing a methodology for non-contact non-destructive mineralogical analysis of iron ore to improve the efficiency and quality of its further processing and preparation for metallurgical processing. results. As measurable characteristic estimates of textural and structural features of iron ore varieties the results of spectral analysis of the reversed radiant ultrasonic signal were used. To implement the measurement results classification procedure, Adaptive Neuro-Fuzzy Inference System is used. At the vector of parameters of membership functions of terms of input variables and the vector of coefficients of linear functions in the conclusions of the rules was formed based on the characteristics of the processed ore and the spectrograms of the backscattered ultrasonic signal. The average accuracy of recognition of magnetite, chlorite-carbonate-magnetite, hematite-magnetite, magnetite-cummngtonite-chlorite-siderite mineral varieties of iron ore of the studied deposit was 93%.

A Differential Diffusion Theory for Participating Media

AbstractWe present a novel approach to differentiable rendering for participating media, addressing the challenge of computing scene parameter derivatives. While existing methods focus on derivative computation within volumetric path tracing, they fail to significantly improve computational performance due to the expensive computation of multiply‐scattered light. To overcome this limitation, we propose a differential diffusion theory inspired by the classical diffusion equation. Our theory enables real‐time computation of arbitrary derivatives such as optical absorption, scattering coefficients, and anisotropic parameters of phase functions. By solving derivatives through the differential form of the diffusion equation, our approach achieves remarkable speed gains compared to Monte Carlo methods. This marks the first differentiable rendering framework to compute scene parameter derivatives based on diffusion approximation. Additionally, we derive the discrete form of diffusion equation derivatives, facilitating efficient numerical solutions. Our experimental results using synthetic and realistic images demonstrate the accurate and efficient estimation of arbitrary scene parameter derivatives. Our work represents a significant advancement in differentiable rendering for participating media, offering a practical and efficient solution to compute derivatives while addressing the limitations of existing approaches.

Reinforcement Learning-Based Model-Free Controller for Feedback Stabilization of Robotic Systems.

This article presents a reinforcement learning (RL) algorithm for achieving model-free control of robotic applications. The RL functions are adapted with the least-square temporal difference (LSTD) learning algorithms to develop a model-free state feedback controller by establishing linear quadratic regulator (LQR) as a baseline controller. The classical least-square policy iteration technique is adapted to establish the boundary conditions for complexities incurred by the learning algorithm. Furthermore, the use of exact and approximate policy iterations estimates the parameters of the learning functions for a feedback policy. To assess the operation of the proposed controller, the trajectory tracking and balancing control problems of unmanned helicopters and balancer robotic applications are solved for real-time experiment. The results showed the robustness of the proposed approach in achieving trajectory tracking and balancing control.

Integrating link count data for enhanced estimation of deterrence functions: A case study of short-term bicycle network interventions

Owing to the lack of demand-related data from interim timespans between consecutive household surveys, it is difficult to assess short-term interventions in the bicycling network and their impact on potential accessibility. In this study, we propose a novel method that incorporates easier and cheaper demand-related data for estimating the parameters of deterrence functions of gravity-based distribution models. In particular, an origin-destination (OD) matrix-updating procedure that uses bicycle count data was incorporated into a two-step line-search algorithm for estimating a two-parameter deterrence function. The method was applied to a case study in the city of São Paulo, where potential accessibility was calculated during the years 2014–2017, which was a period interim to the two most recent household surveys. Three different models were used to estimate the generalized costs of the network links. This method proved useful for capturing accessibility variations over short timespans. Results show how demand is affected by the bicycling network and provide insights into how short-term assessments can benefit decision-making such as providing well-connected and continuous bicycling infrastructure positively influences accessibility. However, they fail as a unique solution apart from land-use policies. Finally, our proposed method can become a valuable tool for decision-making from an accessibility perspective in the short term.