Assumption Of Stability Research Articles

Adaptive Proportional-Integral Control Design for a Class of Continuous Stirred Tank Reactors with Uncertain Parameters

Mathematical model of reaction systems contains experimental parameters such as reaction enthalpies, which may be inaccurate and, therefore, severely affect the computation as well as the implementation of feedback control laws. This paper aims to design an adaptive PI-like controller to regulate a chemical reaction system by means of the Lyapunov theory. More precisely, uncertain model parameters are updated online by solving a set of ordinary differential equations while the global asymptotic convergence of closed-loop system trajectories towards the desired equilibrium is ensured by using the proposed adaptive PI-like controller under the assumption of stability of isothermal conditions. The applicability of theoretical developments is illustrated with an irreversible first-order reaction system having multiple steady states and taking place in a non-isothermal continuous stirred tank reactor. Simulation results show that system trajectories initiated at different conditions are asymptotically stabilized at the desired values and the closed-loop system is robust against the uncertainty of heat exchange coefficient and dilution rate.

Rethinking entrepreneurship in causally entangled crises: A poly-crisis perspective

Over the last few years, the world has witnessed the emergence of a poly-crisis era in which overlapping, causally entangled crises, such as pandemics, war, inflation, natural disasters, etc. converge to challenge assumptions of societal stability upon which much of the field's knowledge base has been developed over the last few decades. In this editorial, we propose a poly-crisis perspective to entrepreneurship and compare it with entrepreneurship under both normal times and a single crisis. In doing so, we highlight the need to reexamine the boundary conditions of our models and to propose some questions, constructs, and methods that deserve increased attention in a world where institutional uncertainty is the rule rather than the exception.

Fine-grained effect sizes.

To make transparent individuals' responses to intervention over time in the systematic review of single-case experimental designs, we developed a method of estimating and graphing fine-grained effect sizes. Fine-grained effect sizes are both case- and time-specific and thus provide more nuanced information than effect size estimates that average effects across time, across cases, or both. We demonstrate the method for estimating fine-grained effect sizes under three different baseline stability assumptions: outcome stability, level stability, and trend stability. We then use the method to graph individual effect trajectories from three single-case experimental design studies that examined the impact of self-management interventions on students identified with autism. We conclude by discussing limitations associated with estimating and graphing fine-grained effect sizes and directions for further development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multivariate process capability analysis for evaluating metal additive manufacturing via electron beam melting

Abstract Electron beam melting (EBM) as one of the relatively new metal AM techniques showed promising and increasing applications. Therefore, there is a need to evaluate the quality of the EBM process using its critical quality characteristics. However, EBM and different AM process parts have many functionally or statistically correlated quality characteristics. Consequently, the quality characteristics of the EBM process should be evaluated together. Therefore, this research aims to evaluate the quality of the EBM process using a multivariate process capability index (MPCI). In this study, the dimensional accuracy in different directions is considered as a quality characteristics. The proposed methodology involves producing a large sample of small specimens of square shape using EBM technology. Three critical dimensions of the specimen in the X, Y, and Z axis are investigated as quality characteristics. The dimensions of quality characteristics are measured using a precise measurement device. The normality and stability assumptions of the collected data are investigated using skewness measure, and multivariate process control chart respectively. Then a large sample of the multivariate normal data is simulated using computer software to estimate the percent of nonconforming (PNC) from the established specification limits, which is used to estimate MPCI. Finally, the capable tolerance of the process is estimated and the sensitivity analysis of variation is investigated. The results show the capability of the EBM process under different specification limits designations. Estimating MPCI revealed that the EBM process is capable under very coarse limits only. Moreover, the sensitivity analysis showed that variation in quality characteristics data is very sensitive for MPCI estimation, especially variation in width quality characteristic.

Non-hyperuniformity of Gibbs point processes with short-range interactions

Abstract We investigate the hyperuniformity of marked Gibbs point processes that have weak dependencies among distant points whilst the interactions of close points are kept arbitrary. Various stability and range assumptions are imposed on the Papangelou intensity in order to prove that the resulting point process is not hyperuniform. The scope of our results covers many frequently used models, including Gibbs point processes with a superstable, lower-regular, integrable pair potential, as well as the Widom–Rowlinson model with random radii and Gibbs point processes with interactions based on Voronoi tessellations and nearest-neighbour graphs.

Identifying core IoT technologies using ARM and FCM: A comprehensive data-driven method

The internet of things (IoT) technology has garnered significant attention in recent years due to its wide-ranging applications. IoT, with its high connectivity capabilities, integrates various industrial, domestic, and agricultural devices into a smart and remotely controllable software and hardware platform. The field of IoT technology is expansive and encompasses a multitude of sub-technologies. Identifying core technologies in this domain is crucial for guiding research and development efforts by companies. Given the interrelation of these core technologies and their combination with recent decision-making approaches, network-based strategies have recently received special attention. The developed methods are based on static conditions and the assumption of stability, while in emerging technologies like IoT, the pace of changes over time is high. This leads to changes in the importance of technologies under various scenarios.In this study, in order to analyze the extracted patent data, association rule mining (ARM) algorithms were applied to identify the relationships between technologies and social network analysis was used to analyze the relationships between technologies and estimate their initial weights. Finally, fuzzy cognitive map (FCM) were used to estimate the final weights of technologies and rank them. The fcm approach allows for simultaneous modeling of both static and dynamic states of the system and, on the other hand, by calculating under various scenarios, suggests a core technology that is sustainable.The research results show that digital information transmission technologies, digital or electrical data processing, and wireless communication networks are the most important sub-technologies of Internet of things.

Is the supermultiplier stable?

AbstractSupermultiplier models, which show how autonomous demand can drive both business cycles and long‐run GDP growth, are based on a stability assumption. In this paper I look at recent efforts to justify this assumption, and argue that they are not convincing. The supermultiplier literature generally assumes that business investment reacts very slowly to changes in the state of the economy, but faster adjustment speeds are consistent with US data and can generate instability.

Almost Sure Stability of Complex-Valued Complex Networks: A Noise-Based Delayed Coupling Under Random Denial-of-Service Attacks.

This article is concerned with stability for stochastic complex-valued delayed complex networks under random denial-of-service (RDoS) attacks. Different from the existing literature on the stability of stochastic complex-valued systems that concentrate on moment stability, we investigate almost sure stability (ASS), where noise plays a stabilizing role. It is noted that, besides the vertex systems influenced by noise, the interactions among vertices are also at the mercy of noise. As a consequence, an innovative noise-based delayed coupling (NDC) in the presence of RDoS attacks is proposed first to accomplish the stability of complex-valued networks, where the RDoS attacks have a certain probability of triumphantly interfering with communications at active intervals of attackers. Namely, RDoS attacks considered are randomly launched at active periods, which is more realistic. In terms of the Lyapunov method and stochastic analysis theory, an almost sure exponential stability (ASES) criterion of the system discussed straightforwardly is developed by constructing a delay-free auxiliary system, while removing the traditional assumption of moment stability. The criterion strongly linked with topological structure, RDoS frequency, attack successful probability, and noise intensity reveals that the higher the noise intensity, the faster the convergence rate is for the stability of the network. In light of the criterion established, we present an algorithm that can be employed to analyze the tolerable attack parameters and the upper bound of the coupling delays, under the prerequisite of guaranteeing the stability of the network. Eventually, the theoretical results are applied to inertial complex-valued neural networks (ICNNs) and an illustrative example is presented to substantiate the efficiency of the theoretical works.

ProMvSD: Towards unsupervised knowledge graph anomaly detection via prior knowledge integration and multi-view semantic-driven estimation

Knowledge graphs (KGs) have found extensive applications within intelligent systems, such as information retrieval. Much of the research has predominantly focused on completing missing knowledge, with little consideration given to examining errors. Unfortunately, during customizing KGs, diverse unpredictable errors are virtually unavoidable to be introduced, and these anomalies significantly impact the performance of applications. Detecting erroneous knowledge presents a formidable challenge due to the costly acquisition of ground-truth labels. In this work, we develop an unsupervised anomaly detection framework named ProMvSD, aiming to adapt KGs of varying scales via serialization components. To overcome the insufficient contextual information provided by the topological structure, we introduce the large language model as a reasoner to extract prior knowledge from extensive pre-trained textual data, thereby enhancing the understanding of KGs. Anomalous triple may result in a larger semantic gap between the head and tail neighborhoods. To uncover latent anomalies effectively, we propose a multi-view semantic-driven model (MvSD) based on the assumptions of self-consistency and information stability. MvSD jointly estimates the suspiciousness of triples from three hyperviews: node-view semantic contradiction, triple-view semantic gap, and pathway-view semantic gap. Extensive experiments on three English benchmark KGs and a Chinese medical KG demonstrate that, for the top 1% of the most suspicious triples, we can detect real anomalies with at most 99.9% accuracy. Furthermore, ProMvSD significantly outperforms state-of-the-art representation learning baselines, achieving a 29.2% improvement in detecting all anomalies.

Numerical study of linear and nonlinear stability in double-diffusive Hadley-Prats flow through a horizontal porous layer with Soret and internal heat source effects

Numerical analysis is constructed to study the onset of the double-diffusion convection mechanism through an infinite parallel permeable channel with heat generation, mass flow and Soret impacts. Homogenous isotropic Darcy’s flow model is deployed to elucidate the porous features. The roll instabilities pertaining to longitudinal and transverse cases are examined through a linear and nonlinear stability analysis. The unit-less eigenvalue problem is constructed through linear and nonlinear stability assumptions and which is solved numerically using a fourth order Runge-Kutta scheme. The critical values of wave and thermal Rayleigh parameters are evaluated. Extended graphical and tabular visualization is presented to describe the onset of the convection mechanism. The results of this semi-numerical investigation may be useful in environmental, geothermal and chemical engineering processes. In addition, the critical R z value is also determined for a range of thermo-physical numbers. Significant modifications in the flow patterns are computed with mass flow parameter and vertical thermal and solutal Rayleigh number. With an increment in Soret (thermo-diffusive) parameter Sr the regime become more unstable. Based on the nonlinear stability analysis, an elevation in solutal Rayleigh number also induces earlier instability. The collective influence of the Lewis number and horizontal concentration parameter is observed to render the system more stable.

Breakup of planar liquid sheets injected at high speed in a quiescent gas environment

Using a combination of mean flow spatial linear stability and two-dimensional volume-of-fluid (VoF) simulations, the physics governing the instability of high-speed liquid sheets being injected into a quiescent gas environment is studied. It is found that the gas shear layer thickness $\delta _G$ plays an influential role, where for values $\delta _G/H\lesssim 1/8$ , the growth of sinuous and varicose modes is nearly indistinguishable. Here, $H$ is the liquid sheet thickness. With larger values of $\delta _G/H$ , a second peak develops in the lower wavenumber region of the dispersion relation, and becomes increasingly dominant. This second peak corresponds to a large-scale sinuous mode, and its critical wavelength $\lambda _{crit,sinuous}$ is found to scale as $\lambda _{crit,sinuous}/H = 14.26 (\delta _G/H)^{0.766}$ . This scaling behaviour collapses onto a single curve for various combinations of the liquid-based Reynolds ( $Re_L$ ) and Weber ( $We_L$ ) numbers, provided that $\delta _G/H > O({10^{-1}})$ . For the varicose modes, the shape of the dispersion relation does not change with variations in $\delta _G/H$ , and the liquid shear layer thickness has an almost negligible influence on the growth of instabilities. Two-dimensional VoF simulations are employed to examine the validity of the linear stability assumptions. These simulations also show that the dominant sinuous mode remains active as the process transitions into the nonlinear regime, and that this mode is ultimately responsible for fragmenting the sheet. Based on an energy budget analysis, the most influential contributors to the growth of the sinuous mode are the gas Reynolds shear stress and the lateral working of pressure on the gas side.

Dynamic relationship among immediate release fentanyl use and cancer incidence: A multivariate time-series analysis using vector autoregressive models

Background A substantial increase in the incidence of immediate release fentanyl (IRF) use was reported in Spain from 2012 to 2017. Purpose This study aimed to investigate the relationship dynamically with cancer incidence in order to provide empirical evidence of inappropriate use of IRF with respect to the pathology. Research design A vector autoregresive (VAR) model was constructed using data from a nationwide electronic healthcare record database in primary care in Spain (BIFAP) according to the following step procedure: (1) split data into training data for modelling and test for validation (2) assessing for time series stationarity; (3) selecting lag-length; (4) building the VAR model; (5) assessing residual autocorrelation; (6) checking stability of the VAR system; (7) evaluating Granger causality; (8) impulse response analysis and forecast error variance decomposition (9) prediction performance with validation data. Results The analysis showed a strong and linear correlation between IRF and cancer (Pearson correlation coefficient: 0.594 (95% CI: 0.420–0.726). Two VAR models, VAR (2) and VAR (11) were selected and compared. All tests performed for both models satisfied assumptions for stability, predictability and accuracy. Granger causality revealed cancer incidence is a good predictor for IRF use. VAR (2) seemed to be slightly more accurate, according to the RMSE of the test data. Conclusions This study demonstrates that using a robust and structured VAR modelling approach, is able to estimate dynamics associations, involving IRF use and cancer incidence.

DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection

SAR images play a crucial role in ship detection across diverse scenarios due to their all-day, all-weather characteristics. However, detecting SAR ship targets poses inherent challenges due to their small sizes, complex backgrounds, and dense ship scenes. Consequently, instances of missed detection and false detection are common issues. To address these challenges, we propose the DSF-Net, a novel framework specifically designed to enhance small SAR ship detection performance. Within this framework, we introduce the Pixel-wise Shuffle Attention module (PWSA) as a pivotal step to strengthen the feature extraction capability. To enhance long-range dependencies and facilitate information communication between channels, we propose a Non-Local Shuffle Attention (NLSA) module. Moreover, NLSA ensures the stability of the feature transfer structure and effectively addresses the issue of missed detection for small-sized targets. Secondly, we introduce a novel Triple Receptive Field-Spatial Pyramid Pooling (TRF-SPP) module designed to mitigate the issue of false detection in complex scenes stemming from inadequate contextual information. Lastly, we propose the R-tradeoff loss to augment the detection capability for small targets, expedite training convergence, and fortify resistance against false detection. Quantitative validation and qualitative visualization experiments are conducted to substantiate the proposed assumption of structural stability and evaluate the effectiveness of the proposed modules. On the LS-SSDDv1.0 dataset, the mAP50−95 demonstrates a remarkable improvement of 8.5% compared to the baseline model. The F1 score exhibits a notable enhancement of 6.9%, surpassing the performance of advanced target detection methods such as YOLO V8.

Moving plane method for varifolds and applications

abstract: In this paper, we introduce a version of the moving plane method that applies to potentially quite singular hypersurfaces, generalizing the classical moving plane method for smooth hypersurfaces. Loosely speaking, our version for varifolds shows that smoothness and symmetry at infinity (respectively at the boundary) can be promoted to smoothness and symmetry in the interior. The key feature, in contrast with the classical formulation of the moving plane principle, is that smoothness is a conclusion rather than an assumption. We implement our moving plane method in the setting of compactly supported varifolds with smooth boundary and in the setting of varifolds without boundary. A key ingredient is a Hopf lemma for stationary varifolds and varifolds of constant mean curvature. Our Hopf lemma provides a new tool to establish smoothness of varifolds, and works in arbitrary dimensions and without any stability assumptions. As applications of our new moving plane method, we prove varifold uniqueness results for the catenoid, spherical caps, and Delaunay surfaces that are inspired by classical uniqueness results by Schoen, Alexandrov, Meeks and Korevaar-Kusner-Solomon. We also prove a varifold version of Alexandrov's Theorem for compactly supported varifolds of constant mean curvature in hyperbolic space.

Time Series Modeling and Forecasting of Drug-Related Deaths in Iran (2014-2016).

Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. The recent policy in Iran is strongly focused on deterring drug use and replacing illicit drugs with legal ones. This study aimed to investigate drug-related deaths in Iran in 2014-2016 and forecast the death toll by 2019. In this longitudinal study, Box-Jenkins time series analysis was used to forecast drug-related deaths. To this end, monthly counts of drug-related deaths were obtained from March 2014 to March 2017. After data processing, to obtain stationary time series and examine the stability assumption with the Dickey-Fuller test, the parameters of the Autoregressive Integrated Moving Averages (ARIMA) model were determined using autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs. Based on Akaike statistics, ARIMA (0, 1, 1) was selected as the best-fit model. Moreover, the Dickey-Fuller test was used to confirm the stationarity of the time series of transformed observations. The forecasts were made for the next 36 months using the ARIMA (0,1,2) model and the same confidence intervals were applied to all months. The final extracted data were analyzed using R software, Minitab, and SPSS-23. According to the Iranian Ministry of Health and the Legal Medicine Organization, there were 8883 drug-related deaths in Iran from March 2014 to March 2017. According to the time series findings, this count had an upward trend and did not show any seasonal pattern. It was forecasted that the mean drug-related mortality rate in Iran would be 245.8 cases per month until 2019. This study showed a rising trend in drug-related mortality rates during the study period, and the modeling process for forecasting suggested this trend would continue until 2019 if proper interventions were not instituted.

Computing Homotopy Classes for Diagrams

We present an algorithm that, given finite diagrams of simplicial sets X, A, Y, i.e., functors {mathcal {I}}^textrm{op}rightarrow {textsf {s}} {textsf {Set}}, such that (X, A) is a cellular pair, dim Xle 2cdot {text {conn}}Y, {text {conn}}Yge 1, computes the set [X,Y]^A of homotopy classes of maps of diagrams ell :Xrightarrow Y extending a given f:Arightarrow Y. For fixed n=dim X, the running time of the algorithm is polynomial. When the stability condition is dropped, the problem is known to be undecidable. Using Elmendorf’s theorem, we deduce an algorithm that, given finite simplicial sets X, A, Y with an action of a finite group G, computes the set [X,Y]^A_G of homotopy classes of equivariant maps ell :Xrightarrow Y extending a given equivariant map f:Arightarrow Y under the stability assumption dim X^Hle 2cdot {text {conn}}Y^H and {text {conn}}Y^Hge 1, for all subgroups Hle G. Again, for fixed n=dim X, the algorithm runs in polynomial time. We further apply our results to Tverberg-type problem in computational topology: Given a k-dimensional simplicial complex K, is there a map Krightarrow {mathbb {R}}^d without r-tuple intersection points? In the metastable range of dimensions, rdge (r+1) k+3, the problem is shown algorithmically decidable in polynomial time when k, d, and r are fixed.

Comparison of different hydrological and stability assumptions for physically-based modeling of shallow landslides

Physically-based models are reliable techniques for landslide susceptibility assessment, therefore, several models have been proposed in the literature. In this study, two different hydrological models (FSLAM and TRIGRS) and two slope stability calculation methods (infinite slope and limit equilibrium method with spherical failure surface in SCOOPS3D) were compared. The rainfall-triggered shallow landslides that occurred in Kaptanpaşa, Rize (Turkey) in 2017 were selected as a case study. The main focus is investigating the impact of landslide morphology on the success of the slope stability models. For this purpose, landslides in the inventory were grouped according to their morphological characteristics, such as the landslide depth, length, width and their ratios, and their compatibility with model assumptions (e.g. the infinite slope assumption). Then, the model performances in different landslide types were evaluated separately. To calibrate the model parameters a new time-saving, semi-automated calibration strategy, based on a sensitivity analysis, is proposed. Concretely, stability parameters c′∅′Z were calibrated via a MATLAB code by considering the area under the curve (AUC) and extreme conditions: saturated and initial conditions. Overall, SCOOPS3D is deduced to have better performance than infinite slope analysis. However, the accuracy of SCOOPS3D is revealed to decrease for landslides with a high ratio of length/depth and width/depth. From the comparison of hydrological models, FSLAM yielded more plausible results than TRIGRS with the available data. It is demonstrated that the morphological features of the landslides significantly influence the performance of the physically-based stability models. Furthermore, it is highlighted that the resolution of the Digital Elevation Model in comparison to the width and length of the landslides in an inventory can be influential in the performance of the physical model. 10 m resolution of DEM is deemed to be suitable for the study area judging the predictive performance in small landslides.

Least squares solvers for ill-posed PDEs that are conditionally stable

This paper is concerned with the design and analysis of least squares solvers for ill-posed PDEs that are conditionally stable. The norms and the regularization term used in the least squares functional are determined by the ingredients of the conditional stability assumption. We are then able to establish a general error bound that, in view of the conditional stability assumption, is qualitatively the best possible, without assuming consistent data. The price for these advantages is to handle dual norms which reduces to verifying suitable inf-sup stability. This, in turn, is done by constructing appropriate Fortin projectors for all sample scenarios. The theoretical findings are illustrated by numerical experiments.

Do bureaucratic appointees change their minds? Preference stability at the NLRB

ObjectivesQuestions of attitude stability for appointed decisionmakers are vital to our understanding of democracy. I test the assumption of bureaucratic preference stability that is widespread in formal and empirical analyses of the U.S. executive branch.MethodsThe study presents a qualitative analysis of quantitative voting data for members of the National Labor Relations Board from 1948 to 1988ResultsAnalysis reveals a few clear cases of permanent and important preference change and some evidence of change at the very beginning or very end of a few board members' careers. But overall there is remarkable stability in the relative levels of support for labor exhibited by board members as their careers unfold.ConclusionsWith some notable exceptions, an independent regulatory board member's revealed preferences tend to remain stable and consistent with the expectations that any observer would have had at the time of their initial appointment.

Estimation of global ocean surface winds blending reanalysis, satellite and buoy datasets

Data derived from reanalysis projects and satellites are often used to increase the ocean surface wind data's spatiotemporal resolution available at buoy stations. All available datasets present variable accuracies and different performances on wind estimations due to: (i) the proximity to the coast; (ii) the wind strength; (iii) the geographical location; and (iv) the assumption of near-neutral atmospheric stability. An assessment of the effect of these aspects on estimating winds was carried out. To partly reduce the observed inaccuracies, a methodology is proposed to correct available spatiotemporal gridded datasets of ocean surface winds. It resulted in a hybrid model blending reanalysis and satellite, as usual, but additionally enriched with information from buoy measurements. This model shows potential to correct reanalysis datasets even for cases when reanalysis outperforms satellite wind accuracy. This model led to accuracy improvements ranging from 9% to 161% compared with wind estimations from newer and older generations of reanalysis models. The blended model was calibrated and validated between 2011 and 2016 using observations at 155 ocean-moored buoys located in 6 oceanic regions. The estimated wind fields presented the highest accuracy for mid-to-high winds (5–20 m/s) with an overall Mean Absolute Error (MAE) of 0.96 m/s and 11.6° for wind speed and wind direction, respectively.