Order Selection Research Articles

Design of active disturbance rejection controller for the system with unknown model using Laguerre functions

This paper concentrates on the design problem of linear active disturbance rejection control (LADRC) for uncertain systems, whose order and parameters are unknown. In general, LADRC has diversified structural forms due to different order and parameters selection. To clarify the prerequisites for constructing the LADRC strategy, a Laguerre-based LADRC (LLADRC) method is proposed, which provides controller design guidance for systems with unknown models by capturing the impulse response. Then, intensive attention is focused on establishing internal stability conditions of the feedback system and parameter tuning rules of the prescribed controller. To gain a better understanding, the internal correlation between LLADRC and standard LADRC of the same order is analyzed. It is shown that observer and controller gains of the standard LADRC can be tuned via a workable LLADRC to achieve better control performance. Finally, a numerical example and an application design of DC motor speed control are studied, both of which demonstrate the feasibility and validity of the proposed method.

Hybrid data mining and data-driven algorithms for a green logistics transportation network in the post-COVID era: A case study in the USA

This study examines the problem of item allocation in a post-COVID environment with various products and a large customer base. The number of customers has increased due to the rise of internet access and the growing willingness to shop online. Problems such as the timely delivery of goods or services, the selection and destination of orders in decentralized warehouses, and the allocation of warehouses to customers are difficult to overcome with a large variety of items and many customers. It has been proposed that mathematical modeling in combination with meta-heuristic solution techniques solve these problems. However, solving mathematical models is very time-consuming and labor-intensive because there are many different location situations. Due to computing power and memory capacity advances, researchers have been looking at data-driven solutions to these problems. This study aims to tackle the diversity of commodities and the number of consumers in the post-COVID era by proposing a hybrid data-driven approach that combines data mining and mathematical modeling to solve mathematical location models with high accuracy in less time. This paper was implemented based on data from real cases in the USA.

Estimation of the domain of attraction for continuous-time saturated positive polynomial fuzzy systems based on novel analysis and convexification strategies

In this paper, the domain of attraction (DOA) of the continuous-time positive polynomial fuzzy systems subject to input saturation is estimated by using the level set of the linear copositive Lyapunov function. To relax the estimation of DOA, the restriction on the level set is removed by embedding the expression of the level set into the stability conditions and positivity conditions. Referring to the nonconvex terms caused by above novel analysis strategy, some polynomial inequality lemmas are proposed to handle them; the nonconvex terms caused by imperfect premise matching (IPM) nonlinear membership functions are dealt with by sector nonlinear methods and advanced Chebyshev membership-function-dependent (MFD) methods. In this advanced MFD method, the state space segmentation and polynomial order selection of the Chebyshev approximation method are improved based on breakpoints of the first derivative and curvature, respectively, which is helpful to reduce the conservatism and computational burden of the result. Thus, this advanced Chebyshev MFD method not only optimizes the convexification strategy, but also can further be extended to estimate the DOA when it is used to introduce the membership functions information for convex stability and positivity conditions. Finally, a numerical example and the lipoprotein metabolism and potassium ion transfer nonlinear model are presented to validate the effectiveness and feasibility of the aforementioned analysis and convexification strategies in the expansion of DOA estimation.

Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification

People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted. An optimization-based feature selection approach (OBFSA) is incorporated between feature selection and tumour classification in order to reduce the dimension of this retrieved feature. Finally, classification is performed using the Sparse Bayesian extreme learning machine (SBELM) classifier. The execution process of this proposed methodology takes an MRI image from the free accessible source. By computing and detecting four different parameters, the experimental analysis of the proposed approach displays the accuracy, specificity, and sensitivity values. This model can assist us in quickly diagnosing brain tumours, potentially saving the lives of patients.

Rumor Forwarding Prediction Model on Weibo Based on Uncertain Time Series

The rapid spread of rumors on social media is mainly caused by individual retweets. This paper applies uncertain time series analysis to analyze a rumor retweeting behavior on Weibo. First, the rumor forwarding is modeled using uncertain time series, including order selection, parameter estimation, residual analysis, uncertain hypothesis test and forecast and the validity of using uncertain time series analysis is further supported by analyzing the characteristics of the residual plot. The experimental results show that the uncertain time series analysis can better predict the next stage of rumor forwarding. The results of the study have important practical significance for rumor management and the management of social media information dissemination.

Electric load prediction using SARIMAX

ABSTRACT Electricity demand variation is one of the prevalent phenomena in Nepal. This variation can cause excess electricity during low-demand periods and a shortage during high-demand periods. An early prediction of electricity demand allows for levelling such fluctuations by introducing dynamic tariffs based on varying demands. This study aims to use Python’s time series models to make predictions about electrical demands and propose a dynamic tariff system. The dataset of Panama City’s hourly electrical load is brought into use. Two models, namely the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Factors (SARIMAX) and the BATS model, are deemed to be the two best time series models. The selection of optimal seasonal and non-seasonal orders in SARIMAX is made by comparing Bayes Information Criterion (BIC). For the SARIMAX model, a daily dataset has been taken by summing the hourly data for each day, whereas for the BATS model, hourly data for the year 2018 has been used. The SARIMAX model resulted in the best fit with the evaluation metrics values: Root Mean Square Error (RMSE) of 975,336.88, Mean Average error (MAE) of 739.09, and R2 score of 0.75. However, SARIMAX’s limitation is that the use of daily data does not allow dynamic tariff prediction, which is possible with the BATS model.

Assessment of cognition: dementia and delirium : In consideration of the AWMF guidelines 038-013 and 084-002LG

Cognitive disorders are multifaceted and the range of neuropsychological instruments is correspondingly extensive; however, most examiners have to limit themselves to asmall selection in order to master them safely. In geriatric patients the various forms and stages of dementia dominate. Delirium must be distinguished from these as an acute life-threatening event. The personal and external medical history as well as clinical observation are the first steps in the assessment of cognition; the selection of test procedures is graduated and bears the patient's benefit in mind. When compiling atoolbox for use in one's own field of work, in addition to instruments focusing on different degrees of severity, alternatives should also be considered for use in the presence of comorbidities that reduce the validity (visual, hearing and fine motor disorders) and the examination situation should be taken into account.

Penalized composite likelihood estimation for hidden Markov models with unknown number of states

Estimating hidden Markov models (HMMs) with unknown number of states is a challenging task. In this paper, we propose a new penalized composite likelihood approach for simultaneously estimating both the number of states and the parameters in an overfitted HMM. We prove the order selection consistency and asymptotic normality of the resultant estimator. Simulation studies and an application demonstrate the finite sample performance of the proposed method.

Mule deer selection of fuel reductions is restricted by site fidelity and structured by circadian and seasonal patterns

Forests in the western US have undergone a profound transformation over the last 100 years due to chronic fire suppression and a cycle of extensive timber harvest followed by little silvicultural activity. Forested landscapes in this region are now dominated by intermediate age, denser stands of coniferous trees that reduce transmission of light and precipitation to the understory, with potential consequences for ungulate nutrition. We used a 20-year dataset of mule deer locations (560 animal-years of data) to evaluate long-term patterns of selection of forest stands that had been thinned and burned compared to untreated reference stands. We evaluated within home-range (3rd order) selection at 3 temporal scales (hourly, daily, annually) and landscape-scale (2nd order) use annually. We found that annual selection of treated stands during summer generally increased with two peaks, once immediately after treatments were applied and again 15 years later. Mule deer also showed strong seasonal and temporal patterns in selection of treated forest stands. Deer selected treated stands at night in July and August immediately after treatments. After 4–6 years, mule deer selected treated stands both during the day and at night in May, July, and August, but with stronger selection at night. We hypothesize that preference for treated forest stands were driven by differences in forage quantity and quality among forest stands. Vegetation surveys during years of peak selection showed that treated stands had significantly higher biomass and digestible energy of neutrally selected forage and lower biomass of avoided forage species, although there was no difference in biomass of preferred forage species. A subset of female mule deer with multiple years of data did not shift home ranges to overlap with treated forest stands over time (2nd order use) but did show increasing selection over time for treated forest stands within their home ranges (3rd order). Our results show that thinning and burning of forest stands can benefit mule deer both immediately and for up to 15 years after treatment and that researchers should evaluate temporal and seasonal patterns in studies of habitat selection. Given the diverse habitat needs of mule deer, treatments would be most beneficial if they were scattered across large landscapes and staggered in time to provide a diversity of forage and cover types.

ArcMatch: high-performance subgraph matching for labeled graphs by exploiting edge domains

Consider a large labeled graph (network), denoted the target. Subgraph matching is the problem of finding all instances of a small subgraph, denoted the query, in the target graph. Unlike the majority of existing methods that are restricted to graphs with labels solely on vertices, our proposed approach, named can effectively handle graphs with labels on both vertices and edges. ntroduces an efficient new vertex/edge domain data structure filtering procedure to speed up subgraph queries. The procedure, called path-based reduction, filters initial domains by scanning them for paths up to a specified length that appear in the query graph. Additionally, ncorporates existing techniques like variable ordering and parent selection, as well as adapting the core search process, to take advantage of the information within edge domains. Experiments in real scenarios such as protein–protein interaction graphs, co-authorship networks, and email networks, show that s faster than state-of-the-art systems varying the number of distinct vertex labels over the whole target graph and query sizes.

Application of the blastomere count variations "skip value" in the embryo AI assessment

Objective: To explore the correlation between blastomere count variations "skip value" which extracted from by time-lapse technology (TLT) combined with artificial intelligence (AI) and morphological features of in vitro fertilization (IVF) embryo, and to test its feasibility in clinical applications. Methods: This study was a diagnostic experiment (AI reassessment of embryo transferred patients), a total of 6 545 embryos from 1 226 patients who underwent IVF at the Women and Children's Hospital of Chongqing Medical University from December 2020 to December 2021 were retrospectively analyzed, of which 2 869 embryos were attempted to cultured to blastocyst stage by TLT. The embryo dynamic map (EDM) was drawn by Embryo Viewer, a TLT recording software, based on embryo developmental kinetics. The self-developed AI embryo evaluation software identified and recorded the number of cleavages in real time during embryonic development, and compared with the EDM, the correlation between the skip value formed by the change of cleavage sphere counts and the outcomes of the embryos was analyzed. The correlation among skip value, morphological score of embryo, implantation rate and live birth rate were performed by Spearman and step-up logistic regression. The receiver operating characteristic (ROC) curve was selected for reporting there relationship of skip value and morphology. Finally, predicting power of skip value for implantation and live birth rate were performed by ROC analysis. Results: The total skip values extracted from the blastomere count of embryos (72 hours post-fertilization) were negatively correlated with abnormal cleavage, blastocyst formation rate, day 3 (D3)-cell score, uneven size and fragmentation (the β values were -0.268, -0.116, -0.213, -0.159 and -0.222, respectively; all P<0.001); positively correlated with D3-cell number (β=0.034; P<0.001); negatively correlated with blastocyst formation rate and implantation rate (OR=0.97, 95%CI: 0.93-0.99, P=0.034; OR=0.96, 95%CI: 0.93-0.98, P=0.044). The power of predicting implantation were similar between the order selection of skip values and traditional morphology criteria [area under curve (AUC): 0.679 vs 0.620]. Live birth rate were negatively correlated with female age (OR=0.91, 95%CI: 0.88-0.93; P<0.001), D3 general score (OR=0.77, 95%CI: 0.59-0.99; P=0.045) and order selection of skip values (OR=0.98, 95%CI: 0.96-0.99; P=0.038), while positively correlated with retrieved oocyte number and endometrial thickness in embryo transferred (OR=1.08, 95%CI:1.05-1.11, P<0.001; OR=1.09, 95%CI:1.06-0.12, P<0.001, respectively) from multivariate regression analysis, and the power of predicting live birth was 0.666 for AUC. Conclusions: The skip value and its order form is a systematic quantification of embryo development, correlated with embryo developmental quality and clinical outcome. It could be an addition parameter for embryo culture and selection.

Assessment of Genetic Variability and Component Relationship of Seed Yield and Its Contributing Traits in Greengram (Vigna radiata L. Wilczek)

After pigeonpea and chickpea, greengram is the third most significant pulse crop in Asia. Pulses are the primary source of protein needed to satisfy both the daily need and the daily consumption for a balanced diet. Pulses are essential to achieving the diet's protein requirements for humans. Because they are the least expensive type of protein, they are referred to as poor people's meat. It is the primary source of vital amino acids for India's vegetarian population. There are twenty genotypes of Greengram in this experiment, plus one check. Three replications of the experiment were carried out at RBD during the month of Zaid in 2023. Thirteen characters' worth of data were collected in order to investigate genetic advancement, variability, heritability, correlation, and path analysis. The majority of the traits under study have significant genetic diversity in the available germplasm, according to analysis of variance. According to the mean performance of grain yield and its constituents, CO-7 performed better than AMULYA, SML-20-10, and IPM-2-14. A detailed examination of the variability coefficients showed that there was no environmental influence on the expression of the traits under study, as evidenced by the small difference between PCV and GCV. For biological yield, number of pods per plant, and number of seed yield per plant, high to moderate estimations of GCV &amp; PCV were noted. For the seed index, plant height, harvesting index, and quantity of seeds per pod, high estimates of heritability were noted. Since they are less affected by environmental factors and have a greater potential for genetic determination, high heritability estimates can be used for genetic improvement. For the seed index, number of branches per plant, and number of clusters per plant, low estimations of genetic advance were noted. According to correlation, plant height, major branches per plant, clusters per plant, pods per plant, biological yield, and harvest index all positively and significantly correlated with grain yield per plant. Path analysis revealed that plant height, cluster size per plant, biological yield, and harvest index have a direct impact on yield at both the phenotypic and genotypic levels. These characteristics are therefore acknowledged as the effective and prospective characteristics for direct selection in order to increase Greengram productivity in the current experimental materials.

Lag order selection for long-run variance estimation in econometrics

. Estimating the long-run variance (LRV) is crucial for several econometric issues. Constructing reliable heteroskedasticity autocorrelation consistent (HAC) variance-covariance matrices and implementing efficient generalized method of moments (GMM) estimation procedures require a consistent LRV estimate. A good VARHAC estimator (HAC matrix with the spectral density at frequency zero constructed using a VAR spectral estimation) requires accurately estimating the sum of autoregressive (AR) coefficients; however, a criterion that minimizes the innovation variance does not necessarily yield the best spectral estimate. This article implements an optimal VARHAC estimator using an alternative information criterion, considering the bias in the sum of the parameters for the AR estimator of the spectral density at frequency zero.

Recreating chronic respiratory infections in vitro using physiologically relevant models.

Despite the need for effective treatments against chronic respiratory infections (often caused by pathogenic biofilms), only a few new antimicrobials have been introduced to the market in recent decades. Although different factors impede the successful advancement of antimicrobial candidates from the bench to the clinic, a major driver is the use of poorly predictive model systems in preclinical research. To bridge this translational gap, significant efforts have been made to develop physiologically relevant models capable of recapitulating the key aspects of the airway microenvironment that are known to influence infection dynamics and antimicrobial activity in vivo In this review, we provide an overview of state-of-the-art cell culture platforms and ex vivo models that have been used to model chronic (biofilm-associated) airway infections, including air-liquid interfaces, three-dimensional cultures obtained with rotating-wall vessel bioreactors, lung-on-a-chips and ex vivo pig lungs. Our focus is on highlighting the advantages of these infection models over standard (abiotic) biofilm methods by describing studies that have benefited from these platforms to investigate chronic bacterial infections and explore novel antibiofilm strategies. Furthermore, we discuss the challenges that still need to be overcome to ensure the widespread application of in vivo-like infection models in antimicrobial drug development, suggesting possible directions for future research. Bearing in mind that no single model is able to faithfully capture the full complexity of the (infected) airways, we emphasise the importance of informed model selection in order to generate clinically relevant experimental data.

System identification based on the cross Gramian

In order to make mechanical structures more reliable, research is being carried out into methods of detecting and localizing damage to structures, known as Structural Health Monitoring (SHM). One method for continuously monitoring a structure is called State Projection Estimation Error (SP2E) and was developed at the University of Applied Sciences Leipzig. This method is based on state space systems, which are generated by output-only system identification. It is state-of-the-art to create a stabilization diagram to choose the best model order. Since the monitoring process is continuous, a state space model is created every 10 minutes, and therefore it is not possible to look at a stabilization diagram. Thus, the system identification process has to be automated. A novel approach developed in the SPP 100+ research program takes place via the theory of the cross Gramian. The cross Gramian is defined by the Hankel operator, which maps the past input to the future output. This Gramian indicates the interaction between the states and the input/output of a system and can be seen as the energy capacity of a system. By comparing the power of the measurement with the identified energy capacity, the model order selection can be automated. There are limits to the existence of the cross Gramian for multiple-input, multiple-output (MIMO) systems. The system has to be square, stable and symmetric. The covariance driven system identification never provides a symmetric system. A way to create a symmetric system out of the asymmetric measurement data is presented by the use of a symmetrizer, such that the cross Gramian can always be computed. With this symmetrizer a method to estimate the system matrix A by a stable Lyapunov equation, which is classically computed by a numerically unstable pseudo inverse, is presented.

Prediction of flame transfer function and combustion instability on a partially premixed swirling combustor by the system identification and CFD methods

Combustion instability can lead to high amplitude oscillations of pressure in the combustion chamber, which would seriously affect the normal operation of the combustion system. In order to reduce the cost and time of combustion chamber design and testing, combustion instability of the partially premixed swirl combustor was predicted, by solving the Helmholtz equation coupled the flame transfer unction (FTF). In the experimental study, by changing the equivalence ratio, the pressure in the combustion chamber appeared to oscillate in different forms including combustion noise, intermittent oscillation and limit cycle oscillation, while the frequency of the self-excited oscillations was obtained to be 165 Hz at the equivalence ratio of 0.7. In Computational Fluid Dynamics (CFD) Simulations, by applying Discrete Random Binary Signal (DRBS) excitation at the inlet, the FTF has been calculated by system identification method, where the excitation results of perturbation signals with different magnitudes were studied. And the result was basically consistent with the FTF calculated by the single-frequency excitation method, which means that the system identification method is suitable for calculating the FTF of the partially premixed swirl combustor. The pulse excitation method has been proposed to determine the selection of the filtering order, and the accuracy of the method has been validated through comparing the FTF results obtained by using different filter orders for discrimination. Moreover, coupling the FTF into the Helmholtz equation, the oscillation frequency of the system was predicted as 159.7 Hz, compared with the experimentally measured oscillation frequency of 165 Hz, which verified that the model could be effectively used for the prediction of oscillation frequency in the partially premixed combustion systems.

A Qualitative Study of the Psychological Effects of Motivational Quotes

Motivational quotes wield and obtain a sense of power towards inspiring individuals to strive towards their goals, harnessing both internal and external motivations. The absence of motivational quotes tends to serve as catalysts, igniting the drive to propel one through challenges and pursue excellence. Externally, motivational quotes often encapsulate universal truths or wisdom, offering perspective and guidance from varied sources and figures. This paper explores the effectiveness of motivational quotes in medical and educational contexts, with a focus on their impact on individuals' behavior. While numerous studies have reported a generally positive attitude toward the use of motivational quotes, specific concerns have emerged. This paper has observed that motivational quotes, delivered through various media, have successfully motivated individuals to take positive actions, such as adhering to medication regimens and quitting smoking. However, some studies have highlighted potential limitations, including overexposure leading to reduced effectiveness, the contextual relevance of motivational quotes, and the distinction between external and internal motivation. This paper presents an overview of the existing research, offering insights into the mixed opinions on the use of motivational quotes for motivation. It calls for future studies to consider contextual factors, audience mindset, media selection, and quote suitability in order to enhance their efficacy.

Logic-based Benders decomposition for order acceptance and scheduling on heterogeneous factories with carbon caps

We study an integrated order acceptance and scheduling problem for heterogeneous factories with carbon caps, order-dependent processing speeds, and setup times. Three joint decisions including order selection, order assignment and order sequencing are made to maximize profits, i.e., total revenue minus total production and tardiness cost. Firstly, we develop a mixed integer programming model to simultaneously optimize the three decisions and propose several dominance rules to enhance the model. Secondly, due to the particular structure of the problem, we propose a logic-based Benders decomposition (LBBD) method that decomposes the complicated original problem into a master problem and a number of subproblems. The master problem aims to determine order acceptance and order assignment, and each subproblem is for determining order sequencing in each factory. A branch and bound algorithm is then designed to quickly solve the subproblems. The branch and check framework is implemented to accelerate the solving process of the LBBD. Finally, numerical experiments verify that the proposed dominance rules play a promising role in enhancing the model, and the results of the algorithm comparison experiments show the significant advantages of proposed LBBD algorithm combined with the branch and check framework. Sensitivity experiments reveal that carbon cap serves as a major constraint on order acceptance.

ARX-Naïve Bayes based structural damage identification

ABSTRACT The nature of damage identification is close to a pattern recognition process that classifies different damage patterns. The naïve Bayes classifier (NBC) can effectively handle multiple-classification problems by choosing patterns with high probabilities. Therefore, by absorbing the autoregressive model with exogenous inputs (the ARX model), an ARX-Naïve Bayes damage identification strategy has been proposed in which the autoregressive coefficients of the ARX model are taken as the sensitive damage feature. The classification training and test sample datasets are then built on these coefficients corresponding to various damage scenarios. The model order of an AR model is first determined for the subsequent order selection of the ARX model, whose autoregressive coefficients are further used to construct the NBC. This procedure can enhance the pattern recognition robustness to uncertainties such as measurement noises. Different damage patterns are determined by calculating the sum of logarithmic likelihoods of testing samples. The effectiveness of the proposed method has been verified against a bridge benchmark model having different damage scenarios under the noise pollution. In addition, an experimental five-story shear frame structure was adopted for validation, showing that compared with the SVM algorithm suitable for handling binary classification problems, the proposed method excels in multi-classification of damage patterns.

Development of a dedicated process simulator for the digital twin in apparel manufacturing: a case study

PurposeThe purpose of this study is to introduce a dedicated simulator to automatically generate and simulate a balanced apparel assembly line, which is critical to the digital twin concept in apparel manufacturing. Given the low automation level in apparel manufacturing, this is a first step toward the implementation of a smart factory based on cyber-physical systems.Design/methodology/approachThe mixed task assignment algorithm was implemented to automatically generate a module-based apparel assembly line in the developed simulator. To validate the developed simulator, a case study was conducted using process analysis data of technical jackets obtained from an apparel manufacturer. The case study included three scenarios: calculating the number of workers, selecting orders based on factory capacity and managing unexpected worker absences.FindingsThe developed simulator is approximately 97.2% accurate in assigning appropriate tasks to workstations using the mixed task assignment algorithm. The simulator was also found to be effective in supporting decision-making for production planning, order selection and apparel assembly line management. In addition, the module-based line generation algorithm made it easy to modify the assembly line.Originality/valueThis study contributes a novel approach to address the challenge of low automation levels in apparel manufacturing by introducing a dedicated simulator. This dedicated simulator improves the efficiency of virtual apparel assembly line generation and simulation, which distinguishes it from existing commercial simulation software.