Order Selection Research Articles

Nonlinear Model Order Selection: A GMM Clustering Approach Based on a Genetic Version of EM Algorithm

Choosing an accurate model order is one of the important sections in system identification. Traditionally, the model order selection of a nonlinear system depends on a predetermined model. However, it requires excess calculation and is impossible to get rid of the trouble of the structural design of the model, once the specific model was determined. A false nearest neighbor (FNN) algorithm that only relies on input-output data to estimate the model order is proposed here. Due to the FNN algorithm is sensitive to its own threshold which is a crucial constant for evaluating the model structure, Gaussian mixture model (GMM) clustering based on a genetic version of the expectation-maximization (EM) algorithm and minimum description length (MDL) criterion is developed in this paper, where the order can be determined without relying on a specific model. The GMM clustering is proposed to calculate the threshold of the FNN. Then, the genetic algorithm and MDL criteria are embedded to optimize the calculation of the EM algorithm as reduce the influence of initial values and not prone to fall into local extreme values as well. Three examples are given here to indicate the superiority of this technique: simulation of a strongly nonlinear system, isothermal polymerization process, and Van der Vusse reaction in relevant reference. Finally, some typical modeling methods are conducted to confirm the validity of this approach.

An exposition of least square Monte Carlo approach for real options valuation

The least square Monte Carlo simulation (LSM) approach is a state-of-the-art approach built upon approximate dynamic programming for the selection of single or multiple exercise options, and it has been extensively used for sequential decision-making and real options valuation. Although it has been broadly discussed and employed in many real-world applications, relatively less attention has been given to some of its implementation details. This paper aims to contribute to an improved understanding of sequential decision-making and real options valuation using the LSM algorithm. In this paper, we illustrate and argue the impact of only including the in-the-money (ITM) paths and the choice of regression functions for a specific example. A simple oil production problem with two common embedded options (shrink versus expands) has been utilized for the analysis.The analyses conducted in this article for the considered decision situation confirm that when at least one of the options (decision alternatives) is relevant to the out-of-the-money (OOTM) paths, it is crucial to consider all the paths to benefit from all the flexibilities. Furthermore, the choice of regression function is shown to have minor effect on the optimal strategies and the expected project value as long as the options are deeply ITM or OOTM. However, when the options are poorly ITM or OOTM, we might reach a different solution than what would have been achieved had we used an exact dynamic programming approach. In addition, excluding OOTM paths from the analysis adversely affects the option valuation part of the LSM for the current application, leading to lower project values. In contrast, the impact of path exclusion on regression performance is less emphasized. This work also verifies the robustness of the LSM approach for the selection of the polynomial order used for tuning the regression function. While some of the findings herein are problem-specific, a similar methodology can be used to evaluate the implementation details and refine problem settings in other real options problems.

Estimation and order selection for multivariate exponential power mixture models

Finite mixture model is a promising statistical model in investigating the heterogeneity of population. For multivariate non-Gaussian density estimation and approximation, in this paper, we consider to use multivariate exponential power mixture models. We propose the penalized-likelihood method with a generalized EM algorithm to estimate locations, scale matrices, shape parameters, and mixing probabilities. Order selection is achieved simultaneously. Properties of the estimated order have been derived. Although we mainly focus on the unconstrained scale matrix type in multivariate exponential power mixture models, three more parsimonious types of scale matrix have also been considered. Performance based on simulation and real data analysis implies the parsimony of the exponential power mixture models, and verifies the consistency of order selection.

Non-small-cell lung cancer prediction using radiomic features and machine learning methods

One of the primary causes of deaths related to cancer all over the world is Lung cancer. The history of the patient and his histological classification in terms of lung cancer has provided critical information regarding the characteristics of tissues and anatomical locations. There are many different studies that have depicted the radiomic features and their power of prediction in the detection of lung cancer. But its quantitative size in terms of data is large and has been resulting in major challenges in the algorithms of classification. In order to overcome this, symbolic approach to data analysis which employs many different quantitative data is proposed. The work further investigates different techniques of feature selection in order to predict the histologic subtypes of lung cancer by using either symbolic data or the radiomic features. These features have been extracted by using a gray-level co-occurrence matrix (GLCM), the Gabor filter and the fusion that was achieved by making use of concatenation once there is a normalization of the Z score. The results of the experiment have proved that the proposed method had a better performance compared to the other methods.

Modified ant colony optimization with selecting and elimination customer and re-initialization for VRPTW

Vehicle routing problem with time windows (VRPTW) is a special kind of vehicle routing with adding time windows constraints and has a variety of applications in logistics. Many researchers have attacked the VRPTW by approximate solutions. Ant colony optimization (ACO) is a classical method to solve the VRPTW problem but the constraints of VRPTW are not used to consider customer selection. Most ACO-based optimization algorithms can suffer from the complexity of the VRPTW such as trapping in local optimum. In this paper, we present a novel ACO-based optimization method for VRPTW by using customer selection in order to decrease or solve the inefficiency of the customer selection of the ACO process. Moreover, we enhance performance searching of ACO in order to eliminate these small routes from the ACO process. Finally, we proposed the re-initialization technique in order to decrease or solve trapping in local optimum. Experiments conducted on fifty-six maps dataset have shown that the proposed method achieves encouraging performance compared to other ACOs.

Modeling Grain Yield in China with Uncertain Time Series Analysis

Grain yield is an important factor related to the stable operation of a country’s social economy. This paper employs uncertain time series analysis (UTSA) to model rice grain yield in China, including order selection, parameter estimation, residual analysis, uncertain hypothesis test and forecast. By analyzing the characteristics of the residual plot, the reason of using uncertain time series analysis instead of probabilistic time series analysis is explained.

Subaxillary Replacement Flap Compared with the Round Block Displacement Technique in Oncoplastic Breast Conserving Surgery: Functional Outcomes of a Feasible One Stage Reconstruction.

For selected women diagnosed with breast cancer (BC), partial reconstructive techniques involve displacement or replacement procedures to improve cosmesis without compromising oncological safety. This study aims to evaluate the surgical outcomes of the round block (RB) compared with the subaxillary flap (SF) technique for patients with upper outer tumor. Thirty-three patients treated with oncoplastic conserving surgery (15 RB and 18 SF) were enrolled in this retrospective study. After carrying out a comparison of baseline characteristics, all cases were recruited for postoperative evaluation of oncological and cosmetic parameters. Moreover, we investigated several scoring combinations to check whether they could discriminate surgeon and patient satisfaction according to different functional results. Median age (p < 0.05), average tumor size (p > 0.05), estimated resection volume (p > 0.05), and nodal involvement (p > 0.05) were slightly higher in the SF group. A greater frequency of DCIS (p < 0.05) in the RB series correlated with reintervention for positive margins (p > 0.001). At a mean follow-up of 19 months, no locoregional recurrences were recorded and early and late complications were comparable (p > 0.05). The overall satisfaction with cosmesis was characterized by similar proportions of good results (p > 0.05), with some details more related to each procedure. The proposed techniques represent effective solutions for reshaping that follows upper outer wide excision, achieving comparable complication rates, low reinterventions, and good aesthetic results in relation to technical and social functioning evaluations. However, it is crucial to establish a careful patient selection in order to manage correct surgical planning while predicting any potential sequelae or complication.

How anthropogenic factors influence the dissolved oxygen in surface water over three decades in eastern China?

The aquatic environment, linked to the sustainable development of human existence and ecological environment, is influenced comprehensively by anthropogenic and natural activities. In light of the continuously low concentration of dissolved oxygen (DO) in surface water in plain river networks and the phenomenon of delay in the improvement of surface water quality, this research aims to introduce a method that may be utilized in identifying the critical driving forces of DO in surface water and their lagging characteristics, which will contribute to the assessment and adjustment of water quality drivers and/or policies. The research analyzes a typical small watershed in a river network region of the Yangtze River Delta plain as the study area, collecting 35-year (1986–2020) data on several water quality parameters, decades of anthropogenic activities, and two natural factors. The time series methods of vector autoregressive model, Granger causality tests, forecast error variance decompositions, and impulse response functions (hereinafter referred to as VAR+), which are rarely applied in related research, were employed in this study and proved helpful for screening out pivotal drivers and capturing the lagging responses of DO level to driving forces at each lagged time. Results show that there exists a fluctuating drop in DO level in surface water from 1986 to 2008 and a steady climb from 2008 to 2020, with the lowest DO level being present in 2008. The impulsive perturbations of phosphate fertilizer consumption (PFC), motor vessel number, and precipitation minimally increase DO concentration, while the impulsive perturbation of gross domestic product (GDP) causes the sharpest drop in DO level. With these perturbations, the driving force of PFC persists for approximately seven years, and the driving forces of water temperature, permanent population, and GDP persist for only five years. Future research could be conducted with spatial hysteresis, selection of lag order and variable quantity within the model, as well as intermediate variables between drivers and DO level for exploring driving pathways and mechanisms.

Lost but not blown away: How do losers of party leadership contests react?

Many Western parties have opened up the process of leadership selection in order to increase the party’s attractiveness, but negative reactions of losers in such contests might undermine these efforts. It has extensively been documented that losers of elections or referendums become less supportive of the political system, but the question is whether such a winner-loser gap also occurs in the context of intra-party elections. We examine unique panel data collected before and after the leadership elections of the Flemish Christian-Democratic Party and Liberal-Democratic Party and investigate the difference in change in attitudes and behavior of party members who voted for the losing candidates and those who voted for the winner. Contrary to earlier research on candidate selection, we find that only decision acceptance differs between winners and losers, while there is no gap in support for the electoral process, party membership satisfaction, and members’ activity within the party.

Transcriptome and Regional Association Analyses Reveal the Effects of Oleosin Genes on the Accumulation of Oil Content in Brassica napus.

Rapeseed stores lipids in the form of oil bodies. Oil bodies in the seeds of higher plants are surrounded by oleosins. Adjusting oleosin protein levels can prevent the fusion of oil bodies and maintain oil body size during seed development. However, oil contents are affected by many factors, and studies on the complex molecular regulatory mechanisms underlying the variations in seed oil contents of B. napus are limited. In this study, a total of 53 BnOLEO (B. napus oleosin) genes were identified in the genome of B. napus through a genome-wide analysis. The promoter sequences of oleosin genes consisted of various light-, hormone-, and stress-related cis-acting elements, along with transcription factor (TF) binding sites, for 25 TF families in 53 BnOLEO genes. The differentially expressed oleosin genes between two high- and two low-oil-content accessions were explored. BnOLEO3-C09, BnOLEO4-A02, BnOLEO4-A09, BnOLEO2-C04, BnOLEO1-C01, and BnOLEO7-A03 showed higher expressions in the high-oil-content accessions than in low-oil-content accessions, at 25, 35, and 45 days after pollination (DAP) in two different environments. A regional association analysis of 50 re-sequenced rapeseed accessions was used to further analyze these six BnOLEO genes, and it revealed that the nucleotide variations in the BnOLEO1-C01 and BnOLEO7-A03 gene regions were related to the phenotypic variations in seed oil content. Moreover, a co-expression network analysis revealed that the BnOLEO genes were directly linked to lipid/fatty acid metabolism, TF, lipid transport, and carbohydrate genes, thus forming a molecular network involved in seed oil accumulation. These favorable haplotypes can be utilized in molecular marker-assisted selection in order to further improve seed oil contents in rapeseed.

Fault diagnosis of HVAC AHUs based on a BP-MTN classifier

HVAC Air Conditioning Units (AHU) adjust and deliver air to rooms through fans and ducts to meet human comfort needs. Fault diagnosis of AHUs helps to reduce energy consumption and meet human comfort needs, and thus is significant. As a network, the Multi-dimensional Taylor Network (MTN) approximates a nonlinear function with a polynomial network. It is suitable for embedding in a control system since it has a much simpler structure than a neural network while having high accuracy. However, the traditional MTN is usually used for model fitting but not for classification. To solve this problem, a Back Propagation Multi-dimensional Taylor Network (BP-MTN) classifier is proposed in this paper to diagnose the faults of AHUs. This BP-MTN classifier has three main features: 1) a fully connected layer is added after the output layer of the traditional MTN to solve the mismatch between the dimensionality of fault features and the number of categories; 2) the softmax layer is added in the traditional MTN to realize the classification; 3) ReLU function is added in the traditional MTN to improve the classification accuracy and reduce the model complexity; 4) the Back-Propagation (BP) algorithm based on the small batch gradient descent algorithm is used to train the BP-MTN classifier rather than the nonlinear least square used in the traditional MTN. Additionally, this paper explores the selection of polynomial orders and activation functions of BP-MTN through extensive experiments. The experimental results show that the BP-MTN can achieve the accurate classification of AHU faults effectively.

The role of forensic investigation in systemic risk enquiry: Reflections from case studies of disasters in Istanbul, Kathmandu, Nairobi, and Quito

The systemic nature of risk is increasingly acknowledged within scholarship, policy and practice relating to disaster management. However, a number of conceptual and methodological challenges arise in advancing empirical inquiry in this regard. These challenges relate to how the boundaries of the system are determined both spatially and temporally, how expertise from across disciplines is integrated to allow for consideration of institutional and broad socio-economic drivers of risk in addition to physical drivers, and, crucially, how causality operates within system complexity. The potential of forensic investigations of disasters that typically deploy in-depth case studies to overcome these obstacles is evaluated on the basis of causal mapping with experts from a range of disciplinary backgrounds in Istanbul, Kathmandu, Nairobi and Quito. It is found that such investigations can serve to interrogate the fundamental value of any given system and its spatial and temporal bounds, generate collective mental models of the system from which risk emerges, and drive reflection on its root causes. However, it is critical that forensic investigation approaches carefully consider participant selection and facilitation in order to effectively operationalise the systemic risk concept in complementarity with other approaches.

A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data

Subgraph matching aims to find similar substructures in a single graph according to a given query graph and is known as a basic query for graph data management. There exist many categories of subgraph matching solutions. Subgraph isomorphism, which is thought of an NP-complete problem, is an initial solution for the subgraph matching task. To speed up the procedure, graph simulation has been presented to match subgraphs with a polynomial complexity of time. Unfortunately, graph simulation usually loses topologies of matched subgraphs because of its loose restrictions. In this paper, we propose an approximation approach named kSGM (top- k S ubraph G raph M atching) for subgraph matching based on twig patterns. First, we transform query graphs into twig patterns and match candidate substructures in graph data. Second, we present an optimized join strategy along with top-k mechanism, including join order selection based on cost evaluation and optimized pruning based on maximum/minimum possible score. Finally, we design experiments on real-life and synthetic graph data to evaluate the performance of our work. The results show that our proposed kSGM obviously reduces the time complexity and guarantee the correctness for answering the queries of subgraph matching compared to existing algorithms.

Order selection with confidence for finite mixture models

The determination of the number of mixture components (the order) of a finite mixture model has been an enduring problem in statistical inference. We prove that the closed testing principle leads to a sequential testing procedure (STP) that allows for confidence statements to be made regarding the order of a finite mixture model. We construct finite sample tests, via data splitting and data swapping, for use in the STP, and we prove that such tests are consistent against fixed alternatives. Simulation studies and real data examples are used to demonstrate the performance of the finite sample tests-based STP, yielding practical recommendations of their use as confidence estimators in combination with point estimates such as the Akaike information or Bayesian information criteria. In addition, we demonstrate that a modification of the STP yields a method that consistently selects the order of a finite mixture model, in the asymptotic sense. Our STP is not only applicable for order selection of finite mixture models, but is also useful for making confidence statements regarding any sequence of nested models.

Evidence of potential synergy between aquaculture and offshore renewable energy

Worldwide increased demand for offshore renewable energy (ORE) industries and aquaculture requires developing efficient tools to optimize the use of the offshore space, reducing anthropic pressure. The synergetic development of marine renewable energy infrastructure with mariculture has been hypothesized as a way to reduce costs through shared infrastructure. In the Irish Sea, blue mussels (Mytilus edulis L.) represent 40 - 50 % of the total gross turnover of Welsh shellfish industries and the industry has been operating sustainably for over 50 years in North Wales. However, the region is also attractive for tidal energy projects, with strong tidal currents (&gt; 2m/s) occurring, and offshore wind farms, with shallow waters (approx. 50 m) and consistent winds. In this context, it is of scientific and economic interest to study the potential impact of ORE on shellfish larvae recruitment. A numerical approach has been developed using an Eulerian hydrodynamic model coupled with a Lagrangian particle tracking model, which allowed the simulation of tidal currents, wind-driven currents and larval dispersal. Results show: 1) interannual variability of density distribution of larvae; and 2) strong connectivity between commercial shellfish beds and ORE sites. This study shows the importance of ORE site selection in order to: 1) reduce biofouling on ORE infrastructures and 2) develop multi-use platforms at sea to combine needs for ORE and for mariculture.

Эффективность применения хелатных микроудобрений на формирование зелёной биомассы зернового сорго возделываемого в засушливых условиях Саратовской области

During the research period, morphological characteristics were measured in grain sorghum varieties of the FGBNU RosNIISK "Rossorgo" selection in order to identify the effect of chelated microfertilizers. The complex of agronomic features included the following indicators of grain sorghum productivity: plant height, length of the flag and largest leaf, biomass yield. According to the results of the research, the influence of chelated microfertilizers on the morphological characteristics of grain sorghum cultivated in the Saratov Volga region was revealed. Leaf treatment of vegetative grain sorghum plants had a significant effect on the height of grain sorghum plants from 3.3 to 5.2%, and also contributed to a significant increase in biomass by 17.3-27.6%. Keywords: GRAIN SORGO, BIOMASS YIELD, PLANT HEIGHT, FLAG AND LARGE LEAF LENGTH, MICROFERTILIZERS, MORPHOMETRIC FEATURES, VARIETY

Collaborative production and predictive maintenance scheduling for flexible flow shop with stochastic interruptions and monitoring data

Flexible flow shop problem (FFSP) has been extensively researched in recent years. However, machine deterioration and stochastic production interruptions noticeably affect the regular job process in practice, incurring great cost. It is significant to incorporate the condition-based predictive maintenance scheduling and the production modification for interruptions with traditional FFSP. Nevertheless, this issue is rarely investigated in the existing FFSP studies. To solve the FFSP with maintenance and stochastic interruptions (FFSP-MSI), this paper proposes a collaborative optimization policy of production and maintenance (COPPM) by considering stochastic interruptions and monitoring data utilization for total cost reduction. For maintenance scheduling, a real-time prognosis updating method is leveraged based on monitoring data to capture the individual machine degradations. Assisted by the prognosis output, the optimal maintenance intervals are dynamically derived through a cost rate model. For production optimization, a variable neighborhood search (VNS) algorithm is presented to obtain the nearly- optimal initial production plan and the production modification for stochastic interruptions caused by machine failures and random jobs. This proposed COPPM comprehensively determines the cost-effective maintenance cycle, job order and machine selection for each job. Computational experiments of the wind turbine blade process are presented to certify the economic effectiveness of COPPM. Compared with the traditional production and maintenance scheduling method, the COPPM achieves 13.68 % total cost reduction in average under various problem scales.

Angle-incremental range estimation for FDA-MIMO radar via hybrid sparse learning

In this paper, a target parameter estimation problem is addressed for the recently emerging frequency diverse array multiple-input multiple-output (FDA-MIMO) radar system, utilizing sparse learning. The scene is modeled as a two dimensional (2D) angle-incremental range grid. To solve the resulting sparse problem, the recently proposed user-parameter free algorithms including block sparse learning via iterative minimization (BSLIM), iterative adaptive approach (IAA), sparse iterative covariance-based estimation (SPICE), likelihood-based estimation of sparse parameters (LIKES), and orthogonal matching pursuit (OMP) are applied which achieve excellent parameter estimation performance. However, these algorithms do not exhibit a uniform performance under different scenarios such as the number of available snapshots and the number of present targets. In this paper, we propose a hybrid estimation approach which utilizes the model order selection (MOS), and automatically selects the best algorithm for the scenario under test. The performance analyses show that the proposed hybrid algorithm presents higher accuracy in terms of root mean square error (RMSE), as compared with the discussed sparse algorithms, and converges to the Cramér-Rao lower bound (CRLB).

PASS: power allocation and SIC order selection of cache-aided NOMA in vehicular networks

PASS: power allocation and SIC order selection of cache-aided NOMA in vehicular networks

Pengaruh variasi bevel pada proses pengelasan SMAW terhadap kekuatan tarik material

The strength of welded joints is influenced by a wide variety of factors. One of them is due to structural changes as a result of the heating process. In order for the connection between the two metal parts to have good quality, it is necessary to have a proper welding and the connection and the shape of the weld bevel are in accordance with the use of the results of the weld. Parameters in Shielded Metal Arc Welding (SMAW) welding include current strength, voltage, electrical polarity, and the angle of the joint used. These parameters make the basis for the right selection in order to get a good quality or connection quality. The purpose of this study was to determine the effect of bevel variations on the tensile strength test of the SMAW welding process connection on medium carbon steel material. The welding process is carried out using RB 26 type electrodes using a current of 100 A at 1 G welding position, welding is carried out on the material using V, I and double V bevel with 1 specimen per bevel. Based on the research that has been done, it can be concluded that the type of joint bevel has an important influence on the results of the weld. From the variation of the bevel that has the highest tensile strength value, it is V bevel rather than double V bevel and 1 bevel. Due to the variation of V bevel has a small distortion angle compared to double V bevel and I bevel. Optimal electric welding results depend on the welding process technique.