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Increasing energy prices and market volatilities force energy intensive industries to implement energy management systems in order to ensure competitiveness. In this paper, we focus on the strategic level of energy management, specifically, on the analysis of the internal and external conditions that form the basis for strategic development. For this, we selected the case of the Austrian paper and pulp industry. We applied a hybrid method by combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process which was based on a survey of expert opinion. The results show that cost-related factors predominate. According to our analysis, the four most important factors are all directly linked to energy costs, energy efficiency, and the energy market. Experts pay little attention to environmental issues or to energy market volatilities. Apart from that, the combination of an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process promises to be a valuable tool for strategic energy management. This is especially true for energy intensive companies and/or sectors, since it enables decision makers to take strategic decisions based on a systematic understanding of the main issues. [ABSTRACT FROM AUTHOR]

The Low Earth Orbit (LEO) region is populated by tens of thousands of objects, which include not only active satellites, but also space debris which can pose a real danger to space operations, and therefore have to be monitored closely. The work presented in this paper addresses the problem of wide field LEO detection and astrometric reduction using a portable, low cost wide field system. The work will address the challenges of astrometric reduction errors for wide FOV images, which lead to the need for multiple calibrations, which pose a challenge for real time operation. We propose a mechanism for aggregating calibration data as the system runs in real time, which does not slow down the real time operation but continuously increases the accuracy of the results. The proposed mechanism can be adapted for both staring and sidereal tracking observation modes, and to all types of astrometry software or streak detection algorithms. [ABSTRACT FROM AUTHOR]

A variety of factors will affect the combination and distribution of the molds used in the production of prefabricated components on the mold table, and will produce many combination and distribution results. In order to solve such problems and quickly find the best distribution results of the molds on the mold table, this paper proposed a mold combination method based on the combination of backtracking method and improved Eclat algorithm. Firstly, it built a binary tree model based on the mold combination relationship in the historical data, searched the binary tree through the backtracking method, and obtained all theoretically feasible mold combination results. Then it pre-placed these combination results by using the improved BL positioning algorithm on the mold table with a fixed area, and screened out the mold combination results that met the actual placement conditions. It used Eclat algorithm to obtain the optimal combination set of mold adaptation from the selected combination results, determined the association rules between different types of molds,and used the rules to guide the allocation process of molds on the mold table in the new component production task. At the same time, this paper improved the Eclat algorithm by introducing the partition idea and the priori constraint idea based on probability, so as to improve its operation efficiency when dealing with large-scale data. Finally, through the simulation test of the case data in the prefabricated component manufacturing enterprise, it verified the effectiveness of the method to quickly solve the problem of mold table combination allocation. The results show that the mold combination allocation based on backtracking and DEclat algorithm method has a good effect on the utilization of the mold area, the reduction of the mold changing time and the reduction of the total completion time. [ABSTRACT FROM AUTHOR]

Traditional approaches to stereo visual simultaneous localization and mapping (SLAM) rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and line segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop-closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in three-dimensional elements, which can be exploited to infer valuable, high-level scene structures, such as planes, empty spaces, ground plane, etc. (not addressed in this paper). Our proposal has been tested with several popular datasets (such as EuRoC or KITTI), and is compared with state-of-the-art methods such as ORB-SLAM2, revealing a more robust performance in most of the experiments while still running in real time. An open-source version of the PL-SLAM C++ code has been released for the benefit of the community. [ABSTRACT FROM AUTHOR]

This paper is a more in-depth discussions for selected problems discussed in [1], which are inspired by college entrance exam practice problems from China. When initially seeing the problem during an exam, one may have no clue how the problem is created. In this paper, we begin by presenting the essential algebraic manipulation skills that Chinese high school students are expected to apply to answer the question. We then continue the discussion by exploring various scenarios that utilize technological tools. We shall see many unexpected surprising outcomes. More importantly, we summarize how a problem can be extended to a more general setting whenever is possible. [ABSTRACT FROM AUTHOR]

Abstract Sine Cosine Algorithm is a recently developed algorithm based on the characteristics of sine and cosine trigonometric functions, to solve global optimization problems. This paper introduces a novel improved version of sine cosine algorithm, which enhances the exploitation ability of solutions and reduces the overflow of diversity present in the search equations of classical SCA. The proposed algorithm is named as ISCA. The key feature in the proposed algorithm is the hybridization of exploitation skills of crossover with personal best state of individual solutions and integration of self-learning and global search mechanisms. To evaluate these skills in ISCA, a classical set of well-known benchmark problems, standard IEEE CEC 2014 benchmark test and a recent set of benchmark problems, IEEE CEC 2017 have been taken. Several performance metrics (such as convergence, statistical test, performance index), employed on ISCA, ensure the robustness and efficiency of the algorithm. In the paper, the proposed algorithm ISCA is also used to solve five well-known engineering optimization problems. At the end of the paper, the proposed algorithm is also used for multilevel thresholding in image segmentation. The numerical experiments and analysis demonstrate that the proposed algorithm (ISCA) can be highly effective in solving real-life optimization problems. Highlights • A new method called ISCA is proposed for global optimization problems. • The ISCA improves the SCA using crossover and personal best memory of agents. • The classical, CEC 2014 and CEC 2017 benchmarks are used to examine ISCA. • The ISCA is used for engineering problems and image thresholding problem. • Comparisons illustrate the improvement on the performance of ISCA. [ABSTRACT FROM AUTHOR]

Abstract Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a L ocalized M ultiple K ernel learning approach for A nomaly D etection (L M K A D) using OCC, where the weight for each kernel is assigned locally. Proposed L M K A D approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of L M K A D on 25 benchmark datasets from various disciplines. This performance is evaluated against existing M ulti K ernel A nomaly D etection (M K A D) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. L M K A D achieves significantly better Gmean scores while using a lesser number of support vectors compared to M K A D. Friedman test is also performed to verify the statistical significance of the results claimed in this paper. [ABSTRACT FROM AUTHOR]

The exact approximation of write latency for NWR databases under various consistency levels can serve the building and operating of database clusters, by finding the optimal combination of cluster size and replication factor that minimizes the building and operating cost. Existing benchmarking or queue simulating based approaches can only give incomplete results as they are limited to specific configurations and testbeds. This paper depicted the first close-form analysis of(n,r k) fork-join queueing process of Cassandra(a typical NWR database) write operations, based on which this paper proposed the first theoretical write latency model for NWR databases. The model was capable of giving more comprehensive latency results. Experiments validate the close-form analysis of(n,r,k) fork-join queues and the write latency model respectively on simulated queues and a Cassandra cluster. [ABSTRACT FROM AUTHOR]

In this paper, we first present a modified Chambolle–Pock primal-dual method (MCPPDM) to solve a convex composite optimization problem which minimizes the sum of two convex functions with one composed by a linear operator. It is well known that the Chambolle–Pock primal-dual method (CPPDM) with the combination parameter being 1 is an application of the proximal point algorithm and thus is convergent, however, when the combination parameter is not 1, the method may be not convergent. To choose flexibly the combination parameter, we develop a slightly modified version with little additional computation cost. In CPPDM, one variable is updated twice but another variable is updated only once at each iteration. However, in the modified version, two variables are respectively updated twice at each iteration. Another main task of this paper is that we reformulate some well-known sparse recovery problems as special cases of the convex composite optimization problem and then apply MCPPDM to address these sparse recovery problems. A large number of numerical experiments have demonstrated that the efficiency of the proposed method is generally comparable or superior to that of existing well-known methods such as the linearized alternating direction method of multipliers and the graph projection splitting algorithm in terms of solution quality and run time. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields. Note to Practitioners—Though directly applicable to general business process discovery, this paper is motivated by our collaboration with the company HEVA (Lyon, France) and health practitioners on patient care pathway discovery. The French hospitalization database that contains hospitalization history of all patients is used for this purpose and our goal is to determine the most meaningful process model of the patient hospitalization history. The hierarchical event structure of this paper provides a natural way of representing relationship between hospitalization events and is readily obtainable from the classical ICD-10 codes. The formal mathematical model and our optimization algorithm allow the end users to best balance between the faithfulness of the process model and its complexity. A case study of cardiovascular patients is presented to show the capability of the proposed approach to clearly capture the major patient pathways before and after the implementation of defibrillators. The results of this paper are highly valuable for doctors and public health decision makers, as crucial information is provided on patient care pathways for any selected cohort. [ABSTRACT FROM AUTHOR]

Community detection is significative in the complex network. This paper focuses on community detection based on clustering algorithms. We tend to find out the central nodes of the communities by centrality algorithms. Firstly, we define the distance between nodes using similarity. Then, a new centrality measuring the local density of nodes is put forward. Combining the independence of the centrality, the nodes in the network can be divided into four classes. Leveraging the product of centrality and independence, the central nodes in the network are easily identified. We also find that we can distinguish bridge nodes from central nodes using centrality and independence. This research designs a community detection algorithm combining centrality and independence. Simulation results reveal that our centrality is more effective than existing centralities in measuring local density and identifying community centers. Compared with other community detection algorithms, results prove the effectiveness of our algorithm. This paper just shows one application of independence of the centrality. There may be more useful applications of it. [ABSTRACT FROM AUTHOR]

This paper presents a novel facial expression recognition (FER) technique based on support vector machine (SVM) for the FER. Here it is called the FERS technique. First, the FERS technique develops a face detection method that combines the Haar-like features method with the self-quotient image (SQI) filter. As a result, the FERS technique possesses better detection rate because the face detection method gets more accurate in locating face regions of an image. The main reason is that the SQI filter can overcome the insufficient light and shade light. Subsequently, three schemes, the angular radial transform (ART), the discrete cosine transform (DCT) and the Gabor filter (GF), are simultaneously employed in the design of the feature extraction for facial expression in the FERS technique. More specifically, they are employed in constructing a set of training patterns for the training of an SVM. The FERS technique then exploits the trained SVM to recognize the facial expression for a query face image. Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods under consideration in the paper. [ABSTRACT FROM AUTHOR]

For a simplicial complex Δ, the effect of the expansion functor on combinatorial properties of Δ and algebraic properties of its Stanley-Reisner ring has been studied in some previous papers. In this paper, we consider the facet ideal I(Δ) and its Alexander dual which we denote by JΔ to see how the expansion functor alters the algebraic properties of these ideals. It is shown that for any expansion Δα the ideals JΔ and JΔα have the same total Betti numbers and their Cohen-Macaulayness is equivalent, which implies that the regularities of the ideals I(Δ) and I(Δα) are equal. Moreover, the projective dimensions of I(Δ) and I(Δα) are compared. In the sequel for a graph G, some properties that are equivalent in G and its expansions are presented and for a Cohen-Macaulay (respectively, sequentially Cohen-Macaulay and shellable) graph G, we give some conditions for adding or removing a vertex from G, so that the remaining graph is still Cohen-Macaulay (respectively, sequentially Cohen-Macaulay and shellable). [ABSTRACT FROM AUTHOR]

Assigning papers to reviewers is a large, long and difficult task for conference chairs and scientific committees. The paper reviewer assignment problem is a multi-agent problem which requires understanding reviewer expertise and paper topics for the matching process. This paper proposes to elaborate on some features used to compute reviewer expertise and aggregate multiple factors to find the fittest combination of reviewers for each paper. Expertise information is gathered implicitly from publicly available information and a reviewer profile is generated automatically. An Ordered Weighted Averaging (OWA) aggregation function is used to summarize information coming from different sources and rank candidate reviewers for each paper. General constraints for the Reviewer Assignment Problem (RAP) have been incorporated into a real case example: (i) conflicts of interest between a reviewer and authors should be avoided, (ii) each paper must have a minimum number of reviewers, and (iii) each reviewer load cannot exceed a certain number of papers. [ABSTRACT FROM AUTHOR]

Integer vulnerability is a common and hidden problem of software security. Even mature software written by experienced programmers cannot avoid integer vulnerability. Therefore, it is very important for testers to detect the bugs. However, testing the bugs will face the test oracle problem. As far as we know, the metamorphic testing can effectively alleviate the test oracle problem. In this paper, a method of combinational metamorphic relations (CMR) is proposed, which solves the test oracle problem and is more effective than metamorphic relation for Integer vulnerability detection. A case study results shows that the combinational metamorphic relations are more effective in bugs-detection than the single metamorphic relations. [ABSTRACT FROM AUTHOR]

• We combine kernel alignment with subspace segmentation for MKC. • The algorithm automatically fuses unlabeled data from multiple sources. • The proposed algorithm is robust to noise and outliers. • An effective optimization method for the resultant optimization problem. • Experimental results demonstrate superior performance of the algorithm. Multiple kernel clustering (MKC) is an important research topic during the last few decades. It optimally combines a group of pre-specified base kernels to improve clustering performance. Though demonstrating promising performance in various applications, this task is still challenging due to lack of reliable discriminative guidance for the base kernel combination. Moreover, noise from either corrupted data or inappropriately selected base kernel parameters would undermine the intrinsic manifold and makes the problem even harder. In this paper, we integrate subspace segmentation into MKC and propose a robust subspace segmentation-based multiple kernel clustering (SS-MKC) algorithm to address these issues. In our formulation, we unify the constrained kernel polarization and subspace segmentation into a single procedure, where the resultant affinity matrix embedded with robust subspace structural information is utilized to guide the linear combination of base kernels. In addition, we carefully design the noise representation matrix as well as two sparse constraints, i.e., the ℓ 1 -norm and the probability constraints, to eliminate the adverse effect of noise among base kernels. We then propose a novel Alternative Direction Method of Multiplier (ADMM)-based algorithm to solve the resulting optimization problem. Extensive experiments have been conducted on both synthetic and public benchmark datasets, and the results well demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art MKC methods. [ABSTRACT FROM AUTHOR]

The effect of centre-of-gravity heights on the high-speed performance measures of long combination vehicles including truck with double centre-axle trailers, Nordic, and A-double combination vehicles is investigated. The high fidelity three-dimensional models, used in this research, are validated against physical test data. These models are often accurate in terms of the actual dynamic behaviours of the vehicle. On the other hand, the simple yaw-plane single-track models with linear tires require less number of vehicle parameters. In this paper, it is investigated how accurate the estimations of performance measures are at high forward speeds by such single-track linear (STL) models. The influence of load height is especially studied. The high-speed performance-based standard or PBS measures considered are rearward amplifications of both lateral acceleration and yaw velocity; lateral load transfer; yaw damping and high-speed transient offtracking. The results show that tire relaxation has a large effect and it is rather easy to add to an STL model, so it is assumed to be modelled in STL. With realistically high payload and a required accuracy of PBS measures of approximately 10%, only the accuracy of rearward amplification of yaw velocity calculated by the frequency response is fulfilled by the STL. With low payload, the same statement is valid, but with around 5% in required accuracy. The roll dynamics effects are more important than the tire non-linearities. [ABSTRACT FROM AUTHOR]

In this paper, to reveal the influence of multilayer network structure on opinion diffusion in social networks, we study an opinion dynamics model based on DeGroot model on multilayer networks. We find that if the influence matrix integrating the information of connectedness for each layer and correlation between layers is strongly connected and aperiodic, all agents' opinions will reach a consensus. However, if there are stubborn agents in the networks, regular agents' opinions will finally be confined to the convex combinations of the stubborn agents'. Specifically, if all stubborn agents hold the same opinion, even if the agents only exist on a certain layer, their opinions will diffuse to the entire multilayer networks. This paper not only characterizes the influence of multilayer network topology and agent attribute on opinion diffusion in a holistic way, but also demonstrates the importance of coupling agents which play an indispensable role in some social and economic situations. [ABSTRACT FROM AUTHOR]

The wrapper feature selection method can achieve high classification accuracy. However, the cross-validation scheme of the wrapper method in evaluation phase is very expensive regarding computing resource consumption. In this paper, we propose a new statistical measure named as LW-index which could replace the expensive cross-validation scheme to evaluate the feature subset. Then, a new feature selection method, which is the combination of the proposed LW-index with Sequence Forward Search algorithm (SFS-LW), is presented in this paper. Further, we show through plenty of experiments conducted on nine UCI datasets that the proposed method can obtain similar classification accuracy as the wrapper method with centroid-based classifier or support vector machine, and its computation cost is approximate to the compared filter methods. [ABSTRACT FROM AUTHOR]

The design of high-performance application-specific multi-core processor systems still is a time consuming task which involves many manual steps and decisions that need to be performed by experienced design engineers. The ASAM project sought to change this by proposing an automatic architecture synthesis and mapping flow aimed at the design of such application specific instruction-set processor (ASIP) systems. The ASAM flow separated the design problem into two cooperating exploration levels, known as the macro-level and micro-level exploration. This paper presents an overview of the micro-level exploration level, which is concerned with the analysis and design of individual processors within the overall multi-core design starting at the initial exploration stages but continuing up to the selection of the final design of the individual processors within the system. The designed processors use a combination of very-long instruction-word (VLIW), single-instruction multiple-data (SIMD), and complex custom DSP-like operations in order to provide an area- and energy-efficient and high-performance execution of the program parts assigned to the processor node. In this paper we present an overview of how the micro-level design space exploration interacts with the macro-level, how early performance estimates are used within the ASAM flow to determine the tasks executed by each processor node, and how an initial processor design is then proposed and refined into a highly specialized VLIW ASIP. The micro-level architecture exploration is then demonstrated with a walk-through description of the process on an example program kernel to further clarify the exploration and architecture specialization process. The main findings of the experimental research are that the presented method enables an automatic instruction-set architecture synthesis for VLIW ASIPs within a reasonable exploration time. Using the presented approach, we were able to automatically determine an initial architecture prototype that was able to meet the temporal performance requirements of the target application. Subsequently, refinement of this architecture considerably reduced both the design area (by 4x) and the active energy consumption (by 2x). [ABSTRACT FROM AUTHOR]

This paper proposed a cluster algorithm based on the combination ofLDA(Latent Dirichlet allocation) probabilistic topic model and VSM (Vector Space Model), with the three-tier framework adopted containing text, topic and feature word. Although LDA alone has the ability to seek out the hidden topic knowledge, it is hard for the low-dimensional model to remain the integrity of the text information, leading to insufficient capacity for distinguishing texts. The paper is set to launch the cluster analysis in turns of feature words and topic through integrating two model above. With a better mix of LDA and VSM, the clustering effect will be improved, paralleling determining the optimal clustering number K of the K-means algorithms and optimum topic number T of LDA model. In order to design the algorithms more scientifically and effectively, silhouette coefficient and Dunn coefficient have been brought in to make assessments. [ABSTRACT FROM AUTHOR]

Building proper reference ontologies is a hard task. There are a number of methods and tools that traditionally have been used to support this task. These include the use of foundational theories, the reuse of domain and core ontologies, the adoption of development methods, as well as the support of proper software tools. In this context, an approach that has gained increasing attention in recent years is the systematic application of ontology patterns . However, a pattern-based approach to ontology engineering requires: the existence of a set of suitable patterns that can be reused in the construction of new ontologies; a proper methodological support for eliciting these patterns, as well as for applying them in the construction of these new models. The goal of this paper is twofold: (i) firstly, we present an approach for deriving conceptual ontology patterns from ontologies. These patterns are derived from ontologies of different generality levels, ranging from foundational to domain ontologies; (ii) secondly, we present guidelines that describe how these patterns can be applied in combination for building reference domain ontologies in a reuse-oriented process. In summary, this paper is about the construction of ontology patterns from ontologies, as well as the construction of ontologies from ontology patterns. [ABSTRACT FROM AUTHOR]

Time series prediction is a challenging research topic, especially for multi-step-ahead prediction. In this paper, a novel multi-step-ahead time series prediction model is proposed based on combination of the Kalman filtering model (KFM) and the echo neural networks (ESN). Recently, the studies demonstrate the ESN model is a promising strategy for multi-step-ahead time series prediction, at the same time, the KFM is a recursion-based sequence information processing approach, which has been used effectively for prediction, filtering and smooth of time series data. In this paper, we consider to use the recursion-based KFM to enhance performance of the ESN-based direct prediction model. A novel graph model named the E-KFM that generated from combination of the ESN and the KFM is developed to predict multi-step-ahead time series data. The simulation and comparison results show that the proposed model is more effectiveness and robustness. [ABSTRACT FROM AUTHOR]

The first paper in this series initiated a study of Sylow theory for quasigroups and Latin squares based on orbits of the left multiplication group. The current paper is based on so-called pseudo-orbits, which are formed by the images of a subset under the set of left translations. The two approaches agree for groups, but differ in the general case. Subsets are described as sectional if the pseudo-orbit that they generate actually partitions the quasigroup. Sectional subsets are especially well behaved in the newly identified class of conflatable quasigroups, which provides a unified treatment of Moufang, Bol, and conjugacy closure properties. Relationships between sectional and Lagrangean properties of subquasigroups are established. Structural implications of sectional properties in loops are investigated, and divisors of the order of a finite quasigroup are classified according to the behavior of sectional subsets and pseudo-orbits. An upper bound is given on the size of a pseudo-orbit. Various interactions of the Sylow theory with design theory are discussed. In particular, it is shown how Sylow theory yields readily computable isomorphism invariants with the resolving power to distinguish each of the 80 Steiner triple systems of order 15. [ABSTRACT FROM AUTHOR]

In this paper we introduce %CEM, a macro package allowing researchers to automatically perform coarsened exact matching (CEM) in SAS environment. CEM is a non-parametric matching method widely used by researchers to avoid the confounding influence of pre-treatment control variables to improve causal inference in quasi-experimental studies. %CEM introduces a completely automated process which allows SAS users to efficiently perform CEM in fields in which large data sets are common and where SAS is the most popular statistical tool. In addition, such a macro may be used to test several coarsening combinations of numeric variables. This option also provides a visual representation of thematching frontier, thus enabling researchers to select the optimal setting which takes into account both theimbalance and the percentage of matched units. The paper concludes with an empirical application comparing computational performance and results obtained using alternative available software (SAS, R and STATA) using multiple administrative data sets from a large regional database. [ABSTRACT FROM PUBLISHER]

Purpose The purpose of this paper is threefold: to classify wildcards into three particular types sharing similar characteristics; use the Fuzzy TOPSIS as a new method in foresight to turn qualitative ideas into quantitative ones; and apply a combination of Fuzzy TOPSIS and a panel of experts to prioritize weak signals.Design/methodology/approach In this paper, the authors classify wildcards into three particular types which share similar character: natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). Wildcards point to unexpected and surprising events including important results that can form watershed in the development of a specific trend. In addition, the authors present a Fuzzy TOPSIS model which can be used in various cases to prioritize a number of weak signals and put them in order, so that the most important ones are likely to yield the wildcard in the futureFindings The authors presented a classification of wildcards with the same characteristics being natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). The authors also prioritized the weak signals to deal with the most important ones and take appropriate action in advance so as to minimize possible damages and maximize the benefits of potential wildcards in an uncertain environment.Originality/value In this paper, the authors report on the prioritizing of weak signals by applying Fuzzy TOPSIS and classify wildcards. This is significant because, by identifying the most important weak signals, appropriate actions can be taken in the future if necessary. The paper should be of interest to readers in the area of participatory foresight. [ABSTRACT FROM AUTHOR]

Prototype selection is one of the most common preprocessing tasks in data mining applications. The vast amounts of data that we must handle in practical problems render the removal of noisy, redundant or useless instances a convenient first step for any real-world application. Many algorithms have been proposed for prototype selection. For difficult problems, however, the use of only a single method would unlikely achieve the desired performance. Similar to the problem of classification, ensembles of prototype selectors have been proposed to overcome the limitations of single algorithms. In ensembles of prototype selectors, the usual combination method is based on a voting scheme coupled with an acceptance threshold. However, this method is suboptimal, because the relationships among the prototypes are not taken into account. In this paper, we propose a different approach, in which we consider not only the number of times every prototype has been selected but also the subsets of prototypes that are selected. With this additional information we develop GEEBIES, which is a new way of combining the results of ensembles of prototype selectors. In a large set of problems, we show that our proposal outperforms the standard boosting approach. A way of scaling up our method to large datasets is also proposed and experimentally tested. • We propose a new method for combining prototype selection algorithms. • The method achieves better combined storage reduction and testing error than previous approaches. • Stratified random sampling is successfully applied to scale up the method. [ABSTRACT FROM AUTHOR]

It is well known that under some conditions, a convex combination of the drastic product T D and the strict triangular norm is still a triangular norm. Uninorms, as a special kind of generalization of both triangular norms and triangular conorms, have been widely used in fuzzy set theory. In this paper, the convex combination of uninorms is studied. Some properties of the class of uninorms with the underlying triangular norm T D are presented firstly. Then the convex combination of this class of uninorms is discussed and it is shown that only in some trivial cases, the convex combination is still a uninorm. [ABSTRACT FROM AUTHOR]

In this paper, we study the mountain pass characterization of the second eigenvalue of the operator -Δpu-ΔJ,pu and study shape optimization problems related to these eigenvalues. [ABSTRACT FROM AUTHOR]

There are well-known expressions for natural frequencies and mode shapes of a Euler-Bernoulli beam which has classical boundary conditions, such as free, fixed, and pinned. There are also expressions for particular boundary conditions, such as attached springs and masses. Surprisingly, however, there is not a method to calculate the natural frequencies and mode shapes for a Euler–Bernoulli beam which has any combination of linear boundary conditions. This paper describes a new method to achieve this, by writing the boundary conditions in terms of dynamic stiffness of attached elements. The method is valid for any boundaries provided they are linear, including dissipative boundaries. Ways to overcome numerical issues that can occur when computing higher natural frequencies and mode shapes are also discussed. Some examples are given to illustrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we point out that a recent characterization of geodesic convex hull on Hadamard manifolds is not rigorous and explain why the characterization does not hold like it in linear spaces. Therefore, a definition of geodesic pseudo-convex combination is proposed to show that the Knaster–Kuratowski–Mazurkiewicz theorem still holds under some mild conditions on Hadamard manifolds. [ABSTRACT FROM AUTHOR]

This paper first gives a summarization of different combination strategies, and then the combina- tion strategy for station coordinates and earth rotation parameters (ERP) based on solution level is intro- duced. The one-year products from nine analysis centers of International GNSS Service (IGS) are used to validate the combination method. As far as the differences between the combined solutions and the results of IGS are concerned, the combined coordinates, ERP and geocenter motion parameters show well agree- ment with IGS combination. The agreement of coordinate is 0.5 mm and 1.0 mm for horizontal and verti- cal， respectively. While the precision of pole motion and its rate are respectively about 7.0×10-6 ″ and 40.0×10-6 ″/d, and the precision of length of day (LOD) parameter is better than 7.7×10-6 s/d. The im- plemented program of station coordinates and ERP combination will be helpful for the international GNSS monitoring and assessment system (iGMAS) products combination. [ABSTRACT FROM AUTHOR]

In this paper, multi-switching combination synchronization for spatiotemporal coupled systems are studied. For the local system in spatiotemporal coupled systems, different local dynamic behaviors are taken into consideration. With the Backstepping method, synchronization controllers are designed for different possible combined errors and corresponding theorems are derived according to the Lyapunov stability theory. In the stability design, multi-switching combination synchronizations can be translated into corresponding combination projective synchronization with different projective factors. In addition, complexities including time delays are considered in the synchronization process. At last, simulation results show the robustness and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Comparison and evaluation of elasto‐plastic verification alternatives DIN EN 1993‐1‐1 describes methods for verifying internal forces in cross‐sections in order to determine the plastic cross‐sectional load carrying capacity of rolled I‐profiles and rectangular hollow sections. Furthermore, Kindmann describes in [1, 2] the Partial Internal Forces Method (TSV), which with further developments provides two alternatives. In this paper, the authors summarize the basics of these methods of verification for rolled I‐profiles and rectangular hollow profiles. Particular attention will be paid to applicability. Furthermore, the absolute interaction spaces for each verification alternative are generated and compared based on two example profiles. This is done for the internal forces combinations N‐Vz‐My, N‐Vy‐Mz, N‐My‐Mz and N‐Vy‐Vz‐My‐Mz. Similarities and differences are highlighted. The evaluation of the verification alternatives is carried out regarding applicability, safety and accuracy. While the application is subjective, previous work reveals an objective tendency of the latter attributes. The outlook sums up the practicability of the alternatives discussed. [ABSTRACT FROM AUTHOR]

In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback–Leibler divergence for associating each kernel weight to its relevance. From the obtained results of validation on nonstationary and non-Gaussian brain activity (simulated and real-world EEG data), WM-MK proves that the accuracy of the source estimation raises by more effectively exploiting the measured nonlinear structures with high time and space complexity. [ABSTRACT FROM AUTHOR]

• Novel technique is proposed to diagnose vibration data from rolling bearing. • Method indicates perfect fault signature in the presence of multiple impulsive components. • Bi-frequency representation, matrix factorization, spatial denoising are used. • Presented results are based on real data from reciprocating compressor operating in industrial conditions. In this paper, authors present the original procedure for local damage detection in rolling bearings based on vibration data. The aim is to obtain envelope spectrum (ES) of the signal component related to damage, that is clear and easy to interpret. The method is especially aimed at cases, when multiple cyclic impulsive components are present and interfere with each other, which makes ES of such signal very difficult to evaluate. In order to deal with such situation properly, authors propose to choose Cyclic Spectral Coherence (CSC) map as a two-dimensional data representation that will be the basis for the analysis. Nonnegative Matrix Factorization (NMF) is used to analyze such map in two ways: first, it helps to initially separate cyclic components by producing a set of filters for input vibration data, and second, to identify proper damage-related frequency components in envelope spectrum. In addition, an intermediate step of spatial denoising allows enhancing the properties of CSC map. Finally, Monte Carlo simulation improves statistical significance of the result and increases robustness by reducing the impact of random initialization effects. The method has been evaluated using real-life vibration data measured on rolling bearing operating in the industrial gas compressor. [ABSTRACT FROM AUTHOR]

Let ℤ and ℕ be the set of integers and the set of positive integers, respectively. For a 1 , a 2 , ... , a k , n ∈ ℕ , let N (a 1 , a 2 , ... , a k ; n) be the number of representations of n by a 1 x 1 2 + a 2 x 2 2 + ⋯ + a k x k 2 , and let t (a 1 , a 2 , ... , a k ; n) be the number of representations of n by a 1 x 1 (x 1 − 1) 2 + a 2 x 2 (x 2 − 1) 2 + ⋯ + a k x k (x k − 1) 2 (x 1 , ... , x k ∈ ℤ). In this paper, by using Ramanujan's theta functions φ (q) and ψ (q) , we reveal many relations between t (a 1 , a 2 , ... , a k ; n) and N (a 1 , a 2 , ... , a k ; 8 n + a 1 + ⋯ + a k) for k = 3 , 4. [ABSTRACT FROM AUTHOR]

Experimental random data, in general, present a skewed behaviour. Thus, asymmetrical generalized distributions are of interest. The generalized logistic distributions (GLDs) are good candidates to model skewed data because their probability density functions (p.d.f.) and characteristic functions are mathematically simple. In this paper, exact expressions in terms of the H-function are, for the first time, derived for the p.d.f. and for the cummulative distribution function of the linear combination of GLDs of type IV with different location, scale and shape parameters. Also, exact and approximate expressions are derived for R = P(X < Y ). Numerical examples illustrate the correctness of the expressions derived. [ABSTRACT FROM AUTHOR]

This study is based on the structure of hyper GR-algebras, an algebra that is partially related on some class of hyper BCI-algebras. This allows us to create a new structure and investigate how this two algebras are related to each other. A pseudo hyper GR-algebra involves two hyper operations and a set of axioms that come in pairs or a combination of both making it interesting like some algebras established. This paper focuses on some properties of pseudo hyper GR-algebras and its ideals. Moreover, pseudo hyper GR-ideals were defined and classified to determine their relationship to each other. [ABSTRACT FROM AUTHOR]

In this paper, our aim is to use the SBA numerical method (combination of Adomian method and Picard successive approximations) and Fourier method or method of separation of variables to construct the solution of some wave equations. We compare the two methods and apply them to some wave equations. [ABSTRACT FROM AUTHOR]

Highlights • Novel technique for growing model-based trees for ordinal responses proposed. • Mixture models estimated at nodes accounting for uncertainty in ordinal scores. • Splitting criteria featuring both inferential and fitting issues introduced. • Effective graphical visualization of the results and flexibility. • Application to survey data and benchmark study to assess the proposal. Abstract The paper introduces a new technique for growing trees for ordinal responses in the model-based framework. We consider the class of cub mixtures which is particularly appropriate to model perceptions and evaluations, as it designs the response process as the combination of a personal feeling and an inherent uncertainty. In our proposal, the partitioning process is based on the local estimation of cub regression models to profile responses according to feeling and uncertainty conditional to the splitting variables. In this regard, two alternative splitting criteria are introduced featuring both inferential and fitting issues. Moreover, the chosen modelling framework allows for advantageous visualization of the classification results. The proposal is illustrated using real data from a survey conducted by the Italian National Statistical Office, with focus on perceived trust towards the European Parliament. A benchmark study is conducted to settle the proposal among the available tree methods for ordinal responses. [ABSTRACT FROM AUTHOR]

Taking into account the actual photometric characteristics of a road surface to design and then adjust a lighting installation is seldom done. In a lighting renovation, one of the Commission Internationale de l'Eclairage's standard r-tables is arbitrarily chosen although they are no longer representative of the characteristics of current road surfaces. The objective of the study is to assess an optimized, evolutive pavement and lighting combination, called Lumiroute®. To do this, two conventional designed sections were compared with two Lumiroute® sections. On-site measurements of road photometry, luminance and power consumption were conducted at regular intervals for three years. This paper presents the results of the photometric study together with an economic analysis. The Lumiroute® sections offer optimized performance and increased efficiency in comparison with the ordinary sections, particularly with regard to light and energy performance. [ABSTRACT FROM AUTHOR]

In this paper, existing feasible slot/pole combinations of the fractional slot permanent magnet (FSPM) machines are extended. In comparison with the existing slot/pole combinations, the proposed ones could result in a higher winding factor, lower torque ripple, and lower cogging torque. This diversity in the number of the slot/pole combinations brings the designers more alternative solutions. To reach such slot/pole combinations three-slot pitch coils are applied. The existing slot/pole combinations are limited to the designs with an integer number of slots per phase in one periodicity of the FSPM machine. In the presented work, FSPM machines with fractional numbers of the slots per phase in one periodicity of the machines are introduced. However, the phase back electromotive force signals are balanced. Moreover, to achieve a slot/pole combination with small unbalance magnetic force (UMF) and low THD value of the armature MMF, the multilayer winding technique is applied. In addition, a simple technique is presented to find an arrangement of the multilayer winding with very small UMF. Extensive finite element simulations are used to compare the FSPM machines with the proposed slot/pole combinations with the traditional FSPM machines. [ABSTRACT FROM AUTHOR]

The fast and precise positioning of flexible mechanical structures is often corrupted by the unwanted dynamics in the form of a residual vibration. Therefore, we would like to find an appropriate control strategy that is capable to suppress this effect. The control strategies can be basically divided into two main groups: feedback control and feedforward control. The feedback control with the information from integrated sensors is capable to ensure the stability and robustness, but it may require large actuator effort, and it may be difficult to design satisfactory controllers for rapid movements. The feedforward methods including command/input shaping are based on the model of the system and usually require no additional sensors. They can significantly eliminate residual vibration, but feedforward methods cannot deal with disturbances, and the quality of their performance is strongly determined by the precision of the used model on which they are based. This paper proposes the novel solution to these problems, the so-called SHAVO (SHAper + serVO control) strategy that combines advantages of both approaches. Compared to other methods combining command shaping and feedback controller, the SHAVO approach differs in two key features. Firstly, it uses a different structure, the model of the system is used not only for shaper synthesis but also for predicting system outputs and states. Secondly, the shaper itself is highly optimized with arbitrary adjustable time length, not an impulse series, not limited by the system's natural frequency. [ABSTRACT FROM AUTHOR]

Highlights • Width estimation is difficult in vertical type adjacent pittings. • Circumferential MFL component is best suited to estimate the widths of the adjacent pittings. • Combination of three-axis MFL components can discriminate the individual pits and improve the depth sizing accuracy. Abstract This paper presents a novel approach for estimation of the diameters and depths of the adjacent pittings using combination of three axial MFL signal components and a multi-valued calibration surface. Since closely-spaced pittings influence each other, accelerate the corrosion growth rates, and are finally combined to one cluster, it's meaningful to discriminate multiple adjacent pittings from single pittings by analyzing their properties. The study is carried out using extensive 3D finite element analysis focusing on the spatial distribution of MFL components generated by adjacent corrosion pits. Various types of adjacent pittings with different depths are considered by modifying the separation distance between two pits. Besides, MFL signals from experimental measurements of adjacent pittings are obtained and the interaction between signals will be studied. Finally, quantitative estimation of adjacent pittings is analyzed in detail and the improvements in characterization of defects that resulted from this investigation are reported. [ABSTRACT FROM AUTHOR]

A new (k , n) threshold secret image sharing scheme with two decoding options is introduced in this paper. The proposed scheme combines color-black-and-white visual cryptography scheme (CBW-VCS) and polynomial based secret image sharing (PSIS) together, to offer stacking-to-see decryption and lossless image reconstruction. To construct the color shares, a general (k , n) threshold CBW-VCS is firstly given. A grayscale secret image is converted to a p -radix image and a binary image. The p -radix image is encrypted by the (k , n) PSIS under mod p operation, and n p -radix shadows are obtained. Then, a color share generation algorithm with data embedding is proposed to construct n color shares. Theoretical analysis and sufficient experiments are provided to show the effectiveness and advantages of the proposed scheme. [ABSTRACT FROM AUTHOR]

Abstract Multi-frequency carrier-phase linear combinations are greatly beneficial to improving the performance of ambiguity resolution (AR), cycle slip correction as well as precise positioning. However, as a limiting factor for further improvements in Global Navigation Satellite System (GNSS) high-precision positioning, the effect of multipath is also accentuated due to the measurement combination. In this paper, with real GPS and BDS triple-frequency observations, we analyse the influence of the carrier-phase multipath in three typical triple-frequency combinations: extra-wide-lane (EWL) combination, ionosphere estimation with ambiguity-corrected EWL/wide-lane (WL) combinations and the geometry-free and ionosphere-free (GIF) combination for narrow-lane (NL) AR. For more intuitive reflection of the influence caused by the carrier-phase multipath, zero-baseline tests with no multipath influence were carried out for comparison. The results from consecutive orbital repeat periods were also used to confirm this influence. Experiments show that for the BDS (1, 4, −5) and GPS (1, −6, 5) EWL combinations, the multipath errors could be combined with the biases of several metres in units of distance and over 0.5 cycles in units of cycles. Therefore in single-epoch EWL AR, besides the empirical precision, the bias caused by carrier-phase multipath should be also fully considered. The coefficients of the ionosphere estimation and GIF model are so large that the multipath errors are accentuated many times. Experiments results indicate that even smoothed or averaged with several hours, the errors of ionosphere estimators could be still over 10 cm; the biases of NL ambiguities with GIF model could be still over 0.5 cycles, especially for BDS GEO satellites. [ABSTRACT FROM AUTHOR]