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In this paper we deal with conditional aggregation-based survival functions recently introduced by Boczek et al. (2020). The concept is worth to study because of its possible implementation in real-life situations and mathematical theory as well. The aim of this paper is the comparison of this new notion with the standard survival function. We state sufficient and necessary conditions under which the generalized and the standard survival function equal. The main result is the characterization of the family of conditional aggregation operators (on discrete space) for which these functions coincide.

In this article, a low complexity finite-control-set model predictive control (FCS-MPC) based on the discrete space vector modulation (DSVM) is proposed for T-type three-phase three-level (3P-3L) converters. Different from the conventional FCS-MPC, 48 virtual voltage vectors (VVs) of the converter are constructed by real VVs based on the DSVM. Thus, the performance of 3P-3L converters is significantly improved and the peak amplitude of high-order harmonics concentrates at the sampling frequency. Furthermore, two-stage FCS-MPC based on virtual VVs is proposed to reduce the computation burden. Its first stage selects one of six virtual VVs that minimizes the current tracking error. Then, these candidate VVs located in the same sector as the optimal virtual VV selected in the first stage are evaluated in the second-stage optimization. Thus, the computational efficiency has been greatly improved. To verify the validity of the proposed control method and show its superiority over the conventional FCS-MPC, experimental results are presented.

© 2021 Karabuk UniversityThe recently proposed waveform called universal filtered multicarrier (UFMC) has already managed to get the attention of science world due to its superior features and it has been accepted as one of the foremost fifth generation (5G) waveform contenders. However, due to being a type of multicarrier waveform, the UFMC suffers from the problem of high peak-to-average power ratio (PAPR). In this paper, a new discrete elephant herding optimization-based partial transmit sequence (DEHO-PTS) scheme is suggested to reduce the PAPR of UFMC signals to minimum levels. Due to the fact that the optimization of phase rotation factors multiplied by the sub-blocks in the conventional partial transmit sequence (PTS) technique is a combinatorial optimization problem to be solved in discrete space, in order to integrate the elephant herding optimization (EHO) algorithm into the PTS scheme, we developed its discrete version (DEHO) in this paper. In the simulations, the capability of the suggested DEHO-PTS procedure to minimize the PAPR, suppress the out-of-band emission and decrease the bit error rate of the UFMC signal amplified via a nonlinear high power amplifier was analyzed. Simulation results clearly indicate that our proposed DEHO-PTS procedure can be a serious option for UFMC waveform to minimize the PAPR values of transmission signals due to its significant PAPR reduction, side lobe suppression and bit error rate performances with low computational complexity.

PurposeThe aim of the study is to show that merging two areas of mathematics – topology and discrete optimization – could result in a viable option to solve classical or specialized integer problems.Design/methodology/approachIn the paper, discrete topology concepts are applied to propose a metaheuristic algorithm that is capable to solve binary programming problems. Particularly, some of the homotopy for paths principles are used to explore the solution space associated with four well-known NP-hard problems herein considered as follows: knapsack, set covering, bi-level single plant location with order and one-max.FindingsComputational experimentation confirms that the proposed algorithm performs in an effective manner, and it is able to efficiently solve the sets of instances used for the benchmark. Moreover, the performance of the proposed algorithm is compared with a standard genetic algorithm (GA), a scatter search (SS) method and a memetic algorithm (MA). Acceptable results are obtained for all four implemented metaheuristics, but the path homotopy algorithm stands out.Originality/valueA novel metaheuristic is proposed for the first time. It uses topology concepts to design an algorithmic framework to solve binary programming problems in an effective and efficient manner.

We consider properties of an approximation space with a neighbourhood operator. Two types of topologies τ and σ are introduced on the approximation space using a neighbourhood operator and we show that they are the same Alexandrov topology, τ = σ . Also, for a reflexive and transitive neighbourhood operator, the topological space ( U , σ ) is a Hausdorff space if and only if σ is the discrete topology P ( U ) , where U is a non-empty set which is not need to be finite. In addition, we prove an algebraic property that a pair ( n , a p r _ ) of operators defined by a neighbourhood operator n forms a Galois connection.

Minkowski spacetime provides a background framework for the kinematics and dynamics of classical particles. How the framework implements the motion of matter is not specified within special relativity. In this paper we specify how Minkowski space can implement motion in such a way that ’quantum’ propagation occurs on appropriate scales. This is done by starting in a discrete space and explicitly taking a continuum limit. The argument is direct and illuminates the special tension between ’rest’ and ’uniform motion’ found in Minkowski space, showing how the formal analytic continuations involved in Minkowski space and quantum propagation arise from the same source.

The Ellis semigroup of a discrete dynamical system (X, f) is denoted by E(X, f). We only consider surjective continuous functions $$f:[0,1]\rightarrow [0,1]$$ such that $$\{ f^n : n\in {\mathbb {N}}\}$$ is infinite. The main result asserts that the Ellis semigroup E([0, 1], f) is always a compactification of the natural numbers with the discrete topology. We also prove some results estimating the cardinality of the Ellis semigroup of certain dynamical systems: we show that if $$f:[0,1]\rightarrow [0,1]$$ is a continuous function with positive topological entropy, then $$|E([0,1],f)|= 2^{2^{\aleph _0}}$$ , and if $$f:[0,1]\rightarrow [0,1]$$ is a $$2^{n}$$ -function for some $$n \in {\mathbb {N}}$$ , then $$|E([0,1],f)-\{f^n:n\in {\mathbb {N}}\}|=2^n.$$

Motivation With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. Results In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Finally, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13 and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta. Availabilityand implementation The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold. Supplementary information Supplementary data are available at Bioinformatics online.

The application of topology optimization methods in architecture, while useful for conceptual design explorations, seems to be limited by the practical realization of continuum-type design outcomes. One way to overcome this limitation is setting up design and fabrication techniques, through which continuum domains become discrete structures. This study investigates to which extent discrete optimized systems can be built using a hybrid approach combining 3D printing and analogue fabrication techniques. The procedure is based on an algorithm in Grasshopper (Rhinoceros) that translates continuum topologies obtained in MATLAB into discrete systems, providing alternatives depending on the targeted volume fraction, the intended surface smoothness of the structural components and building material. The study focuses on fabrication aspects and structural performance of discrete structures using 3D printed nodes. Experimental tests evaluate the compressive strength of different types of filaments with varied infill percentages. Final prototypes are fabricated using a hybrid technique involving the use of 3D printed nodes to assemble bar-arrays comprising wooden members. Results provide a critical appraisal of the limitations and potentialities of 3D printing for hybrid fabrication of real scale structures.

Multicarrier transmission strategy provides great benefits in wireless communication technology. For this reason, all of the waveform contenders proposed for the fifth generation and beyond technologies are built on this strategy. Universal filtered multicarrier (UFMC) is one of the foremost representatives of the related waveform contenders. However, utilizing the multicarrier transmission strategy causes the UFMC to generate transmission signals with high peak-to-average power ratio (PAPR), which leads to serious deteriorations in the communication quality. In order to solve this issue, it is crucial to reduce the PAPR of transmission signal before giving it to the wireless channel. With the aim of developing an efficient PAPR reduction scheme for the UFMC waveform, we first developed a new discrete version of the invasive weed optimization (DIWO) algorithm and integrated it to one of the classical PAPR reduction techniques called partial transmit sequence (PTS) as a phase optimizer. By doing so, we obtained an advanced scheme called DIWO–PTS. The function of DIWO algorithm in this new scheme is to optimize the phase sequences in discrete space. The usage of DIWO as a phase optimizer enables PTS to reach better phase combinations in less number of searches. Thus, it becomes possible to boost the PAPR reduction performance of the PTS scheme to a quite high level. Simulation results support the advantage of DIWO-based phase optimization. In the simulations, neither of the algorithms used for comparison in our paper could reach the performance level achieved by DIWO algorithm due to its efficient optimization capability.