Theoretical Algorithm Research Articles

A New Ai–Zhang Type Interior Point Algorithm for Sufficient Linear Complementarity Problems

In this paper, we propose a new long-step interior point method for solving sufficient linear complementarity problems. The new algorithm combines two important approaches from the literature: the main ideas of the long-step interior point algorithm introduced by Ai and Zhang and the algebraic equivalent transformation technique proposed by Darvay. Similar to the method of Ai and Zhang, our algorithm also works in a wide neighborhood of the central path and has the best known iteration complexity of short-step variants. However, due to the properties of the applied transforming function in Darvay’s technique, the wide neighborhood definition in the analysis depends on the value of the handicap. We implemented not only the theoretical algorithm but a greedy variant of the new method (working in a neighborhood independent of the handicap) in MATLAB and tested its efficiency on both sufficient and non-sufficient problem instances. In addition to presenting our numerical results, we also make some interesting observations regarding the analysis of Ai–Zhang type methods.

Methods for Underwater Gravity Measurement Error Compensations Based on Correlation Analysis

The measurement of Earth’s gravitational field is important in geophysics, geodynamics, geodesy, oceanography, and space science. The ocean covers 71% of the earth’s surface; therefore, measuring the ocean’s gravitational field is crucial. Compared with shipborne gravimetry, underwater gravimetry near the seafloor is closer to gravity sources and can obtain short-wavelength gravity information that is useful for small-scale deposit detection and seawater intrusion monitoring. This article focuses on gravimetric errors caused by the poor dynamics of the carrier; an error compensation method for underwater gravimetry based on correlation analysis is proposed. By analyzing the error sources that affect the dynamics of the carrier, the relationship between the gravimetry error and impact factors related to the dynamics was established, and the model’s parameters were estimated by the least-squares fitting method. The experimental data show that this method can effectively compensate for gravimetric errors caused by carrier dynamics and provide the theoretical basis and algorithm model for underwater gravimetry in the bottom-tracking mode.

Weak Galerkin finite element method for linear elasticity interface problems

In this paper, we apply a weak Galerkin finite element method to a linear elasticity interface model. Since the solution may become discontinuous while crossing the interface, we first discretize the model by double-valued weak functions on the interface. Then, in order to facilitate theoretical analysis and algorithm implementation, we substitute interface conditions into the weak Galerkin formulation and construct a weak Galerkin method with single-valued functions on the interface. Furthermore, we prove the well-posedness of the weak Galerkin scheme and derive a priori error estimates in energy norm and L2 norm. Finally, we present some numerical experiments to demonstrate the efficiency and the locking-free property of our method.

Five material tissue decomposition by dual energy computed tomography

The separation of mixtures of substances into their individual components plays an important role in many areas of science. In medical imaging, one method is the established analysis using dual-energy computed tomography. However, when analyzing mixtures consisting of more than three individual basis materials, a physical limit is reached that no longer allows this standard analysis. In addition, the X-ray attenuation coefficients of chemically complicated basis materials may not be known and also cannot be determined by other or previous analyses. To address these issues, we developed a novel theoretical approach and algorithm and tested it on samples prepared in the laboratory as well as on ex-vivo medical samples. This method allowed both five-material decomposition and determination or optimization of the X-ray attenuation coefficients of the sample base materials via optimizations of objective functions. After implementation, this new multimodal method was successfully tested on self-mixed samples consisting of the aqueous base solutions iomeprol, eosin Y disodiumsalt, sodium chloride, and pure water. As a first proof of concept of this technique for detailed material decomposition in medicine we analyzed exact percentage composition of ex vivo clots from patients with acute ischemic stroke, using histological analysis as a reference standard.

Organization of accelerated recovery and early rehabilitation of surgical patients within the framework of the service of anesthesiology and resuscitation: literature review

Accelerated recovery after surgery, according to publications, has been developed since 1980 and is a multimodal strategy aimed at reducing the physiological response to surgical trauma, rapid recovery and early discharge of the patient. This study aimed to analyze the feasibility of introducing a methodology for accelerated recovery of the patient after surgery and early discharge from the surgical hospital. A study of the literature and formulation of a theoretical algorithm for the implementation of the process of accelerated recovery after surgery or fast track. According to publications, accelerated recovery from surgery has been developed since 1980 and is a multimodal strategy intended to reduce the physiological response to surgical trauma and accelerating recovery and early discharge of the patient. This concept consists of scientifically based approaches to the perioperative and postoperative management of surgical patients and represents a radical revision of the established traditional approach. Solving organizational issues of implementing the fast track concept in multidisciplinary hospitals in our country is a key factor toward wider dissemination and implementation of accelerated recovery after surgery.

Impact of meteorological conditions on tropospheric ozone and associated with parameterization methods for quantitative assessment and monitoring

In recent years, the heavy ozone pollution events around the world have shown a sudden frequently increase, which has aroused widespread concern in the government and the public. It is well known that O3 is driven by photochemical reactions triggered by solar radiation (direct and indirect solar radiation), the O3 concentration calculated by chemical mechanism is mostly significantly lower than the actual O3 observation. Based on the study of the effect of meteorological conditions on the “additional increment” of O3 in three representative regions of Beijing, Hangzhou and Guangzhou from 2015 to 2020, an innovation diagnostic theory algorithm that the cross-cutting effects of atmospheric clouds on the chemical pattern of O3 solar radiation is established in this study. On this basis, a parametric evaluation method of O3 is established. The novelty of this study is 1) Comprehensive influence of the meteorological conditions and photochemical reactions mechanisms on the cross-cutting effects of O3 concentration are given. Especially low-level clouds in the troposphere, which have significantly large variable effects on the reflection and refraction of O3 through solar radiation. Theory quantitative algorithm of cloud scattering, cloud height, cloud volume and cloud structure changes, as well as feedback effects caused by water vapor condensation, which closely related to the transformation of O3 precursors are given. 2) Based on this, a parameterization method for quantitative O3 assessment and monitoring, which is a Parameterization for Linking Ozone pollution with Meteorological conditions. 3) Applying the theoretical algorithm and parameterization method of this study, comparing the changes of O3 in 2018 with 2019, an objective quantitative distinction between emission reduction and meteorological impact was made, showing that emission reduction still played a leading role, with a contribution rate of about 27%. This shows that the created quantitative algorithm of atmospheric cloud theory and the innovative parameterization method can provide an objective quantitative basis for O3 pollution decision-making and public emission reduction.

Improved event-triggered robust adaptive control for marine vehicle with the fault compensating mechanism

This paper investigates an improved event-triggered robust adaptive control algorithm for the marine surface vehicle with the fault compensating mechanism. A dynamic event-triggered mechanism is designed with a concise form to reduce the updating frequencies of the system state. Different from the exiting results, the proposed algorithm can ensure that both the controller and the state transmitter are activated synchronously only at the triggering instants. That can ameliorate the communication obstruction in both the channels of sensor-to-controller and controller-to-actuator. To improve the fault-tolerant capability of the theoretical algorithm, gain-related adaptive parameters are derived to compensate for gain uncertainties and possible actuator faults. In addition, a compensating term via the robust neural damping technique is introduced to stabilize the unpredictable perturbation from the model uncertainties and the external disturbance. Through the Lyapunov criterion, the corresponding closed-loop system is with the semi-global uniformly ultimately bounded (SGUUB) stability. The simulation results are illustrated to verify the effectiveness of the algorithm.

Necessary and Sufficient Conditions of Formation-Containment Control of High-Order Multiagent Systems With Observer-Type Protocols

The analysis and design problems of formation-containment control for high-order linear time-invariant (LTI) multiagent systems (MASs) on directed graphs with observer-based output-feedback protocols are given in this work. To expand the feasibility of state formation configuration, two well-structured compensation signals are introduced for the leaders and followers in the protocols, respectively. Benefitting from the compensation signal of followers, the decoupling between formation control of leaders and containment control of followers is achieved. Thus, a necessary and sufficient condition is first established such that the formation-containment control for high-order LTI MASs can be achieved. Moreover, a heuristic iterative algorithm is developed to compute the controller gains, observer gains, as well as the compensation signals. Finally, two numerical examples are implemented to illustrate the time-varying formation-containment control of high-order MASs, which shows the validity and practicability of the theoretical results and algorithm.

Theoretical construction and algorithms specifying determinants of ±1 matrices of the spectrum

Theoretical construction and algorithms specifying determinants of ±1 matrices of the spectrum

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding.

Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms (HIOAs). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms (HIOAs) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

GrAfSS: a webserver for substructure similarity searching and comparisons in the structures of proteins and RNA.

The GrAfSS (Graph theoretical Applications for Substructure Searching) webserver is a platform to search for three-dimensional substructures of: (i) amino acid side chains in protein structures; and (ii) base arrangements in RNA structures. The webserver interfaces the functions of five different graph theoretical algorithms – ASSAM, SPRITE, IMAAAGINE, NASSAM and COGNAC – into a single substructure searching suite. Users will be able to identify whether a three-dimensional (3D) arrangement of interest, such as a ligand binding site or 3D motif, observed in a protein or RNA structure can be found in other structures available in the Protein Data Bank (PDB). The webserver also allows users to determine whether a protein or RNA structure of interest contains substructural arrangements that are similar to known motifs or 3D arrangements. These capabilities allow for the functional annotation of new structures that were either experimentally determined or computationally generated (such as the coordinates generated by AlphaFold2) and can provide further insights into the diversity or conservation of functional mechanisms of structures in the PDB. The computed substructural superpositions are visualized using integrated NGL viewers. The GrAfSS server is available at http://mfrlab.org/grafss/.

An innovative multi-layer shell model for in-pile thermo-mechanical behavior analysis of plate-type fuel assemblies

In this study, an innovative large-deformation multi-layer shell model is developed. In order to improve the efficiency of simulating the thermo-mechanical coupling behavior in complex plate-type fuel assemblies, the fuel plates are simulated by 2D shell element, with the irradiation swelling and creep effects involved. The specific theoretical frame for the kinematic models and three-dimensional constitutive models is presented, together with the generalized force update algorithm. The application of the developed shell model in the thermal–mechanical behavior simulation of a monolithic U-Mo/Al fuel assembly indicates that (1) the out-of-plane displacements of the side plates can be precisely calculated, with the calculation time ∼92% shorter than that of a full 3D simulation of 308-day irradiation behavior; (2) the out-of-plane displacements of the fuel plates excluding the peripheral ones can also be well obtained, with small relative deviations to the full 3D simulation results; (3) using 3D solid models for the peripheral fuel plates, a fuel assembly finite element (FE) model with 2D&3D geometric models for the fuel plates is proposed with a total computation time decrease of 64%, and the precise of deformation simulation results can be improved comprehensively. This research provides important theoretical models and algorithms for the irradiation-induced deformation analysis of complex plate-type fuel assemblies.

On a semilinear parabolic problem with non-local (Bitsadze–Samarskii type) boundary conditions in more dimensions

This paper studies a semilinear parabolic equation in a bounded domain Ω⊂Rd along with nonlocal boundary conditions. The boundary values are linked to the values of a solution on an interior (d−1)-dimensional manifold lying inside Ω. Firstly, the solvability of a steady-state problem is addressed. Secondly, involving the semi-discretization in time, a constructive algorithm for approximation of a solution to a transient problem is developed. The well-posedness of the problem in a weighted Hilbert space is shown. Convergence of approximations is addressed and the error estimated are derived. Numerical experiments support the theoretical algorithms.

(Retracted) Image processing technology in Shanghai Stock Exchange options based on state-space model

The current relevant models for the analysis of Shanghai Stock Exchange (SSE) options, whether for the study of theoretical algorithms or the application of verification, are still at the beginning of the research stage. Based on this, we combine the status quo of China’s SSE options to construct a state-space model with a certain flexibility and combine image processing technology to extract model features. At the same time, we obtained the experimental data of this study through the network data collection method and analyzed the performance of the algorithm by comparison method, recorded the data obtained by the model operation, and turned the result into a visually identifiable feature result through image processing. The research indicates that the state-space model has certain effects on the analysis of the SSE option and can provide theoretical reference for subsequent related research.

Three-Dimensional Spectrum Occupancy Measurement Using UAV: Performance Analysis and Algorithm Design

Spectrum sharing, as an approach to significantly improve spectrum efficiency in the era of 6th generation mobile networks (6G), has attracted extensive attention. Radio Environment Map (REM) based low-complexity spectrum sharing is widely studied where the spectrum occupancy measurement (SOM) is vital to construct REM. The SOM in three-dimensional (3D) space is becoming increasingly essential to support the spectrum sharing with space-air-ground integrated network being a great momentum of 6G. In this paper, we analyze the performance of 3D SOM to further study the tradeoff between accuracy and efficiency in 3D SOM. We discover that the error of 3D SOM is related with the area of the boundary surfaces of licensed networks, the number of discretized cubes, and the length of the edge of 3D space. Moreover, we design a fast and accurate 3D SOM algorithm that utilizes unmanned aerial vehicle (UAV) to measure the spectrum occupancy considering the path planning of UAV, which improves the measurement efficiency by requiring less measurement time and flight time of the UAV for satisfactory performance. The theoretical results obtained in this paper reveal the essential dependencies that describe the 3D SOM methodology, and the proposed algorithm is beneficial to improve the efficiency of 3D SOM. It is noted that the theoretical results and algorithm in this paper may provide a guideline for more areas such as spectrum monitoring, spectrum measurement, network measurement, planning, etc.

Identification of a coherent twin relationship from high-resolution reciprocal-space maps.

Twinning is a common crystallographic phenomenon which is related to the formation and coexistence of several orientation variants of the same crystal structure. It may occur during symmetry-lowering phase transitions or during the crystal growth itself. Once formed, twin domains play an important role in defining physical properties: for example, they underpin the giant piezoelectric effect in ferroelectrics, superelasticity in ferroelastics and the shape-memory effect in martensitic alloys. Regrettably, there is still a lack of experimental methods for the characterization of twin domain patterns. Here, a theoretical framework and algorithm are presented for the recognition of ferroelastic domains, as well as the identification of the coherent twin relationship using high-resolution reciprocal-space mapping of X-ray diffraction intensity around split Bragg peaks. Specifically, the geometrical theory of twinned ferroelastic crystals [Fousek & Janovec (1969). J. Appl. Phys. 40, 135-142] is adapted for the analysis of the X-ray diffraction patterns. The necessary equations are derived and an algorithm is outlined for the calculation of the separation between the Bragg peaks, diffracted from possible coherent twin domains, connected to one another via a mismatch-free interface. It is demonstrated that such separation is always perpendicular to the planar interface between mechanically matched domains. For illustration purposes, the analysis is presented of the separation between the peaks diffracted from tetragonal and rhombohedral domains in the high-resolution reciprocal-space maps of BaTiO3 and PbZr1-xTixO3 crystals. The demonstrated method can be used to analyse the response of multi-domain patterns to external perturbations such as electric field, change of temperature or pressure.

Theoretical algorithm for fast calculation of interatomic bonds as applied to metallic hydrogen and carbon condensates

A method for calculating the potential energy of interatomic interaction, an alternative to the use of significant computer resources, is proposed. The algorithm was tested on the example of 1S-1S bonds, in particular, in hydrogen, as well as 2S-2S and 2 P-2P bonds, in particular, in carbon. It is shown that bound states can be formed in all three cases, but the interacting atoms must overcome the potential barrier. The presence of a barrier makes it possible to explain a number of known features in the formation of the corresponding condensates.

A game theory‐based clustering and multi‐hop routing scheme in wireless sensor networks for energy minimization

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.

A novel computational strategy for defining the minimal protein molecular surface representation.

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

Preface

Due to recent pandemic, 2022 5th International Conference on Advanced Algorithms and Control Engineering (ICAACE 2022) was held virtually online during January 20-22, 2022. The decision to hold the virtual conference was made in compliance with many restrictions and regulations that were imposed by countries around the globe. Such restrictions were made to minimize the risk of people contracting or spreading the COVID-19 through physical contact. There were 140 individuals who attended this online conference, represented many countries including Malaysia, India, Iran, and China. ICAACE 2022 is to bring together innovative academics and industrial experts in the field of advanced algorithms and control engineering to a common forum. The primary goal of the conference is to promote research and developmental activities in advanced algorithms and control engineering. And another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the second part, we invited two professors as our keynote speakers. Professor Jinming Wen, Jinan University, China. He is mainly engaged in the basic theoretical research and algorithm design of lattice cipher and compressed sensing. And then we had Assoc. Prof. Pavel Loskot, IEEE Senior Member, Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJUI), China. His current research interest focuses on problems involving statistical signal processing and importing methods from Telecommunication Engineering and Computer Science to other disciplines in order to improve the efficiency and the information power of system modeling and analysis. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Algorithms and Data Structures, Electrical systems, Machine Learning, Intelligent data analysis and Data Mining, Mathematical and computer modelling, Systems and automation, Process control and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard. We would like to acknowledge all of those who supported ICAACE 2022. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers. We sincerely hope that the ICAACE 2022 turned out to be a forum for excellent discussions that enabled new ideas to come about, promoting collaborative research. We are sure that the proceedings will serve as an important research source of references and knowledge, which will lead to not only scientific and engineering findings but also new products and technologies. The Committee of ICAACE 2022 Committee member are available in the pdf