Theory-based Algorithm Research Articles

REMR: Identification of RNA Editing-mediated MiRNA Regulation in Cancers

Dysregulation of adenosine-to-inosine (A-to-I) RNA editing has been implicated in cancer progression. However, a comprehensive understanding of how A-to-I RNA editing is incorporated into miRNA regulation to modulate gene expression in cancer remains unclear, given the lack of effective identification methods. To this end, we introduced an information theory-based algorithm named REMR to systematically identify 12,006 A-to-I RNA editing-mediated miRNA regulatory triplets (RNA editing sites, miRNAs, and genes) across ten major cancer types based on multi-omics profiling data from The Cancer Genome Atlas (TCGA). Through analyses of functional enrichment, transcriptional regulatory networks, and protein-protein interaction (PPI) networks, we showed that RNA editing-mediated miRNA regulation potentially affects critical cancer-related functions, such as apoptosis, cell cycle, drug resistance, and immunity. Furthermore, triplets can serve as biomarkers for classifying cancer subtypes with distinct prognoses or drug responses, highlighting the clinical relevance of such regulation. In addition, an online resource (http://www.jianglab.cn/REMR/) was constructed to support the convenient retrieval of our findings. In summary, our study systematically dissected the RNA editing-mediated miRNA regulations, thereby providing a valuable resource for understanding the mechanism of RNA editing as an epitranscriptomic regulator in cancer.

A novel property to modify weighted [formula omitted] minimization for improved compressed sensing

Weighted l1 minimization schemes are common methods to achieve compressed sensing (CS). However, they fail in the presence of inaccurate prior knowledge or improper scaling of weights due to inappropriately assigned large weights causing large and destructive errors in signal recovery. This paper proposes a theory-based algorithm to identify and correct such destructive weights for each signal entry. The enhancement is achieved through a novel sparsity-inducing property (SIP) which establishes a necessary condition for successful signal recovery. SIP outperforms existing properties such as coherence, restricted isometry property, and nullspace property by indicating which signal entries fail to be recovered. This unique advantage enables us to correct destructive weights that do not satisfy the SIP condition, making signal recovery successful where it previously failed. Results from many numerical experiments demonstrate that our proposed method can improve the signal recovery capability, robustness, and stability of the weighted l1 minimization for a wide range of applications, including sparse and compressive signal recovery, noise-aware recovery, sparse error correction, fast image acquisition, and sub-Nyquist sampling.

A Double Exponential Particle Swarm Optimization with non-uniform variates as stochastic tuning and guaranteed convergence to a global optimum with sample applications to finding optimal exact designs in biostatistics

Nature-inspired metaheuristic algorithms, like Particle Swarm Optimization (PSO), are powerful general-purpose optimization tools but they invariably do not come with rigorous theoretical justifications and can fail to find a global optimum. By treating PSO as a random search optimization process and repairing the famous Global Search Convergence Theorem by imposing an additional condition in the proof, we create a novel theory-based algorithm called the Double Exponential Particle Swarm optimization algorithm (DExPSO) that converges to a global optimum. In particular, we show the common practice of using uniform variates as stochastic components in PSO and related algorithms does not satisfy the sufficient conditions in DExPSO and hence may provide a reason why PSO and other nature-inspired algorithms, like QPSO, LcRiPSO, and CSO can fail to converge. Additionally, in more complicated design problems, we show that DExpSO tends to converge to the support points of the optimal design more frequently and faster than PSO and its variants do. Moreover, there is a possibility to modify other PSO variants to DExPSO variants, and such hybridization offers promising improvement in the quality of the global search. Our applications include finding designs that minimize the integrated mean squared prediction error and locally D-optimal exact designs for a 68-compartmental model to assess radioactive particles retained in the human lung after exposure. Because PSO, and more generally, metaheuristics are used across disciplines, including ecology, pharmacokinetics and pharmacodynamics studies, agriculture, engineering, and computer science, there are potentially broad and deep implications of our results.

A game theoretic approach for spectral and energy efficient D2D communication in 5G-IoT networks

In the context of fifth generation (5G) technology, Device-to-Device (D2D) communication plays a pivotal role, requiring swift and intelligent decision-making in mode selection and device discovery. This study addresses the challenge of rapid mode selection and device discovery within 5G communication networks, focusing specifically on enhancing spectral and energy efficiency for Internet of Things (IoT) applications. A novel self-centered game theory-based algorithm is introduced to optimize spectral efficiency and support intelligent mode selection. Additionally, the utilization of the support vector machine (SVM) expedites mode selection decisions. For D2D discovery, the Frank-Wolfe method is adopted, significantly improving the differentiation between D2D and Cellular users based on signal strength and interference, thereby enhancing spectral efficiency. The proposed approach maximizes spectral efficiency while adhering to strict power and interference constraints, intelligently partitioning bandwidth into two subparts using game theoretic principles to amplify spectral efficiency. Furthermore, the emphasis on energy efficiency is underscored through iterative calculations to achieve maximum energy-efficient spectral allocation. Numerical analyses validate the efficacy of the proposed technique, revealing substantial improvements in accurately predicted labels. As the number of devices increases, precision and recall rates experience noteworthy enhancements, ultimately leading to superior bandwidth utilization. This research presents a significant contribution to the field of 5G communication, particularly concerning energy efficiency, which is paramount for IoT applications. By accelerating D2D connectivity and optimizing energy and spectrum resources, it advances the goals of energy-efficient D2D communication within 5G-IoT networks.

RETRACTED ARTICLE: Efficient identity security authentication method based on improved R-LWE algorithm in IoT environment

In recent years, various smart devices based on IoT technology, such as smart homes, healthcare, detection, and logistics systems, have emerged. However, as the number of IoT-connected devices increases, securing the IoT is becoming increasingly challenging. To tackle the increasing security challenges caused by the proliferation of IoT devices, this research proposes an innovative method for IoT identity authentication. The method is based on an improved ring-learning with errors (R-LWE) algorithm, which encrypts and decrypts communication between devices and servers effectively using polynomial modular multiplication and modular addition operations. The main innovation of this study is the improvement of the traditional R-LWE algorithm, enhancing its efficiency and security. Experimental results demonstrated that, when compared to number theory-based algorithms and elliptic curve cryptography algorithms at a 256-bit security level, the enhanced algorithm achieves significant advantages. The improved algorithm encrypted 20 data points with an average runtime of only 3.6 ms, compared to 7.3 ms and 7.7 ms for the other algorithms. Similarly, decrypting the same amount of data had an average runtime of 2.9 ms, as opposed to 7.3 ms and 8 ms for the other algorithms. Additionally, the improved R-LWE algorithm had significant advantages in terms of communication and storage costs. Compared to the number theory-based algorithm, the R-LWE algorithm reduced communication and storage costs by 3 °C each, and compared to elliptic curve cryptography, it reduced them by 4 °C each. This achievement not only enhances the efficiency of encryption and decryption but also lowers the overall operational costs of the algorithm. The research has made significant strides in improving the security and efficiency of IoT device identity authentication by enhancing the R-LWE algorithm. This study provides theoretical and practical foundations for the development and application of related technologies, as well as new solutions for IoT security.

Data-Driven Insight into the Reductive Stability of Ion-Solvent Complexes in Lithium Battery Electrolytes.

Lithium (Li) metal batteries (LMBs) are regarded as one of the most promising energy storage systems due to their ultrahigh theoretical energy density. However, the high reactivity of the Li anodes leads to the decomposition of the electrolytes, presenting a huge impediment to the practical application of LMBs. The routine trial-and-error methods are inefficient in designing highly stable solvent molecules for the Li metal anode. Herein, a data-driven approach is proposed to probe the origin of the reductive stability of solvents and accelerate the molecular design for advanced electrolytes. A large database of potential solvent molecules is first constructed using a graph theory-based algorithm and then comprehensively investigated by both first-principles calculations and machine learning (ML) methods. The reductive stability of 99% of the electrolytes decreases under the dominance of ion-solvent complexes, according to the analysis of the lowest unoccupied molecular orbital (LUMO). The LUMO energy level is related to the binding energy, bond length, and orbital ratio factors. An interpretable ML method based on Shapley additive explanations identifies the dipole moment and molecular radius as the most critical descriptors affecting the reductive stability of coordinated solvents. This work not only affords fruitful data-driven insight into the ion-solvent chemistry but also unveils the critical molecular descriptors in regulating the solvent's reductive stability, which accelerates the rational design of advanced electrolyte molecules for next-generation Li batteries.

Game Theory-Based Decision-Making and Iterative Predictive Lateral Control for Cooperative Obstacle Avoidance of Guided Vehicle Platoon

This paper presents a systematic decision-making and lateral control framework to realize cooperative obstacle avoidance (OA) of the guided vehicle platoons safely and integrally in multiple scenarios. In the control framework, a centralized decision-making strategy is developed to determine optimal overtaking maneuver (i.e., OA) in the dynamic driving environment, and a distributed lateral controller with a safe driving corridor planner is designed for lateral reference trajectories tracking. The proposed decision-making strategy formulates the influence mechanism between the controlled platoon and potential surrounding vehicles based on an interactive behavior model. Moreover, a game theory-based algorithm is applied to calculate safe timing of overtaking and select efficient overtaking mode while avoiding the negative impact on the surrounding vehicles. For the distributed lateral control strategy, the control problem is solved by a linear time-varying model predictive control (LTV-MPC) algorithm so as to reduce the computational burden. Furthermore, to improve the accuracy of LTV-MPC algorithm in varying scenarios, a tailored iterative control logic is presented. The performance of the proposed framework is evaluated on the dSPACE hardware-in-loop platform. The results show that the proposed framework works well under various test scenarios involving multiple static and moving obstacles.

Joint Optimization of Computing Offloading and Service Caching in Edge Computing-Based Smart Grid

With the continuous expansion of the power Internet of Things and the number of Smart Devices (SDs), the data generated by SDs has exponentially increased. The traditional cloud-based smart grid cannot meet the low latency and high reliability requirements of emerging applications. By moving computing, data, and services from the centralized cloud to Edge Servers (ESs), edge computing exhibits excellent performance in communication delay and traffic reduction. Simultaneously, service caching also shows attractive advantages in handling the surge in data traffic. This paper considers the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulates the problem as a Mixed-Integer Non-Linear Program, aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative Computing Offloading and Resource Allocation Method (CCORAM) is proposed to solve the problem, which includes two low-complexity algorithms. Specifically, a gradient descent allocation algorithm is first proposed to determine the computing resource allocation strategy, and then a game theory-based algorithm is proposed to determine the computing strategy. Simulation results show that CCORAM with low-complexity is very close to the optimal method, and performs much better than other benchmark methods.

Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships

Chalcogenide glasses (ChGs) possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite their ubiquitous usage, these materials’ composition–property relationships remain poorly understood, impeding the pace of their discovery. Here, we use a large experimental dataset comprising ∼24 000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning (ML) models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Young’s modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models are the largest regarding the compositional space and the number of properties covered for ChGs. Further, we use Shapley additive explanations, a game theory-based algorithm, to explain the properties’ compositional control by quantifying each element’s role toward model predictions. This work provides a powerful tool for interpreting the model’s prediction and designing new ChG compositions with targeted properties. Finally, using the trained ML models, we develop several glass-selection charts that can potentially aid in the rational design of novel ChGs for various applications.

Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs

With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.

Algorithm development to improve intervention effectiveness for parents with mental health signs and symptoms.

In this study we aimed to describe and compare groups formed by a rules-based algorithm to prospectively identify clients at risk of poor outcomes in order to guide tailored public health nursing (PHN) intervention approaches. Data-driven methods using standardized Omaha System PHN documentation. Clients ages 13-40 who received PHN home visiting services for both the Caretaking/parenting and Mental health problems (N=4109). We applied a theory-based algorithm consisting of six rules using existing Omaha System data. We examined the groups formed by the algorithm using standard descriptive, inferential statistics, and Latent Class Analysis. Clients (N=4109) were 25.1 (SD=5.9) years old and had an average of 7.3 (SD=3.2) problems, 250 (SD=319) total interventions, and 32 (SD=44) Mental health interventions. Overall outcomes improved after PHN interventions (p<.001 for all) and having more Mental health signs/symptoms was negatively associated with outcome scores (p<.001 for all). This algorithm may be helpful in identifying high-risk clients during a baseline assessment who may benefit from more intensive mental health interventions. Findings show there is value using the Omaha System for PHN documentation and algorithm clinical decision support development. Future research should focus on algorithm implementation in PHN clinical practice.

Performance comparison of successive interference cancellation decoding order for base station association and power control in small cell networks incorporating handoff optimization

The co-channel interference and frequent handoff are two important issues that have a great impact on the spectrum and energy efficiency in the non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) in the small cell networks (SCN). This paper investigates the performance of SIC decoding order based on the users’ channel state information (CSI) and random SIC decoding order under the perfect and imperfect conditions for the handoff optimization in SCNs with NOMA. The effect of SIC decoding order in the joint base station association and power control problem is studied by considering the motion of mobile users (MUs). Two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs, under the maximum allowable transmit power and minimum average-rate constraints. Two objectives are combined into a single-stage optimization problem through the weighted sum method. The formulated problem is solved using a game theory-based algorithm and primal decomposition theory. The simulation results show that changing the SIC decoding order can lead to performance improvement in the uplink NOMA- based SCNs. Also, it is shown that the proposed algorithm can significantly reduce the frequent handoffs in small cell networks.

Multiagent RL Aided Task Offloading and Resource Management in Wi-Fi 6 and 5G Coexisting Industrial Wireless Environment

With the emergence of industrial Internet of Things (IIoT), intensive computation workload will be imposed to industrial end units (IEUs). By leveraging mobile edge computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This article proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel joint task scheduling and resource allocation approach comprises the following two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power and an online multiagent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared “intuition” and “cost optimal” approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multiaccess-node MEC systems.

Adaptive simulation and integration method for wheel-rail contact problems in locomotive traction studies

Knowledge of creep forces at wheel-rail interface when high power is applied to the locomotive wheels has been the subject of many recent studies. The common wheel-rail interaction methods are generally divided by two theories – Hertzian and non-Hertzian. The first method uses Hertzian contact for the normal problem and the tangential problem is divided into two algorithms – Polach’s theory and the Simplified theory-based algorithm referred to as Modified Fastsim. Kalker’s original Fastsim-based method shows a better accuracy for these two Hertzian-based algorithms. The second method is based on the non-Hertzian theory, and it uses the Exact theory also developed by Kalker and implemented in the Extended Contact library to calculate forces at the wheel-rail interface. Considering that the second method is more accurate, this paper aims to present an adaptive simulation and integration method (ASIM) which is positioned in the middle between the two common methods and is designed to introduce a transition from the non-Hertzian to Hertzian contact in the normal problem with the application of the Modified Fastsim for the calculation of contact forces at the wheel-rail interface. The proposed method is aimed to be used in vehicle system dynamics studies performed in multibody software packages. A specially developed wheel-rail coupling incorporating geometrical, normal, and tangential modules is integrated into Gensys multibody software and benchmarked with the standard Extended Contact based wheel-rail coupling by means of locomotive traction tests that use a full mechatronic model of a high adhesion locomotive. The computational performance and accuracy of the proposed method are assessed and discussed.

Deep-Learning-Based Resource Allocation for Time-Sensitive Device-to-Device Networks.

Ultra-reliable and low-latency communication (URLLC) is considered as one of the major use cases in 5G networks to support the emerging mission-critical applications. One of the possible tools to achieve URLLC is the device-to-device (D2D) network. Due to the physical proximity of communicating devices, D2D networks can significantly improve the latency and reliability performance of wireless communication. However, the resource management of D2D networks is usually a non-convex combinatorial problem that is difficult to solve. Traditional methods usually optimize the resource allocation in an iterative way, which leads to high computational complexity. In this paper, we investigate the resource allocation problem in the time-sensitive D2D network where the latency and reliability performance is modeled by the achievable rate in the short blocklength regime. We first design a game theory-based algorithm as the baseline. Then, we propose a deep learning (DL)-based resource management framework using deep neural network (DNN). The simulation results show that the proposed DL-based method achieves almost the same performance as the baseline algorithm, while it is more time-efficient due to the end-to-end structure.

AN EFFICIENT GRAPH THEORY-BASED ALGORITHM FOR SHIP TRAJECTORY PLANNING

The research presented in this paper is dedicated to the development of a path planning algorithm for a moving object in a dynamic environment. The marine environment constitutes the application area. A graph theory-based path planning method for ships is introduced and supported by the results of simulation tests and comparative analysis with a heuristic Ant Colony Optimization approach. The method defines the environment with the use of a visibility graph and uses the A* algorithm to find the shortest, collision-free path. The main contribution is the development of an effective graph theory-based algorithm for path planning in an environment with static and dynamic obstacles. The computational time does not exceed a few seconds. Obtained results allow to state that the method is suitable for use in an intelligent motion control system for ships.

Individual tree detection and crown segmentation based on metabolic theory from airborne laser scanning data

Laser scanning technology has enabled to study three-dimensional (3D) structures in forests. For example, airborne laser scanning (ALS) point cloud has been applied to detect individual trees and segment tree crowns. However, the accuracy of such approach remains a challenge because of the intersected crowns and complicated understories. We developed a metabolic theory-based algorithm for individual tree detection and crown segmentation from ALS data. The algorithm is composed of two parts, of which one is an unscaled transporting distance-based top-to-bottom detection approach, and the other is a scaled transporting distance-based segmentation approach. The unscaled transporting distance for detection is the absolute distance from tree root to crown and then to ALS point based on the tree structure models, whereas the scaled transporting distance for segmentation is the unscaled distance that is scaled by an initial tree height obtained during detection. This is based on a basic metabolic theory that vascular plants tend to minimize the material transporting distance from root to leaves. Hence, seven types of materials transporting distance models were built based on monopodial branching structure or crown-centered structure. The performance of the proposed approach was then further examined and compared with two typical canopy height model-based approaches and one typical point cloud-based approach, taking forest in Oxfordshire, UK, as a case study. The results showed that our approach can reach a recall of 1.00, a precision of 0.96, and an F-score of 0.98 and can reach to much higher accuracy for tree height (R2 = 0.8045) than the comparison approaches (R2 < 0.2) in the study plot. One of the main reasons that led to such low accuracy of comparison approaches is much overestimation of understory height with a mean error that is 2.9 times higher than that of our approach on average. Furthermore, ALS point-to-point level accuracy assessment shows 9.7% more ALS points were truly assigned in our approach than that of comparison approaches. It is noticed that the algorithm presented is not sensitive to the two key parameters: p (a percentage determining the threshold of unscaled transporting distances) ranging from 32.0% to 34.0% and λ (the proportion between the assumed crown center height and tree height) ranging from 0.70 to 0.90 based on our data set. Such high accuracy of our approach can greatly improve detections of individual tree and crown segmentation, especially in delineating understories in complex-structured forest.

A New Game Theory-Based Algorithm for Target Coverage in Directional Sensor Networks

A New Game Theory-Based Algorithm for Target Coverage in Directional Sensor Networks

Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation.

The autoxidation of triglyceride (or triacylglycerol, TAG) is a poorly understood complex system. It is known from mass spectrometry measurements that, although initiated by a single molecule, this system involves an abundance of intermediate species and a complex network of reactions. For this reason, the attribution of the mass peaks to exact molecular structures is difficult without additional information about the system. We provide such information using a graph theory-based algorithm. Our algorithm performs an automatic discovery of the chemical reaction network that is responsible for the complexity of the mass spectra in drying oils. This knowledge is then applied to match experimentally measured mass spectra with computationally predicted molecular graphs. We demonstrate this methodology on the autoxidation of triolein as measured by electrospray ionization-mass spectrometry (ESI-MS). Our protocol can be readily applied to investigate other oils and their mixtures.

Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations

Flying base stations (FlyBSs) enable ubiquitous communications in the next generation mobile networks with a flexible topology. However, a deployment of the FlyBSs intensifies interference, which can result in a degradation in the throughput of cell-edge users. In this paper, we introduce a flexible soft frequency reuse (F-SFR) that enables a self-organization of a common SFR in the networks with an unpredictable and dynamic topology with the FlyBSs. We propose a graph theory-based algorithm for an allocation of resource plans, which is understood as a bandwidth allocation and a transmission power setting in the context of SFR. Furthermore, we introduce a low-complexity implementation of the proposed resource allocation using deep neural network (DNN) to significantly reduce the computation complexity. We show that the proposed F-SFR increases the throughput of cell-edge users by 16% to 26% and, at the same time, improves the satisfaction of the cell-edge users by up to 25% compared to the state-of-the-art solutions. We also demonstrate that the proposed scheme ensures a higher fairness in the throughput among the users with respect to the state-of-the-art solutions. The implementation via DNN also outperforms all state-of-the-art solutions despite its very low complexity.