Feasibility Of Algorithm Research Articles

Care-seeking pathways and diagnostic delays in extrapulmonary TB patients

SETTING: This study was conducted at a private tertiary hospital engaged with the TB control programme in the city of Lahore, Pakistan.OBJECTIVE: To assess the healthcare-seeking pathways, different delays and factors associated with delays among the patients who presented in the outpatient department with tuberculous lymphadenitis and pleuritis, the most common manifestations of extrapulmonary TB.DESIGN: This cross-sectional study was conducted prospectively from April 2016 to August 2017.RESULTS: The median age of the 339 patients analysed was 22 years (IQR 17–30); tuberculous lymphadenitis was predominant in females (63%), while pleuritis affected more males (64%). Overall, 62% reported seeking care from healthcare providers before diagnosis, of whom 62% sought care from private facilities, 32% visited facilities >2 times and 8% visited traditional healers. Diagnostic delay was associated with tuberculous lymphadenitis, age 15–44 years, poor socio-economic status and poor TB knowledge.CONCLUSION: There was considerable delay in the management of extrapulmonary TB patients, and the health-system delay was the major contributor, leading to increased patient suffering. Efforts towards minimising health-system delay need to be prioritised for patient screening and diagnosis, with a feasible algorithm that is workable in resource-limited settings.

Performance Analysis of Probabilistic Roadmaps in Simulation for Mobile Robot Path Planning

Many challenges, including path-planning for a mobile robot, revolve around finding the lowest-cost path through a graph. A quick path-planning algorithm for a mobile robot in a dynamic environment is proposed by integrating Dijkstra's algorithm. Dijkstra's method and Probabilistic Roadmaps are used to determine an initial path from the starting point to the goal. This method searches for an optimal path from the robot's current location to this local target, and as the number of nodes increases, a new path that brings the robot from the current place to the goal is obtained. Meanwhile, the re-planning time is substantially reduced. The simulation results validate the suggested algorithm's feasibility and effectiveness.

Distributed Coordination LFC Approach for Interconnected Power Systems Under Detection and Compensation Mechanism Targeting DoS Attacks

A distributed coordination load frequency control (LFC) approach is proposed for interconnected power systems, and a detection and compensation mechanism targeting denial-of-service (DoS) attacks is designed in this work. Firstly, different from traditional LFC, a hybrid-type distributed control strategy is proposed, which takes full advantage of local control information and coordination control information. Then, for discovering DoS attacks timely, a feasible detection algorithm targeting DoS attacks is presented. Furthermore, in order to weaken the harm of cyber-attacks, a triggered-type compensation mechanism for control data is proposed. By constructing appropriate Lyapunov-Krasovskii functionals, a stability criterion with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance index is developed. At last, the feasibility of detection and compensation mechanism targeting DoS attacks and the effectiveness of proposed control approach are demonstrated by presenting contrastive illustrative examples.

Quadratic Programming for Continuous Control of Safety-Critical Multiagent Systems Under Uncertainty

This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actuation system describes the dynamic response of the actual velocity to the velocity reference signal. The notion of input-to-output stability (IOS) is employed to characterize the essential velocity-tracking capability of the actuation system. The standard QP algorithms for collision avoidance may be infeasible due to uncertain actuator dynamics. Even if feasible, the solutions may be non-Lipschitz because of possible violation of the full rank condition of the active constraints. Also, the interaction between the controlled integrator and the uncertain actuator dynamics may lead to significant robustness issues. Based on the current development of nonlinear control theory and numerical optimization methods, this paper first contributes a new feasible-set reshaping technique and a refined QP algorithm for feasibility, robustness, and local Lipschitz continuity. Then, we present a nonlinear small-gain analysis to handle the inherent interaction for guaranteed safety of the closed-loop multi-agent system. The proposed method is illustrated by numerical simulation and a physical experiment.

A novel method of restoration path optimization for the alternating current–direct current bulk power grid after a major blackout

AbstractThe restoration control of the modern alternating current–direct current (AC–DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line‐commutated converter high‐voltage direct current (LCC‐HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC‐HVDC is first analysed from the aspects of start‐up and operation of LCC‐HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC‐HVDC is derived in detail based on the system strength indices of the short‐circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non‐tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39‐bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC–DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC–DC power grid.

An EM algorithm for estimating the parameters of the skew generalized t-normal distribution with application to robust finite mixture modeling

The present article describes an EM-type algorithm for estimation of the skew generalized t-normal (SGTN) distribution. The family of SGTN distributions can provide certain types of flexibility such as heavy tails and high kurtosis. The complexity of the SGTN distribution is traced to the ratio of the t density and distribution function of a normal distribution in the likelihood equations. To cope with this problem, we develop a feasible ECME algorithm for computing maximum likelihood estimates of model parameters via a selection mechanism. The proposed approach provides a robust parameter estimation method for the finite mixture model. Standard errors for the parameter estimates can be obtained via a general information-based method. Experimental results on simulated data and one real data example demonstrate the efficacy and usefulness of the proposed methodology.

Applications of chaotic quantum adaptive satin bower bird optimizer algorithm in berth-tugboat-quay crane allocation optimization

With the advent of the post-pandemic era, the global economic situation has tightened, and the economic benefits of container ports have also been affected. This paper attempts to build a novel berth-tugboat-quay crane joint scheduling method for container ports to reduce the economic cost of container ports. Firstly, from the perspective of the economic cost of container ports, considering random economic factors, this paper proposes a new berth-tugboat-quay crane joint dispatch optimization model, namely E-BUQC, by taking the minimum time cost of vessels in port, the cost of additional freight distance for trucks, the cost of tugboat operation, and the minimum operation cost of quay cranes as the objective function. Then, in order to obtain a relatively high-quality solution, based on chaotic mapping and quantum computing, this paper constructs a new chaotic quantum adaptive satin bower bird optimizer algorithm, namely CQASBO, formulates exclusive coding rules for the E-BUQC model, develops a feasible integer algorithm, and proposes a new joint scheduling solution method of berth-tugboat-quay crane, named E-BUQC_CQASBO. Subsequently, based on the simulation examples of two ports in northern China, the new solution method is tested. The simulation test results show that the E-BUQC model obtains an appropriate solution; The CQASBO constructed in this paper has better performance than other traditional comparison algorithms selected in this paper and can provide a better solution when solving the E-BUQC model.

Resource Optimization Strategy in Phased Array Radar Network for Multiple Target Tracking When Against Active Oppressive Interference

The phased array radar network has the ability to track multiple targets simultaneously through the cooperative work of several active nodes, which brings significant advantages in tracking target under oppressive interference. In this article, an efficient resource optimization strategy (ROS) is developed in this scenario. The optimization model is formulated with the aim of countering active oppressive interference by dynamically assigning tracking tasks and allocating transmit power to each node. The architecture of information fusion considered in this article is centralized so that the detection performance and the parameter estimation accuracy can be improved. Since the predicted conditional Cramer–Rao lower bound (PC-CRLB) can provide an accurate performance lower bound for target tracking accuracy, the objective function is designed to improve the sum of weighted PC-CRLBs of multiple targets. By introducing the relaxation technique and the sorting algorithm, this mixed integer nonlinear programming problem is converted into a two-stage nonconvex problem, and the efficient solutions are obtained through the modified Zoutendijk method of feasible directions algorithm. Numerical simulations demonstrate the effectiveness of the proposed ROS.

An Information-Theoretic Method to Automatic Shortcut Avoidance and Domain Generalization for Dense Prediction Tasks.

Deep convolutional neural networks for dense prediction tasks are commonly optimized using synthetic data, as generating pixel-wise annotations for real-world data is laborious. However, the synthetically trained models do not generalize well to real-world environments. This poor "synthetic to real" (S2R) generalization we address through the lens of shortcut learning. We demonstrate that the learning of feature representations in deep convolutional networks is heavily influenced by synthetic data artifacts (shortcut attributes). To mitigate this issue, we propose an Information-Theoretic Shortcut Avoidance(ITSA) approach to automatically restrict shortcut-related information from being encoded into the feature representations. Specifically, our proposed method minimizes the sensitivity of latent features to input variations: to regularize the learning of robust and shortcut-invariant features in synthetically trained models. To avoid the prohibitive computational cost of direct input sensitivity optimization, we propose a practical yet feasible algorithm to achieve robustness. Our results show that the proposed method can effectively improve S2R generalization in multiple distinct dense prediction tasks, such as stereo matching, optical flow, and semantic segmentation. Importantly, the proposed method enhances the robustness of the synthetically trained networks and outperforms their fine-tuned counterparts (on real data) for challenging out-of-domain applications.

THE COMPARISON OF 2D DOSE PATIENT-SPECIFIC QUALITY ASSURANCE BETWEEN MONTE CARLO-CONVOLUTION AND MODIFIED CLARKSON INTEGRATION ALGORITHM

A sophisticated machine of radiotherapy treatment process follows the complexity of the quality assurance (QA) measurement. Non-measurement QA becomes one of the solutions to reduce the medical physicists’ workload. However, this method has not been clinically established. This study compared two non-measurement methods of patient-specific quality assurance (PSQA) to find the feasible algorithm for the adaptive radiotherapy process. Monte Carlo-based (MC) PSQA used a phase space file of the medical linear accelerator (Linac) to obtain the photon energy fluence and forward projected to the isoplane. In contrast, Modified Clarkson Integration-based (MCI) used a non-uniform fluence map in the isoplane. For the modulated intensity, we used a pair of the dynamic log files of the multileaf-collimator (MLC) and then employed them in the algorithms. The dose distributions of MC and MCI methods were compared to the treatment planning system (TPS) using gamma index analysis. We found that the gamma pass rates (GPR) for MC-TPS and MCI-TPS were 99.54% and 99.57%, respectively. Further, the dose distribution in the off-axis region for the MCI method showed lesser accuracy due to the higher secondary dose contribution. The linac log file information can be used and calculated into a 2D dose distribution using both MC and MCI methods, providing high-accuracy results.

A new low-cost feasible projection algorithm for pseudomonotone variational inequalities

A new low-cost feasible projection algorithm for pseudomonotone variational inequalities

Computation Efficiency Optimization for Millimeter-Wave Mobile Edge Computing Networks With NOMA

In this paper, by improving the computation efficiency (CE) and ensuring the fairness among users, we study the CE optimization for millimeter-wave mobile edge computing (mmWave-MEC) Networks with NOMA, where both the analog beamforming (ABF) and hybrid beamforming (HBF) architectures under the partial offloading mode are considered. Firstly, according to the max-min fairness criterion, the CE maximization problem is formulated to jointly optimize the ABF and the local resource allocation of each user. An efficient CE optimization algorithm based on the penalized successive convex approximation is proposed to solve this non-convex problem. Then, the max-min CE optimization problem in mmWave-MEC with HBF is studied, where the joint design of the HBF and the local resource allocation of each user is carried out. By using the penalty function and the inexact block coordinate descent method, a feasible CE optimization algorithm is developed to tackle this challenging problem. Simulation results verify the convergence of the proposed algorithms and show that the proposed computation-efficient resource allocation schemes can improve the CE effectively, and mmWave-MEC with HBF can obtain higher CE than that with ABF. Besides, the NOMA scheme exhibits superior performance over the conventional orthogonal multiple access scheme in terms of CE.

Clustering asymmetrical data with outliers: Parsimonious mixtures of contaminated mean-mixture of normal distributions

Mixture modeling has emerged as a statistical tool to perform unsupervised model-based clustering for heterogeneous data. A framework of using contaminated mean-mixture of normal distributions as the components of the mixture model is designed to accommodate asymmetric data with outliers. Fourteen parsimonious variants of the postulated model are introduced by employing an eigenvalue decomposition of the component scale matrices. Simultaneously clustering and outliers detection is an outstanding advantage of the proposed model in analyzing non-normally distributed data. A computationally feasible and flexible EM-type algorithm is outlined for obtaining maximum likelihood parameter estimates. Moreover, the score vector and empirical information matrix for calculating asymptotic standard errors of the parameter estimates are derived by offering an information-based approach. The applicability of the proposed method is demonstrated through the analysis of simulated and real datasets with varying proportions of outliers.

Diagnosis for Chinese patients with light chain amyloidosis: a scoping review

Background Amyloid light chain (AL) amyloidosis is the most common systemic amyloidosis. The objective of this scoping review was to map the available literature on the diagnosis of AL amyloidosis in China. Materials and Methods The published academic papers related to the diagnosis of AL amyloidosis were screened from 1 January 2000 to 15 September 2021. Chinese patients who have suspected AL amyloidosis were included. The included studies were categorized into accuracy studies and descriptive studies based on if the studies supplied the diagnostic accuracy data or not. The information on the diagnostic methods reported by included studies was synthesized. Results Forty-three articles were included for the final scoping review, with 31 belonging to descriptive studies and 12 having information on diagnostic accuracy. Although cardiac involvement was second top in Chinese patients with AL amyloidosis, a cardiac biopsy was rare. Next, we found light chain classification and monoclonal (M-) protein identification were essential methods for the diagnosis of AL amyloidosis in China. In addition, some combined tests (e.g. immunohistochemistry and serum free light chain, immunohistochemistry and immunofixation electrophoresis, and serum free light chain and immunofixation electrophoresis) can increase the sensitivity of the diagnosis. Finally, several adjuvant methods (e.g. Imaging, N-terminal-pro hormone BNP, and brain natriuretic peptide test) were important for AL amyloidosis diagnosis. Conclusion This scoping review details the characteristics and results of the recently published studies on diagnosing AL Amyloidosis in China. Biopsy is the most important method for AL Amyloidosis diagnosis in China. In addition, combined tests and some adjuvant methods played essential roles in the diagnosis. Further research is required to determine an acceptable and feasible diagnostic algorithm after symptom onset. REGISTRATION: INPLASY2022100096 KEY MESSAGES This scoping review details the characteristics and results of the recently published studies on diagnosing Amyloid light chain (AL) Amyloidosis in China. Biopsy is the most important method for AL Amyloidosis diagnosis in China. Combined tests and some adjuvant methods played essential roles in the diagnosis.

Multidimensional rank-one convexification of incremental damage models at finite strains

This paper presents computationally feasible rank-one relaxation algorithms for the efficient simulation of a time-incremental damage model with nonconvex incremental stress potentials in multiple spatial dimensions. While the standard model suffers from numerical issues due to the lack of convexity, our experiments showed that the relaxation by rank-one convexification delivering an approximation to the quasiconvex envelope prevents mesh dependence of the solutions of finite element discretizations. By the combination, modification and parallelization of the underlying convexification algorithms, the novel approach becomes computationally feasible. A descent method and a Newton scheme enhanced by step-size control prevent stability issues related to local minima in the energy landscape and the computation of derivatives. Numerical techniques for the construction of continuous derivatives of the approximated rank-one convex envelope are discussed. A series of numerical experiments demonstrates the ability of the computationally relaxed model to capture softening effects and the mesh independence of the computed approximations. An interpretation in terms of microstructural damage evolution is given, based on the rank-one lamination process.

H∞ observer-based sliding mode control for uncertain discrete-time singularly perturbed systems

This paper studies the sliding mode control (SMC) problem of discrete-time singularly perturbed systems (DTSPSs) with external perturbations and nonlinear inputs. First, to estimate the state of the original system, a novel state observer is constructed. Under this condition, the observer error system is ensured to be input-to-state stable (ISS) and satisfies the given H∞ performance index. Then, an appropriate sliding mode control law is proposed to satisfy the reachability condition. On this basis, the ISS criterion for the sliding mode dynamics (SMDs) is given, and the controller gain matrix is also obtained. In addition, a feasible algorithm is given to compute the upper bound for the small parameter. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed method.

Interior-point algorithm for symmetric cone horizontal linear complementarity problems based on a new class of algebraically equivalent transformations

AbstractWe generalize a primal-dual interior-point algorithm (IPA) proposed recently in (Illés T, Rigó PR, Török R Unified approach of primal-dual interior-point algorithms for a new class of AET functions, 2022) to $$P_*(\kappa )$$ P ∗ ( κ ) -horizontal linear complementarity problems (LCPs) over Cartesian product of symmetric cones. The algorithm is based on the algebraic equivalent transformation (AET) technique with a new class of AET functions. The new class is a modification of the class of AET functions proposed in (Illés T, Rigó PR, Török R Unified approach of primal-dual interior-point algorithms for a new class of AET functions, 2022) where only two conditions are used as opposed to three used in (Illés T, Rigó PR, Török R Unified approach of primal-dual interior-point algorithms for a new class of AET functions, 2022). Furthermore, the algorithm is a feasible algorithm that uses full Nesterov-Todd steps, hence, no calculation of step-size is necessary. Nevertheless, we prove that the proposed IPA has the iteration bound that matches the best known iteration bound for IPAs solving these types of problems.

A path-following interior-point algorithm for monotone LCP based on a modified Newton search direction

In this paper, we propose a short-step feasible full-Newton step path-following interior-point algorithm (IPA) for monotone linear complementarity problems (LCPs). The proposed IPA uses the technique of algebraic equivalent transformation (AET) induced by an univariate function to transform the centering equations which defines the central-path. By applying Newton’s method to the modified system of the central-path of LCP, a new Newton search direction is obtained. Under new appropriate defaults of the thresholdτwhich defines the size of the neighborhood of the central-path and ofθwhich determines the decrease in the barrier parameter, we prove that the IPA is well-defined and converges locally quadratically to a solution of the monotone LCPs. Moreover, we derive its iteration bound, namely,O(√nlogn\ϵ) which coincides with the best-known iteration bound for such algorithms. Finally, some numerical results are presented to show its efficiency.

Meta-Learned User Preference Estimator with Attention Network for Cold-Start Recommendation

One crucial challenge in the recommendation research field is the cold-start problem. Meta-learning is a feasible algorithm to reduce the error of cold-start recommendation because it can adjust to new tasks rapidly through relatively few updates. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. We proposed a recommendation model called attentional meta-learned user preference estimator that combines attention network and meta-learning. This method enhances the ability of modelling the personalized user interest by learning the weights between users and items based on attention mechanism, then improves the performance of cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%.

Optimization on Elastoplasticity of Functionally Graded Shells of Revolution under Axisymmetric Loading

Depending on the load level, structures can experience a material nonlinearity known as elastoplasticity, which has an important role in the behaviour of structures. In order to avoid the elastoplastic behaviour, it is necessary to find the optimal thickness distribution, which corresponds to the minimum mass that provides an elastic behaviour for a certain load level. The elastoplasticity analysis of functionally graded axisymmetric shells under axisymmetric mechanical loading, and the subsequent optimization, was performed by using a simple conical frustum finite element model with two nodal circles; three degrees of freedom per node, which was based on Kirchhoff’s theory allowing for shear deformation; and using a reduced numerical integration procedure that is essential for its success when applied to thin shells. The formulation accounts for the calculation of the displacements and through-thickness stress distribution, including the effective stress. In this work, the thickness was the design variable in the optimization procedure and the mass was the objective function that needed to be minimized subject to a constraint imposed on the effective stress. The optimization solutions were obtained by using a feasible arc interior point gradient-based algorithm. Some illustrative examples were performed, and the corresponding results are presented and discussed.