Meta-heuristic combining prior online and offline information for the quadratic assignment problem

The construction of promising solutions for NP-hard combinatorial optimization problems (COPs) in meta-heuristics is usually based on three types of information, namely a priori information, a posteriori information learned from visited solutions during the search procedure, and online information collected in the solution construction process. Prior information reflects our domain knowledge about the COPs. Extensive domain knowledge can surely make the search effective, yet it is not always available. Posterior information could guide the meta-heuristics to globally explore promising search areas, but it lacks local guidance capability. On the contrary, online information can capture local structures, and its application can help exploit the search space. In this paper, we studied the effects of using this information on metaheuristic's algorithmic performances for the COPs. The study was illustrated by a set of heuristic algorithms developed for the quadratic assignment problem. We first proposed an improved scheme to extract online local information, then developed a unified framework under which all types of information can be combined readily. Finally, we studied the benefits of the three types of information to meta-heuristics. Conclusions were drawn from the comprehensive study, which can be used as principles to guide the design of effective meta-heuristic in the future

Internal Leakage Fault Detection and Tolerant Control of Single-Rod Hydraulic Actuators

The integration of internal leakage fault detection and tolerant control for single-rod hydraulic actuators is present in this paper. Fault detection is a potential technique to provide efficient condition monitoring and/or preventive maintenance, and fault tolerant control is a critical method to improve the safety and reliability of hydraulic servo systems. Based on quadratic Lyapunov functions, a performance-oriented fault detection method is proposed, which has a simple structure and is prone to implement in practice. The main feature is that, when a prescribed performance index is satisfied (even a slight fault has occurred), there is no fault alarmed; otherwise (i.e., a severe fault has occurred), the fault is detected and then a fault tolerant controller is activated. The proposed tolerant controller, which is based on the parameter adaptive methodology, is also prone to realize, and the learning mechanism is simple since only the internal leakage is considered in parameter adaptation and thus the persistent exciting (PE) condition is easily satisfied. After the activation of the fault tolerant controller, the control performance is gradually recovered. Simulation results on a hydraulic servo system with both abrupt and incipient internal leakage fault demonstrate the effectiveness of the proposed fault detection and tolerant control method

Adaptive Robust Actuator Fault Accommodation for a Class of Uncertain Nonlinear Systems with Unknown Control Gains

An adaptive robust fault tolerant control approach is proposed for a class of uncertain nonlinear systems with unknown signs of high-frequency gain and unmeasured states. In the recursive design, neural networks are employed to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unknown sign of the virtual control direction. By incorporating the switching function σ algorithm, the adaptive backstepping scheme developed in this paper does not require the real value of the actuator failure. It is mathematically proved that the proposed adaptive robust fault tolerant control approach can guarantee that all the signals of the closed-loop system are bounded, and the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples

FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless Communication Networks

With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. However, when a federated learning system runs on wireless communication networks, limited wireless resources, heterogeneity of clients, and network transmission failures affect its performance and accuracy. In this study, we propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training accuracy and performance in wireless communication networks. We utilize a tiering algorithm that dynamically divides clients into different tiers according to specific indicators and assigns specific timeout thresholds to each tier to reduce the training time required. To improve the accuracy of the model without increasing the training time, we introduce a cross-tier client selection algorithm that can effectively select the tiers and participants. Simulation experiments show that our scheme can make the model converge faster and achieve a higher accuracy in wireless communication networks

Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

Podocytes, specialized epithelial cells that envelop the glomerular capillaries, play a pivotal role in maintaining renal health. The current description and quantification of features on pathology slides are limited, prompting the need for innovative solutions to comprehensively assess diverse phenotypic attributes within Whole Slide Images (WSIs). In particular, understanding the morphological characteristics of podocytes, terminally differentiated glomerular epithelial cells, is crucial for studying glomerular injury. This paper introduces the Spatial Pathomics Toolkit (SPT) and applies it to podocyte pathomics. The SPT consists of three main components: (1) instance object segmentation, enabling precise identification of podocyte nuclei; (2) pathomics feature generation, extracting a comprehensive array of quantitative features from the identified nuclei; and (3) robust statistical analyses, facilitating a comprehensive exploration of spatial relationships between morphological and spatial transcriptomics features.The SPT successfully extracted and analyzed morphological and textural features from podocyte nuclei, revealing a multitude of podocyte morphomic features through statistical analysis. Additionally, we demonstrated the SPT's ability to unravel spatial information inherent to podocyte distribution, shedding light on spatial patterns associated with glomerular injury. By disseminating the SPT, our goal is to provide the research community with a powerful and user-friendly resource that advances cellular spatial pathomics in renal pathology. The implementation and its complete source code of the toolkit are made openly accessible at https://github.com/hrlblab/spatial_pathomics

Transfusion-dependent non-severe aplastic anemia: characteristics and outcomes in the clinic

Transfusion-dependent non-severe aplastic anemia (TD-NSAA) is a rare condition of bone marrow failure that can persist for a long time or develop into severe aplastic anemia (SAA). Little is known about the clinical and laboratory characteristics, and disease prognosis and outcomes in TD-NSAA patients. The clinical and laboratory data of 124 consecutive TD-NSAA patients in the Chinese Eastern Collaboration Group of Anemia from December 2013 and January 2017 were analyzed retrospectively. In 124 TD-NSAA patients, the median age was 32 years (range: 3-80) and the median disease course was 38 months (range: 3-363). Common complications were iron overload (53/101, 52.5%), liver and kidney dysfunction (42/124, 33.9%), diabetes mellitus/impaired glucose tolerance (24/124, 19.4%), and severe infection (29 cases, 23.4%). 58% of patients (57/124) developed severe aplastic anemia with a median progression time of 24 months (range: 3-216). Patients with absolute neutrophil count (ANC) <0.5×109/L, severe infection, or iron overload had a higher probability of progression to SAA (P=0.022, P=0.025, P=0.001). Patients receiving antithymocyte globulin (ATG) plus Cyclosporin A (CsA) had a higher overall response rate compared to those receiving CsA alone (56.7% vs 19.3%, P < 0.001). The addition of ATG was the favorable factor for efficacy (P=0.003). Fourteen patients developed secondary clonal hematologic disease: eleven patients with paroxysmal nocturnal hemoglobinuria, two patients with myelodysplastic syndromes, and one patient with acute myeloid leukemia, respectively. Ten patients (8.1%) died with a median follow-up of 12 months (range: 3- 36 months). Patients with TD-NSAA usually have a prolonged course of disease, and are prone to be complicated with important organ damage and disease progression to SAA. Intensive immunosuppressive therapy based on ATG might be an appropriate approach for TD-NSAA.Clinical trial registration:http://www.chictr.org.cn/edit.aspx?pid=125480&htm=4, identifier ChiCTR2100045895

Effect of particle degradation on electrostatic sensor measurements and flow characteristics in dilute pneumatic conveying

Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic conveying often lead to particle degradation. The resulting particle size reduction and particle number increase will impact on the flow characteristics, and subsequently affect the electrostatic type of flow measurements. This study investigates this phenomenon using both experimental and numerical methods. Particle degradation was induced experimentally by recursively conveying the fillite material within a pneumatic pipeline. The associated particle size reduction was monitored. Three electrostatic sensors were embedded along the pipeline to monitor the flow. The results indicated a decreasing trend in the electrostatic sensor outputs with decreasing particle size, which suggested the attenuation of the flow velocity fluctuation. This trend was more apparent at higher conveying velocities, which suggested that more severe particle degradation occurred under these conditions. Coupled computational fluid dynamics and discrete element methods (CFD–DEM) analysis was used to qualitatively validate these experimental results. The numerical results suggested that smaller particles exhibited lower flow velocity fluctuations, which was consistent with the observed experimental results. These findings provide important information for the accurate application of electrostatic measurement devices in pneumatic conveyors