Random Scheme Research Articles

Approach for multi-valued integer programming in multi-material topology optimization: Random discrete steepest descent (RDSD) algorithm

The present study models the multi-material topology optimization problems as the multi-valued integer programming (MVIP) or named as combinatorial optimization. By extending classical convex analysis and convex programming to discrete point-set functions, the discrete convex analysis and discrete steepest descent (DSD) algorithm are introduced. To overcome combinatorial complexity of the DSD algorithm, we employ the sequential approximate integer programming (SAIP) to explicitly and linearly approximate the implicit objective and constraint functions. Considering the multiple potential changed directions for multi-valued design variables, the random discrete steepest descent (RDSD) algorithm is proposed, where a random strategy is implemented to select a definitive direction of change. To analytically calculate multi-material discrete variable sensitivities, topological derivatives with material contrast is applied. In all, the MVIP is finally transferred as the linear 0–1 programming that can be efficiently solved by the canonical relaxation algorithm (CRA). Explicit nonlinear examples demonstrate that the RDSD algorithm owns nearly three orders of magnitude improvement compared with the commercial software (GUROBI). The proposed approach, without using any continuous variable relaxation and interpolation penalization schemes, successfully solves the minimum compliance problem, strength-related problem, and frequency-related optimization problems. Given the algorithm efficiency, mathematical generality and merits over other algorithms, the proposed RDSD algorithm is meaningful for other structural and topology optimization problems involving multi-valued discrete design variables.

Optimization of a Groundwater Pollution Monitoring Well Network Using a Backpropagation Neural Network-Based Model

Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwater monitoring well network optimizations. A local sensitivity analysis method was used in this study to analyze the hydrogeological parameters of a simulation groundwater solute transport model. The results showed a strong effect of longitudinal dispersion and transverse dispersion on the output results of the simulation model, and a good fit between the backpropagation neural network (BPNN)-based alternative model’s results and those obtained using the solute transport simulation model, accurately reflecting the input and output relationship of the simulation model. The optimized groundwater monitoring layout scheme consisted of four groundwater monitoring wells, namely no. 7, no. 16, no. 23, and no. 24. These wells resulted in a groundwater fluoride pollution rate of 98.44%, which was substantially higher than that obtained using the random layout scheme. In addition, statistical analysis of the fluoride groundwater pollution results obtained using the Monte Carlo random simulation highlighted continuous and high groundwater fluoride levels in the second and third pollution sources and their downstream groundwater. Therefore, more attention should be devoted to these sources to ensure the effective remediation of groundwater pollution in the study area.

Gorilla algorithm based on double random perturbation and its engineering application

Aiming at the problems of artificial gorilla troop optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low optimization accuracy, an artificial gorilla troop optimization algorithm based on double random perturbation strategy is proposed. Firstly, the Halton sequence is introduced to initialize the population to increase the diversity of the population; secondly, a multi-dimensional random number strategy is used in the algorithm optimization stage and an adaptive position search mechanism is proposed in the exploration stage to improve the convergence speed of the algorithm; thirdly, a double random perturbation strategy is proposed to solve the group effect of gorillas and enhance the ability of the algorithm to jump out of the local optimum; finally, a dimension-by-dimensional update strategy is adopted to update individual positions, which improves the convergence accuracy of the algorithm. Through the comparison of the optimization results of 10 benchmark test functions and the Wilcoxon rank sum test, it can be seen that the improved algorithm has greatly improved the optimization accuracy and convergence speed. In addition, through the experimental comparative analysis of an engineering optimization problem, the superiority of the proposed algorithm in dealing with real engineering problems is further verified.

An Innovative Path Planning Algorithm for Complex Obstacle Environments with Adaptive Obstacle Density Adjustment: AODA-PF-RRT*

To address the limitations of low node utilization and inadequate adaptability in complex environments encountered by Rapidly-exploring Random Tree (RRT) algorithms during the expansion phase, this study presents an enhanced path planning algorithm—AODA-PF-RRT* (Adaptive Obstacle Density Adjustment-PF-RRT*). The proposed algorithm implements a random extension strategy for nodes that fail collision detection, thereby improving node efficiency. Furthermore, it dynamically partitions the area surrounding sampling points and calculates local obstacle density in real time. By leveraging this density information, the algorithm flexibly adjusts both the number of expansion points and the dichotomy threshold, significantly enhancing its responsiveness to environmental changes. We rigorously demonstrate the algorithm’s probabilistic completeness and asymptotic optimality. Simulation and benchmarking results demonstrate that the AODA-PF-RRT* algorithm not only generates smooth and high-quality paths compared to existing algorithms but also maintains low computational costs in complex environments, showcasing exceptional stability and robustness.

Grain boundary crystallography and segregation in Ni-based superalloy INC738 manufactured by electron-beam powder bed fusion in as-built and annealed conditions

The excellent high-temperature properties of Ni-based superalloy INC738 are due to its hierarchical microstructure, making it an ideal engineering material for high-temperature applications. Engineering parts are now increasingly made via electron beam powder bed fusion (EPBF), an additive manufacturing technique suitable for such hard-to-weld Ni-based superalloys, due to lower thermal gradients and unmatched scan path control. The thermal cycles induced by EPBF impact characteristics of the γ-matrix, γ’ precipitates, secondary phases such as carbides, grain boundary (GB) solute segregation and, in turn, properties including GB cohesion and strength. However, a more thorough understanding of the GB microstructure evolution with focus on GB chemistry and character is required to optimise properties. We systematically investigate texture, grain structure, GB habit planes, and GB segregation in INC738 fabricated with linear versus random EPBF scanning strategies. We show that random scanning is a suitable strategy to inhibit cracking, refine grains, and decrease segregation of Cr, Mo, C, and B at GBs. For both scanning strategies, γ/γ GBs predominantly terminate on {100} planes and are decorated with C, B, Mo, and W. Upon 2 h annealing at 1180 °C and 1250 °C, the GB character and texture are shown to remain stable despite a reduction in GB interfacial excess. After 24 h annealing at 1250 °C, GB segregation and depletion are nearly eradicated, while static recrystallisation is observed with a predominant formation of annealing twins and GBs terminating on {111} planes. These findings are critical for defect-free additive manufacturing of INC738 and similar grades for superior high-temperature performance.

LightGBM integration with modified data balancing and whale optimization algorithm for rock mass classification

The accurate prediction of uneven rock mass classes is crucial for intelligent operation in tunnel-boring machine (TBM) tunneling. However, the classification of rock masses presents significant challenges due to the variability and complexity of geological conditions. To address these challenges, this study introduces an innovative predictive model combining the improved EWOA (IEWOA) and the light gradient boosting machine (LightGBM). The proposed IEWOA algorithm incorporates a novel parameter l for more effective position updates during the exploration stage and utilizes sine functions during the exploitation stage to optimize the search process. Additionally, the model integrates a minority class technique enhanced with a random walk strategy (MCT-RW) to extend the boundaries of minority classes, such as Classes II, IV, and V. This approach significantly improves the recall and F1-score for these rock mass classes. The proposed methodology was rigorously evaluated against other predictive algorithms, demonstrating superior performance with an accuracy of 94.74%. This innovative model not only enhances the accuracy of rock mass classification but also contributes significantly to the intelligent and efficient construction of TBM tunnels, providing a robust solution to one of the key challenges in underground engineering.

Agroforestry stand age influence physical and chemical soil parameters

The role of agroforestry in improving soil parameters is well known. However, there is debate as to how age of agroforestry practice affects physical and chemical parameters especially in the tropical region of Sub-Saharan Africa where adoption of the practice is fairly recent. To understand those effects, a study was conducted using soil samples taken in farms adopting/non adopting agroforestry practises, selected using a stratified, random sampling strategy. Soil was sampled from adopters and non-adopters using soil auger. At least five sub-samples were collected from each location and the soil mixed to get an integrated soil sample for analysis. The physical (sand, clay, silt and bulk density), chemical properties (pH, total nitrogen [TN], total phosphorus [TP], total organic carbon [TOC], carbon nitrogen ratio [C/N] and carbon to phosphorus ratio [C/P]) were analyzed in the soil. The exchangeable bases (K, Ca, Mg and Na) as well as micronutrients (Mn, Cu, Fe and Zn) were also analyzed. The results indicated that sand was significantly (P < 0.05) higher among non-adopters compared to adopters while silt and bulk density was significantly (P < 0.05) higher among the adopters compared to the non-adopters. Sand levels decreased while silt and bulk density significantly increased with increasing agroforestry stand age. The TN, TOC and C/P ratio were significantly (P < 0.05) higher among adopters and increased consistently with age of adoption, while C/N was higher among non-adopters and decreased with increasing age of agroforestry stand. The trend in exchangeable bases and micro-nutrients in the soil were similar, where higher concentrations occurred among adopters and displayed an increase with regard to age of agroforestry stand. Our results support the hypothesis that age of agroforestry practice affects soil in parameter-specific patterns.

Brief Peer-Supported Web-Based Skills Training in Affective and Interpersonal Regulation (BPS webSTAIR) for Trauma-Exposed Veterans in the Community: Randomized Controlled Trial.

Peer-supported mobile health (mHealth) programs hold the promise of providing a low-burden approach to increasing access to care and improving mental health. While peer support has been shown to improve engagement in care, there is limited investigation into the impact of peers on symptom outcomes. Trauma-exposed populations frequently endure co-occurring posttraumatic stress and depressive symptoms as well as difficulties in day-to-day functioning. This study evaluated the potential benefits of a peer-supported, transdiagnostic mHealth program on symptom outcomes and functioning. This randomized controlled trial tested the effectiveness of Brief Peer-Supported (BPS) web-based Skills Training in Affective and Interpersonal Regulation (webSTAIR), a 6-module transdiagnostic digital program derived from Skills Training in Affective and Interpersonal Regulation and compared to waitlist control in a community sample of veterans who screened positive for either posttraumatic stress disorder (PTSD) or depression. A total of 178 veterans were enrolled in this study using a 2:1 randomization scheme with 117 assigned to BPS webSTAIR and 61 assigned to waitlist control. PTSD and depressive symptoms as well as emotion regulation and psychosocial functioning were assessed at pretreatment, posttreatment, and 8-week follow-up time points. Mixed-effects models were used to assess change in outcome measures across time points. Exploratory analyses were conducted to determine whether the type and number of peer interactions influenced outcomes. Significant interaction effects were observed for all outcomes such that participants randomized to BPS webSTAIR reported significantly greater improvement at the posttreatment time point compared to waitlist control with moderate effect sizes for PTSD (d=0.48), depression (d=0.64), emotion regulation (d=0.61), and functional impairment (d=0.61); gains were maintained at 8-week follow-up. An initial cohort of participants who were required to engage with a peer coach to progress through the modules interacted more frequently with peers but completed fewer modules compared to a later cohort for whom peer engagement was optional. Overall, those who completed more modules reported greater improvement in all outcomes. BPS webSTAIR was effective in improving PTSD and depression symptoms, emotion regulation, and psychosocial functioning in community veterans. Peer-supported, transdiagnostic mHealth programs may be a particularly efficient, effective, and low-burden approach to improving mental health among trauma-exposed populations. Investigation of peer-supported programs among other populations is necessary to evaluate the generalizability of the findings. Analyses comparing peer support that was required versus optional indicated that some veterans may not need or want peer support. Future research should evaluate how best to deliver peer support and for whom it is most beneficial. If successful, peer-supported tech programs may increase the Veteran Affairs workforce as well as improve veteran mental health services and outcomes. ClinicalTrials.gov NCT04286165; https://clinicaltrials.gov/study/NCT04286165.

Pistachio Consumption Increases Macular Pigment Optical Density in Healthy Adults: A Randomized Controlled Trial

BackgroundPistachios are a bioavailable source of the xanthophyll lutein. Along with zeaxanthin, these plant pigments are major components of macular pigment (MP) in the human retina. MP can be non-invasively measured and is referred to as MP optical density (MPOD). MPOD is modifiable with dietary interventions that include lutein and zeaxanthin (L/Z). Higher MPOD protects the eye from light damage and is positively associated with eye health. ObjectivesThis dietary intervention study aimed to evaluate the effect of pistachio consumption on MPOD. MethodsThis single-blinded, randomized controlled trial compared a 12-week pistachio intervention (2 oz/d) with usual diet (UD) on MPOD and serum L/Z in middle-aged to older healthy adults (n = 36) in a 1:1 randomization scheme. Participants were selected for habitually low L/Z intake and low baseline MPOD. MPOD was measured using heterochromatic flicker photometry at 4 retinal eccentricities during baseline, week 6, and week 12 study visits. Serum L/Z was analyzed using high-performance liquid chromatography. Primary statistical analysis was conducted on an intent-to-treat basis using repeated-measure analysis of variance. ResultsCompared with UD, MPOD of the participants in the pistachio intervention group (PIS) had significantly increased (P < 0.001) at all eccentricities over the initial 6-wk period. This increase was maintained at week 12. MPOD in the UD participants did not change during the 12-week period. Serum lutein concentration followed a similar pattern to MPOD; serum cis-lutein and zeaxanthin did not change in either group over the 12-wk intervention. ConclusionsThe results of our study demonstrate that a dietary intervention with pistachios is efficacious in increasing MPOD in healthy adults selected for habitually low intake of L/Z and low baseline MPOD. This suggests that pistachio consumption could be an effective dietary strategy for preserving eye health. Future studies need to evaluate the generalizability of our findings to other populations.This trial was registered at clinicaltrials.gov as NCT05283941.

Automated fault diagnosis of rotating machinery using sub domain greedy Network Architecture search

Network Architecture Search (NAS) automates hyperparameter adjustments in deep learning models, offering a potent solution for building intelligent fault diagnosis models. Despite its potential, NAS faces challenges in this application. The primary issues include the excessive time required when searching across the full data domain and the inefficacy of the common random search strategy in yielding high-performance sub networks. This study addresses these challenges through several key initiatives: It defines a specific library of hyperparameters tailored for intelligent fault diagnosis tasks; constructs sub data domain model clusters to reduce the computational costs associated with NAS; and introduces a hyperparameter search strategy leveraging a greedy algorithm, which enhances the search efficiency for optimal diagnostic sub network and minimizes its prediction errors. When applied to datasets involving gear and bearing faults, the proposed method demonstrates a reduction in both the time cost of searches and the prediction errors of sub networks, compared to traditional Network Architecture Search. Moreover, it outperforms manually tuned deep learning models by simultaneously optimizing multiple sets of hyperparameter and automatically generating higher accuracy diagnostic sub networks.

Controlling and optimizing the transport (search) efficiency with local information on a class of scale-free trees.

The scale-free trees are fundamental dynamics networks with extensive applications in material and engineering fields owing to their high reliability and low power consumption characteristics. Controlling and optimizing transport (search) efficiency on scale-free trees has attracted much attention. In this paper, we first introduce degree-dependent weighted tree by assigning each edge (x,y) a weight wxy=(dxdy)θ, with dx and dy being the degree of nodes x and y, and θ being a controllable parameter. Then, we design a parameterized biased random walk strategy with the transition probability depending on the local information (the degree of neighboring nodes) and a parameter θ. Finally, we evaluate analytically the global mean first-passage time, which is an important indicator for measuring the transport (search) efficiency on the underlying networks, and find the interval for parameter θ where transport (search) efficiency can be improved on a class of scale-free trees. We also analyze the (transfinite) walk dimension for our biased random walk on the scale-free trees and find one can obtain arbitrary transfinite walk dimension in an interval by properly tuning the biased parameter θ. The results obtained here would shed light on controlling and optimizing transport (search) efficiency on objects with scale-free tree structures.

Relation semantic fusion in subgraph for inductive link prediction in knowledge graphs

Inductive link prediction (ILP) in knowledge graphs (KGs) aims to predict missing links between entities that were not seen during the training phase. Recent some subgraph-based methods have shown some advancements, but they all overlook the relational semantics between entities during subgraph extraction. To overcome this limitation, we introduce a novel inductive link prediction model named SASILP (Structure and Semantic Inductive Link Prediction), which comprehensively incorporates relational semantics in both subgraph extraction and node initialization processes. The model employs a random walk strategy to calculate the structural scores of neighboring nodes and utilizes an enhanced graph attention network to determine their semantic scores. By integrating both structural and semantic scores, SASILP strategically selects key nodes to form a subgraph. Furthermore, the subgraph is initialized with a node initialization technique that integrates information about neighboring relations. The experiments conducted on benchmark datasets demonstrate that SASILP outperforms state-of-the-art methods on inductive link prediction tasks, and verify the effectiveness of our approach.

Strategic deployment in the deep: Principled underwater sensor placement optimization with three-dimensional acoustic map.

Underwater acoustic sensors are vital for monitoring marine environments and detecting targets, but their optimal placement presents challenges, particularly in deep-sea environments. This paper addresses the question of determining the optimal sensor placement in a specific ocean region through a principled optimization approach. While previous studies mainly utilized heuristic algorithms without exploiting problem-specific structures, this work explores leveraging the complex three-dimensional acoustic environment through principled modeling and tailored optimization. Specifically, intricate three-dimensional multi-directional acoustic maps are constructed for each sensor. Based on these maps, the sensor placement problem is then cast as an integer linear programming, allowing the study to leverage established theoretical results from operations research. Additionally, an alternative algorithm with its performance indicator is presented to find near-optimal solutions efficiently and can empirically reach over 99% coverage of the optimal solution. Experimental results using real-life data from the South China Sea demonstrate the effectiveness of the proposed approach in achieving much larger detection coverage compared to random and empirical strategies. Notably, the alternative fast algorithm approaches the optimal solution in significantly less time. Furthermore, experiments show that any further simplification of this approach leads to the performance degradation.

Machine-directed Gravitational-wave Counterpart Discovery

Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics or detector dynamics and the behavior policy that maximizes a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent with the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3× higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.

A multi‐dimensional incentive mechanism based on age of update in hierarchical federated learning

AbstractFederated learning represents a decentralized approach to machine learning, enabling numerous devices to collaboratively contribute to model training while ensuring the privacy of individual data. However, the existing incentive mechanism of hierarchical federated learning (HFL) only considers the data contribution of a single round, which needs to be revised. For non‐IID data sets, the continuous selection of any end devices will cause the weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a certain end device to ensure the overall effectiveness. We introduce a metric to describe the importance of updates: age of update (AoU), which can help select end devices not selected in the previous round to promote a faster model convergence. We put forward an incentive mechanism based on AoU, reputation, and data quantity in HFL (ARDHFL). We have derived the optimal equilibrium solution for the three‐stage Stackelberg game. Based on this solution, we can ensure maximum edge‐cloud utility while incentivizing end devices to engage actively in HFL tasks and providing superior data to train the HFL model. Finally, we conducted extensive experiments to prove that ARDHFL can effectively improve the performance. Compared with the fixed scheme, random scheme, FMore and InFEDge, the testing accuracy of ARDHFL in the MNIST dataset has been improved by 29.7%, 9.3%, 6.8% and 6.1%, respectively. In the CIFAR‐10 dataset, it has been improved by 40.2%, 33.1%, 16.4% and 14.2%, respectively, and demands fewer communication iterations to achieve the same testing accuracy.

A graphics processing unit accelerated sparse direct solver and preconditioner with block low rank compression

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. In this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

Evaluating sampling techniques for quantifying Asiatic garden beetle (Maladera formosae) (Coleoptera: Scarabaeidae) infestations in commercial mint.

The Asiatic garden beetle, Maladera formosae Brenske (AGB), has become a significant pest of commercial mint fields in northern Indiana. Larval feeding on mint roots can cause stunted growth and plant death when densities are high. Sampling approaches that provide reliable estimates of larval densities in mint have not been established, leaving farmers without the knowledge necessary to implement integrated pest management (IPM) strategies. To address this knowledge gap, we evaluated strategies for estimating AGB larval densities and plant performance in commercial mint systems. We used 2 sampling methods to collect larval density and plant performance data from 3 mint fields and conducted simulations to optimize sampling intensity (accuracy and precision) and sampling scheme (random vs. systematic) using these data. Additionally, we examined the sensitivity and efficiency of each sampling method. Compared to the cup-cutter method, the quadrat method provided the most accurate and precise estimates of larval density and plant performance, with ≤ 7 samples required per 0.2 ha. Quadrat excavation was also more sensitive, increasing the probability of detecting AGB larvae within a 32 m2 plot by 76.7%, and requiring significantly less time to survey an equivalent volume of soil for AGB larvae. When the quadrat method was employed, random sampling schemes provided below-ground biomass estimates that were significantly closer to the true mean of the sampling area. The results of this research will facilitate the development of IPM decision-making tools for farmers and support future research for AGB and other soil insect pests affecting mint production.

Model‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments

AbstractStepped wedge cluster randomized experiments (SW‐CREs) represent a class of unidirectional crossover designs. Although SW‐CREs have become popular, definitions of estimands and robust methods to target estimands under the potential outcomes framework remain insufficient. To address this gap, we describe a class of estimands that explicitly acknowledge the multilevel data structure in SW‐CREs and highlight three typical members of the estimand class that are interpretable. We then introduce four analysis of covariance (ANCOVA) working models to achieve estimand‐aligned analyses with covariate adjustment. Each ANCOVA estimator is model‐assisted, as its point estimator is consistent even when the working model is misspecified. Under the stepped wedge randomization scheme, we establish the finite population Central Limit Theorem for each estimator. We study the finite‐sample operating characteristics of the ANCOVA estimators in simulations and illustrate their application by analyzing the Washington State Expedited Partner Therapy study.

Cyclic bond behavior and bond stress – slip model of steel strands in steel fiber reinforced concrete

To study the bond performance of steel strands in steel fiber reinforced concrete (SFRC), bonding tests under monotonic and cyclic loads were performed on the specimens with different steel fiber volume fractions (SFVFs), bond lengths, nominal diameters of steel strands and SFRC strengths. The results showed that the relative rotation between the steel strand and SFRC occurred when the steel strands were vertically pulled out from SFRC due to the smooth and continuous helical shape of the steel strands. For the specimens with the same parameters, the maximum bond stress under cyclic load decreased by 1.67–22.88 % than that under monotonic load. The maximum bond stress under cyclic load increased with the increasing SFVFs or SFRC strength. Notably, the maximum bond stress under cyclic load also increased with the increasing bond length or diameter of steel strands, significantly different from that of ordinary ribbed steel bars and FRP bars. The cumulative energy dissipation (CED) of bond specimens increased with the diameter of steel strands and SFRC strength. Besides, the SFVFs had a positive effect on the CED, but simultaneously increasing diameter of steel strands would weaken the positive effect of SFVFs, and simultaneously increasing bond length had no obvious on the CED. Finally, a bond stress – slip model of steel strands in SFRC was established, suitable for calculating bond stress – slip curves under random and complicated loading schemes. By comparing the calculation and test results of bond stress – slip curves, it was proved that the proposed model had an acceptable accuracy and could describe the influence laws of SFVF, bond length, nominal diameter of steel strand and SFRC strength well.

Research on random dynamic cylinder deactivation control strategy of engine based on nonlinear model prediction

In the process of cycle cylinder deactivation, the different proportion of cylinder deactivation between adjacent working cycles will cause the problem of uneven operation of each cylinder. In this paper, a random dynamic cylinder deactivation (RD-CDA) control method based on nonlinear model prediction is proposed. The RBF neural network model of the RD-CDA system is built. The K-means algorithm is applied to the offline training of the center position c of the hidden layer node. The P-nearest algorithm is used to train the hidden layer node width σ offline. The recursive least squares (RLS) algorithm is applied to train the output layer weight vector w online. The online adaptive change of the model is realized, and the model verification is carried out. Then, the nonlinear model predictive control system structures with the minimum torque fluctuation and brake specific fuel consumption rate (BSFC) are built respectively. Under different working conditions, the average torque fluctuation per cycle is improved by about 9% to 18.75%, and the average BSFC is improved by about 2 % -3.9 %, which verifies the effectiveness of this strategy in reducing the dynamic fluctuation of random dynamic cylinder deactivation torque and improving fuel economy.