Loading Sequence Research Articles

The effect of molnupiravir and nirmatrelvir on SARS-CoV-2 genome diversity in severe models of COVID-19.

Immunocompromised individuals are susceptible to severe COVID-19 and potentially contribute to the emergence of variants with altered pathogenicity due to persistent infection. This study investigated the impact of immunosuppression on SARS-CoV-2 infection in k18-hACE2 mice and the effectiveness of antiviral treatments in this context during the first 7 days of infection. Mice were immunosuppressed using cyclophosphamide and infected with a B lineage of SARS-CoV-2. Molnupiravir and nirmatrelvir, alone and in combination, were administered and viral load and viral sequence diversity was assessed. Treatment of infected but immune compromised mice with both compounds either singly or in combination resulted in decreased viral loads and pathological changes compared to untreated animals. Treatment also abrogated infection of neuronal tissue. However, no consistent changes in the viral consensus sequence were observed, except for the emergence of the S:H655Y mutation. Molnupiravir, but not nirmatrelvir or immunosuppression alone, increased the transition/transversion (Ts/Tv) ratio, representative of A>G and C>U mutations and this increase was not altered by the co-administration of nirmatrelvir with molnupiravir.Notably, immunosuppression itself did not appear to promote the emergence of mutational characteristic of variants of concern (VOCs). Further investigations are warranted to fully understand the role of immunocompromised individuals in VOC development, especially by taking persistence into consideration, and to inform optimised public health strategies. It is more likely that immunodeficiency promotes viral persistence but does not necessarily lead to substantial consensus-level changes in the absence of antiviral selection pressure. Consistent with mechanisms of action, molnupiravir showed a stronger mutagenic effect than nirmatrelvir in this model.

A multi-agent reinforcement learning approach for ART adaptive control in automated container terminals

Intelligent transportation equipment represented by Artificial Intelligence Robot of Transportation (ART) has been gradually applied to automated container terminals (ACT). ARTs have higher intelligence and distributed autonomous decision-making capability compared to AGVs. The production and operation of ACT require strict loading plans based on container types and destination ports, which puts strict requirements on the ART loading task sequence. ART travel time from the yard to the quay is highly uncertain due to special scenarios such as traffic flow convergence and turning in narrow areas, which will lead to long waits for ART due to out-of-sequence arrivals at the quay. Therefore, it is necessary to regulate the speed of ART to adjust the timing of ART arriving at the quay. However, existing scheduling systems tend to ignore the impact of ART speed and loading sequence constraints on terminal operations. To optimize the multi-ART control problem of ACT, the waiting time of ART at the quay is taken as the optimization goal, and the speed regulation of ART during each task is defined as a Markov decision process. Then, by incorporating reinforcement learning and a multi-agent approach, an adaptive decision-making method is developed to generate the optimal speed regulation strategy. This method interacts with the multi-agent simulation environment to obtain real-time status information and learns strategies online to improve the intelligence of individual agents and the collaborative capabilities of the agent group. Finally, a multi-agent simulation model embedded with a reinforcement learning agent is established on AnyLogic software. Simulation results show that this method can effectively reduce the waiting time of ART, improve the coherence and efficiency of loading operations, and exhibit good performance in online scenarios that consider emergency tasks. The results indicate that the speed of ART in ACTs with parallel layout of the yard can impact the operational efficiency of the system. Furthermore, by appropriately defining the reward function and training method, collaborative behaviour among equipment can be enhanced, ultimately optimizing the overall performance of the system.

Fatigue Life Estimation under High Temperature and Variable Loading of AA7001-T6 Using Shot Peening

In this study, the fatigue life of aluminum alloys (7001-T6) was predicted with shot peening at various temperatures. Surface treatment with shot peening steel balls is a mechanism for reducing damage. An experimental investigation was conducted to find the degree of fatigue accumulation for AA7001-T6 under rotational bending loading and stress ratio R = −1. The experiments were conducted at RT (25 ℃), 330 ℃, and SP + 330 ℃ temperatures. A modified damage stress model that considers damage at various load levels was recommended for forecasting the fatigue life under high temperatures. The model and experimental results were compared to determine the most damage (Miner’s rule). The experimental results of the fatigue life indicated that the increased testing temperature reduced the fatigue life. However, using shot peening at high temperatures increased the fatigue life by 8% when loading sequence L-H and 10% when loading sequence H-L. The results showed a satisfactory degree of safety for the present model. Nevertheless, Miner’s model featured two models: one for low–high loading and high-low loading. The results were proper for prolonging fatigue life.

A new fatigue equation for asphalt mixtures considering loading sequence effects

The fatigue life of asphalt mixture has been found to significantly depend on loading sequences. However, the widely used traditional linear fatigue equations are difficult to determine the fatigue life of asphalt mixture subjected to different loading sequences. This study aims to improve these fatigue equations by establishing a fatigue equation considering loading sequence effects. First, the semi-circular bending (SCB) constant amplitude fatigue tests were conducted under the high loading amplitude (σhigh) and low (σlow) loading amplitude, respectively. Then, two of the loading sequences, namely the low-high stress (σlow-σhigh) and high-low stress (σhigh-σlow), were selected to carry out the SCB variable amplitude fatigue tests. Furthermore, the loading sequence effects on the life fractions and fatigue life of asphalt mixtures were analyzed. Finally, the damage equivalent criterion was successfully applied to establish the nonlinear fatigue damage accumulation model and fatigue equation considering loading sequence effects. It was found that the cumulative life fractions of asphalt mixtures under constant amplitude loading are equal to 1, indicating the linear fatigue damage accumulation. The cumulative life fractions corresponding to the σlow-σhigh and σhigh-σlow are greater than 1 and less than 1, respectively, indicating the nonlinear fatigue damage accumulation. The established nonlinear fatigue damage accumulation model can accurately characterize the dependence of fatigue damage accumulation on loading sequences. The established fatigue equations can improve the traditional linear fatigue equations to some extent and accurately predict the fatigue life of asphalt mixtures corresponding to the loading sequences of the σlow-σhigh and σhigh-σlow.

Influence of precast microbial reinforcement on lateral responses of monopiles

Microbially Induced Calcium Carbonate Precipitation (MICP) provides an environmentally friendly solution for reinforcing large diameter monopiles for offshore wind turbines (OWTs). This study presents an investigation into the lateral responses of monopiles with precast microbial reinforcement using a low-pH one-phase method. Both static and cyclic loading tests were carried out. The results of static loading tests show that the failure mode of the bio-reinforced monopile was an overall overturn failure. The lateral bearing capacity was increased by 50% and the bending moment was reduced by about 25% with the bio-reinforcement. Further investigation was conducted on the secant stiffness, damping ratio, and accumulated deformation of the bio-reinforced monopile under various cyclic loadings. With the bio-reinforcement, the accumulated deformation under one-way cyclic loading can be reduced by 30%–60%. The influences of cyclic loading parameters and loading sequence on pile stiffness were clarified. The growth ratio of pile stiffness due to bio-reinforcement under one-way loading was between 1.65 and 2.82, and decreased with increasing load amplitude. The ratio was smaller under two-way loading. Three competing factors on pile stiffness were identified: cyclic compaction of the unreinforced sandy soil, weakening of the bio-reinforced soil and soil subsidence around the bio-reinforced soil.

Load Prediction in Double-Channel Residual Self-Attention Temporal Convolutional Network with Weight Adaptive Updating in Cloud Computing.

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively.

A modified Manson-Halford model based on improved WOA for fatigue life prediction under multi-level loading

The Manson-Halford (M-H) nonlinear cumulative damage model is widely applied for fatigue life analysis problems under multi-level loading. In this model, the influence of loading sequence on the fatigue life can be better considerer, but the loading interaction effect is ignored. An improved whale optimization algorithm (IWOA) by integrating multiple strategies is proposed. The ability of global search and local exploitation is balanced and improved through nonlinear convergence factor, adaptive weighting factors and the Cauchy reverse learning strategies. In order to fully account for loading interaction effect, loading weighting factors are introduced to modify the M-H model, and the parameters are optimized through the global search properties of IWOA. The model is evaluated on multi-level loading fatigue experimental data from five metal materials and two aluminum alloy welded joints. The results suggest that the proposed IWOA has better optimization accuracy compared to the standard whale optimization algorithm (WOA). The proposed modified M-H model has better prediction performance compared to the four traditional cumulative damage models, which can be effectively applied to multi-level loading fatigue life analysis problems under actual working conditions. The proposed model is useful for the study of fatigue life evaluation methods.

Topology optimization of the flat steel shear wall based on the volume constraint and strain energy assumptions under the seismic loading conditions

In structural engineering systems, shear walls are two-dimensional vertical elements designed to endure lateral forces acting in-plane, most frequently seismic and wind loads. Shear walls come in a variety of materials and are typically found in high-rise structures. Because steel shear walls are lighter, more ductile, and stronger than other concrete shear walls, they are advised for usage in steel constructions. It is important to remember that the steel shear wall has an infill plate, which can be produced in a variety of forms. The critical zones in flat steel shear walls are the joints and corners where the infill plate and frame meet. The flat infill plate can be modified to improve the strength and weight performance of the steel shear walls. One of these procedures is Topology Optimization (TO) and this method can reduce the weight and also, increase the strength against the cyclic loading sequences. In the current research paper, the TO of the infill steel plate was considered based on the two methods of volume constraint and maximization of strain energy. Four different volumes (i.e., 60%, 70%, 80%, and 90%) were assumed for the mentioned element in the steel shear wall. The obtained results revealed that the topology configuration of CCSSW with 90% volume constraint presented the highest seismic loading performance. The cumulated energy for this type of SSW was around 700 kJ while it was around 600 kJ for other topology optimization configurations.

Time-dependent reliability analysis for structures under multilevel loads based on fatigue damage theory

This paper proposes an efficient method to evaluate the time-dependent fatigue reliability of structures under complex multilevel loads using a finite set of fatigue test data obtained under single-level loading conditions. The method preserves the simplicity of linear damage accumulation theory while considering the interactions among multiple load levels and the influence of load sequence on reliability. Leveraging the linear damage rule, a cumulative damage probability model was established under single-level constant amplitude loading. This model incorporates a fatigue life degradation function to characterize the cumulative damage effect of load cycles and corrects the fatigue reliability curve under single-level constant amplitude loading. By employing the principle of fatigue damage equivalence, the method determines the damage equivalence at the load switching point using the damage curve method, enabling the conversion of equivalent reliability under multilevel loads. The proposed method was validated by combining the test data obtained under three working conditions, including two-level loads loading, load blocks loading, and changing loads loading. The results demonstrate that the proposed method can accurately and efficiently evaluate the time-dependent fatigue reliability under different types of multilevel loads, using only a limited set of fatigue life data under single constant amplitude loads.

Influence of clay content on repeated liquefaction resistance of sand using element test

Soil liquefaction is a major concern because of its potential to cause building collapse, roadbed settlement, and sand eruptions. Field investigations have shown that clayey sand, when liquefied during mainshock, may undergo multiple liquefaction events during the subsequent aftershocks. Although previous research has primarily focused on the initial liquefaction event in clayey sand, research on the mechanism responsible for repeated liquefaction is limited. Consequently, a series of cyclic triaxial experiments are conducted on sand with varying clay contents, and these samples are subjected to various earthquake sequences. The primary objective of this study is to evaluate the cyclic behavior of sandy soil with clay content and analyze its repeated liquefaction resistance under different load sequences. A comprehensive assessment is conducted to understand the evolution of the repeated liquefaction resistance. The test results indicate that clayey sand exhibits the lowest liquefaction resistance during the second liquefaction event. Repeated liquefaction is dominated by stress-induced anisotropy at earlier events and relative density at the final events.

Dynamic behaviour of bridges under critical conventional and regular trains: Review of some regulations included in EN 1991-2

In the field of structural analysis dedicated to the study of vibrations of high-speed railway bridges, one reference load model is the well-known HSLM-A, which limits of validity are stated in Eurocode EN 1991-2, Annex E. In a recent paper published in the Journal of Rail and Rapid Transit, the authors investigated the degree of coverage provided by HSLM-A to critical articulated trains. Now in the present article, the authors have extended those analyses to critical conventional and regular trains as well. This is an important aspect because HSLM-A as such is an articulated-type model, so it is of interest to understand how it deals with covering the various resonance phenomena generated by other train types. Therefore, the main goal of this work is to establish whether the conventional and regular trains that stem from the validity rules given in Annex E/EN 1991-2, produce vibratory effects that are duly covered by HSLM-A. Following the aforementioned validity rules, one first aspect analysed is the importance of near-to-integer wheelbase ratios in the coupled vibrations produced by conventional trains. Subsequently, seven realistic, conventional and regular high-speed train models have been synthesised; these models have been made publicly available in Mendeley Data, and comprise almost 3800 different sequences of axle loads. Finally, the response of simply-supported bridges has been analysed with a view to compare the seven synthesised models versus HSLM-A. The exceedance and required speed increase have been computed for both displacements and accelerations, in a comprehensive ensemble of spans and speeds. The results provide a diagnosis of the degree of coverage of HSLM-A with respect to those conventional and regular trains compliant with Annex E/EN 1991-2.

A multi-energy load forecasting method based on complementary ensemble empirical model decomposition and composite evaluation factor reconstruction

Ensuring precise multi-energy load forecasting is crucial for the effective planning, management, and operation of Integrated Energy Systems (IES). This study proposes a novel multivariate load forecasting model based on time-series decomposition and reconstruction to handle the complex, high-dimensional multi-energy load data in IES and enhance forecasting accuracy. Initially, the model conducts a thorough correlation analysis and variable screening to minimize irrelevant data interference. It then applies denoising by decomposing the load sequence into modal components with distinct characteristics, using the complementary ensemble empirical mode decomposition (CEEMD). To overcome the unstable prediction accuracy inherent in time-domain decomposition methods, this study introduces an innovative composite evaluation factor (CEF) that reconstructs the modal components after considering their complexity, coupling, and frequency. The final predictions are generated using the proposed MTL-CNN-BiLSTM model, optimized with the attention mechanism. The results show that the proposed model significantly reduces error accumulation compared to traditional time-domain analysis methods, achieving a 37.40% reduction in average forecasting error and a 30.73% increase in forecasting efficiency.

On a lack of stability of parametrized BV solutions to rate‐independent systems with nonconvex energies and discontinuous loads

AbstractWe consider a rate‐independent system with nonconvex energy under discontinuous external loading. The underlying space is finite‐dimensional and the loads are functions in . We investigate the stability of various solution concepts w.r.t. a sequence of loads converging weakly* in with a particular emphasis on the so‐called normalized, ‐parametrized balanced viscosity solutions. By means of three counterexamples, it is shown that common solution concepts are not stable w.r.t. weak* and even intermediate (or strict) convergence of loads in the sense that a limit of a sequence of solutions associated with these loads need not be a solution corresponding to the load in the limit. We moreover introduce a new solution concept, which is stable in this sense, but our examples show that this concept necessarily allows “solutions” that are physically meaningless.

Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage

Pickup vehicle scheduling in steel logistics parks is an important problem for determining the outbound efficiency of steel products. In a steel logistics park, each yard contains different types of steel products, which provides flexible yard selection for each pickup operation. In this case, the yard allocation and the loading sequence for each vehicle must be considered simultaneously in pickup vehicle scheduling, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize the makespan of pickup vehicles and the makespan of steel logistics parks simultaneously. The optimization problem is formulated as a multi-objective mixed-integer linear programming model, and an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is firstly proposed to initialize the vehicle pickup sequence for each yard. Then, an insertion decoding method is designed to improve the scheduling efficiency, utilizing the idle time of a yard. Furthermore, local search technology based on critical paths is proposed for the ESPEA to improve the solution quality. Experiments are executed based on data collected from a real steel logistics park. The results confirm that the ESPEA can significantly reduce both the makespan of each pickup vehicle and the makespan of the steel logistics park.

The Sequence of Heating and Loading Affects Shear Properties of Granite Fractures Under High Temperature

The Sequence of Heating and Loading Affects Shear Properties of Granite Fractures Under High Temperature

New insights into the load sequence effect: Experimental characterization and incremental modeling of plain high-strength concrete under mode II fatigue loading with variable amplitude

Reinforced concrete (RC) infrastructure typically endures repeated subcritical loading throughout its operational lifespan, potentially compromising structural integrity. A realistic loading condition of these structures consists of constantly changing maximum and minimum load events. Characterizing the fatigue response of concrete proves challenging due to its heterogeneous and quasi-brittle nature, leading to varying fatigue lifetimes depending on the loading sequence. Understanding the impact of this so-called load sequence effect stands as a critical facet in ensuring structural longevity and reliability of current infrastructure. This paper offers insights into this phenomenon, focusing on unraveling the intricate behavior of plain concrete under multistage fatigue loading. Since the experimental investigation of the load sequence effect is highly complex due to the large scatter of test results, a newly developed, mode II test setup was employed. The advantage of this test setup resides in the precise specification of stress state and fracture zone. The systematic investigation of over 50 specimens elucidates the pronounced load sequence effect and provides insight into the underlying mechanisms. Based on these experimental results, a deformation-based incremental modeling approach was developed which considers the effect of loading sequence and provides not only the fatigue life but also the entire evolution of deformation. The experimental recalculation underscores the efficacy of this approach over the conventional Palmgren–Miner (P–M) rule.

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

AbstractOptimizing short‐term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load data, resulting in accuracy reduction. To achieve accurate and efficient short‐term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN‐GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. Then, the overall framework of short‐term load forecasting based on CNN‐GRU‐Attention network is proposed, and the coupling relationship between each stage is revealed. Finally, the developed framework is implemented on realistic load dataset of distribution networks, and the experimental results verify the effectiveness of the proposed method. Compared with the state‐of‐the‐art models, the CNN‐GRU‐Attention model outperforms in different evaluation metrics.

Strain-based running-reliability characterisation in time-domain for risk monitoring under various load conditions

This aim of this paper is to characterise the strain-based fatigue life data in time-domain using the newly modelled running-reliability technique that considers the load sequence effect. Current established conventional strain life models do not consider dependence for fatigue life of low or high amplitudes, on which with occur first in the load history. Finite element analysis is carried out to ensure the strain signals are captured at the most critical region during road test at various conditions. Fatigue life of 2.74 × 104 to 6.07 × 105 cycle/block with mean cycle to failure of 4.32 × 106 to 7.00 × 106 cycle/block is predicted based on the cycle sequence effect using cycle-counting method. The newly modelled running-reliability technique is formulated to extract the features of high amplitude excitation obtained from the strain signals for characterising the fatigue reliability features under load sequence effect. Hence, the reliability-hazard relationship for fatigue reliability characterisation of strain-based approach in time-domain using running-reliability technique.

Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model

Current load forecasting methods struggle with the randomness and complexity of multiple loads in regional integrated energy systems, resulting in less accurate predictions. To address this problem, this paper proposes a novel two-phase decomposition hybrid forecasting model that combines complementary ensemble empirical mode decomposition, sample entropy, variational mode decomposition, and genetic algorithm-bidirectional long short term memory. The proposed approach begins by decomposing the load sequence into multiple intrinsic mode function components at different frequencies using complementary ensemble empirical mode decomposition. Afterward, the sample entropy values are calculated for each intrinsic mode function. The intrinsic mode function with the highest sample entropy value is subjected to a two-stage decomposition using the variational mode decomposition method. Through this technique, a series of stationary components is acquired. Subsequently, the bidirectional long short term memory model is optimized using the genetic algorithm, and the genetic algorithm-bidirectional long short term memory approach is employed to predict all the decomposed components. Finally, the prediction results are combined and reconstructed to obtain the final prediction value. Experimental results demonstrate the effective handling of non-stationary load sequences by the forecasting model, showcasing the highest level of accuracy in regional integrated energy system multiple loads forecasting.

Brain-inspired spiking neural networks in Engineering Mechanics: a new physics-based self-learning framework for sustainable Finite Element analysis

AbstractThe present study aims to develop a sustainable framework employing brain-inspired neural networks for solving boundary value problems in Engineering Mechanics. Spiking neural networks, known as the third generation of artificial neural networks, are proposed for physics-based artificial intelligence. Accompanied by a new pseudo-explicit integration scheme based on spiking recurrent neural networks leading to a spike-based pseudo explicit integration scheme, the underlying differential equations are solved with a physics-informed strategy. We propose additionally a third-generation spike-based Legendre Memory Unit that handles large sequences. These third-generation networks can be implemented on the coming-of-age neuromorphic hardware resulting in less energy and memory consumption. The proposed framework, although implicit, is viewed as a pseudo-explicit scheme since it requires almost no or fewer online training steps to achieve a converged solution even for unseen loading sequences. The proposed framework is deployed in a Finite Element solver for plate structures undergoing cyclic loading and a Xylo-Av2 SynSense neuromorphic chip is used to assess its energy performance. An acceleration of more than 40% when compared to classical Finite Element Method simulations and the capability of online training is observed. We also see a reduction in energy consumption down to the thousandth order.