Robust Approach Research Articles

A new approach to multiaxial fatigue life prediction: A multi-dimensional multi-scale composite neural network with multi-depth

Many multiaxial fatigue life prediction methods tend to increase additional parameters and model depth as the complexity of the loading path increases. This leads to issues such as poor model robustness, limited flexibility, and single-dimensional approaches. In this study, a multi-dimensional multi-scale composite neural network with multi-depth is proposed to address these challenges and enhance prediction accuracy. Initially, physical and sensitive features serve as input data for the proposed model, to enhance the richness of input features. Subsequently, a multi-dimensional feature extraction module is deployed to extract feature information from the composite data. To process these features, an improved multi-domain query cascaded transformer network (IMQCT) is employed as the feature processing module of the proposed model. The proposed model is verified to have better prediction accuracy and extrapolation capability by using experimental data from nine materials and comparing its performance with six machine learning models.

Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach

Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach

Addressing Wind Power Forecast Errors in Day-Ahead Pricing With Energy Storage Systems: A Distributionally Robust Joint Chance-Constrained Approach

Addressing Wind Power Forecast Errors in Day-Ahead Pricing With Energy Storage Systems: A Distributionally Robust Joint Chance-Constrained Approach

Adaptive Robust Control of Uncertain Euler-Lagrange Systems Using Gaussian Processes.

This article proposes a novel adaptive robust control approach based on Gaussian processes (GPs) for the high-precision tracking problem of uncertain Euler-Lagrange (EL) systems with time-varying external disturbances. Given a prior dynamic model, the GP regression (GPR) technique is employed to obtain a nonparametric data-based uncertainty model, including its probabilistic confidence intervals. Based on the adaptive sliding mode control (ASMC) framework, the posterior means of GPs are utilized for dynamic compensation, whereas the posterior variances are applied to adjust the feedback gains. This proposed control strategy is robust against significant system uncertainty with low feedback gains. A novel adaptive law for updating hyperparameters based on tracking error feedback is presented, thereby improving the performance of both tracking control and GP modeling simultaneously. Compared to existing likelihood-based optimization methods, this hyperparameter adaptive law enables data-efficient and fast uncertainty learning for control applications. The proposed control strategy guarantees the semiglobal asymptotic convergence to zero tracking error with a specified probability. Simulations using an underwater robot model demonstrate that the utilization of GPs and hyperparameter adaptive law significantly improves the performance of tracking control and uncertainty learning.

Dual therapy of cancer using optimal control supported by swarm intelligence.

The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.

Concurrent Learning Robust Adaptive Fault Tolerant Boundary Regulation of Hyperbolic Distributed Parameter Systems.

This article develops a robust adaptive boundary output regulation approach for a class of complex anticollocated hyperbolic partial differential equations subjected to multiplicative unknown faults in both the boundary sensor and actuator. The regulator design is based on the internal model principle, which amounts to stabilize a coupled cascade system, which consists of a finite-dimensional internal model driven by a hyperbolic distributed parameter system (DPS). To this end, a systematic sliding mode equipped with a backstepping approach is developed such that the robust state feedback control can be realized. Moreover, since the available information is a faulty boundary measurement at the right side point, state estimation is required. However, due to the presence of boundary unknown faults, we need to solve an issue of joint fault-state estimation. Restrictive persistent excitation conditions are usually required to guarantee the exact estimation of faults but are unrealistic in practice. To this end, a novel concurrent learning (CL) adaptive observer is proposed so that exponential convergence is obtained. It is the first time that the spirit of CL is introduced to the field of DPSs. Consequently, the observer-based adaptive boundary fault tolerant control scheme is developed, and rigorous theoretical analysis is given such that the exponential output regulation can be achieved. Finally, the effectiveness of the proposed methodology is demonstrated via comparative simulations.

High Precision Robust Real-Time Lightweight Approach for Railway Pantograph Slider Wear Estimation

High Precision Robust Real-Time Lightweight Approach for Railway Pantograph Slider Wear Estimation

Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

Privacy-Preserving Collaborative Intrusion Detection in Edge of Internet of Things: A Robust and Efficient Deep Generative Learning Approach

Privacy-Preserving Collaborative Intrusion Detection in Edge of Internet of Things: A Robust and Efficient Deep Generative Learning Approach

IDNA-OpenPrompt: OpenPrompt learning model for identifying DNA methylation.

Introduction: DNA methylation is a critical epigenetic modification involving the addition of a methyl group to the DNA molecule, playing a key role in regulating gene expression without changing the DNA sequence. The main difficulty in identifying DNA methylation sites lies in the subtle and complex nature of methylation patterns, which may vary across different tissues, developmental stages, and environmental conditions. Traditional methods for methylation site identification, such as bisulfite sequencing, are typically labor-intensive, costly, and require large amounts of DNA, hindering high-throughput analysis. Moreover, these methods may not always provide the resolution needed to detect methylation at specific sites, especially in genomic regions that are rich in repetitive sequences or have low levels of methylation. Furthermore, current deep learning approaches generally lack sufficient accuracy. Methods: This study introduces the iDNA-OpenPrompt model, leveraging the novel OpenPrompt learning framework. The model combines a prompt template, prompt verbalizer, and Pre-trained Language Model (PLM) to construct the prompt-learning framework for DNA methylation sequences. Moreover, a DNA vocabulary library, BERT tokenizer, and specific label words are also introduced into the model to enable accurate identification of DNA methylation sites. Results and Discussion: An extensive analysis is conducted to evaluate the predictive, reliability, and consistency capabilities of the iDNA-OpenPrompt model. The experimental outcomes, covering 17 benchmark datasets that include various species and three DNA methylation modifications (4mC, 5hmC, 6mA), consistently indicate that our model surpasses outstanding performance and robustness approaches.

Measurement-device-independent quantum random number generation over 23 Mbps with imperfect single-photon sources

Quantum randomness relies heavily on the accurate characterization of the generator implementation, where the device imperfection or inaccurate characterization can lead to incorrect entropy estimation and practical bias, significantly affecting the reliability of the generated randomness. Measurement-device-independent (MDI) quantum random number generation (QRNG) endeavors to produce certified randomness, utilizing uncharacterized and untrusted measurement devices that are vulnerable to numerous attack schemes targeting measurement loopholes. However, existing implementations have shown insufficient performance thus far. Here, we propose a high-speed MDI-QRNG scheme based on a robust measurement tomography approach against the imperfection of single-photon sources. Compared with the conventional approach, the decoy-state method is introduced to obtain more accurate tomography results and a tighter lower bound of randomness. Finally, by using a high-speed time-bin encoding system, we experimentally demonstrated the scheme and obtained a reliable min-entropy lower bound of 7.37×10−2 bits per pulse, corresponding to a generation rate over 23 Mbps, which substantially outperforms the existing realizations and makes a record in discrete-variable semi-device-independent QRNGs.

Top 10 Tips on Safety From the Air Medical Transport Industry.

The air medical transport industry places a high value on developing and maintaining a culture of safety due to the higher risk nature of its operations. The dynamic nature of response and transport, inherent risks involved with flight, lack of supporting resources, weather conditions, and austere nature of the transport environment are all factors that highlight the need for enhanced safety. As such, the air medical transport industry has developed a robust and unique approach to provider and patient safety involving many tactics not otherwise used in other areas of health care. This article describes some of the unique safety features and approaches that are commonplace in the air medical transport industry and proposes a means for these initiatives to other areas of the health care system.

A Distributionally Robust Optimization Approach for Local Electrical Market Interaction With Wholesale Electrical Market and Prosumers Within the Framework of Transactive Energy Concepts

A Distributionally Robust Optimization Approach for Local Electrical Market Interaction With Wholesale Electrical Market and Prosumers Within the Framework of Transactive Energy Concepts

Effective mathematics learning through APOS theory by dint of cognitive abilities

The paper dwells on the contributions of APOS theory to the development of teaching and learning of mathematics in school. APOS is an acronym for <i>action</i>, <i>process</i>, <i>object</i>, and <i>schema</i>. The theory emerges as an extension to constructivism but with a more focused and robust learner-centered approach to the teaching and learning of mathematics. Proponents of the theory believed that learning occurs initially as an <i>action </i>or <i>activity </i>in learners’ cognitive settings, independent of learners’ environment, triggered by cognitive coherence, then it is transformed to <i>process</i>,<i> </i>where learner now waits for internalization of the earlier <i>activity</i>, preparatory to the occurrence of learning. At <i>object </i>level, learner now considers what has been learnt earlier to have been fully internalized into mathematical <i>object(s)</i>. Lastly, at <i>schema </i>level, the <i>object</i> learnt is assumed to have been embedded in the learners’ <i>schema</i>–a cognitive structure formed as a result of accumulated learning experience, and a complete mental image of what has been learnt is said to have been formed. Against the backdrop of this, the paper looks at how this theory had changed the narrative about teaching and learning of mathematics vis-à-vis the bearing of the theory to other cognitive abilities of the learner such as intelligence and creativity. In the end, the paper suggests the application of APOS theory in teaching and learning mathematics at all levels of learning in Nigeria and beyond.

Dynamic carbon allocation trade‐off: A robust approach to model tree biomass allometry

Abstract Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry because traditional allometries ignore the dynamics of allocation. Herein, we characterised allometric scaling using the dynamic ratio (r) of the stem biomass (SB) to AGB and a dynamic exponent. The allometric models were biologically parameterised by the r values for initial, intermediate and final ages rather than only a regression result. The scaling was tested using field measurements of 421 species and 2213 different‐sized trees in pantropical regions worldwide. We found that the scaling fluctuated with tree size, and this fluctuation was driven by the trade‐off relationship of biomass allocation between the tree crown and stem depending on the dynamic crown trait. The allometric scaling between SB and AGB varied from 0.8 to 1.0 for a tree during its entire lifecycle. The fluctuations presented a general law for the allometric scaling of the pantropical tree biomass and size. Our model quantified the trade‐off and explained 94.1% of the allometric relationship between the SB and AGB (93.8% of which between D2H and AGB) for pantropical forests, which resulted in a better fit than that of the traditional model. Considering the effects of the trade‐off on modelling, the actual biomass of large trees could be substantially greater than conventional estimates. These results highlight the importance of coupling growth mechanisms in modelling allometry and provide a theoretical foundation for better describing and predicting forest carbon accumulation.

Fuzzy-random robust flexible programming on sustainable closed-loop renewable energy supply chain

Photovoltaic (PV) systems have become a widely accepted method of harnessing solar energy in recent decades. In addition to the growing use of renewable energies are being used due to the depletion of fossil fuel supplies and the corresponding pollution generated by these non-renewable fuels. Since PV systems possess a limited lifespan, and are predicted to eventually become outdated, sustainability and recycling that should be incorporated into the structure of the solar system supply chain. The management of high and low moisture waste (H&LMW) is crucial due to its harmful environmental impacts, including sludge waste, carbon dioxide (CO2), and acid compounds. This study explores the integration of research on H&LMW and PV systems, focusing on energy generation from biogas, incineration power plants, and PV systems. In this respect, we propose a multi-objective mixed-integer programming model to construct a sustainable closed-loop renewable energy supply chain by considering forward and reverse flow through solar, biogas, and incineration power plants. Reducing CO2 with power-to-gas technology is thought to be an environmentally friendly approach. A novel fuzzy-random robust flexible programming approach based on Me measure is proposed, which overcomes the limitations of addressing the uncertainty of the parameters. Next, in order to solve the suggested multi-objective model, a novel strategy known as utility function based multi-choice conic goal programming is presented. A case study explores the sustainable challenges of a closed-loop renewable energy supply network in India’s renewable energy sector. The experimental results indicate that 9 candidate locations are selected for solar wafer production facilities, while 10, 9, and 15 locations are chosen for solar cell production, module manufacturing, and solar power plants, respectively. A sensitivity analysis reveals that the proportion of air pollution limitations possessively rises from 100% to 80% corresponding to the aggregate cost increases from INR 8021452 to INR 8056542.

Understanding sustainable growth in the face of economic policy uncertainties

AbstractThis paper investigates the impact of economic policy uncertainty (EPU) in China, measured by a news-based index, on the sustainable growth of 2771 Chinese non-financial A-share firms (including 1674 SMEs and 1097 non-SMEs) from 2010 to 2022. We also analyze how green business practices moderate the impact of EPU on firms' sustainable growth. After implying various econometric models, this study finds that EPU has a significant negative effect on the sustainable growth of Chinese firms, which is more pronounced among non-SMEs. Heterogeneity analysis reveals that the negative impact of EPU on CSG is more pronounced in big firms, highly leveraged firms, firms with low liquidity and low financialization in China. We further reveal that this negative impact of EPU stems from trade policy uncertainties. Furthermore, findings reveal that green business activities positively moderate the association between EPU and sustainable growth, and that this moderating impact is more pronounced among SMEs. These findings are consistent with various robustness approaches. This paper provides useful insights for enterprises to improve their sustainable growth in the context of economic policy uncertainties by adjusting their number of employees, board of directors, average age of staffs, financialization level and debt flexibility.

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Smart Electrospun Nanofibers from Short Peptidomimetics Based on Pyrrolo-pyrazole Scaffold.

We prepared a small library of short peptidomimetics based on 3-pyrrolo-pyrazole carboxylate, a non-coded γ-amino acid, and glycine or alanine. The robust and eco-friendly synthetic approach adopted allows to obtain the dipeptides in two steps from commercial starting materials. This gives the possibility to shape these materials by electrospinning into micro- and nanofibers, in amounts required to be useful for coating surfaces of biomedical relevance. To promote high quality of electrospun fibers, different substitution patterns were evaluated, all for pure peptide fibers, free of any polymer or additive. The best candidate, which affords a homogeneous fibrous matrix, was prepared in larger amounts, and its biocompatibility was verified. This successful work is the first step to develop a new biomaterial able to produce pristine peptide-based nanofibers to be used as helpful component or stand-alone scaffolds for tissue engineering or for the surface modification of medical devices.

Multi‐Functional Spin Photogalvanic Device Based on 2D Half‐Metallic Ferromagnets

AbstractGenerating fully spin‐polarized currents (FSPC) and pure spin currents (PSC) are of great importance for spintronics. Half‐metallic ferromagnets, which generate 100% spin polarization, are considered as one of the most promising materials for applications in spintronics. However, the knowledge of intrinsic half‐metallic materials in the spin‐dependent photogalvanic effect is still poorly understood. Using first‐principle transport calculations, a robust approach is introduced to obtain FSPC and PSC by spin‐dependent photogalvanic effect in intrinsic half‐metallic materials. Based on the recently synthesized monolayer half‐metallic C(CN)3, a 2D spin photogalvanic device is built and it is demonstrated that the FSPC can be achieved in a wider photon energy range under both linearly and elliptically polarized light due to the half‐metallicity. It is intriguing to note that the device can be easily manipulated to transition between two modes: one for generating FSPC and another for generating PSC by setting the two leads in anti‐parallel configurations. Furthermore, a significant spin‐valve effect can be attained across various photon energies for both linearly and elliptically polarized light. The research shows that half‐metallic materials are an ideal platform for studying and generating FSPC and PSC, which presents a remarkable avenue for the development of advanced spintronic devices in the next generation.