Gradient Optimization Research Articles

An enrichment strategy based on hydrophobic tagging and reversed-phase chromatographic separation for the analysis of lysine-containing peptides

Lysine (K) is widely used in the design of lysine-targeted crosslinkers, structural elucidation of protein complexes, and analysis of protein-protein interactions. In "shotgun" proteomics, which is based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), proteins from complex samples are enzymatically digested, generating thousands of peptides and presenting significant challenges for the direct analysis of K-containing peptides. In view of the lack of effective methods for the enrichment of K-containing peptides, this work developed a method which based on a hydrophobic-tag-labeling reagent C10-S-S-NHS and reversed-phase chromatography (termed as HYTARP) to achieve the efficient enrichment and identification of K-containing peptides from complex samples. The C10-S-S-NHS synthesized in this work successfully labeled standard peptides containing various numbers of K and the labeling efficiency achieved up to 96% for HeLa cell protein tryptic digests. By investigating the retention behavior of these labeled peptides in C18 RP column, we found that most K-labeled peptides were eluted once when acetonitrile percentage reached 57.6% (v/v). Further optimization of the elution gradient enabled the efficient separation and enrichment of the K-labeled peptides in HeLa digests via a stepwise elution gradient. The K-labeled peptides accounted for 90% in the enriched peptides, representing an improvement of 35% compared with the number of peptides without the enrichment. The dynamic range of proteins quantified from the enriched K-containing peptides spans 5-6 orders of magnitude, and realized the detection of low-abundance proteins in the complex sample. In summary, the HYTARP strategy offers a straightforward and effective approach for reducing sample complexity and improving the identification coverage of K-containing peptides and low-abundance proteins.

Rational design of a setaria-like NiTe2/MoS2 semi-coherent heterogeneous interface for enhancing diffusion kinetics in potassium-ion batteries

Constructing unique heterostructures is a highly effective approach for enhancing the K+ storage capability of transition metal selenides. Such structures generate internal electric fields that significantly reduce the charge transfer activation energy. However, achieving a flawless interfacial region that maintains the optimal energy level gradient and degree of lattice matching remains a considerable challenge. In this study, we synthesised Setaria-like NiTe2/MoS2@C heterogeneous interfaces at which three-dimensional MoS2 nanosheets are evenly embedded in NiTe2 nanorods to form stabilised heterojunctions. The NiTe2/MoS2 heterojunctions display distinctive electronic configurations and several active sites owing to their low lattice misfits (δ = 13 %), strong electric fields, and uniform carbon shells. A NiTe2/MoS2@C anode in a potassium-ion battery (KIB) exhibited an impressive reversible capacity of 125.8 mAh/g after 1000 cycles at a rate of 500 mA g−1 and a stable reversible capacity of 111.7 mAh/g even after 3000 cycles at 1000 mA g−1. Even the NiTe2/MoS2@C//perylene tetracarboxylic dianhydride full battery configuration maintained a significant reversible capacity of 92.4 mAh/g after 100 cycles at 200 mA g−1, highlighting its considerable potential for application in KIBs. Calculations further revealed that the well-designed NiTe2/MoS2 heterojunction significantly promotes K+ ion diffusion.

Ambient noise imaging for municipal solid waste landfill structure detection based on the common-midpoint two-station analysis with distributed acoustic sensing

SUMMARY Distributed acoustic sensing (DAS) enables high-density sampling of seismic wavefields at low cost compared to conventional geophones. This capability facilitates structural detection of a municipal solid waste (MSW) landfill, which is important for protecting the surrounding ecosystem. However, processing the vast amount of data from DAS array for ambient noise imaging can be computationally intensive. To address this, we employed the common-midpoint two-station (CMP-TS) analysis to enhance the efficiency of ambient noise imaging in the MSW landfill. CMP-TS analysis involves selecting pairs of traces at equal distances on both sides with the subarray midpoint as symmetry, which reduces the number of DAS array recordings for cross-correlation calculations. After positioning the DAS arrays linearly on top of the MSW landfill to automatically collect ambient noise, we used the CMP-TS analysis in the cross-correlation calculations to speed up the measurement of dispersion. The S-wave velocity structure of the study region was obtained quickly by inverting the extracted dispersion curves using the gradient optimization method. Ambient noise imaging based on CMP-TS analysis with DAS was applied to a test of an area-type MSW landfill. The resulting S-wave velocity section revealed a discontinuous low-velocity zone, validated by the high-density resistivity method. This low-velocity zone was interpreted as containing leachate from waste decomposition, and its discontinuity may be caused by excessive differences in the waste residues settling rates under compaction. Employing CMP-TS analysis in ambient noise data collected by DAS offers more cost-effective monitoring and a reliable basis for environmental pollution prevention and control.

Gradient estimates for the insulated conductivity problem: The non-umbilical case

We study the insulated conductivity problem with inclusions embedded in a bounded domain in Rn, for n≥3. The gradient of solutions may blow up as ε, the distance between the inclusions, approaches to 0. We established in a recent paper optimal gradient estimates for a class of inclusions including balls. In this paper, we prove such gradient estimates for general strictly convex inclusions. Unlike the perfect conductivity problem, the estimates depend on the principal curvatures of the inclusions, and we show that these estimates are characterized by the first non-zero eigenvalue of a divergence form elliptic operator on Sn−2.

Implementation of Improved U-Net and Optimized XGBoost-SVM Classifier for Early Detection of Masses and Microcalcifications in Breast

Purpose: In contemporary time, breast cancer has been extensively found among women at a global rate of 24.2% as of 2018. This exposes the significant and imperative need for detecting masses and micro calcifications in the breast to avoid death rates. The existing histopathological images have gained a golden standard considering they would afford reliable results. However, these images have been entangled with various complexities including insufficient image contrast, noise, and misdiagnosis. These pitfalls might negatively impact the detection rate for which automatic recognition has become vital. With the momentous evolvement of Machine Learning (ML) and Deep Learning (DL), various researchers have endeavoured to consider ML and DL for accomplishing this prediction. However, they need to improve in accuracy rate due to ineffective feature extraction, and most studies averted to consider segmentation. Hence, this study regards accomplishing a high classification and segmentation process. Materials and Methods: The study proposes Modified Weight Updated Convolutional Block-U-Net (MWu-Conv-U-Net) to handle the image dimensions optimally. In this case, U-Net based model is regarded by improvising it with the inclusion of an additional convolutional layer in individual encoder-decoder. Further, the study proposes an Optimized Weight Updated eXtreme Gradient Boosting-Support Vector Machine (OWu-XGBoost-SVM) for determining the optimal gradient, which would eventually enhance the prediction rate. Results: The overall performance is assessed through performance metrics to confirm its effectiveness in classifying and segmenting the Breast Cancer Histopathological (BACH) image dataset. Comparison is undertaken with conventional systems in accordance with metrics (recall, F1-score, precision, and accuracy). The results revealed the efficacy of the proposed system with 99% accuracy, 99% F1-score, 99% precision, and 99% recall. Conclusion: High accuracy procured through the analysis reveals its efficacy and hence it could be applicable for real-time execution for assisting medical experts in early breast cancer prognosis.

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

Design and additive manufacturing of bionic hybrid structure inspired by cuttlebone to achieve superior mechanical properties and shape memory function

Lightweight porous materials with high load-bearing, damage tolerance and energy absorption (EA) as well as intelligence of shape recovery after material deformation are beneficial and critical for many applications, e.g. aerospace, automobiles, electronics, etc. Cuttlebone produced in the cuttlefish has evolved vertical walls with the optimal corrugation gradient, enabling stress homogenization, significant load bearing, and damage tolerance to protect the organism from high external pressures in the deep sea. This work illustrated that the complex hybrid wave shape in cuttlebone walls, becoming more tortuous from bottom to top, creates a lightweight, load-bearing structure with progressive failure. By mimicking the cuttlebone, a novel bionic hybrid structure (BHS) was proposed, and as a comparison, a regular corrugated structure and a straight wall structure were designed. Three types of designed structures have been successfully manufactured by laser powder bed fusion (LPBF) with NiTi powder. The LPBF-processed BHS exhibited a total porosity of 0.042% and a good dimensional accuracy with a peak deviation of 17.4 μm. Microstructural analysis indicated that the LPBF-processed BHS had a strong (001) crystallographic orientation and an average size of 9.85 μm. Mechanical analysis revealed the LPBF-processed BHS could withstand over 25 000 times its weight without significant deformation and had the highest specific EA value (5.32 J·g−1) due to the absence of stress concentration and progressive wall failure during compression. Cyclic compression testing showed that LPBF-processed BHS possessed superior viscoelastic and elasticity energy dissipation capacity. Importantly, the uniform reversible phase transition from martensite to austenite in the walls enables the structure to largely recover its pre-deformation shape when heated (over 99% recovery rate). These design strategies can serve as valuable references for the development of intelligent components that possess high mechanical efficiency and shape memory capabilities.

Ensemble Random Forest-based Gradient Optimization based Energy Efficient Video Processing System for Smart Traffic Surveillance System

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods.

Increase of coercivity and composition distribution in dysprosium-diffused NdFeB sintered magnets treated by vacuum evaporation method

This study examines the distribution of heavy rare-earth elements (HRE) and magnetic properties in sintered NdFeB permanent magnets treated with Dy vacuum evaporation grain boundary diffusion, considering variations in time and temperature. The HRE distribution and magnetic properties were analyzed using an electron probe microanalyzer (EPMA), glow discharge optical emission spectrometer (GDOES), and hysteresis loop meter. The optimal temperature gradient for achieving the best results was found to be between 850 °C and 950 °C for a duration of 10 h. This resulted in iHc of 1415 kJ/m and (BH)max of 396.2 kJ/m3, which is classified as N52H grade. In comparison, the untreated NdFeB magnets had iHc of 1105 kJ/m and (BH)max of 400.3 kJ/m3, classified as N52 M grade. The results suggest that 0.167 wt% consumption of vacuum evaporated Dy alloy can enhance the 28 % in iHc of the magnet and 5 % increase in (BH)max compared to the untreated ones. This innovative manufacturing method improves the utilization of rare heavy earth and enhances the overall magnetic properties. This material has the potential to be used in the development of low-weight rare earth-sintered NdFeB magnets with high intrinsic coercivity. This study's findings advance energy-efficient technologies such as electric vehicles and offshore wind turbines, aiding global energy conservation efforts.

An improved log-cosine variation slime mold - simplified gated recurrent neural network for the high-precision state of charge estimation of lithium-ion batteries

Accurate estimation of SOC is crucial for the safe and efficient utilization of electric vehicles, portable devices and renewable energy systems. Neural networks have become a powerful tool in the field of SOC estimation, but they have a complex structure, difficult parameter tuning, and high performance requirements. For this reason, this paper proposes a simplified gated recurrent neural network (SGRN) that greatly simplifies the unitary network structure and parameters. A Nadam gradient optimizer is introduced to update the network weight parameters, and the network hyperparameters are optimized using the log-cosine variant slime mold algorithm (LVSM). The algorithm is validated under HPPC, BBDST, and DST operating conditions under 25 °C, 10 °C, and − 5 °C. The results show that the LVSM-SGRN algorithm has higher accuracy, lower performance consumption, and better temperature adaptability than the conventional LSTM algorithm as well as the EKF algorithm. This contributes to the integration and large-scale industrialization of the algorithm, and comprehensively improves the safety of lithium-ion batteries and accuracy of SOC estimation.

Shape and location recovery of laser excitation sources in photoacoustic imaging using topological gradient optimization

This study focuses on source term inversion in fractional partial differential equations, specifically applied to photoacoustic imaging. This work contributes to advancing imaging techniques and provides practical insights for medical diagnostics and materials characterization. Our aim in this paper is to develop an accurate method for recovering the location and the shape of a laser excitation source from partial boundary data. To achieve this, we reformulate our inverse problem as an optimization challenge. We utilize topological sensitivity analysis to establish an asymptotic expansion of a relevant shape function. These theoretical findings form the basis for a rapid and precise detection algorithm. Additionally, we present several numerical experiments that demonstrate the effectiveness and accuracy of our proposed approach.

Conjugate gradient‐based active noise cancellation

AbstractActive noise cancellation schemes should synthesize proper secondary perturbations without relevant prior information. Their performance depends strongly on their adaptive mechanisms, which, sometimes, do not fulfill the severe requirements of practical setups. This research demonstrates for the first time that the adaptive conjugate gradient optimization method can be used for active noise cancellation tasks. Additionally, a stochastic model is devised for estimating its steady‐state solution, even when the secondary path is not correctly estimated. Such a theoretical analysis demonstrates the asymptotic unbiasedness of the proposed scheme when the secondary plant is perfectly estimated. Simulations reveal that the devised method can attain better asymptotic performance and a higher convergence rate than the traditional FX‐LMS algorithm, at the expense of an increased computational burden. Furthermore, simulations show that the method outperforms the standard ones for nonstationary inputs (such as speech signals).

Efficient learning in spiking neural networks

Spiking neural networks (SNNs) are a large class of neural model distinct from ‘classical’ continuous-valued networks such as multilayer perceptrons (MLPs). With event-driven dynamics and a continuous-time model in contrast to the discrete-time model of their classical counterparts, they offer interesting advantages in representational capacity and energy consumption. Spiking networks may also be more biologically plausible, offering more insights into neuroscience. However, developing models of learning for SNNs has historically proven challenging: as discrete-time systems, their dynamics are much more complex and they cannot benefit from the strong theoretical developments in MLPs such as convergence proofs and optimal gradient descent. Nor do they gain automatically from algorithmic improvements that have produced efficient matrix inversion and batch training methods. Most of the existing research has focused on the most well-studied learning mechanism in SNNs, spike-timing-dependent plasticity (STDP), and although there has been progress, there are also notable pathologies that have often been solved with a variety of ad-hoc techniques. While efforts have been made to map SNNs to classical convolutional neural networks (CNNs), these have not yet shown any decisive efficiency advantage over conventional CNNs. More promising research directions lie in the realm of pure spiking learning models that exploit the inherent temporal dynamics (and often leverage recurrency). Metrics are needed; one possibility would be a measure of total energy cost per unit reduction in error. This tutorial overview looks at existing techniques for learning in SNNs and offers some thoughts for future directions.

Statistical Assessment of Bayesian Optimization Gradient and Hessian Computation Based Improved Random Forest Classifier for Non-Linear Classification Problems

Statistical Assessment of Bayesian Optimization Gradient and Hessian Computation Based Improved Random Forest Classifier for Non-Linear Classification Problems

Optimizing pressure gradient on the MHD peristaltic blood flow of nanofluids by - response surface methodology

This study focuses on optimization of pressure gradient on MHD peristaltic nanofluid flow by response surface methodology (RSM). The governing equations, including continuity, motion, nanoparticle, and concentration force are solved by disregarding inertial forces and employing the approximation of long-wavelength. The perturbation method is used to solve the resultant nonlinear coupled partial differential equation analytically. Mathematical and graphical outputs for concentration, temperature, and pressure rise, considering all physical parameters, are presented. Numerical computation is applied to assess expressions for friction forces and pressure rise. Magnetohydrodynamics has various applications in various microchannel designs for efficacious flow control in pumping fluids for both pulsating and non-pulsating continuous flow. Finally, Response Surface Methodology (RSM) is used for performing sensitivity analysis and its optimization. ANOVA tables are generated with the help of MINITAB-19 which is a statistical software. The sensitivity results are displayed in tabular and graphical form and concluded that M is more sensitive than other input parameters for ΔPat the low level and the input parameter Nt is most sensitive among others at middle and high levels. Further, Temperature and pressure of the flow has different responses for various values of magnetic, thermophoresis and Brownian motion parameter.

Recognition of Longitudinal Cracks on Slab Surfaces Based on Particle Swarm Optimization and eXtreme Gradient Boosting Model

Recognition of Longitudinal Cracks on Slab Surfaces Based on Particle Swarm Optimization and eXtreme Gradient Boosting Model

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

One data-driven vibration acceleration prediction method for offshore wind turbine structures based on extreme gradient boosting

With the development of offshore wind power towards large-scale and deep-sea, the safety risks and the difficulty of intelligent operation and maintenance of offshore wind turbine (OWT) during operation periods have increased significantly. Structural vibration response is an important factor affecting the safety and stability of OWT. Accurate prediction on vibration through environmental conditions and operational factors can identify the safety risks of OWT in advance, and reduce harmful vibration through appropriate operation strategy regulation. Therefore, a vibration acceleration prediction method of OWT structures was proposed in this research based on Harris hawks optimization (HHO) and extreme gradient boosting (XGBoost). Firstly, approximately two months of monitoring data from a 3 MW OWT were extracted to establish the database for training the machine learning models. Then, the key hyperparameters of XGBoost were optimized by the HHO algorithm and compared with other typical algorithms. Finally, the trained prediction model was applied to the regularity analysis of different conditions and long time series data. The analysis results demonstrate that the proposed method can effectively predict the vibration acceleration response of OWT structures using operational and environmental characteristics, and has higher accuracy and stability than other methods, with R2 of 0.9715 and mean absolute percentage error (MAPE) of 6.55%. Simultaneously, it also shows acceptable accuracy on the long-term measured data because the MAPE of chosen summer, autumn, and winter samples were calculated as 9.60%, 11.76%, and 7.21%, respectively. The establishment of the HHO-XGBoost vibration prediction model can be applied in the subsequent prediction of the vibration trend, which provides a guarantee for the safe and stable operation of OWT.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Coupled optimization of auxiliary channels and porosity gradient of GDL for PEMFC

In this study, we proposed a novel under-rib auxiliary channel and developed a systematic methodology combining computational fluid dynamics (CFD), an artificial neural network (ANN), and a particle swarm optimization (PSO) algorithm for the coupled optimization of the auxiliary-channel structure and gas diffusion layer (GDL) porosity gradient in a proton exchange membrane fuel cell (PEMFC). First, we developed a three-dimensional multiphase numerical model of a non-isothermal parallel flow-field PEMFC. ANN was then applied to obtain a mapping relationship between auxiliary channels structure, GDL porosity gradient and output performance of PEMFC with finite CFD data. Finally, PSO was employed to perform an optimization search to determine the optimal parameter pairing, resulting in a 16.7 % increase in maximum power density and a 20.6 % increase in the current operating range. In addition, the optimized PEMFC exhibited superior mass transfer and water removal capabilities, where the average oxygen concentration in the intermediate section of cathode GDL was increased by 33.3 % and the maximum liquid water saturation was reduced by 23.5 %. Moreover, the coupled optimization of the auxiliary channels and the GDL porosity gradient can increase power density by at least 8.7 % and current density by 9.1 % compared to single component optimization.