Multimodal Characteristics Research Articles

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed. A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58-0.85] and a specificity of 0.91 [0.87-0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21-14.50], the negative likelihood ratio (NLR) was 0.28 [0.16-0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96-78.97]. The area under the curve (AUC) was 0.93 [0.90-0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis. In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. PROSPERO (CRD42024497599).

An MIP-Based PFAS Sensor Exploiting Nanolayers on Plastic Optical Fibers for Ultra-Wide and Ultra-Low Detection Ranges-A Case Study of PFAS Detection in River Water.

In this work, a novel optical-chemical sensor for the detection of per- and polyfluorinated substances (PFASs) in a real scenario is presented. The proposed sensing approach exploits the multimode characteristics of plastic optical fibers (POFs) to achieve unconventional sensors via surface plasmon resonance (SPR) phenomena. The sensor is realized by the coupling of an SPR-POF platform with a novel chemical chip based on different polymeric nanolayers over the core of a D-shaped POF, one made up of an optical adhesive and one of a molecularly imprinted polymer (MIP) for PFAS. The chemical chip is used to launch the light into the SPR D-shaped POF platform, so the interaction between the analyte and the MIP's sites can be used to modulate the propagated light in the POFs and the SPR phenomena. Selectivity tests and dose-response curves by standard PFOA water solutions were carried out to characterize the detection range sensor response, obtaining a wide PFAS response range, from 1 ppt to 1000 ppt. Then, tests performed on river water samples collected from the Bormida river paved the way for the applicability of the proposed approach to a real scenario.

Cr3+-Activated Deep Trap Electron-Trapping Biphasic Mixture of Zn (Al,Ga)2O4 and Zn2GeO4 for Multilevel Information Storage by Trap Multiplexing and Wavelength Multiplexing.

Double wavelength emitting phosphors with hypersensitive thermal response are important for optical/thermal sensors and multiplexing optical data storage technology. However, it is a daunting challenge to develop multicolor electron-trapping materials with superheat sensitivity and anomalous quenching luminescence characteristics for information storage. Here, based on the selection of host lattice and the control of doping ion types, we have designed Mn2+- and/or Cr3+-activated persistent materials with excellent fluorescence properties. Among these phosphors, Mn,Cr co-activated biphasic mixture exhibits not only sensitive anomalous thermosensitive fluorescence properties but also multicolor and multimode fluorescence emission characteristics derived from Mn and Cr simultaneously. The corresponding multimode fluorescence mechanism is proposed based on afterglow energy transfer and deepening trap effect. Especially, based on the excellent fluorescence performances, these phosphors as information storage medium demonstrate multilevel information storage by a combination of the trap multiplexing and wavelength multiplexing technologies. The study here provides not only the ideas for designing and synthesizing new electronic materials but also a material foundation for the fourth generation of optical information storage and encryption.

Deep learning-based approach for identifying vortex-induced vibrations in stay cables

Due to variations in wind speed profiles along the length of bridge stay cables, vortex-induced vibrations (VIV) exhibit multimodal characteristics, presenting challenges for VIV identification. Currently, the VIV identification is concentrated on the stable stage of VIV, lacking an available early warning system for detecting the initial developing stage of VIV. In this study, a deep learning-based approach that integrates energy distribution ratio features derived from frequency band wavelet packet decomposition to recognize VIV of stay cable was proposed. Firstly, vibration characteristics induced by vortices in cable-stayed bridges were analyzed based on field monitoring data from the bridge health monitoring system, aiming to propose suitable feature indicators for VIV identification. Secondly, using root mean square as label classification, a deep learning model was constructed, incorporating convolutional neural networks, long short-term memory networks, and attention mechanisms. Finally, four different stages in the evolution of stay cables VIV were identified utilizing field monitoring datasets to analyze the optimal parameter. Meanwhile, effective early warning recognition was achieved through the classification and recognition of confusion matrix. This study provides technical support for early warning systems and structural condition assessment concerning bridge stay cable VIV.

CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images

Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involves the presence of multiple immature follicles in the ovaries, which can disrupt normal ovulation and lead to hormonal imbalances and associated health complications. Routine diagnostic methods rely on manual interpretation of ultrasound (US) images and clinical assessments, which are time-consuming and prone to errors. Therefore, implementing an automated system is essential for streamlining the diagnostic process and enhancing accuracy. By automatically analyzing follicle characteristics and other relevant features, this research aims to facilitate timely intervention and reduce the burden on healthcare professionals. The present study proposes an advanced automated system for detecting and classifying PCOS from ultrasound images. Leveraging Artificial Intelligence (AI) based techniques, the system examines affected and unaffected cases to enhance diagnostic accuracy. The pre-processing of input images incorporates techniques such as image resizing, normalization, augmentation, Watershed technique, multilevel thresholding, etc. approaches for precise image segmentation. Feature extraction is facilitated by the proposed CystNet technique, followed by PCOS classification utilizing both fully connected layers with 5-fold cross-validation and traditional machine learning classifiers. The performance of the model is rigorously evaluated using a comprehensive range of metrics, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis. The model demonstrated a commendable accuracy of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96.54\\%$$\\end{document} when utilizing a fully connected classification layer, as determined by a thorough 5-fold cross-validation process. Additionally, it has achieved an accuracy of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.75\\%$$\\end{document} when employing an ensemble ML classifier. This proposed approach could be suggested for predicting PCOS or similar diseases using datasets that exhibit multimodal characteristics.

Prediction of epileptogenicity in patients with tuberous sclerosis complex using multimodal cerebral MRI

ObjectiveEpilepsy is the most common complication and cause of morbidity and mortality in tuberous sclerosis complex (TSC). Surgery is associated with an increased probability of achieving seizure-freedom. However, the preoperative noninvasive localisation of epileptogenic tubers remains challenging. This study aimed to identify multimodal magnetic resonance imaging (MRI) biomarkers of epilepsy in patients with TSC and develop a prediction model of epileptogenicity in these patients. MethodsPatients with TSC, with or without epilepsy, were recruited. All patients underwent MRI scanning, including T1WI, T2WI, T2W-FLAIR, DTI, and multi-parametric MR with a flexible design (MULTIPLEX). We compared the multimodal cerebral MRI characteristics of the cortical tubers, subependymal nodules, and perilesional tissue between patients with TSC with or without epilepsy and developed a prediction model for epileptogenicity. ResultsAmong the patients with TSC, 32 with and 16 without epilepsy underwent MRI. Higher proton-density mapping (PD) of cortical tubers and decreased fractional anisotropy (FA), increased mean diffusivity (MD), and increased radial diffusivity (RD) of subependymal nodules were associated with epileptogenicity in both the centre and perilesional tissue, independent of TSC gene variation. Based on the above findings, we developed a prediction model for epileptogenicity with an area under the curve of 0.973, specificity of 0.909, and sensitivity of 0.963 (P < 0.001). ConclusionIn patients with TSC, high PD of the cortical tubers, decreased FA, and elevated MD/RD of the subependymal nodules were significantly associated with epileptogenicity. A prediction model based on multimodal cerebral MRI characteristics has the potential to evaluate the likelihood of epilepsy in patients with TSC.

68Ga]Ga-LNC1007 versus 2-[18F]FDG in the evaluation of patients with metastatic differentiated thyroid cancer: a head-to-head comparative study.

This head-to-head comparison study aimed to compare the performance of [68Ga]Ga-FAPI-RGD (LNC1007) and 2-[18F]FDG PET/CT in the evaluation of patients with metastatic differentiated thyroid cancer (mDTC). Ten unexplained hyperthyroglobulinemia (UHTg) patients and 20 patients with definite metastatic lesions of thyroid cancer (DmDTC) were enrolled in the study. All patients underwent both [68Ga]Ga-LNC1007 and 2-[18F]FDG PET/CT within 1week. The final diagnosis was based on histopathological results and a comprehensive evaluation of laboratory tests and multimodal imaging characteristics. In patients with UHTg, [68Ga]Ga-LNC1007 PET/CT detected more metastatic lymph nodes (LNs) (17 vs. 15, P = 0.317) and lung lesions (2 vs. 0) than 2-[18F]FDG. In patients with DmDTC, [68Ga]Ga-LNC1007 PET/CT also detected more true positive lesions than 2-[18F]FDG (Total: 133 vs. 103, LN: 20 vs. 15, lung: 18 vs. 10, bone: 87 vs.73). [68Ga]Ga-LNC1007 PET/CT demonstrated significantly higher SUVmax (Total: 6.30 vs. 3.84, LN: 8.28 vs. 4.82, Lung: 3.31 vs. 1.49, Bone: 5.73 vs. 3.87, all P < 0.05) and TBR (Total: 6.92 vs. 4.93, LN: 6.48 vs. 4.16, Lung: 5.16 vs. 2.57, Bone: 7.22 vs. 5.41, all P < 0.05) in true positive lesions compared to 2-[18F]FDG. Specifically, the sensitivity of [68Ga]Ga-LNC1007 PET/CT was higher than that of 2-[18F]FDG in detecting lung and bone metastases (94.7% vs. 52.6% and 100% vs. 83.9%, all P < 0.05). [68Ga]Ga-LNC1007 PET/CT exhibited better specificity and accuracy in diagnosing LNs (96.9% vs. 66.7% and 96.3% vs. 68.5%, all P < 0.05). However, the specificity of [68Ga]Ga-LNC1007 for bone metastasis was inferior to 2-[18F]FDG (15.4% vs. 88.5%, P < 0.05). Compared with 2-[18F]FDG, [68Ga]Ga-LNC1007 PET/CT could detect more metastatic lesions, with higher SUVmax and TBR, in patients with mDTC. [68Ga]Ga-LNC1007 had better accuracy in the diagnosis of LN and lung metastasis. Trial registration ClinicalTrials.gov NCT05515783. Registered 01 May 2022. URL of registry https://classic. gov/ct2/show/NCT05515783.

Insights into Retinal Metastasis from Systemic Carcinoma: A Systematic Review of Clinical and Multimodal Imaging Characteristics.

Background/Objectives: Retinal metastasis (RM) is an exceptionally rare clinical condition, comprising less than 1% of ocular metastasis cases. This study aims to investigate the clinical features of RM originating from systemic carcinoma. Methods: A systematic review (PROSPERO ID: CRD42022344724). Results: The systematic review revealed 58 cases involving 61 eyes. Most of the cases (82.8%) had a known primary malignancy before RM was diagnosed. The main primary sites were the lung (45.8%), gastrointestinal tract (23.7%), and breast (16.9%). The lung was the most common (80.0%) carcinoma in cases with unknown primary sites. The median survival after RM diagnosis was 11 months. The main clinical patterns were patchy retinal infiltrates (35.7%), an elevated retinal mass (63.8%), and punctate retinal infiltrates (7.1%). Subretinal fluid, vitreous seeding, and choroidal invasion were noted in 57.4%, 41.0%, and 6.6% of eyes, respectively. Key multimodal imaging features were early hypofluorescence with mid-to-late hyperfluorescence on fluorescein angiography (84.6%) and hyper-reflectiveness on optical coherence tomography (70.8%). A final best-corrected visual acuity of ≤20/200 was noted in 66.7% of the eyes. Conclusions: Patchy retinal infiltrates, an elevated retinal mass, and punctate retinal infiltrates are the major clinical patterns of RM from systemic carcinoma. The primary carcinoma sites are the lung, gastrointestinal tract, and breast. Systemic and visual prognoses were unfavorable.

Semi-Supervised Encrypted Malicious Traffic Detection Based on Multimodal Traffic Characteristics.

The exponential growth of encrypted network traffic poses significant challenges for detecting malicious activities online. The scale of emerging malicious traffic is significantly smaller than that of normal traffic, and the imbalanced data distribution poses challenges for detection. However, most existing methods rely on single-category features for classification, which struggle to detect covert malicious traffic behaviors. In this paper, we introduce a novel semi-supervised approach to identify malicious traffic by leveraging multimodal traffic characteristics. By integrating the sequence and topological information inherent in the traffic, we achieve a multifaceted representation of encrypted traffic. We design two independent neural networks to learn the corresponding sequence and topological features from the traffic. This dual-feature extraction enhances the model's robustness in detecting anomalies within encrypted traffic. The model is trained using a joint strategy that minimizes both the reconstruction error from the autoencoder and the classification loss, allowing it to effectively utilize limited labeled data alongside a large amount of unlabeled data. A confidence-estimation module enhances the classifier's ability to detect unknown attacks. Finally, our method is evaluated on two benchmark datasets, UNSW-NB15 and CICIDS2017, under various scenarios, including different training set label ratios and the presence of unknown attacks. Our model outperforms other models by 3.49% and 5.69% in F1 score at labeling rates of 1% and 0.1%, respectively.

Hybrid of imperialist competitive algorithm and particle swarm optimization for parameter extraction of photovoltaic cells

To improve the efficiency of photovoltaic systems, it is essential to obtain the parameters of photovoltaic cells through an identification process. However, due to the nonlinear and multimodal characteristics, accurately and reliably identifying the parameters of photovoltaic cells still remains a challenging task. In this paper, a hybrid of the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), ICA-PSO, is proposed to effectively identify the parameters of photovoltaic cells. The position updating strategy of PSO is adopted to replace the colony’s position updating strategy in the ICA. The hybrid algorithm ICA-PSO integrates the multi-swarm search characteristic and the powerful exploration ability of PSO together, leading to an enhanced optimization performance. Experimental results of applying ICA-PSO to parameter identification of photovoltaic cells show that ICA-PSO can extract the parameters of photovoltaic cells with higher accuracy and reliability, thus outperforming many other methods presented in the literature.

LCMA-Net: A light cross-modal attention network for streamer re-identification in live video

With the rapid expansion of the we-media industry, streamers have increasingly incorporated inappropriate content into live videos to attract traffic and pursue interests. Blacklisted streamers often forge their identities or switch platforms to continue streaming, causing significant harm to the online environment. Consequently, streamer re-identification (re-ID) has become of paramount importance. Streamer biometrics in live videos exhibit multimodal characteristics, including voiceprints, faces, and spatiotemporal information, which complement each other. Therefore, we propose a light cross-modal attention network (LCMA-Net) for streamer re-ID in live videos. First, the voiceprint, face, and spatiotemporal features of the streamer are extracted by RawNet-SA, Π-Net, and STDA-ResNeXt3D, respectively. We then design a light cross-modal pooling attention (LCMPA) module, which, combined with a multilayer perceptron (MLP), aligns and concatenates different modality features into multimodal features within the LCMA-Net. Finally, the streamer is re-identified by measuring the similarity between these multimodal features. Five experiments were conducted on the StreamerReID dataset, and the results demonstrated that the proposed method achieved competitive performance. The dataset and code are available at https://github.com/BJUT-AIVBD/LCMA-Net.

Investigation of a cascaded piezoelectric transducer with cone horn for multi-mode power ultrasound application

To meet the ultrasonic application requirement of high power intensity and large mechanical displacement, a new theoretical method is used to study the multi-mode characteristic of the cascaded piezoelectric transducer with cone horn in this paper. Based on equivalent circuit and Kirchhoff’s law to obtain the frequency equation and vibration velocity expression of the transducer, then the resonance frequency and effective electromechanical coupling coefficient can be calculated, the velocity amplification ratio can be obtained in the meantime. This method is distinguish from traditional theoretical analysis, which avoids complex transformation of the multi-excitation sources in cascaded structure, and can calculate more performance parameters. The relationships between these performance parameters and the excitation position of the piezoelectric stack, the length and output end radius of the cone horn are analyzed and compared with the numerical simulation, and the optimized parameters of the transducer size are given. A transducer is manufactured for experimental test, the results show that the experimental value is in good agreement with theoretical calculation and finite element simulation. This work is expected to be used in the optimal analysis of the multi-mode transducer in high power ultrasonic field.

Novel multi-mode anti-counterfeiting encryption material of CaAl12O19:Eu, Er with multi-color down-conversion luminescence, up-conversion luminescence, dynamic luminescence, and photochromism

Multi-mode dynamic anti-counterfeiting materials can provide complex anti-counterfeiting performance and ensure the anti-counterfeiting strategy becomes more secure. Herein, a new type of multi-mode anti-counterfeiting encryption material of CaAl12O19:Eu, Er with different Er doping concentration was developed by sol–gel method. Interestingly, the CaAl12O19:Eu, Er phosphor and its composite have multi-mode anti-counterfeiting characteristics of multi-color down-conversion luminescence, up-conversion luminescence, dynamic luminescence, and photochromism. Effect of different Er doping concentration on the down-conversion luminescence, up-conversion luminescence, dynamic luminescence, and photochromism of CaAl12O19:Eu, Er was systematically investigated, and the relevant mechanisms were discussed. These anti-counterfeiting features can be simultaneously applied in both bright and dark fields, which can achieve high-level anti-counterfeiting in both spatial and temporal dimensions. The CaAl12O19:Eu, Er phosphors cannot be easily replaced by other materials with the same anti-counterfeiting properties. They display good application foreground in the field of anti-counterfeiting encryption.

CytoKernel: Robust kernel embeddings for assessing differential expression of single cell data.

High-throughput sequencing of single-cell data can be used to rigorously evlauate cell specification and enable intricate variations between groups or conditions. Many popular existing methods for differential expression target differences in aggregate measurements (mean, median, sum) and limit their approaches to detect only global differential changes. We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. cytoKernel is specifically designed to assess the differential expression of single cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single cell data, by calculating the pairwise divergence/distance between distributions of subjects. It can detect both patterns involving aggregate changes, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the False Discovery Rate (FDR) and shows favourable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry data sets. The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http://bioconductor.org/packages/cytoKernel.

Nonlinear dynamic transfer partial least squares for domain adaptive regression

Aiming to address soft sensing model degradation under changing working conditions, and to accommodate dynamic, nonlinear, and multimodal data characteristics, this paper proposes a nonlinear dynamic transfer soft sensor algorithm. The approach leverages time-delay data augmentation to capture dynamics and projects the augmented data into a latent space for constructing a nonlinear regression model. Two regular terms, distribution alignment regularity and first-order difference regularity, are introduced during data projection to address data distribution disparities. Laplace regularity is incorporated into the nonlinear regression model to ensure geometric structure preservation. The final optimization objective is formulated within the framework of partial least squares, and hyperparameters are determined using Bayesian optimization. The effectiveness of the proposed algorithm is demonstrated through experiments on three public datasets.

Preparing for Artificial General Intelligence (AGI) in Health Professions Education: AMEE Guide No. 172

Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI.

Leak detection and localization of fluid-filled pipeline using accelerometer pairs and mode separation method

Acoustic methods are commonly used to detect and locate leaks in pipelines, but they often overlook important multimodal characteristics of acoustic waves, limiting their applicability. Based on propagation theories regarding F(1,1) mode signal extraction, a new and easy-to-use method for leak detection and localization in pressurized pipelines is proposed. The F(1,1) mode signals are obtained using accelerometer pairs and mode separation method, which are then used to train detection models and to estimate the time difference of arrival (TDOA) for leak localization. Mode separation empowers the data-driven models with physically meaningful samples, aiming to broaden their applicability. The mode consistency between TDOA and velocity is achieved to ensure accurate leak localization. Experiments in buried water-filled ductile iron pipe (experiment A) and in fuel-filled iron pipe (experiment B) are conducted and the longest test distance reaches 72 m. The results validate that the employment of mode separation notably improves current leak detection and localization methods which primarily rely on original signals. The recognition rates rise about 3 %∼11 %, and the false alarm rates reduce about 1 %∼2 %, even when confronted with pipe junction interferences. Also, the leak localization errors are overall lower, with absolute errors below 2 m and relative errors less than 5 % of the test segment length for most cases.

Photovoltaic model parameters identification using diversity improvement-oriented differential evolution

Fast and accurate parameter identification of the photovoltaic (PV) model is crucial for calculating, controlling, and managing PV generation systems. Numerous meta-heuristic algorithms have been applied to identify unknown parameters due to the multimodal and nonlinear characteristics of the parameter identification problems. Although many of them can obtain satisfactory results, problems such as premature convergence and population stagnation still exist, influencing the optimization performance. A novel variant of Differential Evolution, namely, Diversity Improvement-Oriented Differential Evolution (DIODE), is proposed to mitigate these deficiencies and obtain reliable parameters for PV models. In DIODE, an adaptive perturbation strategy is employed to perturb current individuals to mitigate premature convergence by enhancing population diversity. Secondly, a diversity improvement mechanism is proposed, where information on the covariance matrix and fitness improvement of individuals is used as a diversity indicator to detect stagnant individuals, which are then updated by the intervention strategy. Lastly, a novel parameter adaptation strategy is employed to maintain a sound balance between exploration and exploitation. The proposed DIODE algorithm is applied to parameter identification problems of six PV models, including single, double, and triple diode and three PV module models. In addition, a large test bed containing 72 benchmark functions from CEC2014, CEC2017, and CEC2022 test suites is employed to verify DIODE’s overall performance in terms of optimization accuracy. Experiment results demonstrate that DIODE can secure accurate parameters of PV models and achieve highly competitive performance on benchmark functions.

Tunable bandgaps of guided waves by periodic shunting circuits in multilayered piezoelectric plates

The multi-mode and dispersion characteristics of guided waves in plate bring great challenges to the manipulation of guided waves and controlling vibration and noise. In this study, the Stroh formalism is developed to establish a theoretical model for deriving the dispersion relations of guided waves in a multilayered piezoelectric plate. The dispersion characteristics under different electrical boundary conditions introduced by periodic shunting circuits are discussed to reveal the influences of external circuits on guided wave propagation. The proper shunting circuit is not only the key point to obtain the specific frequency of bandgap, but also the effective method to control the bandwidth. Furthermore, for the separation of symmetric and anti-symmetric modes of guided waves, the multilayered piezoelectric plate with a mirror plane is studied by using an equivalent model. Results indicate that the propagation behaviors of guided waves in multilayered piezoelectric plates can be interpreted by developed Stroh formalism, and their bandgaps can be effectively manipulated by periodically external circuits. The tunable bandgaps in this work could be helpful for designing smart devices of wave isolation based on multilayered piezoelectric plate.

Surrogate and Autoencoder-Assisted Multitask Particle Swarm Optimization for High-Dimensional Expensive Multimodal Problems

In practice, some optimization problems require expensive calculation and exhibit multimodal characteristics simultaneously. These problems are called high-dimensional expensive multimodal optimization problems. When addressing such problems, existing surrogate-assisted evolutionary algorithms (SAEAs) encounter the “curse of dimensionality," which severely affects their capability to search optimal solutions. Therefore, this study proposed a surrogate and autoencoder-assisted multitask particle swarm optimization algorithm. First, an autoencoder-embedded multitask evolutionary framework was established to transform a high-dimensional multimodal optimization problem into multiple low-dimensional subproblems or subtasks. Further, a multi-level surrogate model management mechanism combining mirror learning was proposed. An appropriate local surrogate model can be rapidly generated for each modality of the problem. Moreover, a dual-mode local exploitation strategy was developed to improve the capability of swarm to exploit each subtask. The proposed algorithm was compared with seven existing SAEAs on 33 benchmark functions and the aeroengine aerodynamic design optimization problem. Experimental results revealed that the proposed algorithm can obtain multiple highly competitive optimal solutions, including global optimal solutions.