Domain Mapping Research Articles

Effect of random roughness on Stokes flow

Microscopic irregularity (roughness) of bounding surfaces affects macroscopic dynamics of fluid flows. Its effect on bulk flow is usually quantified empirically by means of a roughness coefficient. A new approach, which treats rough surfaces (e.g. parallel plates) as random fields whose statistical properties can be inferred from measurements, is presented. The mapping of a random flow domain onto its deterministic counterpart, and the subsequent stochastic averaging of the transformed Stokes equations, yield expressions for the effective viscosity and roughness coefficient in terms of the statistical characteristics of the irregular geometry of the boundaries. The analytical nature of the solutions allows one to handle surface roughness characterized by short correlation lengths, a challenging feature for numerical stochastic simulations.

CATH v4.4: major expansion of CATH by experimental and predicted structural data.

CATH (https://www.cathdb.info) is a structural classification database that assigns domains to the structures in the Protein Data Bank (PDB) and AlphaFold Protein Structure Database (AFDB) and adds layers of biological information, including homology and functional annotation. This article covers developments in the CATH classification since 2021. We report the significant expansion of structural information (180-fold) for CATH superfamilies through classification of PDB domains and predicted domain structures from the Encyclopedia of Domains (TED) resource. TED provides information on predicted domains in AFDB. CATH v4.4 represents an expansion of ∼64 844 experimentally determined domain structures from PDB. We also present a mapping of ∼90 million predicted domains from TED to CATH superfamilies. New PDB and TED data increases the number of superfamilies from 5841 to 6573, folds from 1349 to 2078 and architectures from 41 to 77. TED data comprises predicted structures, so these new folds and architectures remain hypothetical until experimentally confirmed. CATH also classifies domains into functional families (FunFams) within a superfamily. We have updated sequences in FunFams by scanning FunFam-HMMs against UniProt release 2024_02, giving a 276% increase in FunFams coverage. The mapping of TED structural domains has resulted in a 4-fold increase in FunFams with structural information.

Geological and mining mapping approach by coupling geological and geophysical field data: application to the Central Domain of the Mbere division (Adamawa-Cameroon)

This study is based on the coupling of geological field data with geophysical studies in the geological mapping of the Central Domain of the Mbere Division (Adamawa-Cameroon). The first step was to calculate the residual grid associated with the geophysical data that will be used for the mapping of the basement. The second step before mapping consisted in calculating the analytical signal associated with the residual grid, used to interpret the geological formations under cover or in intrusion. Subsequently, a geological mapping approach based on the categorisation of geophysical signatures into ranges using the Encom Discover program was adopted. This approach is based on the analysis of the relief of geophysical anomalies. The process of categorisation which is done iteratively, takes into consideration in the same georeferenced space, the resolution of the input data, the variation of the field considered and the structural model previously interpreted. The result of the categorisation is then compared to the spatial distribution of outcrop data to build the geological model. The application of this approach to the Central Domain of the Mbere Division led to the production of a synthetic geological map at a scale of 1/75,000. The superposition of this map to the topographic model allows to observe a concordance between the Cretaceous basins represented by their edge faults and the topographic depression zones. The various mineral indicators (primary and secondary) collected during the fieldwork, combined with the geological model (1:75,000 scale) as well as the Cretaceous deposits and vein intrusion models were used to create the mineral indicators and targets map for this sector. All these models can thus be used to underpin mineral exploration projects in the study area.

The short conserved region-2 of LARP4 interacts with ribosome-associated RACK1 and promotes translation.

LARP4 interacts with poly(A)-binding protein (PABP) to protect mRNAs from deadenylation and decay, and recent data indicate it can direct the translation of functionally related mRNA subsets. LARP4 was known to bind RACK1, a ribosome-associated protein, although the specific regions involved, and relevance had been undetermined. Here, yeast two-hybrid domain mapping followed by other methods identified positions 615-625 in conserved region-2 (CR2) of LARP4 (and LARP4B) as directly binding RACK1 region 200-317. Consistent with these results, AlphaFold2-multimer predicted high confidence interaction of CR2 with RACK1 propellers 5-6. CR2 mutations strongly decreased LARP4 association with cellular RACK1 and ribosomes by multiple assays, whereas less effect was observed for PABP association, consistent with independent interactions. CR2 mutations decreased LARP4 ability to optimally stabilize a β-globin mRNA reporter containing an AU-rich element (ARE) more significantly than a β-globin and other reporters lacking this element. While polysome profiles indicate the β-glo-ARE mRNA is inefficiently translated, consistent with published data, we show that LARP4 increases its translation whereas the LARP4-CR2 mutant is impaired. Analysis of nanoLuc-ARE mRNA for production of luciferase activity confirmed LARP4 promotes translation efficiency while CR2 mutations are disabling. Thus, LARP4 CR2-mediated interaction with RACK1 can promote translational efficiency of some mRNAs.

ProCogGraph: a graph-based mapping of cognate ligand domain interactions.

Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts. We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands. The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.

Making the effect visible - OX40 targeting nanobodies for in vivo imaging of activated T cells.

Human OX40 (hOX40/CD134), a member of the TNF receptor superfamily, is mainly expressed on activated T lymphocytes. Triggered by its ligand OX40L (CD252), it provides costimulatory signals that support the differentiation, proliferation and long-term survival of T cells. Besides being a relevant therapeutic target, hOX40 is also an important biomarker for monitoring the presence or infiltration of activated T cells within the tumor microenvironment (TME), the inflammatory microenvironment (IME) in immune-mediated diseases (IMIDs) and the lymphatic organs. Here, we developed novel single domain antibodies (nanobodies, Nbs) targeting hOX40 to monitor the activation status of T cells by in vivo molecular imaging. Nbs against hOX40 (hOX40-Nbs) were selected from an immunized Nb-library by phage display. The identified hOX40-Nbs were characterized in vitro, including determination of their specificity, affinity, stability, epitope recognition and their impact on OX40 signaling and T cell function. A lead candidate was site-specifically conjugated with a fluorophore via sortagging and applied for noninvasive in vivo optical imaging (OI) of hOX40-expressing cells in a xenograft mouse model. Our selection campaign revealed four unique Nbs that exhibit strong binding affinities and high stabilities under physiological conditions. Epitope binning and domain mapping indicated the targeting of at least two different epitopes on hOX40. When analyzing their impact on OX40 signaling, an agonistic effect was excluded for all validated Nbs. Incubation of activated T cells with hOX40-Nbs did not affect cell viability or proliferation patterns, whereas differences in cytokine release were observed. In vivo OI with a fluorophore-conjugated lead candidate in experimental mice with hOX40-expressing xenografts demonstrated its specificity and functionality as an imaging probe. Considering the need for advanced probes for noninvasive in vivo monitoring of T cell activation dynamics, we propose, that our hOX40-Nbs have a great potential as imaging probes for noninvasive and longitudinal in vivo diagnostics. Quantification of OX40+ T cells in TME or IME will provide crucial insights into the activation state of infiltrating T cells, offering a valuable biomarker for assessing immune responses, predicting treatment efficacy, and guiding personalized immunotherapy strategies in patients with cancer or IMIDs.

Methodology for the selection of learning measures for the competence development of assembly workers

ABSTRACT Mass personalization and shorter product life cycles complicate the assembly of manufacturing companies. To handle the complexity, a high number of employees execute varying assembly tasks. Their work is characterized by learning new tasks frequently to produce different variants. To achieve good learning results in a short amount of time, a suitable individual assembly workers’ competence development process is crucial. Therefore, this publication shows the development of a user methodology to plan the competence development process focusing on selecting learning measures that fit both the work situation and learning employees. The presented user methodology is based on a standardized description and selection procedure for learning measures by using results of a Domain Mapping Matrix, Delphi study, and expert interviews. The publication contributes to improved competence development to support the strategic empowerment of the workforce and to increase the flexibility in manual assembly.

CPDet: Circle-Permutation-Aware Object Detection for Heat Exchanger Cleaning

Shell–tube heat exchangers are commonly used equipment in large-scale industrial systems of wastewater heat exchange to reclaim the thermal energy generated during industrial processes. However, the internal surfaces of the heat exchanger tubes often accumulate fouling, which subsequently reduces their heat transfer efficiency. Therefore, regular cleaning is essential. We aim to detect circle holes on the end surface of the heat exchange tubes to further achieve automated positioning and cleaning tubes. Notably, these holes exhibit a regular distribution. To this end, we propose a circle-permutation-aware object detector for heat exchanger cleaning to sufficiently exploit prior information of the original inputs. Specifically, the interval prior to the extraction module extracts interval information among circle holes based on prior statistics, yielding prior interval context. The following interval prior fusion module slices original images into circle domain and background domain maps according to the prior interval context. For the circle domain map, prior-guided sparse attention using prior a circle–hole diameter as the step divides the circle domain map into patches and performs patch-wise self-attention. The background domain map is multiplied by a hyperparameter weak coefficient matrix. In this way, our method fully leverages prior information to selectively weigh the original inputs to achieve more effective hole detection. In addition, to adapt the hole shape, we adopt the circle representation instead of the rectangle one. Extensive experiments demonstrate that our method achieves state-of-the-art performance and significantly boosts the YOLOv8 baseline by 5.24% mAP50 and 5.25% mAP50:95.

MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge Model

Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a 5% RMSE and 7% SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality.

Applying the Panarchy Framework to Examining Post-Pandemic Adaptation in the Undergraduate Medical Education Environment: A Qualitative Study

Phenomenon: The COVID-19 pandemic necessitated an abrupt shift to online medical education, disrupting learning across knowledge, skills, and social connections. Post-pandemic, medical schools must evaluate how these disruptions shaped student experiences to optimize the return to in-person learning. Approach: This cross-sectional qualitative study explored medical students’ perceptions of their learning environment during the post-pandemic reintegration period in Oman. Fifty-four preclinical and clinical students participated in six focus group interviews. Content analysis identified key topics characterizing students’ perceptions of change and change processes in the post-pandemic learning environment. The panarchy framework, developed to characterize complex adaptive systems in nature, was used to frame the results. Findings: The return to in-person environments presented a mix of renewed connectivity, involving collaborative benefits alongside transitional adjustment strains. Five major topics characterizing student perceptions of change and change processes in their post-pandemic learning environment were identified: learning skills, developing clinical competence, faculty interactions, physical atmosphere, and social connections. Managing academic schedules and cognitive load as the learning environment opened challenged students’ learning skills. Learners valued a renewed opportunity for interactive application of knowledge through collaboration, patient contact, and empathy skill-building to feel prepared for future practice. Returning to in-person instruction renewed a sense of community and peer support networks disrupted by pandemic isolation. Some students continued to struggle with study-life imbalance and felt ill-equipped to handle post-pandemic demands. Improved access to student support and wellness services was emphasized to ease transitional stresses. Students defined an ideal learning climate as supportive, active, personalized, relevant, challenging, accessible, and collaborative. Insights: While pandemic disruptions posed challenges, they provide opportunities to strengthen the educational system’s resilience moving forward. Our findings highlight an opportunity for medical educators and learners to capitalize on the innovations that emerged during this period, integrating technology with interactive learning activities and reconnecting students with the core values of the medical profession. Applying the panarchy framework to frame this adaptive process could enable the tracking of multi-level interactions within the medical education environment and the evaluation of interventions targeted at identified areas of concern. Further exploration to achieve complete mapping of specific environmental domains onto the panarchical cycles merits future investigation to build integrated resilience frameworks.

Deep shared learning and attentive domain mapping for cross-domain recommendation

Cross-domain recommendations (CDR) present a viable solution and are increasingly used to address the cold-start problem. Recently, CDR methods are utilizing deep models to generate latent preferences from context vectors or rating matrices and transfer these preferences between domains. However, many of these models focus on learning latent preferences using domain-related information and often disregard preference patterns from the contrary domain. Incorporating the contrary domain preference patterns into deep models can improve the generation of more effective latent representations. Moreover, existing CDR models face challenges in effectively transferring mapped preferences between domains due to the large features disparity between them. In this study, we tackle these problems and present a novel Deep Shared Learning and Attentive Domain Mapping (DSAM) approach for CDR. Specifically, we propose a variant of Long Short-Term Memory (LSTM) called shared learning LSTM, which incorporates the learning of cross-domain preference patterns alongside domain-specific user/item embeddings derived from textual reviews to dynamically generate shared contextual representations in each domain. We further exploit a multi-head self-attentive network to match item-specific knowledge from the source and target domains into different subspaces. We aggregate this learned knowledge to predict rating scores for cold-start users in the target domain. We efficiently optimize this framework in an end-to-end fashion. Experimental results on five real-world datasets demonstrate the effectiveness of our proposed approach against various groups of recommendation models. Additionally, we provide insights to help understand the model architecture and its robustness in handling cold-start users.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

Enhancing Inter-AUV Perception: Adaptive 6-DOF Pose Estimation with Synthetic Images for AUV Swarm Sensing

The capabilities of AUV mutual perception and localization are crucial for the development of AUV swarm systems. We propose the AUV6D model, a synthetic image-based approach to enhance inter-AUV perception through 6D pose estimation. Due to the challenge of acquiring accurate 6D pose data, a dataset of simulated underwater images with precise pose labels was generated using Unity3D. Mask-CycleGAN technology was introduced to transform these simulated images into realistic synthetic images, addressing the scarcity of available underwater data. Furthermore, the Color Intermediate Domain Mapping strategy is proposed to ensure alignment across different image styles at pixel and feature levels, enhancing the adaptability of the pose estimation model. Additionally, the Salient Keypoint Vector Voting Mechanism was developed to improve the accuracy and robustness of underwater pose estimation, enabling precise localization even in the presence of occlusions. The experimental results demonstrated that our AUV6D model achieved millimeter-level localization precision and pose estimation errors within five degrees, showing exceptional performance in complex underwater environments. Navigation experiments with two AUVs further verified the model’s reliability for mutual 6D pose estimation. This research provides substantial technical support for more complex and precise collaborative operations for AUV swarms in the future.

Research on the design of growable solid wood children’s beds

To meet children’s needs for beds at different stages of growth, a product design model integrating the KANO Model, the Hierarchical Analysis Method, and Axiomatic Design (KANO-AHP-AD) was adopted to conduct a design study on the growability of solid wood children’s beds. In accordance with the KANO model, the demands for children’s beds coming from questionnaires among families with children were classified and sorted, and the demand indicators were then summarized. Secondly, AHP was introduced to establish a multilevel hierarchical model, construct a judgment matrix of design elements, and calculate the weights of these elements to improve the accuracy of users’ demand weights. Then, AD was used to complete the mapping of the demand domain, function domain, and design domain of children’s beds and to judge their reasonableness through the matrix. Such a design allows users to evaluate and verify the rationality of the program. Through modular design techniques, the growability requirements were fulfilled. A low number of product modules were freely combined to form product types with multiple functions to meet customers’ needs for personalization and functional diversification. The work has value for the design of growable children’s beds, thus contributing to sustainable development and environmental protection causes.

A feature extension and reconstruction method with incremental learning capabilities under limited samples for intelligent diagnosis

Existing data-driven Intelligent Fault Diagnosis (IFD) methods often struggle with continuous learning and integrating new diagnostic knowledge. These methods necessitate a complete retraining process when new fault data emerges. Furthermore, collecting sufficient data for model training is impractical in real-world applications. This study proposes a Feature Extension and Reconstruction (FER) method for model incremental updating with limited samples. FER employs scalable and modular feature extractors for continuous representation learning of new fault modes. In addition, a domain mapping algorithm based on optimal transport is devised to leverage old classification nodes in aiding new nodes’ training, enhancing models’ generalization performance under limited samples. A feature reconstruction technique is also developed to compress the expanded multi-backbone model, eliminating redundant parameters and dimensions for adaptability in subsequent incremental tasks. Validated on simulated transmission systems and real planetary gearboxes, FER demonstrates a better stability-plasticity trade-off in incremental tasks with limited training samples, offering a novel solution for adaptive IFD model updates in sample-constrained scenarios.

Tectonic structures of the Dome Fuji region, East Antarctica, based on new magnetic data

The Oldest Ice Reconnaissance (OIR) airborne geophysical survey in East Antarctica was flown over approximately 170,000 km2 of the Dome Fuji region in 2016/17. The survey’s results support new insights into the subglacial geology and its meaning for the tectonic histories of the supercontinents Rodinia and Gondwana. The new magnetic and radar-derived bed topography data are integrated with previously acquired magnetic and gravity data, allowing the mapping of crustal domains within and beyond the survey’s limits. The magnetic data reveal three distinct domains within the survey region, delineated by N–S oriented boundaries, partly aligned with gravity domains following upward continuation transformations for both datasets. Additionally, four primary sets of magnetic lineaments were identified, exhibiting correlations with topographic and gravity patterns. These correlations indicate the continuation of the Tonian Oceanic Arc Super Terrane (TOAST) southward of its previously known southern limit. Moreover, an E–W-trending magnetic anomaly, the Elbert magnetic anomaly, suggests the suture between the recently-proposed subglacial Valkyrie craton and the TOAST. Furthermore, the analysis reveals a broad scale shear zone, named here the OIR shear zone, which formed as a result of oblique collision of the Ruker and Valkyrie cratons during the amalgamation of Gondwana.

Enhanced domain transfer deep fuzzy echo state network for rotating machinery fault diagnosis based on current signal

Although vibration-based fault diagnosis methods have achieved remarkable results in rotating machinery, they are still limited by various factors, such as environment noise and additional sensor installation. Meanwhile, due to inevitable distribution differences in practical engineering, deep transfer learning models are commonly used in fault diagnostic fields. Although deep architectures can extract representative domain-invariant features, they typically result in substantial computational burdens and training time. Therefore, to address the above problems, an enhanced domain transfer deep fuzzy echo state network (EDFESN) is proposed for rotating machinery fault diagnosis based on current signals. Firstly, current signals derived from the driving motor are directly collected without additional sensors. Secondly, an enhanced domain mapping method is raised to solve the problem of data distribution discrepancies between cross domain. Furthermore, a deep fuzzy echo state network (DFESN) integrated with fuzzy clustering is constructed, which utilizes layer-wise fuzzy-tuning learning paradigm instead of backpropagation step to reduce training time. Two datasets from gearbox and bearing both verify the effectiveness of the proposed method. Compared with other popular transfer methods, EDFESN not only achieves highest accuracy, but also has fastest training speed.

Exploring the PDZ, DUF, and LIM Domains of Pdlim5 in Dendrite Branching.

The branched architecture of neuronal dendrites is a key factor in how neurons form ordered networks and discoveries continue to be made identifying proteins and protein-protein interactions that direct or execute the branching and extension of dendrites. Our prior work showed that the molecular scaffold Pdlim5 and delta-catenin, in conjunction, are two proteins that help regulate the branching and elongation of dendrites in cultured hippocampal neurons and do so through a phosphorylation-dependent mechanism triggered by upstream glutamate signaling. In this report we have focused on Pdlim5's multiple scaffolding domains and how each contributes to dendrite branching. The three identified regions within Pdlim5 are the PDZ, DUF, and a trio of LIM domains; however, unresolved is the intra-molecular conformation of Pdlim5 as well as which domains are essential to regulate dendritic branching. We address Pdlim5's structure and function by examining the role of each of the domains individually and using deletion mutants in the context of the full-length protein. Results using primary hippocampal neurons reveal that the Pdlim5 DUF domain plays a dominant role in increasing dendritic branching. Neither the PDZ domain nor the LIM domains alone support increased branching. The central role of the DUF domain was confirmed using deletion mutants in the context of full-length Pdlim5. Guided by molecular modeling, additional domain mapping studies showed that the C-terminal LIM domain forms a stable interaction with the N-terminal PDZ domain, and we identified key amino acid residues at the interface of each domain that are needed for this interaction. We posit that the central DUF domain of Pdlim5 may be subject to modulation in the context of the full-length protein by the intra-molecular interaction between the N-terminal PDZ and C-terminal LIM domains. Overall, our studies reveal a novel mechanism for the regulation of Pdlim5's function in the regulation of neuronal branching and highlight the critical role of the DUF domain in mediating these effects.

CoDIAC: A comprehensive approach for interaction analysis reveals novel insights into SH2 domain function and regulation.

Protein domains are conserved structural and functional units and are the functional building blocks of proteins. Evolutionary expansion means that domain families are often represented by many members in a species, which are found in various configurations with other domains, which have evolved new specificity for interacting partners. Here, we develop a structure-based interface analysis to comprehensively map domain interfaces from available experimental and predicted structures, including interfaces with other macromolecules and intraprotein interfaces (such as might exist between domains in a protein). We hypothesized that a comprehensive approach to contact mapping of domains could yield new insights. Specifically, we use it to gain information about how domains selectivity interact with ligands, whether domain-domain interfaces of repeated domain partnerships are conserved across diverse proteins, and identify regions of conserved post-translational modifications, using relationship to interaction interfaces as a method to hypothesize the effect of post-translational modifications (and mutations). We applied this approach to the human SH2 domain family, an extensive modular unit that is the foundation of phosphotyrosine-mediated signaling, where we identified a novel approach to understanding the binding selectivity of SH2 domains and evidence that there is coordinated and conserved regulation of multiple SH2 domain binding interfaces by tyrosine and serine/threonine phosphorylation and acetylation, suggesting that multiple signaling systems can regulate protein activity and SH2 domain interactions in a regulated manner. We provide the extensive features of the human SH2 domain family and this modular approach, as an open source Python package for COmprehensive Domain Interface Analysis of Contacts (CoDIAC).

A Novel Chaos Control Strategy for a Single-Phase Photovoltaic Energy Storage Inverter

The single-phase photovoltaic energy storage inverter represents a pivotal component within photovoltaic energy storage systems. Its operational dynamics are often intricate due to its inherent characteristics and the prevalent usage of nonlinear switching elements, leading to nonlinear characteristic bifurcation such as bifurcation and chaos. In this paper, a deep investigation of a single-phase H-bridge photovoltaic energy storage inverter under proportional–integral (PI) control is made, and a sinusoidal delayed feedback control (SDFC) strategy to mitigate the nonlinear characteristics is proposed. A frequency domain mapping model of the system is established, then, by analyzing the Jacobian matrix and equilibrium points, the bifurcation diagram is formed, and finally, the stable operational domains are determined under double and triple bifurcation parameters. Through simulation experiments, the efficacy of this strategy is validated. The findings show that through the control strategy, the stable operational envelope of the inverter can be greatly expanded and the nonlinear dynamic phenomena can be notably suppressed.