Topological Structure Research Articles

Subthalamic nucleus deep brain stimulation modulates speech graphs in patients with Parkinson’s disease

AbstractGraph theory enables a direct quantification of topological properties of any arbitrary network. Its application in neuroscience has unveiled topological changes of brain networks associated with various neurodegenerative diseases. This study used the graph theory to understand speech deficits in patients with Parkinson’s disease (PD). In particular, this study investigated the effect of subthalamic nucleus deep brain stimulation (STN-DBS) on the topology of speech graphs. Sixty patients with PD completed a standard semantic fluency test with DBS switched ON and OFF. A control group of sixty matched nonsurgical PD patients completed the test once. All verbal responses were recorded, transcripted, and transformed into directed speech graphs. Volumes of tissue activated (VTA) were estimated for three STN subregions, including sensorimotor, associative, and limbic parts. First, the patients with DBS OFF produced smaller and denser speech graphs than nonsurgical patients, showing fewer nodes, higher density, shorter diameter, and shorter average shortest path. Second, DBS partially reversed the effect of surgery, leading to larger and sparser speech graphs with more nodes, lower density, longer diameter, and longer average shortest path (ON versus OFF). Third, however, the left associative VTA negatively correlated with the DBS-induced diameter and average shortest path changes (ON versus OFF), suggesting that the patients with greater left associative STN stimulation tended to produce smaller and denser speech graphs. This study demonstrates that STN-DBS can partially restore the topological structure of speech graphs in PD patients. However, stimulating the left associative STN appears to disrupt speech graphs.

Layered hybrid superlattices as designable quantum solids.

Crystalline solids typically show robust long-range structural ordering, vital for their remarkable electronic properties and use in functional electronics, albeit with limited customization space. By contrast, synthetic molecular systems provide highly tunable structural topologies and versatile functionalities but are often too delicate for scalable electronic integration. Combining these two systems could harness the strengths of both, yet realizing this integration is challenging owing to distinct chemical bonding structures and processing conditions. Two-dimensional atomic crystals comprise crystalline atomic layers separated by non-bonding van der Waals gaps, allowing diverse atomic or molecular intercalants to be inserted without disrupting existing covalent bonds. This enables the creation of a diverse set of layered hybrid superlattices (LHSLs) composed of alternating crystalline atomic layers of variable electronic properties and self-assembled atomic or molecular interlayers featuring customizable chemical compositions and structural motifs. Here we outline strategies to prepare LHSLs and discuss emergent properties. With the versatile molecular design strategies and modular assembly processes, LHSLs offer vast flexibility for weaving distinct chemical constituents and quantum properties into monolithic artificial solids with a designable three-dimensional potential landscape. This opens unprecedented opportunities to tailor charge correlations, quantum properties and topological phases, thereby defining a rich material platform for advancing quantum information science.

Analysis and Design of a Recyclable Inductive Power Transfer System for Sustainable Multi-Stage Rocket Microgrid with Multi-Constant Voltage Output Characteristics—Theoretical Considerations

After a traditional one-time rocket is launched, most of its parts will fall into the atmosphere and burn or fall into the ocean. The parts cannot be recycled, so the cost is relatively high. Multi-stage rockets can be recovered after launch, which greatly reduces the cost of space launches. Moreover, recycling rockets can reduce the generation of waste and reduce pollution and damage to the environment. With the reduction in rocket launch costs and technological advances, space exploration and development can be carried out more frequently and economically. It provides technical support for the sustainable use of space resources. It not only promotes the sustainable development of the aerospace field but also has a positive impact on global environmental protection, resource utilization, and economic development. In order to adapt to the stage-by-stage separation structure of the rocket, this paper proposes a new multi-stage rocket inductive power transfer (IPT) system to power the rocket microgrid. The planar coil structure is used to form wireless power transfer between each stage of the rocket, reducing the volume of the magnetic coupling structure. The volume of the circuit topology structure is reduced by introducing an auxiliary coil. An equivalent three-stage S/T topology is proposed, and the constant voltage output characteristics of multiple loads are analyzed. A multi-stage coil structure is proposed to supply power to multiple loads simultaneously. In order to eliminate undesired magnetic coupling between coils, ferrite cores are added between coils for effective electromagnetic shielding. The parameters of the magnetic coupling structure are optimized based on the finite element method (FEM). A prototype of the proposed IPT system is built to simulate a multi-stage rocket. A series of experiments are conducted to verify the advantages of the proposed IPT system, and the three-stage rocket system efficiency reached 88.5%. This project is theoretical. Its verification was performed only in the laboratory conditions.

A multi-task learning based line parameter identification method for medium-voltage distribution network

Accurate line parameters are critical for and dispatch in distribution systems. External operating condition variations affect line parameters, reducing the accuracy of state estimation and power flow calculations. While many methods have been proposed and obtained results rather acceptable, there is room for improvement as they don’t fully consider line connections in known topologies. Furthermore, inaccuracies in measurement devices and data acquisition systems can introduce noise and outliers, impacting the reliability of parameter identification. To address these challenges, we propose a line parameter identification method based on Graph Attention Networks and Multi-gate Mixture-of-Experts. The topological structure of the power grid and the capabilities of modern data acquisition equipment are utilized to capture. We also introduce a multi-task learning framework to enable joint training of parameter identification across different branches, thereby enhancing computational efficiency and accuracy. Experiments show that the GAT-MMoE model outperforms traditional methods, with notable improvements in both accuracy and robustness.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Fluid Grey 2: How Well Does Generative Adversarial Networks Learn Deeper Topology Structure in Architecture That Matches Images?

Taking into account the regional characteristics of ‘intrinsic’ and ‘extrinsic’ properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facilitate architects and users to participate in the design. Therefore, this study hopes to prove that I2I GAN also has the potential to recognize topological relationships autonomously. Therefore, this research proposes a method for quickly detecting the ability of pix2pix to learn topological relationships, which is achieved by adding two Grasshopper-based detection modules before and after GAN. At the same time, quantitative data is provided and its learning process is visualized, and changes in different input modes such as greyscale and RGB affect its learning efficiency. There are two innovations in this paper: 1) It proves that pix2pix can automatically learn spatial topological relationships and apply them to architectural design. 2) It fills the gap in detecting the performance of Image-based Generation GAN from a topological perspective. Moreover, the detection method proposed in this study takes a short time and is simple to operate. The two detection modules can be widely used for customizing image datasets with the same topological structure and for batch detection of topological relationships of images. In the future, this paper may provide a theoretical foundation and data support for the application of architectural design and urban renewal that use GAN to preserve spatial topological characteristics.

Design of a two-stage photovoltaic grid-connected system with dual closed-loop control

Abstract This paper designs a two-stage photovoltaic grid-connected system with dual closed-loop control, cascading the topological structures of photovoltaic cells, boost chopper circuits, and inverter circuits. The front-stage boost chopper circuit control utilizes the incremental conductance method to achieve maximum power point tracking control. The rear-stage inverter circuit employs a dual closed-loop control system with an outer voltage loop and an inner current loop, incorporating a proportional-integral controller, to ensure stable DC-side voltage and unity power factor for grid connection. A simulation model of the two-stage photovoltaic grid-connected system designed in this paper was constructed and analyzed through simulation runs. The firmness of the DC-side voltage and the achievement of unity power factor during grid connection confirmed the operability and resultiveness of the cascaded topology and the control method.

GB-RVFL: Fusion of randomized neural network and granular ball computing

The random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the need for inverting the entire training matrix. To address these issues, we propose granular ball RVFL (GB-RVFL) model, which uses granular balls (GBs) as inputs instead of training samples. This approach enhances scalability by requiring only the inverse of the GB center matrix and improves robustness against noise and outliers through the coarse granularity of GBs. Furthermore, RVFL overlooks the dataset’s geometric structure. To address this, we propose graph embedding GB-RVFL (GE-GB-RVFL) model, which fuses granular computing and graph embedding (GE) to preserve the topological structure of GBs. The proposed GB-RVFL and GE-GB-RVFL models are evaluated on KEEL, UCI, NDC and biomedical datasets, demonstrating superior performance compared to baseline models.

Intelligent Design Method and System of Tin Spraying Steel Mesh for Complex Combiner

ABSTRACT In the process of spraying tin into the combiner cavity, employing the Tin Spraying Steel Mesh (TSSM) is crucial. This operation is akin to covering a complex, maze-like area with multiple islands and branches. Manual operation is labor-intensive, time-consuming, and challenging to ensure high-quality results. Therefore, this paper presents an intelligent design method and system for the TSSM, based on graphic processing. Initially, isolated islands are used as nodes to establish the topology structure of complex paths. Then, an improved Delaunay triangulation method is utilized to extract intermediate paths for regions with branching paths, resulting in the division into multiple single path regions. Following this, the island regions undergo equal arc length segmentation, while the single path regions are subjected to random bidirectional segmentation to obtain the final TSSM. Finally, a TSSM intelligent segmentation system is developed by seamlessly integrating this method into the AutoCAD software platform using ObjectARX technology. The TSSM obtained by this method meets the quality requirements, demonstrating consistent segmentation direction and uniform gaps. Furthermore, the system can adjust segmentation parameters to cater to different combiner specifications, thereby significantly improving design efficiency and establishing itself as an indispensable auxiliary tool in the automated design of combiners.

Deep incomplete multi-view clustering via Multi-level Imputation and Contrastive Alignment

Deep incomplete multi-view clustering (DIMVC) aims to enhance clustering performance by capturing consistent information from incomplete multiple views using deep models. Most existing DIMVC methods typically employ imputation-based strategies to handle missing views before clustering. However, they often assume complete data availability across all views, overlook potential low-quality views, and perform imputation at a single data level, leading to challenges in accurately inferring missing data. To address these issues, we propose a novel imputation-based approach called Multi-level Imputation and Contrastive Alignment (MICA) to simultaneously improve imputation quality and boost clustering performance. Specifically, MICA employs an individual deep model for each view, which unifies view feature learning and cluster assignment prediction. It leverages the learned features from available instances to construct an adaptive cross-view graph for reliable view selection. Guided by these reliable views, MICA performs multi-level (feature-level, data-level, and reconstruction-level) imputation to preserve topological structures at different levels and ensure accurate missing feature inference. The complete features are then used for discriminative cluster assignment learning. Additionally, an instance- and cluster-level contrastive alignment is conducted on the cluster assignments to further enhance semantic consistency across views. Experimental results show the effectiveness and superior performance of the proposed MICA method.

Nanocellulose/metal-organic frameworks composites for advanced energy storage of electrodes and separators

Nanocellulose is widely utilized in various fields due to its high surface area, excellent mechanical strength, and environmental friendliness. However, nanocellulose is an insulating material with typically low electrical conductivity. Metal-organic frameworks (MOFs) are appealing because of their huge specific surface areas, highly flexible pore size and porosity, diverse topological structure, changeable surface chemistry, and good chemical and thermal strength. The high porosity and surface area, good electrical conductivity, excellent mechanical strength, and flexibility of nanocellulose/MOF composites (NC/MOFs) make it a promising material for energy storage. This paper provides a review of the synthesis and application of NC/MOFs for energy storage of electrodes and separators. It presents the physical and chemical properties of nanocellulose and MOF. Particularly, two preparation strategies for NC/MOFs, the in-situ synthesis method and the ex-situ blending method, are introduced. Then the applications of NC/MOFs in energy storage of electrodes and separators are delivered, including supercapacitors, lithium-ion batteries, sodium-ion batteries, and others. Finally, the challenges and prospects of NC/MOFs for energy storage are addressed.

Monotonicity, asymptotics and level sets for principal eigenvalues of some elliptic operators with shear flow

We investigate the joint effects of diffusion and advection on principal eigenvalues of some elliptic operators with shear flow. Some monotonicity and asymptotic behaviors of principal eigenvalues, with respect to diffusion rate and flow amplitude, are established. These analyses lead to a classification of topological structures of level sets for principal eigenvalues, as a function of diffusion rate and flow amplitude. Our analytical results provide a unifying viewpoint to understand mixing enhancement and dispersal-induced growth, which are apparently two unrelated phenomena, one in fluid mechanics and the other in population dynamics. In our analysis, some limiting Hamilton-Jacobi equations, blowup argument and limiting generalized principal eigenvalue problems play critical roles.

A Self-Powered Interface Circuit for Piezoelectric and Photovoltaic Energy Extracting

This paper presents a high-efficiency multi-source energy harvesting system consisting of a piezoelectric rectifier, a photovoltaic maximum power point tracking circuit and a fast self-startup circuit for powering wireless sensor nodes. Synchronous electric charge extraction techniques (SECE) are utilized to extract the piezoelectric energy when the deflection of piezoelectric energy harvester beam reaches its peak. Compared with other circuits, the proposed SECE technique can achieve piezoelectric and photovoltaic extraction simultaneously with a concise flyback topology structure. A novel maximum power point tracking method is adopted to enhance the robustness of operation against changing operating condition and improve the photovoltaic extraction efficiency. To remove the need for a battery, a simple and efficient self-startup circuit is introduced. The energy harvesting circuit is fabricated in 180 nm CMOS process. Measurement results show a fast self-startup at a relatively low light intensity of 120 lux is realized. The energy harvesting circuit is capable of outputting 189 μW power at 3.3 V piezoelectric open circuit voltage and 0.6 V photovoltaic input voltage.

Automatically Detecting Anchor Cells and Clustering for scRNA-Seq Data Using scTSNN.

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging. To this end, we proposed a tensor shared nearest neighbor anchor clustering for scRNA-seq data, named scTSNN, which first makes use of the tensor affinity learning module to mine the local-global balanced topological structures among cells, next designs density-based shared nearest neighbor measurement method to automatically detect anchor cells, finally partitions the non-anchor cells to obtain the clustering results. Validated on synthetic datasets and scRNA-seq datasets, scTSNN not only exactly detects the complicated structures but also has better performance in accuracy and robustness compared with the state-of-the-art methods. Moreover, case studies on mammalian cells and cervical cancer tumor cells demonstrate the selected anchor cells of scTSNN benefit the cell pseudotime inference and rare cell identification, which show good application and research value of scTSNN.

Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning

Backgroundand purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients. MethodsWe recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs. ResultsAll participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937. ConclusionThe alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.

Reliability-based topology optimization of imperfect structures considering uncertainty of load position

In this paper, a novel optimization technique is implemented to explore the effects of considering uncertain load positions. Therefore, the integration of reliability-based design into structural topology optimization, while considering imperfect geometrically nonlinear analysis, is proposed. By comparing the results obtained from perfect and imperfect geometrically and materially nonlinear analyses, this study examines the impact of nonlinearity on probabilistic and deterministic analyses. Concerning probabilistic analysis, the originality of this research lies in its incorporation of the position of the applied load as a stochastic variable. This distinctive approach complements the consideration of other relevant parameters, including volume fraction, material properties, and geometrical imperfections, with the overarching goal of capturing the variability arising from real-world conditions. For the assessment of uncertainties, normal distribution is assumed for all these parameters. Normal distributions are chosen due to their advantages in terms of simplicity, ease of implementation, and computational efficiency. These characteristics are particularly beneficial when dealing with complex optimization algorithms and extensive analyses, as is the case in our research. The proposed algorithm is validated according to the results of benchmark problems. Structural examples like cantilever beam, pinned-shell, and L-shaped beam problems are further explored within the context of imperfect geometrically nonlinear reliability-based topology optimization, with specific regard to the probabilistic aspect of the location of the externally applied loads. Moreover, the results of the suggested approach suggest that the inclusion of a probabilistic design strategy has influenced topology optimization. The reliability index acts as a controlling constraint for the resulting optimized configurations, including the mean stress values associated with the resulting topologies.

Topology optimization of multi-material structures subjected to dynamic loads

Traditional designs for multi-material structures are mostly based on static loads. However, engineering structures are often subjected to dynamic loads. This paper firstly and systematically conducts an in-depth study on the topological design of multi-material structures under dynamic loads. In this method, the definition of design variable adopts the ordered solid isotropic material with penalization (ordered-SIMP) method to normalize the design variable, without introducing any additional variables, the HHT-α method is employed to solve the dynamic optimization problem. To address the dynamic optimization problems with constraints on mass and cost, a convex optimization method based on the concept of sensitivity separation is used to update the design variables, and search for the structural topologies. Finally, this paper provides some numerical examples with respect to time-varying, material parameters, load amplitude, load direction and multiple loads, to discuss in depth the topological and numerical results of these examples.

Multi-branch fusion graph neural network based on multi-head attention for childhood seizure detection

The most common manifestation of neurological disorders in children is the occurrence of epileptic seizures. In this study, we propose a multi-branch graph convolutional network (MGCNA) framework with a multi-head attention mechanism for detecting seizures in children. The MGCNA framework extracts effective and reliable features from high-dimensional data, particularly by exploring the relationships between EEG features and electrodes and considering the spatial and temporal dependencies in epileptic brains. This method incorporates three graph learning approaches to systematically assess the connectivity and synchronization of multi-channel EEG signals. The multi-branch graph convolutional network is employed to dynamically learn temporal correlations and spatial topological structures. Utilizing the multi-head attention mechanism to process multi-branch graph features further enhances the capability to handle local features. Experimental results demonstrate that the MGCNA exhibits superior performance on patient-specific and patient-independent experiments. Our end-to-end model for automatic detection of epileptic seizures could be employed to assist in clinical decision-making.

TTMGNet: Tree Topology Mamba-Guided Network Collaborative Hierarchical Incremental Aggregation for Change Detection

Change detection (CD) identifies surface changes by analyzing bi-temporal remote sensing (RS) images of the same region and is essential for effective urban planning, ensuring the optimal allocation of resources, and supporting disaster management efforts. However, deep-learning-based CD methods struggle with background noise and pseudo-changes due to local receptive field limitations or computing resource constraints, which limits long-range dependency capture and feature integration, normally resulting in fragmented detections and high false positive rates. To address these challenges, we propose a tree topology Mamba-guided network (TTMGNet) based on Mamba architecture, which combines the Mamba architecture for effectively capturing global features, a unique tree topology structure for retaining fine local details, and a hierarchical feature fusion mechanism that enhances multi-scale feature integration and robustness against noise. Specifically, the a Tree Topology Mamba Feature Extractor (TTMFE) leverages the similarity of pixels to generate minimum spanning tree (MST) topology sequences, guiding information aggregation and transmission. This approach utilizes a Tree Topology State Space Model (TTSSM) to embed spatial and positional information while preserving the global feature extraction capability, thereby retaining local features. Subsequently, the Hierarchical Incremental Aggregation Module is utilized to gradually align and merge features from deep to shallow layers to facilitate hierarchical feature integration. Through residual connections and cross-channel attention (CCA), HIAM enhances the interaction between neighboring feature maps, ensuring that critical features are retained and effectively utilized during the fusion process, thereby enabling more accurate detection results in CD. The proposed TTMGNet achieved F1 scores of 92.31% on LEVIR-CD, 90.94% on WHU-CD, and 77.25% on CL-CD, outperforming current mainstream methods in suppressing the impact of background noise and pseudo-change and more accurately identifying change regions.

A new Outlook on Omega Closed Functions in Bitopological Spaces and Related Aspects

Many studies have employed a variety of techniques to further investigate topological space, particularly the notion of bitopological spaces, due to the significance of topological space in data processing as well as certain implementations. Numerous extended topological structures have been laid out subsequently. Of those abstractions, functions in topology was one of which was most noteworthy. In order to assist in this trend, we focused our research on the idea of open and closed sets, which is one of the strongest techniques available to present scientists for the study of computer graphics and digital topology.New functions, pairwise ω−closed functions, which are strictly weaker than pairwise closed functions, will be introduced in this study. By applying the P˝−space definition, whose is a P−space modification. Additionally, we establish different projection and product theories pertaining to pairwise Lindelöf of and pairwise paracompact spaces utilizing P˝−spaces. We analyze images and inverse images that have been chosen topological attributes for every one of these functions. In the final analysis, we explore several counterexamples that correspond to the offered definitions and theorems.