Path Connectivity Research Articles

E-TransUNet: TransUNet provides a strong spatial transformation for precise deforestation mapping

Forest loss significantly contributes to climate change. As a result, scientists aim to understand its causes and evaluate forest loss classification maps. While Vision Transformers (ViTs) have demonstrated superior performance compared to convolutional neural networks (CNNs) in computer vision applications, they face challenges in recognizing and analyzing remote sensing images. These challenges include computational complexity, which increases processing costs, and the requirement for more labeled input data compared to CNNs. To address these issues, this paper introduces enhanced transformer UNet (e-TransUNet), which integrates spatial transformation architecture to enhance feature description in the skip connection path of the attention TransUNet for precise deforestation mapping. The model is validated in two prominent South American forest biomes—the Amazon and Atlantic forests. Experimental results highlight the significant improvement achieved by e-TransUNet, particularly in the Atlantic rainforests, where it boosts recall, overall accuracy, F1-score, dice coefficient, and precision by approximately 2, 2, 3, 4, and 4 percentage points, respectively, compared to the base TransUNet segmentation architecture. The code will be made publicly available at https://github.com/aj1365/e-TransUNet.

Assessing the influence of simulated environmental gradients on the spatial heterogeneity of landscape patterns in the Tibetan Plateau

Landscape patterns are pivotal in the realms of land use planning and ecological development, yet there remains a dearth of comprehensive research pertaining to the prediction of changes in landscape pattern characteristics. Within this study, we adopt the PLUS-CA-Markov and Fragstats models to forecast landscape patterns on the Tibetan Plateau spanning the period from 2030 to 2050. Through qualitative and quantitative analyses, we explore the spatiotemporal characteristics of landscape pattern changes between 2000 and 2050, concurrently identifying correlations among landscape pattern indices. Moreover, acknowledging the distinctive environmental gradients encompassing the plateau, notably elevation, slope, temperature, and precipitation, we investigate their implications on landscape pattern changes. Our findings indicate that: (1) Grassland degradation exhibited the utmost severity between 2000 and 2020, primarily attributed to overgrazing and climate-induced glacial melt. In contrast, cropland, forest, and water showcased divergent trends from 2020 to 2050 when compared to the preceding two decades, indicative of the efficacy of climate change control measures. (2) The distribution of landscape patterns on the Tibetan Plateau exhibited a considerable level of instability, marked by a decline in aggregation, reduced diversity and complexity, and amplified ecological connectivity between 2000 and 2020, signifying a partial amelioration in ecological quality. Between 2020 and 2050, landscape aggregation decreased alongside landscape fragmentation and the number of connectivity paths, signifying a discernible degradation of the plateau's ecosystem. (3) The most significant trade-off relationship was observed between landscape division index and largest patch index, while the synergistic relationship between landscape shape index and mean shape index was more pronounced. (4) Landscape aggregation, division, and largest patch index demonstrated non-linear quadratic trends in relation to elevation and temperature. Landscape shape index and patch density exhibited irregular non-linear effects. Largest patch index was predominantly influenced by slope, whereas division index was most affected by precipitation.

Assessment of an auto-routing method for topology generation of aircraft power supply systems

During early phases of aircraft development, overall systems design is performed to estimate relevant system design parameters such as mass, power consumption, and compliance with architectural requirements (redundancy). The GeneSys software framework has been developed for parametric modeling of aircraft on-board systems for overall systems design. To this end, model-based architecture definition, topology generation (components positioning and connections routing), and system sizing are conducted. The topology generation is currently performed using design templates containing knowledge-based heuristics derived from systems topologies of existing aircraft. However, using such design templates limits their generic application to novel aircraft models because exceptions for geometric differences between these models need to be implemented. This especially applies to the topology generation of power supply systems (electric, hydraulic) due to their high number of interfaces to consumer systems. To overcome these limitations, an automated routing method is presented in this paper as a generic approach for topology generation. With this method, the system topology is generated based on a defined routing network, which comprises areas of the aircraft for allowed components positioning and connections routing. Using Dijkstra’s algorithm, the shortest path connections are found within the established routing network. Furthermore, boundary conditions for routing, for instance adding a cost function for power segregation, can be defined. An assessment of the automated routing method applied on the electric power supply system is performed using qualitative parameters such as the capability to adapt the routing network. Quantitative parameters like cable length and system mass are assessed as well.

Evaluation of Rain Attenuation Models for 5G Frequencies: Regions in Turkey

Rain attenuation in 5G satellite communications needs to be considered in planning the communication settings due to the fact that it causes significant attenuation especially above 10 GHz and in low elevation angle Earth-space connection paths. Even though various rain attenuation models have been developed by using measured meteorological data, it is important to know especially which model provides better results for 5G frequencies since direct satellite communication over 5G channels is planned to be used in the near feature. Hence, in this study, prominent rain attenuation models such as the Simple Attenuation Model, Garcia-Lopez model, and ITU-R P.618-14 model were examined. Using these models, rain attenuations occurring at elevation angles of 30°, 60°, and 85° were identified in three different cities in Turkey. Average annual precipitation data from the General Directorate of Meteorology of Turkey for the years 1930-2023 were utilized. The studies on various rain models using different parameters revealed that the attenuation in the 470-780 MHz frequency band is 2.849x10-4 dB, whereas there is a 12.63 dB attenuation in the range of 40.5-43.5 GHz. It was observed that the attenuation increases further with frequencies above 43.5 GHz. Therefore, it is evident that rain attenuation is a crucial parameter in satellite communication systems, particularly in 5G and beyond. This study has revealed that ITU-R P.618-14 model is the best suitable one for high elevation angles and at high frequencies (above 40GHz). As for lower elevation angles Garcia model is more appropriate.

Structure of the human KMN complex and implications for regulation of its assembly

Biorientation of chromosomes during cell division is necessary for precise dispatching of a mother cell’s chromosomes into its two daughters. Kinetochores, large layered structures built on specialized chromosome loci named centromeres, promote biorientation by binding and sensing spindle microtubules. One of the outer layer main components is a ten-subunit assembly comprising Knl1C, Mis12C and Ndc80C (KMN) subcomplexes. The KMN is highly elongated and docks on kinetochores and microtubules through interfaces at its opposite extremes. Here, we combine cryogenic electron microscopy reconstructions and AlphaFold2 predictions to generate a model of the human KMN that reveals all intra-KMN interfaces. We identify and functionally validate two interaction interfaces that link Mis12C to Ndc80C and Knl1C. Through targeted interference experiments, we demonstrate that this mutual organization strongly stabilizes the KMN assembly. Our work thus reports a comprehensive structural and functional analysis of this part of the kinetochore microtubule-binding machinery and elucidates the path of connections from the chromatin-bound components to the force-generating components.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials.

BackgroundSingle-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New methodUsing intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. ResultsThe proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methodExisting methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. ConclusionsThese results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Topological Characteristics of the Pore Network in the Tight Sandstone Using Persistent Homology.

Tight sandstone reservoirs have become important areas for unconventional reservoir development, and their pore network is a key feature for identifying tight sandstone, which affects fluid migration path and reservoir development efficiency. However, the connectivity characteristics of the pore network at different scales have remained unclear owing to the numerous pores and uneven pore shape. Here, using pore size distributions from many hundreds of tight sandstone samples and subsequent topological data analysis, we construct the topological structure of the pore network in the Yanchang Formation tight sandstone of the Ordos Basin in China and visualize the topological characteristics of the pore network with distances. We show that there are three connected groups within the pore structure of the tight sandstone. The topology of the pore network resides on a trident ring manifold, suggesting that the pore network in the tight sandstone encompasses three obvious dominant connection paths. One prominent bar on the H0 dimension in the barcode indicates a two-point connection from nanoscale to microscale in the pore network. Three prominent bars with varying durations on the H1 dimension indicate the presence of three separate multipoint connections within a limited extent in the pore network. Connectivity of combined pores is good and controlled by the topological structure of the pore network. This demonstration of pore connections on a trident ring manifold provides a population-level visualization of the pore network in the tight sandstone.

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

Optimization of Pupil Fit Parameters for Contact Hole Pattern CD Difference Improvement

AbstractIn the stacked semiconductor structure, the contact hole layer is widely used in Dram/3D NAND as a connection path between layers. During the lithographical process of the contact hole layer, a critical issue known as CD difference appears. When faced with this issue in a mass production environment, solutions are limited. In particular, the only solution for the issue caused by a difference of pupil shape is to change the illumination ID, which requires a full design change of the pupil. This is challenging and causes differences in the recipe and equipment. As a way to fundamentally solve this problem, the ellipticity among the pupil fit parameters is optimized to minimize CD differences. Through simulation, the difference between the issue pupil and the reference pupil was confirmed, and the pupil fit parameter was calculated. The pupil shape was predicted by applying the calculated pupil fit parameter. Pattern simulation was performed using the predicted pupil shape, and conditions suitable for the direction of improvement were established to confirm improved CD difference. In both cases, the improvement tendency was confirmed in the predicted direction and was suitable for a mass production environment.

Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm

Underwater autonomous path planning is a critical component of intelligent underwater vehicle system design, especially for maritime conservation and monitoring missions. Effective path planning for these robots necessitates considering various constraints related to robot kinematics, optimization objectives, and other pertinent factors. Sample-based strategies have successfully tackled this problem, particularly the rapidly exploring random tree star (RRT*) algorithm. However, conventional path-searching algorithms may face challenges in the marine environment due to unique terrain undulations, sparse and unpredictable obstacles, and inconsistent results across multiple planning iterations. To address these issues, we propose a new approach specifically tailored to the distinct features of the marine environment for navigation path planning of underwater vehicles, named bidirectional cached rapidly exploring random tree star (BCRRT*). By incorporating bidirectional path planning and caching algorithms on top of the RRT*, the search process can be expedited, and an efficient path connection can be achieved. When encountering new obstacles, ineffective portions of the cached path can be efficiently modified and severed, thus minimizing the computational workload while enhancing the algorithm’s adaptability. A certain number of simulation experiments were conducted, demonstrating that our proposed method outperformed cutting-edge techniques like the RRT* in several critical metrics such as the density of path nodes, planning time, and dynamic adaptability.

Partitioning the effects of coffee-Urochloa intercropping on soil microbial properties at a centimeter-scale

Conservation farming and crop diversity increase soil health, organic matter content, and soil microbial activity. However, the design and implementation of sustainable practices often lack a detailed understanding of their impacts on microbial communities in soil. Here, we studied the effects of an Arabica coffee (Coffea arabica) – Brachiaria (Urochloa spp.) intercropping system on soil microbial properties by partitioning at the centimeter-scale the topsoil layers. In particular, we collected and analyzed four soil layers in a vertical stratification gradient of 0–2.5 cm (layer 1), 2.5–5.0 cm (layer 2), 5–10 cm (layer 3), and 10–15 cm (layer 4). Soil samples were subjected to chemical analysis, bacterial community profiling, and quantification of microbial enzymatic activity for β-glucosidase and acid-phosphatase, in addition to the quantification of N-fixation and P-solubilization gene abundances. We found that intercropping increased acid-phosphatase activity at layer 1, β-glucosidase activity at layers 2 and 3, and the amount of soil organic matter and total magnesium at layers 2 and 3. Intercropping increased the relative abundance of the N-fixation gene at layer 3 and bacterial diversity at layers 1 and 3. Overall, intercropping significantly changed the soil bacterial community structure and resulted in a more interconnected co-occurrence network (i.e., greater node connectivity, network density, and lower edges average path length). Taken together, this study provides evidence for the positive impact of intercropping in the soil at a centimeter-scale vertical stratification. It corroborates the notion that plant diversity stimulates microbial activity and species interactions in soil.

通讯网络连接的最短路径问题探究

通讯网络连接的最短路径问题探究

Analysis of Connectivity and Inheritance Path of Traditional Dance Elements Based on Graph Theory

Abstract With the progress and development of the times, the excavation and protection of national traditional dance is the historical necessity of its development and inheritance. This paper carries out research on the analysis of traditional dance movements using digital technology and motion capture technology. The traditional dance digital inheritance technology path is proposed to decompose the traditional dance into four digital media: model, movement, audio and video, and determine the digital display and inheritance process of traditional dance activities in the diffusion dimension. Based on the segmentation algorithm of graph theory, the traditional dance action image is segmented, the optical flow map is used as the input of this stage, and the entropy value of each optical flow map is calculated so as to realize the extraction of action features. Finally, a dataset of six typical movement combinations of traditional dance was constructed to optimize the traditional dance movements of the trainers. The research results show that the traditional dance dataset is constructed reasonably, the recognition model has good performance for traditional dance classification, and the optimal classification accuracy rate is 0.9645 under the Euler Angle feature, which effectively records and preserves the traditional dance movements, and provides a new way for the connection and inheritance of folk dances.

Intelligent Prediction Technology for Inter-well Connectivity Paths in Deep Fracture-type Reservoirs

Intelligent Prediction Technology for Inter-well Connectivity Paths in Deep Fracture-type Reservoirs

Optimization of school-enterprise cooperation path for engineering majors based on robust optimization algorithm

Abstract This research delineates the unique attributes of the industry-university-research collaboration network, formulating an innovation network specifically designed for engineering disciplines predicated on university-industry partnerships. It introduces a set of measurement indices aimed at evaluating the structural properties of this network. To address the prevalent dissatisfaction within conventional collaborative network decision-making scenarios, this study proposes a theoretical framework comprised of “baseline,” “target,” and “status quo” strategies. This framework systematically standardizes attribute values and constructs a decision-maker satisfaction function. Utilizing this function, the study identifies the optimal solution by ranking the satisfaction levels associated with various alternatives within the cooperative innovation network. Further, this paper examines the characteristics and evolutionary trends of the regional network structure of industry-university-research cooperation in the context of 5G technology, encompassing engineering departments, educational institutions, and corporate entities. It also analyzes the optimization outcomes of six preferred linkage types within the engineering majors’ university-enterprise industry-university-research innovation network under a scenario of enhanced network relationships. By comparing the weighted degree distributions between the optimally optimized network and the actual network and employing the entropy value method along with the TOPSIS method, the study evaluates the disparities in network structures between the two scenarios and identifies the optimal connection paths. The comprehensive score of the optimization effect of university-enterprise-industry-university-research cooperation and innovation network structure of engineering majors in the context of network relationship strengthening is ranked as Q 3 > Q 5 > Q 6 > Q 2 > Q 4 > Q 1, and the comprehensive score of the strong-weak connection type is the highest at 0.6774. That is, maintaining the connecting edges between “strong enterprise” nodes and “weak enterprise” nodes in the industry-university-research cooperation and innovation network has the best effect on the optimization of university-enterprise industry-university-research cooperation and innovation network for engineering majors.

Strategi Pengembangan Heritage Tourism Di Kompleks Percandian Batujaya, Kabupaten Karawang, Jawa Barat

This study aims to analyze the existing conditions of the 4A tourism product components and examine the development strategy of heritage tourism in Batujaya Temple Complex. The research method used is qualitative descriptive with a SWOT analysis approach using the IFAS and EFAS matrices, Internal-External matrix, and SWOT analysis matrix. Data were collected through observation, interviews, and documentary study, using purposive sampling as the technique for selecting informants. The results of the study show that Batujaya Temple Complex offers attractions such as Candi Jiwa, Candi Blandongan, wells, unur-unur, and Batujaya Cultural Heritage Site Museum. The accessibility through roads is good, but there is no public transportation available. Amenities provided include an auditorium, mosque, toilets, gazebos, canteen, inter-temple connecting paths, and parking lots. However, there are deficiencies such as the absence of an information center, CCTV, insufficient toilets and gazebos, suboptimal inter-temple connecting paths, and limited parking space. The only ancillary service in the complex is the Caretaker. Other challenges include the lack of travel agencies and minimal participation of the local community due to the inactivity of the Pokdarwis (Tourism Awareness Group). The resulting development strategies include 4 SO (Strengths-Opportunities) strategies, 5 WO (WeaknessesOpportunities) strategies, 2 ST (Strengths-Threats) strategies, and 3 WT (Weaknesses-Threats) strategies. These strategies can be implemented through concrete steps such as establishing an information center, installing CCTV, adding toilets and gazebos, improving inter-temple connecting paths, expanding parking areas, enhancing promotion, collaborating with the private sector, revitalizing Pokdarwis, providing education and training to the local community, developing integrated tour packages, establishing public transportation, and planning tsunami mitigation measures. Keywords: heritage tourism, existing conditions, development strategy

Evaporating hydrovoltaics with asymmetric electrodes

Power generation through natural evaporation shows great potential for green energy technologies. However, it remains a great challenge to balance the coupling effect between the interfacial potential and streaming potential. Here, we propose a new evaporating-hydrovoltaic strategy with asymmetric electrodes to optimize the coupling effect. This device uses active electrode to enhance interfacial interactions while inert electrode to avoid mutual blocking of interfacial potentials. The polytetrafluoroethylene (PTFE) membrane acts as a conductive connection path and induces a streaming potential. It is found that configuration of C (inert electrode)- PTFE (positively charged porous material)-Cu (active electrode) along the water gradient, achieves the optimal power generation. Exposing such a centimeter-sized device to environmental condition can generate ∼0.36 V voltage and lasts for ∼10 h without significant changes. Such high performance is ascribed to the consistency of potential level between interfacial potential and streaming potential. The findings provide a general guideline for sustainable power generation based on a synergistic effect of interfacial potential and streaming potential, which has far-reaching implications to the future design of hydrovoltaics.

Network Embeddedness and Company Competitiveness

ABSTRACT The purpose of the paper is to create a research model which analyses the impact of companies’ network embeddedness on their competitiveness through the lenses of knowledge cooperation in the network, competitor orientation and knowledge-oriented leadership. The data was collected using telephone interviews with 283 managers at medium-high technology companies. The findings revealed two relatively strong path connections between network embeddedness and competitiveness: one through knowledge cooperation in the network and competitor orientation and the other through knowledge-oriented leadership. In revealing the interconnections between network embeddedness and competitiveness, this paper argues for an indirect influence of the former on the latter.

Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping

Multi-hop path reasoning plays a crucial role in temporal knowledge graph reasoning, aiming to infer deep complex relationships and obtain interpretable reasoning results. Previous reasoning models primarily focus on designing temporal knowledge graphs with rich paths between entities but struggle to handle path sparsity, known as sparse temporal knowledge graphs. Sparse temporal knowledge graphs store only essential knowledge information, resulting in missing connection paths between certain entities and encountering issues of sparse rewards and information scarcity. To tackle these challenges, this paper proposes a multi-hop path reasoning model over sparse temporal knowledge graph based on path completion and reward shaping (STKGR-PR). STKGR-PR dynamically completes missing paths using a temporal embedding model to alleviate path sparsity. To tackle the issue of sparse rewards caused by reduced hit rates, we propose semantic composition-based reasoning path embeddings derived from relation embeddings and timestamp embeddings for reward shaping. Considering the limited information contained in sparse temporal knowledge graphs, we incorporate time vectors into the embedding and multi-hop path reasoning models to enhance the accuracy of reasoning paths and results. Experimental results conducted on six sparse temporal datasets sampled from ICEWS14 and ICEWS515–05 demonstrate that STKGR-PR outperforms state-of-the-art multi-hop path reasoning models over temporal knowledge graphs across all evaluation metrics, while ensuring interpretability.

Extracting connectivity paths in digital core images using solution of partial minimum eigenvalue problem

Abstract We show theoretically and numerically that the lowest non-trivial eigenvector function for a specific eigenproblem has almost constant values in high conductivity channels, which are different in separate channels. Therefore, based on these distinct values, all separate connected clusters of open pores can be identified in digital cores.