Nodes In Space Research Articles

The Effect of the Combination Polymers – Grape Marc in the Development of Tomato Growing in Soilless

This study examined the impact of two fertilizers (FI (CRFA) and F2 (Arizona University) and three substrates (S1(100% coconut fiber), S2 (50% coconut fiber, 50% grape marc, and 7g of polymers), and S3 (50% coconut fiber, 50% grape marc, and 13g of polymers)) on tomato growth in a soilless system. Results showed that Fertilizer F2 was the most effective overall. Substrate S3 excelled in node count and flower bud spacing, likely due to improved moisture retention and nutrient availability. Substrate S1 supported optimal stem length and node spacing. Substrate S2 yielded the highest tomato production. These findings suggest that optimizing fertilization and substrate composition can significantly improve tomato yield and quality in soilless systems. This has implications for sustainable agriculture by reducing reliance on soil-based cultivation and potentially increasing crop productivity. Further research in this area could lead to more efficient and environmentally friendly farming practices.

Exploring the Influence of Microtopography on the Spatial Genes of Urban Historical Streets and Alleys in Xuzhou

Spatial genes represent the fundamental interplay among the morphological characteristics of historical districts. Identifying and analyzing these morphological elements can enhance our understanding of urban spatial development, uncover spatial meanings, and provide informed recommendations for future development. This study focuses on the Xuzhou Huilongwo historical district, employing geographic information system, Global Mapper, and other digital technologies to determine the area’s microtopographic features. Qualitative methodologies extract the spatial genes of street segments, entry spaces, and node spaces. By summarizing the microtopography’s influence on street and alley characteristics, valuable spatial samples were selected and visually represented for analysis. This included examining the street segment interface, entry space sequences, and the planar morphology of node spaces. The findings reveal that Huilongwo architecture aligns with topographical features, exhibiting a multi-directional distribution. Height differences help establish street boundaries and enhance pathways’ experiential quality. Additionally, topography significantly influences street spaces, leading to undulating sequences in entry spaces. This study provides insights into the preservation and enhancement of streets and alleys within Xuzhou’s historical district.

Comparison of Single and Multiple Intratracheal Administrations for Pulmonary Toxic Responses of Multi-Walled Carbon Nanotubes in Rats.

The purpose of the present study is to contribute to the establishment of a standard method for evaluating the adverse effects of nanomaterials by intratracheal administration. Low and high doses of multi-walled carbon nanotubes (MWCNTs) were administered to rats in a single administration or the same final dose as the single administration but divided over four administrations. Bronchoalveolar lavage examination on day 14 showed an inflammatory reaction and cytotoxicity in the lung, generally greater at the higher dose, and tending to be greater in the rats with four administrations at both the low and high doses. Histopathologic findings showed increased alveolar macrophages and MWCNT deposition (fibers phagocytosed by alveolar macrophages and fibers that were not phagocytosed) in the alveolar space, granulomatous changes, and MWCNT deposition in bronchus-associated lymphoid tissue (BALT) and lung-related lymph nodes on days 14, 28, and 91. In addition, alveolar type II epithelial hyperplasia was observed on day 91, and fibrosis of the alveolar wall was observed on days 28 and 91. Fewer alveolar macrophages with phagocytosed MWCNTs were present at day 91 compared to day 28. MWCNT deposition tended to be higher in the BALT after a single administration, whereas deposition was higher in the lung-related lymph nodes after four administrations. MWCNTs were considered to be transported from the lungs or BALT to the lymph nodes over time. There were no significant differences in MWCNT deposition in the lung after the single administration compared with four administrations at either the low or high doses, and the histopathological findings were similar after single and four administrations, at both the low and high doses. Based on the above findings, a toxicity evaluation of the nanomaterials can be sufficiently performed by intratracheal administration, even with a single intratracheal administration.

Enhanced In-Network Caching for Deep Learning in Edge Networks

With the deep integration of communication technology and Internet of Things technology, the edge network structure is becoming increasingly dense and heterogeneous. At the same time, in the edge network environment, characteristics such as wide-area differentiated services, decentralized deployment of computing and network resources, and highly dynamic network environment lead to the deployment of redundant or insufficient edge cache nodes, which restricts the efficiency of network service caching and resource allocation. In response to the above problems, research on the joint optimization of service caching and resources in the decentralized edge network scenario is carried out. Therefore, we have conducted research on the collaborative caching of training data among multiple edge nodes and optimized the number of collaborative caching nodes. Firstly, we use a multi-queue model to model the collaborative caching process. This model can be used to simulate the in-network cache replacement process on collaborative caching nodes. In this way, we can describe the data flow and storage changes during the caching process more clearly. Secondly, considering the limitation of storage space of edge nodes and the demand for training data within a training epoch, we propose a stochastic gradient descent algorithm to obtain the optimal number of caching nodes. This algorithm entirely takes into account the resource constraints in practical applications and provides an effective way to optimize the number of caching nodes. Finally, the simulation results clearly show that the optimized number of caching nodes can significantly improve the adequacy rate and hit rate of the training data, with the adequacy rate reaching 84% and the hit rate reaching 100%.

Developing a Method to Evaluate Public Space Perception in River Valley Plain Villages of the Loess Gully Area Based on Villagers’ Perspectives

The natural conditions in the loess gully area are harsh, and the construction of rural public spaces often lacks scientific guidance. It is essential to develop an optimization method for public spaces that is oriented toward the needs of villagers. For this study, the numerous river valley plain villages in Northern Shaanxi were selected as typical examples, with a focus on Liangqu Village as a case study. First, a public space perception evaluation system was established using the semantic differential method, which comprised four criterion-layer factors and nineteen indicator-layer factors. Perception evaluations were conducted for two types of public spaces: nodes and lanes. The results indicate that “facility completeness”, “accessibility”, and “activity diversity” were the most significant factors affecting villagers’ perceptions of node spaces. In contrast, “safety”, “landscape richness”, and “facility completeness” were the most critical factors influencing villagers’ perceptions of road spaces. Based on the evaluation results, the optimization needs of public spaces were categorized into four levels, and factors that urgently require improvement such as “facility completeness” and “safety” were identified. This study analyzed villagers’ perceptions and needs related to public spaces, providing scientific guidance for establishing a bottom-up design method for optimizing public spaces.

Three Gorges Seismological Network: Monitoring Seismicity at the Three Gorges Reservoir Forebay

Abstract In this study, we report on a nodal seismic deployment targeting the forebay area of the Three Gorges Reservoir. This deployment aimed to enhance the detection of microearthquakes and refine the imaging of subsurface geological structures. The network’s first phase of operation spanned from 2 September 2020 to 16 November 2020. It consisted of 78 short-period seismometers with an approximate interstation distance of 5 km. Using advanced deep-learning algorithms for phase detection and event location, we identified 1165 seismic events in this period, which exceeds the 623 events identified in the manual catalog using the same dataset. The catalog’s magnitude of completeness is ML ∼−0.3. The experiment from this deployment can offer guidelines for future microseismic monitoring networks, allowing for the strategic design of nodal seismometer spacing and other key parameters to meet specific observational requirements.

Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network

Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.

A depth‐integrated SPH framework for slow landslides

AbstractSlow and very slow landslides can cause severe economic damage to structures. Due to their velocity of propagation, it is possible to take action such as programmed maintenance or evacuation of affected zones. Modeling is an important tool that allows scientists, engineers, and geologists to better understand their causes and predict their propagation. There are many available models of different complexities which can be used for this purpose, ranging from very simple infinite landslide models which can be implemented in spreadsheets to fully coupled 3D models. This approach is expensive because of the time span in which the problems are studied (sometimes years), simpler methods such as depth‐integrated models could provide a good compromise between accuracy and cost. However, there, the time step limitation due to CFL condition (which states that the time step has to be slower than the ratio between the node spacing and the physical velocity of the waves results in time increments which are of the order of one‐10th of a second on many occasions. This paper extends a technique that has been used in the past to glacier evolution problems using finite differences or elements to SPH depth‐integrated models for landslide propagation. The approach is based on assuming that (i) the flow is shallow, (ii) the rheological behavior determining the velocity of propagation is viscoplastic, and (iii) accelerations can be neglected. In this case, the model changes from hyperbolic to parabolic, with a time increment much larger than that of classic hyperbolic formulations.

Eccentric Compression Behavior of Truss-Reinforced Cross-Shaped Concrete-Filled Steel Tubular Columns.

In the paper, the eccentric compression behavior of the truss-reinforced cross-shaped concrete-filled steel tubular (CCFST) column is investigated. A total of eighteen CCFST columns were tested under eccentric compression, and the key test variables included the reinforced truss node spacing (s = 140 mm and 200 mm), slenderness ratio (λ = 9.2, 16.6, and 23.1), and eccentricity ratio (η = 0, 0.08, and 0.15). The failure mode, deformation characteristic, stress distribution, strain distribution at the mid-span of the steel tube, and the eccentric compression bearing capacity were assessed. The results show that due to the addition of reinforced truss, the steel plates near the mid-span of eccentrically compressed CCFST columns experienced multi-wave buckling rather than single-wave buckling after the peak load was reduced to 85%, and the failure mode of concrete also changed from single-section to multi-section collapse failure. Comparisons were made with the unstiffened specimen. The ductility coefficient of the stiffened specimen with eccentricity ratios of 0.08-0.15 and node spacings of 140 mm~200 mm increased by 70~83%, approaching that of the multi-cell specimens with an increasing steel ratio of 1.8%. In addition, by comparing the test results with the calculation results of four domestic and international design codes, it was found that the Chinese codes CECS159-2018 and GB50936-2014, and the Eurocode 4 (2004) can be better employed to predict the compression bearing capacity of truss-reinforced CCFST columns.

A data-driven LSTMSCBLS model for soft sensor of industrial process

Abstract In the chemical industry, data-driven soft sensor modeling plays a crucial role in efficiently monitoring product quality and status. Industrial data in these applications typically exhibit significant temporal characteristics, meaning that current information is influenced by data from previous periods. Effectively extracting and utilizing these temporal features is essential for achieving accurate soft sensor modeling in complex chemical scenarios. To address this challenge, this study proposes a data-driven Broad Learning System (BLS) model, which combines Long Short-Term Memory (LSTM) networks with an adaptive algorithm known as the Stochastic Configuration Algorithm (SC), referred to as LSTMSCBLS. The model operates in two stages: temporal feature extraction and final prediction. In the temporal feature extraction stage, the integration of the LSTM network with a feature attention mechanism allows for efficient extraction of temporal features from high-dimensional time-series data. In the final prediction stage, the SC is integrated into the BLS, effectively mitigating issues related to node space redundancy and the determination of the number of nodes. The effectiveness and superiority of the proposed model are demonstrated through two industrial case studies involving a debutanizer column and a sulfur recovery unit.

Analysis of the dynamic response for Kirchhoff plates by the element-free Galerkin method

Dynamic response analysis involves examining structural behavior under dynamic loading conditions. Transient dynamic analysis emerges as a method employed to assess the responses of deformable bodies. Due to the depth analysis of transient responses for thin plates, the associated error analysis work becomes particularly important. In this work, we conduct stability and convergence analysis of the element-free Galerkin (EFG) method for the dynamic response of Kirchhoff plate model. We start by discretizing the thin plate dynamics using the EFG method for the spatial domain and the central difference scheme for the temporal domain. Stability and convergence analysis for transient responses, including deflection and moment responses, are derived similarly to those in two-dimensional transient structural dynamic analysis. The key to the analysis is transforming the governing equations and clamped boundary conditions into a weak formulation with penalty terms using the penalty method. The main results demonstrate a significant correlation between the error estimate and both nodal spacing and time step size. Additionally, the continuity of the approximation functions and the selection of penalty factors significantly affect numerical accuracy. To validate the theoretical results, we conduct numerical experiments involving clamped square and L-shaped plates with uniform and nonuniform node distributions, as well as a perforated plate model. Furthermore, we investigate the impact of node influence domains and penalty factors on numerical accuracy, facilitating parameter selection for practical engineering applications.

SLIVER: Unveiling large scale gene regulatory networks of single-cell transcriptomic data through causal structure learning and modules aggregation

Prevalent Gene Regulatory Network (GRN) construction methods rely on generalized correlation analysis. However, in biological systems, regulation is essentially a causal relationship that cannot be adequately captured solely through correlation. Therefore, it is more reasonable to infer GRNs from a causal perspective. Existing causal discovery algorithms typically rely on Directed Acyclic Graphs (DAGs) to model causal relationships, but it often requires traversing the entire network, which result in computational demands skyrocketing as the number of nodes grows and make causal discovery algorithms only suitable for small networks with one or two hundred nodes or fewer. In this study, we propose the SLIVER (cauSaL dIscovery Via dimEnsionality Reduction) algorithm which integrates causal structural equation model and graph decomposition. SLIVER introduces a set of factor nodes, serving as abstractions of different functional modules to integrate the regulatory relationships between genes based on their respective functions or pathways, thus reducing the GRN to the product of two low-dimensional matrices. Subsequently, we employ the structural causal model (SCM) to learn the GRN within the gene node space, enforce the DAG constraint in the low-dimensional space, and guide each factor to aggregate various functions through cosine similarity. We evaluate the performance of the SLIVER algorithm on 12 real single cell transcriptomic datasets, and demonstrate it outperforms other 12 widely used methods both in GRN inference performance and computational resource usage. The analysis of the gene information integrated by factor nodes also demonstrate the biological explanation of factor nodes in GRNs. We apply it to scRNA-seq of Type 2 diabetes mellitus to capture the transcriptional regulatory structural changes of β cells under high insulin demand.

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

SummaryInternet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

Computing nodes for plane data points by constructing cubic polynomial with constraints

To construct a parametric polynomial curve for interpolating a set of data points, the interpolation accuracy and shape of the constructed curve are influenced by two principal factors: parameterization of the data points (computing a node for each data point) and interpolation method. A new method of computing nodes for a set of data points was proposed. In this paper, the functional relationship between data points and corresponding nodes in cubic polynomials was established. Using this functional relationship, a functional cubic polynomial with one degree of freedom can pass through four adjacent data points. The degree of the freedom can be represented by two adjacent node intervals can be obtained by minimizing the cubic terms of the cubic polynomial. Since each node is computed in different node spaces, a method for constructing a quadratic curve is presented, which transforms all the quadratic curves into a unified form to compute nodes. Nodes computed using the new method exhibit quadratic polynomial precision, i.e., if the set of data point is taken from a quadratic polynomial F(t), the nodes by the new method are used to construct a interpolation curve, an interpolation method reproducing quadratic polynomial gives quadratic polynomial F(t). The primary advantage of the proposed method is that the constructed curve has a shape described by data points. Another advantage of the new method is that the nodes computed by it have affine invariance. The experimental results indicate that the curve constructed by the nodes using the new method has a better interpolation accuracy and shape compared to that constructed using other methods.

Simulating Free-Space Optical Communications to Support a Li-Fi Access Network in a Smart City Concept

Smart city development has grown rapidly in the decades since 4G and 5G technologies have been released. Moreover, a highly reliable network is required to support the Internet of Things (IoT) and mobile access within a city. Light Fidelity (Li-Fi) technology can provide huge bitrate transmission and high-speed communications. In the research, a backbone based on Free-Space Optical (FSO) communication (FSO) is designed through simulation to provide a Li-Fi access network with a high capacity data rate. The originality of the proposed method is the implementation of double filtering techniques, which gives an advantage when forwarding the signal to a node and improves the quality of the signal received by the Li-Fi. The FSO as the Optical Relaying Network (ORN) is designed with a configuration of 12 channels of Dense Wavelength Division Multiplexing (DWDM) amplified by optical amplifiers in the transmitter and receiver. The signal output is filtered by a Fiber Bragg Grating (FBG) and a Gaussian filter. In the simulation, the ORN has node spacing in the range of 500 m to 2,000 m. Then, the data transmission rate at 120 Gbps is provided by the implementation of DWDM channels to serve as an access network. From the simulation, the FSO backbone can optimally deliver highly reliable Li-Fi access networks. When the nodes are spaced in a 500–2,000 m range, the Bit-Error-Rate (BER) performance is produced at the order of 10−6.

Meshless interface tracking for the simulation of dendrite envelope growth

The growth of dendritic grains during solidification is often modelled using the Grain Envelope Model (GEM), in which the envelope of the dendrite is an interface tracked by the Phase Field Interface Capturing (PFIC) method. In the PFIC method, an phase-field equation is solved on a fixed mesh to track the position of the envelope. While being versatile and robust, PFIC introduces certain numerical artefacts. In this work, we present an alternative approach for the solution of the GEM that employs a Meshless (sharp) Interface Tracking (MIT) formulation, which uses direct, artefact-free interface tracking. In the MIT, the envelope (interface) is defined as a moving domain boundary and the interface-tracking nodes are boundary nodes for the diffusion problem solved in the domain. To increase the accuracy of the method for the diffusion-controlled moving-boundary problem, an h-adaptive spatial discretization is used, thus, the node spacing is refined in the vicinity of the envelope. MIT combines a parametric surface reconstruction, a mesh-free discretization of the parametric surfaces and the space enclosed by them, and a high-order approximation of the partial differential operators and of the solute concentration field using radial basis functions augmented with monomials. The proposed method is demonstrated on a two-dimensional h-adaptive solution of the diffusive growth of dendrite and evaluated by comparing the results to the PFIC approach. It is shown that MIT can reproduce the results calculated with PFIC, that it is convergent and that it can capture more details in the envelope shape than PFIC with a similar spatial discretization.

Error analysis of the element-free Galerkin method for a nonlinear plate problem

In this work, we derive a geometrically nonlinear plate model based on the Kirchhoff hypothesis and the large deflection hypothesis, and provide an error analysis of the corresponding element-free Galerkin method. The penalty method is employed to enforce boundary conditions. The error estimate explicitly depends on nodal spacing, number of monomial basis functions, continuity of weight functions, and penalty factors, which provides some practical choices among these key parameters in engineering applications. In addition, we offer guidance on selecting appropriate penalty factors to improve numerical accuracy. Numerical experiments, involving clamped square and circle plates with uniform and nonuniform nodes, as well as a plate model with a corner singularity, are presented to validate the theoretical results.

Hyperbolic Graph Diffusion Model

Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a 48% improvement in the quality of graph generation with highly hierarchical structures.

Assessment of Local Radial Basis Function Collocation Method for Diffusion Problems Structured with Multiquadrics and Polyharmonic Splines

This paper aims to systematically assess the local radial basis function collocation method, structured with multiquadrics (MQs) and polyharmonic splines (PHSs), for solving steady and transient diffusion problems. The boundary value test involves a rectangle with Dirichlet, Neuman, and Robin boundary conditions, and the initial value test is associated with the Dirichlet jump problem on a square. The spectra of the free parameters of the method, i.e., node density, timestep, shape parameter, etc., are analyzed in terms of the average error. It is found that the use of MQs is less stable compared to PHSs for irregular node arrangements. For MQs, the most suitable shape parameter is determined for multiple cases. The relationship of the shape parameter with the total number of nodes, average error, node scattering factor, and the number of nodes in the local subdomain is also provided. For regular node arrangements, MQs produce slightly more accurate results, while for irregular node arrangements, PHSs provide higher accuracy than MQs. PHSs are recommended for use in diffusion problems that require irregular node spacing.

Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning.

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.