Nodal Coordinates Research Articles

Supply-demand Security Assessment of Water-energy-food Systems: A Perspective on Intra-city Coupling and Inter-city Linkages of Ecosystem Services

Ensuring the supply-demand security of water-energy-food systems (WEF) is paramount in sustainable cities. Leveraging ecosystem services (ESs) as a bridge between WEF supply and demand, this study proposes a conceptual framework for assessing the supply-demand security of WEF from the perspective of intra-city coupling and inter-city linkages of WEF-related ESs. Considering 14 Liaoning cities, we developed supply-demand indices for three ESs: water yield, carbon storage, and food production. The supply-demand security pattern of WEF was evaluated using Spearman's rank correlation, the Copula model, the coupling coordination degree model, the gravity model, and social network analysis, using these indices. The results show that the supply-demand security of WEF was higher in eastern Liaoning cities and weakened westward. Dandong had the highest supply-demand security, with a 98% probability of achieving moderate surplus in WEF resources. The regions of Dandong-Liaoyang-Anshan-Yingkou-Panjin-Jinzhou and Tieling-Fushun-Benxi formed two extreme WEF coupling coordination gravity networks. Liaoyang, Panjin, and Fuxin emerged as hubs in the WEF coupling coordination gravity networks and exhibited the highest degree and betweenness centrality values. Additionally, Fushun, Liaoyang, Dandong, and Tieling were identified as WEF high coupling coordination nodes. This supply-demand security assessment framework for WEF offers a scientific basis for developing sustainable city strategies.

Elastodynamic analysis of rotating solids by novel nodal position finite element method

This study develops a novel 8-node isoparametric hexahedral element using the Nodal Position Finite Element Method (NPFEM) for elastodynamic analysis of rotating solids. The element also incorporates the flexural modes directly into its element shape function to alleviate the shear locking when modeling the bending deformation of solids. Unlike conventional displacement-based finite element methods, which require the decoupling of elastic deformation from rigid-body motions, the NPFEM eliminates this process by directly representing strain and kinetic energies through nodal position coordinates, which avoids potential approximation errors in the decoupling process. To validate the accuracy and efficacy of this new NPFEM solid element, numerical simulations of a beam under static and dynamic loads are conducted and benchmarked against the theoretical solutions. Then, dynamic analysis of a rotating blade demonstrates that the NPFEM element can directly account for the centrifugal stiffening effect and superharmonic resonance of rotating blades without resorting to conventional methods. The successful implementation of this NPFEM element in complex simulations highlights its potential to provide significant advancements in computational mechanics.

Weighted Centroid Amorphous Algorithm for Position Error Minimization in WSN

ABSTRACTWireless Sensor Networks (WSNs) have numerous applications, one of which is localization. Localization is crucial for determining the position of unknown sensor nodes in the deployment environment. Localization techniques are broadly classified into two categories: range based and range‐free. Range‐free localization techniques are gaining popularity as they are easy to implement and do not require external hardware. In this proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. Ten nearest beacon nodes are considered to determine the coordinates of a single unknown node. The hop value and hop size of that unknown node are calculated from the 10 nearest beacon nodes, and by using the hop value and hop size, the weight is calculated. After the weight estimation, the weighted sum is calculated, and using the weighted sum, the coordinates of an unknown node are determined. Simulation results indicate that the proposed Weighted Centroid Amorphous method achieves superior accuracy, with rates of 90.41%, 89.57%, and 86.10% compared to the traditional Amorphous, improved Amorphous, and Ensemble approach, respectively.

Probabilistic Modeling Geometric Tolerance and Low Cycle Fatigue Life of Gas Turbine Compressor Blade

Abstract This paper presents a probabilistic low cycle fatigue (LCF) assessment for geometric tolerances on high load contact surfaces of gas turbine compressor blades. The typical patterns of the geometric deviations for the root contact flank of the compressor blades are identified and characterized according to coordinate measurement machine (CMM) measurements of the blade root. These typical patterns are closely related to the root form manufacture tools and process. Finite element (FE) models for the typical geometric deviation patterns are created based on nodal coordinates transformation of the surface nodes on the blade root contact flanks and radius. An optimized blade root profile tolerance is established, which enables significant cost saving. The elastic-plastic FE analysis with nonlinear contact model, material strain-life test, response surface, and constrained Monte Carlo simulation are used to create a probabilistic LCF life model for the optimized tolerance. The model quantifies the effect of the geometric deviations, blade mass, and material property on the blade LCF life. The result shows that with the optimized tolerance, the probability of blade LCF failure is very low and acceptable. It is also shown that the strain life material property is the most critical factor for the LCF failure. The root profile tolerance and blade mass are seen to have a much weaker effect on the blade LCF life.

A force-density framework for flexible multi-body dynamic analysis of clustered tensegrity structures

This paper develops a versatile and effective force-density framework for the flexible multi-body dynamic analysis of clustered tensegrity structures. In this framework, the force density is selected as the basic variable instead of force, and the clustered tensegrity structure is mathematically described in a vector and matrix form, encompassing topology, geometry, material, and force properties. A non-negative variable is defined as an indicator of the member stress state, and a complementary function is constructed to address the discontinuity issues that arise from the unidirectional axial stiffness of cables. Dynamic formulas are established within this force-density framework, with nodal coordinates selected as generalized parameters and formulations constructed in a matrix form. A complementary framework is established as an alternative for solving the dynamic equations, transforming the isolated steps of Newton’s iteration and cable state judgment (slack or tension) into a unified one, bringing more potential for improving solving efficiency. Numerical simulations are carried out to validate the approach, demonstrating that it effectively reveals the dynamic oscillation, tension changes, and cable slack behavior of clustered tensegrity structures during shape control. Comparative studies highlight the advantage of computational efficiency. The method proposed in this paper provides a robust mathematical model for studying clustered tensegrity structures, particularly regarding the shape control of deployable, active, and intelligent structures, aiding in understanding dynamic oscillation, tension changes, and cable slack behavior during their deformation. The methods can also be applied to cable net structures and other prestressed pin-jointed systems.

Grid Optimization of Free-Form Spatial Structures Considering the Mechanical Properties

In recent years, the application of free-form surface spatial grid structures in large public buildings has become increasingly common. The layouts of grids are important factors that affect both the mechanical performance and aesthetic appeal of such structures. To achieve a triangular grid with good mechanical performance and uniformity on free-form surfaces, this study proposes a new method called the “strain energy gradient optimization method”. The grid topology is optimized to maximize the overall stiffness, by analyzing the sensitivity of nodal coordinates to the overall strain energy. The results indicate that the overall strain energy of the optimized grid has decreased, indicating an improvement in the structural stiffness. Specifically, compared to the initial grid, the optimized grid has a 30% decrease in strain energy and a 43.3% decrease in maximum nodal displacement. To optimize the smoothness of the grid, the study further applies the Laplacian grid smoothing method. Compared to the mechanically adjusted grid, the structural mechanical performance does not significantly change after smoothing, while the geometric indicators are noticeably improved, with smoother lines and regular shapes. On the other hand, compared to the initial grid, the smoothed grid has a 21.4% decrease in strain energy and a 28.3% decrease in maximum nodal displacement.

Nanozymes in Biomedical Applications: Innovations Originated From Metal-Organic Frameworks.

Nanozymes exhibit significant potential in medical theranostics, environmental protection, energy development, and biopharmaceuticals due to their exceptional catalytic performance. Compared with natural enzymes, nanozymes have the advantages of simple preparation and purification, convenient production and low cost. Therefore, it is very important to prepare nanozymes quickly and efficiently, which not only helps to expand their application scope, but also can further exert their great potential in various fields. Metal-organic frameworks (MOF) materials serve as versatile substrates for constructing nanozymes, offering unique advantages like adjustable structure, high specific surface area, and porous channels. MOF coordination nodes constructed from metal ions or metal clusters have unique properties that can be leveraged to tailor nanozyme characteristics for different applications. This review describes and analyzes recent methods for constructing nanozymes using MOF materials, and explores their application prospects in biomedicine. By expounding the preparation techniques and biomedical applications of nanozymes, this review aims to inspire researchers to develop innovative nanozyme materials and explore new application directions.

Influence of tool wear on geometric surface modeling for TC4 titanium alloy milling

Addressing the insufficient accuracy of milling surface roughness prediction model, the formation causes of different roughness were analyzed, and a surface roughness prediction model which takes tool wear into account was established based on an improved Z-MAP algorithm. Combining tool geometry and tool wear morphology, a mathematical model of surface profile was built; And the motion trajectory equations of the milling cutter teeth were established via machining kinematics. The servo rectangular encirclement and Newton iterative method were adopted to improve the Z-MAP algorithm. Combining the Z-MAP algorithm with the insert motion trajectory equations, the values at different height coordinates of the workpiece grid nodes were obtained, allowing numerical simulation of workpiece surface geometry and analysis of the effect of cutting trajectory overlap on the machined surface morphology. TC4 titanium alloy milling experiment was conducted to validate the simulation model. The simulated surface roughness Sa exhibited significant deviation from experimental values before tool wear, with a maximum error of 53.08 %. Nevertheless, the simulated values of Sa were closer to experimental values after tool wear, with a maximum error of 15.45 % and a minimum of only 3.07 %. Additionally, the predicted results for the bearing length ratio Rmr(c) were consistent with experimental values. The mathematical model for surface roughness taking tool wear into account effectively predicted the shape profile and surface roughness of the machined surface, providing theoretical guidance and technical support for practical production.

A new triangular thin shell element based on the absolute nodal coordinate formulation for complex surfaces

Abstract This work proposed a new Hermite triangular thin shell element based on the absolute nodal coordinate formulation to model thin shells with complex curved surfaces. Three techniques are adopted to enhance its universality and efficiency. Firstly, local numerical curvilinear coordinates are used on each node for those curved surfaces whose global curvilinear coordinates cannot be obtained analytically, and the Lie group interpolation is used for obtaining the curvilinear coordinates on the non-nodal domain. Secondly, the slope vector of the element is obtained by cross-producing the two gradient vectors only on each node; but interpolated inside the element to ensure its continuity. Additionally, this processing maintains the linear relationships between the shape functions and nodal coordinates, resulting in constant elastic constants. Thirdly, the enhanced assumed strains (EAS) and the assumed natural strains(ANS) methods are adopted respectively to accelerate the convergence speed and avoid locking problems of the element. Several numerical examples show that this new element is universal for irregularly curved surfaces without locking problems. In addition, the efficiency is much higher than the traditional triangular shell element.

Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making.

Investigating the impact of body node coordinator position on communication reliability in wireless body area networks

Investigating the impact of body node coordinator position on communication reliability in wireless body area networks

Analysis of Vibration Characteristics of Spatial Non-Uniform Tensioned Thin-Film Structures Based on the Absolute Nodal Coordinate Formulation.

Due to their lightweight characteristics, spatial thin-film structures can generate vibrations far exceeding their film thickness when subjected to external loads, which has become a key factor limiting their performance. This study examines the vibration characteristics of tensioned membrane structures with non-uniform elements subjected to impacts in air, leveraging the Absolute Nodal Coordinate Formulation (ANCF). This model takes into account the wrinkling deformation of thin films under pre-tension and incorporates it into the dynamic equation derived using the absolute node coordinate method. A detailed discussion was conducted on the influence of non-uniform elements, situated at different locations and side lengths, on the vibration characteristics of the thin film. The analytical results obtained from the vibration model were compared with the experimental results, validating the effectiveness of the vibration model. This provides a theoretical foundation for the subsequent vibration control of thin films.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

IoT-FAR: A multi-sensor fusion approach for IoT-based firefighting activity recognition

Inadequate training poses a significant risk of injury among young firefighters. Although Human Activity Recognition (HAR) algorithms have shown potential in monitoring and evaluating performance, most existing studies focus on daily activities and have difficulty distinguishing complex firefighting tasks. This study introduces the Internet of things (IoT)-based wearable firefighting activity recognition (IoT-FAR) system which employs a multi-modal sensor fusion approach to achieve comprehensive activity recognition during firefighting training. The IoT-FAR comprises five wearable body sensor nodes and a coordinator node. This study explores the significance of features extracted from the surface electromyography, heart rate, and inertial measurement units in firefighting training activity recognition. A hybrid machine learning (HML)-based network is proposed, which integrates three models: one trained with all features (MA), another with upper body features (MU), and a third with lower body features (ML). The proposed HML-SVM-RBF1-RF2 network achieves superior performance, with a mean recall of 93.94%, mean precision of 90.94%, and a mean accuracy rate of 98.29%. Additionally, the study introduces the specialized firefighting training associated activities (SFTAA) dataset, which includes endurance training activities involving self-contained breathing apparatus (SCBA) conducted by eighteen firefighters. This dataset represents preliminary work towards building a comprehensive dataset covering various events and scenarios for tracking firefighter activities. The IoT-FAR system also demonstrates the potential use of misclassified activities as evaluation metrics for assessing firefighter training performance.

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm.

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

Конечно-элементное моделирование нелинейных задач упругости в абсолютных узловых координатах на неструктурированных шестигранных сетках

We consider a finite element approach to solving the problem of elasticity theory in terms of absolute nodal coordinate formulation (ANCF), in which large body displacements are described in a global reference frame without using any local coordinate system. The main feature of the method is the absence of gyroscopic effects and, as a result, constancy of the mass matrix and the vector of the generalized force of gravity. In contrast to the traditional ANCF approach, the sets of nodal degrees of freedom of the finite element are formed only on the basis of absolute coordinates of nodes, which allows us to solve the problem using, in particular, unstructured hexahedral meshes. To construct the stiffness matrix, we apply a second-order automatic differentiation algorithm, which ensures its symmetrical form (Hessian matrix) and is analytically accurate in calculating the derivative. This approach also makes it possible to carry out calculations for models of hyperelastic materials without the corresponding Piola-Kirchhoff tensor. It has been shown that within the framework of discretization of the equation of motion, along with the well-known Newmark numerical integration scheme, it is possible to use the HTT-α scheme, which is unconditionally stable, second-order accurate and dissipative for high frequencies. We present several examples of solving static and dynamic elasticity problems for compressible and incompressible models of hyperelastic materials, in which the functions of internal energy density of the body are specified in terms of the deformation gradient.

Tunable multi-stability of conical Kresling origami structures utilizing local imperfections

The classic Kresling origami structure has been widely studied in the past two decades because of its interesting mechanical properties, including compressive-twist coupling deformation and bistability. It is also known that the conical derivative of Kresling origami can achieve a wider range of structural configurations while preserving the bistability of the original design. Moreover, different origami structures exhibit different responses to local geometric or material imperfections which are often inevitable in practical applications. In this study, we utilize the bar-and-hinge model to convert local imperfections to corresponding variations in nodal coordinates and equivalent stiffness values. Subsequently, we examine the response of conical Kresling origami structures to certain local imperfections. It is demonstrated that the effect of geometric imperfections on the folding properties of such structures is more substantial than that of material imperfections. We show that the multistability of conical Kresling origami structures may undergo a radical transformation when the value of the imperfection exceeds a certain threshold. Furthermore, based on responses to local imperfections, a derivative of the conical Kresling origami structure is designed which manifests tristability. This work develops a strategy for the form-finding of origami structures with tunable multistability, and can be generalized to analyze combined results from multiple local imperfections.

RPL*: An Explainable AI-based routing protocol for Internet of Mobile Things

The Internet of Mobile Things (IoMT) is an emerging paradigm of Internet of Things (IoT) with special focus on enabling mobility to the ‘things’. Several IoMT applications such as group of robots or drones performing collaborative search and rescue operation, identification of mines, warehouse management, goods delivery, etc can be considered as examples of IoMT systems. In the applications mentioned above, the nodes may send the information in a multi-hop manner to the root or coordinator node which may be static or mobile. While the Routing Protocol for Low Power and Lossy Networks (RPL) is extensively utilized in static IoT networks, it encounters significant limitations in handling mobility and providing resilience against routing attacks in mobile IoT networks. In this work, we propose a modified RPL, RPL* which is robust to handling mobility in nodes and is resilient towards routing attacks. In RPL*, any deviation from the normal behaviors of the network are identified as anomalies using an unsupervised Explainable Artificial Intelligence (XAI) strategy. In RPL*, we propose a novel mobility detection mechanism that will identify the mobility in the network in an energy efficient manner without incurring additional communication overhead. To maintain the connectivity with parent node, we propose a novel proactive connectivity management mechanism in RPL* which will ensure a smooth transition from one parent to another if required, thus avoiding the network partitioning due to mobility. The performance analysis of the system has demonstrated an improvement in packet delivery ratio of the mobile nodes by 40% due to the proposed RPL* when compared to RPL. Also, the proposed XAI strategy provided an F1-score of over 95% for the detection of sink hole and black hole attacks in the tested IoMT network scenarios. It was observed that RPL* improves the performance of the IoMT network when compared to RPL. However it may be noted that the mechanisms introduced to support mobility does not lead to a drop in PDR or increase in control packet overhead for static networks. Hence, RPL* can be considered as an alternative to RPL for IoT as well as IoMT networks.

Distance correction range-free localization algorithm for WSNs

Accurate location information is essential for the effective deployment of Wireless Sensor Networks (WSNs) applications. Recently, researchers have developed numerous localization algorithms, with multi-hop methods being particularly attractive due to their cost-effectiveness and robustness, but their accuracy is still unsatisfactory. For better localization accuracy, this paper presented a distance correction range-free localization algorithm (DCRA). Firstly, the proposed algorithm estimates the distance between neighboring nodes based on neighbor connectivity to improve the accuracy of single-hop distance estimation. Secondly, during the multi-hop distance estimation phase, path similarity metrics are employed to ascertain the optimal path correction coefficients, thereby minimizing multi-hop distance estimation errors. Finally, based on the estimated distances, the node localization is converted into an optimization problem, which is then solved using an enhanced particle swarm algorithm to obtain the node coordinates. Simulation results show that the distance estimation accuracy of the DCRA algorithm in different node density scenarios is improved by more than 31 % and 12 % compared to the DV-Hop and DV-RND algorithms, respectively; and in terms of localization accuracy the DCRA algorithm is improved by more than 58 % and 35 % compared to the DV-Hop and DV-RND algorithms, respectively.