Minimum Path Research Articles

Finding k Shortest Paths in Cayley Graphs of Finite Groups

We present a new method for finding k shortest paths between any two vertices in the Cayley graph Cay(G,S)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Cay}(G, S)$$\\end{document} of a finite group G with its generating set S closed under inverses. By using a reduced convergent rewriting system R for G, we first find the lexicographically minimal shortest path between two vertices in Cay(G,S)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Cay}(G, S)$$\\end{document}. Then, by symmetrizing the length-preserving rules of R, we provide a polynomial time algorithm (in the size of certain rewrite rules, the lexicographically minimal shortest path, and k) for finding k shortest paths between two vertices in Cay(G,S)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Cay}(G, S)$$\\end{document}. Our implementation of finding k shortest paths between two vertices in Cay(G,S)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {Cay}(G, S)$$\\end{document} is also discussed.

Wireless routeing scheme for UAV-assisted access networks based on the link path loss

ABSTRACT In this work, a group of unmanned aerial vehicles (UAVs) is aimed to function as relay nodes that provide a multi-hop wireless link between ground users. The communication route between the sender and receiver is established with awareness of the link path loss between the cooperating UAV relay nodes. Then, Dijkstra’s shortest path algorithm is used to find the optimal route that has the minimum total path loss among all other possible routes. MATLAB simulation results show a high-performance gain when using Dijkstra’s algorithm for establishing the communication route compared with the baseline approach in which the route is formed based on the shortest-distance path through the UAV relay nodes. It is worth mentioning that both shadow fading and nonfading radio environments were taken into account. Moreover, the performance is further improved when the standard deviation of shadow fading increases. The work also investigates the probability of selecting each of the UAV nodes in forming the multi-hop link considering the total simulated realizations. Here, it is shown that more variability is observed in the indexes of the traversed UAV nodes when routeing through a random fading environment compared to a nonfading environment.

Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction

A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment.

Enhancing software quality assurance in ubiquitous learning environments through mutation testing and diverse test oracles

Ubiquitous learning enables software analysts to acquire knowledge and design test oracles from online resources including developer documentations, forums and collaborative platforms. Test oracles are primarily designed by humans and may contain quality defects. Mutation testing can facilitate the adaptability of ubiquitous learning to enhance software quality assurance. Unfortunately, mutation testing generates a large number of faulty versions known as mutants to analyze the quality of test oracles which is computationally expensive. In this paper, we proposed minimal path selection strategy to select fewer and non-trivial mutants to analyze diverse test oracles realizing ubiquitous learning environments. The proposed selection strategy explores the relationship between faulty conditions and output statements to identify feasible paths. To establish a ubiquitous learning setup, diverse test oracles are selected by incorporating user input, automated scripts and developer insights. This approach aims to enhance learner engagement and effectively design test oracles. The empirical evaluation and state-of-the-art comparison of various Java-built software programs demonstrated the potential of ubiquitous learning in software quality assurance by reducing 75% of the generated mutants. Furthermore, only 64%–85% of adequate test oracles were required to meet the desired quality assurance criteria. The study highlights the significance of personalized and adaptive approaches to assure software quality in ubiquitous learning environments.

Dynamic performance analysis and optimization research on solar dual-stage evaporation multigeneration system

A novel solar dual-stage evaporation multigeneration system is proposed, with analysis on the impact of the parabolic trough collector area (APTC) and the volume of the heat storage tank (VHST) on performance under dynamic conditions. The particle swarm optimization- genetic algorithm and the minimum path method optimized the system’s overall performance. Results show that reducing APTC and increasing VHST can enhance energy performance within a certain range, but beyond specific thresholds, changes in APTC and VHST no longer affect it. As APTC and VHST increase, economic performance initially improves but then declines. The particle swarm optimization- genetic algorithm increased optimization efficiency and avoided local optima. When using the minimum path method, the dimensionless cumulative error was 0.63, balancing thermal and economic performance. Using toluene as the working fluid, the optimized system achieved thermal and exergy efficiencies of 56.06 % and 9.15 %, respectively, with a net present value of 58.12 M$, a payback period of 5.23 years, and an equivalent CO2 reduction of 24.8 kt, outperforming the solar organic Rankine cycle system in energy efficiency, economic performance, and carbon reduction potential. The solar dual-stage evaporation multigeneration system achieved the best performance with toluene as the working fluid, while heptane provided longer running time and greater carbon reduction potential. At low rated power generation, performance differences between toluene and heptane were minimal, while as the rated power generation increased, toluene showed significant advantages in thermal and economic performance.

Evaluation of reliability and characteristics measure using exponential decay distribution of non‐series parallel network

AbstractThe purpose of this paper is to analyze the reliability and characteristics of the non‐series‐parallel network. The illustrative non‐series parallel networks, that is, the Twin T‐Network and the network with six components of resistor and capacitor, have been converted into a series of parallel network combinations by use of the minimal path technique. The stochastic variables related to component failure rate have been demonstrated using exponential decay distribution. We obtain the reliability, mean time to failure, and sensitivity with the use of the exponential decay distribution. The reliability and its attributes are examined by assuming different values of scale and shape parameters at different points of time. Using Owen's methodology, we obtained minimal signature, tail signature, expected time, expected cost, and Barlow‐Proschan index were determined. The performance of all reliability characteristics has been observed graphically for arbitrary values of parameters. A real‐world example is used to introduce the point.

Location of 5G base station antenna in substation taking into account path loss and RF radiation

Aiming at the engineering problem that 5G base station antenna is difficult to locate efficiently in complex electromagnetic environment, a two-stage positioning method of 5G base station antenna substation is proposed, which considers signal path loss and antenna RF radiation. Firstly, the path loss solution model of the 5G base station antenna signal in the substation is established, and the RF radiation solution model generated by the coupling excitation of 5G high-frequency electromagnetic wave and the metal shell of the electrical equipment is constructed based on the big element physical optics method. Secondly, combining the advantages of particle swarm optimization (PSO) and interior point algorithm, the antenna positioning method considering path loss and RF radiation is constructed. Among them, in the first stage, the minimum loss of 5G signal path in the substation is taken as the goal, and the antenna stations in the substation are searched globally to get the optimal pre-selected position. In the second stage, the antenna position is calibrated according to the pre-selected position, so that the interference of RF radiation is minimized, and the final position is output. Finally, a 500 kV 5G substation in Henan Province was taken as the simulation object. Compared with the original scheme, the simulation results ensured the minimum 5G path loss in the substation and took into account the electromagnetic compatibility of the equipment in the substation. The effectiveness of the location strategy for maintaining the safe operation of the substation is verified, and it can provide a reference for the 5G base station antenna positioning project of the substation.

Theoretical Investigation into Polymorphic Transformation between β-HMX and δ-HMX by Finite Temperature String

Polymorphic transformation is important in chemical industries, in particular, in those involving explosive molecular crystals. However, due to simulating challenges in the rare event method and collective variables, understanding the transformation mechanism of molecular crystals with a complex structure at the molecular level is poor. In this work, with the constructed order parameters (OPs) and K-means clustering algorithm, the potential of mean force (PMF) along the minimum free-energy path connecting β-HMX and δ-HMX was calculated by the finite temperature string method in the collective variables (SMCV), the free-energy profile and nucleation kinetics were obtained by Markovian milestoning with Voronoi tessellations, and the temperature effect on nucleation was also clarified. The barriers of transformation were affected by the finite-size effects. The configuration with the lower potential barrier in the PMF corresponded to the critical nucleus. The time and free-energy barrier of the polymorphic transformation were reduced as the temperature increased, which was explained by the pre-exponential factor and nucleation rate. Thus, the polymorphic transformation of HMX could be controlled by the temperatures, as is consistent with previous experimental results. Finally, the HMX polymorph dependency of the impact sensitivity was discussed. This work provides an effective way to reveal the polymorphic transformation of the molecular crystal with a cyclic molecular structure, and further to prepare the desired explosive by controlling the transformation temperature.

One path, two solutions: Network-based analysis identifies targetable pathways for the treatment of comorbid type II diabetes and neuropsychiatric disorders

Comorbid diseases complicate patient outcomes and escalate healthcare costs, necessitating the need for a deeper mechanistic understanding. Neuropsychiatric disorders (NPDs) such as Neurotic Disorder, Major Depression, Bipolar Disorder, Anxiety Disorder, and Schizophrenia significantly exacerbate Type 2 Diabetes Mellitus (DM2), often leading to suboptimal treatment outcomes. The neurobiological mechanisms underlying this comorbidity remain poorly understood. To address this gap, we developed a novel pathway-based network computational framework to identify critical shared disease mechanisms between DM2 and these five prevalent comorbid NPDs. Our approach involves reconstructing an integrated DM2 ∩ NPDs KEGG pathway-pathway network and employs two complementary analytical methods, including the "minimum path to comorbidity" method to identify the shortest path fostering comorbid development. This analysis uncovered shared pathways like the PI3K-Akt signaling pathway and highlighted key nodes such as calcium signaling, MAPK, estrogen signaling, and apoptosis pathways. Dysregulation of these pathways likely contributes to the development of DM2-NPDs comorbidity. These findings have significant clinical implications, as they identify promising therapeutic targets that could lead to more effective treatments addressing both DM2 and NPDs simultaneously. Our model not only elucidates the intricate molecular interactions driving this comorbidity but also identifies promising therapeutic targets, paving the way for innovative treatment strategies. Additionally, the framework developed in this study can be adapted to study other complex comorbid conditions, advancing personalized medicine for comorbidities and improving patient care.

Exploring the minimum cost conflict mediation path to a desired resolution within the inverse graph model framework

The existing inverse graph model for conflict resolution (GMCR) research primarily concentrates on identifying the required preferences of decisions makers (DMs) such that a desired state is an equilibrium. However, the process of transitioning from the current state to the desired equilibrium is not explored. In this paper, we propose a minimum adjustment cost model taking account of preference adjustment costs to identify the required preferences for a desired state to be an equilibrium. Subsequently, we introduce the concept of transition costs for the first time to quantify expenses involved in guiding a DM transition from one state to another and develop a minimum cost conflict mediation path model. This model aims to identify the most efficient path that minimizes the cost of transitioning from the current state to the desired equilibrium. Moreover, to accommodate the consideration of multiple desired equilibria, we extend the minimum cost conflict mediation path model to analyze and determine the optimal path for transitioning from the current state to one of the identified desired equilibria with the overall minimum cost. Furthermore, to address uncertainty surrounding transition costs, we formulate a probability maximizing conflict mediation path model that considers a limited budget available for the mediation process. Finally, a real-world dispute, the fracking conflict in the province of New Brunswick, Canada, is utilized to demonstrate the application of the proposed models.

Shutdown dose rate calculation for fission reactors: An application of the MS-CADIS method to OPAL

There have been considerable advances of shutdown dose rate (SDDR) calculation methods for fusion related problems, however applications of these methods in the fission reactor field have hitherto been sparse. The present study attempts to bridge this gap by investigating the applicability of SDDR calculation methods for fission reactor problems. Specifically, we aim to assess and validate whether recent advances in SDDR methods can be successfully applied in fission research reactors. To this end, we estimate the shutdown dose rate distribution at the Open Pool Australian Light water reactor (OPAL) using the rigorous two step (R2S) computational method, and we compare the calculated results with the experimental data. This method utilizes a 3D reactor model implemented in the Monte Carlo N-Particle (MCNP) transport code, the AutomeD VAriaNce reduction Generator (ADVANTG) code for geometry discretization and variance reduction calculations, and the Oak Ridge Isotope GENeration (ORIGEN) inventory code for activation calculations. To ensure robustness, we employ two variance reduction techniques, Forward Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) and Multi-Step Consistent Adjoint Driven Importance Sampling (MS-CADIS). To the best of our knowledge, this is the first MS-CADIS method implementation for fission reactor problems. The SDDR is estimated at ten locations within the experimental hall, all situated more than 4 m away from the reactor core.The paper shows that, the experimental observations are within the lower and upper bounds of the simulation results for 4 out of 10 locations, while the remaining observations are within a factor of 7, with one significant outlier. The calculated average dose rate is within 5% of the nominal values of the experimental observations for 3 locations. The computational results are within statistical uncertainty by using two different variance reduction techniques, with significant computational advantage of MS-CADIS over FW-CADIS for SDDR calculations. The results indicate that the combination of SDDR distribution maps, estimated dose rate energy dependance, and activation information are powerful tools in identifying the radioisotopes and reactor components dominating the SDDR. These results can contribute to better radiation safety practices in contaminated areas, by enabling the minimal dose path planning or by improving radiation shielding.

Studies on highly stable Na7Fe3(P2O7)4 as anode materials for lithium-ion batteries and migration behavior of lithium ions

In this paper, Na7Fe3(P2O7)4 target material is firstly evaluated as potential anode material for lithium ion batteries. It is found to have good electro-chemical performance and high stability as anode material for lithium batteries. It shows a capacity of 290 mAh g−1 for 200 cycles and increases to 388 mAh g−1 for 1000 cycles at a current density of 150 mA g−1. The Coulomb efficiency is maintained at about 98 % during the long cycling. The ex-situ experimental results indicate that Na7Fe3(P2O7)4 has high structural stability and the changes of cell volume and cell parameters are <0.62 % during charging and discharging. The results of GITT data show that the diffusion coefficients range from 3.15 × 10−11 cm2 S−1 to 1.12 × 10−12 cm2 S−1.The Li+/Na+ diffusion paths are estimated by the Bond valence-energy landscape (BEL) method and the exact energy barriers and minimal energy paths (MEPs) are determined using the nudged elastic band (NEB) method implemented in density functional theories (DFT). A two dimensional Na+ diffusion network is observed and Na+ diffusion along c-axis is prohibited. For the initial discharge stage, Na+ migrates in the Na+ transportation channel with an energy barrier of 0.48 eV along a-axis and 0.46 eV along b-axis. For the discharge later on, the intercalated Li+ enters the nearby Na+ vacancies and also migrate in the Na+ channel except Li+ at 8f Lid sites, which will stay where it is firstly introduced because of the higher energy barrier of 0.75 eV. The immobility of Lid is one of the important reasons for the irreversible capacity loss after the initial charge/discharge cycle.

Unified theory for the scaling of the crossover between strong and weak disorder behaviors of optimal paths and directed or undirected polymers in disordered media.

In this paper, we are concerned with the crossover between strong disorder (SD) and weak disorder (WD) behaviors in three well-known problems that involve minimal paths: directed polymers (directed paths with fixed starting point and length), optimal paths (undirected paths with a fixed end-to-end or spanning distance), and undirected polymers (undirected paths with a fixed starting point and length). We present a unified theoretical framework from which we can easily deduce the scaling of the crossover point of each problem in an arbitrary dimension. Our theory is based on the fact that the SD limit behavior of these systems is closely related to the corresponding percolation problem. As a result, the properties of those minimal paths are completely controlled by the so-called red bonds of percolation theory. Our model is first addressed numerically and then approximated by a two-term approach. This approach provides us with an analytical expression that seems to be reasonably accurate. The results are in perfect agreement with our simulations and with most of the results reported in related works. Our research also leads us to propose this crossover point as a universal measure of the disorder strength in each case. Interestingly, that measure depends on both the statistical properties of the disorder and the topological properties of the network.

Network reliability of a stochastic flow network by wrapping linear programming models into a Monte-Carlo simulation

A stochastic flow network (SFN) serves as a fundamental framework for real-life network-structured systems and various applications. Network reliability NRd is defined as the probability that an SFN can successfully send at least d units of demand from a source to a terminal. Current analytical algorithms for the network reliability evaluation are classified into an NP-hard problem. This limitation hinders the ability of decision-makers to monitor and manage decisions for an SFN flexibly and immediately. Therefore, this paper develops an algorithm to estimate network reliability by wrapping developed linear programming (LP) models based on minimal paths (MPs) into a Monte-Carlo simulation. The developed LP models present satisfaction of the demand d in the SFN in terms of minimal paths. The effectiveness and efficiency of the proposed algorithm are verified using a series of numerical investigations. Contributions are manifold: (1) an integrated model with the simulation and LP models is provided to estimate network reliability in terms of the MPs, thereby filling a crucial gap in existing research; (2) the scalability and efficiency of the proposed method are shown for the complex SFNs; (3) decision-making capabilities can be provided under real-time reliability predictions.

Stable route selection for adaptive packet transmission in 5G-based mobile communications

ABSTRACT The poor connectivity among mobile nodes introduces uncertainty in packet loss as the path link is not measured in this network. The focus is placed on communication cost to achieve valid packet transmission. Because the high-distance path selected for packet transmission incurs higher communication costs, it increases energy consumption and packet loss rate. So, the proposed dispersed path selection for communication (DPAC) method is constructed to obtain the best minimum distance routing path. This path operates with the help of queue variation is handled the data packets maintenance, and the time slot exceeds its limit. Packets are kept waiting to increase the packet broadcasting efficiency. A multipath jamming detection algorithm is constructed to provide a link-based path packet overload detection scheme to identify the packet overload. It also separates the path based on its characteristics to control overload. It reduces energy consumption and packet loss rate.

An Integrated Route and Path Planning Strategy for Skid–Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (IRRT*) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT* planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT* algorithm outperformed RRT and RRT* with 96.1% and 97.6% smoother paths, respectively. The IRRT* also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture.

A parallel algorithm for reliability assessment of multi-state flow networks based on simultaneous finding of all multi-state minimal paths and performing state space decomposition

This article presents a new efficient method for reliability evaluation of two-terminal Multi-state Flow Networks (MFNs). Indirect State Space Decomposition (SSD) methods are usually more efficient than direct SSD methods. However, indirect SSD methods require searching for all d-MPs/d-MCs. The presented method requires only an MFN, a system demand requirement d, and the probability distributions of states of components as an input. The presented algorithm uses a partition technique to decompose a state space into smaller and distinct subspaces. The algorithm tries to determine a d-flow with a lower bound for each subspace. If a flow exists and it is a d-MP, the algorithm computes the probability that a capacity vector belongs to this subspace and is bounded below by the d-MP and decomposes this subspace into smaller subspaces. For all subspaces without d-MP, the presented algorithm uses parallel computations and the SSD method to compute the probability that a capacity vector belongs to this subspace and is bounded below by a d-MP. The numerical experiments show that the efficiency of the presented method improves in comparison with other indirect SSD methods if the number of available logical processes and the number of d-MPs increase.

MULTI-HOP RELAY SELECTION FOR COOPERATIVE SENSING IN COGNITIVE RADIO NETWORKS

Spectrum sensing is one of the essential blocks of the cognitive radio (CR) system. Cooperative spectrum sensing (CSS) enhances sensing performance by exchanging information among secondary users (SUs). The paper addresses a situation where some SUs cannot communicate their local information with the fusion center (FC) due to real-time circumstances, i.e., shadowing, large distances, increased signal interference, etc. The issue can be resolved by introducing relay nodes to assist such SUs in transmitting local information to the corresponding destination (FC), making relay selection an essential component on which the system's performance depends. This paper proposes a critical path method (CPM) based multi-hop relay selection technique to select a set of relay nodes combined to form a relay with minimum path loss. The performance of the proposed scheme is compared with that of the existing methodologies to demonstrate its robustness and efficiency in terms of end-to-end path loss and reliability. In addition, a closed-form expression of outage probability is also developed to withstand the results.

Robot Motion Planning Based on an Adaptive Slime Mold Algorithm and Motion Constraints

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time.

Enhancing multi-channel electrode structures in ceramic fuel cells: Influence of channel geometry on mass transport and electrochemical performance

Solid oxide fuel cell (SOFC) is a promising electrochemical power generation technology that contributes to the decarbonization of the energy field. The electrode-supported design enables the use of thinner electrolytes, but inevitably brings additional gas diffusion resistance, which in turn leads to higher anodic concentration polarization. Therefore, additional structural design of the electrodes is necessary to ensure efficient operation and mass transport resistance minimization. In this study, anode-supported micro-tubular SOFCs with different multi-channel geometries have been attempted, during which samples with 4 channels exhibit unique elliptical structure due to meticulous manipulation. In addition, the 4-channel samples have been compared to the single-channel and 7-channel counterparts, exploring the influential mechanism of the anode micro-structure on gas transfer and electrochemical performances. The study found that the 4-channel cell has the best geometric controllability, with its minimum gas diffusion path reduced to below 50 μm, such thin electrochemical active region (EAR) accounts for >50 % of the entire external surface. Efficient utilization of the geometric surface area considerably facilitates the fuel transport to reach anode/electrolyte interface. By enhancing the mass transport efficiency, the maximum power density of the 4-channel cell reached 1.40 W cm−2 (750 °C), which is 22.8 % and 102.9 % higher than that of the 7-channel and single-channel cells under the same conditions. The highly efficient anode design additionally exhibited exceptional structural integrity and mechanical robustness, thereby ensuring the steady operation of the cell.