Path Computation Research Articles

Structural and topological analysis of thiosemicarbazone-based metal complexes: computational and experimental study of bacterial biofilm inhibition and antioxidant activity

The structural and electronic behavior of thiosemicarbazone (TSC)-based metal complexes of Mn (II), Fe (II), and Ni (II) have been investigated. The synthesized metal complexes were characterized using elemental analysis, magnetic susceptibility, molar conductivity, FTIR, and UV–Vis spectroscopy, the computational path helped with further structural investigation. The solubility test on the TSC and its complexes revealed their solubility in most organic solvents. DFT computational analysis was performed, and quantum reactivity parameters of the octahedral optimized complexes were calculated to describe the reactivity via the stability states of the synthesized complexes. FMOs map was generated to confirm similar findings and MEP analysis was applied to elaborate the important electrophilic and nucleophilic sites on the studied surfaces. Also, other important topological analyses such as electron localization function and reduced density gradient, to establish the favorable noncovalent interactions, were studied. In silico molecular docking approach was studied against the gram-positive bacteria Bacillus cereus to predict the potent inhibition behavior of the studied complexes. The findings summarized the inhibition prediction of the most interactive [NiL2Cl2], then [FeL2Cl2] complexes as confirmed by the binding energy values (− 7.1 kacl/mol and − 6.4 kacl/mol, respectively). Another In silico results, with gram-positive bacteria (S. aureus), estimated similar results of the experimental finding, where [MnL2Cl2] (− 9.2 kcal/mol) is the more effective predicted antibacterial inhibitor. Fluorescence microscopy was used to examine the inhibition of bacterial biofilm, and the DPPH assay was used to measure antioxidant activity, followed by an understanding of the behavior of the current complexes toward free radicals’ removal. The findings observed less aggregated bacterial strains covered with the studied complexes leading to less dense biofilm covering.

Topo-Geometrically Distinct Path Computation Using Neighborhood-Augmented Graph, and Its Application to Path Planning for a Tethered Robot in 3-D

Topo-Geometrically Distinct Path Computation Using Neighborhood-Augmented Graph, and Its Application to Path Planning for a Tethered Robot in 3-D

Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.

A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning.

Autonomous vehicles (AVs) offer significant potential to improve safety, reduce emissions, and increase comfort, drawing substantial attention from both research and industry. A critical challenge in achieving SAE Level 5 autonomy, full automation, is path planning. Ongoing efforts in academia and industry are focused on optimizing AV path planning, reducing computational complexity, and enhancing safety. This paper presents a novel approach using Lyapunov Optimization (LO) for local path planning in AVs. The proposed LO model is benchmarked against two conventional methods: model predictive control and a sampling-based approach. Additionally, an AV prototype was developed and tested in Norman, Oklahoma, where it collected data to evaluate the performance of the three control algorithms used in this study. To minimize costs and increase real-world applicability, a vision-only solution was employed for object detection and the generation of bird's-eye-view coordinate data. Each control algorithm, i.e., Lyapunov Optimization (LO) and the two baseline methods, were independently used to generate safe and smooth paths for the AV based on the collected data. The approaches were then compared in terms of path smoothness, safety, and computation time. Notably, the proposed LO strategy demonstrated at least a 20 times reduction in computation time compared to the baseline methods.

INT-MC: Low-Overhead In-Band Network-Wide Telemetry Based on Matrix Completion

In-band Network Telemetry (INT) enhances real-time, high-resolution network monitoring capabilities by incorporating fine-grained internal state information into packets. Utilizing INT for network-wide visualization can significantly bolster network management and operation. Although existing studies have made significant contributions, they have not been able to simultaneously meet the following two objectives: 1) Comprehensive visibility of the network's internal state, which refers to obtaining information on all switch ports and their corresponding link states; 2) Low measurement overhead, which involves reducing measurement bandwidth overhead and minimizing the impact of the measurement process on the network. We designed INT-MC to meet both of these objectives. INT-MC is an efficient and cost-effective network-wide telemetry solution based on matrix completion. By modeling link metadata as a matrix and leveraging its low-rank property, INT-MC selectively measures certain links. It then employs matrix completion algorithms to infer information about unmeasured links, thereby achieving low-overhead network-wide telemetry. Designing paths to cover the target links involves an NP-complete problem, and the high computational complexity may delay the measurement tasks. To circumvent this, we propose an improved scheme based on Eulerian digraph decomposition, transforming the path calculation problem into a high-information path selection problem, significantly reducing computational costs. We have implemented an INT-MC prototype within the NSFNet topology, consisting of 14 Tofino switches and 10 end-hosts, and conducted extensive experiments and evaluations. The results indicate that INT-MC incurs only 16% of the measurement overhead compared to existing network-wide telemetry solutions, while achieving nearly identical accuracy. Even under high-frequency measurements of 20 times per second, the bandwidth overhead of INT-MC is approximately 0.075% of the total bandwidth, exerting minimal impact on the network.

Design Calculation and Shaping of the Hydro-Steam Turbine Flow Path with Helical Nozzles

Design Calculation and Shaping of the Hydro-Steam Turbine Flow Path with Helical Nozzles

Geographic Information System of Early Childhood School Mapping Using Android-Based Dijkstra Algorithm

The PAUD school mapping geographic information system (GIS) is an innovative Android-based application designed to efficiently assist users in finding the closest route to PAUD schools. By leveraging GPS technology, this system displays a detailed geographic map that guides users effectively through their surroundings. At the core of its functionality is the Dijkstra Algorithm, which ensures the calculation of the shortest path to the desired location, making navigation straightforward and reliable. The design process employs UML (Unified Modeling Language) to create a clear structure and user-friendly interface, enhancing the overall user experience. Developed in Java and supported by the Android Studio platform, this GIS provides essential information about PAUD addresses, their statuses, and available facilities. This comprehensive approach allows users to make informed decisions about educational opportunities for their children. The system has undergone rigorous testing to validate its effectiveness. This practical application demonstrates the system's capability in real-world scenarios and highlights its role in improving access to early childhood education. Furthermore, the PAUD mapping geographic information system is a valuable community resource. By delving into geographic data and providing actionable insights, it aims to bridge gaps in educational access. Ultimately, this GIS is an essential tool for parents and educators, facilitating informed choices and enhancing the journey toward quality education for young learners. Its integration of modern technology with educational accessibility makes it an ultimate resource in fostering early childhood development.

Abstraction and Path Computation for Video Game Path Finding with Changing Maps

Efficient grid-based path finding is important in video games especially for larger maps and when moving many agents. Algorithms based on abstraction have an order of magnitude faster search time performance than A* at the cost of a small amount of memory and increased suboptimality. Some approaches also compute and store paths to improve search performance. This paper evaluates new and existing algorithm variants for abstraction and path computation and investigates their performance for video game path finding with map changes. The results show that abstraction has significant advantages over A* and can be implemented efficiently for changing maps. Computing, storing, and reusing paths also has benefits especially when several searches can be performed before the map changes.

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.

Efficient quantum mechanical minimum free energy path calculation by combining path integral hybrid Monte Carlo and climbing image nudged elastic band methods, and its application to the addition reaction of hydrogen isocyanide to formaldehyde.

We propose an efficient algorithm for a minimum free energy path calculation based on the path integral hybrid Monte Carlo (PIHMC) method by combining the climbing image-nudged elastic band (CI-NEB) and the thermodynamic integration (TI) methods. Here, the CI-NEB and the TI methods are used to find a transition state along the reaction path and evaluate the free energy path, respectively. Our algorithm is applied to the Walden inversion reaction of the hydronium ions (H3O+). The numerical results show that the computational effort by our algorithm is significantly reduced compared to that of the previously proposed algorithm combining PIHMC without losing accuracy. We also demonstrate the importance of temperature and isotope effects on the addition reaction of hydrogen isocyanide to formaldehyde. In this reaction, the nuclear quantum effect causes the structural change at the TS and decreases the energy barrier.

Optimal Path Calculation for Multi-ring Based Packet–Optical Transport Networks

Multi-domain optical transport networks are inherently non-interoperable and require integrated orchestration and path provisioning mechanisms at the network-wide level. Moreover, ensuring the network’s survivability is a critical issue. While the MPLS-TP (multi-protocol label switching-transport profile) defines various protection and recovery mechanisms as standards, it does not address methods for calculating and selecting protection and recovery paths. Therefore, an algorithm is needed to calculate and set up paths to ensure quick protection and recovery across the entire integrated network, minimizing conflicts in protection and recovery at the packet optical integrated network level. In this paper, we proposed an algorithm that calculates and sets a path that enables rapid protection and recovery in an MPLS-TP network composed of a multi-ring-mesh topology. To this end, this study proposes the concept of a transparent node (T-node) for calculating link-disjoint SPF (shortest path first) in a multi-ring network with dual or more rings. A T-node is a node in a ring with more than a dual ring and indicates that the node has been used once in route calculation. Therefore, during path calculation, a T-node can be used as a source node and an intermediate node but not as a destination node.

Resilience enhancement for interdependent power systems by AI-assisted disaster response

The failure of urban Electrical Distribution Systems (EDSs) can lead to direct or indirect consequences for their interdependent critical infrastructures, for example, the hospitals, the fire department, etc., negatively affecting the well-being of citizens in the city. To improve the resilience of the coupled power network, the Operational Interdependence Simulator (OIS) is proposed to help prioritize the queue of failure recovery and Minimum Spanning Tree and Shortest Path Tree topology reconfiguration to minimize critical load downtime. Additionally, AI agents are introduced to assist decision-making processes. By utilizing machine learning and training in thousands of offline scenarios, the system can be used for real-time calculation of the optimal recovery paths when given the number and location of electrical faults. The IEEE 70-node distribution system case validated the high prediction accuracy and post-disaster topology reconfiguration while considering the interdependencies.

Analysis and evaluation of suitability of high-pressure dynamic constitutive model for concrete under blast and impact loading

Concrete demonstrates complex dynamic mechanical behaviors under the influence of blast or impact loads, and there are inherent limitations in experimental and theoretical methods when dealing with highly nonlinear problems. As computational technologies and mechanics continue to evolve, it is possible to conduct high-fidelity simulations of the transient response of concrete structures subjected to intense dynamic loads. Such simulations play a crucial role in revealing the propagation laws of stress waves, the progression of damage, the mechanisms of structural failure, and in conducting protective engineering design. An accurate concrete material model is essential for conducting numerical studies. This article reviews the development of high-pressure dynamic constitutive models for concrete in recent years from both experimental research and theoretical modeling perspectives, focusing on the analysis and evaluation of the modeling methods and main shortcomings of the equation of state(EOS), strength model, and damage model. Using single-element numerical simulations under a single loading path and numerical calculations of engineering cases under complex loading paths, a systematic analysis and comparison were conducted on the predictive capabilities of the HJC, RHT, KCC, and CSC models, as well as the newly developed Kong-Fang and Yan-Chen models. It pointed out the impact of high-pressure mechanical behavior of concrete and the cumulative effect of damage under hydrostatic pressure on the calculation results. Finally, a discussion was conducted on the inherent flaws, applicability, and research difficulties of local constitutive models of concrete in the finite element method. This provides a reference for the selection and research of constitutive models when conducting numerical analysis of the blast and impact resistance of concrete structures.

Probabilistic Path Computation and Frequency Assignment to Mitigate Spectral Fragmentation in Elastic Optical Networks

Probabilistic Path Computation and Frequency Assignment to Mitigate Spectral Fragmentation in Elastic Optical Networks

Autonomous trajectory planning method for aircraft based on ripple diffusion algorithm

Aiming at the problem that route optimization in dynamic continuous airspace (non-discrete road network) is difficult to ensure optimality and has low computational efficiency, a non-circular ripple diffusion algorithm is proposed. Firstly, considering the influence of time-varying airflow and obstacle area in dynamic airspace, the airspace wind speed model, route network and dynamic network weight model are established, and 6 airspace environments and 4 road network structures with the influence of time-varying airflow and obstacle area are constructed. Then, the effects of traditional dynamic path optimization algorithm, co-evolutionary path optimization algorithm and non-circular ripple diffusion algorithm on route optimization are compared in 1000 experiments, taking the optimized path length, flight time and calculation time as indicators. The simulation results show that the traditional dynamic path optimization algorithm has the worst effect under the three indicators, and the calculation time of non-circular ripple diffusion algorithm is significantly less than that of the other two algorithms, which effectively improves the engineering calculation efficiency.

Stretching vibration driven adiabatic transfer kinetics for photoexcited hole transfer from semiconductor to adsorbate

Interfacial hole transfer from a photoexcited semiconductor to surface adsorbates is pivotal for initiating solar-to-chemical energy conversion, yet the atomic-level transfer kinetics remains elusive. Using the methoxy/TiO2(110) system as an archetype, here we elucidate the hole transfer mechanism from hole-trapping lattice oxygen to the methoxy adsorbate at gas/solid and liquid/solid interfaces through molecular dynamics simulations and static minimum energy path calculations. Instead of direct nonadiabatic hopping, we uncover an adiabatic migration pathway adapted to local substrate relaxation, driven by a bond-stretching mechanism supported by stronger Ti-O stretching vibrations. Notably, this mechanism persists at the aqueous methoxy/TiO2(110) interface, albeit hindered by interfacial water and coadsorbates. Surprisingly, the hole transfer barriers across various photoexcited adsorbate/TiO2 interfaces correlate more closely with the vertical excitation energies of the adsorbates rather than their redox potentials, indicating an early-type transition-state nature. These insights deepen our understanding of elementary hole transfer kinetics in surface photochemistry.

Low-temperature oxidation of methane mediated by Al-doped ZnO cluster and nanowire: a first-principles investigation.

First-principles calculations are performed to investigate the catalytic oxidation of methane by using N2O as an oxidizing agent over aluminum (Al)-doped Zn12O12 cluster and (Zn12O12)2 nanowire. The impact of Al impurity on the geometry, electronic structure, and surface reactivity of Zn12O12 and (Zn12O12)2 is thoroughly studied. Our study demonstrates that Al-doped ZnO systems have a better adsorption ability than the corresponding pristine counterparts. It is found that N2O molecule is initially decomposed on the Al site to provide the N2 molecule, and an Al-O intermediate which is an active species for the CH4 oxidation. The conversion of CH4 into CH3OH over AlZn11O12 and (AlZn11O12)2 requires an activation energy of 0.45 and 0.29eV, respectively, indicating it can be easily performed at normal temperatures. Besides, the overoxidation of methanol into formaldehyde cannot take place over the AlZn11O12 and (AlZn11O12)2, due to the high energy barrier needed to dissociate C-H bond of the CH3O intermediate. Dispersion-corrected density functional theory calculations were performed through GGA-PBE exchange-correlation functional combined with a numerical double-ζ plus polarization (DNP) basis set as implemented in DMol3. To include the relativistic effects of core electrons of Zn atoms, DFT-semicore pseudopotentials were adopted. The DFT + D scheme proposed by Grimme was used to involve weak dispersion interactions within the DFT calculations. The reaction energy paths were generated by the minimum energy path calculations using the NEB method.

Shortest path counting in complex networks based on powers of the adjacency matrix.

Complex networks describe a broad range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a crucial role in network science, where the computation of shortest path lengths and numbers has garnered substantial focus. It is well known that powers of the adjacency matrix can calculate the number of walks, specifying their corresponding lengths. However, developing methodologies to quantify both the number and length of shortest paths through the adjacency matrix remains a challenge. Here, we extend powers of the adjacency matrix from walks to shortest paths. We address the all-pairs shortest path count problem and propose a fast algorithm based on powers of the adjacency matrix that counts both the number and the length of all shortest paths. Numerous experiments on synthetic and real-world networks demonstrate that our algorithm is significantly faster than the classical algorithms across various network types and sizes. Moreover, we verified that the time complexity of our proposed algorithm significantly surpasses that of the current state-of-the-art algorithms. The superior property of the algorithm allows for rapid calculation of all shortest paths within large-scale networks, offering significant potential applications in traffic flow optimization and social network analysis.

Network analysis for the steady-state thermodynamic uncertainty relation.

We perform network analysis of a system described by the master equation to estimate the lower bound of the steady-state current noise, starting from the level 2.5 large deviation function and using the graph theory approach. When the transition rates are uniform, and the system is driven to a nonequilibrium steady state by unidirectional transitions, we derive a noise lower bound, which accounts for fluctuations of sojourn times at all states and is expressed using mesh currents. This bound is applied to the uncertainty in the signal-to-noise ratio of the fluctuating computation time of a schematic Brownian computation plus reset process described by a graph containing one cycle. Unlike the mixed and pseudo-entropy bounds that increase logarithmically with the length of the intended computation path, this bound depends on the number of extraneous predecessors and thus captures the logical irreversibility.

A technique to improve the fixed-sphere decoding algorithm for Spectrally Efficient Frequency Division Multiplexing systems

Spectral Efficient Frequency Division Multiplexing (SEFDM) enhances spectral efficiency by narrowing subcarrier bandwidth, however it introduces severe inter-carrier interference (ICI). Currently, the most effective solution is the Fixed Sphere Detection (FSD) algorithm. In previous studies, we found that in order to better reduce the computational complexity of FSD, it is always through adding preprocessing and other pre-algorithms. Although this approach can effectively fix the search radius g in FSD, the complexity of the FSD algorithm itself has not been reduced. Since FSD determines the exploration path by fixing the tree width (Tw), and the selection of exploration paths can directly affect the final search for the optimal candidate values. In this paper we analyze the traditional algorithm from three angles - computational path, ICI generation, and computational complexity, and propose an Optimization Fixed Sphere Detection (OFSD) algorithm. It reduces the number of calculations required when initially selecting exploration paths by narrowing the search range. This allows us to avoid most unnecessary calculations during path selection. In this way, we can greatly reduce the computational complexity. Based on the simulation results, the proposed algorithm in the paper achieves a significant reduction in computational complexity compared to the FSD algorithm (up to 25% at maximum), while the performance drops by at most 0.12 dB when BCF is 0.8 and drops by at most 0.09 dB when BCF is 0.9.