Efficient Path Research Articles

Fostering the transition to viable supply chains under risk/disaster events: A hierarchical analysis framework

AbstractThis study examines the state of the art in research on risk/disaster operations management in supply chains (SCs) and closed loop SCs (CLSCs) from a viability perspective. In addition, it integrates key research elements that could act as enablers of more efficient evolution paths in SC/CLSC management. To this end, the existing literature is categorized according to the nature of the disruptive event (risk or disaster), the event formulation, the structure of SC network (SC/CLSC), the source of the disruptive event, the decision‐making level (strategic, tactical or operational), the aim of risk/disaster management (viability–sustainability) and the relevant treatment methods. The target of this literature taxonomy process is threefold: (a) literature analysis with respect to risk and disaster management in SCs; (b) the formulation of a framework for selecting the most suitable treatment policies to manage risk/disaster events at strategic, tactical and operational levels, with the aim of achieving transition to viable SC networks; and (c) the codification of a methodological approach when developing models for achieving viability. The latter is introduced by proposing a dynamic model as an exemplary model to explain the state evolution of an SC network governed by dynamics subjected to viability constraints and, also, to reveal regulatory feedback mechanisms to achieve desired states.

Mechanical and Lattice Thermal Properties of Si-Ge Lateral Heterostructures.

Two-dimensional (2D) materials have drawn extensive attention due to their exceptional characteristics and potential uses in electronics and energy storage. This investigation employs simulations using molecular dynamics to examine the mechanical and thermal transport attributes of the 2D silicene-germanene (Si-Ge) lateral heterostructure. The pre-existing cracks of the Si-Ge lateral heterostructure are addressed with external strain. Then, the effect of vacancy defects and temperature on the mechanical attributes is also investigated. By manipulating temperature and incorporating vacancy defects and pre-fabricated cracks, the mechanical behaviors of the Si-Ge heterostructure can be significantly modulated. In order to investigate the heat transport performance of the Si-Ge lateral heterostructure, a non-equilibrium molecular dynamics approach is employed. The efficient phonon average free path is obtained as 136.09 nm and 194.34 nm, respectively, in the Si-Ge heterostructure with a zigzag and armchair interface. Our results present the design and application of thermal management devices based on the Si-Ge lateral heterostructure.

Efficient Path Planning in Medical Environments: Integrating Genetic Algorithm and Probabilistic Roadmap (GA-PRM) for Autonomous Robotics

Path-planning is a crucial part of robotics, helping robots move through challenging places all by themselves. In this paper, we introduce an innovative approach to robot path-planning, a crucial aspect of robotics. This technique combines the power of Genetic Algorithm (GA) and Probabilistic Roadmap (PRM) to enhance efficiency and reliability. Our method takes into account challenges caused by moving obstacles, making it skilled at navigating complex environments. Through merging GA's exploration abilities with PRM's global planning strengths, our GA-PRM algorithm improves computational efficiency and finds optimal paths. To validate our approach, we conducted rigorous evaluations against well-known algorithms including A*, RRT, Genetic Algorithm, and PRM in simulated environments. The results were remarkable, with our GA-PRM algorithm outperforming existing methods, achieving an average path length of 25.6235 units and an average computational time of 0.6881 seconds, demonstrating its speed and effectiveness. Additionally, the paths generated were notably smoother, with an average value of 0.3133. These findings highlight the potential of the GA-PRM algorithm in real-world applications, especially in crucial sectors like healthcare, where efficient path-planning is essential. This research contributes significantly to the field of path-planning and offers valuable insights for the future design of autonomous robotic systems.

Efficient Path Planning Algorithm Based on Laser SLAM and an Optimized Visibility Graph for Robots

Mobile robots’ efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms.

Enhancing energy efficiency of the utility system through R-curve analysis by integrating absorption cooling systems

This paper presents a novel approach for upgrading utility systems by integrating absorption cooling systems using R-curve analysis. The key contributions include a comprehensive methodology for drawing the R-curve, determining priority paths, and optimizing the system without additional capital costs; a step-by-step case study illustrating the process of integrating two absorption chillers and analyzing their impact on cogeneration efficiency and the R-curve; equations for calculating cogeneration efficiency and power efficiency for different turbine paths to prioritize the most efficient paths; and findings revealing that integrating absorption chillers can significantly impact system performance, potentially increasing or decreasing cogeneration efficiency depending on the specific system. The proposed approach enables engineers to optimize utility systems by leveraging existing equipment and integrating absorption cooling efficiently, with the R-curve analysis providing a powerful tool for visualizing and optimizing the system to achieve the best balance of power, cooling, and heating. The findings demonstrate that careful analysis and optimization of the utility system using R-curve techniques can unlock significant energy savings and emissions reductions by prioritizing the most efficient turbine paths and integrating absorption cooling optimally.

(Battery Student Slam 8 Award Winner) A Simple and Fast Method to Assemble Flexible and Stable Quasi-Solid-State Zn Ion Pouch Cell

Aqueous zinc-ion batteries (AZIBs) have been investigated intensively as effective energy storage devices that are environmentally friendly, safe, and have a lower cost than current Li-ion batteries.[1-5] Being outstandingly safe due to usage of non-organic electrolyte, AZIBs could also be used as power supply for wearable electronics. This also put high standard requirements in terms of flexibility, stability and electrochemical performance to AZIBs. On the other hand, the general challenges, such as the formation of the dendrites on the Zn anodes, hydrogen evolution and side reactions, have been obstructing the practical application of AZIBs. In this presentation, a simple method to assemble quasi-solid-state Zn ion pouch cell with excellent mechanical performance, flexibility and electrochemical performance was developed, combining in-situ electrodeposition of MnO2 cathode and aramid nano fiber-based hydrogel with Zn plate as anode. In-situ and cycling measurements of the electrodeposition of MnO2 on carbon nanotube mat shows excellent capacitance and superior cycling stability. In addition, Kevlar derivatized aramid nanofiber-based hydrogel shows extremely strong strength when combined with polyvinyl alcohol, which prolonged the lifetime of the cell by preventing Zn dendrite growth largely.[5,6] Moreover, the method to assemble the AZIBs pouch cell enables us to optimize the process, decrease the steps, and save time effectively by taking advantage of hydrogel formation stage and avoiding conventional verbose slurry-casting-drying steps. This work paves an efficient path to assemble flexible and stable AZIBs pouch cells with high capacity and cycling stability.[1] Tang, Boya, et al. "Issues and opportunities facing aqueous zinc-ion batteries." Energy & Environmental Science 12.11 (2019): 3288-3304.[2] Li, Chang, et al. "Toward practical aqueous zinc-ion batteries for electrochemical energy storage." Joule 6.8 (2022): 1733-1738.[3] Yu, Feng, et al. "An aqueous rechargeable zinc-ion battery on basis of an organic pigment." Rare Metals 41.7 (2022): 2230-2236.[4] Lin, Yuexing, et al. "Dendrite-free Zn anode enabled by anionic surfactant-induced horizontal growth for highly-stable aqueous Zn-ion pouch cells." Energy & Environmental Science 16.2 (2023): 687-697.[5] Wang, Peng, and Petru Andrei. "High Performance Separator and Hydrogel Based on Aramid Fibers for Zn Ion Batteries." Electrochemical Society Meeting s 242. No. 4. The Electrochemical Society, Inc., 2022.[6] Xu, Lizhi, et al. "Water‐rich biomimetic composites with abiotic self‐organizing nanofiber network." Advanced Materials 30.1 (2018): 1703343.

Harmonic Oscillator Staging Coordinates for Efficient Path Integral Simulations of Quantum Oscillators and Crystals.

Imaginary-time path integral (PI) is a rigorous quantum mechanical tool to compute static properties at finite temperatures. However, the stiff nature of the internal PI modes poses a sampling challenge. This is commonly tackled using staging coordinates, in which the free particle (FP) contribution of the PI action is diagonalized. We introduce novel and simple staging coordinates that diagonalize the entire action of the harmonic oscillator (HO) model, rendering it efficiently applicable to (exclusively) systems with harmonic character, such as quantum oscillators and crystals. The method is not applicable to fluids or systems with imaginary modes. Unlike FP staging, the HO staging provides a unique treatment of the centroid mode. We provide implementation schemes for PIMC and PIMD simulations in NVT ensemble. Sampling efficiency is assessed in terms of the precision and accuracy of estimating the energy and heat capacity of a one-dimensional HO and an asymmetric anharmonic oscillator (AO). In PIMC, the HO coordinates propose collective moves that perfectly sample the HO contribution, then (for AO) the residual anharmonic term is sampled using standard Metropolis method. This results in a high acceptance rate and, hence, high precision, in comparison to the FP staging. In PIMD, the HO coordinates naturally prescribe definitions for the fictitious masses, yielding equal frequencies of all modes when applied to the HO model. This allows for a substantially larger time step sizes relative to standard staging, without affecting accuracy or integrator stability. For completeness, we also present results using normal mode (NM) coordinates, based on both HO and FP models. While staging and NM coordinates show similar performance (for FP or HO), staging is computationally preferable due to its cheaper scaling with the number of beads. The simplicity and the enhanced sampling gained by the HO coordinates open avenues for efficient estimation of nuclear quantum effects in more complex systems with harmonic character, such as real molecular bonds and quantum crystals.

Large-area superconducting nanowire arrays fabricated by nano laser direct writing

We report on the fabrication, structural, and electrical transport characterizations of superconducting nanowire arrays based on nano-laser direct writing (NLDW). The thermal excitation-induced micro/nano fabrication based on the interactions between NLDW and superconducting Nb films was experimentally analyzed and simulated. Experimentally, the arrays of superconducting nanowires have an area of 190 × 190 μm2 with a critical transition temperature Tc of 7.8 K and a critical current density Jc of 20.8 MA cm−2 at 4.0 K. The width and arrangement of the nanowires are precisely controlled, exhibiting a minimal loss of 0.6 K in Tc and excellent Jc after LDW. The width of superconducting nanowires could be further optimized by adjusting parameters such as laser power, irradiation gap, and delay of points. Compared with electron beam lithography and focused ion beam, to some extent, the nanowires fabrication process based on NLDW provides a more efficient and cost-effective path for fabricating large-area superconducting circuits and devices.

Two decades of research on “masstige” marketing: A systematic literature review and future research agenda

AbstractIn the 21st century, luxury marketing increasingly utilizes “masstige marketing” as a strategic approach. This approach combines luxury and mass brands and targets middle‐class consumers with an aim to make high‐end brands more accessible and affordable. However, despite a surge in research interest over the past two decades, the literature on masstige marketing remains scattered. Therefore, the present moment offers an appropriate time to consolidate the existing literature and lay the groundwork for future researchers. To achieve this aim, this paper endeavors to narrow the literature divide between luxury and affordability to establish a strong conceptual boundary using SPAR‐4 protocol and the TCCM approach. It thoroughly examines 75 papers published between 2003 and 2023 to identify critical research gaps. Furthermore, following manual coding and inductive analysis, the review outlines the theoretical perspective like masstige and bandwagon (Theory); identifies the contextual elements like B2B space and culture (Context); reveals the antecedents, gaps, and outcome variables, that is, CBBE and purchase intention (Characteristics); and suggest diverse approaches, that is, mixed and experimental methods (Methodology). The article also confirms the incorporation of themes, that is, value‐based and masstige‐evolution dimensions, by using Corbin and Strauss open, axial, and selective coding. Lastly, by employing in‐depth knowledge gap analysis, the review proposes future research directions as an efficient path for obtaining a holistic understanding of masstige marketing dynamics.

Correction: Assuring Efficient Path Selection in an Intent-Based Networking System: A Graph Neural Networks and Deep Reinforcement Learning Approach

Correction: Assuring Efficient Path Selection in an Intent-Based Networking System: A Graph Neural Networks and Deep Reinforcement Learning Approach

Experiment and path optimization on Large-Space Multi-Area crowd evacuation

Abstract In large spaces where there are multiple evacuation zones, the flow patterns during evacuation are intricate, leading to congestion in emergency scenarios, and affecting evacuation efficiency. To address this, we proposed an optimization method for crowd evacuation paths in large-space multi-area environment settings under fire scenarios by utilizing multi-agent simulation. Using Pathfinder software, disorganized evacuation simulations are conducted within expansive multi-zonal settings to pinpoint areas with inadequate evacuation capabilities. Subsequent evacuation experiments are performed for verification, facilitating adjustments to agent-based model parameters for evacuees in fire scenarios, and carrying out organized evacuation simulations. Through meticulous simulation analysis, we aim to optimize the most efficient evacuation paths under organized responses in large-space multi-area settings. Results indicate that in emergency conditions due to fire, the evacuation speeds reach 1.8 m/s, and significant reductions in total evacuation time are achieved through simulation optimization, with the time required for the slowest individuals to evacuate decreasing from 240.32 s to 209.32 s. Furthermore, the flow rate per second at congested exits diminishes from 2.01 persons/s to 1.39 persons/s, highlighting the efficacy of the proposed path optimization method.

Hybrid fuzzy logic and gravitational search algorithm based routing for wireless sensor networks

Over the recent years, the wireless sensor network (WSN) has garnered significant attention from both researchers and the general populace. Its application in diverse environmental scenarios, including weather monitoring, temperature regulation, humidity tracking, and military surveillance, extends beyond conventional boundaries. WSNs consist of numerous nodes, each functioning as a sensor with the primary responsibility of data sensing. These nodes operate under constraints such as power, energy, efficiency, and deployment considerations. Moreover, the power and other resources cannot be replaced and renewed therefore prolonging the network lifetime has become the main aspect of WSNs. Energy aware routing play’s important role to ensure the efficient data transmission with minimal power consumption and ensures the prolonged network lifetime. In this work, we focus on optimizing the routing process therefore we present a hybrid model which uses fuzzy logic for path identification and gravitational search optimization (GSA) for efficient path selection. The fuzzy logic considers energy consumption, residual energy, distance and delay parameters to identify the most suitable path for data transmission. The experimental analysis shows a significant improvement in network lifetime, delay an aspect delivery.

Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs

Node ranking in heterogeneous graphs, which quantifies the relative importance of nodes, can often be improved by incorporating information from relevant paths. Graph database and heterogeneous graph neural network (HGNN) are two main approaches to better solve this problem. Graph databases support efficient path queries for flexible path types but require manual design to combine results for node ranking. Conversely, current HGNNs can automatically integrate semantic information from multiple linear path types for accurate node ranking. However, our experiments show that they fail to outperform a multi-layer perceptron model that utilizes features extracted from multiple nonlinear conditional paths, which can be handled by graph databases. Therefore, we aim to enable HGNN to take advantage of these path types for better performance. However, HGNNs require a generalized path schema to define the structure of input paths, and incorporating each additional path type will significantly increase the required system memory and sampling time for HGNNs. To address these limitations, we introduce CompNode, a novel framework based on a new unified path schema definition called Complex-path, which is used to describe all the required path types, including nonlinear conditional path types. Then, we design a pre-aggregation method to reduce the required system memory and sampling time by pre-aggregating the same type of complex-path. Furthermore, we develop a model that combines semantic information from all aggregated complex-paths for accurate node ranking. Real-world experiments on identifying top potential high-value customers show CompNode outperforms state-of-the-art HGNNs by 20% in average precision and the previously deployed graph database method by 252% in success rate.

Automated line scan profilometer based on the surface recognition method

Existing high-precision measurement devices face numerous challenges when dealing with complex surfaces, including high manual involvement, low measurement efficiency, and high costs. It is even more difficult to obtain complete surface data, particularly when confronted with unknown surfaces. This paper combines a fringe projection sensor with a high-precision line-structured light sensor to construct a line scan profiler. It proposes a surface recognition measurement method to overcome these challenges. This method fully leverages the fringe projection sensor's wide-field advantage to identify the object's surface shape and pose quickly. Subsequently, through the point cloud extraction and path planning algorithms proposed in this paper, efficient and complete line scan measurement paths are automatically generated, thus achieving the automation of the measurement process. Experimental measurements of aspherical lenses, freeform reflective mirrors, and artificial knee joint models validate the advantages of this method over existing methods in terms of measurement efficiency, automation capability, and applicability.

An Empirical Study for the Dynamic and Personalised Learning Experience of the AI Course Generator

In a world that is quickly evolving, the demand for continuous learning and upskilling is critical for personal and professional growth. However, many learners struggle to create personalised, efficient learning paths tailored to their unique needs due to the limitations of traditional course creation methods, which require significant human input and expertise. This project aims to address this problem by developing "modulo," an innovative platform designed to automate the creation of personalised and structured learning paths. The objectives of “modulo” are to leverage artificial intelligence and external APIs to generate customised study plans for any chosen subject, integrate curated YouTube tutorials and supplemental materials, and enhance the learning experience with adaptive quizzes tailored to user progress. The methodology follows an Iterative-Waterfall approach, combining structured phases with iterative cycles to incorporate feedback and adapt to emerging challenges. The system architecture is built on a microservices framework, with a frontend developed using React and Next.js, and a backend supported by Supabase with Prisma for database management, NextAuth for user authentication, and Stripe for payment processing. The result is a scalable and maintainable platform that empowers diverse user groups by enhancing education accessibility. “modulo” provides a dynamic and personalised learning experience, making a meaningful impact on self-directed learning

Safe and Efficient Exploration Path Planning for Unmanned Aerial Vehicle in Forest Environments

This study presents an enhanced exploration path planning for unmanned aerial vehicles. The primary goal is to increase the chances of survival of missing people in forest environments. Exploration path planning is an essential methodology for exploring unknown three-dimensional spaces. However, previous studies have mainly focused on underground environments, not forest environments. The existing path planning methods for underground environments are not directly applicable to forest environments. The reason is that multiple open spaces exist with various obstacles, such as trees, foliage, undergrowth, and rocks. This study mainly focused on improving the safety and efficiency to be suitable for forests rather than underground environments. Paths closer to obstacles are penalized to enhance safety, encouraging exploration at a safer distance from obstacles. A potential field function is applied based on explored space to minimize overlapping between existing and new paths to increase efficiency. The proposed exploration path planning method was validated through an extensive simulation analysis and comparison with state-of-the-art sampling-based path planning. Finally, a flight experiment was conducted to verify further the feasibility of the proposed method using onboard real hardware implementation in a cluttered and complex forest environment.

Research on Path Planning Method for Autonomous Patrol Robots

For autonomous patrol robots, how to complete multi-point path planning efficiently is a crucial challenge. To address this issue, this work proposes a practical and efficient path planning method for patrol robots. Firstly, the evaluation function of the traditional A* method is improved to ensure that the planned path maintains a safe distance from the obstacles. Secondly, a Dubins curve is used to optimize the planned path to minimize the number of turning points while adhering to kinematic constraints. Thirdly, a trajectory-preserving strategy is proposed to preserve the continuous trajectory, linking multi-points for future inspection tasks. Finally, the proposed method is validated through both simulation and real-world experiments. Experimental results demonstrate that our proposed method performs exceptionally well in terms of safety, actual trajectory distance, and total execution efficiency.

Nanoengineering construction of g-C3N4/Bi2WO6 S-scheme heterojunctions for cooperative enhanced photocatalytic CO2 reduction and pollutant degradation

S-scheme heterojunction photocatalysts featuring efficient charge transport paths provide a promising strategy for cooperative achieving photocatalytic CO2 reduction reaction (CO2RR) and Rhodamine B (RhB) degradation. In this work, S-scheme heterojunctions composed of g-C3N4 nanosheets coated on Bi2WO6 nanosheets were prepared by a one-step hydrothermal method, resulting in broad-spectrum light absorption, high electron–hole separation efficiency, and excellent photocatalytic CO2 reduction and RhB oxidization performance. Mechanistic analysis revealed the formation of a directional charge transport path at the interface of the heterojunction, which maintained a high oxidation and reduction potential and improved the efficiency of photoexcited carrier separation. In particular, the synergistic reaction system showed excellent photoredox activity compared with the two separate half-reactions. The optimal catalyst 15CN/BWO displayed the highest CO2 conversion efficiency, with CO and CH4 generation rates of 4.3 and 2.7 μmol g−1h−1, respectively, and the degradation efficiency of the composite heterojunction was significantly higher than that of its constituent materials. This work provides a new possibility for designing a novel dual-function photocatalytic reaction system.

A Free-Space Diffraction BSDF

Free-space diffractions are an optical phenomenon where light appears to "bend" around the geometric edges and corners of scene objects. In this paper we present an efficient method to simulate such effects. We derive an edge-based formulation of Fraunhofer diffraction, which is well suited to the common (triangular) geometric meshes used in computer graphics. Our method dynamically constructs a free-space diffraction BSDF by considering the geometry around the intersection point of a ray of light with an object, and we present an importance sampling strategy for these BSDFs. Our method is unique in requiring only ray tracing to produce free-space diffractions, works with general meshes, requires no geometry preprocessing, and is designed to work with path tracers with a linear rendering equation. We show that we are able to reproduce accurate diffraction lobes, and, in contrast to any existing method, are able to handle complex, real-world geometry. This work serves to connect free-space diffractions to the efficient path tracing tools from computer graphics.

Area ReSTIR: Resampling for Real-Time Defocus and Antialiasing

Recent advancements in spatiotemporal reservoir resampling (ReSTIR) leverage sample reuse from neighbors to efficiently evaluate the path integral. Like rasterization, ReSTIR methods implicitly assume a pinhole camera and evaluate the light arriving at a pixel through a single predetermined subpixel location at a time (e.g., the pixel center). This prevents efficient path reuse in and near pixels with high-frequency details. We introduce Area ReSTIR , extending ReSTIR reservoirs to also integrate each pixel's 4D ray space, including 2D areas on the film and lens. We design novel subpixel-tracking temporal reuse and shift mappings that maximize resampling quality in such regions. This robustifies ReSTIR against high-frequency content, letting us importance sample subpixel and lens coordinates and efficiently render antialiasing and depth of field.