Field Environment Research Articles

Preparation Methods and Quantum Hall Effect of Graphene

Graphene is a two-dimensional ultra-thin functional material. It has the characteristics of light weight, and it exhibits stable chemical properties. In recent years, it has become an excellent candidate material for the modification of various functional devices due to its excellent electrical performance. The performance of graphene is significantly influenced by the preparation method. Therefore, it is necessary to summarize the preparation methods of graphene. In addition, these functional devices typically operate in complex external environments, including magnetic fields, temperature fields, and electric fields. Therefore, studying the quantum Hall effect of graphene is of great significance for the use of graphene based devices. This article is written in order to provide a quicker understanding of this field. The preparation methods of graphene are briefly described. Among these methods, several industrialized methods of graphene production are also highlighted. Then, the progresses of quantum Hall effect in graphene are also presented. This work is of great significance for promoting the application of graphene based devices in complex external field environments.

Evaluation of the Corrosion Resistance of 904L Composite Plate in a High-Temperature and High-Pressure Gas Field Environment

In order to study the corrosion resistance of 904L composite plate pressure vessels under a high-temperature and high-pressure gas field environment, the pitting corrosion and stress corrosion cracking resistance of a 904L composite plate body and weld material were compared with those of a 2205 composite plate and 825 composite plate, which are used in high-temperature and high-pressure gas field environments. The results showed that the pitting resistance of the 904L composite plate was lower than that of the 825 composite plate and higher than that of a 2205 solid-solution pure material plate and a 2205 composite plate. The corrosion resistance of the 625 welding material is higher than that of the E385 welding material. In the simulation of the corrosion environment of a high-temperature and high-pressure gas field, the corrosion rates of the 904L composite plate body, welding seam, and surfacing welding were all less than 0.025 mm/a, indicating slight corrosion, and the sensitivity coefficient of chloride stress corrosion cracking was less than 25%, indicating low sensitivity. The 904L composite plate met the requirements of corrosion resistance for pressure vessel materials in a high-temperature and high-pressure gas field environment.

A vulnerable soil environment study in karst areas: a bibliometric analysis

Karst landforms are widely distributed around the world, and karst rocky desertification has occurred on a large scale in many countries and regions, causing significant adverse impacts on local natural environments and societies. The improvement and rational use of karst soil is a key aspect of rocky desertification governance. Karst soil science studies are of great value in karst regions and are essential for controlling karst rocky desertification and ecological restoration. In order to understand the research hotspots and the development directions in the field of vulnerable karst soil environment, we undertook bibliometrics citation analysis on 1913 contributions to the literature written in the range from 2001 to 2019 based on the “Web of Science” core collection citation index database. Hopefully, this work will help to set up a scientific foundation for further studies. Using CiteSpace visualization software, we analyzed the distribution of disciplinary categories, reference co-citation clusters, and keyword clusters in the literature. The results show the basic characteristics and evolution of the literature related to karst pedology. We then recognized the main intellectual bases in the domain of karst soil science. This study also revealed the research hotspots and trends in this field. Through a bibliometrics citation analysis of research on karst vulnerable soil environment, the present study provides a quantitative and objective understanding of development directions that have emerged in this field over the past 19 years, offering a reference for future research.

Systematically Measuring Ultradiffuse Galaxies. VII. The H i Survey Overview

We present the results from the neutral hydrogen (H i) follow-up survey of 378 optically detected ultradiffuse galaxy (UDG) candidates from the Systematically Measuring Ultra-Diffuse Galaxies survey using the Robert C. Byrd Green Bank Telescope. We detect H i in 110 targets and determine 37 to be UDGs and 73 to be low-surface-brightness (LSB) dwarfs based on their effective radii and central surface brightnesses. In line with previous studies, we find that: (i) our H i detections have on average bluer g − r colors and more irregular morphologies than our H i nondetections, (ii) our H i detections populate the tail end of the star-forming main sequence from the ALFALFA catalog with marginally lower specific star formation rates, and (iii) H i detections are mostly in relatively isolated (i.e., field) environments, while most nondetections have at least one nearby neighbor in projection. We find that the H i mass to stellar mass ratios (i.e., gas richnesses) scale with the physical sizes for UDGs and LSB dwarfs alike, suggesting that mechanisms other than bursty star formation feedback may be at play for UDGs. However, we find a stronger trend between gas richnesses and physical sizes if we define UDGs using their effective surface brightness instead of their central surface brightness. We are in the process of using this unprecedented sample of UDG candidates to carry out detailed follow-up studies (i.e., star formation and environmental analysis, comparisons to simulations) and are obtaining resolved H i observations for several of them.

Synthesis and antimicrobial activity of yttrium-doped cerium oxide nano-composite against wound pathogen

Abstract Nanocomposite materials have attracted considerable interest in several disciplines owing to their distinctive characteristics and possible uses. The primary objective of this study was to synthesize and characterize a nanocomposite material consisting of cerium that has been doped with yttrium. The shape, structure, and properties of the synthesized nanocomposite was characterized using various characterization techniques including ultraviolet spectrophotometry, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), X-ray diffraction (XRD), zeta potential measurements, dynamic light scattering (DLS), and Fourier-transform infrared spectroscopy (FTIR).The antibacterial activity of the nanocomposite was assessed against a range of pathogenic bacteria including Gram-positive bacteria - Enterococcus faecalis, Staphylococcus aureus, and Gram-negative bacteria- Escherichia coli, Pseudomonas aeruginosa using standard microbiological techniques. The results indicate that the nanocomposite possesses strong antibacterial properties against a wide range of microorganisms, showcasing its potential as a highly effective antimicrobial agent for diverse applications in the biological, environmental, and industrial fields. Hemolysis is the process of red blood cell destruction, resulting in the release of hemoglobin into the surrounding fluid. In this study, 0.7:0.3M nanocomposite had showed effective biocompatibility with less than 5%. Furthermore, this particular composite remained identical in different time intervals, while other composites would normally fluctuate. Research has shown that meticulous material design and surface modification can reduce the harmful effects on red blood cells, hence improving the safety of nanocomposite for therapeutic purposes. In summary, this study offers valuable information on the creation, analysis, and assessment of nanocomposite materials with antimicrobial properties.

A Lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU on YOLOv8

The underwater target detection is the most important part of monitoring for environment, ocean, and other fields. However, the detection accuracy is greatly decreased by the poor image quality resulted from the complex underwater environments. The storage and computing power of underwater equipments are not enough for complex underwater target detection technology. Therefore, many YOLO series algorithms have been applied to underwater target detection. On the basis of YOLOv8, a lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU, AGW-YOLOv8, is proposed in this paper. Firstly, by the combination of limited contrast adaptive histogram equalization and wavelet transform(LCAHE-WT), the fidelity and detail of images are improved; Secondly, by CBAM, the key channel features can be effectively extracted with retaining spatial information to improve the performance of the network when dealing with complex image tasks; Thirdly, by GSConv, composed of depth-wise separable convolution and regular convolution, the model parameters and computational complexity are reduced; Fourth, the SE attention mechanism is integrated into the C2f module of the neck, and the channel dimension is weighted to make the network more focus on important features, and to further enhance the feature extraction capability; Finally, by the dynamic nonmonotonic mechanism of WIoU, the gradient gain can be reasonably distributed, the harmful gradients of extreme samples can be reduced, and the generalization ability and overall performance of the model are improved. By the experiments on the URPC2020 data-set, it can been proved that the mAP of AGW-YOLOv8 can reaches 82.9%, is 2.5% higher than that of YOLOv8; and the parameters is 2.95M, is lower than 3.01M of YOLOv8.

Strong Metal-Support Interactions in Heterogeneous Oxygen Electrocatalysis.

Molecular oxygen redox electrocatalysis involves oxygen reduction and evolution as core reactions in various energy conversion and environmental technology fields. Strong metal-support interactions (SMSIs) based nanomaterials are regarded as desirable and state-of-the-art heterogeneous electrocatalysts due to their exceptional physicochemical properties. Over the past decades, considerable advancements in theory and experiment have been achieved in related studies, especially in modulating the electronic structure and geometrical configuration of SMSIs to enable activity, selectivity, and stability. In this focuses on the concept of SMSI, explore their various manifestations and mechanisms of action, and summarizes recent advances in SMSIs for efficient energy conversion in oxygen redox electrocatalysis applications. Additionally, the correlation between the physicochemical properties of different metals and supports is systematically elucidated, and the potential mechanisms of the structure-activity relationships between SMSIs and catalytic performance are outlined through theoretical models. Finally, the obstacles confronting this burgeoning field are comprehensively concluded, targeted recommendations and coping strategies are proposed, and future research perspectives are outlined.

Developing a Taught Module on ‘Law, Religion, and Environment’

Building on the Arab Barometer seventh wave survey, which highlights the regional priorities on climate change, water resources, and environmental development in Middle East and North Africa, this paper explores the potential for a module on “Law, Religion and Environmental Sustainability”. The module aims to mainstream environmental sustainability in the legal education curriculum through forging the intersections between law, religion, and the environment. The growing body of interdisciplinary research on religion and environmentalism mainly involves scholars from academic disciplines such as sociology, theology, and religious studies, but not law. The Arab Barometer results indicate that citizens’ environmental concerns are mostly linked to the micro level, without directly reflecting on related macro level issues. However, religious beliefs and faith systems often cut across the micro and macro levels, and could potentially advance environmental and sustainable development objectives. Moreso, there have been pockets of non-international armed conflicts that are in some ways tied to religious beliefs and cultural values on access to natural resources such as land and water. This paper addresses how to train law students to embrace and contribute to the emerging field of religion and environmentalism. Such training would equip law graduates to (a) have a good understanding of the contemporary social, economic and political context of environmental governance and sustainable development laws; (b) appreciate the interlinkages between law, religion and environment at a theoretical and practical level; and (c) be better equipped to maximise the opportunities and resolve tensions that may arise from mainstreaming legal principles, including human rights and justice, in advancing sustainable development goals, including environmental sustainability. , , CurriculumDevelopment, MENA region, Legal Education,

Universal limit on spatial quantum superpositions with massive objects due to phonons

It has been a long-standing goal to bring massive objects into a superposition of different locations in real space, not only to confirm quantum theory in new regimes, but also to explore the interface with gravity. The main challenge is usually thought to arise from forces or scattering due to environmental fields and particles that decohere the large object's wave function into a statistical mixture. We unveil a decoherence channel which cannot be eliminated by improved isolation from the environment. It originates from sound waves within the object, which are excited as part of any splitting process and carry partial information. This puts stringent constraints on future spatial superpositions of large objects. Published by the American Physical Society 2024

Mechanisms of Cannabis Growth Promotion by Bacillus velezensis S141.

Cannabis sativa L. has a variety of uses, including fiber production, food, oil, and medicine. In response to environmental concerns regarding chemical fertilizers, Bacillus velezensis S141 was examined as a plant-growth-promoting bacterium (PGPB) for cannabis. This study evaluated the effects of S141 on cannabis growth and utilized transcriptomic analysis to identify the responsive pathways. Inoculation with S141 significantly increased growth in laboratory and field environments, with most of the bacteria residing in the leaves, followed by the stems and roots, as determined by quantitative polymerase chain reaction (qPCR). Transcriptomic analysis revealed 976 differentially expressed genes. Upregulated genes were associated with metabolism, cellular processes, and catalytic activities, especially in the biosynthesis of phenylpropanoid, plant-pathogen interactions, and hormone signaling pathways. S141 mutants deficient in the production of auxin and cytokinin displayed reduced growth enhancement, which affirmed the roles of these hormones in cannabis development. These findings emphasize the potential of S141 as a sustainable growth promoter for cannabis and provide insights into the underlying pathways it influences.

Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

Sensitivity improvement of a deuterium-deuterium neutron generator based in vivo neutron activation analysis (IVNAA) system.

Our lab has been developing a deuterium-deuterium (DD) neutron generator-based neutron activation analysis (NAA) system to quantify metals and elements in the human body in vivo. The system has been used to quantify metals such as manganese, aluminum, sodium in bones of a living human. The technology provides a useful way to assess metal exposure and to estimate elemental deposition, storage and biokinetics. It has great potential to be applied in the occupational and environmental health fields to study the association of metal exposure and various health outcomes, as well as in the nutrition field to study the intake of essential elements and human health. However, the relatively low sensitivity of the system has greatly limited its applications. Neutron moderation plays an important role in designing an IVNAA facility, as it affects thermal neutron flux in irradiation cave and radiation exposure to the human subject. This study aims to develop a novel thermal neutron enhancement method to improve the sensitivity of the in vivo neutron activation analysis (IVNAA) system for elemental measurement but still maintain radiation dose. Utilizing a compact DD neutron source, we propose a new and practical moderator design that combines high density polyethylene with heavy water to enhance thermal neutrons by reducing thermal neutron absorption. All material dimensions are calculated by PHITS, a general-purpose Monte Carlo simulation program. The improvement of the new design predicted by the Monte Carlo simulation for the quantification of one of the elements, manganese was verified by experimental irradiation of manganese-doped bone equivalent phantoms. For the same radiation dose, a 67.9% thermal neutron flux enhancement is reached. With only 4.2% increase of radiation dose, the simulated thermal neutron flux and activation can be further increased by 84.2%. A 100% thermal neutron enhancement ratio is also achievable with a 20% dose increase. The experimental results clearly show higher manganese activation gamma ray counts for each specific phantom, with a significantly reduced minimum detection limit. Additionally, the photon dose was suppressed. The thermal neutron enhancement method can increase the number of useful neutrons significantly but maintain the radiation dose. This greatly decreased the detection limit of the system for elemental quantification at an acceptable dose, which will broadly expand the application of the technology in research and clinical use. The method can also be applied to other neutron medical applications, including neutron imaging and radiotherapy.

Magnetic zinc ferrite nanostructures: Recent advancements for environmental and biomedical applications

Fabrication of spinel ferrite nanomaterials has gained immense attention in environmental and biomedical applications owing to their excellent magnetic properties. Among different spinel ferrites, zinc ferrite (ZnFe2O4) nanomaterials are promising transition metal oxide ferrites with excellent magnetic properties and biocompatibility. This review discusses the size and morphology-dependent physicochemical properties of ZnFe2O4 nanostructures and their correlation with the environmental and biomedical fields. Next, we provide a comprehensive overview of various synthesis and characterization techniques for fabricating ZnFe2O4 nanostructures. Examples of ZnFe2O4 nanomaterials and their nanocomposites used for environmental remediation, drug delivery, cancer theranostics, anticancer activity, imaging, biosensing, and gene therapy are further discussed. Finally, this review article offers insights into ZnFe2O4 nanostructures with their impacts, opportunities, and challenges for the future progress of environmental and biomedical applications.

An effective unsupervised domain adaptation for in-field potato disease recognition

Accurate disease recognition through computer vision is crucial for the intelligent management of potato production. Popular data-driven classification methods face challenges including limited labelled data and poor model portability. Unsupervised Domain Adaptation (UDA) addresses these challenges with a novel learning strategy. However, the complex field environment introduces a significant domain shift problem due to varying conditions. Existing UDA methods usually concentrate on aligning global data distribution and employ a single structure for disease feature extraction, thereby limiting their efficacy in true field environment. To tackle this challenge of potato disease recognition, the Multi-Representation Adaptive Network (MRSAN) based on subdomain alignment is presented. MRSAN effectively aligns feature distributions across diverse data by minimising distribution differences among relevant subdomains. Simultaneously, the multi-representation extraction method captures finer details from various perspectives in the disease images. The combination of these two approaches efficiently mitigates the adverse effects caused by various interference factors in field environment. Based on the acquisition conditions of light variation and disease progression, two field potato disease image datasets are created, containing five and six kinds of potato leaf disease, respectively. Extensive transfer experiments are conducted on the two datasets. MRSAN achieves average classification accuracies of 87.03% and 80.06% on the datasets for the corresponding transfer tasks, outperforming the other compared methods. This not only validates the effectiveness of MRSAN but also demonstrates its robust ability to generalise across changes in regard to light variation and disease progression.

Genome-wide mapping of quantitative trait loci conferring resistance to stripe rust in spring wheat line PI 660072.

Two major QTL for resistance to stripe rust were mapped on chromosome 2BL and 4BL in spring wheat PI 660072, and their KASP markers were developed. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat worldwide. Identifying resistance genes is crucial for developing resistant cultivars to control the disease. Spring wheat PI 660072 (Triticum aestivum) has been identified to possess both adult-plant resistance (APR) and all-stage resistance (ASR) to stripe rust. To elucidate the genetic basis of the resistance in PI 660072, a mapping population consisting of 211 F5-F7 recombinant-inbred lines (RILs) was developed from a cross of PI 660072 with susceptible spring wheat Avocet S. The mapping population was phenotyped for stripe rust responses across five field environments from 2020 to 2022 and genotyped using the 15K SNP (single nucleotide polymorphism) array to map stripe rust resistance loci. The mapping population was also tested at the seedling stage with predominant Chinese Pst races CYR31, CYR32, CYR34 and PST-YX1-3-1 in the greenhouse. Stripe rust resistance genes were identified using the quantitative trait locus (QTL) mapping approach. Two QTL were identified with QYrPI660072.swust-2BL mapped on the long arm of chromosome 2B for ASR and QYrPI660072.swust-4BL on the long arm of chromosome 4B for APR. To facilitate marker-assisted selection breeding, Kompetitive allele specific PCR (KASP) markers, KASP-1269 for QYrPI660072.swust-2BL and KASP-3209 for QYrPI660072.swust-4BL, were developed. These markers could be used to introgress the effective resistance QTL into new wheat cultivars.

LT-DeepLab: an improved DeepLabV3+ cross-scale segmentation algorithm for Zanthoxylum bungeanum Maxim leaf-trunk diseases in real-world environments.

Zanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation is often threatened by frequent diseases, leading to significant yield declines. Deep learning-based methods for crop disease recognition have emerged as a vital research area in agriculture. This paper presents a novel model, LT-DeepLab, for the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves and trunks in complex field environments. The proposed model enhances DeepLabV3+ with an innovative Fission Depth Separable with CRCC Atrous Spatial Pyramid Pooling module, which reduces the structural parameters of Atrous Spatial Pyramid Pooling module and improves cross-scale extraction capability. Incorporating Criss-Cross Attention with the Convolutional Block Attention Module provides a complementary boost to channel feature extraction. Additionally, deformable convolution enhances low-dimensional features, and a Fully Convolutional Network auxiliary header is integrated to optimize the network and enhance model accuracy without increasing parameter count. LT-DeepLab improves the mean Intersection over Union (mIoU) by 3.59%, the mean Pixel Accuracy (mPA) by 2.16%, and the Overall Accuracy (OA) by 0.94% compared to the baseline DeepLabV3+. It also reduces computational demands by 11.11% and decreases the parameter count by 16.82%. These results indicate that LT-DeepLab demonstrates excellent disease segmentation capabilities in complex field environments while maintaining high computational efficiency, offering a promising solution for improving crop disease management efficiency.

Sulphate influence on strength development of cemented paste backfill with superplasticizer under field-like curing conditions

This study investigates the impact of sulphate on the strength development of cemented paste backfill (CPB) with polycarboxylate ether-based superplasticizers (PES) under curing conditions mimicking real field environments. These conditions include thermal (T, non-isothermal field temperature), hydraulic (H, drainage), mechanical (M, overburden stress), and chemical (C, PES, sulphate ions) factors. CPB samples with varying sulphate and PES contents were examined through mechanical and microstructural tests under these THMC conditions. The findings reveal that traditional methods for assessing sulphate influence on CPB strength, typically under standard lab conditions, do not adequately capture the mechanical response of CPB structures to sulphate in field scenarios. Real field curing conditions such as temperature, drainage, and stress significantly affect CPB’s mechanical response to sulphate. CPB samples incorporating PES and sulphate show notable improvements in early-age strength, emphasizing the beneficial role of elevated temperatures and the complex relationship between drainage, sulphate, and strength development. Elevated curing temperatures counterbalance sulphate's negative effects, boosting initial strength, while drainage conditions play a critical role in strength development by facilitating the removal of sulphate ions and reducing capillary water. Additionally, stress application at the start of curing aids solid particle rearrangement, promoting the release of free water and enhancing mechanical strength. The competitive adsorption between sulphate ions and PES on the cementitious matrix influences the dispersion of particles and strength development. This research offers crucial technical insights for designing durable CPB mixes suited to various on-site curing conditions, particularly enhancing initial strength across diverse field curing scenarios.

High-throughput identification of fusarium head blight resistance in wheat varieties using field robot-assisted imaging and deep learning techniques

Fusarium head blight (FHB) has attracted much attention in food science and agriculture for its threat to wheat yields and food safety, due to the production of mycotoxins like deoxynivalenol (DON). Breeding of wheat varieties with improved FHB resistance is essential for controlling this disease. However, identifying resistance in different materials during variety selection remains time-consuming and labor-intensive. Therefore, this paper proposed a high-throughput method for the evaluation of FHB disease symptoms. It enabled the semiautomatic acquisition of images of individual wheat ears in a field environment with the aid of a field robot. The images obtained were semantically segmented to get a single wheat ear, from which the infected spikelets (ISs) were extracted, and then the disease degree was calculated. The results showed that the accuracy value of the individual wheat ear using DeepLabV3+ reached 0.996. The accuracy value of ISs was over 0.98. The mean Accuracy of this method for identifying the resistance of varieties was 0.967, and the precision of a single severity grade reached 0.980. The results indicate that the proposed method enables the acquisition and extraction of disease phenotype and rapid identification of resistance to FHB. The study also provides a reference for accurately identifying phenotypes of wheat ears in other field environments and for selecting and breeding other fungal-toxin-resistant varieties.

Academic Traces of Urban Furniture: A Bibliometric Perspective

Urban furniture, which increases the quality and aesthetics of urban life, has recently been the subject of academic studies. However, a bibliometric and visual analysis in this field has not been conducted in sufficient number. This paper presents a quantitative and descriptive study of 1,063 documents from the Web of Science database (1975-2022) using RStudio's 'Bibliometrix' package. Key bibliometric indicators include 887 documents, 619 references, 582 articles, 12.25 citations per document, 2,558 authors, 1,386 keywords, keywords of 2,968 authors, and a collaboration index of 16.8%. The study found higher scientific productivity in China and the USA. Research status, influential sources, countries, institutions, authors, keywords and data relationships were analysed. In particular, it was observed that academic interest increased after 2000 and focused on technology, environment and urban disciplines rather than architecture. The keywords (Author's Keywords and Keywords plus) vary, with a focus on the urban field of study and the health and environment field of study, but both keywords are increasing. The paper recommends increased interdisciplinary co-operation to broaden the scope of urban furniture research. This is one of the first comprehensive bibliometric and content analyses on urban furniture.

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.