Complex Operational Environments Research Articles

Repository (R)evolution: Metadata, Interoperability, and Sustainability

Introduction: Successfully managing an open-access repository requires constant attention to user community priorities in order to inform the development or selection of a platform that fulfills constantly evolving functional demands in an increasingly complex operational environment. This paper uses AgEcon Search (AES) as an example of the way that varying platforms address the metadata and other platform needs of a repository. AES is a successful subject repository with an international scope that has resided on several different platforms in its 25-year lifespan. Elements and Considerations: Critical among the technical requirements of a repository is interoperability with other information sources and the ability to accommodate and describe different types of objects, including data. Experienced in the use of easy and widely used Dublin Core (DC), as well as Machine-Readable Cataloging 21 (MARC 21)-based repository platforms, we discuss both metadata schemas from administrative and user perspectives. Reconsidering underlying metadata issues might positively impact both technical and administrative issues that are currently restricting the development of robust, interoperable systems. As managers of AES, we are uniquely placed to discuss both technical and sustainability issues. Conclusions: Although many institutional and subject repositories are on platforms that use DC for their metadata, other options are available. MARC, the well-established library standard, can provide the wide range of fields needed to fully and accurately describe the variety of document and data types that are included in repositories.

Oil pipeline multiple leakage detection and localization based on sensor fusion

Oil pipeline simultaneous leak localization solely using pressure and flow rate measurements at its boundaries is inherently challenging. Localizing simultaneous leaks in a long-distance oil pipeline solely using pressure and flow rate measurements at its boundaries presents significant challenges. This difficulty is attributed to the presence of noisy measurements and the interdependence of two equations that incorporate variables associated with leaks, leading to inherent uncertainty. Additionally, existing leak detection methodologies predominantly utilize a single sensor, which contributes to a high incidence of false alarms, particularly in complex operational environments. Developing nonlinear equations based on momentum and continuity principles is proposed as a comprehensive solution for estimating multiple leak locations in oil pipelines. In this study, multiple leaks in long-distance oil pipelines are estimated using a two-stage decision-making scheme. Arrays of sensors are proposed for improving the reliability and accuracy of simultaneous leak estimations, replacing the traditional reliance on single sensors along pipelines. The Fisher fusion technique combines inaccurate measurements to improve estimation accuracy. This method integrates sensor measurements even when noise is present, demonstrating its effectiveness. Comprehensive simulationsare conductedusing OLGA multiphase flow simulation software to evaluate the robustness and practicality of the proposed approach under various real-time conditions found in long-distance oil pipelines. The system’s state convergence and precision in simultaneous leak estimations highlight the effectiveness of the proposed strategy in oil pipeline control. Across various scenarios, the leak detector showed a 28 % improvement for a 50 Km oil pipeline.

Infrared target detection algorithm based on multipath coordinate attention mechanism

Abstract The current generation of infrared target detection algorithms frequently exhibits a high degree of dependency on parameter configurations within complex operational environments. This often results in a reduction in detection accuracy, an increase in the number of model parameters, and a slowing of the detection process. To address these limitations, a new algorithm, CGhostNet-Attention-YOLO (CAY), is proposed in this paper. Firstly, we designed a lightweight backbone network, CGhostNet, with the objective of improving feature extraction efficiency, thereby enabling accurate and real-time feature extraction. Furthermore, we proposed a multipath coordinate attention mechanism, which incorporates both channel and positional information, thereby facilitating enhanced context awareness and the comprehension of relationships between different positions. This effectively enhances the model’s ability to comprehend the overall meaning and addresses the issue of missed detections in infrared targets, significantly improving detection accuracy. Moreover, we employed the Inner-SIoU loss function to accelerate model convergence, reduce loss, and enhance the robustness of the model. Finally, comparative experiments were conducted on our dataset (IFD) as well as publicly available datasets, including FLIR, Pascal VOC, and NEU-DET. The results demonstrate that the CAY algorithm achieved a mean Average Precision (mAP@0.5) of 81.3% on the IFD dataset, 86.1% on the FLIR dataset, 79.2% on the Pascal VOC dataset, and 79.9% on the NEU-DET dataset, with a 27% reduction in the number of parameters. These findings validate the feasibility of the proposed algorithm.

Enhanced Impedance Control of Cable-Driven Unmanned Aerial Manipulators Using Fractional-Order Nonsingular Terminal Sliding Mode Control with Disturbance Observer Integration

The article presents a novel control strategy for cable-driven aerial manipulators (UAMs) aimed at enhancing impedance control during contact operations in complex environments. A fractional-order nonsingular terminal sliding mode control (FONTSMC) integrated with a disturbance observer (DOB) is proposed to improve the robustness and precision of the UAM under lumped disturbances. This developed approach utilizes the flexibility of fractional calculus, the finite-time stability of nonsingular terminal sliding mode, and the real-time disturbance estimation capabilities of the DOB to ensure smooth and compliant contact interactions. The effectiveness of the proposed control strategy is validated through comprehensive simulation studies, which demonstrate significant improvements in control performance, stability, and disturbance rejection when compared to traditional methods. The results indicate that the FONTSMC-DOB framework is highly suitable for complex aerial manipulation tasks, offering both theoretical and practical insights into the design of advanced control systems for UAMs.

THE MUTLISTAKEHOLDER INITIATIVE APPROACH TO EUROPEAN CRISIS MANAGEMENT. CIVILIAN CSDP AND ENGAGEMENT WITH LOCAL NONSTATE ACTORS

: The Common Security and Defence Policy (CSDP) missions are deployed in complex operational environments, where a plethora of national, regional and international actors are present. These present circumstances enable the existence of an - either direct or indirect - but inexorable engagement between all stakeholders involved in similar activities. While the vast majority of current debates and research focus solely on the cooperative dimension of the CSDP in the framework of multilateralism and regionalism, the topic of the EU's engagement with local stakeholders is scarcely explored. Therefore, drawing on the multistakeholder initiative approach (MSI) in economic theory, this paper assesses the engagement of CSDP missions with civil societies and private sector entities in theatres of operations. Employing a single case study approach of inquiry, consisting of the EUCAP Somalia mission this paper aims at answering the research questions: how does EUCAP Somalia engage with local non-state actors, what are the opportunities associated with this engagement and to what extent can a MSI approach be implemented by EUCAP Somalia?By deploying a qualitative research design, this paper uses document analysis within a single case study approach of inquiry. The results of this approach show two distinct tendencies. While EUCAP Somalia informally engages with local non-state actors, this engagement is rather insufficient. Results of this research further show that CSDP missions represent an important tool of EU diplomacy and their engagement with local non-state actors positively impacts the implementation of two of their core principles: local ownership and human security.

Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning

The wide application of robotic technology in various industries from industrial automation to medical assistance is gradually changing our production and lifestyle, and has attracted widespread attention in many fields. However, existing robotic control systems often grapple with limited flexibility and poor adaptability to complex environments, particularly in highly dynamic operational contexts. Against this backdrop, the integration of deep learning technologies offers new possibilities in enhancing robotic perception and decision-making, especially in visual perception and motion planning. To address these challenges, we have introduced a novel robotic arm control network model, MPC-WGAN-Faster R-CNN, which combines Model Predictive Control concepts with Wasserstein Generative Adversarial Networks and Faster R-CNN visual recognition technology. This integration aims to improve the precision and adaptability of robotic arm operations in complex environments.

Physics of Ferroelectric Wurtzite Al1−xScxN Thin Films

AbstractAl1−xScxN emerges as a revolutionary ferroelectric material within the III‐N family. It combines exceptional switchable polarization (80–165 µC cm−2), highly tunable coercive fields (1.5–6.5 MV cm−¹), and a wide bandgap (4.9–5.6 eV). Unlike conventional ferroelectrics, Al1−xScxN exhibits remarkable compatibility with both CMOS and III‐N technologies. It can be fabricated on plastic substrates at low temperatures, demonstrating excellent flexibility and biocompatibility. Remarkably, Al1−xScxN maintains superior performance in harsh environments due to its outstanding thermal stability (up to 1100 °C). These unique characteristics position Al1−xScxN as a highly promising candidate for a wide range of applications, including high‐performance memory, in‐memory computing, neuromorphic computing, and next‐generation wearable and implantable devices, particularly for operation in complex environments. Despite its potential, Al1−xScxN faces challenges such as high coercive fields, significant leakage currents, and limited polarization reversal cycle life. Addressing these challenges require a deeper understanding of the fundamental physics controlling Al1−xScxN films. This review explores the origins of Al1−xScxN's ferroelectricity and phase stability, delves into the fundamental theory of wurtzite ferroelectricity, investigates mechanisms for controlling spontaneous polarization and coercive fields, examines recent research progress in Al1−xScxN ferroelectric devices, and outlines future development directions for this exciting material.

Wireless Online Rotation Monitoring System for UAV Motors Based on a Soft-Contact Triboelectric Nanogenerator.

Real-time monitoring of UAV motor speed is crucial for enhancing control performance and ensuring flight safety. However, this task faces challenges such as difficult sensor installation and high costs. This study introduces a wireless rotational speed sensing system based on a UAV-rotary triboelectric nanogenerator (UR-TENG). By employing a carefully designed structure with soft contact and a freestanding-triboelectric-layer mode, UR-TENG exhibits characteristics like low friction, affordability, ease of production, and self-powering capability. This eliminates the need for an external power source and addresses the complexity of installation in the limited space of UAVs. Experimental findings demonstrate that UR-TENG possesses high sensitivity and stability, maintaining the structural integrity of the UAV. The goodness of fit is notably high at 0.99959, with a maximum error rate of only 0.014 within a range of 6270 rpm. Moreover, UR-TENG integrates with a microcontroller unit (MCU) and external circuitry to form a monitoring system. This system transmits electrical signals to a PC via a Wi-Fi module, facilitating real-time rotational speed sensing and anomaly detection. Finally, a practical application demonstration on a UAV validates the adaptability of UR-TENG to complex operational environments. This study presents a promising approach for online rotation monitoring of UAV motors, with potential for commercialization, and introduces new avenues for TENG application in UAV technology.

Study on Chassis Leveling Control of a Three-Wheeled Agricultural Robot

Three-wheeled agricultural robots possess the advantages of high flexibility, strong maneuverability, and low cost. They can adapt to various complex terrains and operational environments, making them highly valuable in the fields of crop planting, harvesting, irrigation, and more. However, the horizontal stability of the three-wheeled agricultural robot chassis is compromised when working in harsh terrain, significantly impacting the overall operational quality and safety. To address this issue, this study designed a leveling system based on active suspension and proposed a stepwise leveling method based on an adaptive dual-loop composite control strategy (ADLCCS-SLM). Firstly, in the overall control of the three-wheeled chassis, a stepwise leveling method (SLM) was introduced. This method allows for rapid leveling by incrementally adjusting one or two suspensions, effectively avoiding the complex interactions between suspension components encountered in traditional methods involving the simultaneous linkage of three suspensions. Next, in terms of suspension actuator control, an adaptive dual-loop composite control strategy (ADLCCS) was proposed. This strategy employs a dual-loop composite control both internally and externally and utilizes an improved adaptive genetic algorithm to adjust critical control parameters. This adaptation optimizes the chassis leveling performance across various road conditions. Finally, the effectiveness of the proposed ADLCCS-SLM was validated through simulation and experimental testing. The test results showed that the control effect of the proposed method was significant. Compared to the traditional multi-suspension linkage leveling method based on PID, the peak values of pitch angle and roll angle were reduced by 31.8% and 33.3%, respectively.

IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality

Due to its asymptotic optimality, the Rapidly-exploring Random Tree star (RRT*) algorithm is widely used for robotic operations in complex environments. However, the RRT* algorithm suffers from poor path quality, slow convergence, and unstable generation of high-quality paths in the path planning process. This paper proposes an Improved Bi-Tree Obstacle Edge Search Artificial Potential Field RRT* algorithm (IBPF-RRT*) to address these issues. First, based on the RRT* algorithm, this paper proposes a new obstacle edge search artificial potential field strategy (ESAPF), which speeds up the path search and improves the path quality simultaneously. Second, a bi-directional pruning strategy is designed to optimize the bi-directional search tree branch nodes and combine the bi-directional search strategy to reduce the number of iterations for convergence speed significantly. Third, a novel path optimization strategy is proposed, which enables high-quality paths to be generated stably by creating an entirely new node between two path nodes and then optimizing the paths using a pruning strategy based on triangular inequalities. Experimental results in three different scenarios show that the proposed IBPF-RRT* algorithm outperforms other methods in terms of optimal path quality, algorithm stability, and the number of iterations when compared to the RRT*, Q-RRT*, PQ-RRT*, F-RRT* and CCPF-RRT* algorithms, and proves the effectiveness of the proposed three strategies.

Watch the Skies: A Study on Drone Attack Vectors, Forensic Approaches, and Persisting Security Challenges

In the rapidly evolving landscape of drone technology, securing unmanned aerial vehicles (UAVs) presents critical challenges and demands unique solutions. This paper offers a thorough examination of the security requirements, threat models, and solutions pertinent to UAVs, emphasizing the importance of cybersecurity and drone forensics. This research addresses the unique requirements of UAV security, outlines various threat models, and explores diverse solutions to ensure data integrity. Drone forensics, a field dedicated to the investigation of security incidents involving UAVs, has been extensively examined and demonstrates its relevance in identifying attack origins or establishing accident causes. This paper further surveys artifacts, tools, and benchmark datasets that are critical in the domain of drone forensics, providing a comprehensive view of current capabilities. Acknowledging the ongoing challenges in UAV security, particularly given the pace of technological advancement and complex operational environments, this study underscores the need for increased collaboration, updated security protocols, and comprehensive regulatory frameworks. Ultimately, this research contributes to a deeper understanding of UAV cybersecurity and aids in fostering future research into the secure and reliable operation of drones.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Towards an understanding of the digital transformation of facility management in healthcare: perspectives from practice

PurposeFacility management (FM) in healthcare is an organisational function that provides non-medical activities, ensuring operational functionality of critical health infrastructure. FM leaders are under pressure to implement the digital transformation, confronted with a highly complex and challenging operational environment. However, the current scope and understanding of the digital transformation in FM is rather limited, with a strong focus on technology. This perspective is inadequate to realise a digital transformation in healthcare organisations and requires a new framework. To ensure the practical applicability, a clearer depiction on how FM practitioners in the field, currently understand the digital transformation, is necessary.Design/methodology/approachA sequential exploratory mixed-methods design was chosen with a qualitative data collection and quantitative data analysis phase. A total of 23 semi-structured research interviews with professionals from FM in healthcare in Switzerland were carried out. Topic modeling was used to analyse, identify and cluster topics.FindingsIn total, nine distinct digital transformation topics were generated: ‘addressing future organisational and people needs’, ‘end-user-oriented processes’, ‘digitalisation management vs. digital tools’, ‘major organisational change’, ‘information exchange and work culture’, ‘digital systems’, ‘pressure’, ‘company-wide strategy’ and ‘management support and communication involving non-medical support services’. The findings indicate that FM practitioners perceive the digital transformation beyond the purely technological viewpoint, although they lack the know-how and instruments necessary to implement the digital transformation.Originality/valueThis study employs an innovative research design, resulting in foundational insights on how FM in healthcare perceives the digital transformation. As such, the study expands the notion on what the digital transformation means for the FM field.

Application of Acoustic Emission Technology for Bearing Condition Monitoring on Passenger Ropeway

Rolling bearings are important stressed components of driving wheels and detour wheels on passenger ropeway, and their failure may lead to accidents and casualties. After the ropeway is installed and put into operation, the rolling bearings in the wheels are difficult to disassemble. Once disassembled, the bearing may be damaged. Due to the installation position and the complex operation environment, the health status of the rolling bearings is a blind spot for ropeway inspection and detection. Acoustic emission detection technology is used to monitor the rolling bearings of passenger ropeways. After a large number of field tests, the acoustic emission monitoring program and signal analysis method of rolling bearing on ropeway were proposed. The parameter characteristics of acoustic emission signals obtained in bearing operating were analyzed from two aspects: the ropeway operating cycle and the bearing operating cycle. Spectrum analysis of typical signals was performed, and the spectrum characteristics of typical acoustic emission signals of rolling bearing during the operation of the ropeway were obtained. On-site tests results show that using acoustic emission technology to perform condition monitoring and fault diagnosis of low-speed rolling bearings on passenger ropeways has good application prospects.

Structural Strength Analysis and Optimization of Commercial Aircraft Nose Landing Gear under Towing Taxi-Out Conditions Using Finite Element Simulation and Modal Testing

In the field of civil aviation, the nose landing gear is a critical component that is prone to damage during taxiing. With the advent of new technologies such as towing taxi-out and hub motors, the nose landing gear faces increasingly complex operational environments, thereby imposing higher performance demands. Ensuring the structural safety of the nose landing gear is fundamental for the successful application of these technologies. However, current research on aircraft nose landing gear under these new conditions is somewhat lacking, particularly in terms of reliable analysis models for real-world scenarios. This study focuses on a typical Class C aircraft, specifically the B-727 model, for which a finite element model of the nose landing gear is developed. Modal testing of the aircraft’s nose landing gear is conducted using the impact hammer method, and the results are compared with those from the simulations. The experimental data indicate that the error range for the first seven natural frequencies is between 0.23% and 9.27%, confirming the high accuracy of the developed landing gear model. Furthermore, with towing taxi-out as the primary scenario, a dynamic model of the aircraft towing system is established, and an analysis on the structural strength and topological optimization of the nose landing gear under various conditions, including high speeds and heavy loads, is performed. The results show that the developed model can effectively support the analysis and prediction of the mechanical behavior of the nose landing gear. Under high-speed, heavy-load conditions, the nose landing gear experiences significantly increased loads, with the maximum deformation primarily occurring at the lower section of the shock strut’s outer cylinder. However, no damage occurred. Additionally, under these conditions, an optimized structural design for the landing gear was identified, which, while ensuring structural strength, achieves a 22.32% reduction in the mass of the outer cylinder, also ensuring safety in towing taxi-out conditions.

Pilot performance during simulated point and boundary avoidance tracking tasks

Abstract Helicopters are used in complex and harsh operational environments, such as search and rescue missions and firefighting, that require operating in ground proximity, tracking targets while avoiding impacting obstacles, namely a combination of point tracking (positive) and boundary avoidance (negative) objectives. A simulation task representing simplified helicopter dynamics is used to investigate point tracking and boundary avoidance tasks. The variance and regression analysis are used to study the effects of task conditions on participants’ tracking errors and input aggression. The overall tracking error shows a negative correlation with input aggression. The participants tend to have higher input aggression and lower tracking error near the boundaries, exposing the switching of manipulation input strategies under different task conditions. It also suggests a potential way of designing simulation tasks for human operators manipulating helicopters and a trigger for investigating pilots’ biodynamic feedthrough.

SCREW DRUM STRUCTURAL PARAMETERS ANALYSIS ON PROPULSION PERFORMANCE IMPACT

Screw propeller can perform engineering operations in complex environments and have a wide range of applications. To improve its operational efficiency, it is necessary to analyze the influence of the parameters of the screw drum on the performance indicators of the operation, in order to confirm the structural parameters under the optimal operational efficiency. By coupling the Discrete Element Method (DEM) with RecurDyn, the simulation of the locomotion process of a screw propulsion mechanism in a sand environment is conducted. Output results such as travel speed, maximum traction force, and maximum torque are measured. Through computation, performance indicators including slip ratio, traction coefficient, and dimensionless power can be obtained. By coupling the Discrete Element Method (DEM) with RecurDyn, the simulation of the locomotion process of a screw propulsion mechanism in a sand environment is conducted. Output results such as actual speed, maximum traction force, and maximum torque are measured. Through computation, performance indicators including slip ratio, traction coefficient, and dimensionless power can be obtained. Analyzing the numerical indicators of the propulsion system, it is determined that the optimal structural parameters for the screw propulsion mechanism to operate in a sand environment are a helix angle of 40°, aspect ratio of 4, blade height ratio of 0.15, and the number of blades of 4.

International legal regulation of reforming the Armed Forces of Ukraine to NATO standards. studying the experience of command groups' activities in the armies of NATO member states and partner countries

The paper investigates the concept of mission command within the framework of military management, focusing on its application in modern military operations. Mission command, characterized by centralized intent and decentralized execution, is examined in terms of its origins, principles, and effectiveness in enhancing speed and initiative in military actions. The author conducts a comprehensive analysis of how mission command has been utilized by various military forces worldwide, assessing its alignment with contemporary strategic concepts and its contribution to achieving strategic objectives. Special attention is paid to its relevance and applicability in the context of the Ukrainian Armed Forces reform, particularly in enhancing command and control structures. The research highlights mission command as a vital component of effective military leadership, facilitating adaptability and responsiveness in complex operational environments. The findings underscore the importance of integrating mission command principles into modern military management systems to optimize decision-making processes and achieve mission success. Overall, the study provides valuable insights into the practical implementation and impact of mission command on military operations, offering recommendations for its further development and refinement within the Ukrainian Armed Forces and beyond.

Investigating social sustainability practices in global supply networks: A fuzzy-set qualitative comparative analysis

Given the paucity of research that examines the impact of Social Sustainability Practices (SSP) on performance outcomes, this study investigates the adoption and influence of these practices on firms' and suppliers’ overall performance, whilst also considering the level of complexity of operational environments of global supply networks. A Fuzzy-sets Qualitative Comparative Analysis (FsQCA) approach is adopted, based on eight primary case studies within the food, chemical and textile sectors, with data collected through semi-structured interviews conducted with representatives from each company.The research identified four key categories of SSP: informative, control, collaborative, and supply network redesign practices. The findings offer empirical support for the notion that SSP adoption yields improved performance outcomes for both the firm and its suppliers. These positive effects encompass enhanced firm profitability, increased supplier efficiency, and heightened supplier innovativeness. Nevertheless, the research uncovered that the relationship between adopting SSP and their impact on performance is not always straightforward. In certain instances, a combination of these practices may be necessary to achieve the desired performance level.In addition, a prudent consideration of the operational complexity present in global supply networks is crucial for informed decision-making pertaining to the selection and adoption of the most appropriate SSP. Driving profits in complex operational environments requires integrating both informative and collaborative practices, whilst tight control is necessary to improve supplier efficiency, regardless of the extent of complexity. Collaboration also emerged as a core factor of success in enhancing supplier innovation regarding social sustainability. Decision makers should also collaborate with external entities, particularly Non-Governmental-Organisations (NGOs) and industry associations/peers, to educate suppliers, thus creating an auspicious environment where suppliers are accountable for their own sustainability performance. This research carries significant practical implications. Managers should evaluate diverse combinations of SSP to confront global sustainability challenges through the utilization of customized strategies that can yield enhanced performance results. Expanding sustainability beyond primary suppliers can often be challenging in a global supply networks, given a firm's limited oversight, transparent knowledge and control over lower tier suppliers. Consequently, managers should seek to strengthen controls and oversight within the manageable segments of the supply network, all the while facilitating self-adaptation within the extended supply network with the adoption of informative and collaborative practices that extend reach, homogeneity, and improve overall governance.

Research on the Safety Evaluation Method for Quayside Container Cranes Based on the Best–Worst Method–Pythagorean Fuzzy VIKOR Approach

In the port domain, quayside container cranes are an indispensable component of maritime freight transport. These cranes are not only costly but also associated with safety accidents that often result in casualties and property loss, severely impacting port operations and the surrounding environment. Given their complex operational environment, rapid technological updates, high dependency on human factors, and the challenges of maintenance and inspection, the safety of quayside container cranes is a significant concern for port enterprises and managers. This paper, based on the operational modes and structural characteristics of the cranes, divides them into five main systems and identifies twenty-eight safety evaluation indicators, covering a comprehensive range of risk factors from equipment integrity to operator behavior, as well as environmental factors. However, numerous pain points exist in the safety risk evaluation process of quayside container cranes, such as fuzziness, uncertainty, and complex multi-criteria decision-making (MCDM) environments. These issues make traditional safety evaluation methods inadequate in accurately reflecting the actual safety conditions. Therefore, this paper proposes a safety evaluation method for quayside container cranes based on the Best–Worst Method (BWM) and Pythagorean hesitant fuzzy VIKOR. This method effectively overcomes the uncertainties and fuzziness of traditional safety evaluation methods by integrating the decision maker’s preference information from the BWM and the fuzzy handling capability of Pythagorean hesitant fuzzy sets, enhancing the accuracy and reliability of the evaluation results. A case study was conducted on a quayside container crane at a specific port. Through empirical analysis, the feasibility of the proposed method was validated. Overall, the safety evaluation method for quayside container cranes based on the BWM and Pythagorean hesitant fuzzy VIKOR proposed in this paper enriches the theoretical research on the safety risk assessment of quayside container cranes and offers a new approach and tool for port enterprises and managers in practice.