Complex Operational Environments Research Articles

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.

Research on assessment of air traffic control operation quality based on track data

AbstractEvaluating the quality of air traffic control operations is crucial for enhancing airspace management. Thus, this paper proposes a data mining approach for conducting a comprehensive assessment of control operation quality (COQ) in increasingly complex operation environments. First, the authors establish a COQ evaluation index system that combines both subjective and objective measures. Key index parameters are determined using wavelet filtering and interval estimation techniques on the basis of data mining results. Second, the authors apply an entropy‐weighted cloud model to label data samples and classify COQ into ‘excellent’, ‘good’, and ‘fair’ levels. Finally, the authors establish an support vector machine‐based COQ assessment model using XGBoost feature combinations to verify the practical feasibility and scientific validity of their approach.

Research on eVTOL Air Route Network Planning Based on Improved A* Algorithm

With the continuous opening of low-altitude airspace and the development of aircraft such as electric vertical takeoff and landing (eVTOL) vehicles, urban air traffic has become a sustainable and green development direction for future transportation. Air route networks, as a mainstream design scheme for air traffic, are able to provide prerequisites for eVTOL and other green aircraft to enter urban airspace for safe operation, among which air route planning is a fundamental component of air route network design. Currently, most of the research on aircraft path planning is performed in free airspace, lacking the analysis and processing for the complex operation environment, which has led to the high risk and large operation cost of path planning results, failing to meet the demand for safe and efficient development in the future. Aiming at the above problems, eVTOL-oriented air route planning research was carried out. Firstly, the urban low-altitude airspace structure was planned, and the operational levels of eVTOL were clarified; this was followed by introducing the urban dynamic air–ground risk factors and constructing a dynamic risk assessment model containing risk level information; finally, the improved A* algorithm based on the risk cost was employed to plan the eVTOL air route network, which finally realized the purpose of short path length and low total risk. The simulation results showed that the route generated by the improved A* algorithm could reduce the risk cost by at least 30% with a relatively small path cost, which ensured the operation efficiency and safety of eVTOLs and laid the foundation for the further sustainable and green development of urban airspace in the future.

Dual-Signal Chemical Exchange Saturation Transfer (Dusi-CEST): An Efficient Strategy for Visualizing Drug Delivery Monitoring in Living Cells.

We report a dual-signal chemical exchange saturation transfer (Dusi-CEST) strategy for drug delivery and detection in living cells. The two signals can be detected by operators in complex environments. This strategy is demonstrated on a cucurbit[6]uril (CB[6]) nanoparticle probe, as an example. The CB[6] probe is equipped with two kinds of hydrophobic cavities: one is found inside CB[6] itself, whereas the other exists inside the nanoparticle. When the probe is dispersed in aqueous solution as part of a hyperpolarized 129Xe NMR experiment, two signals appear at two different chemical shifts (100 and 200 ppm). These two resonances correspond to the NMR signals of 129Xe in the two different cavities. Upon loading with hydrophobic drugs, such as paclitaxel, for intracellular drug delivery, the two resonances undergo significant changes upon drug loading and cargo release, giving rise to a metric enabling the assessment of drug delivery success. The simultaneous change of Dusi-CEST likes a mobile phone that can receive both LTE and Wi-Fi signals, which can help reduce the occurrence of false positives and false negatives in complex biological environments and help improve the accuracy and sensitivity of single-shot detection.

The Dimension Of Military Personnel Readiness

The purpose of this article is to provide understanding related to the dimensions of military personnel readiness. The Physical Readiness (PR) and Cognitive Readiness (CR) of military personnel are the key dimensions that significantly affect operational effectiveness. A combination of this readiness is essential to the military organization for preparing military personnel readiness. To build strong resilient military personnel, military organizations need to identify potential military training on how to train military personnel to enhance cognitive and physical readiness. Military personnel require cognitive and physical capabilities for optimal performance to meet the demands of complex operational environments military operations. Therefore, this article provides a consideration related to the dimension of military personnel readiness and the potential for effective training strategies to improve the physical and cognitive readiness of personnel for military organizations.

Evaluation method of composite insulator aging status based on hyperspectral imaging technology

Silicone rubber insulators' long-term operation in complex environments can lead to accelerated aging, causing the risk of flashovers and grid outages. Timely detection of the insulators' aging status is crucial to ensure the stable operation of grid. Therefore, this paper proposes a pixel-level fast, non-contact detection method based on hyperspectral imaging (HSI) technology for insulators' aging state. Firstly, the hyperspectral spectral line of the samples' six aging levels was extracted, and 14 features were extracted using pre-processing methods like segmented principal component analysis (SPCA) to reduce irrelevant information interference. Secondly, the aging state classification model was established, and through comparison using overall accuracy, F1 score, Precision, and Recall, the random forest (RF) model demonstrated optimal performance with a classification accuracy of 96.81%. Finally, this study utilized HSI technology to achieve a pixel-level evaluation of composite insulators' aging status, and the technique's feasibility was verified through specific parameters such as roughness.

Steam oxidation performance of Yb2Si2O7 environmental barrier coatings exposed to CMAS

Environmental barrier coatings (EBCs) are necessary for use of SiC/SiC ceramic matrix composites (CMCs) in gas turbine engines. Due to the complex operational environments, it is increasingly important to understand the interplay of damage modes to project the durability of the EBC/CMC system. Two life-limiting failure mechanisms of EBCs are steam oxidation and molten calcium-magnesium-aluminosilicate (CMAS) attack. Steam oxidation results in the formation of a thermally grown oxide (TGO) that acts as a weak interface and CMAS/EBC interactions cause thermomechanical and thermochemical changes that compromise coating integrity. EBCs consisting of a Yb2Si2O7 topcoat and a silicon bond coat were subjected to low (2 mg/cm2) loadings of two CMAS compositions prior to steam oxidation at 1316 °C for up to 300 h. Exposure to CMAS reduced the TGO thickness and the low CaO content glass resulted in the thinnest oxide scales. However, glass exposure had mechanical consequences that caused premature coating delamination.

LACTA: A lightweight and accurate algorithm for cherry tomato detection in unstructured environments

Developing cherry tomato detection algorithms for selective harvesting robots faces many challenges due to the influence of various environmental factors such as lighting, water mist, overlap, and occlusion. To this end, we present LACTA, a lightweight and accurate cherry tomato detection algorithm specifically designed for harvesting robot operation in complex environments. Our approach enhances the model’s generalization ability and robustness by selectively expanding the original dataset using a combination of offline and online data augmentation strategies. To effectively capture the small target featuresof cherry tomatoes, we construct an adaptive feature extraction network (AFEN) that focuses on extracting pertinent feature information to enhance the identification ability. Additionally, the proposed cross-layer feature fusion network (CFFN) preservesthe model’s lightweight nature while obtaining richer feature representations. Finally, the integration of efficient decoupled heads (EDH) further enhances the model’s detection performance. Experimental results demonstrate the adaptability and robustness of LACTA, achieving precision, recall, and mAP values of 94 %, 92.5 %, and 97.3 %, respectively. Compared to the original dataset, the offline-online combined data augmentation strategy improves precision, recall, and mAP by 1.6 %, 1.7 %, and 1.1 %, respectively. The AFEN + CFFN network structure significantly reduces computational complexity by 28 % and number of parameters by 72 %. With a compact size of only 2.88 M, the LACTA model can be seamlessly deployed into selective harvesting robots for the automated harvesting of cherry tomatoes in greenhouses. The code is available at https://github.com/ruyounuo/LACTA.

Distributed Navigation in Emergency Scenarios: A Case Study on Post-Avalanche Search and Rescue Using Drones

Unmanned aerial vehicles (UAVs) are increasingly employed in civil applications due to their ease of use and adaptability. This paper proposes a distributed navigation strategy for a formation of UAVs in post-avalanche search-and-rescue (SAR) operations. Formations offer a more efficient approach than single UAVs in dynamic and complex operational environments. Additionally, they can distribute different sensors, reducing payload and increasing robustness and overall efficiency. The proposed navigation algorithm relies on the Kalman filter (KF) based on consensus to distribute state estimation, and internodal transformation theory to improve system scalability, preserving the dynamic equivalence between the global and local models. The effectiveness of this approach was tested in two realistic scenarios, resulting in the ability to detect victims and maintain situational awareness while avoiding unsearched areas. The proposed approach offers a promising alternative to human-intensive SAR missions.

Improved active disturbance rejection speed control for autonomous driving of high-speed train based on feedforward compensation.

Due to the complex and changeable train operation environment and the unstable and time-varying parameters, accurate modeling is limited. Therefore, a modified active disturbance rejection control algorithm based on feedforward compensation (FC-MADRC) is proposed targeting the speed control problem of trains under the circumstances of external disturbances, which reduces the dependence on the train model. Firstly, the state space equation is established based on the single-particle mathematical model of the train, and all the running resistances are regarded as disturbances. Secondly, the FC-MADRC algorithm is designed. Based on the terminal attractor function and the novel Sigmoid function, an improved tracking differentiator (ITD) is designed. An improved fal (nsfal) function with better smoothness is constructed by using the properties of the Dirac δ function, and an ameliorative nonlinear state error feedback (ANLSEF) and a modified extended state observer (IESO) are designed based on the nsfal function. Furthermore, based on the thought of PID, the integral term of error is introduced into ANLSEF for the nonlinear operation to reduce the steady-state error of train speed tracking. In order to promote the robustness and control accuracy of the system, the feedforward compensation term and disturbance compensation term are combined to perform dynamic compensation for disturbances in real time. Finally, the simulation is carried out with CRH380A train data. The results indicate that compared with conventional ADRC and 2DOF-PID, FC-MADRC has the more vital anti-disturbance ability and higher tracking accuracy. FC-MADRC has the advantages of solid anti-disturbance, fast response, and high tracking accuracy. Under the premise of external disturbance, it can still achieve accurate speed tracking under different road conditions.

Knowledge Management in Multimedia Communication Using Software Agents

This paper presents the problem of knowledge management related to software agents and agent systems (i.e. systems composed of communicating and cooperating software agents). The author presents basic theoretical issues, but the main focus is on the areas of application of software agents in multimedia communication - both existing and prospective applications. Particular attention has been paid to the applications of software agents for: supporting customer service processes through the Contact Centre system; supporting the distance learning system; creating virtual assistants in the form of conversation bots; searching for information in the Semantic Web, and assisting in setting up a video conference call. Software agents are often referred to as 'intelligent' - due to their intelligent behaviour. At their core, they involve: perception, reasoning, learning, communication and operation in complex environments, using all multimedia techniques and procedures. Software agents and agent systems, as presented in this article, represent a new approach to the analysis, design and implementation of complex (usually decentralised and distributed) computer systems, offering a whole range of concepts, techniques and tools to significantly improve the software development process. The ability of software agents to make autonomous decisions have been a subject of considerations. Among other things, they are based on the example of cognitive sciences, which deal with modelling real processes of  information processing in humans and building programmes that imitate these processes. Research on the common understanding of shared knowledge and knowledge management in agent-based systems focuses on ontologies, understood as a description of the relationship between the way knowledge is represented and the concept represented by that knowledge. In contrast, research on knowledge sharing has focused on communication languages and their associated - interaction protocol and message transport protocol. 

Capital equilibrium strategy for uncertain multi-model systems

Working capital, as a core element of company operation and management, plays an important role in various aspects of the company, particularly in activities such as production, ordering, financing, etc. However, internal and external uncertainties, as well as various time cycles, constitute a complex operational environment that will severely affect the capital level of the node companies in the supply chain finance system, and thus cause serious disruptions to the system stability. Therefore, this paper aims to propose an effective capital equilibrium strategy to achieve the switching control of system capitals, thereby reducing system costs and improving system stability. Considering a two-echelon supply chain finance system, this paper designs a capital equilibrium switching strategy based on the T-S fuzzy theory to achieve the system capital equilibrium. The paper also proves the effectiveness and scientific validity of the proposed method, and compares and analyzes the differences of capital equilibrium strategies under fuzzy robust control and robust control through five numerical simulations. The research results show that fuzzy robust control can better suppress the interference of uncertainty on the switching of capital equilibrium strategy, and make the system more stable.

Crack propagation analysis of slewing bearings in wind turbines applying a modified sub-model technology

As slewing bearings in wind turbines are exposed to complex operational environments, they are prone to crack initiation, propagation and evolution that can lead to contact fatigue problems. Reliable crack extension prediction is a critical requirement. With the goal of obtaining more accurate crack characteristics, this study proposes a crack initiated contact finite element model (CI-CFEM) targeted at the raceway of slewing bearings by introducing a modified sub-model. In the application of the global finite element model (G-FEM) to the slewing bearing system, the rolling displacements of the ball and the boundary displacements are employed in CI-CFEM to take account of external ring deformations and the support conditions. The crack is embedded using FRANC3D, and the stress intensity factor distribution and propagation characteristics of the crack front are investigated. Furthermore, the effect of external load and the design parameters of the slewing bearing on crack propagation are explored, providing insight for optimizing the design of slewing bearings to extend service life. The results reveal that changing external loads and bearing parameters are an effective means of retarding crack propagation rate, while have relatively minor influence on crack growth angle.

Revisiting Malaysia’s port classification system in a complex operational environment to streamline the coordination and management of maritime ports

Malaysia's existing port classification method is confusing and inconsistent, especially in terms of specific lists of ports for each category. This makes it difficult to effectively apply the existing port classification system in Malaysia as the accuracy of the categories is unclear and questionable. An ineffective classification system not only affects the port coordination and benchmarking system but also leads to poor decision-making and ineffective port management. Therefore, this research aims to establish a strategic port classification model with proper definitions for the Malaysian context to overcome the above-mentioned issues. A hybrid multiphase mixed-methods approach was used to establish a proper port classification model that enables Malaysia to face complex environments. This research classified Malaysian ports into three categories; namely, primary, secondary, and tertiary; that have different conceptual definitions. This research provides alternative insights for port authorities and policymakers to enhance the national port coordination system and streamline the port classification system practised in Malaysia. A streamlined port classification system helps create a more efficient and effective way to communicate information about the country’s ports, leading to a better decision-making process and improved port management.

A novel robustness PROMETHEE method by learning interactive criteria and historical information for blockchain technology-enhanced supplier selection

In a complex transaction and operation environment, supply chain usually faces the disruption risk, especially the supplier selection. Fortunately, the blockchain technology not only can provide traceability and decentration for supply chain management, but also can increase supply chain resilience. With the aid of blockchain technology, this paper deeply proposes a two-stage performance measure model to assess suppliers reinforcing supply chain resilience. Firstly, we screen out the assessment criteria of supply chain and blockchain by using complex network. Subsequently, for depicting the hesitation of multiple experts, we formulate the generation mechanism of linguistic distribution. Besides, in the blockchain technology-enabled supply chain, the criteria of supply chain and blockchain are interactive. Therefore, considering the interactive criterion, we combine the PROMETHEE method with the internal interactive index to measure supplier performance in the first stage. Meanwhile, the robustness PROMETHEE method is further improved by designing three types of aggregation models. In the second stage, we also concern the supply chain situation before adopting blockchain technology. In this case, this paper further designs a correction method of supplier performance measure based on supplier historical information in order to select the reliable supplier. Finally, we verify the effectiveness and rationality of our proposed method. In general, this study enriches the evaluation criteria of supplier performance measure by considering the influence of blockchain technology and provides the correspond decision support for blockchain technology-enabled supply chain management.

Fault Diagnosis for Large-Scale Processes Based on Robust Multiblock Global Orthogonal Projections to Latent Structures

Effective fault diagnosis can be obtained by using multiblock global orthogonal projections to latent structures (MBGOPLS), but there exist certain limitations in this method in terms of block division intelligence and model robustness to outliers. Although these issues can be addressed by developing correlation analysis, such as mutual information and copula-correlation, the complex coupling relationship between variables has not been fully examined. In this study, a robust MBGOPLS is proposed to intelligently diagnose faults using a relatively stable model. First, a double hierarchical clustering method is established, in which internal hierarchical clustering is performed based on Euclidean distance to discretize the samples. Subsequently, external hierarchical clustering is adopted based on mutual information distance to divide variables into different blocks, which results in intelligent block division while suppressing the influence of outliers. Second, a robust block regression coefficient matrix (BRCM) is obtained by employing joint <inline-formula> <tex-math notation="LaTeX">$\ell _{2,1}$</tex-math> </inline-formula>-norm minimization on the BRCM and prediction error of the correlation between the block input and output. Furthermore, BRCM is integrated into the MBGOPLS framework. Therefore, the proposed method is robust to outliers while retaining the diagnostic properties of MBGOPLS. Finally, the proposed method is applied in a numerical case and an actual thermal power plant. The results verify the method&#x2019;s applicability and superiority. <i>Note to Practitioners</i>&#x2014;With increasing large-scale, complex and intelligent to achieve anticipant performance, thermal power plant is prone to faults that can lead to unplanned outages. Meanwhile, complex operation mechanism and environment, and diverse data acquisition sensors make the coupling relationship between variables complex, and collected data often contain numerous outliers, which happens frequently in modern industrial process. Therefore, fault diagnosis is critical to modern industrial process, and the diagnosis accuracy and robustness are the main challenges. This forces us to ensure the block division accuracy and model robustness when using multiblock-based fault diagnosis technology. This paper proposes a robust MBGOPLS to intelligently diagnose faults of thermal power plant using a relatively stable model. Additionally, the proposed method can be extended to fault diagnosis for other large-scale processes.

Cyber4Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones

Cyber Security and forensics for Unmanned Aerial Vehicles (UAVs) pose unique requirements, solutions, and challenges. As UAVs become increasingly prevalent for legitimate and illegal use, ensuring their security and data integrity is important. Solutions have been developed to tackle these security requirements. Drone forensics enables the investigation of security incidents involving UAVs, aiding in identifying attackers or determining the cause of accidents. However, challenges persist in the domain of UAV security and forensics. This paper surveys drone threat models, security, and privacy aspects. In particular, we present the taxonomy of drone forensics for investigating drone systems and talk about relevant artifacts, tools, and benchmark datasets. While solutions exist, challenges such as evolving technology and complex operational environments must be addressed through collaboration, updated protocols, and regulatory frameworks to ensure drones’ secure and reliable operation. Furthermore, we also point out the field’s difficulties and potential future directions.