Operational Environment Research Articles

Cooperation objective evaluation in aviation: validation and comparison of two novel approaches in simulated environment.

In operational environments, human interaction and cooperation between individuals are critical to efficiency and safety. These states are influenced by individuals' cognitive and emotional states. Human factor research aims to objectively quantify these states to prevent human error and maintain constant performances, particularly in high-risk settings such as aviation, where human error and performance account for a significant portion of accidents. Thus, this study aimed to evaluate and validate two novel methods for assessing the degree of cooperation among professional pilots engaged in real-flight simulation tasks. In addition, the study aimed to assess the ability of the proposed metrics to differentiate between the expertise levels of operating crews based on their levels of cooperation. Eight crews were involved in the experiments, consisting of four crews of Unexperienced pilots and four crews of Experienced pilots. An expert trainer, simulating air traffic management communication on one side and acting as a subject matter expert on the other, provided external evaluations of the pilots' mental states during the simulation. The two novel approaches introduced in this study were formulated based on circular correlation and mutual information techniques. The findings demonstrated the possibility of quantifying cooperation levels among pilots during realistic flight simulations. In addition, cooperation time is found to be significantly higher (p < 0.05) among Experienced pilots compared to Unexperienced ones. Furthermore, these preliminary results exhibited significant correlations (p < 0.05) with subjective and behavioral measures collected every 30 s during the task, confirming their reliability.

Toward a personalized autonomous transportation system: Vision, challenges, and solutions

The fragmented design of Intelligent Transportation Systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution towards Autonomous Transportation System (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates Blockchain, Federated Learning, and Large-scale Model to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATS empowered by TPE can serve diverse user groups both privately and equally.

Tailoring mechanical, microstructural and toughening characteristics of plasma-sprayed graphene-reinforced samarium niobate coatings for extreme environments

Thermal barrier coatings (TBCs) are advanced ceramic layers applied to metal components to provide insulation and protection against high temperatures in extreme operating environments. This study investigated the effects of graphene nanoplatelet (GNP) reinforcement on samarium niobate (SN: SmNbO4) TBCs for extreme environments. Four ceramic top coat compositions were plasma-sprayed onto Inconel 718 substrates: Yttria-stabilized zirconia (YSZ), SmNbO4 (SN), and SN reinforced with 1 and 1.5 wt% GNPs (SN-1GNP, SN-1.5GNP). The research examined microstructural characteristics, phase evolution, mechanical properties and toughening mechanisms. GNP reinforcement significantly improved coating density, with SN-1.5GNP reaching 97.4 ± 1.64% compared to 91.3 ± 1.69% for SN and 86.6 ± 1.47% for YSZ. Hardness and elastic modulus were enhanced by 86.38% and 57.91% for SN-1GNP, and 101.09% and 65.23% for SN-1.5GNP respectively. Moreover, fracture toughness experienced a significant increase from 1.86 ± 0.4 to 5.48 ± 0.7 MPa·m1/2, facilitated by toughening mechanisms, like splat bridging, GNP pull-out, crack arrest and ferroelastic domain switching. Additionally, the SN-1.5GNP coating exhibited a higher adhesion strength of 36.84 MPa, thereby leading to improved layer distribution and lesser chance of delamination. Compared to YSZ, these findings suggest that GNP-reinforced SN coatings offer enhanced performance for extreme environment applications.

Structure and properties study of in-situ TiC reinforced 316L materials prepared by laser melting deposition based on Ti3SiC2 decomposition

As the service performance requirements for metal components in diverse high-tech industries continue to escalate, a myriad of specialized operational environments pose increasingly formidable challenges to the comprehensive performance of various components. To address these challenges and further enhance the mechanical properties of 316L stainless steel, expand its application range, we have developed in-situ TiC reforced 316L composites. In this paper, we propose a novel method for the fabrication of grain boundary-dispersed, in-situ generated TiC-reinforced 316L metal matrix composites (MMC) through laser melting deposition (LMD), leveraging the decomposition reaction of Ti3SiC2. Samples with different TiC contents were prepared, and their microstructures post-LMD were closely analyzed. Furthermore, the underlying enhancement mechanism of in-situ TiC on the mechanical properties of these samples was thoroughly investigated. The results demonstrate that the in-situ TiC particles are uniformly dispersed along grain boundaries, functioning as additional nucleation sites. This enhances the nucleation rate significantly and contributes to grain refinement. Compared with pure 316L material, the hardness and wear resistance of the composite material are greatly improved, and the hardness increased by up to 35.66 %. In-situ TiC can effectively improve the overall performance of 316L.

Modeling the impact of noise and uncertain operational environment on software release decisions considering testing coverage-based SRGM

The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.

Fixed-time control of teleoperation systems based on adaptive event-triggered communication and force estimators

Abstract A fixed-time control strategy based on adaptive event-triggered communication and force estimators is proposed for a class of teleoperation systems with time-varying delays and limited bandwidth. Two force estimators are designed to estimate the operator force and environment force instead of force sensors. With the position, velocity, force estimate signals, and triggering error, an adaptive event-triggered scheme is designed, which automatically adjusts the triggering thresholds to reduce the access frequency of the communication network. With the state information transmitted at the moment of event triggering while considering the time-varying delays, a fixed-time sliding mode controller is designed to achieve the position and force tracking. The stability of the system and the convergence of tracking error within a fixed time are mathematically proved. Experimental results indicate that the control strategy can significantly reduce the information transmission, enhance the bandwidth utilization, and ensure the convergence of tracking error within a fixed time for teleoperation systems.

Role of document management system for business processes optimization

The automation of work processes and seamless integration with various business services highlight the essential role of digital document management systems (DMS) in enhancing organizational flexibility, productivity, and adaptability. DMS serve as centralized repositories that streamline, automate, and interconnect business processes, thereby facilitating effective document management. The integration of DMS with Enterprise Resource Planning (ERP) systems provides a comprehensive solution for optimizing business workflows, addressing the needs for both operational efficiency and strategic management. The research paper delineates a business model for the life cycle management of electronic documents and records, focusing on the integration of processes such as document creation, management, approval, and secure distribution. This integrated approach enhances organizational structure, optimizes processes, and bolsters security, leading to significant cost reductions and increased work efficiency. The model emphasizes the importance of streamlined and automated document workflows in reducing manual errors and operational bottlenecks. Key features of DMS include advanced mechanisms for digitizing physical documents, ensuring that all data is captured accurately and efficiently. Centralized storage with version control allows for easy retrieval and management of documents, ensuring that the latest versions are always accessible. Additionally, advanced search and accessibility features enable quick and efficient document retrieval, which is crucial for maintaining high productivity levels. The study underscores the pivotal role of DMS in modern enterprises, advocating their implementation as a strategic move to drive operational excellence and competitive advantage. By integrating DMS with ERP systems, businesses can achieve a cohesive and efficient operational environment that supports both day-to-day activities and long-term strategic goals. This comprehensive approach to document management not only optimizes efficiency and reduces errors but also enhances the overall agility and responsiveness of the organization in a rapidly changing economic environment.

Enhanced fault diagnosis of rolling bearings using an improved inception-lstm network

ABSTRACT Rolling bearings are integral to the operation of various mechanical systems, where their condition directly impacts equipment reliability and performance. Accurate fault diagnosis is essential for preventing unexpected failures and minimising downtime in industrial settings. This study presents a novel diagnostic model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, enhanced by an improved Inception module. This integrated approach captures both spatial and temporal features of vibration signals, leading to superior diagnostic accuracy under diverse operating conditions. Experimental results using the Case Western Reserve University (CWRU) bearing dataset demonstrate an impressive average fault diagnosis accuracy of 99.83%, outperforming conventional models. The robustness of the proposed model against noise and its adaptability to varying operational environments underscore its practical value for real-world engineering applications, offering a reliable solution for maintaining the safety and efficiency of critical machinery.

SigBERT: vibration-based steel frame structural damage detection through fine-tuning BERT

PurposeThis article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.Design/methodology/approachSigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.FindingsThe experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.Originality/valueSigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

The Influence of Artificial Intelligence on the Automation of Processes in Electronic Commerce

This study explores the transformative impact of Artificial Intelligence (AI) on automating business processes in electronic commerce (e-commerce), with a focus on enhancing efficiency and customer experience. The research employs Deep Learning (DL) and Machine Learning (ML) as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories. Techniques such as data preprocessing, normalization, sentiment analysis, and advanced predictive models using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) are utilized. Data collection was conducted using web scraping tools like Beautiful Soup and Scrapy, along with APIs from Amazon and Google. The application of AI in e-commerce has led to significant improvements in inventory control, fraud prevention, and customer relations. ML algorithms have enhanced the estimation of product demand and personalized customer interactions, while DL has strengthened product recommendation systems and fraud detection mechanisms. The findings indicate that AI contributes to a more secure, faster, and smarter operational environment in e-commerce. This research highlights the substantial benefits and broad potential of AI in optimizing e-commerce operations, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.

Optimal charging scheduling for Indoor Autonomous Vehicles in manufacturing operations

Indoor Autonomous Vehicles (IAVs) have become instrumental in modern logistics, particularly in dynamic operational environments. Their consistent availability is crucial for both timely task execution and energy efficiency in smart factories. Therefore, an inefficient charging schedule can waste energy, compromising production and warehousing efficiency. In addition, formulating an effective charging schedule is challenging due to the nonlinearity of its design and the uncertainties inherent in smart factory settings. In contrast to previous studies that either simplify the problem using linearization-based methods or approach it with computationally demanding algorithms, this paper efficiently addresses the nonlinear challenge, ensuring a closer alignment with real-world conditions. Hence, a simulation environment is first designed to replicate a smart factory, including validated models of IAVs, Charging Stations (CSs), and a variety of unpredictable static and dynamic obstacles. A comprehensive dataset is then provided from this simulation setup. Utilizing this dataset, a model tailored to ascertain the travel time of IAVs, accounting for inherent uncertainties, is trained using Deep Neural Networks (DNNs). Subsequently, a Mixed-Integer Nonlinear Programming (MINLP) problem is formulated to design the optimization task. Finally, integration of the DNNs’ model with the Branch-and-Bound (BnB) approach, forming the BnBD method, streamlines the determination of the optimal charging schedule. Experimental results highlight significant improvements in charging scheduling, establishing this approach as a viable and promising solution for manufacturing operations.

Integrated optimization of logistics routing problem considering chance preference

PurposeThis study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.Design/methodology/approachThis study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.FindingsThe study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.Practical implicationsIn an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.Originality/valueConsidering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

A review of recent advances and applications of inorganic coating for oil and gas pipe systems

The oil and gas pipe systems are a crucial part of the infrastructure. The structural integrity of pipes is diminishing as a result of environmental damage. In recent years, the oil and gas industry has witnessed significant advancements in the development and application of inorganic coatings for pipeline systems. These coatings are essential for enhancing the durability, safety, and efficiency of pipelines by providing superior resistance to corrosion, abrasion, and chemical attacks. This review paper delves into the latest fabrication strategies for API pipe coatings, with a focus on the electroless method, electrodeposition method, thermal spray method, thermochemical method, and other emerging techniques. Each method is evaluated in terms of its principles, advantages, limitations, and suitability for different operational environments. Among the coating methods, the electroless process is one of the most popular methods. The paper also highlights innovative approaches and materials that have the potential to revolutionize the field. By providing a comprehensive overview of current technologies and their applications, this review aims to guide future research and development efforts, promoting the adoption of advanced inorganic coatings in the oil and gas industry to ensure the longevity and integrity of critical infrastructure.

Research on anti-rollover control of three-axle rescue vehicle based on active suspension and differential braking

Abstract. Due to its intricate operational environment, substantial mass, and elevated center of mass, the three-axle emergency rescue vehicle is more prone to rollover. This study aims to enhance the operational stability and mitigate rollover risks by establishing a rollover dynamic model with 11 degrees of freedom. Using the coupling characteristics of vehicle dynamics and tire force, an anti-rollover control strategy of the three-axle vehicle based on the cooperative work of differential braking and active suspension is proposed. According to the vehicle motion characteristics, the active suspension controller is built by using a fuzzy PID algorithm, and the differential braking controller is built by using the improved adaptive model predictive control algorithm. The step and fishhook tests of differential braking, active suspension control and joint control are carried out respectively. The simulation results show that the joint control strategy can effectively reduce the rollover tendency of the vehicle, and further improve the anti-rollover ability of the vehicle compared with the single system control.

Mind bylaws when confronting board term limit issues

With all of the disruption in nonprofit operating environments during the pandemic, it may have escaped your board's attention that one or more members of the board have termed out. In fact, it's even possible that the terms of entire boards actually ended during the pandemic and associated lockdowns, without anyone noticing. But does that mean the board's decisions since then are all moot and void? Not necessarily, according to Benjamin Takis, co‐founder of Sustainability Education 4 Nonprofits.

Monitoring airborne pathogen transmission for enhanced safety at food processing facilities

AbstractFood manufacturing and processing are part of the nation's critical infrastructure. Due to the recent global spread of the SARS-CoV-2 virus, the potential contamination of the food chain and the resulting public health implications are of high consequence to society. The current primary food manufacturing and processing facilities already have various mechanisms such as hazard analysis and critical control point (HACCP) system in place. However, the widespread microbial infections in these facilities raise concerns that they will not only threaten the welfare of food processing workers, but also have a potentially greater consequence on the public if the food is contaminated with an infectious agent.Despite the increasingly recognised role of the environment in the spread of microbes, the effect of air properties remains poorly understood. Heating, ventilation, and air conditioning (HVAC) systems in meat processing facilities not only provide a means of transport for viruses and bacteria but may also deposit them on surfaces where they can survive for days. To maintain a stable and safe food chain supply during the pandemic, the challenges to ensure safe food supply and protect the workers' health must be quickly addressed through sustainable, safe and economic approaches. With these two imminent challenges in mind, the overall goal of this review article is to provide a comprehensive overview of the role of the environment in the impaction and resuspension of bioaerosols, focusing on airborne bacteria and viruses. The review includes the latest results of modeling the spread of microbial aerosols in the airflow and the development of preventive measures to mitigate virus contamination in the unique environment of meat processing operations. By understanding how the environmental factors and seasonality affect the infectivity and spread of airborne pathogens, mitigation measures can be designed to minimise future infections within and beyond these facilities.

Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

Advances and Challenges in the Hunting Instability Diagnosis of High-Speed Trains.

With the continuous increase in train running speeds and the rapid complexity of operation environments, running stability of the high-speed train is facing significant challenges. A series of abnormal vibration issues, caused by hunting instability, have emerged, including bogie instability alarm, carbody swaying, and carbody shaking, posing a significant threat to the safe and stable operation of high-speed trains. Therefore, the monitoring and diagnosis of hunting instability have become important research topics in rail transit. This review follows the development of fault diagnosis for bogie hunting instability and carbody hunting instability. It first summarizes the existing evaluation standards and innovative diagnostic methods. Due to the current limitation of hunting instability evaluation standards, which can only detect large-amplitude hunting, this paper addresses the gap in evaluation criteria for early-stage, small amplitude hunting instability diagnosis. A thorough overview of the progress made by researches in this field of research is given, emphasizing three primary facets: diagnostic signal sources, diagnostic features, and diagnostic targets. Furthermore, given that existing methods only classify faults into small and large amplitudes, which does not meet the practical need for quickly and accurately identifying fault types and severity during operation, this review introduces existing works on the detailed assessment and fault tracing of hunting instability, as well as the mechanisms underlying its occurrence, with the aim of achieving a comprehensive diagnosis of hunting instability. Finally, the limitations of current methods and the future development trends in hunting instability diagnostics are discussed and summarized. This paper provides readers with a framework for the research process of hunting instability diagnosis, offering valuable references and innovative perspectives for their future research efforts.

Energy storage chemistry: Atomic and electronic fundamental understanding insights for high-performance supercapacitors

The scarcity of fuels, high pollution levels, climate change, and other major environmental issues are critical challenges that modern societies are facing, mostly originating from fossil fuels-based economies. These challenges can be addressed by developing green, eco-friendly, inexpensive energy sources and energy storage devices. Electrochemical energy storage materials possess high capacitance and superior power density. To engineer highly efficient next-generation electrochemical energy storage devices, the mechanisms of electrochemical reactions and redox behavior must be probed in operational environments. They can be studied by investigating atomic and electronic structures using in situ x-ray absorption spectroscopy (XAS) analysis. Such a technique has attracted substantial research and development interest in the field of energy science for over a decade. The mechanisms of charge/discharge, carrier transport, and ion intercalation/deintercalation can be elucidated. Supercapacitors generally store energy by two specific mechanisms—pseudocapacitance and electrochemical double-layer capacitance. In situ XAS is a powerful tool for probing and understanding these mechanisms. In this Review, both soft and hard x rays are used for the in situ XAS analysis of various representative electrochemical energy storage systems. This Review also showcases some of the highly efficient energy and power density candidates. Furthermore, the importance of synchrotron-based x-ray spectroscopy characterization techniques is enlightened. The impact of the electronic structure, local atomic structure, and electronically active elements/sites of the typical electrochemical energy storage candidates in operational conditions is elucidated. Regarding electrochemical energy storage mechanisms in their respective working environments, the unknown valence states and reversible/irreversible nature of elements, local hybridization, delocalized d-electrons spin states, participation of coordination shells, disorder, and faradaic/non-faradaic behavior are thoroughly discussed. Finally, the future direction of in situ XAS analysis combined with spatial chemical mapping from operando scanning transmission x-ray microscopy and other emerging characterization techniques is presented and discussed.

Energy-Efficient and Accelerated Resource Allocation in O-RAN Slicing Using Deep Reinforcement Learning and Transfer Learning

Abstract Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models.