Complex Operational Environments Research Articles

Structure and an unstable business operating environment: Revisiting Burns and Stalker’s organisation-environment theory in Zimbabwe’s manufacturing sector

Background: Turbulent socioeconomic contexts coupled with volatile political environments pose a serious survival threat to business organisations. Complex operational environment of this dimension most often resists application of conventional management theories and practices. Organisational managers are therefore constantly challenged to adopt contingency strategies that will not only keep their organisations afloat, but also entrench competitive advantage that could effectively sustain operations. Aim: To update Burns and Stalker’s theory on structure and business environments. Setting: The dynamics of the Zimbabwe’s economy has assumed an extraordinary proportion of complexity due to intractable political instability and hostile economic environment. Methods: Using a survey research design and employing quantitative research strategy, this article examines the underlining propositions that defined the seminal work of Burns and Stalker regarding strategy adoption by organisations in a dynamic operating environment. Primary data was collected from 189 randomly selected managers in 350 manufacturing firms operating in Zimbabwe using a structured questionnaire. Data were analysed using structural equation modelling. Results: The major finding of the present study suggests that firms adopt a hybrid structure when confronted with an unstable operating environment. Conclusion: The finding is inconsistent with that of Burns and Stalker, who concluded that firms adopt organic structure in an unstable operating environment. While Burns and Stalker’s study was conducted in a relatively stable socioeconomic context, the present study was conducted in an operating environment that is characterised by turbulent socioeconomic and political instability. These environmental divergences could have influenced the outcome of both studies.

Dynamic task assignment method based on NSGA-II algorithm in command and control field

In order to improve the adaptability of command and control organizations to complex operational environments and effectively solve the unexpected events that may be encountered in the operation of C2 organizations, this paper studies the dynamic task assignment of C2 organizations and proposes multi-objective tasks assignment method based on NSGA-II algorithm. Firstly, two kinds of emergencies such as platform damage and task increase in the course of combat are analyzed, and the mathematical expression method is given. Secondly, a multi-objective optimization model aiming at maximizing task completion quality and minimizing plan adjustment cost is constructed. And the adaptive NSGA-II algorithm is designed to solve the model. Finally, the simulation experiment shows that the constructed model can effectively deal with the above-mentioned emergencies, and the designed algorithm can obtain the better Pareto frontier and realize the dynamic task assignment.

Teleoperation for space manipulator based on complex virtual fixtures

This paper presents a kind of complex virtual fixture (VF) to help space robots perform on-orbit operations in complex environments while ensuring operations safety. The main purpose of the VF is to provide virtual force feedback to adjust the manners of the operator throughout the remote operation process. The complex VF is comprised of a tube-type VF and a velocity-based VF. The tube-type VF ensures that the end effector approaches the target safely and at high speeds over long distances, and the velocity-based VF enables the end of the robot to observe the target within a safe and short distance near the target. Combined with dynamic prediction and path planning, the complex VF can improve the flexibility and efficiency of the operation, and avoid collisions in dynamic environments. The proposed methods are verified by several typical space manipulation tasks, the virtual experiment environment of which is built in CHAI3D. The comparative results indicate that the complex VF can reduce operation time and improve efficiency and accuracy.

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments.

Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling designs aimed at mitigating fatigue are typically evaluated in controlled laboratory environments, but the effectiveness of translating such strategies to operational environments can be challenging to assess. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in a complex operational environment. We studied 44 airline pilots over 34 days while they flew a fixed schedule, which included a baseline data collection with 5 days of mid-morning flights, four early flights, four high-workload mid-day flights, and four late flights that landed after midnight. Each work block was separated by 3-4 days of rest. To assess sleep, participants wore a wrist-worn research-validated activity monitor continuously and completed daily sleep diaries. To assess the circadian phase, pilots were asked to collect all urine produced in four or eight hourly bins during the 24 h after each duty block for the assessment of 6-sulfatoxymelatonin (aMT6s), which is a biomarker of the circadian rhythm. To assess subjective fatigue and objective performance, participants were provided with a touch-screen device used to complete the Samn-Perelli Fatigue Scale and Psychomotor Vigilance Task (PVT) during and after each flight, and at waketime, mid-day, and bedtime. Using these methods, it was found that sleep duration was reduced during early starts and late finishes relative to baseline. Circadian phase shifted according to duty schedule, but there was a wide range in the aMT6s peak between individuals on each schedule. PVT performance was worse on the early, high-workload, and late schedules relative to baseline. Overall, the combination of these methods was practical and effective for assessing the influence of sleep loss and circadian phase on fatigue and performance in a complex operational environment.

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Cooperative Prescribed Performance Tracking Control for Multiple High-Speed Trains in Moving Block Signaling System

If high-speed trains move under a moving block signaling (MBS) system, it is a quite challengeable and significant issue to cooperate the multiple high-speed trains, such that they are separated safely and governed steadily. As an intelligent, comprehensive, and advanced modern train operation control system, the MBS system will definitely, in the near future, replace the widely used fixed block signaling system due to its modern communication, multi-source positioning, and advanced control method. Due to the involvement of several subsystems and lots of track-side equipment, such as radio block center (RBC), global system for mobile communications-railway (GSM-R), and so on, it is a nontrivial task to develop a reliable MBS system under complex operational environments, particularly without a pivotally cooperative control method for the movement of multiple high-speed trains. This paper addresses the cooperative control for multiple high-speed trains to achieve prescribed performance tracking, i.e., the speed and the position of high-speed trains are guaranteed to be confined to specific speed limitations and allowed distances ratified by automatic train protection and moving authority, respectively. The proposed control requires no prior information of the empirical parameters of the operational resistances and online adjusts by proper adaptation laws. Theoretical analysis and simulation results are given to demonstrate the effectiveness of the proposed control methods.

Fostering entrepreneurial innovation ecosystems: lessons learned from the European Institute of Innovation and Technology

While business practices and management research has evolved towards more systemic approaches building on the notion of ecosystem to relate organisations with their complex operational environment, the linear model of innovation is still highly pervasive in governmental innovation policies. In this paper we contribute to the narrowing the research gap in the field on ecosystems by combining two often separate streams of literature and subsequently coining the term ‘entrepreneurial innovation ecosystems’. We then examine the European Institute of Innovation and Technology (EIT), a recent policy experiment aimed at developing Pan-European ecosystems to stimulate high-impact innovative entrepreneurship, which is done by integrating higher education, research and business activities (i.e. via knowledge triangle integration). While the high political profile of the EIT has constrained partly its freedom to experiment, a European-wide networked excellence and business logic in managing KICs has created new insights on experimental governance models that merit to be explored further. Building on an action research case study, the paper documents these developments and opens up an avenue for further work regarding experimental governance of Pan-European entrepreneurial innovation ecosystems.

Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN

This paper presents an improved Faster R–CNN model for a field robot platform (FRP) aimed at automatically extracting image features and quickly and accurately detecting maize seedlings during different growth stages under complex field operation environments, with the goal of preparing for intelligent inter-tillage in maize fields. A FRP with five industrial USB cameras for data collection was used to capture a large number of sample images. The shooting angle range of the industrial USB cameras is 0–90°. The photographs were used to create an image database containing twenty thousand images of soil, maize and weeds. Ten selected pretrained networks were used to replace the network of the CNN feature computing component of the classic Faster R–CNN. A Faster R–CNN with VGG19 processed by the pretrained networks method is proposed. The Faster R–CNN algorithm used in this work represents a deep learning architecture that distinguishes maize seedlings and weeds under three field conditions: Full-cycle, Multi-weather and Multi-angle. This work achieved greater than 97.71% precision in the detection of maize seedlings with respect to soil and weeds. The precision rate of six-leaf to seven-leaf maize seedlings was 2.74% lower than that of the total test set. The precision rate under sunny conditions was 1.97% lower than that of the total test set. The precision rate of an angle shot of 0° was 0.95% lower than that of the total test set. The proposed model has significant potential for autonomous weed and maize classification under actual operating conditions.

A new integrated collision risk assessment methodology for autonomous vehicles

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware motion models that take inter-vehicle dependencies into account by utilizing the Bayesian interference are employed to mutually control multiple factors. However, communications between vehicles are often assumed and the developed models are required many parameters to be tuned. Consequently, they are computationally very demanding. Even in the cases where these desiderata are fulfilled, current approaches cannot cope with a large volume of sequential data from organically changing traffic scenarios, especially in highly complex operational environments such as dense urban areas with heterogeneous road users. To overcome these limitations, this paper develops a new risk assessment methodology that integrates a network-level collision estimate with a vehicle-based risk estimate in real-time under the joint framework of interaction-aware motion models and Dynamic Bayesian Networks (DBN). Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. Results indicated an enhancement of the interaction-aware model by up to 10%, when traffic conditions are deemed as collision-prone. Hence, it was concluded that a well-calibrated collision prediction classifier provides a crucial hint for better risk perception by autonomous vehicles.

Risk-informed decision-making in asset management as a complex adaptive system of systems

Decision-making is an essential activity in asset management (AM). It is influenced by various factors (strategic, technical/technological, economic, organisational, regulatory, safety, markets, etc.). Sound decision-making in AM ought to take into account relevant factors in order to balance risks, opportunities, performance, costs and benefits. Additionally, modern organisations evolve in complex operational and business environments and are exposed to significant uncertainties. In such a context, decision-making in AM becomes more challenging. This study proposes a holistic three-step risk-informed decision-making (RIDM) methodology developed for AM, where RIDM is considered a complex adaptive system of systems. The methodology is applied in a case study to analyse possible modification strategies for a nuclear power plant's emergency core cooling system. Through the RIDM process, quantitative models and other factors have been taken into account in order to obtain the necessary comprehensive insights regarding the decision to be made.

Broad Utility: Architecting Flexible and Robust Systems for a Complex Operational Environment

The current and future Operational Environment (OE) for the United States (U.S.) military is becoming increasingly complex. This complexity requires Systems Engineers and Architects to develop new approaches for evaluating the variability inherent in the OEs of today and tomorrow. In response to this growing capability gap, the U.S. Department of Defense (DoD) established the Engineered Resilient Systems (ERS) program. A core property of ERS is Broad Utility—the ability of a system to, “perform effectively in a wide range of operations across multiple futures despite experiencing disruptions.” [1] This paper discusses on-going research intended to provide system designers with an approach to architecting systems for Broad Utility. Specifically, this research seeks to accomplish three objectives: (1) Identify gaps in current U.S. DoD doctrine impacting the ability to architect for Broad Utility; (2) Develop an integrated network model of the Operational Environment that highlights how OE variables can impact system effectiveness; and (3) Propose an approach for ensuring that system architectures exhibit Broad Utility, through a process of mapping system Flexibility and Robustness to the variables of the OE. By employing this approach early in the Systems Engineering process, system designers can increase the likelihood that the resulting system responds appropriately to a changing environment.

Design and Implementation of Plastic Deformation Behavior by Cartesian Impedance Control Based on Maxwell Model

Compliance has become one prerequisite of robots designed to work in complex operation environment where dynamic and uncertain physical contact or impact takes place frequently and even intentionally. Impedance control is a typical complaint control methodology. Standard impedance control is based on dynamics described by a spring and damper model connected in parallel way, which endues the robot an elastic behavior. In contrast, plastic deformation can be realized by Maxwell model in which spring and damper connect in series. In this study, a novel Cartesian impedance controller is constructed based on the Maxwell model. Implementation in a robot manipulator is executed to validate and analyze the proposed control law. A plastic deformation behavior of the robot manipulator is produced and certain extent compliance is achieved under the unpredictable impact or contact force exerted by human or other environment objects.

Online health assessment of wind turbine based on operational condition recognition

To reduce the operation and maintenance (O&M) costs, the health assessment of wind turbine has received more and more attention in recent years. However, it is difficult to evaluate the health condition of wind turbine due to the complex and non-stationary operation environment. This paper proposes a data-driven approach for online health assessment of wind turbine based on operational condition recognition. First, the operational condition parameters are selected by analyzing the monitoring data of wind turbine. Considering the time-varying of operation environment, the operational conditions are divided into four subspaces utilizing the K-means clustering algorithm. Then, using the historical state parameters data under normal operation, a health benchmark model is constructed in each operational condition space based on Gaussian Mixture Model (GMM). Further, a Softmax model is trained according to the results of operational condition classification, which is used to identify the online operational condition of wind turbine. Moreover, an overall health index (HI) based on Mahalanobis distance is developed to assess the health condition of wind turbine. Finally, the method is verified by the actual supervisory control and data acquisition (SCADA) data of a wind field in northwestern China. The test results show that the proposed approach can track the running state of the wind turbine accurately and play a good role in early fault warning.

Press, Politics and Democratic Stability

It’s axiomatic to note that, there is indeed an inextricable or close nexus between the realm of mass communication and that of politics. This to a large extent derives from the goal the two seek to achieve. Communication is the oil that lubricates the engine of political and social relations. Politics is meant to realise the goal of the society or to promote the common good. The myriads of political activities in the state and around its agencies can have great impact on the society at large through the instrumentality of mass communication. Arguably, while the complex operational environments of the mass media in older democracies are relatively vibrant and congenial, the same cannot be said of the new democracies. Yet as agents that are meant to check executive recklessness on the one hand and as instruments of sensitisation and mobilisation, the press in the advanced democracies have their own limitations that can hardly be ignored. The peculiar domain in which mass media operate in the less developed societies raises many germane issues that demands answers. The paper seeks to interrogate the nature and character of politics visible in African states that engendered hostile environment for the press. The study contends that there is the dire need for a paradigm shift from sensational reporting to messages that are capable of tilting the mass of the people towards the realisation of the goal of the society, including democratic stability

The Research on Comprehensive Performance Evaluation of Axial Piston Pump Based on AHP

The axial piston pump is an important industrial power element, and its performance directly affects the operation of the system. However, owing to its complex structure and harsh operational environment, the actual operational performance of the axial piston pump is difficult to be assessed accurately, which makes ensuring the normal operation of the hydraulic system difficult. To improve the evaluation method for an axial piston pump, a comprehensive performance evaluation system was proposed based on analytic hierarchy process (AHP), which could assess the performance of an axial piston pump on the basis of theoretical analysis, test, operator interaction, and application. Considering a model of load-sensitive axial piston pump with good operability as an example, the model development and simulation of the pump were carried out based on AMESim and the pump was tested using a developed performance test bed for axial piston pump. The weights of factors in the evaluation system were determined on the basis of the simulation results, analyses, and calculation. The above results were used to comprehensively analyze the tested pump and design a set of comprehensive performance evaluation software. The evaluation result was nearly identical to the actual usage, which verified the feasibility of the designed evaluation system.

The Conundrum of DDR Coordination: The Case of South Sudan

This article analyses the nature of coordination between the various stakeholders during the design and implementation of a disarmament, demobilisation and reintegration (DDR) process. It makes detailed reference to the contemporary DDR programme in South Sudan as this African country is a relevant example of significant international and local efforts to facilitate DDR coordination in a fragile and complex political and operational environment. The analyses showed that in South Sudan, coordination appeared to have been negatively affected by hierarchical, convoluted and inflexible organisational structures and arrangements. In addition, further contributing factors included: inadequate communication; uncertainty over roles and responsibilities; and unequal access to financial resources. Moreover it was apparent that these arrangements and dynamics fostered inter-organisational tensions and eroded trust between stakeholders. This ultimately resulted in fragmented and sub-standard DDR outcomes.

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy.

Organization: A new focus on mine safety improvement in a complex operational and business environment

The daily operations in the mining industry are still a significant source of risk with regard to occupational safety and health (OS & H). Various research studies and statistical data world-wide show that the number of serious injuries and fatalities still remains high despite substantial efforts the industry has put in recent years in decreasing those numbers. This paper argues that the next level of safety performance will have to consider a transition from coping solely with workplace dangers, to a more systemic model taking organizational risks in consideration. In this aspect, lessons learned from the nuclear industry may be useful, as organizational learning processes are believed to be more universal than the technologies in which they are used. With the notable exception of major accidents, organizational performance has not received all the attention it deserves. A key element for reaching the next level of performance is to include organizational factors in low level events analyses, and approach the management as a risk control system. These factors will then appear not only in the event analysis, but in supervision activities, audits, change management and the like. Many recent event analyses across various industries have shown that organizational factors play a key role in creating conditions for triggering major accidents (aviation, railway transportation, nuclear industry, oil exploitation, mining, etc.). In this paper, a perspective that may be used in supervisory activities, self-assessments and minor events investigations, is presented. When ingrained in an organizational culture, such perspective has the highest potential for continuous safety improvement.

Inverse kinematics research using obstacle avoidance geometry method for EAST Articulated Maintenance Arm (EAMA)

This paper proposed a new method for solving inverse kinematics (IK) of a redundant manipulator called EAST Articulated Maintenance Arm (EAMA), which is applied in the fusion reactor EAST (Experimental Advanced Superconducting Tokamak) and used to complete some maintenance tasks in the complex areas. However, it is difficult to realize remote control due to its redundancy, coupling structure and the complex operational environment. The IK research of the robot played a vital role to the manipulator’s motion control algorithm of remote handling (RH) technology. An Obstacle Topology Partition Projection (OTPP) approach integrated with Modified Inverse Depth First Search (MIDFS) method was presented. This is a kind of new geometric algorithm in order to solve the problem of IK for a high-redundancy manipulator. It can also be used to find a solution satisfying collision avoidance with optimal safety distance between the manipulator and obstacles. Simulations and experiments were conducted to demonstrate the efficiency and accuracy of the proposed method.

Complex Adaptive Behavior of Hybrid Teams

The challenges in uncertain, dynamic and complex military operation environments exceed the problem-solving capabilities of individuals. Problem-solving has become a team task. These [hybrid] teams, which typically include machine and human elements, utilize autonomy and artificial intelligence to enhance the quality of actionable information and decision-making capabilities in solving complex problems. For this to be effective, shared mental models must be developed by teams. This demands adaptive behavior of team members to establish a common understanding, and its members to respond to the changes in complex dynamic environments.In this paper, we introduce a mathematical formalization of an interaction platform designed to support individuals working in heterogeneous, hybrid teams. The purpose of the platform is to facilitate convergent adaptive behavior and interoperability.Hilbert space is used to provide a mathematical foundation and coherent axiomatic structure. Individual and shared mental models are represented in the form of superposition of vector states in a conceptual space. Hilbert Space allows for the inclusion of phenomena, such as spooky activation, entanglement, or emergence that are representative of complex social dynamics.