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Small highly saturated interior permanent magnet- synchronous machines (IPMSMs) show a very nonlinear behaviour. Such machines are mostly controlled with a closed-loop cascade control, which is based on a d-q two-axis dynamic model with constant concentrated parameters to calculate the control parameters. This paper aims to present the identification of a complete current- and rotor position-dependent d-q dynamic model, which is derived by using a finite element method (FEM) simulation. The machine’s constant parameters are determined for an operation on the maximum torque per ampere (MTPA) curve. The obtained MTPA control performance was evaluated on the complete FEM-based nonlinear d-q model.

A FEM model was used to determine the nonlinear properties of the complete d-q dynamic model of the IPMSM. Furthermore, a fitting procedure based on the nonlinear MTPA curve is proposed to determine adequate constant parameters for MTPA operation of the IPMSM.

The current-dependent d-q dynamic model of the machine models the relevant dynamic behaviour of the complete current- and rotor position-dependent FEM-based d-q dynamic model. The most adequate control response was achieved while using the constant parameters fitted to the nonlinear MTPA curve by using the proposed method.

The effect on the motor’s steady-state and dynamic behaviour of differently complex d-q dynamic models was evaluated. A workflow to obtain constant set of parameters for the decoupled operation in the MTPA region was developed and their effect on the control response was analysed.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

This article aims to establish a dynamic Energy Performance Contract (EPC) risk allocation model for commercial buildings based on the theory of Incomplete Contract. The purpose is to fill the policy vacuum and allow stakeholders to manage risks in energy conservation management by EPCs to better adapt to climate change in the building sector.

The article chooses a qualitative research approach to depict the whole risk allocation picture of EPC projects and establish a dynamic EPC risk allocation model for commercial buildings in China. It starts with a comprehensive literature review on risks of EPCs. By modifying the theory of Incomplete Contract and adopting the so-called bow-tie model, a theoretical EPC risk allocation model is developed and verified by interview results. By discussing its application in the commercial building sector in China, an operational EPC three-stage risk allocation model is developed.

This study points out the contract incompleteness of the risk allocation for EPC projects and offered an operational method to guide practice. The reasonable risk allocation between building owners and Energy Service Companies can realize their bilateral targets on commercial building energy-saving benefits, which makes EPC more attractive for energy conservation.

Existing research focused mainly on static risk allocation. Less research was directed to the phased and dynamic risk allocation. This study developed a theoretical three-stage EPC risk allocation model, which provided the theoretical support for dynamic EPC risk allocation of EPC projects. By addressing the contract incompleteness of the risk allocation, an operational method is developed. This is a new approach to allocate risks for EPC projects in a dynamic and staged way.

Evaluating the economic effects of climate change is a pivotal step for planning adaptation in developing countries. For Bangladesh, global warming has put it among the most vulnerable countries in the world to climate change, with increasing temperatures and sea-level rise. Hence, the purpose of this paper is to examine how climate change impacts the economy in Bangladesh in the case of climate scenarios.

Using a dynamic computable general equilibrium (CGE) model and three climate change scenarios, this paper assesses the economy-wide implications of climate change on Bangladesh’s economy and agriculture. It is clear from the examination of the CGE model that the impacts of climate change on agricultural sectors were felt more sharply, reducing output by −3.25% and −3.70%, respectively, and increasing imports by 1.22% and 1.53% in 2030 and 2050, compared to the baseline.

The findings reveal that, relative to baseline, agricultural output will decline by a range of −3.1% to −3.6% under the high climate scenario (higher temperatures and lower yields). A decrease in agricultural output results in declines in agricultural labor and household income. Household income falls in all categories, although it drops the most in urban less educated households with a range of −3.1% to −3.4%. On the other hand, consumption of commodities will fall by −0.11% to −0.13%, according to the findings. Although climate change impacts had a relatively small effect on gross domestic product, reducing it by −0.059% and −0.098% in 2030 and 2050, respectively.

As agricultural output, household consumption and income decline, it will impact the majority of the population’s health in Bangladesh by increasing malnutrition, hidden hunger, poverty, changing food environment, changing physical and mental health status and a changing health-care environment. Therefore, population health and food security will be a top socioeconomic and political concern for Bangladesh Government.

The examination of the dynamic CGE model is its originality. In conclusion, the evidence generated here can provide important information to policymakers and guide government policies that contribute to national development and the achievement of food security targets. It is also necessary to put more emphasis on climate change issues and address potential risks in the following years.

Digital transformation is a foundational change in how firms operate and deliver value to customers by using digital technologies to create new business opportunities. The purpose of this study is to offer a conceptual framework by reorganizing the elements of digital transformation, including resources, technology, capabilities and performance, into a workable process and investigating how firms integrate these resources, build new capabilities and transform them into enhanced performance.

This framework builds three blocks: resource integration, organizational capabilities and outcomes, exploring the impact of resource integration on outcomes through organizational capabilities. For resource integration, this study adopts a resource-based view (RBV) and service-dominant logic (SDL) to integrate organizational resources, including information technology (IT)-based resources, which play a role in moderating the effect of resource integration. Moreover, the author argues that firms’ capabilities have two levels: higher-order capabilities and lower-order capabilities, which will convert these resources through the capabilities into organizational performance.

This framework is built to understand the process of digital transformation and its antecedents for firms’ performance in business environments. Drawing on RBV, it provides a more holistic perspective that has been linked to resource integration, organizational capabilities and outcomes at the firm level. In this way, the theoretical basis for diminishing implicitness associated with the current perspective of digital transformation can be strengthened.

This paper offers a coherent discussion of digital transformation and explains the process of digital transformation, thus advancing prior work. The major contribution is connecting the process of digital transformation through which firms integrate resources, i.e. digital technologies and valuable, rare, inimitable and nonsubstitutable (VRIN) and nonVRIN resources as well, to build organizational dynamic capabilities based on RBV and SDL.

This study aims to determine the factors and dynamic systems behaviour of essential medicine stockout in public health-care supply chains. The authors examine the constraints and effects of mental models on medicine stockout to develop a dynamic theory of medicine availability towards saving patients’ lives.

This study uses a mixed-method approach. Starting with a survey method, followed by in-depth interviews with stakeholders within five health-care supply chains to determine the dynamic feedback leading to stockout and conclude by developing a network mental model for medicines availability.

The authors identified five constraints and developed five case mental models. The authors develop a dynamic theory of medicine availability across cases and identify feedback loops and variables leading to medicine availability.

The need to include mental models of stakeholders like manufacturers and distributors of medicines to understand the system completely. Group surveys are prone to power dynamics and bias from group thinking. This survey’s quantitative output could minimize the bias.

This study uniquely uses a mixed-method of survey method and in-depth interviews of experts to assess the essential medicine stockout in Nigeria. To improve medicine availability, the authors develop a dynamic network mental model to understand the system structure, feedback and behaviour driving stockouts. This research will benefit public policymakers and hospital managers in designing policies that reduce medicine stockout.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Tandem wing aircrafts belong to an unconventional configurations group, and this type of design is characterised by a strong aerodynamic coupling, which results in lower induced drag. The purpose of this paper is to determine whether a certain trend in the wingspan impact on aircraft dynamic stability can be identified. The secondary goal was to compare the response to control of flaps placed on a front and rear wing.

The aerodynamic data and control derivatives were obtained from the computational fluid dynamics computations performed by the MGAERO software. The equations of aircraft longitudinal motion in a state space form were used. The equations were built based on the aerodynamic coefficients, stability and control derivatives. The analysis of the dynamic stability was done in the MATLAB by solving the eigenvalue problem. The response to control was computed by the step response method using MATLAB.

The results of this study showed that because of a strong aerodynamic coupling, a nonlinear relation between the wing size and aircraft dynamic stability proprieties was observed. In the case of the flap deflection, stronger oscillation was observed for the front flap.

Results of dynamic stability of aircraft in the tandem wing configuration can be found in the literature, but those studies show outcomes of a single configuration, while this paper presents a comprehensive investigation into the impact of wingspan on aircraft dynamic stability. The results reveal that because of a strong aerodynamic coupling, the relation between the span factor and dynamic stability is nonlinear. Also, it has been demonstrated that the configuration of two wings with the same span is not the optimal one from the aerodynamic point of view.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.