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The childbirth process is a complex and vital event that requires careful analysis and improvement. This experience can shape a woman's perspective on motherhood and even affect her mental health. Healthcare providers must prioritize improving the birth experience for women. In this interdisciplinary research, a combination of business process modeling (BPM) and medicine have been used with the aim of realizing an improved delivery experience and increased maternal satisfaction.

The data collection of this study was done by observing 518 childbirth processes and interviewing the chief of labor, chief residents, and midwives in the obstetrics and gynecology department of a hospital in Tehran from October 2022 to February 2023.

The research has been done in four main stages. The first phase is to model the primary process and sub-processes of normal vaginal delivery (NVD). The second phase is validation using expert confirmation and process mining (PM). The third phase is the analysis of the causes of maternal dissatisfaction in labor. The fourth phase of the heuristics redesigning and improving the process, in which for the first time three new categories have been presented including hospital-based, patient-based, and medical technique-based results show BPM intervention effect can be far-reaching in improving patient care and optimizing operational efficiency.

This study is one of only a few to adopt a process-oriented perspective to show how BPM can be used in clinical processes and has specifically examined an essential clinical process, i.e. childbirth.

1. Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.

2. A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.

3. Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.

4. Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.

5. Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.

6. Providing a framework for clinical process modeling and improvement containing steps and tools.

Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.

A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.

Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.

Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.

Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.

Providing a framework for clinical process modeling and improvement containing steps and tools.

A hydraulic elevator including the hydraulic actuator and cabin is highly nonlinear with many parameters and variables. Its state-space model is in non-companion form and uncertain due to the parametric errors, flexibility of the ropes, friction and external load disturbances. A model-based control cannot perform well while a precise model is not available and all state variables cannot be measured. To overcome the problems, this paper aims to develop a direct adaptive fuzzy control (DAFC) for the hydraulic elevator.

The controller is an adaptive PD-like Mamdani type fuzzy controller using position error and velocity error as inputs. The design is based on the stability analysis.

The proposed control can overcome uncertainties, guarantee stability, provide a good tracking performance and operate as active vibration suppression by tracking a smooth trajectory. The controller is not involved in the nonlinearity, uncertainty and vibration of the system due to being free from model. Its performance is superior to a PD-like fuzzy controller due to being adaptive as illustrated by simulations.

The proposed DAFC is applied for the first time on the hydraulic elevator. Compared to classic adaptive fuzzy, it does not require all system states. In addition, it is not limited to the systems, which have the state-space model in companion form and constant input gain, thus is much less computational and easier to implement.

The vehicle routing problem (VRP) is critical for the successful execution of logistics activities. However, there is strong evidence that efficiently solving the VRP is often complicated and requires more powerful – and possibly intelligent – support tools. In accordance with this necessity, the present study proposes a decision support system (DSS) applicable to the VRP, which includes both initial planning and replanning phases to support the real-time operations.

The proposed DSS lies at the basis of resilience thinking to provide a capacity to absorb and withstand the impact of disruptions, where resilience is connected with the factors of preparedness, flexibility and redundancy. These factors are approached in this study through a number of operational strategies in the reactive and proactive modes. The DSS includes a multi-layer perceptron neural network to predict changes that may arise in dynamic contexts, a modified k-means clustering algorithm to group customers with both static and dynamic attributes and two mixed-integer programming models to produce primary and alternate routing plans.

The research is motivated by the operational challenges faced by a collaborative networked clinical laboratory, which seeks to enhance efficiency and productivity in the daily management of medical sample collection and delivery through the implementation of increased automation. The findings reveal that centralized planning leads to heightened vulnerability in route planning and increased costs for replanning. Furthermore, the effectiveness of resilience-enhancement strategies varies based on the source and level of uncertainty.

The contributions of this paper are as follows: incorporating resilience thinking into the operational planning of logistics services, managing the decision-making of transport and collection companies through a DSS framework to ensure proper support to real-time operations, addressing the clustered VRP in a dynamic setting and adopting forecasting approaches to cover possible sources of dynamism.

This paper aims to evaluate the effect of optimal use of rooftop photovoltaic (PV) systems on improving the loss of life (LOL) of distribution transformers, reducing power losses as well as the unbalance rate of the 69-bus distribution network.

The problem is studied in three scenarios, considering different objective functions as multi-objective optimization in balanced and unbalanced operations. Meta-heuristic golden ratio optimization method (GROM) is used to determine the optimal size of the rooftop PV in the network.

The simulation results show that in all scenarios, the GROM by optimally installing the rooftop PV is significantly capable to reduce the transformer distribution loss of loss, unbalance rate and power loss as well as reduce the temperature of the oil and transformer winding. Also, the lowest %LOL, power loss and unbalance rate occurred in the second scenario for the balanced network and first scenario, respectively. In addition, the results showed that the unbalance of the network results in increased power losses and LOL of the distribution transformer.

The better capability of GROM is proved compared with the grey wolf optimization algorithm with better objective function and by achieving better values of LOL, unbalance rate and power loss. The results also showed that the %LOL, unbalance and power losses are weakened compared to without considering the PV cost but the achieved results are realistic and cost-effective.