Mirpouya Mirmozaffari

Process Mining (PM) effectively diagnoses inefficiencies in complex healthcare workflows, such as chemotherapy protocols. However, current methodologies often remain retrospective or rely on loosely coupled simulations, leaving a critical methodological void: the inability to quantify the aggregate, system-wide operational impact of eliminating specific, diagnosed workflow deviations. This gap prevents decision-makers from forming evidence-based strategies for resource allocation. We address this by introducing the PM²–Predictive Impact Model (PIM) framework, a novel, fully embedded process-native methodology that unifies conformance checking, predictive monitoring, and quantitative scenario analysis within a singular, closed-loop structure. Using event logs from an Iranian Radiotherapy and Oncology Center, we modeled a normative seven-step pathway (Fitness = 0.97, Precision = 1.00) and identified high-impact deviations, including skipped approvals and resequencing, enabling a direct causal linkage between deviation categories and system performance. PIM simulation demonstrated that removing these deviations yields statistically significant reductions in managerially relevant KPIs: Cycle Time (8.00%) and Workload (6.00%), which were robust to parameter uncertainty (p < 0.001). The PM²–PIM framework thus transforms retrospective diagnosis into proactive, quantitatively justified strategic planning, providing oncology services with a reproducible, low-cost, and evidence-rich basis for prioritizing interventions and achieving sustained performance gains.

High-voltage (HV) cable systems—particularly those insulated with cross-linked polyethylene (XLPE)—are increasingly deployed in both AC and DC applications due to their excellent electrical and mechanical performance. However, their long-term reliability is challenged by partial discharges (PD), insulation aging, space charge accumulation, and thermal and electrical stresses. This review provides a comprehensive survey of the state-of-the-art technologies and methodologies across several domains critical to the assessment and enhancement of cable reliability. It covers advanced condition monitoring (CM) techniques, including sensor-based PD detection, signal acquisition, and denoising methods. Aging mechanisms under various stressors and lifetime estimation approaches are analyzed, along with fault detection and localization strategies using time-domain, frequency-domain, and hybrid methods. Physics-based and data-driven models for PD behavior and space charge dynamics are discussed, particularly under DC conditions. The article also reviews the application of numerical tools such as FEM for thermal and field stress analysis. A dedicated focus is given to machine learning (ML) and deep learning (DL) models for fault classification and predictive maintenance. Furthermore, standards, testing protocols, and practical issues in sensor deployment and calibration are summarized. The review concludes by evaluating intelligent maintenance approaches—including condition-based and predictive strategies—framed within real-world asset management contexts. The paper aims to bridge theoretical developments with field-level implementation challenges, offering a roadmap for future research and practical deployment in resilient and smart power grids. This review highlights a clear gap in fully integrated AC/DC diagnostic and aging analyses for XLPE cables. We emphasize the need for unified physics-based and ML-driven frameworks to address HVDC space-charge effects and multi-stress degradation. These insights provide concise guidance for advancing reliable and scalable cable assessment.

As the global transition to sustainable energy accelerates, wind power remains pivotal in reducing carbon emissions and achieving renewable energy targets. However, greater reliance on wind energy systems increases susceptibility to cyberattacks, notably False Data Injection (FDI) attacks, which manipulate operational data and undermine the decision-making critical for efficient energy production. This study introduces a novel analytical framework to assess the impact of FDI attacks on variable-speed wind turbine output power. Simulations, conducted using a MATLAB-based induction generator model, evaluate the effects of injecting false data into parameters such as wind speed, blade pitch angle, and generator angular speed. Results demonstrate that FDI attacks targeting wind speed induce significant power output deviations, causing decision-making errors that threaten operational reliability. In contrast, pitch angle manipulations have negligible effects on power generation. These findings emphasize the urgent need for robust cybersecurity measures to protect wind energy infrastructure from evolving cyber-threats. This research advocates advanced detection and mitigation strategies to enhance system resilience, ensuring wind power’s role in a low-carbon future. By identifying critical vulnerabilities, the analysis informs policymakers and industry stakeholders, guiding investments in cybersecurity to safeguard renewable energy systems. Such efforts are essential to maintain operational stability and support global sustainability goals, reinforcing wind power’s contribution to clean energy transitions.

This paper introduces a two-stage protection coordination framework designed for grid-connected and islanded microgrids (MGs) that integrate distributed generations (DGs) and energy storage systems (ESSs). The first stage focuses on determining the optimal location and sizing of DGs and ESSs within the islanded MG to ensure a stable and reliable operation. The objective is to minimize the combined annual investment and expected operational costs while adhering to the optimal power flow equations governing the MG, which incorporates both DGs and ESSs. To account for the inherent uncertainties in load and DG power generation, scenario-based stochastic programming (SBSP) is used to model these variations effectively. The second stage develops the optimal protection coordination strategy for both grid-connected and islanded MGs, aiming to achieve a rapid and efficient protective response. This is achieved by optimizing the settings of dual-setting overcurrent relays (DSORs) and determining the appropriate sizing of fault current limiters (FCLs), using operational data from the MG’s daily performance. The goal is to minimize the total operating time of the DSORs in both primary and backup protection modes while respecting critical constraints such as the coordination time interval (CTI) and the operational limits of DSORs and FCLs. To solve this complex optimization problem, the Crow Search Algorithm (CSA) is employed, ensuring the derivation of reliable and effective solutions. The framework is implemented on both 9-bus and 32-bus MGs, demonstrating its practical applicability and evaluating its effectiveness in real-world scenarios. The proposed method achieves an expected total daily relay operation time of 1041.36 s for the 9-bus MG and 1282 s for the 32-bus MG. Additionally, the optimization results indicate a reduction in maximum voltage deviation from 0.0073 p.u. (grid-connected mode) to 0.0038 p.u. (islanded mode) and a decrease in daily energy loss from 1.0114 MWh to 0.9435 MWh. The CSA solver outperforms conventional methods, achieving a standard deviation of 1.13% and 1.21% for two optimization stages, ensuring high reliability and computational efficiency. This work not only provides valuable insights into the optimization of MG protection coordination but also contributes to the broader effort of enhancing the reliability and economic viability of microgrid systems, which are becoming increasingly vital for sustainable energy solutions in modern power grids.

Wind power output fluctuations, driven by variable wind speeds, create significant challenges for grid stability and the efficient use of wind turbines, particularly in high-wind-penetration areas. This study proposes a combined approach utilizing an ultra-capacitor energy storage system and fuzzy-control-based pitch angle adjustment to address these challenges. The fuzzy control system dynamically responds to wind speed variations, optimizing energy capture while minimizing mechanical stress on turbine components, and the ultra-capacitor provides instantaneous buffering of power surpluses and deficits. Simulations conducted on a 50 kW DFIG wind turbine powering a 23 kW load demonstrated a substantial reduction in power fluctuations by a factor of 3.747, decreasing the power fluctuation reduction scale from 13.04% to 3.48%. These results highlight the effectiveness of the proposed system in improving the stability, reliability, and quality of wind energy, thereby advancing the broader adoption of renewable energy and contributing to sustainable energy solutions.

The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA’s status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

Today, the chemical corrosion of metals is one of the main problems of large productions, especially in the oil and gas industries. Due to massive downtime connected to corrosion failures, pipeline corrosion is a central issue in many oil and gas industries. Therefore, the determination of the corrosion progress of oil and gas pipelines is crucial for monitoring the reliability and alleviation of failures that can positively impact health, safety, and the environment. Gas transmission and distribution pipes and other structures buried (or immersed) in an electrolyte, by the existing conditions and due to the metallurgical structure, are corroded. After some time, this disrupts an active system and process by causing damage. The worst corrosion for metals implanted in the soil is in areas where electrical currents are lost. Therefore, cathodic protection (CP) is the most effective method to prevent the corrosion of structures buried in the soil. Our aim in this paper is first to investigate the effect of stray currents on failure rate using the condition index, and then to estimate the remaining useful life of CP gas pipelines using an artificial neural network (ANN). Predicting future values using previous data based on the time series feature is also possible. Therefore, this paper first uses the general equipment condition monitoring method to detect failures. The time series model of data is then measured and operated by neural networks. Finally, the amount of failure over time is determined.

1. Discuss the study's limitations and future research suggestions. 2. I strongly suggest that the paper be proofread and reread meticulously again, particularly regarding the spelling and grammatical mistakes. 3. Flowchart is beneficial; it’s als...

In this paper, the problem of finding an assignment of “n” surgeries to be presented in one of “m” identical operating rooms (ORs) or machines as the surgical case scheduling problem (SCSP) is proposed. Since ORs are among NP-hard optimization problems, mathematical and metaheuristic methods to address OR optimization problems are used. The job or surgical operation ordering in any OR is a permanent part of all sequencing and scheduling problems. The transportation times between ORs are defined based on the type of surgical operations and do not depend on distance, so there is no surgical operation waiting time for transferring. These problems are called no-wait open-shop scheduling problems (NWOSP) with transportation times. The transportation system for the problems is considered a multi-transportation system with no limitation on the number of transportation devices. Accordingly, this study modeled a novel combined no-wait open-shop surgical case scheduling problem (NWOSP-SCSP) with multi-transportation times for the first time to minimize the maximum percentile of makespan for OR as a single objective model. A mixed-integer linear program (MILP) with small-sized instances is solved. In addition to the small-sized model, a novel metaheuristic based on a hybrid simulated annealing (SA) algorithm to solve large-sized problems in an acceptable computational time is suggested, considering the comparison of the SA algorithm and a new recommended heuristic algorithm. Then, the proposed hybrid SA and SA algorithms are compared based on their performance measurement. After reaching the results with a numerical analysis in Nova Scotia health authority hospitals and health centers, the hybrid SA algorithm has generated significantly higher performance than the SA algorithm.