Monte Carlo Simulation Model Research Articles

Predictive Model of the Effects of Skin Phototype and Body Mass Index on Photobiomodulation Therapy for Orofacial Disorders

Monte Carlo techniques have been extensively used for planning laser-based clinical procedures such as photobiomodulation. However, the effects of several biological tissue characteristics regarding its morphological structure and physiological parameters have not been carefully addressed in many applications. Specifically, many questions remain concerning the effect of skin phototype and body mass index on the effectiveness of photobiomodulation for extraoral therapies. To address these questions, a Monte Carlo simulation model of the effects of body mass index-dependent skin structure on different Fitzpatrick skin types was developed, specifically tailored for the morphological characteristics of cheek tissue. The model describes the settings of a typical oral photobiomodulation treatment protocol for pain relief, namely the use of 660 nm and 808 nm laser wavelengths and a therapeutic dose of 2.0J/cm2 on the masseter muscle. The simulations were used to train a machine learning predictive model aimed at accelerating the treatment planning stage and assessing the importance of patient-specific parameters. A multiple-regression approach was adopted to predict muscle dose and treatment time for effective delivered dose. Body mass index had little effect on epidermal energy deposition, but an important impact on muscle dose parameters. Phototype also influenced muscle dose, but to a lesser extent than body mass index. The results of this study can be used to develop customized dosimetry phototherapy protocols to promote more effective and safe clinical outcomes.

MIMC-β：microdosimetric assessment method for Internal exposure of β-emitters based on mesh-type cell cluster model

Abstract The method combining Monte Carlo (MC) simulation and mesh-type cell models provides a way to accurately assess the cellular dose induced by β-emitters. Although this approach allows for a specific evaluation of various nuclides and cell type combinations, the associated time cost for obtaining results is relatively high. In this work, we propose a Microdosimetric assessment method for Internal exposure of β-emitters based on Mesh-type Cell cluster models (abbreviated as MIMC-β). This approach is applied to evaluate the dose in various types of cells (human bronchial epithelial cells, BEAS-2B; normal human liver cells, L-O2; and normal human small intestine epithelial cells, FHs74Int) exposed to β-emitters. Furthermore, microdosimetric quantity based on the cell cluster model are employed to estimate the RBE of β-emitters. The results indicate that this method can accurately and rapidly predict cellular doses caused by different types of β-emitters, significantly mitigating the efficiency challenges associated with directly employing MC to estimate the overall dose of the mesh-type cell cluster model. In comparison with results obtained from direct simulations of uniform administration of β- sources using PHITS for validation, the cellular cluster overall S-values obtained through MIMC-β show discrepancies mostly below 5%, with the minimum deviation reaching 1.35%. Small sampling sizes within the cell nucleus led to larger average lineal energies. In comparison to C-14, the differences in cellular cluster average lineal energy for Cs-134, Cs-137, and I-131 are negligible, resulting in close numerical estimations of RBE based on lineal energy. The MIMC-β can be extended to diverse cell types and β-emitters. Additionally, the RBE assessment based on the cell cluster model offers valuable insights for predicting radiobiological damage resulting from internal exposure by β-emitters. This method is expected to find applicability in various realistic scenarios, including radiation protection and radioligand therapy.

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

An optimization method for deformable mirror configuration in multi-conjugate adaptive optics systems

Multi-conjugate adaptive optics (MCAO) is a crucial technology for achieving high-resolution imaging over a wide field of view with modern ground-based optical telescopes. The configuration of deformable mirrors (DMs) is a key component in the analysis and optimization of MCAO performance. Currently, the search for the optimal DM configuration often relies on iterative and time-consuming Monte Carlo simulations. This issue arises from the lack of an appropriate optimization method for DM configurations. The primary objective of this paper is to establish an optimization method for DM configurations in MCAO systems. We established a quantitative criterion for evaluating DM configurations by analyzing their correction capabilities for turbulence aberrations at different altitudes. Then, we optimized the DM configurations based on this criterion. This method provides a new theoretical foundation and practical tool for the design and performance optimization of MCAO systems. Based on the pupil phase structure function, we established a DM configuration evaluation criterion, namely the non-conjugate correction index (NCCI). Using NCCI as the optimal criterion, combined with the particle swarm optimization algorithm, we searched for the optimal solution across different DM configuration spaces. We conducted simulations based on the turbulence profiles of typical telescope sites. We validated our proposed theoretical model against Monte Carlo simulation models and find that the NCCI error ranges from 0.05 to 0.1. For optimizing DM conjugate heights, the results of our optimization algorithm differ by less than 1 km from those obtained via Monte Carlo simulations. Regarding the performance of the DM optimization algorithm, the average convergence accuracy error is less than 0.1 km, and the average convergence speed is approximately ten iterations. Additionally, our optimization method runs in just a few minutes; Monte Carlo simulations, in comparison, require several dozen hours.

Dosimetry model for photobiomodulation based on anthropometric and hemodynamic variables in patients with orofacial pain post-Covid-19: Study protocol for randomized clinical trial.

Orofacial pain and tension headache are symptoms that affect a large portion of the population, compromising productivity, social ability, and functional development. The treatment for reducing painful sensation should be chosen carefully, as pharmacological treatment may bring side effects and overload the organism of patients in pain. Low-level laser therapy has been used with local and systemic [vascular] applications for pain control. However, there is still uncertainty in the literature about the ideal dosimetric parameters for photobiomodulation treatment according to patient characteristics. The objective of this project is to validate a dosimetry model based on the relationship between the effects of photobiomodulation with anthropometric and hemodynamic variables, both in local application and systemic application in patients with symptoms of orofacial pain and tension headache. For this purpose, 180 participants with orofacial pain post-covid eligible participants will be randomly assigned to Group 1-Local Photobiomodulation, Group 2-Vascular Photobiomodulation, Group 3-Placebo Local Photobiomodulation, or Group 4-Placebo Vascular Photobiomodulation [Therapy EC-DMC device, São Carlos, Brazil,- 660 nm, 100mW] using stratified block randomization. Before the application, sociodemographic information such as age, skin phototype [classified by the Fitzpatrick scale], weight, height, body mass index [BMI], oxygen saturation [SaO2], blood pressure [BP], heart rate [HR], and thickness of skin, fat, and facial muscles will be collected. During the application, we will collect local temperature, SaO2, BP, and HR. Before and after laser application, blood levels of lactate and hemoglobin, BP, and HR will be measured in the first and last session. In addition to demographic, anthropometric, and hemodynamic variables, the penetrated energy will be quantified using a power meter, and information from orofacial pain and headache symptom questionnaires will be analyzed. The Monte Carlo simulation technique will be used to systematically study the relationship between the light penetration profile into the target tissues and the most relevant variables, namely BMI, tissue layer thicknesses, and skin phototype. Light transmittance, measured in vivo and simulated, will be compared to validate a personalized dosimetry model. The results of this study contribute to validating a Monte Carlo Simulation model to calculate the appropriate dosimetry for photobiomodulation therapies in the control of patients with Post-Covid-19 orofacial pain. Trial registration number: NCT06065969.

Long-Lived Triplets from Singlet Fission in Pentacene-Decorated Helical Supramolecular Polymers.

Singlet fission (SF), which involves the conversion of a singlet excited state into two triplet excitons, holds great potential to boost the efficiency of photovoltaics. However, losses due to triplet-triplet annihilation hamper the efficient harvesting of SF-generated triplet excitons, which limits an effective implementation in solar energy conversion schemes. A fundamental understanding of the underlying structure-property relationships is thus crucial to define design principles for cutting-edge SF materials, yet it remains elusive. Herein, we harness helical supramolecular polymers decorated with pentacene side groups to elucidate intermolecular SF dynamics in solution and promote the formation of long-lived mobile triplets. By leveraging the hydrogen bonding-driven assembly of benzene-1,3,5-tricarboxamide (BTA) cores into one-dimensional scaffolds, we direct the organization of appended pentacene motifs into long-range ordered helical frameworks. Dynamic interactions between weakly coupled SF pendants mediate singlet conversion within hundreds of picoseconds, affording triplet quantum yields well above 100%. Moreover, analysis of triplet dynamics with a Monte Carlo simulation model reveals that triplet diffusion along the supramolecular fibers is favored over annihilation, resulting in independent triplets exhibiting considerably slow decay on the time scale of tens of microseconds. The molecular packing within the assembly is tuned by subtle changes in monomer design to increase the rate and efficiency of SF while ensuring exceptionally long-lived mobile triplets, allowing to maintain triplet quantum yields exceeding 100% for at least 100 ns. This work opens new opportunities to exploit self-assembled supramolecular polymers as functional templates to achieve long-lived SF-generated triplets.

Monte Carlo Simülasyonu ile Yenidoğan Transportunda Fizyolojik Stabilite Risk Skor Değişiminin Tahminlemesi

Yenidoğan yoğun bakım hastalarının transportu yüksek risk ve mortaliteye sahiptir. Bu nedenle transport süreci dikkatle ele alınmalı ve değerlendirilmelidir. Bu sürecin performansının değerlendirildiği transport skorlama sistemleri bulunmaktadır. Bunlardan hastanın fizyolojik stabilite risk skoru değişimini ölçen Transport Risk Index of Physiologic Stability (TRIPS) skorlaması yaygın olarak kullanılmaktadır. TRIPS skor değeri transportun ilk 15 dakika ve sonunda olmak üzere en az iki defa ölçülür. Bu çalışmada 2011-2022 yılları arasında 1117 sayıda hastanın teslim alındığında ilk TRIPS skoru ve teslim edilmeden hemen önce ölçülen TRIPS skor değerleri ile bebeklerin kayıtlı ağırlık parametre değeri alınmıştır. Retrospektif olarak yapılan bu çalışmada hastanın ağırlığına bağlı olarak ölçülen iki TRIPS skor değeri arasındaki fark, Monte Carlo simülasyon modeli ile tahminlenmiştir. Her hasta ağırlık grubu için ayrı ayrı ortalama TRIPS skor farkı taminlenerek, farklı hasta gruplarının transport süreci birbirleri ile kıyaslanarak gruplar arası farklar tespit edilmiş ve süreç analiz edilmiştir. Bu analiz sürecin iyileştirilmesi, planlanması ve kararların alınmasına katkı sağlayacaktır.

Cost Analysis of Pure Hypochlorous Acid Preserved Wound Cleanser versus Mafenide for the Irrigation of Burn Wounds.

Over 40,000 patients in the United States (US) require hospitalization for burns annually. The treatment regimen can cost more than $6,000 a day and requires the use of numerous supplies to ensure the graft takes for successful wound healing. Irrigation of the wound is a critical step for burn treatment, yet little is known about the cost-effectiveness of different irrigation modalities. In a recent study, pure hypochlorous acid preserved wound cleanser (pHA) was shown to be safe and effective compared to mafenide. This study estimated the associated costs of two common wound irrigation modalities, pHA and mafenide solution, for the treatment of patients with burns. In this study, a patient-level Monte Carlo simulation model using data from a randomized control trial (RCT) was used to conduct the cost analysis from the US Hospital perspective. Based upon 100,000 simulated patients, pHA was expected to save $133 ($123 to $144, 10th to 90th percentile) for the hospital compared to using a mafenide solution over 14 days. Adoption of pHA should be considered a cost-saving strategy when treating patients with burns.

Monte Carlo simulation and PMF model for assessing human health risks associated with heavy metals in groundwater: a case study of the Nubian aquifer, Siwa depression, Egypt

Monte Carlo simulation and PMF model for assessing human health risks associated with heavy metals in groundwater: a case study of the Nubian aquifer, Siwa depression, Egypt

Understanding the regulation of protein synthesis under stress conditions

Protein synthesis regulation primarily occurs at translation initiation, the first step of gene translation. However, the regulation of translation initiation under various conditions is not fully understood. Specifically, the reason why protein production from certain mRNAs remains resistant to stress while others do not show such resilience. Moreover, why is protein production enhanced from a few transcripts under stress conditions, whereas it is decreased in the majority of transcripts? We address them by developing a Monte Carlo simulation model of protein synthesis and ribosome scanning. We find that mRNAs with strong Kozak contexts exhibit minimal reduction in translation initiation rate under stress conditions. Moreover, these transcripts exhibit even greater resilience to stress when the scanning speed of 43S ribosome subunit is slow, albeit at the cost of reduced initiation rate. This implies a trade-off between initiation rate and the ability of mRNA to withstand stress. We also show that mRNAs featuring an upstream ORF can act as a regulatory switch. This switch elevates protein production from the main ORF under stress conditions; however, minimal to no proteins are produced under the normal condition. Because, in stress, a larger fraction of 43S ribosomes bypasses the upstream ORF due to its weak Kozak context. This, in turn, increases the number of scanning ribosomes reaching the main ORF, whose strong Kozak context can convert them into 80S ribosomes, even under stress conditions. This switching allows an efficient use of cellular resources by producing proteins when they are required. Thus, our computational study provides valuable insights into our understanding of stress-responsive translation-initiation.

Validation of a PGNAA sensor model for bulk material sorting

Global mineral demand is forecast to increase significantly to achieve the transition to renewable energy. Greater volumes of ore of lower grade will have to be mined to meet demand. Techniques to process large volumes of low-grade ore efficiently are being investigated to reduce the cost and impact of mining. One technique is to use sensor information to sort mined material, allowing waste to be discarded early in mineral processing. Prompt gamma neutron activation analysis (PGNAA) is a sensing technique that can provide information on the multi-elemental composition of a bulk sample which can be used for bulk material sorting. This paper presents the development of a Monte Carlo simulation model of a PGNAA sensor for bulk sorting using the Geant4 toolkit. The GEOSCAN sensor (Scantech Australia) was used as a case-study to demonstrate the application of the model. The sensor responses for a range of pure mineral samples (Fe2O3, SiO2, S, Na2CO3 and MnO2) were measured to validate the developed model. The sensitivity of the simulation results to the hadronic and electromagnetic physics models used was tested. It was determined that the PGNAA sensor model can reproduce measurements obtained from the GEOSCAN sensor. In particular, the model can provide a good reproduction of the overall spectral shape and the locations of distinct characteristic peaks. The differences between simulated and experimental results are within 30% on average. It was found that the Geant4 HP neutron model best reproduces the activation peaks observed in experimental measurements. Additionally, the PGNAA spectrum was found to be insensitive to the choice of electromagnetic model for the photon interactions. The validated sensor model provides a useful tool for investigating PGNAA sensor applications including a bulk sorting strategy for new materials, sensor calibration, improvements in signal analysis and optimised sensor design.

Monte Carlo Simulation, Artificial Intelligence and Machine Learning-based Modelling and Optimization of Three-dimensional Electrochemical Treatment of Xenobiotic Dye Wastewater

The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better.Highlights• In-depth analysis of various artificial intelligence optimization techniques.• Prediction efficiency of artificial intelligence and machine learning algorithms.• 98% dye removal and 100% regeneration of graphite intercalation compound.• Advanced statistical analysis of targeted responses and data fitting techniques.• Analysis of uncertainties and variability using Monte Carlo simulation.

Aerodynamic resuspension of irregular flat micro-particles

This study investigates the role of particle shape on the aerodynamic resuspension process of irregular flat micro-particles on a substrate. We propose that these particles resuspend at higher velocities than spherical ones of the same size under the same aerodynamic forces. Two sets of data are analyzed to test the argument, the first from experiments we conducted using crushed glass particles (ranging from 80 μm to 300 μm) and the second from published data on RDX explosive residue particles (sized between 10 μm and 25 μm) published previously.We particularly analyze the shape factors of the particles used in the experiments and introduce them into a Monte Carlo (MC) simulation model. The probabilities for the time evolution of the resuspension process are calculated through a Markov chain of states. The transition probabilities entail the balance between the forces and moments involved in the mechanisms for particle detachment from the surface.The particle resuspension rate as a function of the fluid velocity is evaluated both experimental and numerically. Additionally, we assess the removal efficiency for different particle size ranges whenever possible.Both experimental and numerical results demonstrate that the resuspension fraction of irregular flat particles is significantly lower than for equally sized glass microspheres under the same conditions. Simulations corroborate previous experimental findings, indicating that smaller irregular particles exhibit higher removal efficiency. According to the MC model results, irregular particles detach by sliding rather than rolling.

Monte Carlo Simulation of Quantum-Cutting Nanocrystals as the Luminophore in Luminescent Solar Concentrators

Quantum-cutting luminescent solar concentrators (QC-LSCs) have great potential to serve as large-area solar windows. These QC nanocrystals can realize a photoluminescence quantum yield (PLQY) of as high as 200% with virtually zero self-absorption loss. Based on our previous work, we have constructed a Monte Carlo simulation model that is suitable to simulate the performance of the QC-LSCs, which can take into account the band-edge emissions and near-infrared emissions of the QC-materials. Under ideal PLQY conditions, CsPbClxBr3−x:Yb3+-based LSCs can reach 12% of the size-independent external quantum efficiency (ηext). Even if LSCs have a certain scattering factor, the CsPbClxBr3−x:Yb3+-based LSCs can still obtain an ηext exceeding 6% in the window size (>1 m2). The flux gain (FG) of the CsPbClxBr3−x:Yb3+-based LSC-PV system can reach 14 in the window size, which is a very encouraging result.

Electron backscattering coefficients for Cr, Co, and Pd solids: A Monte Carlo simulation study

We have calculated electron backscattering coefficients, η(Ep), at primary electron energies Ep of 0.1–100 keV for three elemental and intermediate atomic number solids, Cr, Co and Pd, with an up-to-date Monte Carlo simulation model. A relativistic dielectric functional approach is adopted for the calculation of the electron inelastic cross section, where several different datasets of optical energy loss function (ELF) are adopted. The calculated backscattering coefficient is found to be substantially affected by the ELF, where the influence can be seen to follow the f- and ps-sum rules and the resultant energy dependence of electron inelastic mean free path. To understand the uncertainties involved in a comparison with experimental data both the theoretical uncertainty due to the elastic cross-section model and the experimental systematic error for the contaminated surfaces are investigated. A total of 192 different scattering potentials are employed for the calculation of Mott's electron elastic cross section and this theoretical uncertainty is confirmed to be small. On the other hand, the simulation of contaminated Co and Pd surfaces with several carbonaceous atomic layers can well explain the experimental data. The present results indicate that accurate backscattering coefficient data should be either measured from fully cleaned surfaces or obtained from modern Monte Carlo theoretical calculations involving reliable optical constants data. With the recent progress in the accurate measurement of optical constants by reflection electron energy loss spectroscopy technique, constructing a reliable theoretical database of electron backscattering coefficients for clean surfaces of elemental solids is highly hopeful.

Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Limited test data hinder the accurate prediction of mechanical strength and permeability of permeable cement-stabilized base materials (PCBM). Here we show a kriging-based surrogate model assisted artificial neural network (KS-ANN) framework that integrates laboratory testing, mathematical modeling, and machine learning. A statistical distribution model was established from limited test data to enrich the dataset through the combination of markov chain monte carlo simulation and kriging-based surrogate modeling. Subsequently, an artificial neural network (ANN) model was trained using the enriched dataset. The results demonstrate that the well-trained KS-ANN model effectively captures the actual data distribution characteristics. The accurate prediction of the mechanical strength and permeability of PCBM under the constraint of limited data validates the effectiveness of the proposed framework. As compared to traditional ANN models, the KS-ANN model improves the prediction accuracy of PCBM’s mechanical strength by 21%. Based on the accurate prediction of PCBM’s mechanical strength and permeability by the KS-ANN model, an optimization function was developed to determine the optimal cement content and compaction force range of PCBM, enabling it to concurrently satisfy the requirements of mechanical strength and permeability. This study provides a cost-effective and rapid solution for evaluating the performance and optimizing the design of PCBM and similar materials.

A SGS efficiency calibration method for measuring the radioactive waste drum based on Monte Carlo simulation and function model

A SGS efficiency calibration method for measuring the radioactive waste drum based on Monte Carlo simulation and function model

Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity: A Monte Carlo Simulation Study.

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.

Unleashing the Potential of a Hybrid 3D Hydrodynamic Monte Carlo Risk Model for Maritime Structures’ Design in the Imminent Climate Change Era

Submarine pipelines have become integral for transporting resources and drinking water across large bodies. Therefore, ensuring the stability and reliability of these submarine pipelines is crucial. Incorporating climate change impacts into the design of marine structures is paramount to assure their lifetime safety and serviceability. Deterministic design methods may not fully consider the uncertainties and risks related to climate change compared to risk-based design models. The latter approach considers the future risks and uncertainties linked to climate and environmental changes, thus ensuring infrastructure sustainability. This study pioneers a Hybrid 3D Hydrodynamic Monte Carlo Simulation (HMCS) Model to improve the reliability-based design of submarine pipelines, incorporating the effects of climate change. Current design approaches may follow deterministic methods, which may not systematically account for climate change’s comprehensive uncertainties and risks. Similarly, traditional design codes often follow a deterministic approach, lacking in the comprehensive integration of dynamic environmental factors such as wind, waves, currents, and geotechnical conditions, and may not adequately handle the uncertainties, including the long-term effects of climate change. Nowadays, most countries are developing new design codes to modify the risk levels for climate change’s effects, such as sea-level rises, changes in precipitation, or changes in the frequency/intensity of winds/storms/waves in coastal and marine designs. Our model may help these efforts by integrating a comprehensive risk-based approach, utilizing a 3D hydrodynamic model to correlate diverse environmental factors through Monte Carlo Simulations (MCS). The hybrid model can promise the sustainability of marine infrastructure by adapting to future environmental changes and uncertainties. Including such advanced methodologies in the design, codes are encouraged to reinforce the resilience of maritime structures in the climate change era. The present design codes should inevitably be reviewed according to climate change effects, and the hybrid risk-based design model proposed in this research should be included in codes to ensure the reliability of maritime structures. The HMCS model represents a significant advancement over existing risk models by incorporating comprehensive environmental factors, utilizing advanced simulation techniques, and explicitly addressing the impacts of climate change. This innovative approach ensures the development of more resilient and sustainable maritime infrastructure capable of withstanding future environmental uncertainties.

Composite reliability analysis of electric power systems considering climatic and spatial conditions using self-organizing map

This article presents an innovative approach by integrating different models that represent the failure rates of components related to weather events, considering the geographic location and its variation over time, to a customized Monte Carlo simulation model to ensure the draw synchronized with the load and generation curves, and then quantify the impact of these events through the power flow. The study focuses on using a neural network is trained to classify/filter system states in order to reduce the need for electrical studies and speed up the reliability assessment process. The approach is demonstrated with reference to the IEEE-RTS network test case. The results obtained indicate that the inclusion of renewable sources and climatic conditions have a significant impact on reliability indicators, as failure rates subject to these meteorological conditions tend to increase, providing a greater risk of simultaneous shutdowns, and contributing to the worsening of the system severity index. Therefore, the proposed model allows for a faster and more realistic reliability assessment, directing the planning of expansions and reinforcements and allowing risk monitoring during system operation.