Search Results (4,879)

Background/Objectives: Several population pharmacokinetic models of vedolizumab (VDZ) are available for inflammatory bowel disease (IBD) patients. However, their predictive performance in real-world clinical settings remains unknown. This study aims to externally evaluate the published VDZ pharmacokinetic models, focusing on their predictive performance and simulation-based clinical applicability. Methods: A literature search was conducted through PubMed to identify VDZ population pharmacokinetic models. A total of 114 VDZ concentrations from 106 IBD patients treated at the University Medical Center “Zvezdara”, Republic of Serbia, served as the external evaluation cohort. The predictive performance of the models was assessed using prediction- and simulation-based diagnostics. Furthermore, the models were utilized for Monte Carlo simulations to generate concentration–time profiles based on 24 covariate combinations specified within the models. Results: Four published pharmacokinetic models of VDZ were included in the evaluation. Using the external dataset, the median prediction error (MDPE) ranged from 13.82% to 25.57%, while the median absolute prediction error (MAPE) varied between 41.64% and 47.56%. None of the models fully met the combined criteria in the prediction-based diagnostics. However, in simulation-based diagnostics, pvcVPC showed satisfactory results, despite wide prediction intervals. Analysis of NPDE revealed that only the models by Rosario et al. and Okamoto et al. fulfilled the evaluation criteria. Simulation analysis further demonstrated that the median VDZ concentration remains above 12 μg/mL at week 22 during maintenance treatment for approximately 45–60% of patients with the best-case covariate combinations and an 8-week dosing frequency. Conclusions: None of the published models satisfied the combined criteria (MDPE, MAPE, percentages of prediction error within ±20% and ±30%), rendering them unsuitable for a priori predictions. However, two models demonstrated better suitability for simulation-based applications. Full article

Count data often exhibit discrepancies in the frequencies of zeros, which commonly occur across various application domains. These data may include excess zeros (zero inflation) or, less frequently, a scarcity of zeros (zero deflation). In regression models, both situations can arise at different levels of covariates. The zero-modified power series regression model provides an effective framework for modeling such count data, as it does not require prior knowledge of the type of zero modification, whether zero inflation or zero deflation, and can accommodate overdispersion, equidispersion, or underdispersion present in the data. This paper proposes a Bayesian estimation procedure based on the stochastic gradient Hamiltonian Monte Carlo algorithm, effectively addressing many challenges associated with estimating the model parameters. Additionally, we introduce a measure of Bayesian efficiency to evaluate the impact of prior information on parameter estimation. The practical utility of the proposed method is demonstrated through both simulated and real data across different types of zero modification. Full article

In this work, we study the magnetocaloric effect (MCE) in a working substance corresponding to a square lattice of spins with Q possible orientations, known as the “Q-state clock model”. When the Q-state clock model has Q≥5 possible configurations, it presents the famous Berezinskii–Kosterlitz–Thouless (BKT) phase associated with vortex states. We calculate the thermodynamic quantities using Monte Carlo simulations for even Q numbers, ranging from Q=2 to Q=8 spin orientations per site in a lattice. We use lattices of different sizes with N=L×L=82,162,322,642,and1282 sites, considering free boundary conditions and an external magnetic field varying between B=0 and B=1.0 in natural units of the system. By obtaining the entropy, it is possible to quantify the MCE through an isothermal process in which the external magnetic field on the spin system is varied. In particular, we find the values of Q that maximize the MCE depending on the lattice size and the magnetic phase transitions linked with the process. Given the broader relevance of the Q-state clock model in areas such as percolation theory, neural networks, and biological systems, where multi-state interactions are essential, our study provides a robust framework in applied quantum mechanics, statistical physics, and related fields. Full article

This paper constructs a simulation platform for underwater wireless optical single-input single-output (SISO) communication systems and quantitatively evaluates communication performance indicators. To improve channel capacity, we propose an angular circle array MIMO scheme. The path loss, CIR, and channel capacity of the angular circular array MIMO communication system are calculated by using the Monte Carlo method. Results show that the proposed angular circular array MIMO communication system has a higher channel capacity compared to planar circular array MIMO communication systems and SISO communication systems. Full article

Species richness is a crucial factor in maintaining ecological balance and promoting ecosystem services. However, simulating population dynamics is a complex task that requires a comprehensive understanding of ecological systems. The current tools for wildlife research face three major challenges: insufficient multi-view assessment, a high learning curve, and a lack of seamless secondary development with Python. To address these issues, we developed a novel software tool named WAPET (Wildlife Analysis and Population Ecology Tool) (Python 3.10.12). WAPET integrates Monte Carlo simulation with ecological models, including Logistic Growth, Random Walk, and Cellular Automata, to provide a multi-perspective assessment of ecological systems. Our tool employs a fully parameterized input paradigm, allowing users without coding to easily explore simulations. Additionally, WAPET’s development is entirely Python-based, utilizing PySide6 and Mesa libraries and enabling seamless development in Python environments. Our contributions include the following: (I) integrating multiple ecological models for a comprehensive understanding of ecological processes, (II) developing a no-code mode of human–computer interaction for biodiversity stakeholders and researchers, and (III) implementing a Python-based framework for easy extension and customization. WAPET bridges the gap between comprehensive modeling capabilities and user-friendly interfaces, positioning itself as a versatile tool for both experienced researchers and non-computational stakeholders in biodiversity decision-making processes. Full article

Energy consumption modeling in buildings is crucial for calculating energy performance indices and establishing criteria for energy labeling. Different countries utilize diverse approaches to calculate these indices based on energy efficiency regulations and classifications. In recent years, Iran has established energy compliance standards, outlined in Article 19 of the National Building Regulations, to improve the energy efficiency of buildings. This study aims to develop a systematic methodology for assessing energy consumption indicators in residential buildings using the criteria specified in the Iranian National Building Regulations. Our research examines three specific energy standard categories in residential buildings to evaluate the suitability of the energy compliance specifications and identify the distribution of energy indices, rather than relying solely on the fixed values prescribed in the regulations. Initially, three model building shapes were analyzed to demonstrate how different building envelope designs affect energy performance. This study fills a critical research gap by estimating energy consumption indices through a novel methodology that combines regression analysis and Monte Carlo simulation for the three energy classifications specified in Article 19 of the Iranian National Building Regulations. The study employs a permutation approach to evaluate the primary energy consumption indicators and the uncertainties arising from various adjacency configurations. Extensive simulations were conducted, resulting in the development of regression equations that account for the surface area of the building envelope adjacent to the outdoor environment. The Monte Carlo method was used to assess potential fluctuations in the adiabatic area of the building envelope and the area adjacent to the external environment for buildings with varying orientations, allowing for the generation of probability distributions for energy consumption intensities. The sensitivity analysis identified the critical components of the building envelope and their orientation that significantly impact the uncertainty of energy efficiency. The findings revealed that the west and east walls of buildings adjacent to the outdoor environment substantially influence the uncertainty of energy consumption. In contrast, the floor surface and south wall had the least significant effect on annual energy uncertainty. This innovative approach represents a significant advancement in the field. It plays a specific role in energy labeling for buildings by calculating the required standard deviation in energy consumption indices resulting from various envelope adjacencies. This research also has practical implications for building design and energy efficiency measurement. Full article

In this study, models for NOEL (No Observed Effect Level) and NOEC (No Observed Effect Concentration) related to long-term/reproduction toxicity of various organic pesticides are built up, evaluated, and compared with similar models proposed in the literature. The data have been obtained from the EFSA OpenFoodTox database, collecting only data for the Bobwhite quail (Colinus virginianus). Models have been developed using the CORAL-2023 program, which can be used to develop quantitative structure–property/activity relationships (QSPRs/QSARs) and the Monte Carlo method for the optimization of the model. The software provided a model which may be considered useful for the practice. The determination coefficient of the best models for the external validation set was 0.665. Full article

This article presents a kinetic model based on the BGK equation to simulate pedestrian dynamics, with a specific focus on anxiety conditions. The proposed model is based on the idea that for pedestrians in equilibrium, where this term is used to indicate that the system is characterized by a symmetric equilibrium velocity distribution with a relaxation term, the anxiety factor is incorporated into the equilibrium distribution through the preferred velocity, leading to potential symmetry-breaking effects in pedestrian dynamics. In addition, this paper introduces a numerical resolution scheme using the Monte Carlo particle method that effectively captures both symmetric and asymmetric behaviors of pedestrians. This method is applied to simulate crowd evacuation during stressful situations and pedestrian bidirectional flow in a straight corridor. Full article

Ionized metal physical vapor deposition (IMPVD), which is operated at a very low pressure to take advantage of the metal sputtering effect on the target surface, has unique properties compared with conventional DC magnetron sputtering. In this study, we investigated the effect of the rotating magnetic field on the plasma formation of IMPVD to enhance the deposition uniformity. This was accomplished through a multi-step simulation, which enabled plasma analysis, sputtered particle and chemical reaction analysis, and deposition profile analysis. A two-dimensional particle-in-cell Monte Carlo simulation utilizes the exact cross-section data of the Cu ion collisions and calculates the particle trajectories under specific magnetic field profiles. This new methodology gives guidance for the design of the magnetic field profiles of IMPVD and an understanding of the physical mechanism. Full article

Chocolate is one of the most popular and widely consumed confectionery products. However, elevated cadmium (Cd) content in this commodity threatens food safety and human health. It is crucial to monitor the presence of Cd in chocolate and to evaluate its associated health risks. This study assessed the Cd levels in milk and dark chocolates from the Serbian market (n = 155). Cadmium concentrations varied between 0.010 and 0.29 mg/kg. The obtained values were used to evaluate the hazard quotient (HQ) and cancer risk (CR). The estimated weekly intakes (EWIs) were below the tolerable limits for all samples. However, in some samples, the EWI reached 60.9% and 63.5% of the tolerable limit for toddlers and other children, respectively. No health risk was found based on the HQ. On the other hand, based on CR values, all chocolate products can be classified as posing a moderate risk. The Monte Carlo simulation indicated that toddlers and other children were more exposed to non-carcinogenic risk, whereas vegetarians, adults, pregnant women, and other children were more exposed to cancer risk. Sensitivity analysis indicates that body weight, exposure frequency, and ingestion rate are the most influential factors for non-cancer and cancer health risks. Full article

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources. Full article

Among the coronal mass ejections (CMEs) and solar proton events (SPEs) frequently observed by near-Earth spacecraft, the SPE that occurred on 28 October 2021 stands out as a remarkable research event. This is due to the infrequency of reported ground-level enhancements it induced. The CSES (China seismo-electromagnetic satellite) is equipped with high-energy particle detectors, namely, HEPP and HEPD, capable of measuring protons within an energy range of 2 MeV to 143 MeV. These detectors provide valuable opportunities for studying solar activity. By utilizing the Monte Carlo method to simulate the pile-up effect and accounting for the detector’s dead time, with the assistance of real-time incident counting rates, we successfully corrected the spectra in the 10–50 MeV range. The energy spectrum is important for understanding solar proton events. We used the data from the HEPP (high-energy particle package) and HEPD (high-energy particle detector) to obtain the total event-integrated spectrum, which possessed good continuity. Additionally, we compared the observations from the CSES with those from the NOAA satellite and achieved reasonable agreement. We also searched for ground-based responses to this solar activity in China and discovered Forbush decreases detected by the Yang Ba Jing Muon Telescope experiment. In conclusion, the HEPP and HEPD can effectively combine to study solar activity and obtain a smooth and consistent energy spectrum of protons across a very wide energy range. Full article

Micro-nozzles are essential for enabling precise satellite attitude control and orbital maneuvers. Accurate prediction of performance parameters, including thrust and specific impulse, is critical, necessitating careful design of these nozzles. Given the high Knudsen numbers associated with micro-nozzle flows, rarefied gas dynamics often dominate, and conventional computational fluid dynamics (CFD) methods fail to capture accurate flow expansion behavior. The Direct Simulation Monte Carlo (DSMC) method, developed by Bird, is widely used for modeling rarefied flows; however, it has been primarily implemented on platforms like OpenFOAM and FORTRAN, with limited exploration in MATLAB. This study presents the development of a viscosity-based DSMC (μDSMC) simulation framework in MATLAB for analyzing rarefied gas expansion through micro-nozzles. Key boundary conditions, including upstream and downstream pressure conditions and thermal wall treatments with diffuse reflection, are incorporated into the code. The μDSMC results are validated against traditional DSMC outcomes, showing strong agreement. Grid convergence studies indicate that the radial grid size must be less than one-third of the mean free path, with a more relaxed requirement on axial grid size. Flow characteristics within micro-nozzles are evaluated across varying ambient pressures and gas species in terms of the back pressure ratio, effective exit flow ratio, and exit flow velocity. Studies indicated that a minimum back pressure ratio is required, beyond which the effective nozzle flow expansion is achieved. Parametric analysis further suggests that gases with lower molecular weights are preferable for achieving optimal expansion in micro-nozzles under low ambient pressures. Full article

The increasing integration of renewable energy sources (RESs) into power systems has introduced new complexities due to the inherent variability and uncertainty of these energy sources. In addition to the uncertainty in RES generation, the demand-side load of power systems is also subject to fluctuations, further complicating system operations. Addressing these challenges requires effective modeling and assessment techniques to quantify and mitigate the risks associated with system uncertainties. This paper evaluates the impact of various uncertainty modeling techniques on power system reliability with wind farm integration. Furthermore, this paper reviews the state of the art of the various uncertainty and risk modeling techniques in power systems. Through a detailed case study, the performance of these techniques in modeling uncertainties of wind speeds is analyzed. Based on the results, the integration of wind turbines improves the system’s overall reliability when there is a reduction in conventional power plants (CPPs)’ generation, which are dispatchable energy sources providing a stable and flexible supply. However, the generation of wind farms is associated with uncertainty. The results show Monte Carlo simulation combined with the K-Means method is consistently a more accurate uncertainty model for wind speeds, closely aligning with real-case scenarios, compared to other methods such as Markov Chain Monte Carlo (MCMC), robust optimization (RO), and information-gap decision theory (IGDT). Full article

This paper presents research on the security performance of a multi-user interference-based mixed RF/FSO system based on SWIPT untrusted relay. In this work, the RF and FSO channels experience Nakagami-m fading distribution and Málaga (M) turbulence, respectively. Multiple users transmit messages to the destination with the help of multiple cooperating relays, one of which may become an untrusted relay as an insider attacker. In a multi-user network, SWIPT acts as a charging device for each user node. In order to prevent the untrusted relays from eavesdropping on the information, some users are randomly assigned to transmit artificial noise in order to interfere with untrusted relays, and the remaining users send information to relay nodes. Based on the above system model, the closed-form expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) for the mixed RF/FSO system are derived. The correctness of these expressions is verified by the Monte Carlo method. The influences of various key factors on the safety performance of the system are analyzed by simulations. The results show that the security performance of the system is considerably improved by increasing the signal–interference noise ratio, the number of interfering users, the time distribution factor and the energy conversion efficiency when the instantaneous signal-to-noise ratio (SNR) of the RF link instantaneous SNR is low. Full article