Sensitive Analytical Method Research Articles

Accurate Temperature Reconstruction in Radiofrequency Ablation for Atherosclerotic Plaques Based on Inverse Heat Transfer Analysis.

Radio frequency ablation has emerged as a widely accepted treatment for atherosclerotic plaques. However, monitoring the temperature field distribution in the blood vessel wall during this procedure presents challenges. This limitation increases the risk of endothelial cell damage and inflammatory responses, potentially leading to lumen restenosis. The aim of this study is to accurately reconstruct the transient temperature distribution by solving a stochastic heat transfer model with uncertain parameters using an inverse heat transfer algorithm and temperature measurement data. The nonlinear least squares optimization method, Levenberg-Marquardt (LM), was employed to solve the inverse heat transfer problem for parameter estimation. Then, to improve the convergence of the algorithm and reduce the computational resources, a method of parameter sensitivity analysis was proposed to select parameters mainly affecting the temperature field. Furthermore, the robustness and accuracy of the algorithm were verified by introducing random noise to the temperature measurements. Despite the high level of temperature measurement noise (ξ = 5%) and larger initial guess deviation, the parameter estimation results remained closely aligned with the actual values, with an overall ERMS consistently below 0.05. The absolute errors between the reconstruction temperature at the measurement points TC1, TC2, and TC3, and the actual temperature, remained within 0.33 °C, 2.4 °C, and 1.17 °C, respectively. The Levenberg-Marquardt algorithm employed in this study proficiently tackled the ill-posed issue of inversion process and obtained a strong consistency between the reconstructed temperature the actual temperature.

Sensitivity Analysis of the Square Cup Forming Process Using PAWN and Sobol Indices

This study investigates the sensitivity of the square cup forming process. It analyses how the uncertainties in the material properties, friction and process conditions affect the results of the square cup, such as equivalent plastic strain, geometry change, thickness reduction, punch force and springback. The cup flange and the die curvature region are identified as highly affected areas, while the cup bottom is least affected by the uncertainties. Two sensitivity analysis techniques, PAWN and Sobol indices, are compared. In particular, the study shows that PAWN indices require a significantly smaller number of simulations than Sobol indices, making them a more efficient choice for sensitivity analysis. While both PAWN and Sobol indices generally give comparable results, discrepancies arise in the analysis of springback, where PAWN indices show superior accuracy, particularly when dealing with multimodal distributions. This observation highlights the importance of selecting the appropriate sensitivity analysis method based on the nature of the data being analysed. These results provide insights for optimizing stamping processes to reduce production time and costs.

Partial derivative-based dynamic sensitivity analysis expression for non-linear auto regressive with exogenous (NARX) model[sbnd]case studies on distillation columns and model's interpretation investigation

Constructing the reliable dynamic sensitivity profile for the output variable using the machine learning model is a challenging task; however, the dynamic sensitivity trends are helpful to understand the impact of the input variables on the system's performance. In this paper, we have derived the partial-derivative approach-based sensitivity analysis expression for the non-linear auto regressive with exogenous (NARX) model for the first time. The engineering systems-based case studies, i.e., two distillation columns with five and ten stages, respectively are taken which are commonly found in the chemical processing plants. Two output variables, i.e., liquid composition in tray 2 and tray 4 (Y2 and Y4) of a five-stage distillation column, and liquid composition in tray 7 (Y7) of a ten-stage (higher) distillation column are modelled by NARX with respect to time, feed concentration (Xf) and feed flow rate (Lf). The dynamic sensitivity profiles of the output variables with respect to Xf and Lf for the two distillation columns are plotted by the derived partial derivative-based sensitivity expression on the NARX model. Furthermore, the forward difference method of sensitivity analysis (first principle method) is also applied on the ordinary differential equations of the distillation columns to compute the sensitivity values of the output variables. A good agreement in the dynamic sensitivity values of the output variables with respect to the input variables is found for the two sensitivity analysis techniques thereby demonstrating the effectiveness of the partial-derivative approach for the improved NARX's interpretability performance. This research presents the explicit partial-derivative based sensitivity analysis expression for the NARX model which can be utilised for time-series applications and can provide the insights about the model's interpretation performance.

Optimization of insulation thickness of walls and roofs using energy, exergy, economic and environmental (4E) analyses

Buildings play an important role in consumption of energy and carbon dioxide emissions all over the world. The optimum thickness of each insulation material of wall and roof of residential buildings depending on energy, environment, economy and exergy was determined in this study. For this purpose, an optimization model was established based on four different criteria: energy, environment, economics, and exergy. A function was defined containing these four criteria. It has been seen from the results that the optimum insulation thickness of the wall and roof depends on the weight coefficients of the energy, environment, economic and exergy parameters and insulation material types. The results of the economic analysis indicate that the optimum insulation thickness of wall varies from 1.01 to 7.7 cm and the optimum thicknesses of roof varies from 3.25 to 6.7 cm for XPS, EPS and Glasswool insulation materials. According to the results of the enviromental analysis, the optimum thicknesses of wall for different insulation materials are 6.5, 8.6, 9.4, and 9.55 cm and optimum insulation thicknesses of roof are 7.55, 8.1 and 8.2 cm, respectively. The effect of economic and energy parameters on the optimum thickness of the wall and roof for the three insulation materials was investigated using the sensitivity analysis method. It was seen from the results that impacts of interest rate, inflation rate, electricity cost, fuel cost, insulation material cost, heating and cooling degree-days on the optimum insulation thickness of wall and roof and 4EF optimization function were different.

Performance improvement evaluation of latent heat energy storage units using improved bi-objective topology optimization method

Latent heat thermal energy storage (LHTES) based on phase change materials is one of the key technologies to improve energy utilization efficiency and alleviate the mismatch between energy supply and demand. Heat storage capacity and charging/discharging rate are two core factors that determine the comprehensive performance of LHTES units. So a bi-objective transient topology optimization(TO) model is established to freely evolve the optimal structure and volume fraction of LHTES fins to maximize the heat transfer rate based on ensuring high storage capacity. The bi-objective optimization function coupling storage capacity and heat transfer rate is constructed by normalized SAW model. The TO model is formulated using variable density-based design variables of grid cells in physical governing equations, objective functions and constraints. Helmholtz PDE filtering and hyperbolic tangent projection methods are employed to achieve high-precision optimization solutions. Complex transient multi-physics field and bi-objective iterative calculations are performed using the transient adjoint-based sensitivity analysis method and GCMMA algorithm. Results demonstrate that the physical field dynamics response of the design variables at each iteration can more clearly exhibit the evolutionary direction and mechanism of seeking optimal structures. The Pareto frontier indicates TO model responds significantly to the changes in the bi-objective weight ratio ω1:ω2, which also reveals the physical mechanism of the trade-off game among fin topology formation, state parameters distribution and objective changes. Optimal configurations and its performance parameters change regularly with ω1:ω2 decreasing, and has an demonstrable performance mutations occurring at ω1 = 0.65(ω2 = 0.35). The scattered distribution and uniform multi-branch structure of TO fins are key factors in regulating the physical field state. Therefore, TO-fins LHTES exhibits extremely high heat transfer rate, and the charging/discharging time of traditional trapezoid fin LHTES is approximately three times longer than that of TO-fins LHTES under ω1:ω2 = 0.65:0.35.

A Method for Evaluating Coalbed Methane Reservoir Productivity Considering Drilling Fluid Damage

In the process of coalbed methane development, drilling fluid and fracturing fluid cannot achieve absolute compatibility with formation. The incompatibility between the working fluid and reservoir will lead to the intrusion of working fluid into the reservoir and cause reservoir pollution. This is a very common phenomenon. There is a large amount of pulverized coal in the coal seam, and the intrusion of working liquid will be combined with the pulverized coal to form cement to block the seepage space in the reservoir. Since pressure relief and fracturing fluid backflow will be performed at the first time after fracturing, the intrusion range of the working fluid is small, generally reaching 10 m to 50 m. Compared with a conventional gas reservoir or shale gas reservoir, the working fluid loss during CBM development will seriously affect the subsequent production project and even make the gas well lose production capacity. On the other hand, in order to avoid this phenomenon, measures such as acidification or volumetric fracturing are sometimes used to improve the seepage environment near the well and near the fracture. The purpose of this study is to quantitatively evaluate the impacts of working fluid filtration and reservoir reconstruction on production. In this study, a single well productivity evaluation model and sensitivity analysis method considering drilling fluid filtration loss, fracturing fluid filtration loss, reservoir reconstruction and other processes is proposed. The formation mechanism of fluid loss during drilling and fracturing is described, and the productivity evaluation model considering fluid loss is combined with the Langmuir isothermal adsorption equation, steady-state diffusion law, Darcy’s seepage law and Duhamel convolution formation. Combined with the distribution of actual gas reservoir flow characteristics, the sensitivity of single well productivity to gas reservoir porosity, gas reservoir permeability, coal seam adsorption coefficient, working fluid filtration loss and reservoir reconstruction measures are analyzed. Through the analysis and fitting of the actual production data on site, the relationship curve can better fit the field production data, and the evaluation results are in line with the drilling and fracturing conditions at that time and the subsequent production conditions, with small errors. The obtained method is suitable for predicting the productivity of fractured vertical wells in different working conditions and provides a basis for the development and productivity prediction of CBM reservoirs in China and in international cooperation.

Error Modeling and Accuracy Analysis of Parallel Robots for Spacecraft Docking Motion Simulation System

Background:: A large number of on-orbit docking dynamics experiments are conducted in the spacecraft docking motion simulation system with the development of Chinese space science and technology, and therefore, the requirement for high-precision parallel robots has increased. The pose accuracy of parallel robots is one of the most important problems in this application. Objective:: In order to evaluate the pose accuracy of parallel robots in advance, a comprehensive pose accuracy analysis method considering major error sources is presented in this paper, and the influence of major error sources on the pose accuracy is also investigated to summarize the statistics and propagation characteristics of the pose error. Methods:: The first-order error model for the parallel robot is established based on a generalized error model for each hydraulic cylinder and a sensitivity analysis method. Using the error model, a statistical approach to the parallel robot pose accuracy analysis is presented, and the influence of different parameter errors and different poses on the pose accuracy of the parallel robot is investigated. Sensitivity analysis is applied to evaluate the contribution of each parameter error to the position and orientation error of the parallel robot. An automated pose accuracy analysis program that computes and graphically displays the position and orientation error distributions and the sensitivity analysis results is developed. Results: The statistical analysis results of the influence of different parameter errors and different poses on pose accuracy are obtained by using the automated pose accuracy analysis program. The means of the position and orientation errors are close to zero. The standard deviations in the x and y directions are larger than those in the z direction, and these standard deviations are amplified with the increase of the parameter errors. For the given elevation, sensitivity analysis to various parameter errors is performed. It is found that the length error sensitivities of the hydraulic cylinders are less than one, and position error sensitivities of the hook joints A4, A5, and A6 are much greater than those of hook joints A1, A2 and A3. Conclusion:: The elemental error sources belong to one of two groups, i.e., those affecting the hydraulic cylinder length and those affecting the hook joints. The distributions of the position and orientation errors are consistent with statistics theories. The parallel robot is more sensitive to the errors affecting the hook joints than those affecting the hydraulic cylinder length. These will help the designers and users of the parallel robot understand the statistics and propagation characteristics of the pose error. Some recent patents on error modeling and kinematic calibration of parallel robots are also discussed in this article.

There's Something in What We Eat: An Overview on the Extraction Techniques and Chromatographic Analysis for PFAS Identification in Agri-Food Products.

Per- and polyfluorinated alkyl substances (PFASs) are a group of anthropogenic chemicals used in a range of industrial processes and consumer products. Recently, their ubiquitous presence in the environment as well as their toxicological effects in humans have gained relevant attention. Although the occurrence of PFASs is widely investigated in scientific community, the standardization of analytical method for all matrices still remains an important issue. In this review, we discussed extraction and detection methods in depth to evaluate the best procedures of PFAS identification in terms of analytical parameters (e.g., limits of detection (LODs), limits of quantification (LOQs), recoveries). Extraction approaches based on liquid-liquid extraction (LLE), alkaline digestion, and solid phase extraction (SPE), followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) analysis are the main analytical methods applied in the literature. The results showed detectable recoveries of PFOA and PFOS in meat, milk, vegetables, eggs products (90.6-101.2% and of 89.2-98.4%), and fish (96-108%). Furthermore, the low LOD and LOQ values obtained for meat (0.00592-0.01907 ng g-1; 0.050 ng g-1), milk (0.003-0.009 ng g-1; 0.010-0.027 ng g-1), fruit (0.002-0.009 ng g-1; 0.006-0.024 ng g-1), and fish (0.00369-0.017.33 ng g-1; 0.05 ng g-1) also confirmed the effectiveness of the recent quick, easy, cheap, effective, rugged, and safe method (QuEChERS) for simple, speedy, and sensitive ultra-trace PFAS analysis.

Sensitivity Analysis and Optimization of Energy-Saving Measures for Office Building in Hot Summer and Cold Winter Regions

To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.

Quantitation of propofol metabolites by LC–MS/MS demonstrating long detection window for urine drug monitoring

IntroductionChromatographic methods for analysis of propofol and its metabolites have been widely used in pharmacokinetic studies of propofol distribution, metabolism, and clearance. Application of chromatographic methods is also needed in clinical and forensic laboratories for detecting and monitoring propofol misuse. ObjectiveWe report a method for sensitive analysis of propofol, propofol 1-glucuronide (PG), 4-hydroxypropofol 1-glucuronide (1-QG), 4-hydroxypropofol 4-glucuronide (4-QG) and 4-hydroxypropofol 4-sulfate (4-QS) in urine by LC–MS/MS analysis. The method employs a simple dilute-and-analyze sample preparation with stable isotope internal standardization. ResultsValidation studies demonstrate a linear calibration model (100–10,000 ng/mL), with dilution integrity verified for the extended range of concentrations experienced in propofol use. Criteria-based validation was achieved, including an average coefficient of variation of 6.5 % and a percent bias of −4.2 ng/mL. The method was evaluated in 12 surgical patients, with monitoring periods lasting up to 30 days following intravenous propofol administrations of 100–3000 mg on the day of surgery. While the concentration ratio of PG to 4-hydroxy propofol metabolite decreased significantly in the days following surgery, PG maintained the highest concentration in all specimens. Both PG and 1-QG were detectable throughout the monitoring periods, including in a patient monitored for 30 days. Lower concentrations were determined for 4-QG and 4-QS, with evidence of detection up to 20 days. Propofol was not detectable in any urine specimens, thereby proving ineffective for identifying drug use. ConclusionThe validated method for quantifying propofol metabolites demonstrates its applicability for the sensitive detection of propofol misuse over a long window of drug-use detection.

Recoverability and estimation of causal effects under typical multivariable missingness mechanisms.

In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends not only on the usual causal assumptions but also on missingness assumptions that can be depicted by adding variable-specific missingness indicators to causal diagrams, creating missingness directed acyclic graphs (m-DAGs). Previous research described canonical m-DAGs, representing typical multivariable missingness mechanisms in epidemiological studies, and examined mathematically the recoverability of the ACE in each case. However, this work assumed no effect modification and did not investigate methods for estimation across such scenarios. Here, we extend this research by determining the recoverability of the ACE in settings with effect modification and conducting a simulation study to evaluate the performance of widely used missing data methods when estimating the ACE using correctly specified g-computation. Methods assessed were complete case analysis (CCA) and various implementations of multiple imputation (MI) with varying degrees of compatibility with the outcome model used in g-computation. Simulations were based on an example from the Victorian Adolescent Health Cohort Study (VAHCS), where interest was in estimating the ACE of adolescent cannabis use on mental health in young adulthood. We found that the ACE is recoverable when no incomplete variable (exposure, outcome, or confounder) causes its own missingness, and nonrecoverable otherwise, in simplified versions of 10 canonical m-DAGs that excluded unmeasured common causes of missingness indicators. Despite this nonrecoverability, simulations showed that MI approaches that are compatible with the outcome model in g-computation may enable approximately unbiased estimation across all canonical m-DAGs considered, except when the outcome causes its own missingness or causes the missingness of a variable that causes its own missingness. In the latter settings, researchers may need to consider sensitivity analysis methods incorporating external information (e.g., delta-adjustment methods). The VAHCS case study illustrates the practical implications of thesefindings.

Analysis of blade noise with different root boundary conditions considering fluid-structure interaction effect

It is of certain importance for noise reduction to investigate the relationship between the aeroacoustic characteristics of blade and root boundary conditions when considering the fluid-structure interaction (FSI) effect. In this study, a set of precise dynamic models and two-way FSI calculation methods are developed to predict the dynamic load and response of blades more accurately. The natural frequency inaccuracy of the blade model is kept within 5% by comparing the results of the modal test using the ploy-reference time domain method and adopting the sensitivity analysis method with the blade beam stiffness as the modification parameter. Besides, based on the numerical results from the computational fluid dynamics method considering the FSI effect, the aeroacoustic characteristics of blades with different root boundary conditions are analyzed by adopting the Ffowcs Williams-Hawkings equations. The aeroacoustic characteristics of the NACA blade in hover are thoroughly discussed, with emphasis on the impact of different root boundary conditions on rotation noise. The result demonstrates that, the sound pressure level (SPL) difference of loading noise can reach 1.4 dB between blades with various root boundary conditions, while the SPL difference of thickness is only 0.4 dB.

A Computationally Efficient Method for Parameter Sensitivity Analysis of Microbially Explicit Biogeochemical Models Accounting for Long‐Term Behavior

AbstractMicrobial ecological models become increasingly complex owing to the incorporation of many parameters and multiple biotic and abiotic processes. However, little attention has been paid to the variations in the parameter sensitivity during long‐term versus short‐term simulations. Here, we developed a Multi‐Objective Parameter Sensitivity Analysis (MOPSA) method to efficiently identify the important parameters in complex microbial ecological models with multiple response variables of interest in terms of short‐ and long‐term model simulations. We found that MOPSA was more computationally efficient for complex microbial ecological models than Sobol's method because of MOPSA's reliability and low computational sample size. In addition, we address the increased significance of microbial physiology in mediating long‐term than short‐term soil C‐N cycling, indicating that experiment‐model integration practices should examine model behaviors beyond the conventional short‐term experimental period. The outcomes of this study provide an efficient global sensitivity analysis method for parameterization and the scientific foundation for microbial physiology in mediating long‐term microbial ecological processes.

Metabolic Dysregulation and Its Role in Postoperative Pain among Knee Osteoarthritis Patients.

The inflammation observed in joint tissues is linked to pain caused by the production of proinflammatory cytokines. Since the biosynthesis of cytokines requires energy, their production is supported by extensive metabolic conversions of carbohydrates and fatty acids, which could lead to a disruption in cellular homeostasis. Therefore, this study aimed to investigate the association between POP development and disturbances in energy metabolic conversions, focusing on carbohydrate and fatty acid metabolism. Peripheral blood samples were collected from 26 healthy subjects and 50 patients with end-stage OA before joint replacement surgery. All implants were validated by orthopedic surgeons, and patients with OA demonstrated no inherent abnormalities to cause pain from other reasons than OA disease, such as malalignment, aseptic loosening, or excessive bleeding. Pain levels were assessed before surgery using the visual analogue scale (VAS) and neuropathic pain questionnaires, DN4 and PainDETECT. Functional activity was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Three and six months after surgery, pain indices according to a VAS of 30 mm or higher were considered. Total RNA isolated from whole blood was analyzed using quantitative real-time RT-PCR (qRT-PCR) for the expression of genes related to carbohydrate and fatty acid metabolism. Protein levels of the examined genes were measured using an ELISA in the peripheral blood mononuclear cells (PBMCs). We used qRT-PCR because it is the most sensitive and reliable method for gene expression analysis, while an ELISA was used to confirm our qRT-PCR results. Among the study cohort, 17 patients who reported POP demonstrated significantly higher (p < 0.05) expressions of the genes PKM2, LDH, SDH, UCP2, CPT1A, and ACLY compared to pain-free patients with KOA. Receiver-operating characteristic (ROC) curve analyses confirmed the association between these gene expressions and pain development post-arthroplasty. A principle component analysis identified the prognostic values of ACLY, CPT1A, AMPK, SDHB, Caspase 3, and IL-1β gene expressions for POP development in the examined subjects. These findings suggest that the disturbances in energy metabolism, as observed in the PBMCs of patients with end-stage KOA before arthroplasty, may contribute to POP development. An understanding of these metabolic processes could provide insights into the pathogenesis of KOA. Additionally, our findings can be used in a clinical setting to predict POP development in end-stage patients with KOA before arthroplasty.

Analysis of Cannabis and its Products in the Context Forensic Science

Legalization of low-tetrahydrocannabinol-high-cannabidiol cannabis plants is gaining momentum due to growing demand for these products. In some countries, the testing of cannabis and products containing it is a legal requirement: cannabis is identified, the amount of tetrahydrocannabinol or cannabidiol and their precursor acids is assessed. Current research requires more sensitive and reliable analytical methods for accurate identification and quantification of cannabis components. This article purpose is to review the latest advances in the scientific literature on the isolation and analysis of cannabis and cannabis-containing products in the context of forensic science. Research methods included analysis of information on this topic using search engines, including Google, Google Scholar, Web of Science, PubMed, and ScienceDirect. Search requests took into account scientific, analytical and statistical reports in the field of forensic research. Since the forensic expert must be well-versed in modern trends in the analysis of prohibited substances and use the latest data from the analytical and forensic literature in his work, such information will help them choose research methods, taking into account the actual resources and equipment of forensic laboratories. According to the article purpose, advantages and disadvantages of existing methods of extraction and analysis of objects containing cannabis for identification, quantification, profiling and age estimation of cannabis are considered.

Deep eutectic solvents as green and sustainable diluents in headspace gas chromatography for the determination of trace level genotoxic impurities in pharmaceuticals

Genotoxic impurities (GTIs) are potential carcinogens that need to be controlled down to ppm or lower concentration levels in pharmaceuticals under strict regulations. The static headspace gas chromatography (HS-GC) coupled with electron capture detection (ECD) is an effective approach to monitor halogenated and nitroaromatic genotoxins. Deep eutectic solvents (DESs) possess tunable physico-chemical properties and low vapor pressure for HS-GC methods. In this study, zwitterionic and non-ionic DESs have been used for the first time to develop and validate a sensitive analytical method for the analysis of 24 genotoxins at sub-ppm concentrations. Compared to non-ionic diluents, zwitterionic DESs produced exceptional analytical performance and the betaine : 7 (1,4- butane diol) DES outperformed the betaine : 5 (1,4-butane diol) DES. Limits of detection (LOD) down to the 5-ppb concentration level were achieved in DESs. Wide linear ranges spanning over 5 orders of magnitude (0.005–100 µg g−1) were obtained for most analytes with exceptional sensitivities and high precision. The method accuracy and precision were validated using 3 commercially available drug substances and excellent recoveries were obtained. This study broadens the applicability of HS-GC in the determination of less volatile GTIs by establishing DESs as viable diluent substitutes for organic solvents in routine pharmaceutical analysis.

A Risk-Structured Model for the Transmission Dynamics of Anthrax Disease

Anthrax, a zoonotic disease with serious public health consequences, has been the subject of rigorous mathematical and statistical modeling to better understand its dynamics and to devise effective control techniques. In this study, we propose a novel mathematical risk-structured model for anthrax disease spread that includes both qualitative and quantitative evaluations. Our research focuses on the complex interplay between host–anthrax interactions and zoonotic transmission. Our mathematical approach incorporates bifurcation analysis and stability considerations. We investigate the dynamic behavior of the proposed model under various settings, shedding light on the important parameters that determine anthrax transmission and persistence. The normalized forward sensitivity analysis method is used to determine the parameters that are relevant to reducing Rc and, by extension, disease spread. Through scenario simulation of our model, we identify intervention techniques, such as enlightenment of the populace, that will effectively minimize disease transmission. Our findings provide insights into anthrax epidemiology and emphasize the importance of effective disease management. Bifurcation investigations reveal the existence and stability of numerous equilibria, allowing for a better understanding of the behavior of the system under various scenarios. This study adds to the field of anthrax modeling by providing a foundation for informed decision-making regarding public health measures. The use of a mathematical modeling approach improves our ability to anticipate and control anthrax epidemics, ultimately helping to protect both human and animal populations.

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Prevalence and concentration of pesticides in European waters: A protocol for systematic review and meta-analysis.

There is currently a growing interest in the so-called emerging pollutants, such as pesticides, pharmaceuticals, personal hygiene care products, drugs, etc., whose presence in natural ecosystems is not necessarily recent, but the development in latest years of new and more sensitive methods of analysis has allowed their detection. They can be present in the natural environment, food, and many products of everyday origin, which suggests that human exposure to them is massive and universal. Therefore, the study of this type of substances is becoming one of the priority lines of research of the main agencies dedicated to the protection of public and environmental health, such as the World Health Organization (WHO), United States Environmental Protection Agency (USEPA) or European Union (EU). In this sense, it is of vital importance to know the nature and quantity of this type of contaminants, to establish preventive mechanisms that minimize its presence in aquatic systems, with special requirements for human consumption. This study aimed to describe a protocol for a systematic review and meta-analysis to assess the status of pesticides in European waters. We will search for original studies in the PubMed/Medline, Scopus, Web of Science, EMBASE, ScienceDirect databases. Prevalence studies of emerging contaminants (pesticides) in water resources (watersheds, aquifers, rivers, marine and springs), wastewaters (influent and effluent), and drinking water should be included. Two reviewers will independently screen and assess the included studies, with any disagreements being resolved by a third reviewer. We will summarize the findings using a narrative approach and, if possible, conduct a quantitative synthesis (meta-analysis). We will conduct the protocol following the Preferred Reporting Items for Systematic Review and Meta-Analyses Protocols (PRISMA-P) guidelines. The review will summarize the current evidence on the presence of pesticides in European waters such as glyphosate, chlorpyrifos, pyrethroid pesticides, neonicotinoid pesticides, and/or fungicides, in samples of different water resources like wastewaters and drinking water. We expect that this systematic review will establish preventive mechanisms that minimize the presence of pesticides in water in the environment.

Chemical kinetic model reduction based on species‐targeted local sensitivity analysis

AbstractReduction of large combustion mechanisms is usually conducted based on the detection and elimination of redundant species and reactions. Reaction elimination methods are mostly based on sensitivity analysis, which can provide insight into the kinetic system, while species elimination methods are more efficient. In this work, the species‐targeted local sensitivity analysis (STLSA) method is proposed to evaluate the importance of species and eliminate non‐crucial species and their related reactions to simplify kinetic models. This paper comprehensively evaluates the effectiveness of STLSA across various combustion scenarios, including high and low‐temperature ignition and laminar flame speed, using diverse mechanisms like USC Mech II, JetSurf 1.0, POLIMI_TOT_1412, NUIGMech1.1 and so on. Comparisons with graph‐based methods, such as DRG and DRGEP, highlight STLSA's superior efficiency and accuracy. Moreover, STLSA is compared to species‐targeted global sensitivity analysis (STGSA), demonstrating significant computation cost savings and comparable model reduction capabilities. The study concludes that STLSA is a robust and versatile tool for mechanism reduction, offering substantial improvements in computational efficiency while maintaining high accuracy in predicting key combustion properties.