Mathematical Model For Prediction Research Articles

Photochromic webbing structures for monitoring UV-induced mechanical strength degradation

Abstract Webbing structures are critical load-bearing components in a wide array of applications from structural restraint layers in inflatable space habitats to safety harness belts used by construction workers. In the field, webbings are subjected to ultraviolet (UV) irradiation from sunlight, leading to material degradation and a loss of mechanical strength. To date, health monitoring of webbings has relied on empirically correlating UV-induced strength loss with variations in their inherent color, which often yields inconsistent and imprecise results. To fill this gap, we propose a novel class of photochromic webbing structures that afford noninvasive monitoring of UV-induced degradation of their mechanical strength. The webbings’ sensing capabilities are achieved by integrating a class of photochromic yarns, fabricated through a pressurized coating process. Under continuous UV irradiation, the proposed photochromic webbings exhibit a substantial color change, demonstrating a sensing lifetime equivalent to several months in field conditions. We establish a strong correlation between the webbings’ photochromic response and their strength loss, supporting the feasibility of the proposed webbings in monitoring their mechanical integrity. To elucidate the sensing mechanism, we propose a physics-based mathematical model that describes the underlying photochemical reactions. Through an asymptotic analysis, we demonstrate that the model accurately predicts the webbing’s long-term photochromic responses under extended UV irradiation. The proposed photochromic webbing structures and the predictive mathematical model could enhance the safety and integrity of webbing-based engineering systems.

Predictive models, as an idea, to advance the secondary to tertiary transition in science courses

Investigating the transition between the secondary and the tertiary levels is a main theme in mathematics and science education. More so, this paper considers the transition that intersects with the after-effects of COVID-19, or the transition together with an educational context dominated by sociocultural differences and educational disadvantages. With this knowledge in mind, we investigated the effects of predictive mathematical models (multiple regression, logistic regression, and decision trees) to predict <i>at-risk</i> students at three time intervals (weeks one, three, and seven) in the semester. The idea was implemented with a first-year life science class of 130 students. Variables from an academic readiness questionnaire along with early assessment grades were used to build these models. Through a Monte Carlo cross validation method, the performance of the executed predictive models was assessed, and limitations were reported. We argue that the results obtained from predictive models can support both lecturers and students in the transition phase. The idea can be expanded to other courses in STEM fields and other educational contexts.

Blasting safety criterion of existing high speed railway tunnel over tunnel

Drilling and blasting method is still an important method in the current tunnel excavation construction. Controlling the vibration effect of blasting during construction and its influence on the upper span tunnel is the key problem in tunnel construction. Based on Chongqing Science City tunnel excavation blasting project, combined with the tunnel blasting vibration monitoring and testing, this paper analyzes the propagation attenuation law of tunnel blasting vibration along the rock mass, and studies the load characteristics of the explosion stress wave propagating to the existing high-speed railway tunnel. Considering the influence of the buried depth of the blasting source, a mathematical prediction model of the attenuation law of the upper span existing high-speed railway tunnel caused by tunnel blasting is established. Based on the dynamic finite element numerical calculation method, the influence of blasting vibration on the structure of the existing high-speed railway tunnel under construction is analyzed, and the propagation and attenuation law of blasting vibration along the tunnel contour is studied. Based on the ultimate tensile stress criterion, the ultimate shear stress criterion and the Mohr criterion, and compared with the results obtained from the numerical simulation, the blasting safety criterion model of the existing high-speed railway tunnel over the tunnel is established.

Mathematical modeling for prediction of physicochemical characteristics of cardiovascular drugs via modified reverse degree topological indices.

Global health concerns persist due to the multifaceted nature of heart diseases, which include lifestyle choices, genetic predispositions, and emerging post-COVID complications like myocarditis and pericarditis. This broadens the spectrum of cardiovascular ailments to encompass conditions such as coronary artery disease, heart failure, arrhythmias, and valvular disorders. Timely interventions, including lifestyle modifications and regular medications such as antiplatelets, beta-blockers, angiotensin-converting enzyme inhibitors, antiarrhythmics, and vasodilators, are pivotal in managing these conditions. In drug development, topological indices play a critical role, offering cost-effective computational and predictive tools. This study explores modified reverse degree topological indices, highlighting their adjustable parameters that actively shape the degree sequences of molecular drugs. This feature makes the approach suitable for datasets with unique physicochemical properties, distinguishing it from traditional methods that rely on fixed degree approaches. In our investigation, we examine a dataset of 30 drug compounds, including sotagliflozin, dapagliflozin, dobutamine, etc., which are used in the treatment of cardiovascular diseases. Through the structural analysis, we utilize modified reverse degree indices to develop quantitative structure-property relationship (QSPR) models, aiming to unveil essential understandings of their characteristics for drug development. Furthermore, we compare our QSPR models against the degree-based models, clearly demonstrating the superior effectiveness inherent in our proposed method.

Development, modelling and optimization of process parameters on the tensile strength of aluminum, reinforced with pumice and carbonated coal hybrid composites for brake disc application

This study focuses on optimizing double stir casting process parameters to enhance the tensile strength of hybrid composites comprising aluminum alloy, brown pumice, and coal ash, intended for brake disc applications. Analytical techniques including X-ray fluorescence, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy were employed to characterize the composite constituents. The Taguchi method was utilized for experimental design and optimization to determine the optimal weight compositions of brown pumice and coal ash, as well as stir casting parameters (stirrer speed, pouring temperature, and stirring duration). Regression analysis was employed to develop a predictive mathematical model for the tensile strength of the hybrid composites and to assess the significance of process parameters. The optimized composite achieved a predicted tensile strength of 186.81 MPa and an experimental strength of 190.67 MPa using 7.5 vol% brown pumice, 2.5 vol% coal ash, a pouring temperature of 700 °C, stirrer speed of 500 rpm, and stirring duration of 10 min. This represents a 52.23% improvement over the as-cast aluminum alloy’s tensile strength. Characterization results revealed that brown pumice and coal ash contain robust minerals (SiO2, Fe2O3, Al2O3) suitable for reinforcing metal matrices like aluminum, titanium, and magnesium. Thermogravimetric and differential thermal analyses demonstrated thermal stability up to 614.01 °C for the optimized composite, making it suitable for brake disc applications.

Rational Design of Liquid-Liquid Microdispersion Droplet Microreactors for the Controllable Synthesis of Highly Uniform and Monodispersed Dextran Microspheres.

Hydrogel microspheres are biocompatible materials widely used in biological and medical fields. Emulsification and stirring are the commonly used methods to prepare hydrogels. However, the size distribution is considerably wide, the monodispersity and the mechanical intensity are poor, and the stable operation conditions are comparatively narrow to meet some sophisticated applications. In this paper, a T-shaped stepwise microchannel combined with a simple side microchannel structure is developed to explore the liquid-liquid dispersion mechanism, interfacial evolution behavior, satellite droplet formation mechanism and separation, and the eventual successful synthesis of dextran hydrogel microspheres. The effect of the operation parameters on droplet and microsphere size is comprehensively studied. The flow pattern and the stable operation condition range are given, and mathematical prediction models are developed under three different flow regimes for droplet size prediction. Based on the stable operating conditions, a microdroplet-based method combined with UV light curing is developed to synthesize the dextran hydrogel microsphere. The highly uniform and monodispersed dextran microspheres with good mechanical intensity are synthesized in the developed microfluidic platform. The size of the microsphere could be tuned from 50 to 300 μm with a capillary number in the range of 0.006-0.742. This work not only provides a facile method for functional polymeric microsphere preparation but also offers important design guidelines for the development of a robust microreactor.

Prediction model for laser marking colors based on color mixing

The current techniques for coloring surfaces using lasers necessitate the identification of numerous laser marking parameters, which is a laborious process. Furthermore, the quantitative analysis of generating a wide variety of colors through fewer sets of laser marking parameters is a huge challenge. This work employs a nanosecond laser to generate mixed structural colors from micro-nano structures on the surface of stainless steel in order to address these issues. Additionally, the color mixing principle is investigated in relation to these micro-nano structures. On this basis, the spectral reflectance of the primary color is mapped to the linear mixed color space, and the linear mixed color space is constructed by minimizing the linear deviation function. In this space, a precise mathematical model for color prediction is developed, which effectively captures the correlation between the primary color and the resulting mixed color. Four primary colors are created using four sets of laser marking parameters. Mixing these primary colors in varying proportions can achieve more than 100 new tones with rich colors. The average color difference ΔE a b ∗ and ΔE00∗ are 1.98 and 1.80, respectively. By utilizing this model to adjust the proportion of primary colors in each subgraph, an image with vibrant and rich colors is generated, thereby achieving the implementation of a structural color image based on mixed colors.

Creation, Control, and Modeling of NV Centers in Nanodiamonds

AbstractSensing based on Nitrogen‐Vacancy (NV) centers in nanodiamonds (NDs) represents a potentially groundbreaking technology with broad applications. Nevertheless, the optimization of their quantum‐optical properties is still a challenging issue. The present work aims at enhancing and controlling NV centers optical properties in NDs by combining their surface chemistry tuning and proton beam irradiation. Systematic thermal oxidations are carried out to study the evolution of surface chemical groups (IR spectroscopy), as well as their influence on optical properties (photoluminescence spectroscopy, PL decay measurements). Proton irradiation is performed by exploring a wide range of fluences (1014 – 1017 cm−2) in order to precisely control the amount of NV centers, thus defining the conditions that maximize their creation and emission intensity. In addition, NV centers charge state control is achieved by assessing NV−/NV0 ratio upon different surface termination tuning and NV centers amount. Finally, a novel predictive mathematical model is developed, allowing for the evaluation of the efficiencies of the formation of both NV− and NV0. Although the model is tested in the specific case study with proton irradiated NDs, it offers a broad applicability, thus representing a key landmark in the prediction of the outcome of ion‐beam‐based color centers generation in diamond.

Predicting the dynamics of heart rate variability in patients with type 1 diabetes against improving glycemic control

The aim of the study: to analyze the effect of improvement of glycemic control on heart rate variability changes in patients with type 1 diabetes against the background of insulin therapy correction and to predict this dynamics based on the parameters of continuous glucose monitoring. We examined 49 patients with the level of glycosylated hemoglobin ≥7% and <10% without late diabetic complications. The average age – 32.0 (21.5; 38.0) years, the average disease duration – 9.5 (5.0; 17.5) years. The study was based on the simultaneous registration of heart rate variability and continuous glucose monitoring before and 3 months after correction of insulin therapy. Logistic regression analysis and ROC-analysis were used to predict the changes. After 3 months, the patients had significant decrease in the level of glycosylated hemoglobin, glycose variability, and a decrease in the frequency of hypoglycemic episodes. Heart rate variability increased in 73.5% of persons. The presence of hypoglycemia, standard deviation of blood glucose levels and glomerular filtration rate after treatment turned out to be prognostic factors for the predicting improvement in heart rate variability (the proportion of correct prediction of the patient's actual belonging to one or another prognostic group was 76.39%). Based on the calculation of the theoretical values of the positive result probability using the logistic equation, a detailed scale for predicting changes in heart rhythm variability for type 1 diabetes patients was proposed: up to 0.07 – a low probability of a positive result; 0.07-0.29 – the probability of a positive result is below average; 0.29-0.51 – a moderate probability of a positive result; 0.51-0.90 – high probability of a positive result; more than 0.90 – a very high probability of a positive result. We found that improvement of glycemic control leads to an increase in both frequency and time characteristics of heart rate variability. The increase in the likelihood of improvement of heart rate variability in patients with type 1 diabetes was more likely to be associated with reduced glycose variability and fewer hypoglycemic episodes. We developed a predictive mathematical model of heart rate variability based on the continuous glucose monitoring parameters for type 1 diabetes with sensitivity of 88.0% and specificity 68.18%, AUC 0.739 (p=0.001).

Radiation Techniques for Tracking the Progress of the Hydrometallurgical Leaching Process: A Case Study of Mn and Zn

With advancements in hardware and software, non-destructive radiometric analytical methods have become popular in a wide range of applications. A typical case is the study of the leaching process of metals from mineral ores and mine tailings. The objective of the current study was to develop a radiometric method based on neutron activation analysis (NAA), in particular, delayed gamma neutron activation analysis (DGNAA), to monitor the process of Mn and Zn leaching from Ti ore, Cu mine tailings, and Zn-Pb mine tailings. The DGNAA method was performed using a neutron source: a deuterium-tritium (D-T) neutron generator for Mn and a MARIA research nuclear reactor for Zn. Laboratory-scale Mn leaching from Ti ores, Cu tailings, and Zn-Pb tailings was investigated using delayed gamma-rays of 56Mn (half-life of 2.6 h). The dissolution efficiencies of Mn were found to increase with interaction time and HCl concentration (1 to 5 M) and to vary with the leaching temperature (22.5 to 110 °C). Such results were found to agree with those obtained by total reflection X-ray fluorescence (TXRF) spectrometry for the same samples. 65Zn (half-life of 244 days) was chosen to investigate real-time/online leaching of Zn in Ti ore, Cu tailings, and Zn-Pb tailings. During online monitoring, Zn recovery was also reported to increase with increased leaching time. After approximately 300 min of leaching, 80%, 79%, and 53% recovery of Zn in Zn-Pb tailings, Ti ore, and Cu tailings, respectively, were reported. Theoretically, developed mathematical prediction models for 65Zn radiotracer analysis showed that the spherical diffusion model requires much less time to attain saturation compared to the linear diffusion model. The results of NAA for Zn were compared with those obtained by handheld X-ray fluorescence (handheld-XRF) and TXRF analysis. The analyzed samples encompassed leached Ti ore, Cu tailings, and Zn-Pb tailings which were subjected to different conditions of leaching time, temperature, and HCl concentrations. The XRF analysis confirmed that the leaching efficiencies of Zn rise with the increase in leaching time and HCl concentration and fluctuate with leaching temperature. The developed approach is important and can be applied in laboratories and industrial setups for online monitoring of the recovery of any element whose isotopes can be activated using neutrons. The efficiency of the metal-recovery process has a direct impact on the normal operation and economic advantages of hydrometallurgy.

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

AbstractBackground and purposeMultiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.MethodsFor this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.ResultsThe projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model‐based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, “peer‐to‐peer,” and marketing distribution.ConclusionsThis review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

Acceleration analysis and unsteady mathematical prediction of flame spread over thermal insulation layer in u-shaped building façade fires

As one of the most commonly used energy-saving methods in buildings, thermal insulation layer is widely used in building façade and it also endangers the human safety in buildings due to its combustibility. U-shaped facade is a commonly used structure in high-rise buildings since it could improve both the light and ventilation conditions indoors. This paper investigated the flame acceleration over the thermal insulation in u-shaped building facade fires. It was found that the flame spread rate over u-shaped facade had an exponential growth with time as the direction of preheating zone location is the same direction of the heat transfer from the pyrolysis zone. The air entrainment and the upward induced airflow played an important role in the acceleration. As the side wall length increased or the back wall length decreased, the flame spread rate accelerated more rapidly. Furthermore, a mathematical prediction model of the unsteady flame spread rate over u-shaped structure façade was established and validated through experiments. This study provides technical guidance for the fire safety design of building facade.

Development of a mathematical model of productivity for conventional and stepped solar stills using RSM

Solar stills play a vital role as devices for utilizing solar energy to generate drinkable water, especially in dry regions where obtaining clean water poses a considerable challenge. This research concentrates on the expansion of a comprehensive mathematical model for anticipating and enhancing the performance of both conventional and stepped solar stills, employing Response Surface Methodology (RSM). The study investigates the influence of key design parameters, for instance, water depth (mm), mass flow rate (kg/h), and glass thickness (mm), on the productivity of conventional and stepped solar stills. The experimental setup involves the systematic collection of data from both conventional and stepped solar stills under varying operational conditions. Through the application of RSM, a predictive mathematical model is established, capturing the complex interdependencies among the input variables and their impact on the distilled water output, called productivity. This model serves as a valuable tool for understanding and optimizing the performance of solar stills in diverse operating conditions.

Mathematical Modeling and Optimal Stopping Theory-Based Extra Layers for 30-Day Rate Risk Prediction of Readmission to Intensive Care Units

Mathematical Modeling and Optimal Stopping Theory-Based Extra Layers for 30-Day Rate Risk Prediction of Readmission to Intensive Care Units

Accurate identification of lung cancer in the early phase by serum exosomal miR-1271-5p.

e20029 Background: Exosomal miRNAs are associated with the proliferation, migration, and invasion of lung cancer cells. We aim to investigate the clinical significance of serum exosomal miR-1271-5p in early-stage lung cancer (ESLC) mainly characterized by pulmonary nodules to push forward the prevention and treatment of lung cancer. Methods: The serum exosome (sEVs) miR-1271-5p was obtained from a total of 134 subjects from the patients with ESLC and the benign nodule (BLN) and healthy controls, and the expression level of miR-1271-5p were detected by RT-qPCR. Through analysis the clinical feature and CT (Computed Tomography) imaging finding of the subjects confirmed by pathology, independent risk factors for ESLC were obtained according to univariate and multivariate logistic regression analysis. R software was incorporated to build a mathematical prediction model of nomogram, and the clinical significance of sEVs-miR-1271-5p in the prediction and diagnosis of ESLC was verified by ROC curve. Results: The relative expression of sEVs-miR-1271-5p was lower in the patients with ESLC than the patients with BLN and healthy controls. The AUC value of the ESLC predicted by sEVs-miR-1271-5p was 0.9845 (95%CI: 0.9649-1.000, P < 0.0001). The results of multi-factor logistic regression analysis showed that gender (OR = 0.213, p = 0.046), nodule diameter (OR = 0.794, p = 0.031), lobulation (OR = 0.013, p < 0.0001), and sEVs-miR-1271-5p (OR = 0.014, p = 0.037) were independent risk factors for ESLC. Based on these independent risk factors, a prediction model for ESLC was established, with an AUC value of 0.8810 (95% CI: 0.8076-0.9553, P < 0.0001), the sensitivity, specificity, positive and negative predictive value and accuracy of the model were 85%, 80.43%, 79.07%, 86.05% and 82.56% respectively. Conclusions: Serum exosomal miR-1271-5p may break through the early diagnosis of lung cancer and provide important therapeutic targets.

Experimental study of dynamic behavior of impacting droplets on vibrating super-hydrophobic surfaces

The dynamic behaviors of droplets impacting on a vibrating solid surface are complex and interesting, as the differences in the initial phase angle can result in various droplet behaviors. The dynamic behavior of free-falling droplets impacting vibrating superhydrophobic substrates was investigated via high-speed photography. The effects of the initial phase angle (φ), Weber number (We), and vibration frequency (f) on the morphology evolution and energy dissipation were analyzed. Herein, 12 initial phase angles were selected as variables to investigate the evolution of droplet morphology with various initial phase angles. The effect of different initial phase angles on the maximum spreading diameter of droplets can cause variations of over 10%. The initial phase angles enhancing or restricting the droplet spreading were quantitatively defined. The Weber number and initial phase angle which can produce daughter droplets are obtained quantitatively. Meanwhile, a correlation between dimensionless spreading coefficient and dimensionless time was established for various vibration frequencies (f), revealing the effects of f on the maximum spreading diameter of droplet. Furthermore, a mathematical relationship for predicting the maximum spreading diameter of droplet impacting on a vibrating substrate was established based on the derivation of energy conservation. The error of the prediction mathematical model was proved to be less than 2% by the experimental results. These results provide fundamental understanding of droplet impacting on a vibration wall and could be useful for related engineering applications.

Iontophoresis-coupled rapidly dissolving polymeric microneedle patch of naloxone for its enhanced transdermal delivery

The transdermal delivery of naloxone for opioid overdose emergency purposes is a challenge due to its poor rate of diffusion through the layers of skin. This results in delayed delivery of an insufficient amount of the drug within minimal time as is desired to save lives. The ability of dissolving polymeric microneedles to shorten the lag time significantly has been explored and shown to have prospects in terms of the transdermal delivery of naloxone. This is an option that offers critical advantages to the ongoing opioid crisis, including ease of distribution and easy administration, with little to no need for intervention by clinicians. Nonetheless, this approach by itself needs augmentation to meet pharmacokinetic delivery attributes desired for a viable clinical alternative to existing market dosage forms. In this study, we report the success of an optimized iontophoresis-coupled naloxone loaded dissolving microneedle patch which had facilitated a 12- fold increase in average cumulative permeation and a 6-fold increase in drug flux over a conventional dissolving microneedle patch within 60 min of application (p < 0.05). This translates to a 30 % decrease in dose requirement in a mechanistically predicted microneedle patch established to be able to achieve the desired early plasma concentration time profile needed in an opioid overdose emergency. Applying a predictive mathematical model, we describe an iontophoresis-coupled microneedle patch design capable of meeting the desired pharmacokinetic profile for a viable naloxone delivery form through skin.

Statistical and mathematical preliminary interpretation of mechanical test results on sands grouted with colloidal silica

ABSTRACT Colloidal silica is a relatively new grouting low-carbon material used in soil improvement projects. A series of mechanical tests on a sand with different solid contents of the binder, i.e. 40, 30 and 20% solid content have been performed. Unconfined compressive strength at 7, 28 and 56 days, shear tests and permeability tests at 7 days were assessed. A statistical interpretation of these data has been performed and values were interpreted. A mathematical prediction model was suggested to estimate the mechanical strength of the grouted sand as the dilution grade varies. Furthermore, some considerations useful for geotechnical design on the evolution over time of the mechanical characteristics of the grouted sands have been carried out. Results show, from a statistical and mathematical view, that colloidal silica is a promising binder for ground improvement applications.

Interatrial block and abnormal P-wave electrocardiographic parameters as non-invasive predictors of atrial fibrillation

Aim. To identify noninvasive markers of atrial electrical dysfunction and risk of nonvalvular atrial fibrillation (AF) and to develop a predictive mathematical model to estimate the AF risk based on electrocardiographic (ECG) P-wave parameters during sinus rhythm.Methods. The study included 211 patients with cardiovascular pathology (aged median 62 [52; 71] years, 67.8% male, NYHA heart failure class I-III). All patients (follow-up median 45 [26; 67] months) underwent a complex of studies: 12-lead ECG, echocardiography, 24-hour ECG monitoring. Based on surface ECG data during sinus rhythm, parameters of atrial electrical activation were assessed such as Morphology, Voltage and P waves duration (MVP) according to integral analysis by MVP score.Results. During 3.7-year period, 44 (20.8%) patients experienced new-onset sustained AF and 12 (5.69%) patients developed ischemic stroke. As a result of ROC analysis and univariate Cox regression, independent predictors of AF were identified: P-wave prolongation in the DII lead, 3rd degree or advanced interatrial block (aIAB), an increase P-wave terminal force in lead V1 (PTFV1), low-voltage P-wave in the DI lead and сalculated level of abnormal P-wave ≥3 points on the MVP score. Data from multivariate Cox proportional hazards regression analysis confirmed the prognostic significance for three independent predictors of AF: aIAB (hazard ratio (HR) 5.92; 95% confidence interval (CI) [2.48-4.12]; p=0.0001); PTFV1 (HR 1.14; 95% CI [1.04-1.24], p=0.003); low-voltage P-wave in lead DI &lt;0.1 mV (HR 1.03; 95% CI [1.02-1.05]; p=0.0001); and as a result a mathematical model was created to predict AF risk (-2LL =258; χ2=105; p=0.0001). Predictors such as PTFV1 (HR 1.41; 95% CI [1.17-1.72], p=0.0001) and MVP score of abnormal P-waves (HR 1.85; 95% CI [1.27-1.72] 2.70], p=0.001) were associated with a high risk of stroke according to Cox regression model (-2LL= 62.5; χ2=38.4; p &lt;0.001).Conclusion. Complex of ECG markers of atrial electrical dysfunction such as aIAB, PTFV1, level MVP score of abnormal P-wave and low P-wave voltage allows identifying patients at high risk of AF and ischemic stroke.

Development of Predictive Modeling for Removal of Multispecies Biofilms of Salmonella Enteritidis, Escherichia coli, and Campylobacter jejuni from Poultry Slaughterhouse Surfaces.

Salmonella Enteritidis, Escherichia coli, and Campylobacter jejuni are among the most common foodborne pathogens worldwide, and poultry products are strongly associated with foodborne pathogen outbreaks. These pathogens are capable of producing biofilms on several surfaces used in the food processing industry, including polyethylene and stainless steel. However, studies on multi-species biofilms are rare. Therefore, this study aimed to develop predictive mathematical models to simulate the adhesion and removal of multispecies biofilms. All combinations of microorganisms resulted in biofilm formation with differences in bacterial counts. E. coli showed the greatest ability to adhere to both surfaces, followed by S. Enteritidis and C. jejuni. The incubation time and temperature did not influence adhesion. Biofilm removal was effective with citric acid and benzalkonium chloride but not with rhamnolipid. Among the generated models, 46 presented a significant coefficient of determination (R2), with the highest R2 being 0.88. These results provide support for the poultry industry in creating biofilm control and eradication programs to avoid the risk of contamination of poultry meat.