Present Model Research Articles

Detection of dental periapical lesions using retinex based image enhancement and lightweight deep learning model

Dental periapical lesions, commonly associated with inflammation around the tooth apex, pose a significant challenge in early diagnosis and treatment. This study introduces a novel approach for the detection of dental periapical lesions through the integration of Retinex-based image enhancement techniques and a lightweight deep learning model. The Retinex algorithm is employed to enhance the radiographic images, addressing issues related to inconsistent illumination and contrast. Subsequently, a tailored lightweight deep learning model is designed to efficiently extract relevant features from the enhanced images. Present methodology leverages a dataset of dental radiographs to train and evaluate the deep learning model, incorporating a diverse range of periapical lesion cases. The model is optimized for computational efficiency while maintaining high accuracy, making it suitable for deployment in resource-constrained environment. To enhance precision in lesion detection, the U-Net segmentation technique has been incorporated, providing a sophisticated approach to delineate and analyze specific areas of interest within the radiographic images. This addition further refines our diagnostic framework, contributing to the robustness of lesion identification. Experimental results demonstrate the effectiveness of the Retinex-based image enhancement in improving the visibility of periapical lesions. The lightweight deep learning model exhibits promising performance in accurately detecting and classifying dental periapical lesions, showcasing its potential for early and efficient diagnosis. The results obtained from the present model are compared with those from Convolutional Neural Network (CNN) as well as with diagnosis of expert practitioners and the model is observed to perform very well. The study contributes to the advancement of computer-aided diagnostic tools in dentistry, offering a scalable and accessible solution for the identification of dental periapical lesions through the fusion of image enhancement and lightweight deep learning techniques.

Numerical Study on the Dynamics of a Charged Bubble in the Acoustic Field

In this paper, the dynamics of an acoustic bubble with a constant charge in compressible liquid are investigated numerically, which is based on the Gilmore-NASG model to estimate the radial oscillations. The cavitation effects are enhanced due to the presence of the charge on the bubble surface. The obtained results from the present model are compared with that calculated by the previous model within a wide range of parameters (e.g., charge, acoustic pressure amplitude, ultrasound frequency, and liquid temperature). The similar influences of these parameters on bubble collapse intensity can be observed from both models. Since the present model fully considers the compressibility of gas and liquid, it can be applied to a wider parameter range and leads to the larger predicted values. The research in this paper can provide important insights about the effects of charge on bubble dynamics and the acoustic cavitation applications (e.g., sonochemistry, water treatment, and food industry).

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Effect of electron and ion mobility on edge biasing in tokamak plasmas

Abstract We present an improved model for the study of edge biasing in a tokamak plasma that incorporates electron and ion mobility contributions. The non-ambipolar nature of the drifts due to the electron/ion mobility terms influences the space charge separation due to edge biasing and affects plasma dynamics in the edge and SOL regions in a significant manner. In contrast to earlier studies, the present model enables simulation studies at higher biasing voltages. The inclusion of mobility enhances/decreases the effect of negative/positive biasing. The radial profiles of plasma density, electron temperature, radial electric field, and its shear for positive as well as negative biasing are investigated as a function of mobility.

Associations between Adverse Childhood Experiences and Gambling Severity in Youth Online Gamblers: The Mediating Roles of Internalizing, Attention and Externalizing Problems.

This study examined the association between Adverse Childhood Experiences (ACEs) and Gambling Severity (gambling severity), considering the mediating roles of internalizing, externalizing, and attention among youth online gamblers. 762 youth (agemean±SD= 15.03 ± 2.40; agerange = 10-18 years; 75.3% boys) completed the Persian Gambling Disorder Screening Questionnaire (GDSQ-P), Pediatric Symptom Checklist - Youth Report (Y-PSC), and Behavioral Risk Factor Surveillance System Questionnaire (ACE's section, modified by authors). The analysis was done using the SMART PLS software. The reliability and discriminant validity of the provided model were assessed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). According to the results of the PLS-SEM analysis, the present model demonstrated suitable levels of reliability and validity. Adverse Childhood Experiences (ACEs) significantly affected attention, internalizing, externalizing problems, and gambling severity. Additionally, the level of gambling was directly correlated with ACEs. Moreover, the indirect influence of the independent variable on the dependent variable via the mediators was found to be statistically significant (P < .001).These findings suggest that externalizing behaviors, attention problems, and internalizing symptoms mediate the effect of ACEs on gambling severity. Lastly, fitness indices indicated that our proposed model fit the data well (SRMR = 0.06, d_ULS = 1.15, Chi-square = 1291.461, and NFI = 0.71). Our study found that ACEs significantly influence gambling severity among youth online gamblers, with internalizing, externalizing, and attention problems mediating this relationship. Practical implications include integrating ACE screening and targeted interventions for associated mental health issues into youth gambling prevention programs to mitigate the risk of problematic gambling behavior.

Electrode wettability and capacitance of electrical double layer capacitor: a classical density functional theory study

We suggest a coarse-grained water model for use in classical density functional theory (cDFT) to describe aqueous inorganic salt solutions that act as working electrolytes in electrical double-layer capacitors (EDLCs) with electrodes comprising two face-to-face doped carbon monolayers. Focus of the cDFT calculations lies on the influence of solvent electrode wettability (SEW) on capacitance and energy storage behaviors, while also considering its interaction with factors like electrolyte bulk concentration, pore size, electrode voltage, and temperature. New phenomena are disclosed theoretically. Remarkably, this study challenges the traditional notion that energy storage is consistently boosted by enhancing the electrode’s ionophobicity. Contrarily, the SEW effect reduces energy storage below the standard aqueous electrochemical window voltage (around 1.2 V) and only enhances the energy storage as the voltage surpasses a certain threshold up to the optimal window voltage (2 V–2.5 V). Furthermore, a non-monotonic SEW effect on energy storage is demonstrated under appropriate conditions, shedding new light on the complex relationship between ionophobicity and energy storage. Moreover, the present coarse-grained water model enables the prediction of the experimentally observed inverse relationship between temperature and capacitance. In contrast, the widely used electrolyte primitive model predicts the existence of a maximum value. The decisive factor for the impact of SEW on capacitance and energy storage is identified as congestion within the electrode pore, while other factors contribute by affecting this congestion. The present research offers valuable insights, highlighting the significance of SEW in the innovative and strategic design of aqueous inorganic EDLC devices.

#1131 Significant reno-cardio-metabolic protection by the food additive Flexovital in a murine model combining unilateral nephrectomy and Western diet

Abstract Background and Aims Cardiovascular complications are major threats in advanced renal failure and metabolic dysfunction with several unmet medical needs. This study aimed to investigate the therapeutic effects of a special food additive (FLEXOVITAL) in a newly developed mouse model of reno-cardio-metabolic disease, induced by moderate renal failure in combination with a special Western diet. Method Male C57BL/6J mice (4 weeks old, n = 18) were subject to unilateral nephrectomy (UNX), fed a Western diet rich in fat carbohydrates and salt (WD), and were followed for 12 weeks compared with SHAM-operated mice on standard chow (n = 19). One group of UNX+WD mice (n = 8) was fed a food additive (FLEXOVITAL; FLX) containing extracts of Rhodiola rosea, beetroot, and the amino acids arginine and citrulline. Body weight (BW), blood pressure (tail-cuff), endothelial-dependent vasorelaxation (myograph), glucose metabolism (IPGTT), body fat and lean mass composition (DEXA), adipocyte area, renal function (Glomerular Filtration Rate (GFR) by plasma clearance of FITC-inulin), and mitochondrial function (Oroboros, High-resolution respirometry) were measured together with biochemical analysis of heart injury (Troponin-I), inflammation (IL6) and histological analyses of the kidney and heart. Results BW gain was seen in the UNX+WD and SHAM group, and was 25% less in the FLX group (p &amp;lt; 0.05). The fat/lean-mass ratio increased by 17% (p &amp;lt; 0.01) and the adipocyte area by 31% (p &amp;lt; 0.001) in the UNX+WD group but was virtually normalized in the FLX group (p &amp;lt; 0.01). Fasting and non-fasting glucose levels became elevated in the UNX+WD group and were reduced in the FLX group (p &amp;lt; 0.05). Impaired glucose clearance in the UNX+WD group, was partially prevented by FLX. BP significantly increased in the UNX+WD group (MAP = 78 → 90 mmHg, p &amp;lt; 0.001), and was largely prevented by FLX (MAP = 82 mmHg, p &amp;lt; 0.01). Endothelial function was significantly impaired in the UNX+WD group (p &amp;lt; 0.01) and partially preserved in the FLX group (p &amp;lt; 0.05). GFR was reduced by almost 60% in the UNX+WD group but improved with a 75% protective effect in the FLX group (p &amp;lt; 0.05). Significant glomerular injuries with mesangial proliferation were observed in the UNX+WD group (p &amp;lt; 0.001) but were less pronounced in the FLX group (p &amp;lt; 0.01). Tubular injury score (1-10) increased from 1 in SHAM to 6.5 in the UNX+WD group and was partly protected (4.5) in the FLX group (p &amp;lt; 0.05). Troponin-I levels were 15 pg/ml in the SHAM group, markedly increased in the UNX+WD group (p &amp;lt; 0.001), whereas completely reversed in the FLX group (p &amp;lt; 0.01). No significant histological cardiac injuries or deviations could be demonstrated. Inflammatory activity (IL6) increased by 88% in the UNX+WD there was a trend of reduction in mice with FLX. Mitochondrial oxygen efficiency (P/O ratio) was improved in the kidneys of the FLX group compared to the UNX+WD group (p &amp;lt; 0.05). Conclusion In the present multiorgan disease model, significant renal, cardiovascular, and metabolic dysfunction/injuries emerge in mice with a moderate reduction of renal function when fed a Western diet rich in fat, carbohydrates, and salt. Protective effects were noted by dietary FLX treatment in most functional assessments made in the model. This indicates FLX is a potential new treatment in patients with reno-cardio-metabolic disease. The next phase involves confirming these findings in the clinical setting.

The mechanism of phonetic information in voice identity discrimination: a comparative study based on sighted and blind people

PurposeThe purpose of this study is to examine whether phonetic information functions and how phonetic information affects voice identity processing in blind people.MethodTo address the first inquiry, 25 normal sighted participants and 30 blind participants discriminated voice identity, when listening forward speech and backward speech from their own native language and another unfamiliar language. To address the second inquiry, combining articulatory suppression paradigm, 26 normal sighted participants and 26 blind participants discriminated voice identity, when listening forward speech from their own native language and another unfamiliar language.ResultsIn Experiment 1, not only in the voice identity discrimination task with forward speech, but also in the discrimination task with backward speech, both the sighted and blind groups showed the superiority of the native language. This finding supports the view that backward speech still retains some phonetic information, and indicates that phonetic information can affect voice identity processing in sighted and blind people. In addition, only the superiority of the native language of sighted people was regulated by the speech manner, which is related to articulatory rehearsal. In Experiment 2, only the superiority of the native language of sighted people was regulated by articulatory suppression. This indicates that phonetic information may act in different ways on voice identity processing in sighted and blind people.ConclusionThe heightened dependence on voice source information in blind people appears not to undermine the function of phonetic information, but it appears to change the functional mechanism of phonetic information. These findings suggest that the present phonetic familiarity model needs to be improved with respect to the mechanism of phonetic information.

Coupled Experimental Study and Thermodynamic Modeling of the Fe–Si–N System

Some key experiments are carried out to determine the solubility of N in Fe–Si liquid solutions at various compositions, temperatures, and N2 pressures using the sampling method. The Fe–Si–N system is thermodynamically modeled using the CALculation of PHAse Diagrams (CALPHAD) approach for the first time based on critical evaluation of all available experimental data. In the present thermodynamic modeling, the liquid and solid solutions of the Fe–Si–N system are described using the modified quasichemical model and compound energy formalism, respectively. In particular, the Gibbs energies of liquid, FCC_A1, and BCC_A2 solutions are carefully determined to resolve the discrepancies among existing experimental data. The solubility product of β‐Si3N4(s) in Si steels and ferrosilicon alloys, activity coefficients, and interaction parameters of N in liquid and solid Fe–Si–N solutions are determined over a wide temperature range. As an application of the database, the N solubility in Si steels and phase constitution of nitrided ferrosilicon alloys in a wide composition, temperature, and N2 pressure ranges are predicted.

Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation.

To develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets. Cross-sectional study. A total of 325 CT scans from a clinical database. A state-of-the-art deep learning (DL) algorithm (SwinUNETR) was used to train a prediction model for rapid segmentation of 9 key temporal bone structures in a data set of 325 clinical CTs. The data set was manually annotated by a specialist to serve as the ground truth. The data set was randomly split into training (n = 260) and testing (n = 65) sets. The model's performance was objectively assessed through external validation on the test set using metrics including Dice, Balanced accuracy, Hausdorff distances, and processing time. The model achieved an average Dice coefficient of 0.87 for all structures, an average balanced accuracy of 0.94, an average Hausdorff distance of 0.79 mm, and an average processing time of 9.1 seconds per CT. The present DL model for the automated simultaneous segmentation of multiple structures within the temporal bone from CTs achieved high accuracy according to currently commonly employed objective analysis. The results demonstrate the potential of the method to improve preoperative evaluation and intraoperative guidance in otologic surgery.

DNA damage response in a 2D-culture model by diffusing alpha-emitters radiation therapy (Alpha-DaRT)

Diffusing alpha-emitters radiation therapy (Alpha-DaRT) is a unique method, in which interstitial sources carrying 224Ra release a chain of short-lived daughter atoms from their surface. Although DNA damage response (DDR) is crucial to inducing cell death after irradiation, how the DDR occurs during Alpha-DaRT treatment has not yet been explored. In this study, we temporo-spatially characterized DDR such as kinetics of DNA double-strand breaks (DSBs) and cell cycle, in two-dimensional (2D) culture conditions qualitatively mimicking Alpha-DaRT treatments, by employing HeLa cells expressing the Fucci cell cycle-visualizing system. The distribution of the alpha-particle pits detected by a plastic nuclear track detector, CR-39, strongly correlated with γH2AX staining, a marker of DSBs, around the 224Ra source, but the area of G2 arrested cells was more widely spread 24 h from the start of the exposure. Thereafter, close time-lapse observation revealed varying cell cycle kinetics, depending on the distance from the source. A medium containing daughter nuclides prepared from 224Ra sources allowed us to estimate the radiation dose after 24 h of exposure, and determine surviving fractions. The present experimental model revealed for the first time temporo-spatial information of DDR occurring around the source in its early stages.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Strategic Selection of Machine Learning Models for Short-term Trading Optimization

Stock market prediction is a key aspect for a financial analyst and investor, as it facilitates better decisions and improved investment strategies. Towards this end, this project compares how machine learning (ML) models perform in predicting stock market prices with relatively small dataset sizes so as to get the best performing one. This study evaluates the efficiency and accuracy of artificial neural networks, support vector regression, LSTM and decision trees in capturing market trends and forecasting stock movements. The importance of our proposed paper lies in its potential to demystify the complexities of financial markets for students and new entrants in the field of finance. Participants can garner practical insights on how the stock market operates and the application of theoretical ML concepts to financial analytics by identifying the best-performing ML models with limited datasets, which is a common occurrence in real-life situations. This paper aims to analyse the commonly present machine learning models in stock market prediction and choose the most effective model depending upon their performance, which is achieved using limited information (short term data). This would provide an edge in taking small (less-risky) informed financial decisions without extensive study of the stock market.

A Novel Calculate Model of Shear Deformation and Relative Displacement of Pile–Soil Interface in Warm Frozen Soil Foundation

In permafrost regions with warm frozen soil, the pile foundation is commonly used, but most currently available models for the WFS foundation pile–soil system are either highly empirical or overcomplicated, without a simplified theoretical manner in engineering. This study derives a novel and simplified calculated model of the WFS pile–soil system. The model is formulated in terms of the shear deformation theory and load transfer method based on the rigorous deformation mechanism of the WFS foundation soil around the pile. Considering the different depth soil features and the equilibrium state of the pile–soil system, dividing warm frozen soil foundation into three regions (TPPR, ER, and BPPR) to calculate the Dp and Ds can simply obtain the total displacement of pile under different loads. The results demonstrate that the present theoretical model can well predict the WFS foundation load–displacement response of the pile. The present model provides a simple, practical, and effective approach for the estimation of the load–displacement behavior of piles installed in the WFS foundation.

Predictive Modelling and Optimization of the Mechanical Properties of Laser-Coated NB/SiC/Ni Welds Using an ANFIS

In the present work, predictive modelling and optimization with the adaptive network based fuzzy inference system (ANFIS) modelling of the mechanical properties of laser-coated NB/SiC/Ni welds was studied based on the Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties were taken as the output of the ANFIS. The microstructure of welds was analysed using scanning electron microscopy with an energy-dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi’s design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified.

Cadence Paradox in Cycling-Part 2: Theory and Simulation of Maximal Lactate Steady State and Carbohydrate Utilization Dependent on Cycling Cadence.

To develop and evaluate a theory on the frequent observation that cyclists prefer cadences (RPMs) higher than those considered most economical at submaximal exercise intensities via modeling and simulation of its mathematical description. The theory combines the parabolic power-to-velocity (v) relationship, where v is defined by crank length, RPM-dependent ankle velocity, and gear ratio, RPM effects on the maximal lactate steady state (MLSS), and lactate-dependent carbohydrate oxidation (CHO). It was tested against recent experimental results of 12 healthy male recreational cyclists determining the v-dependent peak oxygen uptake (VO2PEAKv), MLSS (MLSSv), corresponding power output (PMLSSv), oxygen uptake at PMLSSv (VO2MLSSv), and CHOMLSSv-management at 100 versus 50per minute, respectively. Maximum RPM (RPMMAX) attained at minimized pedal torque was measured. RPM-specific maximum sprint power output (PMAXv) was estimated at RPMs of 100 and 50, respectively. Modeling identified that MLSSv and PMLSSv related to PMAXv (IPMLSSv) promote CHO and that VO2MLSSv related to VO2PEAKv inhibits CHO. It shows that cycling at higher RPM reduces IPMLSSv. It suggests that high cycling RPMs minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. The present theory-guided modeling approach is exclusively based on data routinely measured in high-performance testing. It implies a higher performance reserve above IPMLSSv at higher RPM. Cyclists may prefer high cycling RPMs because they appear to minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX.

‘Protecting your body and loving yourself’: strategies Black male caregivers use to protect Black girls sexuality in the USA

Black girls in the USA face disproportionate rates of sexually transmitted infections (STIs), HIV, and sexual violence, prompting research into the sociocultural factors such as adultification, race and gender discrimination, and stereotyped messaging, impacting Black girls’ sexual development. While existing literature finds that Black female caregivers utilise protective strategies to mitigate potential harm to Black girls, little is known about the role of Black male caregivers. This qualitative study employed the Becoming a Sexual Black Woman framework and one-on-one interviews to examine how 30 Black male caregivers of girls conceptualised parental protection and elated strategies. The thematic analysis revealed key themes of: Being a Provider, Being a Present Role Model, Protecting Black Girls’ Bodies, Limiting Exposure to Stereotyped Messages, and Providing Emotional Protection. Black male caregivers navigated traditional gendered expectations and limited resources while utilising parental strategies that reflected patriarchal, sometimes misogynistic, and non-patriarchal ideals and norms. The study highlights the attitudes and beliefs of Black male caregivers regarding protecting Black girls from sexual violence and other related adverse outcomes. The findings offer insights for family-based interventions and programmes that empower Black girls to support their sexual development and health in the USA.

Development and preliminary assessment of a machine learning model to predict myocardial infarction and cardiac arrest after major operations

IntroductionAccurate prediction of complications often informs shared decision-making. Derived over 10 years ago to enhance prediction of intra/post-operative myocardial infarction and cardiac arrest (MI/CA), the Gupta score has been criticized for unreliable calibration and inclusion of a wide spectrum of unrelated operations. In the present study, we developed a novel machine learning (ML) model to estimate perioperative risk of MI/CA and compared it to the Gupta score. MethodsPatients undergoing major operations were identified from the 2016–2020 ACS-NSQIP. The Gupta score was calculated for each patient, and a novel ML model was developed to predict MI/CA using ACS NSQIP-provided data fields as covariates. Discrimination (C-statistic) and calibration (Brier score) of the ML model were compared to the existing Gupta score within the entire cohort and across operative subgroups. ResultsOf 2,473,487 patients included for analysis, 25,177 (1.0%) experienced MI/CA (55.2% MI, 39.1% CA, 5.6% MI and CA). The ML model, which was fit using a randomly selected training cohort, exhibited higher discrimination within the testing dataset compared to the Gupta score (C-statistic 0.84 vs 0.80, p < 0.001). Furthermore, the ML model had significantly better calibration in the entire cohort (Brier score 0.0097 vs 0.0100). Model performance was markedly improved among patients undergoing thoracic, aortic, peripheral vascular and foregut surgery. ConclusionsThe present ML model outperformed the Gupta score in the prognostication of MI/CA across a heterogenous range of operations. Given the growing integration of ML into healthcare, such models may be readily incorporated into clinical practice and guide benchmarking efforts.

Understanding aging characteristics of a pyrotechnic initiator through performance modeling in Closed Bomb Tests

AbstractWe conducted an analysis of the natural aging characteristics of an electric pyrotechnic initiator stored for 11 years, using 10‐cm3 Closed Bomb Tests (CBTs). For the comparative analysis, we utilized the closed bomb test data from other three batches that were tested 11 years ago when they were produced. Notably, two aged samples exhibited peculiar phenomena in terms of maximum pressure and the time taken to reach it, both of which are the performance indicators of Closed Bomb Tests. The time taken to reach maximum pressure increased in both aged samples compared to the pristine samples. Additionally, while one aged sample demonstrated a higher maximum pressure, the other displayed a significantly lower value compared to the pristine ones. Through statistical analysis of test results from three batches of the reference samples and one batch of aged samples led us to propose the existence of a natural aging effect on the initiator. The increase in time taken to reach maximum pressure compared to the reference samples can be attributed to the phenomenon of an increasing particle size distribution of spherical combustion particles in the present combustion model. Furthermore, the aging of the binder of the initiator charge likely contribute to a relative increase in the size of the combustion particles due to the binder hardening over time. To explain the phenomenon of the pressure initially reaches its maximum value on the CBT pressure‐time curve and subsequently decrease, we introduce the concept of condensation, where the gas phase transitions into liquid and solid phases.

An approach to the effects of stellar rotation on the theoretical apsidal motion constants. Calculations from 0.40 M_sol to 25.0 M_sol

The most reliable sources for determining absolute stellar parameters are the double-lined eclipsing binary systems. Some of these systems also show apsidal motion, characterized by the variable log k$_2$. This point grants us the possibility of investigating the stellar interior, specifically the degree of stellar mass concentration. The first studies carried out about four decades ago on this topic showed appreciable discrepancies not only with respect to the comparison between the observed absolute dimensions and their theoretical counterparts, but mainly concerning the degree of mass concentration through the analysis of their apsidal motions. Fortunately, this scenario has been gradually improving with the advances in the quality of observational techniques and advances in the input physics of the evolutionary stellar models (e.g. opacities, thermonuclear reactions, equations of state, numerical techniques). These new developments in the input physics has improved the comparison between observations and the values predicted by theory, including the apsidal motion rates. This progress has lead us to investigate second-order effects such as rotation and dynamic tides. In this paper we deal with the effects of rotation on the degree of mass concentration. The stellar models were calculated using the MESA package. The mass range studied here was 0.40 to 25.0 M$_ odot $ for a generic chemical composition characterized by X = 0.70 and Z = 0.02. The present models were computed without taking into account core overshooting in order to highlight the effects of rotation. Each model was followed from the pre-main sequence until the central hydrogen content is of the order of or less than 0.03, covering the range of masses and evolutionary status of the double-lined eclipsing binaries showing apsidal motion. Regarding the calculation of the internal structure coefficients bf we integrated the Radau equation using the fifth-order Runge-Kutta method and a tolerance level of 10$^ $. As an auxiliary tool, homology transformations have been used to explain, qualitatively, how the equation of state, thermonuclear reactions, and convection impact the degree of mass concentration of the models. In our past studies on the effects of rotation on log k$_2$ an average correction was used for all models together. For this a small range of masses (2.0, 7.0, 15.0 M$_ odot $) has been used. In the present paper the corrections due to the effects of rotation in log k$_2$ are presented for each mass individually and for three evolutionary phases. This point is particularly important bf given that these corrections show a clear dependence on the mass and on the evolutionary status. Such corrections can be easily introduced into the theoretical calculation of apsidal motion rates through a linear equation. A typical correction due to the rotation effects is of the order of -0.03 in log k$_2$.