Simulation Outcomes Research Articles

Theoretical and Experimental Study of an Electrokinetic Micromanipulator for Biological Applications

The ability to control and manipulate biological fluids within microchannels is a fundamental challenge in biological diagnosis and pharmaceutical analyses, particularly when buffers with very high ionic strength are used. In this study, we investigate the numerical and experimental study of fluidic biochips driven by ac electrothermal flow for controlling and manipulating biological samples inside a microchannel, e.g., for fluid-driven and manipulation purposes such as concentrating and mixing. By appropriately switching the voltage on the electrode structures and inducing AC electrothermal forces within the channel, a fluidic network with pumping and manipulation capabilities can be achieved, enabling the control of fluid velocity/direction and also fluid rotation. By using finite element analysis, coupled physics of electrical, thermal, fluidic fields, and molecular diffusion transport were solved. AC electrothermal flow was studied for pumping and mixing applications, and the optimal model was extracted. The microfluidic chip was fabricated using two processes: electrode structure development on the chip and silicon mold fabrication in a cleanroom. PDMS was prepared as the microchannel material and bonded to the electrode structure. After implementing the chip holder and excitation circuit, a biological buffer with varying ionic strengths (0.2, 0.4, and 0.6 [S/m]) was prepared, mixed with fluorescent particles, and loaded into the microfluidic chip. Experimental results demonstrated the efficiency of the proposed chip for biological applications, showing that stronger flows were generated with increasing fluid conductivity and excitation voltage. The system behavior was characterized using an impedance analyzer. Frequency response analysis revealed that for a solution with an electrical conductivity of 0.6 [S/m], the fluid velocity remained almost constant within a frequency range of 100 kHz to 10 MHz. Overall, the experimental results showed good agreement with the simulation outcomes.

An improved SPWM control approach with aid of ant lion optimization for minimizing the THD in multilevel inverters

This article presents an innovative asymmetric multilevel inverter (MLI) topology that outperforms conventional counterparts. The introduced topology presents a breakthrough in implementing power electronics control by maximizing specific levels while minimizing switching components. A cutting-edge control scheme for optimal operation of the cascaded half-bridge MLI is presented. The ant lion optimization (ALO) algorithm was implemented to optimize the switching control to reduce the total harmonic distortion (THD) and improve power quality. For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. The suggested method showcases a remarkable reduction in the THD compared to SSPWM. The quality of the resulting waveform was enhanced, and both filter size and cost were significantly reduced. To meet stringent IEEE standards, an LC filter has been designed with minimal size and proper requirements. Experimental results validation of the suggested scheme, using a dSPACE R&D controller board unequivocally, confirmed its robustness and effectiveness. This groundbreaking study not only introduces a superior asymmetric MLI topology but also validates its exceptional performance through comprehensive analysis and experimentation. The experimental waveforms showed good matching with the simulation outcomes. The findings hold immense promise for advancing the field of power system control and revolutionizing the designing and implementation of efficient and cost-effective inverter systems.

Impact of high-resolution land use data on numerical simulations of Colombo’s thermal environment during heatwaves

Land Use and Land Cover (LULC) play a crucial role in the regional climate models, as they influence various physical processes interconnected with energy, mass, and atmospheric interactions. This study has examined the impact of high-resolution LULC data on the accuracy of the Weather Research and Forecasting (WRF) model with the Urban Canopy Model in capturing the thermal environment of the Colombo Metropolitan Region (CMR) during heatwaves (HWs). In the performed simulations we used four LULC datasets: USGS, MODIS30, MODIS15, and Esri (Sentinel2). Our investigation is the first application of the WRF model to study the thermal environment in a tropical city such as Colombo. This is also the first research investigation of HWs in the Sri Lanka context from the mesoscale viewpoint. Additionally, this is the first time high-resolution Esri land-use data have been used in a numerical model to examine the thermal environment. By evaluating the three HW cases, we compared the simulation outcomes with observational data from three meteorological stations in CMR. The results from numerical simulations revealed that the LU4 dataset provided the closest agreement with observed Land Surface Temperatures (LST). Daytime LSTs in urban areas peaked above 45 °C, while nighttime temperatures reached 30 °C in commercial zones. Coastal regions consistently exhibited lower temperatures, with daytime LSTs ranging from 40 to 45 °C and nighttime LSTs from 25 to 30 °C, compared to the urban cores. LU4 showed the closest alignment with Himawari satellite data, ensuring superior accuracy. Temperature anomaly analysis indicated that LU4 reduced daytime temperature anomalies by up to 2.5 °C and nighttime anomalies by 1.0 °C compared to other datasets. Statistical evaluations (R = 0.94 in Colombo) further highlight the importance of high-resolution LULC data for enhancing the accuracy of the WRF model in representing urban thermal environments.

Discrete adaptive sliding mode control for uncertain fractional-order Hammerstein system

This study explores controlling discrete fractional-order Hammerstein systems through two different approaches. The first method involves a detailed exploration of discrete sliding mode control (SMC), while the second method employs adaptive sliding mode techniques to address challenges posed by unknown time-varying system parameters. The aim is to enhance control strategies for these systems by ensuring stability and robustness amid evolving parameters. Stability analysis using Lyapunov Theory is carried out to establish the stability of the proposed control schemes. Comprehensive reachability analysis is then performed to show that the system reaches a quasi-sliding mode in finite time. The effectiveness of the proposed methods is thoroughly evaluated using simulation cases, covering three distinct scenarios. Finally, a discussion section analyzes the simulation outcomes and assesses the efficiency methodologies in handling complex system dynamics. Through comparative analysis and discussion, this study contributes to advancing control strategies tailored for systems characterized by fractional-order dynamics and varying parameters.

Exploring River’s Flood Dynamics: Integrating HEC-RAS 1-D Modelling and Geospatial Techniques for the Karjan River in Gujarat’s Narmada Basin, India

Floods rank among the most devastating natural disasters, causing extensive damage to property and severe impacts on communities. Effective management and mitigation of flood risks necessitate reliable data regarding flood depth, discharge, and extent. The Karjan River Basin’s low-lying regions experienced significant flooding in 2020. This research utilizes the latest new HEC-RAS version for one-dimensional hydrodynamic flood modelling for the Karjan River in low-lying areas and the merging point of the Karjan River Basin in Narmada River, emphasizing geospatial techniques. The study showcases the analytical process of the new HEC-RAS v6 in spatial analysis. River features like bank lines and cross-sections were derived from ALOS Palsar for flood modeling. An unsteady flow simulation was conducted to model water dynamics, yielding water depth results viewable in the HEC-RAS geospatial RAS mapper. Flood depth maps created for 2020 reveal that areas low lying area i.e. Dhamncha, bhadam, Juna Rundh and surrounding area near the merging point of Karjan River are prone to floods when river discharge surpasses 8836 cubic meters per second. Model accuracy was verified by comparing simulated outcomes with historical data from the mentioned events, demonstrating the model’s precision and reliability.

Highly Robust Active Damping Approach for Grid-Connected Current Feedback Using Phase-Lead Compensation

The grid-current-feedback active damping (GCFAD) strategy has been widely utilized in LCL-type cascaded H-bridge static var generator (SVG) systems. Although digital control in GCFAD enhances sampling accuracy, it introduces delays in the subsequent calculation and execution processes, thereby reducing the effective damping region and weakening the robustness of the LCL-type cascaded H-bridge SVG system under a broad domain of grid impedance changes. This paper expands the effective damping area from (0, fR) (fR ∈ (fs/6, fs/3)) to (0, 0.45fs) by introducing a phase-lead compensator into the active damping feedback loop to reduce the delay. This reduces the negative effect of the digital control on the system’s effective damping region and significantly enhances its robustness under an extensive domain of grid impedance variations. The improved GCFAD is employed to establish the system’s discrete domain model and the controller parameters are adjusted in conjunction with the mentioned model. Simulation outcomes indicate the efficiency of the presented approach.

Suspended-core photonic crystal fibers for SERS detection of biomolecules

Abstract Photonic crystal fiber (PCF) can be used as a microfluidic platform for surface-enhanced Raman spectroscopy (SERS)-based measurement of different samples. PCF can provide a long area of interaction for light, metal nanoparticles, and analytes incorporated into its holes. This paper provides a COMSOL simulation model for suspended-core PCF used to evaluate the SERS intensity depending on the fiber's geometry and nanoparticles' coverage density. The simulation is applied to compare three PCFs with varying sizes and lengths. SERS measurements of adenine and DNA validate the simulation outcomes and demonstrate the potential of PCFs for SERS-based detection of diverse samples.

Numerical Investigation and Experimental Verification of Vibration Behavior for a Beam with Cantilever-Hertzian Contact Boundary Conditions

The simple spring structure, with detachable electrical contacts, is a very suitable solution for many applications, such as electromechanical relays and connectors. However, they are prone to exhibit instantaneous interruption faults under mechanical vibration environments. In this paper, the governing equations of the modal analysis of a beam with cantilever-Hertzian contact boundary conditions are presented. Then, the time domain analysis method and frequency domain analysis method for solving the forced vibration response are described explicitly. Next, the effect of the axial force on the modal frequency of a detailed model sourced from the practical relay is investigated by using commercial ANSYS Workbench 2021R1 software. Afterward, the harmonic response of the beam is numerically solved individually by using the transient analysis model and the harmonic analysis model in ANSYS Workbench 2021R1 software. Then, the influences of the damping coefficient and excited frequency on the contact force response are investigated. The experimental results of transient displacement and contact resistance of the beam structure agree well with the simulation outcomes. It is proven that there is a linear relationship between the stiffness coefficient and the mass coefficient, which are used for characterizing the damping of the structures in the time domain method and frequency domain methods.

Parametric Studies of Polyacrylamide Adsorption on Calcite Using Molecular Dynamics Simulation

This study investigates the efficacy of polyacrylamide-based polymers, specifically hydrolysed polyacrylamide (HPAM), in reducing solids production within carbonate reservoirs. Building on our earlier simulation approach, molecular simulations were conducted to examine how these polymers adsorb onto calcite, the main mineral found in carbonate formations. The adsorption process was affected by several factors, including polymer molecular weight, charge density, temperature, and salinity. Generally, increased molecular weight, charge density, and temperature resulted in higher adsorption rates. The effect of salinity was more nuanced, as salt-bridging and charge-screening effects created competing influences. The simulation outcomes correspond closely with experimental results, offering valuable insights for designing and optimizing polymer-based strategies aimed at controlling solids production in carbonate reservoirs.

Compact Tri-Band Bandpass Filter with Wide Upper Stopband Based on Spoof Surface Plasmon Polaritons and Open-/Short-Circuited Stubs

In this paper, a new U-shaped ring spoof surface plasmon polariton (SSPP) structure is proposed as part of a bandpass filter (BPF) combined with short-circuited stubs (SCSs). The U-shaped ring unit offers miniaturization and multiple adjustable parameters. Furthermore, the lower and upper cutoff frequencies of the passband for the BPF can be adjusted by modifying the structural parameters of the SSPP units and SCSs, respectively. To validate the design, a prototype filter was first created with a frequency range of 2 to 3.7 GHz for the passband and an extended stopband that reached up to 15 GHz. On the basis of the designed BPF, a tri-band filter was realized by introducing multiple transmission zeros by loading multiple open-circuited stubs (OCSs) onto the transmission portion of SSPPs. The center frequencies of the three passbands were 1.20 GHz, 2.03 GHz, and 2.96 GHz, respectively. At the same time, the upper stopband rejection reached up to 12 GHz with an attenuation of −30 dB, about 10 times the center frequency of the first passband. The experimental results demonstrate a strong correlation between the measured and simulated outcomes, thereby validating the proposed structure and design methodology. Notably, the filter measures only 0.38λg × 0.13λg, highlighting its compact size as a significant advantage.

Based on AVL-CRUISE Hybrid and Electric Vehicle Simulation and FTP-72 Circle Power Consumption Factor Analysis

With the rapid advancement of technology and the growing scarcity of energy resources, finding effective ways to reduce power consumption has become increasingly crucial. Hybrid and electric vehicles play a vital role in this endeavor, warranting deeper exploration and focus. In this study, I utilized AVL-Cruise software, a powerful tool for simulation and data modeling, to conduct comprehensive simulations of hybrid and electric vehicle performance, with a specific focus on analyzing the FTP72 cycle.The simulation results are highly encouraging. The vehicle achieved a maximum speed of 164 km/h, an acceleration rate of 3 m/s, a 0 to 100 km/h acceleration time of 9 seconds, and a maximum climbing slope of approximately 74%. These figures not only demonstrate that hybrid and electric vehicles can meet performance expectations but also highlight their capability to deliver on key metrics. Furthermore, the total energy consumption was recorded at 4200 kJ, underscoring the efficiency of these vehicles.In addition to the simulation outcomes, I delved into various factors that influence vehicle performance, including vehicle mass, drag coefficient, and motor power.Using MATLAB, I conducted a detailed analysis of the relationships between these factors and energy consumption, incorporating residual analysis and deriving a mathematical equation to describe these interactions.Through this extensive data analysis and model fitting, we gain a deeper understanding of hybrid and electric vehicle dynamics. This research not only contributes to the field but also has the potential to drive further development, broadening the focus and scope of hybrid and electric vehicle technology

Digitizing Low-Frequency Analog Control Circuit Using Bilinear Function Algorithm

In the past, low-frequency analog control circuits were largely used in analog control systems. With the rapid development of modern digital electronic technology, the digitization of traditional low-frequency analog control circuits while retaining the same functions as the original analog control systems has become an important trend in the field of electronic design. For this reason, this study aimed to develop an analog signal digitization model for control signals at a low frequency below 10 Hz. In terms of signal receiving and transmission requirements, the proposed model is configured with analog-to-digital converter (ADC), digital-to-analog converter (DAC), and pulse width modulation (PWM) functions. In the development environment, the input and output signals are first normalized and processed with PWM, enabling the digital signal processor to deal with analog signal receiving, processing, and external transmission. To replace the existing compensator in analog circuits, a digital compensator is used in the digital signal processor. Based on the bilinear function in MATLAB software, the parameter values demanded for the digital compensator can thus be obtained, achieving the automatic calculation of bilinear transformation in the system. Multisim simulation is then used to simulate analog circuit systems for comparison with the digitized outcomes. The experimental results reveal that the performance of the designed digital control circuit matches the simulation outcomes in both Bode gain (dB) and phase responses when the signal frequency is below 10 Hz. The effectiveness of the digitized analog control circuit for low-frequency control signals is therefore confirmed.

Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc.

Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional calibration methods are computationally intensive, requiring iterative, derivative-free optimization algorithms that often take days to converge. This study addresses these challenges by introducing a novel, efficient, and effective calibration method demonstrated on a human L4-L5 IVD FE model as a case study using a neural network (NN) surrogate. The NN surrogate predicts simulation outcomes with high accuracy, outperforming other machine learning models, and significantly reduces the computational cost associated with traditional FE simulations. Next, a Projected Gradient Descent (PGD) approach guided by gradients of the NN surrogate is proposed to efficiently calibrate FE models. Our method explicitly enforces feasibility with a projection step, thus maintaining material bounds throughout the optimization process. The proposed method is evaluated against state-of-the-art Genetic Algorithm (GA) and inverse model baselines on synthetic and in vitro experimental datasets. Our approach demonstrates superior performance on synthetic data, achieving a Mean Absolute Error (MAE) of 0.06 compared to the baselines' MAE of 0.18 and 0.54, respectively. On experimental specimens, our method outperforms the baseline in 5 out of 6 cases. While our approach requires initial dataset generation and surrogate training, these steps are performed only once, and the actual calibration takes under three seconds. In contrast, traditional calibration time scales linearly with the number of specimens, taking up to 8 days in the worst-case. Such efficiency paves the way for applying more complex FE models, potentially extending beyond IVDs, and enabling accurate patient-specific simulations.

Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss

In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy of the solution, different multi-objective case studies are scrutinised based on the combinations of (i) HC and Loss, (ii) HC and VDev, (iii) Loss and VDev, (iv) HC Loss and VDev simultaneously with the effect of demand response. The multi-objective research problem is formulated as non-linear and non-convex programming approach. To solve this complex problem, the modified crow search optimization (MCSO) is proposed. The MCSO achieved the 0.0714 MW of network loss with the optimal integration of distributed generation and is comparable to the well-established optimization algorithms available in literature. From the simulation results, it is found that HC is 3322.31 kW, VDev is 0.4982 p.u and system losses is 1314.86 kWh with demand response program when all the objectives are simultaneously optimized. The simulation outcomes highlight the superiority of the MCSO over others. The application results show the benefits and the beauty of proposed research work.

Using Quantitative Bias Analysis to Adjust for Misclassification of COVID-19 Outcomes: An Applied Example of Inhaled Corticosteroids and COVID-19 Outcomes.

During the pandemic, there was concern that underascertainment of COVID-19 outcomes may impact treatment effect estimation in pharmacoepidemiologic studies. We assessed the impact of outcome misclassification on the association between inhaled corticosteroids (ICS) and COVID-19 hospitalisation and death in the United Kingdom during the first pandemic wave using probabilistic bias analysis (PBA). Using data from the Clinical Practice Research Datalink Aurum, we defined a cohort with chronic obstructive pulmonary disease (COPD) on 1 March 2020. We compared the risk of COVID-19 hospitalisation and death among users of ICS/long-acting β-agonist (LABA) and users of LABA/LAMA using inverse probability of treatment weighted (IPTW) logistic regression. We used PBA to assess the impact of non-differential outcome misclassification. We assigned beta distributions to sensitivity and specificity and sampled from these 100 000 times for summary-level and 10 000 times for record-level PBA. Using these values, we simulated outcomes and applied IPTW logistic regression to adjust for confounding and misclassification. Sensitivity analyses excluded ICS + LABA + LAMA (triple therapy) users. Among 161 411 patients with COPD, ICS users had increased odds of COVID-19 hospitalisations and death compared with LABA/LAMA users (OR for COVID-19 hospitalisation 1.59 (95% CI 1.31-1.92); OR for COVID-19 death 1.63 (95% CI 1.26-2.11)). After IPTW and exclusion of people using triple therapy, ORs moved towards the null. All implementations of QBA, both record- and summary-level PBA, modestly shifted the ORs away from the null and increased uncertainty. We observed increased risks of COVID-19 hospitalisation and death among ICS users compared to LABA/LAMA users. Outcome misclassification was unlikely to change the conclusions of the study, but confounding by indication remains a concern.

Optimization of Federated Learning Communication Costs through the Implementation of Cheetah Optimization Algorithm

Recently, Federated Learning (FL) has promptly gained aggregate interest owing to its emphasis on the data privacy of the user. As a privacy-preserving distributed learning algorithm, FL enables multiple parties to construct machine learning (ML) algorithms without exposing sensitive information. The distributed computation of FL may lead to drawn-out learning and constrained communication processes, which necessitate client-server communication cost optimization. The two hyperparameters that have a considerable effect on the FL performance are the number of local training passes and the ratio of chosen clients. Owing to training preference across different applications, it is challenging for the FL practitioner to manually choose these hyperparameters. Even though FL has resolved the problem of collaboration without compromising privacy, it has a transmission overhead because of repetitive model updating during training. Various researchers have introduced transmission-effective FL techniques for addressing these issues, but sufficient solutions are still lacking in cases where parties are in charge of data features. Therefore, this study develops an Optimization of Federated Learning Communication Costs through the Implementation of the Cheetah Optimization Algorithm (OFLCC-COA) technique. The OFLCC-COA technique is mainly applied for effectually optimizing the communication process in the FL to minimize the data transmission cost with the guarantee of enhanced model accuracy. The OFLCC-COA technique enhances the robust performance in unsteady network environment via the transmission of score values instead of large weights. Besides, the OFLCC-COA technique improves the communication efficiency of the network by transforming the form of data that clients send to servers. The performance analysis of the OFLCC-COA model occurs utilizing different performance measures. The simulation outcomes indicated that the OFLCC-COA model obtains superior performances over other methods in terms of distinct metrics

Resonance-Induced Therapeutic Technique for Skin Cancer Cells.

This study aims to evaluate the viability of a hypothesis for selective targeting of skin cancer cells by exploiting the spectral gap with healthy cells using analytical and numerical simulation. The spectral gap was first identified using a viscoelastic dynamic model, with the physical and mechanical properties of healthy and cancerous skin cells deduced from previous experimental studies conducted on cell lines. The outcome of the analytical simulation was verified numerically using modal and harmonic analysis. Finally, transient analyses were conducted analytically and numerically to evaluate the difference in vibrational response of healthy and cancerous cells when their resonant frequencies were closely matched. For analysis, we used healthy nucleus diameters of 3 µm, 5 µm and 7 µm, whereas 34 kPa was taken as the stiffness of healthy skin epithelial cells. Based on established trends, the nucleus-to-cytoplasm ratio was utilised to predict physical and mechanical properties as cells undergo neoplastic transformation. Analytical and numerical simulation revealed an approximate frequency difference of 50-100 KHz for the different nucleus diameters. The transient simulation revealed a significant difference in the growth rate of cancer cells' vibration amplitude, which was 10 times greater than that of healthy cells. This study highlights that cancer cells are more prone to resonance with tuned ultrasound frequencies, emphasising the need for detailed dynamic models incorporating the basement membrane's influence and experimental validation.

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model

As online education has become increasingly prominent, the primary objective of this study is to evaluate students' opinions of online classes taught by teachers with no prior experience in online teaching, focusing on their teaching style, teaching efficiency, and pedagogy in the online classroom. Online teaching is a kind of teaching system that depends on network management technology. It concludes the teaching method by the process of live courses or recorded courses employing software containing special online teaching environments and any APP software employed for teaching. Social media, with its massive pool of user-generated content and instant feedback, offers a great opportunity to calculate teaching styles in online class management. Therefore, this study offers a Social Media Based Evaluation of Teaching Style in Online Education Systems using Heuristic Search (SMBETS-OESHS) Algorithm. The main objective of the SMBETS-OESHS technique for evaluate teaching styles in online education systems using insights derived from social media platforms. At primary stage, the SMBETS-OESHS model takes place linear scaling normalization (LSN) is implemented for scaling the input data. Next, the bayesian optimization algorithm (BOA) based feature selection process can be employed to allow for the detection of the most relevant features from the data. In addition, the SMBETS-OESHS model exploits stacked sparse autoencoder (SSAE) technique for classification process. In order to achieve optimal performance, the SSAE model parameters are fine-tuned using the improved beetle optimization algorithm (IBOA), ensuring robust evaluation accuracy. The experimental validation outcome of the SMBETS-OESHS algorithm undergoes and the performances are examined over various measures. The simulation outcome stated that the enhanced solution of the SMBETS-OESHS system over the recent techniques.

Integrating Neutrosophic Vague N-Soft Sets with Chimp Optimization Algorithm for Sentiment Analysis on Social Media

The swift development in social media through the internet produces vast data in a real-time scenario that has startling effects on large datasets. It generated the high-level use of sentiments and emotions in social networking media. Sentiment analysis (SA) using a neutrosophic set presents a new technique to handle the integral ambiguity and uncertainty in text datasets. Different from classical approaches, which categorize sentiment as positive, negative, or neutral, the neutrosophic set allows for the comparison analysis of truth-, indeterminacy-, and falsie-membership functions for all the sentiments. This allows a more flexible and nuanced representation of sentiments, which accommodates the contradictions and complexities commonly depicted in natural language. SA can accomplish high performance and depth in interpreting and understanding the emotions expressed in uncertain and diverse text datasets by leveraging a neutrosophic set. This manuscript presents a Neutrosophic Vague N-Soft set with a Chimp Optimization Algorithm for Sentiment Analysis (NVNSS-COASA) technique on Social Media. The NVNSS-COASA technique is initiated by the comprehensive preprocessing stage to normalize and clean the text dataset, which ensures superior input for the succeeding stage. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) mechanism is employed to convert the preprocessed text into mathematical features, which capture the word importance in terms of datasets. Subsequently, a strong NVNSS classifier is employed for accurately categorizing the sentiment. We integrate COA for the parameter tuning to further improve the performance of the method. The simulation outcomes emphasized that the NVNSS-COASA method shows superior outcomes over other techniques. The outcomes indicated that the NVNSS-COASA can able to deliver reliable and precise insights from the text dataset.

Subgroups varying in risk and density highlight the potential for stratified breast cancer screening.

Most breast cancer screening programs rely only on demographic data without considering individual risk factors of the population, which might limit their effectiveness by over- and underscreening specific subgroups. Therefore, the aim of this study is to highlight health and economic disparities in outcomes from such a uniform screening strategy. With the microsimulation model MISCAN, we simulated outcomes of the Dutch screening program considering 16 subgroups varying by their 5-year breast cancer risk and breast density. All outcomes showed significant disparities across risk-density subgroups. Notably, women with extremely dense breasts showed a mortality reduction from the current screening of 16-17 % compared to 25-29 % in other groups. Absolute benefits (breast cancer deaths averted, and life-years gained) increased with risk and varied independently by density. The range of false-positive rates varied almost twofold across the span of subgroups and nearly a ninefold difference in the medical costs incurred with lifelong follow-up. These findings emphasize the potential for stratified screening strategies to improve equity in health outcomes and reduce the burden of breast cancer.