Performed Factor Analysis Research Articles

END-OF-LIFE HEALTH LITERACY: VALIDATION STUDY OF A NEW INSTRUMENT, THE END-OF-LIFE HEALTH LITERACY SCALE (EOL-HLS)

Abstract Measuring health literacy allows to assess individuals’ competencies to deal with health issues; it influences how individuals perceive their health problems, communicate with healthcare providers, or make medical decisions. The end of life is commonly characterized by one or several diseases, healthcare services’ uses and requires individuals to make complex medical decisions. Although the end-of-life concerns everyone, the level of competencies of individuals to get through this stage of life has been little explored. This study aims to fill this gap by validating a new instrument, the End-of-life Health Literacy Scale (EOL-HLS), in a representative sample of older adults aged 58+ living in Switzerland. We use the Swiss wave 8 (2019/2020) of SHARE. Based on the seminal work of Nutbeam (2000), end-of-life health literacy skills are measured using questions on the difficulty in understanding medical interventions, finding information, communicating, deciding in advance, and choosing end-of-life care options. In addition, we compare the findings to the European Health Literacy Survey questionnaire (HLS-EU-Q16). The results confirmed the suitability for performing factor analysis (KMO = 0.924, Bartlett’s test of sphericity statistically significant), a three-factor model was established and showed good fit properties (CFI = 0.964, TLI = 0.958, RMSEA = 0.047, SRMR = 0.067) and good reliability (α = 0.93). The associations found between individuals’ sociodemographic characteristics and the HLS-EU-Q16 were also present in our instrument, but higher EOL-HLS scores were associated with more positive end-of-life outcomes. The EOL-HLS is a reliable and valid instrument to target individuals with low end-of-life health literacy.

Critical Analysis of Performance Factors between Indian and Foreign Soccer Players

Critical Analysis of Performance Factors between Indian and Foreign Soccer Players

Novel optimization approach for stock price forecasting using multi-layered sequential LSTM

Stock markets can often be one of the most volatile places to invest. Statistical analysis of past stock performance and external factors play a major role in the decision to buy or sell stocks. These factors are all used to maximize profits. Stock price index forecasting has been a subject of great research for many years, and several machine learning and deep learning algorithms have been proposed to simplify this complex task, but little success has been found so far. In order to forecast stocks accurately, it is crucial to understand the context-specific dependence of stock prices on their past values. The use of Long Short Term Memory (LSTM), which is capable of understanding long-term data dependencies, can help overcome this obstacle. In this context, this paper proposes a novel optimization approach for stock price prediction that is based on a Multi-Layer Sequential Long Short Term Memory (MLS LSTM) model which makes use of the adam optimizer. Additionally, the MLS LSTM algorithm uses normalized time series data divided into time steps to determine the relationship between past values and future values in order to make accurate predictions. Furthermore, it eliminates the vanishing gradient problem associated with simple recurrent neural networks. The stock price index is forecasted by taking into account past performance information along with past trends and patterns. The results illustrate that a 95.9% prediction accuracy is achieved on the training data set and a 98.1% accuracy on the testing data set with the MLS LSTM algorithm, which dramatically exceeds the performance of other machine learning and deep learning algorithms. The mean absolute percentage error was observed to be 1.79% on the training set and 2.18% on the testing set, respectively. Moreover, the proposed model is able to estimate the stock price with a normalized root mean squared error of 0.019, thus giving an accurate forecast and making it a feasible real-world solution.

Numerical Investigation and Factor Analysis of the Spatial-Temporal Multi-Species Competition Problem

This work considers the spatial-temporal multi­species competition model. A mathematical model is described by a coupled system of nonlinear diffusion reaction equations. We use a finite volume approximation with semi-implicit time approximation for the numerical solution of the model with corresponding boundary and initial conditions. To understand the effect of the diffusion to solution in one and two-dimensional formulations, we present numerical results for several cases of the parameters related to the survival scenarios. We control all non-diffusion parameters, including reproductive growth rate, competition rate, and initial condition of population density of competing species, and compare the dynamic and equilibrium under regular diffusion rate and small diffusion rate; we found that competing species with small diffusion rate can reach a higher equilibrium over the whole geographic domain, but requires more time steps. The random initial conditions' effect on the time to reach equilibrium is investigated. We control other parameters and examine the impact of the initial condition of the species population; we found that regardless of the values of initial conditions in the system, competing species populations will arrive at an equilibrium point. The influence of diffusion on the survival scenarios is presented. We control other parameters and examine the effect of diffusion of species; we found that when the ratio of diffusion rates passes some thresholds, the survival status will change. In real-world problems, values of the parameters are usually unknown yet vary in some range. To evaluate the impact of parameters on the system stability, we simulate a spatial­temporal model with random parameters and perform factor analysis for two and three­species competition models. From the perspective of the numerical experiment, we release control for all parameters and perform factor analysis on simulation results. We found that the initial population condition has a minimum effect on the final population, which aligns with the outcome of our controlled numerical experiment on the initial condition. Diffusion is the dominant factor when diffusion rates are on the same scale as other parameters. This dominant factor aligns with our controlled numerical experiment on diffusion rate, where the change in diffusion rate leads to different survival statuses of species. However, when diffusion rates are 1/10 on the scale of other parameters, reproductive growth rates and competition rates become the dominant factors.

Adolescent dietary patterns in relation to later prostate cancer risk and mortality in the NIH-AARP Diet and Health Study.

Although adolescent diet has been proposed to contribute to prostate cancer (PCa) development, no studies have investigated the relation between adolescent dietary patterns and PCa risk or mortality. Using data from 164,079 men in the NIH-AARP Diet and Health Study, we performed factor analysis to identify dietary patterns at ages 12-13 years and then used Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of total (n = 17,861), non-advanced (n = 15,499), advanced (n = 2362), and fatal PCa (n = 832). Although not entirely consistent across analyses, a higher adolescent plant-based pattern (characterised by vegetables, fruits, and dark bread) score was associated with slightly reduced risks of total (fully adjusted HRQ5vs.Q1 = 0.93, 95% CI: 0.89-0.98, p trend=0.003) and non-advanced PCa (HR = 0.91, 95% CI: 0.87-0.96, p trend<0.001), whereas no associations were observed for advanced or fatal PCa, or for Western modern (characterised by sweets, processed meat, beef, cheese, and pizza) or Western traditional (characterised gravy, eggs, potatoes and white bread) patterns. We found evidence to support a modest, protective role for a plant-based dietary pattern during adolescence on PCa risk. If confirmed in future studies, our findings may help to inform the development of new, primary prevention strategies for PCa.

Social media marketing activities on brand equity and purchase intention among Chinese smartphone consumers during COVID-19

PurposeThe world has been forced to implement movement restriction strategy because of the COVID-19 pandemic, and industries have to embrace online technologies and social media marketing activities (SMMAs) to continue their business operations. Considering the aftermath of COVID-19 on the business world, this study aims to explore the determining elements of SMMAs and analyse how these factors affect brand equity (BE), relationship equity (RE) and purchase intention (PI) among smartphone consumers in China. Five constructs of SMMAs, namely, entertainment (EN), interactivity (IN), trendiness (TR), customisation (CU) and electronic word-of-mouth (WM), were examined to examine the effects of SMMAs on BE and RE. Subsequently, the mediation effects of BE and RE on the relationships of all constructs of SMMAs with PI were analysed.Design/methodology/approachAn online survey was conducted with the participation of 347 Chinese consumers who used social media platform managed by the smartphone brands as their marketing activities during COVID-19. The data were analysed via structural equation modelling using SmartPLS.FindingsThis study’s result showed the significant and positive influence of CU, TR and WM on BE and the strong and positive influence of CU, IN and TR on RE. Furthermore, BE was found to fully mediate the relationships of CU, TR and WM with PI, while RE was found to mediate the relationships of TR and CU with PI. The performance and impact factor analysis revealed RE as the most important factor for PI, followed by BE, CU and TR.Originality/valueThis study extended the authors’ knowledge and understanding about social media marketing, BE and smart phone PI during COVID-19. Findings of this study can assist smartphone brands in China to develop the most effective strategies for SMMAs, which can be tailored for consumers to maximise profits, even during any crisis when physical business activities are deemed difficult. Moreover, this study’s findings can benefit the government and policymakers in developing and regulating rules and regulations for e-commerce and social media commerce for all industries and areas.

Is it all about knowledge? A survey of attitudes toward ADHD among German pediatricians

AimSometimes treatment is not necessarily according to guidelines, which is partly due to attitudes rather than lack of knowledge. In Germany, regional differences concerning prevalence rates of ADHD exist without valid explanation. We contribute with our data to the question of attitudes toward ADHD among pediatricians in Germany.MethodA specially designed questionnaire based on the Knowledge of Attention Deficit Disorders Scale and previous surveys was sent to pediatricians. In addition to descriptive statistics, we performed factor analysis and multiple linear regression analysis.ResultsThe vast majority (85.5%) of 581 respondents have a positive attitude toward ADHD, i.e., ADHD exists and should be treated appropriately. Physicians with positive attitudes were older and more often had a neuropediatric specialization.ConclusionMost of the pediatricians surveyed in Germany assess ADHD and treatment in line with medical guidelines and treat as part of multimodal therapy.

Impact of 16 PF global factors personality traits on perceptual biases of individual investors

The present paper aims to investigate effects of 16 PF global factors personality (16 PF GFP) traits on individuals’ investment related perceptual biases. Data was collected from 206 subjects in India using likert5 point questionnaire and analysed performing factor analysis and regression test. Result indicated that 16 PF GFP influence positively investors’ perceptual biases. This is the only study that applied 16 PF GFP to understand investors’ perceptual biases.

Nasopharyngeal Bacterial Microbiota Composition and SARS-CoV-2 IgG Antibody Maintenance in Asymptomatic/Paucisymptomatic Subjects.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19), ranging from asymptomatic conditions to severe/fatal lung injury and multi-organ failure. Growing evidence shows that the nasopharyngeal microbiota composition may predict the severity of respiratory infections and may play a role in the protection from viral entry and the regulation of the immune response to the infection. In the present study, we have characterized the nasopharyngeal bacterial microbiota (BNM) composition and have performed factor analysis in a group of 54 asymptomatic/paucisymptomatic subjects who tested positive for nasopharyngeal swab SARS-CoV-2 RNA and/or showed anti-RBD-IgG positive serology at the enrolment. We investigated whether BNM was associated with SARS-CoV-2 RNA positivity and serum anti-RBD-IgG antibody development/maintenance 20–28 weeks after the enrolment. Shannon’s entropy α-diversity index [odds ratio (OR) = 5.75, p = 0.0107] and the BNM Factor1 (OR = 2.64, p = 0.0370) were positively associated with serum anti-RBD-IgG antibody maintenance. The present results suggest that BNM composition may influence the immunological memory against SARS-CoV-2 infections. To the best of our knowledge, this is the first study investigating the link between BNM and specific IgG antibody maintenance. Further studies are needed to unveil the mechanisms through which the BNM influences the adaptive immune response against viral infections.

Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasonings are more critical in learning science. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful features: individual skill mastery, ability profile (learning transfer across skills) and problem difficulty by using data mining techniques. IKT’s prediction of future student performance is made using a Tree Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning based student models. IKT also shows better student performance prediction than deep learning based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.

Identification and Comparative Safety Analysis of Overtaking Patterns on Two-Lane Highways: A Simulator Study With Novice Drivers

Overtaking on two-lane highways is one of the riskiest driving maneuvers. However, safety analysis technologies for overtaking patterns and trajectories require development. In this simulator study, 328 overtaking trajectories were collected from 52 novice drivers to analyze difference in overtaking safety performances on two-lane highways under different traffic conditions. Two analyses were conducted: Analysis I focused on structural trajectory differences and classified the trajectory sample into two patterns—“ladder” (LAT) and “triangle” (TRT). In Analysis II, a comparative safety evaluation method was established based on data envelopment analysis to compare overtaking pattern safety; a factor analysis of the safety performance was undertaken by means of ordinary regression models. We found that TRT had a lower spatial consumption and smoother trajectory than LAT, but drivers overtaking using TRT were exposed to higher risk than LAT. Certain environmental and human factors had an influence on the safety levels of the patterns: both deteriorated when drivers were surrounded by vehicles driving at higher speeds, and improved with longer passing sight distances. However, safety levels when overtaking trucks were significantly worse than overtaking cars in the LAT pattern—the opposite was found for TRT. Female drivers’ safety performance was superior in the LAT pattern; no significant gender difference were found with TRT. A longer following distance was found to ameliorate the safety of the TRT pattern, with no effect on LAT. These findings aid understanding of overtaking maneuvers on two-lane highways, and provide recommendations for advanced assistance driving and automatic driving systems.

Abstract 733: Adolescent dietary patterns in relation to later prostate cancer risk and mortality in the NIH-AARP diet and health study

Abstract Background: Although adolescent diet has been proposed to contribute to prostate cancer (PCa) development, no studies have investigated the relation between adolescent dietary patterns and PCa risk or mortality. Method: Using data from the NIH-AARP Diet and Health Study, we performed factor analysis to identify dietary patterns at ages 12-13 years. We further used Cox proportional hazards regression to examine hazard ratios (HRs) and 95% confidence intervals (CIs) of total, non-advanced, advanced, and fatal PCa. Results: During 1,878,681 person-years of follow-up, 17,861 men were diagnosed with PCa, including 15,499 non-advanced, 2,362 advanced, and 832 fatal cancers. Three adolescent dietary patterns were identified: a Western modern pattern characterized by greater intake of sweets, processed meat, beef, cheese, and pizza; a plant-based pattern characterized by greater intake of vegetables, fruits, and dark bread; and a Western traditional pattern characterized by greater intake of gravy, bacon, and chicken, eggs, potatoes, green beans, legumes, and white bread. Although not entirely consistent across analyses, a higher adolescent plant-based pattern score was associated with reduced risks of total PCa (fully-adjusted HRQ5vs.Q1=0.93, 95% CI: 0.89-0.98, p-trend=0.003) and non-advanced PCa (HR=0.91, 95% CI: 0.87-0.96, p-trend&amp;lt;0.001), whereas no associations were observed for Western modern or Western traditional patterns. Conclusions: In the first study, to our knowledge, to investigate adolescent dietary patterns and PCa risk, we found some evidence to support a protective role for a plant-based diet during adolescence on PCa risk. Our findings may inform the development of new, primary prevention strategies for PCa. Citation Format: Tuo Lan, Yikyung Park, Graham A. Colditz, Jingxia Liu, Molin Wang, Kana Wu, Edward Giovannucci, Siobhan Sutcliffe. Adolescent dietary patterns in relation to later prostate cancer risk and mortality in the NIH-AARP diet and health study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 733.

Complete coverage path planning and performance factor analysis for autonomous bulldozer

AbstractWith the development of intelligent machinery, path planning of autonomous bulldozers plays an important role to solve the problems of efficiency in future construction sites. How to plan the path that meets the construction requirements is the key problem. This study investigates the standardized construction workmanship and moving rules of the bulldozer to add rules and path selection strategies to the algorithm. This study establishes the path planning model of bulldozer's leveling, considering the limitations of the traditional bioinspired neural network algorithm, an improved complete coverage path planning method is proposed. An integrated framework is proposed for an automatic bulldozer with a sensor system for a field experiment to verify the feasibility of the method. According to the comparative statistical simulation results, the effectiveness of the method is verified, and the effects of different factors on the path planning results are analyzed.

Comparative Analysis of Performance Factors between Ladies Professional Golf Association and Korea Ladies Professional Golf Association Golfers.

This study aimed to analyze the performance factors of both Ladies Professional Golf Association (LPGA) and Korea Ladies Professional Golf Association (KLPGA) players and suggest which performance factors they should improve to play in world-level games. Data from 180 LPGA and KLPGA players who ranked within the top 60 in prize money rankings from 2018 to 2020 were analyzed. Then, the data from LPGA and KLPGA golfers were compared using the seemingly unrelated estimation method. As a result of analyzing 178 data, excluding two players who had missing values, this study found that among LPGA player data, putting average (PA), sand save (SS), green in regulation (GIR), and birdies (BIR) had a significant impact in 2018. Additionally, scoring average (SA) and top-10 finish (T10) had a significant impact in 2019. However, there were no factors influencing performance in 2020.From the results of analyzing 180 players who ranked within the top 60 in KLPGA prize money rankings, there were no performance factors that significantly affected their performance in 2018. However, driver distance (DD) in 2019 and DD and T10 in 2020 affected performance. In conclusion, short games were the most important factor on the LPGA Tour, and driving distance was the most important trend on the KLPGA Tour. Therefore, KLPGA golfers should train in abilities such as putting and ironshots.

The Effect of Plankton Community Structure in Tributaries at the Downstream of the Musi River, Palembang

River condition is influenced by both natural and human activities. The tributaries also affect the condition of water in the river. The Musi River is the largest in South Sumatra. Its condition is influenced by its nine major tributaries and other rivers. This research applied the descriptive method. Conducted at the five tributaries of the Musi River, this research aimed: 1) to calculate the plankton abundance (N), evenness index (E), the diversity index (H’), and dominance index (D); 2) to perform a correlation test; 3) to perform ANOVA; 4) to perform factor analysis using PCA (principal component analysis). Bacillariophyceae abundance was the highest among other genera, followed by Chlorophyceae, Flagilariophycea, Euglenida, and Chrysophyceae. Crustacea had the highest abundance in the zooplankton class, followed by Tubulenia, Maxillopoda, Mastigophora, and Imbricatea. Plankton's diversity and community stability levels were regarded as moderate; as there was no domination found, the water was classified as heavily polluted. According to the ANOVA hypothesis, the five tributaries' phytoplankton and zooplankton data groups showed different variations. The abundance of phytoplankton was influenced by three factors reaching 76.54%. The condition was indicated by bioindicator pollution: Nitzchia, Chlorococcum, Euglena, Oscillatoria, and Spirogyra. The waters were categorized as Mesotrophic waters, indicating a moderate level of water fertility. At a low pH value, as the diversity index became low, the abundance of phytoplankton also became low. The polluted condition was also shown by the low diversity, which indicated an unstable ecosystem. These waters could only be used for irrigation. The moderate fertility of the waters and its level of contamination affected the downstream Musi River. Proper water management is required to maintain the environmental sustainability. Any parties associated with water management should take steps to improve the water condition downstream of the Musi River.

MACHINE LEARNING TECHNIQUES FOR ASSESSING STUDENTS' ENVIRONMENTS' IMPACT FACTORS ON THEIR ACADEMIC PERFORMANCE

Performance factors analysis has recently gained popularity as a method for assessing how students' environments affect their academic performance. However, most of the progress has been made in analyzing student behaviour during the learning process. Machine Learning provides many powerful methods that could improve student performance prediction. Our aim is to examine all features of students' environmental life using the machine learning paradigm to assess how students' environment affects their grades. These features are divided into three categories (personality, family, and education) and their impact factors are calculated. To improve predictive accuracy, different models (Random Forest, AdaBoost, Decision Tree, Naive Bayes, and Multi-Layer perceptron) are used to score the features in each group according to their contribution to the solution. Results show that personality features are a minor effect on students' academic performance with 53%. Concerning the educational factors, outcomes offer the average impact was 60%. Regarding family factors, results indicate that students' family life significantly affects academic achievement with 64%.

Psychometric evaluation of the Chinese version of the Upper Limb Lymphedema Quality of Life Questionnaire in breast cancer-related lymphedema patients

Purpose The purpose of this study was to translate and evaluate the psychometric properties of the Chinese version of the Upper Limb Lymphedema Quality of Life Questionnaire (C-ULLQoL). Methods Eighty-five participants completed the C-ULLQoL and the Functional Assessment of Cancer Therapy-Breast (C-FACT-B). The Cronbach’s alpha (α) was used to determine the internal consistency, and intraclass correlation coefficients (ICCs) – to evaluate the test-retest reliability. The content validity index (CVI) was assessed by a group of experts. Construct validity was examined by performing factor analysis and criterion validity by observing the correlations between C-ULLQoL with C-FACT-B. Results Cronbach’s α of the total scale was 0.930. ICC scores ranged from 0.874 to 0.938. The content validity of C-ULLQoL was acceptable. Two factors (65.488% of the variance) were extracted by exploratory factor analysis. A significant correlation was observed between C-ULLQoL and C-FACT-B (r = −0.611, p < 0.01). Conclusion The C-ULLQoL is a reliable and valid questionnaire that can be used in clinic and scientific practice for evaluating health-related quality of life in Chinese patients with breast cancer-related lymphedema. IMPLICATIONS FOR REHABILITATION An effective and comprehensive scale to measure the health-related quality of life (HRQoL) is essential because breast cancer-related lymphedema (BCRL) leads to various complications for patients, caregivers, and society. The Chinese version of the Upper Limb Lymphedema Quality of Life Scale (C-ULLQoL) is a valid, reliable, and practical instrument to comprehensively assess HRQoL in Chinese patients with BCRL. The C-ULLQoL can be used in both clinical and research settings to evaluate HRQoL of BCRL patients in China.

Validation and Revision of the Household Emergency Preparedness Instrument (HEPI) by a Pilot Study in the City University of New York.

The aim of this study was to pilot test the Household Emergency Preparedness Instrument (HEPI) with a diverse sample, allowing for assessment of reliability and validity of the instrument. The HEPI is an international, all-hazards questionnaire created to measure disaster preparedness of households, which results in data that can be used to enhance health promotion/disease prevention for individuals and promote resilience for communities. A cross-sectional study of faculty, staff, and students (N = 284) was completed to perform factor analysis to establish the HEPI's construct validity and compare preparedness across groups. The factor analysis revealed 2 dimensions of general preparedness, explaining 35% of the sample variance (Cronbach's α = 0.89): preparedness actions and planning (α = 0.86) and disaster supplies and resources (α = 0.80). This factor analysis resulted in the revision of the subscaling of HEPI questions. Consistent with previous studies, faculty/staff, older age, higher income, and those with previous disaster experience were more prepared. The mean score was 15.28 out of 40 points. The HEPI is easy to administer and explains an acceptable amount of variance. The reliability was strong in this assessment, particularly for a pilot test. Construct, criterion, face, and content validity support the adequacy of the HEPI to capture essentials of household emergency preparedness.

Properties of Bi2O3/epoxy resin–coated composites for protection against gamma rays

In this paper, bismuth oxide (Bi2O3) as the main functional powder, lead (Pb) and tantalum (Ta) as the metal additives, epoxy resin as the matrix and polyester–cotton blended woven fabric as the substrate, Bi2O3 coating nuclear radiation protection composite, Bi2O3/Pb coating nuclear radiation protection composite, and Bi2O3/Ta coating nuclear radiation protection composite with different process parameters were prepared. The cross-section scanning analysis and the influence factor analysis of γ-ray protection performance were carried out, and the mechanical properties of the composites were discussed. The results show that an increase in Bi2O3 content (mass fraction) and an increase in coating thickness can improve the shielding rate of the composite materials to γ-rays. When the thickness of the coating is 1.6 mm and the content of Bi2O3 is 50%, the shielding rate of the composite to γ-rays (at 59.5 keV) reaches 46.1%. The shielding rate of the composite can be increased by adding appropriate metal additives, and the effect of adding Ta is better than that of Pb. The shielding rate of the composite to γ-rays (59.5 keV) can be increased from 28.4% to 31.5% by adding 5% Ta. An increase in Bi2O3 content (mass fraction) and an increase in the coating thickness can aggravate the agglomeration of functional particles in the material. The addition of metal additives can reduce agglomeration to a certain extent. Bi2O3 content, coating thickness, and metal additives all have an effect on the mechanical properties of the composite. If the coating is too thick or the functional particle content is too high, the tensile strength and elongation at break of the composite will be reduced.

IHPSA: An improved bio-inspired hybrid optimization algorithm for task mapping in Network on Chip

System on a chip (SoC) is the leading technology in the recent global world of digitization. The classical bus-based regular communication infrastructures of SoCs cannot handle the growing number of cores. Designers came up with new communication methods known as the network on chip (NoC) to solve this problem. NoC offers scalability, flexibility, modularity, and efficiency. As the number of cores of SoC keeps on increasing due to advancement in technology and shrinking size of transistors, the arrangement of cores on the NoC becomes more significant that can influence the Network on Chip’s overall performance and efficiency. Therefore, there is always a need for efficient and intelligent mapping algorithms for achieving optimal mapping solutions since existing mapping techniques either do not achieve optimal results or possess more execution time. To resolve this issue, we proposed an improved hybrid particle swarm and simulated annealing optimization algorithm iHPSA. The proposed iHPSA combines Improved Particle Swarm Optimization and Simulated Annealing for NoC mapping and incorporates a machine-learning K-means clustering algorithm to segregate tasks into clusters based on communication bandwidth. The Elbow method is used in the K-means clustering algorithm to predict the number of clusters in large applications intelligently.Comparative analysis of different performance factors shows that the proposed approach has performed better than the existing mapping algorithms. The proposed algorithm offers improved performance by reducing communication cost, latency, and power of the network. the proposed iHPSA shows an average enhancement on power reduction of 22.67%, 30.95% , 22.87%, 27.61%, 16.07%, 8.87%, 8.01% ,30% and 9% over PSO,SA,GA,ACO,BA,SCSO,BEMAP,CSO and ILP. Improved energy consumption by 5.70%, 20%, 25%, 24% and 4.09% over PSO, SA ,GA , BEMAP and CSO. iHPSA improved latency by 1.5% over PSO, 6.2% over SA, 6.67% over GA , 9.7% over BEMAP and 1.2% over CSO. The proposed mapping approach’s efficiency is evaluated with a comparative analysis of communication cost, power, energy, and latency for standard and randomly generated benchmark applications. The experimental results revealed that iHPSA outperforms other nature-inspired algorithms by offering an efficient application approach that reduces communication cost, power, energy, and latency.