Regression Equation Research Articles

Advanced ECG Heart Age: A Prognostic, Explainable Machine Learning Approach Applicable to Sinus and Non-Sinus Rhythms

Abstract Background An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular ageing expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm. Aims To develop and prognostically validate a revised, explainable A-ECG Heart Age applicable to both sinus and non-sinus rhythms. Methods An A-ECG Heart Age excluding P-wave measures was derived from the 10-second 12-lead ECG in a derivation cohort using multivariable regression machine learning with Bayesian 5-minute 12-lead A-ECG Heart Age as reference. The Heart Age was externally validated in a separate cohort of patients referred for cardiovascular magnetic resonance imaging by describing its association with heart failure hospitalization or death using Cox regression, and its association with comorbidities. Results In the derivation cohort (n=2771), A-ECG Heart Age agreed with the 5-min Heart Age (R2=0.91, bias 0.0±6.7 years), and increased with increasing co-morbidity. In the validation cohort (n=731, mean age 54±15 years, 43% female, n=139 events over 5.7 [4.8–6.7] years follow-up), increased A-ECG Heart Age gap (≥10 years) associated with events (hazard ratio [95% confidence interval] 2.04 [1.38–3.00], C-statistic 0.58 [0.54–0.62], and the presence of hypertension, diabetes mellitus, hypercholesterolemia, and heart failure (p≤0.009 for all). Conclusions An explainable A-ECG Heart Age gap applicable to both sinus and non-sinus rhythm associates with cardiovascular risk, cardiovascular morbidity, and survival.

Design and Testing of Key Components for a Multi-Stage Crushing Device for High-Moisture Corn Ears Based on the Discrete Element Method

To improve the crushing efficiency and crushing pass rate of high-moisture corn ears (HMCEs), a multi-stage crushing scheme is proposed in this paper. A two-stage crushing device for HMCEs is designed, and the ear crushing process is analyzed. Firstly, a simulation model for HMCEs was established in EDEM software (2018), and the accuracy of the model was verified by the shear test. Subsequently, single-factor simulation experiments were conducted, with the crushing rate serving as the evaluation index. The optimal working parameter ranges for the HMCE device were identified as a primary crushing roller speed of 1200–1600 revolutions per minute (r/min), a secondary crushing roller clearance of 1.5–2.5 mm, and a secondary crushing roller speed of 2750–3750 r/min. A Box–Behnken experiment was conducted to establish a multiple regression equation. With the objective of maximizing the qualified crushing pass rate, the optimal combination of parameters was revealed: a primary crushing roller speed of 1500 r/min, a secondary crushing roller clearance of 2.5 mm, and a secondary crushing roller speed of 3280 r/min. The pass rate of corn cob crushing in the simulation test was 98.2%. The physical tests, using the optimized parameter combination, yielded a qualified crushing rate of 97.5%, which deviates by 0.7% from the simulation results, satisfying the requirement of a qualified crushing rate exceeding 95%. The experimental outcomes validate the rationality of the proposed crushing scheme and the accuracy of the model, providing a theoretical foundation for subsequent research endeavors.

Predicting Microbial Protein Synthesis in Cattle: Evaluation of Extant Equations and Steps Needed to Improve Accuracy and Precision of Future Equations.

Predictions of microbial crude protein (MCP) synthesis for beef cattle generally rely on empirical regression equations, with intakes of energy and protein as key variables. Using a database from published literature, we developed new equations based on the intake of organic matter (OM) and intakes or concentrations of crude protein (CP) and neutral detergent fiber (NDF). We compared these new equations to several extant equations based on intakes of total digestible nutrients (TDN) and CP. Regression fit statistics were evaluated using both resampling and sampling from a simulated multivariate normal population. Newly developed equations yielded similar fit statistics to extant equations, but the root mean square error of prediction averaged 155 g (28.7% of the mean MCP of 540.7 g/d) across all equations, indicating considerable variation in predictions. A simple approach of calculating MCP as 10% of the TDN intake yielded MCP estimates and fit statistics that were similar to more complicated equations. Adding a classification code to account for unique dietary characteristics did not have significant effects. Because MCP synthesis is measured indirectly, most often using surgically altered animals, literature estimates are relatively few and highly variable. A random sample of individual studies from our literature database indicated a standard deviation for MCP synthesis that averaged 19.1% of the observed mean, likely contributing to imprecision in the MCP predictions. Research to develop additional MCP estimates across various diets and production situations is needed, with a focus on developing consistent and reliable methodologies for MCP measurements. The use of new meta-omics tools might improve the accuracy and precision of MCP predictions, but further research will be needed to assess the utility of such tools.

Experimental Study on Proportion Optimization of Rock-like Materials Based on Genetic Algorithm Inversion.

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water-paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σc, tensile strength σt, shear strength τf, elastic modulus E, Poisson's ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water-paste ratio had extremely significant effects on σc, σt, τf, E, ν and c. The iron powder content had a highly significant effect on σc, σt, τf and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σc, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water-paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials.

THE INFLUENCE OF SERVICE QUALITY AND PRICE ON PURCHASE DECISIONS IN CLOTHING STORES AT PARUNG MARKET

This study aims to identify the influence of service quality and price on the purchase decisions of clothing at stores in Parung Market, both partially and simultaneously. The research is associative and quantitative, with consumers of Parung Market stores as the subjects. The sample consists of 90 individuals selected using Slovin’s formula. Data collection was done through questionnaires, and the data were analyzed using various tests, such as validity and reliability tests, classical assumption tests, linear regression, correlation coefficient, determination coefficient, and partial and simultaneous hypothesis testing. All analyses were performed using SPSS version 25. The results show that service quality affects purchase decisions, with a positive relationship demonstrated by the regression equation. The correlation coefficient indicates a fairly strong relationship between service quality and purchase decisions. In addition, price has a significant influence on purchase decisions, with a higher correlation coefficient compared to service quality. Simultaneously, both service quality and price together influence purchase decisions. This relationship is strengthened by the high and significant correlation coefficient. The conclusion of this study is that both service quality and price, individually or together, play an important role in influencing consumer purchase decisions at Parung Market.

Associations between Kidney Disease Progression and Metabolomic Profiling in Stable Kidney Transplant Recipients-A 3 Year Follow-Up Prospective Study.

Background: kidney transplant recipients are exposed to multiple pathogenic pathways that may alter short and long-term allograft survival. Metabolomic profiling is useful for detecting potential biomarkers of kidney disease with a predictive capacity. This field is still under development in kidney transplantation and metabolome analysis is faced with analytical challenges. We performed a cross-sectional study including stable kidney transplant patients and aimed to search for relevant associations between baseline plasmatic and urinary metabolites and relevant outcomes over a follow-up period of 3 years. Methods: we performed a cross-sectional study including 72 stable kidney transplant patients with stored plasmatic and urinary samples at the baseline evaluation which were there analyzed by nuclear magnetic resonance in order to quantify and describe metabolites. We performed a 3-year follow-up and searched for relevant associations between renal failure outcomes and baseline metabolites. Between-group comparisons were made after classification by observed estimated glomerular filtration rate slope during the follow-up: positive slope and negative slope. Results: The mean estimated GFR (glomerular filtration rate) was higher at baseline in the patients who exhibited a negative slope during the follow-up (63.4 mL/min/1.73 m2 vs. 55.8 mL/min/1.73 m2, p = 0,019). After log transformation and division by urinary creatinine, urinary dimethylamine (3.63 vs. 3.16, p = 0.027), hippuric acid (7.33 vs. 6.29, p = 0.041), and acetone (1.88 vs. 1, p = 0.023) exhibited higher concentrations in patients with a negative GFR slope when compared to patients with a positive GFR slope. By computing a linear regression, a significant low-strength regression equation between the log 2 transformed plasmatic level of glycine and the estimated glomerular filtration rate was found (F (1,70) = 5.15, p = 0.026), with an R2 of 0.069. Several metabolites were correlated positively with hand grip strength (plasmatic tyrosine with r = 0.336 and p = 0.005 and plasmatic leucine with r = 0.371 and p = 0.002). Other urinary metabolites were found to be correlated negatively with hand grip strength (dimethylamine with r = -0.250 and p = 0.04, citric acid with r = -0.296 and p = 0.014, formic acid with r = -0.349 and p = 0.004, and glycine with r = -0.306 and p = 0.01). Conclusions: some metabolites had different concentrations compared to kidney transplant patients with negative and positive slopes, and significant correlations were found between hand grip strength and urinary and plasmatic metabolites.

Application of Multiple Linear Regression Method in Steel Column Design under Combined Loading

The article explores using multiple linear regression (MLR) approach to predict the cross-section of beam-column steel members. These members are complex in design, as they must withstand bending and axial forces. The design process involves multiple safety checks according to design standards. The first step in the design procedure is to calculate and verify the axial capacity of the member. Following that, the flexural capacity is calculated. Finally, the combined load capacity is examined, often involving a trial-and-error process. If the initially selected section cannot withstand the applied forces, a new section must be chosen, and the calculations must be repeated. This study employs a multiple linear regression approach to investigate the relationship between member properties (such as length and cross-section) and load design parameters (axial and bending forces). The primary objective is to predict the beam-column steel member’s elastic section modulus (Sx). The regression equation derived from the study is as follows: Sx = 18.000L + 6.839Pr + 47.193Mr – 129.093, with an R-square value of 0.985. The histogram of the equation exhibits symmetry and a standard distribution, while the typical probability plot demonstrates a relatively linear trend. Two examples are presented to evaluate the efficiency of the regression equation. The first example confirms the efficiency of the equation by determining the cross-sectional size by employing the regression equation and using it to verify the safety according to EIT design standards. The second example is the application of the equation for estimating the size of other types of steel members. This study suggests that the regression equation can effectively predict the size of beam-column steel members, providing reliable results for practical design applications.

Design and experiment of bionic skateboard for rice direct seeding machine based on loach body surface

Aiming at the problem of heavy adhesion and large resistance of the rice direct seeding machine slide in the disturbed saturated paddy field environment, the non-smooth surface of the loach body was observed by microscopic observation; the simulation analysis was carried out using Fluent software, revealing the principle of reducing adhesion and drag of the non-smooth surface; the Box-Behnken response surface method was used to design the experiment, and the regression equation of the total resistance of the groove non-smooth surface FBand the groove width w, groove depth hand incoming flow velocity was obtained. vThe optimization analysis results show that when wis 4 mm、v1.25 m/s, 4.5 mm、hthe maximum drag reduction rate reaches 10.052%; based on the optimal parameters, the rice direct seeding machine bionic slide was trial-produced, and the indoor paddy field soil trough test was carried out, and the final drag reduction rate reached 10.23%. This study can provide new ideas for the development of adhesion and drag reduction technology for paddy field soil contact parts.

High-density lipoprotein functionality in cholesterol efflux in early childhood is related to the content ratio of triglyceride to cholesterol

Cholesterol efflux capacity (CEC), commonly measured as a useful risk marker of atherosclerotic cardiovascular disease, depends on high-density lipoprotein (HDL) functionality and its concentration. We defined the relative HDL functionality in cholesterol efflux, not influenced by HDL concentration, as the ratio of measured CEC to standardized CEC (stCEC) based on HDL-cholesterol (HDL-C) of each individual using the curve regression equation obtained from the correlation. HDL-C, CEC, and CEC/stCEC levels in the < 28-day-old participants (neonates) were significantly low compared to those of the ≥ 28-day-old participants, indicating that the low CEC levels in the neonates depend on not only lower HDL-C but also lower HDL functionality. The low level of CEC/stCEC was remarkable in neonates born at < 34 weeks of gestation and did not improved to the reference level (1.000) until the infantile period. The relatively low or high CEC/stCEC ratios in neonates and infants were associated with lower or higher HDL-TG and HDL-TG/HDL-C ratio, respectively. However, no apparent effect of HDL-TG and HDL-TG/HDL-C ratio on CEC/stCEC was observed in the ≥ 1-year-old participants, indicating that HDL functionality in cholesterol efflux could be associated with the various HDL particles with various lipid compositions, but not just with HDL-TG and HDL-TG/HDL-C ratio.

МІЖНАРОДНЕ ІНВЕСТИЦІЙНЕ СПІВРОБІТНИЦТВО ТА ЙОГО РОЛЬ У ПОВОЄННОМУ ВІДНОВЛЕННІ ПРИМОРСЬКИХ ТЕРИТОРІЙ УКРАЇНИ

The article is devoted to the study of the scientific and theoretical foundations of international investment cooperation and the analysis of its impact on the economic development of the coastal areas of Ukraine. It is conducted the critical review of existing definitions of the studied concept. It is proposed the proper definition that considers the socio-economic nature of such cooperation. International investment cooperation should be understood as economic relations formed as a result of international investment and mediating the movement of international investments. It is defined the main purpose of the investigated phenomenon, which consists in making a profit. It is emphasized the key difference between international investment cooperation and other forms of cross-border movement of capital by the presence of a commercial basis that accompanies this process in one way or another. It is considered previous developments in formulating the principles of investment cooperation. It is proposed own list of principles for its implementation: economic expediency, freedom and equality of parties, fair distribution of benefits and risks, regulatory and tax coordination, social, environmental and ethical responsibility, strategic, transparent and contractual nature. By constructing polynomial regression equations of the dependence of the gross product of the coastal regions of Ukraine on direct foreign investment, the regularities of the influence of international investment cooperation on the economic development of the respective territories are investigated. It is determined that such cooperation is characterized by its fundamental importance for the economic growth, its direct impact on the regional economic complex, the marginal possibilities of rational placement of international investments and the unevenness of the positive effect they generate. It is substantiated the practical advantages and limitations of the potential increase in the volume of international investment in the post-war period.

6707 Preconception Lipids, Birthweight, And Pregnancy Outcomes In Pre-existing Diabetes

Abstract Disclosure: J. Chang: None. S. Hsu: None. T. Thaweethai: None. K.E. James: None. L.L. Shook: None. J. Maya: None. C.E. Powe: Consulting Fee; Self; Mediflix Inc. Other; Self; Wolters Kluwer, Up to Date Author. Objective Prior studies suggest that higher preconception triglycerides are associated with higher infant birthweight in people without pre-existing diabetes. People with pre-existing diabetes have high risk for dyslipidemia and large for gestational age (LGA) neonates; however, data is scarce on the association between preconception lipids and pregnancy outcomes in this population. Methods: We retrospectively analyzed pregnancies with pre-existing diabetes who had triglyceride levels available prior to conception. We used generalized estimating equations (GEE) for linear regression to examine the association of preconception log-transformed triglycerides with birthweight percentile (primary outcome) while accounting for pregnancies that occurred in the same person. We used logistic regression via GEE for other pregnancy outcomes, including LGA. We built unadjusted models and models adjusted for maternal age, race/ethnicity, parity, insurance status, marital status, first trimester blood pressure, and BMI and hemoglobin A1C at 12 weeks’ gestation. We assessed for effect modification by diabetes type via stratification and interaction models. Results: Among 217 pregnancies in 189 people, 27% had preconception triglycerides ≥150 mg/dL and 32% resulted in LGA infants. Log-transformed triglycerides were not significantly associated with birthweight percentile in the full cohort in either adjusted (P = 0.62) or unadjusted models (P = 0.35). However, in unadjusted analyses stratified by diabetes type, a one unit increase in log-transformed triglycerides was associated with a 10.4 percentile increase in birthweight in pregnancies with type 2 diabetes (T2D; N=137) (95% confidence interval [CI] [1.12, 19.63], P = 0.03) and a 16.0 percentile decrease in birthweight in those with type 1 diabetes (T1D; N=76) (95% CI [-30.03, -1.95], P = 0.03). These associations were significantly different from each other (P for interaction = 0.002). Associations were attenuated after multivariate adjustment (T2D: β = 6.62, 95% CI [-3.42, 16.66], P= 0.20; T1D: β = -11.12, 95% CI [-23.61, 1.37], P= 0.08). Analyses of LGA incidence were consistent with primary findings, as those with T2D and higher log-transformed triglyceride levels were more likely to have LGA infants (odds ratio [OR] = 3.84, 95% CI [1.71, 8.62], P= 0.001) while an inverse association was seen in T1D (OR = 0.35, 95% CI [0.13, 0.94], P= 0.04). After multivariate adjustment, the association with LGA persisted in T2D (OR = 4.03, 95% CI [1.42, 11.44], P= 0.01) but was attenuated in T1D (OR = 0.34, 95% CI [0.09, 1.27], P= 0.11). Conclusion: Higher preconception triglycerides are associated with higher infant birthweight in people with T2D, while the inverse may be seen in those with T1D. Mechanisms linking preconception triglycerides and birthweight in people with diabetes should be examined in future studies. Presentation: 6/2/2024

Child-Hayling test for French school-aged children: psychometric properties and normative data

ABSTRACT The Hayling Completion Sentence Test (HSCT) is dedicated to assess inhibition of the dominant response and includes two conditions, an automatic condition in which the participants are asked to complete sentences properly and an inhibition condition in which the participants were asked to produce a word completely unrelated to the sentence. The aim of our study was 1) to adapt, 2) to evaluate the psychometric properties and 3) to standardize the HSCT into a French-school-aged pediatric population. We developed the Child-Hayling Test, a child adaptation of the adult French version of the HSCT. The reliability and validity of the Child-Hayling Test were then evaluated in a sample of 134 children aged 6–11 years. In the inhibition condition, children had lower response latency, as they get older. No effect of gender was observed. Reliability indices were low to moderate. Concerning the convergent and divergent validity, response latencies in the Child-Hayling Test correlated with latency scores in the Barre-Joe inhibition test, whereas the Child-Hayling Test scores were not related to children’s lexical abilities. The Child-Hayling Test was then administered to 393 typically developing 6- to 11-year-old children. Normative data were calculated in the inhibition condition using a regression-based approach. Regression equations to calculate Z scores are provided for clinical use. In addition, we proposed a clear guideline on how to score children’s inhibition responses. The Child-Hayling Test provides a useful tool for assessing prepotent response inhibition in children and can be recommended for use in clinical research and practice.

A novel approach to improve population mapping considering facility-based service capacity and land livability

Social sensing data, including points-of-interest (POI) and mobile position, are important data sources for population mapping. However, existing studies using POI data disregard the heterogeneity in facility size among the same type of POI and urban–rural differences. Moreover, mobile position data face biased issues. This study presents a hybrid model that considers facility-based service capacity (FSC) and land livability to map fine-scale population distributions. Based on extracting the FSC index by integrating POI, mobile phone positioning (MPP), and road network data, the district-level census population was disaggregated into 100-m grids using a random forest model. Subsequently, a regression equation was developed from the land use data to correct the estimated residuals. The results showed that the hybrid model exhibited considerably smaller errors than those of the POI density-based method and direct mapping of MPP (RMSE = 5,393.31, 7,348.91, and 7,824.76, respectively), effectively reducing population misallocation in extreme-density regions. Compared to WorldPop and LandScan datasets and a comparative model, our method reduced the RMSE by 30–60%, proving the effectiveness of integrating various social sensing and remote sensing data for improving population mapping. This study improves on an existing method as a thought-provoking step toward advancement in fine-scale population mapping research.

Trends and factors influencing the development of international tourism

The purpose of the article is to determine the return on investment produced by identifying modern trends in the development of international tourism and identifying factors affecting the development of tourism in the Republic of Kazakhstan. In order to achieve the objectives a certain number of regions and places of visitor accommodation were considered, investment in the tourism industry was analysed, and the results of a dynamic model with a distributed lag were obtained for the selected regions. Tourism development is an urgent topic, tourism is a source of income for millions of people working in this field, therefore, increasing the pace of development of the tourism sector is discussed in the Agendas of international organizations and in the annual Messages of the Head of State to the people of Kazakhstan. To determine the strategy for the development of the tourism services market, the factors influencing it were identified and a statistical analysis was carried out. Multiple correlation and regression analysis is one of the methods by which one can investigate the dependence of one variable on several variable factors. And for this purpose, significant factors were selected, and a multiple linear regression equation was constructed. The authors carried out a correlation and regression analysis using the example of regions, and also analyzed the obtained parameters of the multiple linear regression equation. In conclusion, the obtained regression equations in general and the coefficients are statistically significant and reliable at an appropriate level of significance. It allows us to draw conclusions based on the analysis of the obtained parameters of the multiple linear regression equation for the republic.

Health enhancement through activity travel participation and physical activity intensity

IntroductionPast studies investigate the impact of health parameters on activity and transport options. However, limited research has been done on how activity-travel participation influences health outcomes. Physical activity has the potential to improve health characteristics; however, its intensity (duration and frequency) and optimal mode are yet to be determined. Therefore, the current study aims to use physical activity intensity (PAI) as an intermediate variable to study the correlation among daily activities, transport mode choice, and health parameters to pursue sustainable transportation and improve health parameters. MethodsIn September 2013, a multidimensional three-week household survey was used to collect the data containing 191 households and 732 individuals representing 0.029% of the total population of Bandung, Indonesia. SPSS was used for the data normalization and descriptive analysis, whereas R was used for the multilevel regression and structural equation modeling (SEM). Four models were developed between endogenous, mediator, and exogenous variables. ResultsA significant correlation was found between daily activities, transport mode choice, and health parameters with exceptionally acceptable R2 and p-values. The appraised results indicate that strenuous PAI is 2.1% and 0.3% positively correlates with physical health (PH) and social health (SH), showing that it mediates the correlation among transport-related daily activities and health parameters. High-income households are dependent on motorized transport which is 0.1% and 0.2% negatively associated with PH and SH. Whereas, active transport is 2.0% and 0.2% and public transport is 0.2% and 1.5% positively associated with PH and SH. Replacing short car trips with a sustainable transportation system enhances health parameters and reduces GHG emissions from the transportation sector. ConclusionThe current study concluded that the association was mediated by transport-related PAI which enhances health parameters. Policymakers and practitioners promote transport-related physical activity which may help to enhance health parameters and provide a sustainable transportation system.

Comprehensive Evaluation and Selection of Cardamom (Elettaria cardamomum (L.) Maton) Germplasm Using Morphological Traits.

Cardamom (Elettaria cardamomum (L.) Maton) plays a crucial role in Guatemala's agriculture, supporting local families and covering 169,429.29 ha (making it the world's leading producer). Since its introduction to Guatemala in 1910, limited research has focused on unraveling the diversity and defining morphological traits critical for selecting excellent accessions. In this study, we examined 17 morphological traits across 288 accessions to identify key features associated with the germplasm. The comprehensive analysis employed principal component analysis, a morphological composite value (F-value), linear regression, and hierarchical clustering. The Shannon-Wiener diversity index ranged from 0.10 to 2.02, indicating the variation in diversity among traits. Principal component analysis and hierarchical clustering revealed six distinct germplasm groups. The comprehensive analysis facilitated the selection of 14 excellent accessions, and the regression equation incorporating criteria such as plant height, capsule color, panicle number per plant, panicle length, rhizome color, cluster number per panicle, cluster node length, and capsule number per cluster to identify cardamom germplasm. To develop a conservation strategy for the two putative foreign varieties ('Malabar' and 'Mysore'/'Vazhukka') introduced in Guatemala based on plant height, another 12 accessions were selected with a second comprehensive evaluation. This information offers insights into cardamom diversity for informed selection enhancing national utilization, productivity, and conservation.

Unveiling the Genetic Architecture of Semen Traits in Thai Native Roosters: A Comprehensive Analysis Using Random Regression and Spline Function Models.

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1-4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test-day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

A-214 Evaluate LDL-C using NIH equation 2, Friedewald equation, and direct homogeneous measurement

Abstract Background It is essential to accurately assess Low-density lipoprotein cholesterol (LDL-C) levels, a key marker for cardiovascular disease often used for treatment recommendations. The Friedewald equation has been used for many years to calculate plasma LDL-C levels, but it has limitations. Therefore, before implementing the NIH equation, we compared LDL-C results from NIH Equation 2 to the Friedewald formula and then to Atellica direct homogeneous assay LDL-C results. Methods A retrospective study of 1200 randomly selected patients out of 53,258 data points pulled from the laboratory information (LIS) system between November 2021 and October 2023. The study evaluated triglycerides, LDL-C, total cholesterol, and HDL-C measurements. The LDL-C calculated using NIH equation 2 has been set in the LIS as a non-reportable test component since November 1, 2021. The results obtained from NIH equation 2 and Friedewald equation were compared. LDL-C levels were measured using an Atellica direct homogeneous assay for an additional 75 specimens. Deming regression analysis compared measured and calculated LDL-C using both formulae. Results The LDL-C calculated using the Friedewald equation has a mean of 106.1 mg/dL and SD 40.1 mg/dL, while the NIH equation has a mean of 108.5 mg/dL and SD 39.9 mg/dL. The average difference between both equations is 2.5 mg/dL, and there is a significant difference between both calculations using paired t-tests (p-value&amp;lt;0.0001). We first compared the calculated LDL-C derived from the Friedewald formula to the NIH equation 2. The regression analysis equation for the Friedewald formula was y = -2.7 + 1.001 X (r = 0.99). When the Triglyceride values &amp;lt;150 mg/dL (N=879), the average difference is 1.3, and the regression equation is -0.45+1.01X(r=0.99). However, when the Triglyceride value &amp;gt;= 150 mg/dL (N=321), the average bias is 5.4 mg/dL, and the regression equation is -11.2+1.05X(r=0.99). The Friedewald formula was compared to the measured LDL-C in 75 specimens; the regression analysis for the Friedewald formula was y = -19.6 + 1.06 X (r = 0.91, p-value &amp;lt;0.0001). The 75 measured LDL-C results were also compared with the NIH formula. The regression analysis of NIH LDL-C with measured LDL-C was y = -5.6 + 0.981 x (r = 0.90, p-value = 0.0002). Both formulae showed a linear relationship against measured LDL-C. The NIH formula outperformed the Friedewald formula; bias difference -7.8(-6.8%) compared to the Friedwald equation bias -12.9(-12.0%). If the NIH equation had been used during this period, an additional 1,116 patients would have had LDL-C results. This is because the Friedewald equation should not be used if the triglyceride value is above 400 mg/dL. Out of the additional patients, 513 (46%) had a high LDL (&amp;gt;100 mg/dL). Conclusions The NIH formula performed better than the Friedewald formula, with a less negative bias when both calculations were compared to direct homogenous measurement. The average difference between the two formulas showed the lowest value when triglyceride &amp;lt; 150 mg/dL. Moving to NIH equation 2 will benefit our population as we had 2% of our patients in the past two years have triglyceride &amp;gt;400 mg/dL.

A prediction model for improving virtual machine live migration performance in cloud computing using artificial intelligence techniques

Over recent years, virtualization has worked as the powerhouse of the data centers. To positively influence datacenter utilization, power consumption, and management, live migration presents a technique which must be employed. Live migration refers to the method whereby an online virtual machine or an application is moved from one physical server to another without shutting down the client or the program. However, a migration’s qualities, which define service quality, have to be evaluated prior to a migration. In this paper, we propose a novel approach using predictive machine learning and deep learning models to forecast key metrics such as total migration time, downtime, and total data transferred for two migration types: pre-copy migration and post-copy migration. It builds on methods from regression machine learning and deep learning, and hyperparameter optimization, and feature engineering to push past reported solutions. The novelty of this work is the development of the hybrid model that includes the positive aspects from both of the machine learning and the deep learning with an emphasis on the increased accuracy of the model’s predictions. Concretely, the proposed deep learning framework based on GRU provided outstanding performance with 99.6% in total migration time where the transferred data GRU achieved 99.9% Moreover, in Down time target GRU reached 98.3%. This outperforms the other machine learning model and prior studies to become the best model for live migration prediction.

Influence of dispersion of a new type whole crystals seed on crystallization process and sugar quality

The aim of the work was to study effectiveness of the influence of a new type crystal seed with different microcrystals dispersion on the process of sucrose crystallization and sugar quality. The dispersion of microcrystals of the crystal-forming agent with whole crystals was determined and curves of their differential distribution were presented, which showed the average particle sizes of the “Ester K 01” are 18.2 μm and 46.4 μm. The morphological characteristics were determined of particles of the whole crystals seed and the seed slurry produced at a sugar factory. The duration of massecuite boiling was investigated and a comparative characterization of the quality indicators of the resulting massecuite, mother liquor, and sugar was given depending on the use of the whole crystals seed with different dispersion of microcrystals in comparison with the factory slurry. Microphotographs of sugar crystals in the massecuite were obtained with their size recorded 5, 10 and 20 min after the input of various seeds. The morphology of sugar crystals was studied in the finished massecuite. A comparison is given of the granulometric composition of sugar crystals obtained with crystal-forming agents with different dispersion. It was found based on the results of the study that when using the “Ester K 01” (46.4 μm) in thick juice with high turbidity the corresponding crystals are formed faster in the total amount of the massecuite. It was also found that there is a kind of blocking of the process of adjacent growth of crystals from the particles of impurities of thick juice with high turbidity. This contributes to the formation of more uniform, homogeneous crystals of the correct shape. The results are presented in the article of tests of the whole crystal seed “Ester K 01” (18.2 μm) and “Ester K 01” (46.4 μm) at sugar factories using the method of working on the seed magma and the method of shock crystallization respectively. The dependences were obtained of the average linear size, the amount of sugar of the 0.65 mm fraction, the coefficient of variation and the coefficient of uniformity of sugar crystals on the consumption of the whole crystals seed “Ester K 01” (crystal size 46.4 μm). The regression equation was developed in natural form for the process of sucrose shock crystallization. It was established the rational consumption of the crystal-forming agent “Ester K 01” (crystal size 46.4 μm), which ensures the effective crystallization of sucrose for the process of crystallization by the shock induced method.