Regression Coefficients Research Articles

Interaction of a self-expandable stent with the arterial wall in the presence of hypocellular and calcified plaques.

Self-expandable stents manufactured from nitinol alloys are commonly utilized alongside traditional balloon-expandable stents to provide scaffolding to stenosed arteries. However, a significant limitation hampering stent efficacy is restenosis, triggered by neointimal hyperplasia and resulting in the loss of gain in lumen size, post-intervention. In this study, a nonlinear finite element model was developed to simulate stent crimping and expansion and its interaction with the surrounding vessel in the presence of a plaque. The main aim was to determine contact pressures and forces induced at the interface between an artery wall with hypocellular and calcified plaques and an expanded stent. The results demonstrated the drawbacks of plaque calcification, which triggered a sharp contact pressure and radial force surge at the interface as well as a significant rise in von Mises stress within the vessel, potentially leading to rupture and restenosis. A regression line was then established to relate hypocellular and calcified plaques. The adjusted coefficient of determination indicated a good correlation between contact pressures for calcified and hypocellular plaque models. Regarding the directionality of wall properties, contact pressure and force observations were not significantly different between isotropic and anisotropic arteries. Moreover, variations in friction coefficients did not substantially affect the interfacial contact pressures.

Fluorescence Visualization Quantitative Detection of Tetracycline and Nitrofurantoin in Food and Natural Water by Zn2+@Eu-bpdc Composite.

Quantitative detection of tetracycline (TC) and nitrofurantoin (NFT) in food and water is of importance for food safety and environmental protection. Herein, Zn2+ was introduced into a europium metal-organic framework Eu-bpdc (H2bpdc = 2,2'-bipyridyl-5,5'-dicarboxylic acid) to prepare a composite of Zn2+@Eu-bpdc, which was developed as a fluorescence sensor for TC and NFT. The fluorescence mechanism concerns with bpdc2- ligand-to-Eu(III) charge transfer, and the detection mechanism is the inner filter effect. Zn2+@Eu-bpdc is a ratiometric fluorescence sensor for TC with the linear fitting equation of I520/I618 = 1.94 × 104 M-1CTC, whose limit of detection (LOD) is 0.148 μmol·L-1 (μM); it is also a fluorescence "turn-off" sensor for NFT with the fitting equation of (I0-I)/I = 3.62 × 104 M-1CNFT and LOD = 0.0792 μM. Zn2+@Eu-bpdc can detect TC or NFT in lake water, honey, and milk with high accuracy. The emission color changes of paper-based Zn2+@Eu-bpdc depending on CTC or CNFT reveal the visualization detections of TC and NFT. With the red and green values as input signals, smartphone-assisted on-site detection is utilized to recognize the antibiotic residuals of TC and NFT by a self-programmed APP. Zn2+@Eu-bpdc is promising in a smartphone-assisted intelligent platform for on-site detection of TC and NFT.

Experimental and ANN modeling of kerosene fuel desulfurization using a manganese oxide-tin oxide catalyst

This research pioneered the use of a nanocatalyst composed of manganese oxide (MnO2) and stannic oxide (SnO2) to effectively remove dibenzothiophene (DBT) from kerosene fuel through the catalytic oxidative desulfurization (ODS) process, using hydrogen peroxide (H2O2) as the oxidant. Impregnating SnO2 with varying amounts of MnO2 was used to manufacture the catalyst. The oxidation experiment ran in a batch reactor with varying reaction times and temperatures to determine optimal conditions. High MnO2 dispersion over SnO2 was shown by catalyst characterization data. Under optimal operating parameters (catalyst type: 5 % MnO2/SnO2, reaction temperature: 75 °C, and reaction duration: 100 min), the results demonstrated a maximum DBT removal efficiency of 82.84 % from kerosene fuel. This research also provides the construction of Artificial Neural Network (ANN) model to simulate the upgrading of kerosene fuel via desulfurization process. There has been a growing trend toward the diversified use of ANN to represent steady state systems in chemical engineering. MATLAB's code was employed for matching the experimental data to the artificial neural network (ANN) model. The resulted data showed significant agreement between the experimental and predicted outcomes, with regression coefficients (R2) of 0.99902, 0.99986, and 0.99961 and mean square errors (MSE) of 0.266, 0.272, and 0.104 for 0 % MnO2/SnO2, 1 % MnO2/SnO2, and 5 % MnO2/SnO2 respectively. This interactive model provided a solid foundation for understanding the novel behavior of the oxidation process.

9390 Does Znt8a Have An Association With The Duration Of Diabetes? Case Series And Literature Review

Abstract Disclosure: N.M. Rivera Vargas: None. C.S. Botero Suarez: None. S.K. Suryanarayanan: None. S. Saad-Omer: None. Context: Latent autoimmune Diabetes of adults (LADA) is a form of autoimmune diabetes with features of Type 2 Diabetes but positive autoantibodies resembling Type 1 Diabetes. Latent autoimmune diabetes in adults (LADA), has a mixed phenotype with positive islet-cell antibodies (ICA), glutamic acid decarboxylase autoantibodies (GADA), protein tyrosine phosphatase autoantibodies (IA2A), insulin autoantibodies (IAA), or zinc transporter-8 autoantibodies (ZnT8A). Since the discovery of ZnT8A, the relevance and clinical utility of this marker has sparked interest. Although ZnT8A is used as a complementary diagnostic aid in children, its utility in adults seems to be unclear. Objective: We report a case series of 26 subjects referred to the Orlando VA Endocrine clinic with measured ZnT8A. We hypothesize that higher ZnT8A will be found in adult autoimmune diabetes with newly diagnosed diabetes rather than those with a longer duration of diabetes. We also explore the association between ZnT8A and family history, duration of diabetes, diabetes complications, and C-peptide levels. Method: A case series of 26 adult patients with diabetes mellitus with a positive ZnT8A (≥15 U/mL). We reviewed medical records of these cases to include ZnT8A levels, years of diabetes diagnosis, C-peptide levels, and diabetes complications. Patients were classified into subgroups based on ZnT8A levels into < 350 U/mL or ≥ 350 U/mL. Results: The 26 patients reviewed had a mean (SD) age of 46 (13) years. The mean (SD) duration of diabetes was 16 (12) years, and HbA1C was 8.4 (1.1) %. Poisson regression analysis showed a statistically significant inverse relationship between ZnT8A and years of diabetes (regression coefficient: -0.028; 95% confidence interval: -0.03 to -0.25; p-value <0.001). Diabetic ketoacidosis was present in 35% of subjects, while diabetic neuropathy was noted in 58% of subjects with positive ZnT8A. Patients with a high ZnT8Ab above 350 U/mL displayed a weak trend towards a lower C-peptide mean (p-value 0.42), and lower prevalence of DKA (p-value 0.42) which was not statistically significant. Conclusion: In this case series of patients with adult-onset diabetes, there seems to be a statistically significant inverse relationship between ZnT8A levels and duration of diabetes suggesting a reduction of ZnT8A levels over time. Although limited by a small number of subjects, this may suggest the need for screening with autoantibodies in the setting of diabetes earlier in the disease process. Presentation: 6/2/2024

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

The Impact of Taxation on the Cost of Living: A Comprehensive Analysis

This study examines the taxation-related factors affecting Malaysia's cost of living. The rising cost of living, a persistent issue in economics and social well-being, is often linked to global challenges faced by emerging nations. Direct tax has been identified as a primary contributor to the financial strain on households, who must manage various essentials beyond food, clothing, and shelter, such as education, cellular services, and transportation. The study analyzes the relationship between seven independent variables—direct tax, unemployment rate, exchange rate, household consumption expenditure, house price index, subsidies, and net trade—and the cost of living in Malaysia, using 30 years of annual data (1992-2022) with EViews for econometric analysis. This study uses descriptive statistical analysis for the description of data, coefficient matrix analysis and regression analysis for determining the impact of dependent and independent variables. The findings show that the cost-of-living pressures are positively correlated with direct tax, housing price index, and net trade. Data was sourced from the National Property Information Centre (NAPIC), the World Bank, and the Economic Planning Unit (EPU). The analysis underscores the significant impact of direct tax on the cost of living. To mitigate this impact, governments could implement fair and progressive tax reforms, provide targeted subsidies, and promote economic growth to boost income levels. Additionally, social programs and efficient government investment can further reduce household financial burdens.

A Crucible of IoT Applications and Services and the Gambia Revenue Generation

The study investigated the relationship between Internet of Things (IoT) applications and services and The Gambia revenue generation, tax evasion and tax compliance respectively. It identified the challenges impeding the adoption and implementation of IoT applications and services in the revenue generation processes by the Gambia Revenue Authority (GRA). Both primary and secondary data were used in the study. Questionnaire and interview were used to generate the primary data. The study population (2188) comprised senior and mid-tier management personnel from the Ministry of Finance and Economic Affairs (MoFEA), staff members of The Gambia Revenue Authority, and the corporate taxpayers. The sampling approach used in this study was multistage. Personnel from the MoFEA and GRA were purposively selected, while corporate taxpayers were randomly selected. Their populations were then divided into various strata to ensure fair representation. Respondents were randomly picked from each stratum. To ensure an appropriate sample size, 327 individuals were selected. Data obtained were analysed using multiple regression, correlation coefficient, and simple regression analysis. Qualitative analyses were done on the interview responses. The study revealed that connectivity, networking, and data collection improve revenue generation, while increased IoT adoption and higher data utilisation and infrastructure investment reduce tax evasion and boost tax compliance. However, implementation of the IoT has encountered challenges in the form of technological barriers, financial constraints, and skill gaps. In view of these, investment must be made in enhancing connectivity and networking infrastructure, training staff in data analytics and cyber-security to optimise IoT data usage and boost IoT adoption through inducements and awareness programs. The study concluded that IOT applications and services are in the right direction to revolutionise The Gambia revenue generation.

Utilizing computed tomography-based renal parenchymal volumes to calculate split renal function in patients with ureteral stricture disease.

Evaluation of split renal function (SRF) is critical for guiding surgical treatment decisions for patients with ureteral stricture disease (USD). We aimed to determine whether computed tomography (CT)-based renal parenchymal volumes may be used to predict SRF in patients with USD. We retrospectively reviewed all patients undergoing surgical management for USD at a single institution from October 2021 to January 2024. Patients who had preoperative nuclear medicine scan (NMS) and CT scan with intravenous contrast that were obtained within six weeks of each other were included. Interval between NMS and CT could be longer if the affected renal unit was drained with ureteral stent and/or percutaneous nephrostomy. Volume measurements were obtained using the 3D Region of Interest (ROI) Tool on Visage®7 Enterprise Imaging Platform (Visage Inc., San Diego, USA) by two investigators that were blinded to NMS derived SRF. Intraclass correlation coefficient (ICC) was used to assess consistency between investigators. Predictive accuracy was assessed using Pearson correlation coefficient (r) and linear regression. 40 of 160 patients met inclusion criteria. There was excellent reliability in calculating renal parenchymal volume between raters (ICC = 0.990). There was a strong linear correlation between estimated CT SRF and NMS SRF (r = 0.912, p < 0.00001). A linear regression model found RObservedSRF = -0.013 + 1.015(REstimatedSRF), with r2 = 0.832. CT-derived parenchymal volume analysis may be used to estimate SRF in patients with USD. This may obviate the need to obtain preoperative renal scans for SRF measurement in selected patients when assessing surgical management options.

Respiratory Muscle Strength as a Predictor of VO2max and Aerobic Endurance in Competitive Athletes

This study explores the association of respiratory muscle strength with aerobic endurance kinetics among athletes, with a specific focus on maximal oxygen consumption (VO2max). Previous research has elucidated the complex interactions between respiratory and skeletal muscles during exercise, highlighting the critical role of efficient respiration in maximizing athletic performance. The interplay between active skeletal muscles and respiratory muscles, especially the influence of respiratory muscle fatigue on exercise capacity, is well-documented. High-intensity exercise has been shown to activate the respiratory muscle metaboreflex, which can restrict blood flow to working muscles, thereby impacting the energy required for respiration. A total of 41 athletes, drawn from the disciplines of biathlon, judo, and cross-country, participated in this study. Respiratory function tests (RFTs) were administered to assess various respiratory parameters, including changes in chest circumference. Additionally, maximal oxygen consumption (VO2max) and heart rate were measured during a treadmill test. To explore the associations between VO2max and ventilatory parameters—namely, ventilation (VE), oxygen consumption (VO2), carbon dioxide production (VCO2)—as well as respiratory metrics, linear regression analysis was employed. Based on the standardized regression coefficients (β), it was found that maximum expiratory pressure (MEP) (mean ± SD: 130.95 ± 42.82) and inspiratory diaphragmatic circumference values were significantly associated with VE, VO2, and VCO2. Conversely, the other predictor variables did not exhibit a significant effect on VE (mean ± SD: 134.80 ± 36.69), VO2 (mean ± SD: 3877.52 ± 868.47 mL), and VCO2 (mean ± SD: 4301.27 ± 1001.07 mL). Similarly, measurements of chest circumference (mean ± SD: 91.40 ± 10.72 cm), MEP, and diaphragmatic circumference during inspiration (mean ± SD: 95.20 ± 10.21 cm) were significantly associated with VO2max (mean ± SD: 58.52 ± 10.74 mL/kg/min), while the remaining predictor variables did not demonstrate a significant effect on VO2max. Additionally, a multiple linear regression analysis was conducted to examine the combined effects of respiratory muscle strength and ventilatory factors on VO2max. The model, which included interaction terms, explained 89.9% of the variance in VO2max (R2 = 0.899, adjusted R2 = 0.859). Significant interactions were found between MIP and VE (B = −0.084, p = 0.006), as well as MEP and VE (B = 0.072, p = 0.012). These findings suggest that respiratory muscle strength plays a more substantial role in determining VO2max in individuals with higher ventilatory efficiency, highlighting the importance of both respiratory strength and breathing efficiency in aerobic performance. Our findings underscore the importance of considering respiratory muscle strength in assessing and enhancing athletes’ aerobic performance. Integrating objective measurements such as maximal inspiratory and expiratory pressure assessments into routine performance evaluations allows coaches and sports scientists to monitor changes in respiratory function over time and adjust training protocols accordingly.

Empirical Likelihood for Composite Quantile Regression Models with Missing Response Data

Under the assumption of missing response data, empirical likelihood inference is studied via composite quantile regression. Firstly, three empirical likelihood ratios of composite quantile regression are given and proved to be asymptotically χ2. Secondly, without an estimation of the asymptotic covariance, confidence intervals are constructed for the regression coefficients. Thirdly, three estimators are presented for the regression parameters to obtain its asymptotic distribution. The finite sample performance is assessed through simulation studies, and the symmetry confidence intervals of the parametric are constructed. Finally, the effectiveness of the proposed methods is illustrated by analyzing a real-world data set.

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.

Development and Validation of HSPiP- and Optimization-Assisted Method to Analyze Tolterodine Tartrate in Pharmacokinetic Study

A new approach was applied for the development of a precise, simple, and economic analytical process for the accurate analysis of tolterodine tartrate (TOT) in its bulk and tablet using HSPiP- and quality by design (QbD)-assisted methods. The HSPiP program predicted several solvents and their right ratios for the mobile phase, followed by simulating the experimental solubility data in various predicted solvents. QbD was used to identify the impact of the composition and the mobile phase flow rate on the peak area and retention time. TOT was estimated using an Agilent TC C18 column employing an optimized mobile phase. The HSPiP shortened the solvent selection time with high reliability, whereas QbD identified critical factors. The optimized composition and process variables were used to develop an analytical method for TOT estimation. Various analytical validation parameters were estimated with constructed linearity of 5–30 μg/mL and a percent recovery yield value of 100.36%. To ensure the reliability of the optimized method, we estimated validation parameters (linearity, specificity, precision, accuracy, robustness, and ruggedness) to comply with the ICH guidelines. Considering the high recovery yield, good regression coefficient, low detection limit, and low noise ratio, the optimized method was accurate and precise with a high degree of specificity, rapid process, and reproducibility for the quantitative estimation of tolterodine from both oral analytes (I and II). The validated method was implemented for pharmacokinetic study in rats for quantitative estimation of the analytes with high accuracy, sensitivity, and reproducibility.

Case study of simplified reverberation time measurement under multiple conditions using a sound level meter

Recent advancements in the computational capacity of sound level meters have facilitated the simultaneous recording of diverse calculation results and waveform recording data. Traditionally, reverberation time determination entailed manually adjusting a linear regression line to the reverberation decay curve derived from recorded sound levels, followed by reverberation time computation based on the gradient of said line. However, automated techniques have made reverberation time calculation via a sound level meter feasible. This paper discusses a case study evaluating the efficacy and considerations of simplified reverberation time measurements conducted across various conditions, encompassing scenarios with multiple sound sources and diverse indoor environments. The study leveraged recorded data from a sound level meter and employed automated methods simulated on a personal computer.

The relationship between changes in pain intensity and functional disability in persistent disabling low back pain during a course of cognitive functional therapy.

Pain relief and reduced disability are both common treatment targets for persistent disabling low back pain (LBP). Cross-sectional studies show a moderate relationship between functional disability and pain intensity, but little is known about the relationship between changes in pain intensity and functional disability over multiple time points. The objective of this study was to investigate the associations between changes in functional disability and pain intensity and whether changes occurred simultaneously or differentially during a course of cognitive functional therapy for people with persistent disabling LBP. Self-reported measures of pain intensity and patient-specific functional disability were collected prior to each treatment session from 40 participants during a 12-week intervention period. Linear mixed modeling was used to assess simultaneous and lagged associations between pain intensity and functional disability over time. Sensitivity analysis using nonparametric subject-specific methods (simulation modeling analysis) was also performed. Thirty-five participants had sufficient data for analysis. Using the linear mixed-model approach, there was evidence of a moderate and simultaneous association between pain intensity and functional disability over time (regression coefficient = 0.56, 95% confidence interval: 0.44-0.68, p < 0.001). Simulation modeling analysis supported weak to mostly strong associations and supported for simultaneous change in pain and disability for the majority of participants (22 of 35, 64%). Changes in pain intensity and functional disability were moderately related across the intervention. Visual inspection of graphs indicated a very close relationship in some individuals and a decoupling of pain intensity and functional disability in others. The changes in pain intensity and functional disability seem to occur simultaneously in most individuals.

Construction of a Risk Prediction Model for Ureteral Stricture after Ureteroscopic Holmium Laser Lithotripsy.

Introduction: To analyze the influencing factors of ureteral stenosis after ureteroscopic holmium laser lithotripsy. Methods: The clinical data of 427 patients treated with ureteroscopic holmium laser lithotripsy were selected, and the patients were divided into two groups based on the presence or absence of ureteral stenosis after the operation. Univariate and multivariate logistic regression were used to analyze the independent risk factors for postoperative ureteral stenosis, and R software and regression coefficients were used to construct a predictive model. Results: After a 1-year follow-up of 427 patients, 28 patients (6.56%) developed ureteral stenosis; univariate analysis showed that the occurrence of ureteral stenosis after subureteral holmium laser lithotripsy was related to stone diameter, stone incarceration, degree of hydronephrosis, holmium laser injury of mucosa, and operation time (p < 0.05); further logistic regression analysis showed that a large stone diameter, stone incarceration, and moderate to severe hydronephrosis were independent risk factors for ureteral stenosis after ureteroscopic holmium laser lithotripsy (p < 0.05); According to H-L deviation degree and area under receiver operating characteristic curve test, the results show that the model has high accuracy (χ2 = 2.475, p = 0.613) and differentiation (0.875 [95% confidence interval or CI: 0.817-0.919]), and the external verification of the nomogram prediction model was carried out by the verification group. The results showed that the prediction probability of the calibration curve was close to the actual probability and had a good consistency (area under the curve: 0.873 [95 CI: 0.822-0.914]). Conclusion: The established nomogram model exhibits high accuracy and discriminative ability. It can effectively identify high-risk groups, enabling timely prevention of ureteral stenosis and minimizing the risk of postoperative ureteral stenosis.

Pengaruh Kualitas Produk, Gaya Hidup dan Kepribadian terhadap Keputusan Pembelian Sepeda Motor PCX di Kecamatan Blora Kabupaten Blora

The aim of this study was to determine the effect of product quality, lifestyle and personality on purchasing decisions for Honda PCX motorcycles in Blora Blora subdistrict, either partially or simultaneously. The research method used by the population is consumers who come to Honda motorbikes in Blora Blora Regency. The sample of this research is 96 respondents. Data collection techniques using a questionnaire. The analytical tools used are instrument tests (validity test, reliability test), classical assumptions (normality test, multicollinearity and heteroscedasticity test), hypothesis test (multiple linear regression analysis, t test, F test and coefficient of determination test (R2). The results showed that the variables of product quality, lifestyle and personality had a significant influence on purchasing decisions for Honda PCX motorbikes in Blora Blora District partially or simultaneously.

Pengungkapan pada Hubungan Sustainability Report dan Intellectual Capital terhadap Nilai Perusahaan

This research related to company value aims to analyze the relationship between sustainability reports and intellectual capital in manufacturing companies in the industrial and consumption sectors listed on the Indonesia Stock Exchange (BEI) during the 2020-2022 period. The sampling technique was purposive sampling, the results of which were 27 companies that met the research criteria. Data analysis uses SPSS Statistics 26 with descriptive statistical tests, normality tests, multicollinearity tests, heteroscedasticity tests, autocorrelation tests, multiple linear regression analysis, simultaneous tests, partial tests, and coefficient of determination tests. The research results show that sustainability reports do not provide an optimal contribution to company value. However, intellectual capital, especially the employee capital aspect, is a consideration for optimizing its contribution to company value.

Endogenous pain modulation after sleep restriction in migraine: a blinded crossover study

BackgroundPatients with migraine are vulnerable to insufficient sleep, but the impact of sleep restriction is largely unknown. In addition, the importance of sleep may be different in patients with migraine who mostly have attack onsets during sleep, so called sleep-related migraine, compared to patients with non-sleep-related migraine. In this study we investigate the effect of sleep restriction on endogenous pain modulation in patients with migraine and healthy controls. We also compared the effect of sleep restriction in sleep-related and in non-sleep-related migraine.MethodsMeasurements were conducted in 39 patients with migraine between attacks and 31 controls, once after habitual sleep and once after two consecutive nights of partial sleep restriction. There were 29 and 10 patients with non-sleep-related and sleep-related migraine respectively. Test stimulus was 2-min tonic noxious heat to the left volar forearm. Temporal summation was calculated as the regression coefficient for rated pain in the late part of this 2-min stimulation. Conditioning stimulus was right hand-immersion in 7 °C water. Conditioned pain modulation was defined as the difference in rated pain with and without the conditioning stimulus and was calculated for temporal summation and mean rated pain for the test stimulus. The effect of sleep restriction on temporal summation and conditioned pain modulation was compared in migraine subjects and controls using two-level models with recordings nested in subjects.ResultsConditioned pain modulation for temporal summation of heat pain tended to be reduced after sleep restriction in patients with migraine compared to controls (p = 0.060) and, in an exploratory analysis, was reduced more after sleep restriction in sleep-related than in non-sleep-related migraine (p = 0.017). No other differences between groups after sleep restriction were found for temporal summation or conditioned pain modulation.ConclusionPatients with migraine may have a subtly altered endogenous pain modulation system. Sleep restriction may have an increased pronociceptive effect on this system, suggesting a mechanism for vulnerability to insufficient sleep in migraine. This effect seems to be larger in sleep-related migraine than in non-sleep-related migraine.

Finite-Time Adaptive Control for Electro-Hydraulic Braking Gear Transmission Mechanism with Unilateral Dead Zone Nonlinearity

Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time adaptive control strategy was designed for unilateral dead zone nonlinearity. Initially, the unilateral dead zone nonlinearity was reformulated into a matched disturbance term and an unmatched disturbance term to reduce the adverse effects of disturbances, thereby enhancing system controllability. Then, the “complexity explosion” in the design of the control strategy was avoided by command filtering, and the design process of the controller was simplified. Furthermore, the finite-time control theory was employed to boost the system’s convergence speed, thereby enhancing control performance. In order to ensure the stability of the system under the dead zone disturbance, the unknown disturbance terms were estimated. The stability of the control strategy was validated through the finite-time stability theorem and the Lyapunov function. Eventually, simulations and hardware-in-the-loop (HIL) experiments validated the feasibility and availability of the finite-time adaptive control strategy.

B-304 Development of free valproic acid estimating equations for adult epileptic patients

Abstract Background Epilepsy is one of the most common neurological disorders, affecting 1.1% adults in United States. Valproic acid (VPA) is an antiepileptic drug with narrow therapeutic window that requires close monitoring of the blood concentration. VPA is highly protein-bound, and the unbound free VPA (fVPA) is the bioactive form responsible for the pharmaceutical effects as well as the toxicity. However, fVPA is not commonly measured in most laboratories but is a test that needs to be sent out to reference laboratories, and the sendout test has a long turnaround time which delays timely patient care. The aim of the study was to develop fVPA estimating equations, to decrease the turnaround time and reduce adverse events, and ultimately improve patients’ outcomes. Methods We retrospectively collected the laboratory results of total valproic acid (tVPA), fVPA and serum albumin along with the patients’ demographic characteristics and other clinical data from 2013 to 2023 in our institution. While the albumin measurement was divided into normal (≥3.5 g/dl) and hypoalbuminemia (&amp;lt;3.5 g/dl) groups, the tVPA and fVPA were categorized into subtherapeutic, therapeutic, and supratherapeutic ranges. Using stepwise model selection, several linear regression equations were developed to predict fVPA concentration for total patients, patients with normal albumin only, and patients with hypoalbuminemia only. The estimated fVPA was then compared to the measured fVPA. The data were analyzed using SAS 9.4, SPSS 28.0 and EP Evaluator EE12. Results A total of 344 concurrent measurements of tVPA/fVPA/serum albumin from 157 patients were obtained, including 180 measurements from patients with normal albumin levels and 164 measurements from patients with hypoalbuminemia. In the patients with normal albumin levels, 32 (17%) were discordant between tVPA and fVPA. More than half of them had therapeutic tVPA but supratherapeutic fVPA. In contrast, 120 (73%) were discordant for the patients with hypoalbuminemia. About half of them had subtherapeutic tVPA but therapeutic fVPA. Several laboratory and clinical parameters including tVPA, albumin, age, sex, congestive heart failure, obesity, and diabetes mellitus were chosen in the final model. In all patients, our equation demonstrated a concordance rate of 66.3% and a bias of 1.46% between the estimated fVPA and the measured fVPA. Likewise, our equation displayed a concordance rate of 70.0% and a bias of -0.03% for patients with normal albumin levels. For patients with hypoalbuminemia, the concordance rate was 69.5% with a bias of 1.97%. In comparison to three previously published equations, our equations obtained increased concordance and decreased bias between estimated fVPA and measured fVPA. Conclusions Our data indicate that free VPA and total VPA do not correlate well according to the therapeutic ranges, especially in patients with hypoalbuminemia. Compared with previously reported equations, our equations showed enhanced concordance and reduced bias between estimated fVPA and measured fVPA, in both patients with normal albumin levels and patients with hypoalbuminemia. Further validation is needed to evaluate the accuracy and generalizability of the equations before clinical application.