Measurement Of Variables Research Articles

Bilateral hip stability variation in the functional ambulation and kinetic parameters after total hip arthroplasty during leveled walking.

This study determines gender variation, comparing the significance level between men and women related to functional ambulation characteristics after hip arthroplasty. The study focuses on the broader female pelvis and how it affects the rehabilitation regimen following total hip arthroplasty. In this cross-sectional study, 20 cases of right hip arthroplasty were divided into 10 male and 10 female cases, aged 40-65years. The functional ambulation parameters (walking cadence, gait speed, stride length, and gait cycle time) were acquired from the GAITRite device, as well as kinematic values for hip frontal plane displacement and kinetic parameters for ground response force in the medial-lateral direction. An independent t-test showed a significant difference in the kinematic parameter variables for the anterior superior iliac spine, more significant trochanter displacement, and hip abduction angle between the operated and non-operated limbs for each group separately. Regarding the functional ambulation parameters, there was a significant difference in the walking cadence between the operated and non-operated limbs of both male and female groups. Moreover, the output variables of ground reaction force measures revealed significant differences between their operated and non-operated limbs. The linear regression model used was consistent with the current results, demonstrating a weak negative correlation between the abduction angle of the operated hip and gait speed for both male and female groups. Based on the findings, we draw the conclusion that improving a rehabilitated physical therapy program for the abductors of both male and female patients' operated and non-operated limbs is essential for normalizing the ground reaction force value, avoiding focus on the operated hip, and reducing the amount of time that the operated hip's abductors must perform. This involves exposing the surgically repaired limb to the risk of post-operative displacement or dislocation, particularly in female patients.

The Effect of Financial Distress, Profitability, and Leverage on Profit Management with Institutional Ownership as a Moderation in Telecommunications Sector Companies Listed on the IDX

The purpose of this study is to evaluate the influence of financial distress, profitability, and leverage on profit management, as well as to examine the role of institutional ownership as a moderating variable in the relationship between financial distress, profitability, and leverage on profit management. This study uses a quantitative research method. Quantitative research emphasizes more on testing theories through the measurement of research variables with numbers and conducting data analysis with statistical procedures. This study uses a causal design, which is a study that explains the causal relationship of independent variables to dependent variables. The results showed that financial distress, profitability, and leverage had no effect on profit management in telecommunications sector companies listed on the Indonesia Stock Exchange (IDX) for the 2018-2022 period. However, institutional ownership can weaken the influence of financial distress and profitability on profit management, while institutional ownership does not affect the influence of leverage on profit management.

Effects of 6-month administration of tofogliflozin on cardiac function in elderly patients with heart failure with preserved ejection fraction: A retrospective study of a patient cohort.

Patients with type 2 diabetes mellitus are frequently hospitalized for heart failure. The ratio of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity (E/e'), measured by echocardiography, is a simple and convenient indicator of diastolic dysfunction. Various large clinical trials have reported that sodium glucose transporter-2 inhibitor therapy reduced cardiovascular events and hospitalizations in heart failure patients. We examined the effect of tofogliflozin on various physiological and cardiac function. A retrospective analysis was performed on elderly patients aged 65 years or older with type 2 diabetes mellitus attending Himi Municipal Hospital who were taking oral tofogliflozin 20 mg/day. Measurement of physiological and hormonal variables, blood sampling, and echocardiographic evaluations at 0, 1, 3, and 6 months were performed on those with ejection fraction (EF) of 40% or greater at the time of treatment. Statistical analysis was performed using t-tests and mixed-effects models, with brain natriuretic peptide less than or not less than 100 pg/mL, estimated glomerular filtration rate (eGFR) less than or not less than 50 mL/min/1.73 m2, and diuretics administered or not. Hypoglycemic effects were observed at 0, 1, 3, and 6 months. At each time point, EF was retained and E/e' was significantly reduced. On the other hand, most physiological parameters and laboratory results showed no clinical abnormalities. Mixed-effects models showed time-dependent reduction of E/e' in high/low brain natriuretic peptide, high/low eGFR, with or without diuretics between baseline and at 6 months. The interaction with time was significant in high/low eGFR. Tofogliflozin was shown to improve E/e', a measure of diastolic function, while maintaining EF, with hypoglycemic effects and no clinical side effects.

Sensing Classroom Temperature, Relative Humidity, Illuminance, CO2, and Noise: An Integral Solution Based on an IoT Device for Dense Deployments.

Maintaining optimal Indoor Environmental Quality (IEQ) requires continuous measurement of certain variables. To this end, ASHRAE and BPIE recommend that at least the following areas of interest be considered when measuring IEQ: thermal comfort, illuminance, indoor air quality, and noise. At this time, it is not common to find an IoT device that is suitable for dense deployments in schools, university campuses, hospitals, and office buildings, among others, that measures variables in all of the above areas of interest. This paper presents a solution to the problem previously outlined by proposing an IoT device that measures variables across all of the aforementioned areas of interest. Moreover, in a radio frequency network with a tree-like structure of IoT devices, this device is able to assume the roles of sensor and hub node, sensor and router node, and only sensor node. The experimental results are satisfactory, and the detailed system design ensures the replicability of the device. Furthermore, the theoretical analysis paves the way for high scalability.

Natural regeneration of white mangrove (Laguncularia racemosa L.) during the dry season in Ejido La solución in Paraíso, Tabasco, México

Objective: To estimate the natural regeneration and growth in the sapling stage of white mangrove during the dry season. Design/methodology/approach: Measurements of the structural variables of white mangrove, diameter at breast height (DBH), height and crown diameter were made in three sampling plots. A statistical analysis was performed with the data obtained and mean data were obtained from the three sampling plots. Results: DBH data were 0.34, 0.37 and 0.42 m; height 1.34, 1.28 and 1.58 m; and crown 44.18, 38.56 and 39.83 cm, in each sampling plot respectively. Limitations on study/implications: In dry seasons, erosion can expose rocky or sandy substrates that are not suitable for mangrove growth. Findings/conclusions: The white mangrove in its sapling stage during the dry season, presented statistically significant differences in growth between sampling plots using the dasometric variables (DBH, height and crown diameter).

Residual and past varinaccuracy measures

Abstract Analysing the information measures’ dispersion index has garnered increasing attention. In this regard, variability of entropy has been explored for residual and past lifetimes. In a recent paper, Balakrishnan et al. (2023, Commun. Stat. Theory Methods, 53, 5574–5592) have studied the variability of the Kerridge inaccuracy measure. The dynamic measures of varinaccuracy, namely residual varinaccuracy and past varinaccuracy, have been introduced in this article. A detailed course of theoretical results has been investigated with examples. Further, some applications of the residual varinaccuracy have been added to show the functionality of the defined measure.

“Mental health, bonding, family wellbeing, and family functioning in parents of infants receiving neonatal intensive care”.

AimsTo describe self-reported mental health, bonding with the infant, family wellbeing, and family functioning among mothers and non-birthing parents with children receiving neonatal intensive care and to explore factors associated with bonding with the child, family wellbeing, and family functioning. MethodsCross-sectional study included families from three different units in Sweden. Parents received a questionnaire that included self-reported measures of the study variables. ResultsIn total, 75 mothers and 72 non-birthing parents were included. Nearly 40% of the parents reported anxiety symptoms, whereas depression and post-traumatic stress symptoms were uncommon. Mothers reported more mental health issues than non-birthing parents. In the regression analyses depression was associated with problems with bonding and family wellbeing, no other significant associations were identified. ConclusionSymptoms of anxiety were common but might be a natural and transient reaction. Identifying depression is important to be able to provide targeted family support.

Optical measurement of state variables associated with blast wave evolution

Optical measurement of state variables associated with blast wave evolution

초등 읽기부진 아동의 음운인식과 인지기능 요인 간의 관계 분석

The purpose of this study is to provide basic data for acquiring phonological awareness of children with poor reading by grasping the relationship between phonological perception and cognitive function factors of children with poor reading skills in elementary school. The subjects of this study were analyzed with 95 students enrolled in the 1st to 6th grades of elementary school commissioned by four regional education support offices in Jeollanam-do for poor reading. The analysis results are as follows. First, as a result of conducting an independent sample t-test to verify the difference between the measurement variables through intelligence tests, attention tests, and phonological awareness tests of children with poor reading in elementary school, there was a significant difference between grades(low grades, upper grades) in attention and phonological awareness. Second, as a result of conducting correlation analysis to find out the relationship between phonological awareness and measurement factors of children with poor reading in elementary school, attention, language understanding, and working memory had a significant positive correlation. Third, as a result of conducting hierarchical multiple regression analysis to find out the relative influence of the measurement variables on the phonological awareness of children with poor reading in elementary school, it was found that selective attention and working memory abilities had the greatest influence on phonological awareness.

Exposure variables in veterinary epidemiology: are they telling us what we think they are?

This manuscript summarizes a presentation delivered by the first author at the 2024 symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to Dr. Jan Sargeant. Epidemiologic research plays a crucial role in understanding the complex relationships between exposures and health outcomes. However, the accuracy of the conclusions drawn from these investigations relies upon the meticulous selection and measurement of exposure variables. Appropriate exposure variable selection is crucial for understanding disease etiologies, but it is often the case that we are not able to directly measure the exposure variable of interest and use proxy measures to assess exposures instead. Inappropriate use of proxy measures can lead to erroneous conclusions being made about the true exposure of interest. These errors may lead to biased estimates of associations between exposures and outcomes. The consequences of such biases extend beyond research concerns as health decisions can be made based on flawed evidence. Recognizing and mitigating these biases are essential for producing reliable evidence that informs health policies and interventions, ultimately contributing to improved population health outcomes. To address these challenges, researchers must adopt rigorous methodologies for exposure variable selection and validation studies to minimize measurement errors.

Dairy and Headaches: What is the Connection?

Headaches represent a prevalent and burdensome health condition, affecting individuals of all ages worldwide. While dietary factors have been implicated in headache pathophysiology, the association between dairy consumption and headaches remains controversial and inadequately understood. This comprehensive review systematically examines the existing literature to elucidate the relationship between dairy intake and headaches, addressing methodological challenges, potential biases, and gaps in the current knowledge. A thorough search of electronic databases identified relevant observational studies, clinical trials, and mechanistic investigations exploring the impact of dairy consumption on headache incidence, frequency, severity, and duration. Methodological considerations, including study design, measurement of exposure and outcome variables, confounding factors, and sources of bias, were critically evaluated to assess the strength of evidence and validity of findings. Despite heterogeneity across studies, emerging evidence suggests a complex and multifaceted relationship between dairy intake and headaches, influenced by individual characteristics, dietary patterns, headache subtype, and study context. While some studies report a positive association between dairy consumption and headaches, others indicate no significant effect or potential therapeutic benefits of dairy restriction. Mechanistic insights suggest plausible biological mechanisms, including neuroinflammatory pathways, neurotransmitter modulation, vascular effects, and gut-brain interactions, which may mediate the observed associations. Future research directions encompass longitudinal studies, mechanistic investigations, stratified analyses, randomized controlled trials, and exploration of the gut microbiota to further elucidate the underlying mechanisms and inform evidence-based dietary recommendations for headache management. This integrative review underscores the importance of interdisciplinary collaboration and personalized approaches to address the complex interplay between diet, headaches, and overall health.

Tax avoidance, overinvestment, financial reporting quality. Evidence from Italian private firms

PurposeThis study aims to examine whether tax avoidance is associated with overinvestment and the moderating role of financial reporting quality on such association in Italian private firms.Design/methodology/approachThis study uses a multivariate regression analysis based on a sample consisting of 65,535 firm-year observations between 2015 and 2022.FindingsResults show that tax avoidance is positively associated with overinvestment and that such relation is weaker for firms with a higher financial reporting quality than for firms with a lower financial reporting quality. Furthermore, findings hold to a wide range of robustness checks, including alternative measures of main variables, endogeneity and falsification tests.Research limitations/implicationsSince this study focuses on the Italian private firms, the results cannot be extensively generalized.Practical implicationsAs this study highlights the importance of tax avoidance on overinvestment, it can be particularly beneficial for managers, policymakers and other parties interested in assessing factors that lead to a capital allocation in less efficient investments.Originality/valueThis study provides novel evidence about the role of tax avoidance on overinvestment in private firms by mitigating the little attention of prior research in this area. It examines the Italian setting that is particularly of interest given the relevance of private firms in such context and the incentives of managers to reduce the tax burden.

Fibronectin induces a transition from amoeboid to a fan morphology and modifies migration in Entamoeba histolytica.

Cell migration modes can vary, depending on a number of environmental and intracellular factors. The high motility of the pathogenic amoeba Entamoeba histolytica is a decisive factor in its ability to cross the human colonic barrier. We used quantitative live imaging techniques to study the migration of this parasite on fibronectin, a key tissue component. Entamoeba histolytica amoebae on fibronectin contain abundant podosome-like structures. By using a laminar flow chamber, we determined that the adhesion forces generated on fibronectin were twice those on non-coated glass. When migrating on fibronectin, elongated amoeboid cells converted into fan-shaped cells characterized by the presence of a dorsal column of F-actin and a broad cytoplasmic extension at the front. The fan shape depended on the Arp2/3 complex, and the amoebae moved laterally and more slowly. Intracellular measurements of physical variables related to fluid dynamics revealed that cytoplasmic pressure gradients were weaker within fan-shaped cells; hence, actomyosin motors might be less involved in driving the cell body forward. We also found that the Rho-associated coiled-coil containing protein kinase regulated podosome dynamics. We conclude that E. histolytica spontaneously changes its migration mode as a function of the substrate composition. This adaptive ability might favour E. histolytica's invasion of human colonic tissue. By combining microfluidic experiments, mechanical modelling, and image analysis, our work also introduces a computational pipeline for the study of cell migration.

Investigating the influence of measurement uncertainty on chlorophyll-a predictions as an indicator of harmful algal blooms in machine learning models

Advancements in data availability, including high frequency, near real-time multiparameter sensors, laboratory analysis, and in-situ and remote observations, have driven the development of machine learning (ML) models for applications such as toxic Harmful Algal Bloom (HABs) monitoring. However, the performance of ML predictions is influenced by both model uncertainties due to inherent model structures and errors associated with input dataset measurements. For example, measurement uncertainty arises from sample collection, sensor drift and laboratory analysis and sample handling errors. While impacts of model uncertainty are commonly addressed using probabilistic approaches, the effect of measurement uncertainty is less studied due to the limited availability of detailed measurement information. This study focuses on assessing the impact of measurement uncertainty on the ML prediction of chlorophyll-a concentration as an index of HABs in a mesotrophic lake. Using randomized subsets of input measured datasets that mimic possible chlorophyll-a concentration distributions, the study built 1000 Random Forest (RF) and Support Vector Regression (SVR) models. An independent measured dataset was used to validate the ensemble models, allowing for model performance evaluation and the creation of prediction intervals to measure the propagated uncertainty. Our findings showed that the model predictions have MAE that ranged between 0.16 μg/l and 5.19 μg/l, and RMSE ranging between 0.20 μg/l and 7.39 μg/l. The highest uncertainty coverage of 0.71 was observed in the RF model without chlorophyll-a sensor values as a predictor. The study found that the training dataset sizes due to the high frequency and manually sampled nature influence how much measurement uncertainty is covered. The results of this study demonstrate how well ML models can capture various HABs patterns when given diverse measurement variables. Our findings will give researchers insightful information on how to lessen the impact of measurement uncertainty when using ML models as decision-support tools for HABs management.

Spatio-Temporal Variability of Water Quality in the Middle Danube—The Influence of Air Temperature and Discharge

Large watercourses are characterised by significant spatial and temporal changes in water quality due to both natural and anthropogenic impacts. The paper analyses changes in the Danube’s spatial and seasonal water quality in its middle part at five monitoring stations for the period 2018–2022. Examined water quality parameters include oxygen saturation (Os), ammonium (N-NH4), pH, 5-day biochemical oxygen demand (BOD), nitrate nitrogen (N-NO3), orthophosphates (P-PO4), suspended solids (SS), electrical conductivity (EC), and water temperature (WT). Furthermore, the analysis took into account the influence of two additional factors: air temperature (AT) and discharge (D). Throughout the entire period under study, all parameters were characterised by low concentration values, which met the environmental objective of good ecological status. The use of multivariate statistical methods allowed for the identification of EC, N-NH4, WT, Os, SS, and N-NO3 as determining the greatest spatio-seasonal variability of water quality in a selected section of the Danube. Regression models determined WT, EC and nitrogen nitrate changes as depending on AT, flow, and exposure time. Knowing models not only gives a better understanding of the dynamics of changes in water quality in the stretch of the Danube under study but potentially allows the prediction of these parameters based on easily measurable environmental variables.

Technology Use Factoring into Virtual Group Performance

ABSTRACT Virtual teams have become common in business, and their continued growth warrants examination as to the characteristics that contribute to their success. Much of the research acknowledges use of technology as a key element of virtual group success, but quantitative analysis of the independent and dependent variables that contribute to that success is lacking. To be more precise in the examination of variables contributing to project success, and to communicate more effectively with regard to this research, we need to operationally define these contributing factors. Using measurable variables this research examines the effect of technology use on team success with a business plan and cost risk benefit analysis. More granular examination of success variables in virtual groups will make us more precise in understanding and replicating research as well as groups success. This learning exercise predicates on the precepts of social constructivism.

Finite element simulations of smart fracture plates capable of cyclic shortening and lengthening: which stroke for which fracture?

Introduction: Bone healing can be improved by axial micromovement, as has been shown in animals and human patients with external fixators. In the development of smart fracture plates, the ideal amount of stroke for different fracture types in the different healing stages is currently unknown. It was hypothesized that the resulting strain in the fracture gap of a simple tibial shaft fracture does not vary with the amount of axial stroke in the plate, the fracture gap size, and the fracture angle. Methods: With finite element simulations based on body donation computed tomography data, the second invariant of the deviatoric strain tensor (J2), strain energy density, hydrostatic strain, octahedral shear strain, and percentage of the fracture gap in the "perfect healing window" were computed for different gap sizes (1-3mm), angles (5°-60°), and plate stroke levels (0.05-0.60mm) in three healing stages. Multiple linear regression analyses were performed. Results: Findings showed that an active fracture plate should deliver an axial stroke in the range of 0.10-0.45mm. Different optimal stroke values were found for each healing phase, namely, 0.10-0.25mm for the first, 0.10mm for the second, and 0.35-0.45mm for the third healing phase, depending on the fracture gap size and less on the fracture angle. J2, hydrostatic strain, octahedral shear strain and the strain energy density correlated with the fracture gap size and angle (all p < 0.001). The influence of the fracture gap size and angle on the variability (adjusted R2) in several outcome measures in the fracture gap was shown to vary throughout healing. The contribution to the variability of the percentage of the fracture gap in the perfect healing window was greatest during the second healing phase. For J2, strain energy density, hydrostatic strain, and octahedral shear strain, the fracture gap size showed the greatest contribution in the third fracture healing phase, while the influence of fracture angle was independent of the healing phase. Discussion: The present findings are relevant for implant development and to design clinical studies that aim to accelerate fracture healing using axial micromovement.

Measurement of Resonant Frequency of Radio Frequency Converter under Conditions of Significant Electromagnetic Losses

The measurement of resonant frequency in radio wave converter under conditions of high electromagnetic losses presents a formidable challenge. This frequency serves as a crucial parameter directly linked to non-electronic quantities, such as spatial separation thresholds or substance consumption levels. Consequently, accurate measurement of these non-electric variables necessitates precise determination of the transmitter's resonant frequency. However, when electromagnetic losses escalate due to control environment properties, achieving such precision becomes daunting. Common methodologies often hinge on assessing resonant frequency via the transmitter's amplitude-frequency characteristic. However, conventional approaches, typically reliant on electron beam tubes with intricate control mechanisms, falter in accuracy amidst substantial electromagnetic transmission losses. This inadequacy undermines the efficacy of current devices. This paper introduces an unconventional method for resonance frequency measurement and underscores the benefits of the device developed through this novel approach compared to existing ones. The proposed method ensures sustained high measurement accuracy even in the face of significant transmitter electromagnetic losses. Central to this method is the measurement of frequencies ω₁ and ω₂ proximate to the resonant circuit's extreme value at points characterized by identical transmission coefficients. Resonant frequency is then determined as the half-sum of these frequencies during symmetrical frequency modulation. This innovative approach promises to overcome the limitations of traditional resonance frequency measurement methods, offering enhanced precision and reliability in challenging electromagnetic environments.

Evaluation of the relationship between the establishment of the safety, health and environment management system on the performance of employees of the coking complex and refineries - Iranian Zarand Steel Company

Introduction: The present research was conducted with the aim of investigating the relationship between the establishment of safety, health and environment management system on the performance of coking complex employees and refineries of Iranian Zarand Steel Company. Therefore, at first, the dimensions and issues of establishing the safety, health and environment management system and the performance of employees were identified, and then the relationships between the identified variables were presented. Materials and Methods: Two standard questionnaires were used to collect data related to the measurement of research variables, which in total include 2 structures, 3 dimensions, and 20 questions. The content validity method was used to check the validity of the questionnaire. Cronbach's alpha coefficient was calculated as 0.863, which indicated the reliability of the questionnaire. The statistical population of this research includes the employees and experts of Iranian Zarand Steel Company. After ensuring the validity and reliability of the designed scales based on the calculations, the questionnaires were distributed to a sample of 260 people. To check the demographic data, descriptive statistics, K-S test to determine the normality of the data using SPSS24 software, to determine the construct validity using the confirmatory factor analysis test and for the relationships between the variables using the structural equation model using statistical software 3 SMART-PLS was performed. Results: The establishment of the safety, health and environment management system on the performance of employees with a factor load of 0.623 and the T-statistic of the test 9.803, as well as the safety, health and environment management system on the dimensions of employee performance, including increasing knowledge and quality of work, a better relationship with Colleagues, improvement of individual characteristics, development of skills and better supervision, improvement of creativity and improvement of customer orientation of Zarand Iranian Steel Company with factor loading of 0.555, 0.664, 0.430, 0.436, 0.424 and 0.461 respectively and the T-statistics of the test have a significant relationship of 8.086, 12.337, 5.069, 5.792, 5.055 and 6.629. Conclusion: The findings show that the establishment of the HSE management system has a significant effect on the performance of employees and its dimensions, including the quality of occupational health and safety rules, practical compliance with the law, management and cooperation, workplace processes, the use of personal protective equipment and empowering the behavior of managers and employees.

Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria

The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.