Control Variables Research Articles

Body mass index across development and adolescent hair cortisol: the role of persistence, variability, and timing of exposure.

Research suggests a putative role of the glucocorticoid stress hormone cortisol in the accumulation of adiposity. However, obesity and weight fluctuations may also wear and tear physiological systems promoting adaptation, affecting cortisol secretion. This possibility remains scarcely investigated in longitudinal research. This study tests whether trajectories of body mass index (BMI) across the first 15 years of life are associated with hair cortisol concentration (HCC) measured two years later and whether variability in BMI and timing matter. BMI (kg/m2) was prospectively measured at twelve occasions between age 5 months and 15 years. Hair was sampled at age 17 in 565 participants. Sex, family socioeconomic status, and BMI measured concurrently to HCC were considered as control variables. Latent class analyses identified three BMI trajectories: "low-stable" (59.2%, n = 946), "moderate" (32.6%, n = 507), and "high-rising" (8.2%, n = 128). BMI variability was computed by dividing the standard deviation of an individual's BMI measurements by the mean of these measurements. Findings revealed linear effects, such that higher HCC was noted for participants with moderate BMI trajectories in comparison to low-stable youth (β = 0.10, p = 0.03, 95% confidence interval (CI) = [0.02-0.40]); however, this association was not detected in the high-rising BMI youth (β = -0.02, p = 0.71, 95% CI = [-0.47-0.32]). Higher BMI variability across development predicted higher cortisol (β = 0.17, p = 0.003, 95% CI = [0.10-4.91]), additively to the contribution of BMI trajectories. BMI variability in childhood was responsible for that finding, possibly suggesting a timing effect. This study strengthens empirical support for BMI-HCC association and suggests that more attention should be devoted to BMI fluctuations in addition to persistent trajectories of BMI.

European pollen reanalysis, 1980-2022, for alder, birch, and olive.

The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980-2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.

Pengaruh corporate social responsibility disclosure terhadap kinerja keuangan dengan reputasi sebagai variabel moderasi

This study aims to determine the effect of corporate social responsibility disclosure (CSRD) on firm financial performance with corporate reputation as a moderation variable. It focuses on primary and secondary sector companies listed on the IDX in 2014-2022. The dependent variable is firm financial performance, measured using return on assets (ROA). The independent variable, corporate social responsibility disclosure, is measured using the GRI G4. The corporate reputation as moderation variable is measured using the number of awards obtained by the company. The control variables used are company size, leverage, and cash flow. The sampling technique used purposive sampling and obtained as many as 143 companies. This research uses quantitative data, and the method used is regression analysis using the STATA application. The results of this study show that CSRD has a significant positive effect on firm financial performance. These findings provide practical guidelines for companies, stating that it is essential for a company to implement CSRD practices and have a good impact on the company's financial performance, thereby guiding companies in their CSR initiatives.

Identification of sparse nonlinear controlled variables for near‐optimal operation of chemical processes

AbstractFor optimal operation of chemical processes, the selection of controlled variables plays an important role. A previous proposal is to approximate the necessary conditions of optimality (NCO) as the controlled variables, such that process optimality is automatically maintained by tracking constant zero setpoints. In this paper, we extend the NCO approximation method by identifying sparse nonlinear controlled variables, motivated by the fact that simplicity is always favoured for practical implementations. To this end, the ‐regularization is employed to approximate the NCO, such that the controlled variables are maintained simple, even they are specified as nonlinear functions. The sparse controlled variables are solved using the proximal gradient method, implemented within a tailored Adam algorithm. Two case studies are provided to illustrate the proposed approach.

Inverse system based decoupling design and control strategy for crude oil heat exchanger networks

This work focuses on a decoupling control approach for the crude oil heat exchanger network to improve the energy efficiency and achieve continuous energy saving. The desired control variables are identified by the signed directed graph (SDG) model based on the complex network theory. Then the principal component analysis (PCA) method and the evaluation index are employed to construct the initial controller as well as the control variables pairing. The presented system is a multi-input multi-output (MIMO) control system where the coupling problems are considered, and a decoupling control strategy based on the inverse system theory of neural network is proposed. The existence of the inverse system is proved by the reversibility derivation, and the original system is decoupled into two second-order subsystems. Consequently, the proposed decoupling control system is tested on the example of heat exchanger network (HEN) belonging to Crude Distillation Unit. The fluctuating value of temperature is reduced averagely by 57.1% compared with the non-decoupling control strategy and the mean absolute percentage error is within 0.02 under different operating conditions. The results show that the proposed decoupling control method has excellent decoupling performance and can improve the control performance of the system.

Nightmares in the Elderly: Associations with Self-Reported Executive Functions

Study ObjectivesTo describe nightmare phenomenology among community dwelling elderly and to test the hypothesis that reduction in cognitive control is associated with nightmare-related phenomenology including nightmare frequency and severity, greater emotional reactivity, imagery immersion, and dream enactment behaviors (DEBs). MethodsStudy 1: Survey with multiple regression and ANOVAs on N=56 people with frequent nightmares plus N=62 age- and gender-matched controls to quantify the strength of the association between cognitive control variables and nightmare phenomenology. Study 2: Computational simulation of nightmare phenomenology in relation to cognitive control to simulate the empirical findings and to assess the underlying causal theory through computationally supported causal inference. ResultsStudy 1: Regressions demonstrated a strong association between reduction in cognitive control and more extreme nightmare phenomenology, including severity, frequency, daytime effects, and DEBs. Study 2: The computational simulation of nightmare phenomenology in relation to cognitive control is validated relative to regressions from study 1 and offers computational support for the causal theory explaining the associations in study 1. ConclusionsIn aging people, decline in executive cognitive functions (ECFs), cognitive control, and inhibitory processes reduce cognitive control over emotions, thus contributing to unusual nightmare activity, including more extreme nightmare phenomenology such as more severe nightmares, greater emotional reactivity, deeper imagery immersion, and DEBs.

Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach

This paper proposes a novel Multi-scale Interval-valued Decomposition Ensemble (MIDE) framework for forecasting European Union Allowance (EUA) carbon futures prices, which integrates Noise-assisted Multivariate Empirical Mode Decomposition (NAMEMD), Interval-valued Vector Auto-Regressive (IVAR) model, Interval Event Analysis (IEA) method, and Interval Multi-Layer Perceptron (IMLP). First, the original interval-valued carbon prices with other interval-valued control variables are decomposed and integrated into high, medium, and low-frequency components by NAMEMD. Second, IVAR is used to investigate the dynamics of the interval-valued vector system in low-frequency components, while IMLP is employed to characterize the high-frequency components. Besides, the interval event analysis investigates typical events that significantly impact carbon prices in the medium-frequency component. Furthermore, empirical findings indicate that our proposed MIDE learning approach significantly outperforms some other benchmark models in out-of-sample forecasting.

Association of the belief in conspiracy narratives with vaccination status and recommendation behaviours of German physicians

Vaccine hesitancy has been identified as one of the top ten threats to global health by the World Health Organization (WHO). The belief in conspiracy narratives is repeatedly discussed as a major driver of vaccine hesitancy among the general population. However, there is a lack of research investigating the role of the belief in conspiracy narratives in vaccination decisions and recommendation behaviours of physicians. This is particularly relevant as physicians are one of the major and trusted sources of information for patients’ vaccination decisions. This study therefore investigated the association between believing in COVID-19-related conspiracy narratives and physicians’ own COVID-19 vaccination status and their recommendation behavior for COVID-19 and other vaccines (e.g., HPV or flu). In a cross-sectional survey among German physicians (N = 602, April 2022) two conspiracy narratives were assessed, stating that the coronavirus is a hoax or that it is human-made. Additional control variables included trust in health institutions, the rejection of complementary and alternative medicine (CAM), the 5C psychological antecedents of vaccination (confidence, complacency, constraints, calculation, and collective responsibility) and demographic variables. Hierarchical regressions indicated that greater belief in the conspiracy narrative claiming that the coronavirus is a hoax was associated with lower COVID-19 vaccination uptake and fewer COVID-19 vaccination recommendations among physicians. The results for recommendation behavior remain robust even when controlling for other variables. Contrary to our assumption, believing that the coronavirus is human-made was not related to vaccination status nor vaccine recommendation behavior. In conclusion, believing in conspiracy narratives that question the existence and thus also the danger of the virus is an important independent predictor of vaccine hesitancy among physicians that should be addressed in future public health interventions.

Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

Implementation of Accounting Information Systems in Village Fund Management Accountability

This study aims to examine the effect of accounting information systems on accountability for village fund management in Boyolali Regency with control variables of village development index, village fund ceiling, and population. The study was conducted in 40 villages in Boyolali Regency with the criteria of village development index at the developing village stage. Data collection was collected by filling out questionnaires and secondary data for control variables taken from the official government website. The results of this study indicate that accounting information systems have an effect on accountability for village fund management in Boyolali Regency. The control variable of population has a positive effect on accountability for village fund management. The village development index and village fund allocation have a negative effect on accountability for village fund management.

Determinasi Integritas Laporan Keuangan

Financial Statements are a form of corporate management responsibility aimed to stakeholders. The presentation of financial reports must be carried out accurately and free from misstatements in the preparation process. In other words, financial statements must have quality or integrity in its composition. The aim of this research is to determine the influence of company size, financial distress, independent commissioners, and public accounting firm size on the integrity of financial statements in infrastructure sector companies with profitability as a control variable. This research is quantitative research using secondary data from audited financial statements and company annual reports. Based on the results of the tests, it is concluded that 1) company size has no effect on the integrity of financial statements; 2) financial distress has no effect on the integrity of financial statements; 3) independent commissioners have a positive influence on the integrity of financial statements; 4) public accounting firm siz has a positive effect on the integrity of financial statements.

Ecological impacts of vermiculite and submerged macrophytes on sediment enzyme activity in lake restoration: Insights from the mesocosm study

The combination of vermiculite and submerged macrophytes is gradually applied in lake restoration. However, the combined effects on the sediment microenvironment remain unclear. This study conducted indoor ecological experiments using vermiculite doses (0 %, 10 %, 20 %, and 30 %) and types of submerged macrophytes (Vallisneria natans, Hydrilla verticillata, and Potamogeton lucens) as control variables. The study explored the variation in sediment enzyme activity under different combinations of vermiculite and submerged macrophytes, quantifying the driving forces of sediment properties affecting enzyme activity changes. The results showed that the individual and combined effects of vermiculite and submerged macrophytes influenced the variations in sediment enzyme activity. Notably, a 20 % dose of vermiculite combined with Potamogeton lucens significantly improved water environmental conditions, effectively reducing nutrient levels in the overlying water and sediment, while enhancing sediment enzyme activity. The P- acquiring enzyme increased by 0.81 times, while the C- acquiring enzyme improved by an average of 0.83 times. The most significant enhancement in N- acquiring enzyme, with an average increase of 4.93 times. This indicates that a 20 % dose of vermiculite directly enhanced the properties of sediments, promoted plant growth, and accelerated the secretion of microbial enzymes in plant roots. Sediment properties, such as pH, NO3-N, and SOC, are the main factors driving changes in enzyme activity, with average contributions to enzyme activity changes of 54.83 %, 45.59 %, and 47.82 %, respectively. Our study fills in the process of changing microecological conditions in lakes under the combined action of sediment amendment techniques and plants. We recommend using small doses of vermiculite in conjunction with Potamogeton lucens, as it can effectively promote nutrient cycling within lake sediments, offering an environmentally friendly method for managing lake eutrophication.

Peran Penghindaran Kebangkrutan dalam Mengakselerasi Earning Management

This study aims to analyze the impact of financial distress on earnings management within companies listed on the Indonesia Stock Exchange (IDX) from 2019 to 2021. Utilizing a purposive sampling technique, the research focuses on 44 companies that are noted for their special status on the IDX. Key control variables examined include company size, leverage, cash flow operations (CFO), growth, profitability ratios (ROA), equity issuance (EISSUE), and debt issuance (DISSUE). The findings reveal that financial distress significantly and negatively influences earnings management, indicating that distressed companies are less likely to engage in manipulative financial reporting. Furthermore, the study identifies that all control variables contribute to a nuanced relationship between financial distress and earnings management. Specifically, variables such as size, CFO, leverage, ROA, EISSUE, and DISSUE exhibit a significant positive impact on earnings management, suggesting that larger companies with higher leverage and profitability are more prone to earnings manipulation. Conversely, the growth variable demonstrates a significant negative effect, implying that companies experiencing growth are less likely to manage earnings. These insights contribute to a deeper understanding of the dynamics between financial distress and corporate financial behavior, offering valuable implications for investors, regulators, and policymakers.

Revisiting the effects of state anesthesia policy interventions: A comprehensive look at certified registered nurse anesthetist service provision in U.S. hospitals from 2010 to 2021.

Rural hospitals in the United States often rely on nonphysician providers such as advanced practice nurses to care for their patients. One important role that is served by advanced practice nurses is that of anesthesia provider (certified registered nurse anesthetist or CRNA). In 2001, Centers for Medicare & Medicaid Services (CMS) passed an opt-out law affording state governors the right to loosen physician supervision requirements on CRNAs in their respective states, thus potentially improving access in targeted areas. Since then, 24 states have adopted these opt-out provisions. We aim to understand the extent to which the CMS opt-out law has resulted in increased CRNA service provision in hospitals, especially in rural areas. The study used a longitudinal design. We compiled 2010-2021 American Hospital Association data, which includes 4,464 unique U.S. hospitals observed an average of 8 times annually (35,863 total hospital-year observations). We model CRNA services provision at the hospital level using longitudinal mixed effects generalized linear models that incorporate state, county, and hospital control variables. Using descriptive statistics and mixed effects generalized linear models, we discovered that adopting opt-out provisions does not universally result in increased CRNA service provision in U.S. hospitals. Notably, opt-out provisions do not improve access in rural counties. However, in supplemental analysis, we discover some of the conditions under which the likelihood of CRNA service provision is influenced. Hospitals often utilize CRNAs to staff their hospitals. However, many hospitals use both CRNAs and physician anesthesiologists; this can be a potential source of contention and confusion, given the lack of uniformity in thescope of practice policies. We offer some suggestions with regard to the effects of state interventions into the field, and how they might impact this dispute. Lastly, policymakers should consider additional measures to address rural access limitations, as the opt-out policy does not seem to be working as intended.

Identifying the mixed and forced convection flow with transverse magnetic field

AbstractOptimal control of the steady, laminar, magnetohydrodynamics (MHD) mixed convection flow of an electrically conducting fluid is considered under the effect of the transverse magnetic field in a square duct. The viscous and Joule dissipations are included and the flow is driven by a constant pressure gradient. The triple nonlinear set of momentum, induction, and energy equations are solved in dimensionless form by using the mixed finite element method (FEM) with the implementation of Newton's method for nonlinearity with the discretize‐then‐linearize approach. Accordingly, FEM solutions are obtained for various values of the problem parameters to ensure the efficiency of the underlying scheme. This study aims to investigate the problem of controlling the steady flow by using the physically significant parameters of the problem as control variables in the case of a mixed convection flow. In this respect, classification of the type of convection, forced or free, is achieved by controlling the Grashof number (Gr). Besides, single and pairwise controls with Hartmann number (M), Prandtl number (Pr), and magnetic Reynolds number (Rm) are also used to regain the prescribed fluid behaviors and required magnetic field. Control simulations are conducted with the sequential‐least‐squares‐programming (SLSQP) algorithm in the optimization.

Natural resource extraction - Sustainable development relationship and energy productivity moderation in resource-rich countries: A panel Bayesian regression analysis

Social development is essential for improving quality of life, fostering stability, driving economic growth, achieving sustainable goals, enhancing human capital, encouraging civic engagement, building resilience, and fostering innovation, thereby ensuring inclusive, equitable, and sustainable progress. The Social Progress Index (SPI), also known as social development, contains all these features. This study aims to examine the impact of natural resource extraction on social development quadratically using data from 2011 to 2022. The focus of the study is on resource-rich countries. In this study, productive capacity in the energy sector is used as a social development determinant and a natural resource consumption moderator, ensuring sustainability in social development. Analyzing the quadratic relationship allows us to model and understand nonlinear relationships between the predictor and the outcome variable, capturing the effects that change direction or strength at different levels of the predictor. In this study, Bayesian regression is incorporated as an econometric approach. It provides more flexible modelling, incorporates prior knowledge, and offers a full distribution of parameter estimates, leading to better uncertainty quantification and more robust predictions. These estimates confirms a U-shaped natural resource extraction and social development relationship. Energy productive capacities in resource-rich countries not only enhance social development but also ensure the sustainable extraction of natural resources leading to sustainable social development. Population density and biodiversity are incorporated as control variables of the model. The impact of both is positive on social development.

Surface temperature assimilation improving geostationary meteorological satellite surface-sensitive brightness temperature simulations over land

This study explores a possibility of improving Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) simulations over land by assimilating land surface temperature (LST) observations from the National Basic Meteorological Observing Stations of China. The Gridpoint Statistical Interpolation 3D-Var regional data assimilation (DA) system is modified to add LST as a new control variable and its background error variances, horizontal correlations and cross-correlations. The background covariances of LST with other control variables are calculated separately for daytime and nighttime samples in summer and winter seasons. A control experiment (ExpCTL) and three LST DA experiments with (ExpLST) and without (ExpLST_NBC) bias correction or with an average of LST within 2° × 2° grid boxes (ExpLST_SO) are conducted. Considering the fact that surface station observations are point measurements while the satellite TBs measure the total radiation effect of earth's surface within fields-of-view, a bias correction is found necessary for LST DA during daytimes (ExpLST). The biases are quantified by the differences from the Moderate-resolution Imaging Spectroradiometer LST retrievals to compensate for the representative differences. The analyzed fields are then used as input to the Community Radiative Transfer Model to simulate TBs of AHI surface-sensitive channels over land. A long-period statistics shows that ExpLST significantly reduces the observations minus simulations (OB) biases and standard deviations of surface-sensitive TBs in terms of reducing the diurnal variations and season dependences of TB biases over different surface types, which also outperforms ExpLST_NBC and ExpLST_SO at daytime. This study suggests a potential benefit of combining the use of LST observations for assimilating surface-sensitive infrared TBs.

Operating Limits and Control Variables in Photovoltaic Solar Plants with a Net Effective Capacity of 5 MW Connected to the SIN in Colombia

Operating Limits and Control Variables in Photovoltaic Solar Plants with a Net Effective Capacity of 5 MW Connected to the SIN in Colombia

Embracing Technological Advancement: Assessing the Impact of Innovation on Sectorial Employment in Tanzania

Technological innovation is one of the most rapidly advancing and dynamic sector today. It drives growth across all sectors of the economy. However, this rapid growth presents both opportunities and challenges. Taking innovation into account, this study aims to assess the impact of technological innovation on three sectors which are agriculture, industrial, and service sectors. The study employed the autoregressive distributed lag model (ARDL), with data from 2000 to 2022 obtained from the World Bank (WB) and the World Intellectual Property Organization (WIPO). Three ARDL models were used to estimate employment in the agriculture sector, employment in the industrial sector and employment in the service sector with inflation (CPI) and interest rate (INT) as the control variables. The ADF (Augmented Dickey-Fuller) test, PP(Philip-Perron) test, Serial Correlation LM test, BG Heteroscedasticity, and Jarque Bera Normality test were utilized to ensure robust estimates of the models. The findings revealed that innovation has a significant positive influence on employment in the agriculture sector and service sector whereas the industrial sector is negatively influenced by innovation. Nevertheless, inflation has a significant positive effect on employment in the agriculture and service sectors but negatively affects employment in the industrial sector, where a lag in interest rate influence negatively employment in the agricultural sector. Relying on these findings, the study recommends an increase in expenditure on research and development, formulation of policies that will mainstream innovation in all sectors of the economy, and investing more in education and skills development

Ekonomik büyüme ve enerji yoksulluğu arasındaki ilişki: D-8 ülkelerinden bulgular

Energy is critical in production and daily activities in all economies today. Although energy is usually required for economic growth in developed economies, it is also fundamental to economic growth and growth in less developed and developing economies. In this context, this study aims to investigate the linkage between energy access and economic growth in D-8 nations. Inflation, health expenditures, and income were included in the model as control variables. The data set covers the period from 1990 to 2021 and is annual in frequency. The methodology employed includes PANIC and CIPS unit root and Durbin-Hausman co-integration tests. A co-integration relationship was found in the model, and AMG (Augmented Mean Group) was used to perform long-run coefficient estimates. After the coefficient estimation results, we found no statistically significant relationship between energy poverty and economic growth. The energy factor should be used more effectively in poverty reduction. In particular, expanding renewable energy sources will also contribute positively to sustainable growth.