Graphical Criterion Research Articles

Development of PBL-Based Chemistry E-Modules for Colloidal Systems

Integrating digital learning tools has become increasingly important for enhancing student engagement and learning outcomes in the rapidly evolving educational landscape. This research aimed to assess the feasibility of a developed e-module and its impact on student learning outcomes. The development process followed the ADDIE Model, comprising five stages: Analysis, Design, Development, Implementation, and Evaluation. The instruments used included interview sheets, e-module validation sheets, student response questionnaires, and a 20-item test instrument. The e-module was validated by experts based on content, presentation, language, and graphic criteria, achieving an average validity score of 91.6%, indicating strong validity. Following validation, the e-module was implemented with 36 eleventh-grade Science Program (XI MIA) students at a high Islamic school in Medan during the 2023/2024 academic year. The results demonstrated a significant improvement in student learning outcomes, as evidenced by an N-gain score of 0.76, which falls within the high criteria. Additionally, student responses to the e-module were overwhelmingly positive, with an average approval rating of 87%, categorising their feedback as "very good." The findings suggest that the developed e-module is feasible and effective in enhancing student learning outcomes. The high validation scores and positive student feedback indicate that the e-module is well-designed and meets educational standards. It is a valuable tool for improving student engagement and achievement in the classroom.

Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses

Abstract We consider continuous-time survival and event-history settings, where our aim is to graphically represent causal structures allowing us to characterize when a causal parameter is identified from observational data. This causal parameter is formalized as the effect on an outcome event of a (possibly hypothetical) intervention on the intensity of a treatment process. To establish identifiability, we propose novel graphical rules indicating whether the observed information is sufficient to obtain the desired causal effect by suitable reweighting. This requires a different type of graph than in discrete time. We formally define causal semantics for the corresponding dynamic graphs that represent local independence models for multivariate counting processes. Importantly, our work highlights that causal inference from censored data relies on subtle structural assumptions on the censoring process beyond independent censoring; these can be verified graphically. Put together, our results are the first to establish graphical rules for nonparametric causal identifiability in event processes in this generality for the continuous-time case, not relying on particular parametric survival models. We conclude with a data example on Human papillomavirus (HPV) testing for cervical cancer screening, where the assumptions are illustrated graphically and the desired effect is estimated by reweighted cumulative incidence curves.

Development of forecasting of monthly SAR time series in river systems: A multivariate data decomposition-based hybrid approach

The sodium adsorption ratio (SAR) is a critical variable in assessing the quality of water resources, and accurately forecasting its time series is operationally valuable. This study developed a hybrid approach using the multivariate variational mode decomposition (MVMD) model for signal analysis, Boruta model for feature selection, pure linear neural network (PLNN), support vector regression (SVR), Lasso regression, and Elman neural network (ENN) models to forecast monthly SAR time series for rivers. Data from two rivers were used to enhance result reliability, and the developed models were compared with corresponding basic models to evaluate their impact. Numerical and graphical criteria demonstrated the significant superiority of the developed models over the basic ones. Among the basic models, the ENN model exhibited the highest accuracy, while the MVMD-Boruta-ENN model surpasses all investigated models. This finding suggested that the ENN model's structure is more suitable for SAR time series forecasting than other basic models. Analyzing the models' residuals revealed lower mean, standard deviation, skewness, and error range in the developed models, indicating their robust behavior in forecasting the SAR time series. Notably, forecasting extreme SAR values holds greater importance than other values. Anderson-Darling and Kolmogorov-Smirnov tests identified the dominance of the generalized logistic and log-logistic (3-parameters) functions in SAR time series. Probability distribution functions were used to estimate extreme values, and the studied models exhibited more accurate estimations compared to the basic models, indicating their enhanced resilience. The consistent patterns observed in comparing developed and basic models for the entire series, as well as extreme values and residuals across the two investigated rivers, emphasized the reliability of the results.

Population Pharmacokinetics and Absolute Oral Bioavailability of Lasalocid after Single Intravenous and Intracrop Administration in Laying Hens.

Lasalocid sodium is a polyether carboxylic ionophore agent authorized by the EU for use as a coccidiostat in broilers, turkeys, and pullets up to 16 weeks of age, except for laying hens. However, laying hens are the most common nontarget species exposed to lasalocid sodium, mainly due to cross-contamination from feed mills. This exposure may result in potential drug residue deposition in eggs, which could potentially expose consumers to the drug. The breeds commonly used for commercial egg production in Poland are Isa Brown and Green-legged Partridge hens, which have been found to significantly differ in egg-laying performance. This variability may also affect the pharmacokinetics of lasalocid. Data on lasalocid plasma pharmacokinetics in laying hens are lacking. In this study, we aimed to determine typical population pharmacokinetic parameters, absolute oral bioavailability, and how breed may influence the pharmacokinetics of lasalocid. Twenty-layer hens of the two breeds were used in this study. Lasalocid was administered orally at a single dose of either 1 mg or 5 mg/kg body weight or intravenously at a dose of 1 mg/kg body weight, in a crossover design with a three-week washout period between study periods. Blood samples were collected for 72 h, and lasalocid concentrations were measured using high-performance liquid chromatography with fluorescence detection. A population pharmacokinetic analysis was conducted using nonlinear mixed effects modeling. Standard numerical and graphical criteria were used to select the best model, and a stepwise covariate modeling approach was used to determine any influencing factors. The best model was a three-compartment mammillary model with first-order absorption, transit compartments, and linear elimination. The estimated absolute oral bioavailability was low (36%). It was found that breed significantly influenced not only absorption but also the elimination of lasalocid. This study revealed that lasalocid absorption and elimination varied between the two breeds. This variability in pharmacokinetics may result in breed-related differences in drug residue accumulation in eggs, and ultimately, the risk associated with consumer exposure to drug residues may also vary.

Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.

Common Quadratic Lyapunov Functions for Sets of Second-Order Linear Systems: A Simple Graphical Criterion

Common Quadratic Lyapunov Functions for Sets of Second-Order Linear Systems: A Simple Graphical Criterion

Graphical tools for selecting conditional instrumental sets

Summary We consider the efficient estimation of total causal effects in the presence of unmeasured confounding using conditional instrumental sets. Specifically, we consider the two-stage least-squares estimator in the setting of a linear structural equation model with correlated errors that is compatible with a known acyclic directed mixed graph. To set the stage for our results, we characterize the class of linearly valid conditional instrumental sets that yield consistent two-stage least-squares estimators for the target total effect and derive a new asymptotic variance formula for these estimators. Equipped with these results, we provide three graphical tools for selecting more efficient linearly valid conditional instrumental sets: first, a graphical criterion that, for certain pairs of linearly valid conditional instrumental sets, identifies which of the two corresponding estimators has the smaller asymptotic variance second, an algorithm that greedily adds covariates that reduce the asymptotic variance to a given linearly valid conditional instrumental set and, third, a linearly valid conditional instrumental set for which the corresponding estimator has the smallest asymptotic variance that can be ensured with a graphical criterion.

Clinical and laboratory features of autoimmune thyroiditis in subjects of the cohort according to the results of screening

Resume A retrospective analysis of the screening results was carried out (970 subjects of the cohort underwent an in-depth (4-fold) examination (at each visit: ultrasound examination of the thyroid gland, determination of the levels of thyroid-stimulating hormone (TSH) in the blood serum and antibodies to thyroid peroxidase (TPOAb), examination by an endocrinologist) for thyroid diseases. Medical examinations (visits) were carried out every 2 years.It was noted that within the cohort subjects, the most common clinical and laboratory variant of AIT was subclinical with preserved size and function of the thyroid gland or its transient decrease. The frequency of occurrence of the atrophic form of AIT is minimal (only 2/71 patients had a thyroid volume less than 6 ml) as well as the hypertrophic form (in 5/71 cases of AIT the thyroid volume exceeded 24 ml).Analysis of the combination of laboratory and instrumental criteria showed the dependence of the diagnosis of AIT on one of two examinations: Ultrasound (decreased echogenicity) or increased TPOAb. The proportion of each criterion was almost equal (echogenicity was decreased in 10 cases – 14,1%; TPOAb was increased in 16 cases – 22,5%). In most cases (43 cases – 60,6%) there was a combination of two criteria (decreased echogenicity and increased TPOAb). In one case (1,4%), against the background of an increased ratio of antibodies to thyroid peroxidase, an increased level of TSH was noted; ultrasonographic signs were normal. In the second case, against the background of decreased echogenicity of the thyroid gland and low TSH levels, ratio of antibodies to thyroid peroxidase is increased. In all cases, the patients did not report any clinical symptoms, i.e. it manifested as a subclinical form. The TSH level (in 70/71 cases) was within the normal range (0,3-4,0 mIU/l), but its level was significantly higher in patients with AIT (2,4±0,12 mIU/l) than in healthy individuals (TPOAb – normal and thyroid echogenicity – normal) 1,6±0,02 mIU/l; p<0,001.The simultaneous presence of high concentrations of TPOAb in the blood serum and sono -graphic criteria of AIT, including decreased echogenicity of the thyroid gland, increases after 2 years the risk of primary hypothyroidism (PHT) by 12,8 (5,19-31,61) times (p<0,001), in the case of TPOAb content in the blood serum – 29,9 IU/ml and higher, 9,4 (3,86-23,11) times – when the concentration of these autoantibodies TPOAb is more than 60 IU/ml.

Development of handouts on acid-base material with the sorogan learning method

The Sorogan learning method is individual learning with the activities of students giving their learning outcomes (strong) to the teacher. This study aims to determine the feasibility of handouts in improving students' mastery of concepts in acid-base material using the developed Sorogan learning method in terms of validity, practicality, and effectiveness. The research for developing this handout refers to the Research & Development (R&D) development method, which is limited to the limited trial stage. Handout validity consists of content and construct validity. Content validity gets mode 5 with very valid criteria. Construct validity includes graphical, presentation, and linguistic criteria. Graphic criteria get mode 5 with very valid criteria, presentation criteria get mode 4 with valid criteria, and linguistic criteria get mode 4 with valid criteria. The practicality of the handout is seen from the results of observing the activities of the students by obtaining a percentage of 95.71% and the student response questionnaire in terms of the criteria for content, graphics, presentation, and language, getting a percentage respectively 98.33%, 96.67%, 97, 77%, and 100% with very practical criteria. The effectiveness of the handout is seen from the increase in pretest to posttest scores for each student using the N-Gain Score formula, and a value of <g> 0.8 is obtained, or it can be said to increase by 80% with high criteria. Classical completeness gets a percentage of 100% with very effective criteria. It shows that using handouts with the Sorogan learning method in acid-base chemistry learning can improve students' mastery of concepts and learning outcomes. The handout on acid-base material using the Sorogan learning method was declared feasible because it met the validity, practicality, and effectiveness criteria.

Development of guided inquiry-oriented e-worksheets to improve students' science process skills in acid-base material

This study aims to describe the feasibility of guided inquiry-oriented E-worksheets to improve students' science process skills on acid-base material. The research method is Research and Development (R&D), using the 4D development model, which is limited to the development stage and limited trials on 38 eleventh grades at Al-Islam Krian senior high school. The results showed the feasibility of E-worksheets as follows : (1) The validity of E-worksheets based on the criteria of content, presentation, language, and graphics, obtained the mode in E-worksheets 1 and E-worksheets 2, respectively: content criteria 5 and 5; presentation criteria 4.5 and 4.5; language criteria 4 and 4; graphic criteria 5 and 5 with valid and very valid categories, so that the developed E-worksheets are feasible based on the validity criteria. (2) The practicality of the E-worksheets is reviewed from the responses and activities of students. Learner responses based on content, presentation, language, and graphic criteria obtained a percentage of 96.42%; 96.70%; 99.12%; and 100% with a very practical category. Relevant learner activities at meeting 1 and meeting 2 received a percentage of 95.02% and 96.56% in the very practical category. (3) E-worksheets' effectiveness is based on improving science process skills. A normality test showed a significant score on the pretest and posttest of 0.004 and 0.000, respectively, so the significance < 0.05 showed that its data was not distributed normally. Furthermore, pretest and posttest data are analyzed using the Wilcoxon signed rank test, with a significance score of 0.000 < 0.05, showing a significant difference in pretest and posttest results. Data pretest and posttest then also analyze using N-gain with a score from 0.70 to 1.00 with a high category, so students' science process skills are said to increase. E-worksheets effectively improve science process skills.

Graphical characterizations of robust stability in biological interaction networks

Previous studies have inferred robust stability of reaction networks by utilizing linear programs or iterative algorithms. Such algorithms become tedious or computationally infeasible for large networks. In addition, they operate like black boxes without offering intuition for the structures that are necessary to maintain stability. In this work, we provide several graphical criteria for constructing robust stability certificates, checking robust non-degeneracy, verifying persistence, and establishing global stability. By characterizing a set of stability-preserving graph modifications that includes the enzymatic modification motif, we show that the stability of arbitrarily large nonlinear networks can be examined by simple visual inspection. We show applications of this technique to ubiquitous motifs in systems biology such as post-translational modification (PTM) cycles, the ribosome flow model (RFM), T-cell kinetic proofreading, and others. The results of this paper are dedicated in honor of Eduardo D. Sontag’s seventieth birthday and his pioneering work in nonlinear dynamical systems and mathematical systems biology.

The Rosenzweig–MacArthur Graphical Criterion for a Predator-Prey Model with Variable Mortality Rate

We consider a general modified Gause type model of predation, for which the predator mortality rate can depend on the densities of both species, prey and predator. We give a graphical criterion for the stability of positive hyperbolic equilibria, which is an extension of the well-known Rosenzweig–MacArthur graphical criterion for the case of a constant predator mortality rate. We examine the occurrence of a Poincaré–Andronov–Hopf bifurcation and give an expression for the first Lyapunov coefficient. Our model generalizes several models appearing in the literature. The relevance of our results, i.e. the use of the graphical criterion and the expression for the first Lyapunov coefficient, is tested on these models. The global behavior of the system is illustrated by numerical simulations which confirm the local properties of the models near the equilibria.

Determination of the radius of robust stability of the automatic pressure control system in the stabilization column of a catalytic reforming unit

It is proposed to take into account the influence of uncontrolled disturbances on the regulation of pressure in the stabilization column of a catalytic reforming unit by changing the coefficients of the denominator polynomial of the transfer function of the automatic pressure control system based on robust stability methods. To describe the problem in a general form, a general view of the polynomial of the denominator of the transfer function of the automatic pressure control system in the stabilization column of the catalytic reformer is obtained. The purpose of the study is to determine the maximum range of change in the coefficients of the polynomial of the denominator of the transfer function of an automatic pressure control system in the stabilization column of a catalytic reformer, at which robust stability is maintained. Methods - methods of robust stability, automatic control theory. A family of polynomials of the denominator of the transfer function of the automatic pressure control system in the stabilization column of a catalytic reformer with squared uncertainty parameters is considered.The problem of determining the area of robust stability of a family of polynomials is reduced to determining the areas of stability of four edge polynomials and the intersection of all areas to determine the area of robust stability of the family. In general, the problem of determining the robust stability of each edge polynomial is described. To determine the area of robust stability of the family of polynomials of the denominator of the transfer function of the automatic pressure control system in the stabilization column of the catalytic reformer, three polynomials, including the nominal one, are specified. Based on the general description of the problem, the radius of stability of each edge polynomial is determined using a graphical criterion and the construction of a Tsypkin-Polyak hodograph. Results - based on the intersection of the stability regions of four rib polynomials, the robust stability region of the family of polynomials of the denominator of the transfer function of the automatic pressure control system in the stabilization column of the catalytic reformer with a stability radius of 1.5 was determined. Conclusion - this result can be used in the development of an automatic pressure control system in the stabilization column of the catalytic reformer and the choice of regulator settings.

Variable elimination, graph reduction and the efficient g-formula

SummaryWe study efficient estimation of an interventional mean associated with a point exposure treatment under a causal graphical model represented by a directed acyclic graph without hidden variables. Under such a model, a subset of the variables may be uninformative, in that failure to measure them neither precludes identification of the interventional mean nor changes the semiparametric variance bound for regular estimators of it. We develop a set of graphical criteria that are sound and complete for eliminating all the uninformative variables, so that the cost of measuring them can be saved without sacrificing estimation efficiency, which could be useful when designing a planned observational or randomized study. Further, we construct a reduced directed acyclic graph on the set of informative variables only. We show that the interventional mean is identified from the marginal law by the g-formula (Robins, 1986) associated with the reduced graph, and the semiparametric variance bounds for estimating the interventional mean under the original and the reduced graphical model agree. The g-formula is an irreducible, efficient identifying formula in the sense that the nonparametric estimator of the formula, under regularity conditions, is asymptotically efficient under the original causal graphical model, and no formula with this property exists that depends only on a strict subset of the variables.

A Causal Framework for Cross-Cultural Generalizability

Behavioral researchers increasingly recognize the need for more diverse samples that capture the breadth of human experience. Current attempts to establish generalizability across populations focus on threats to validity, constraints on generalization, and the accumulation of large, cross-cultural data sets. But for continued progress, we also require a framework that lets us determine which inferences can be drawn and how to make informative cross-cultural comparisons. We describe a generative causal-modeling framework and outline simple graphical criteria to derive analytic strategies and implied generalizations. Using both simulated and real data, we demonstrate how to project and compare estimates across populations and further show how to formally represent measurement equivalence or inequivalence across societies. We conclude with a discussion of how a formal framework for generalizability can assist researchers in designing more informative cross-cultural studies and thus provides a more solid foundation for cumulative and generalizable behavioral research.

A Complete Criterion for Value of Information in Soluble Influence Diagrams

Influence diagrams have recently been used to analyse the safety and fairness properties of AI systems. A key building block for this analysis is a graphical criterion for value of information (VoI). This paper establishes the first complete graphical criterion for VoI in influence diagrams with multiple decisions. Along the way, we establish two techniques for proving properties of multi-decision influence diagrams: ID homomorphisms are structure-preserving transformations of influence diagrams, while a Tree of Systems is a collection of paths that captures how information and control can flow in an influence diagram.

Unit Selection with Causal Diagram

The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the "benefit function" - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold.

Learning Human Driving Behaviors with Sequential Causal Imitation Learning

Learning human driving behaviors is an efficient approach for self-driving vehicles. Traditional Imitation Learning (IL) methods assume that the expert demonstrations follow Markov Decision Processes (MDPs). However, in reality, this assumption does not always hold true. Spurious correlation may exist through the paths of historical variables because of the existence of unobserved confounders. Accounting for the latent causal relationships from unobserved variables to outcomes, this paper proposes Sequential Causal Imitation Learning (SeqCIL) for imitating driver behaviors. We develop a sequential causal template that generalizes the default MDP settings to one with Unobserved Confounders (MDPUC-HD). Then we develop a sufficient graphical criterion to determine when ignoring causality leads to poor performances in MDPUC-HD. Through the framework of Adversarial Imitation Learning, we develop a procedure to imitate the expert policy by blocking π-backdoor paths at each time step. Our methods are evaluated on a synthetic dataset and a real-world highway driving dataset, both demonstrating that the proposed procedure significantly outperforms non-causal imitation learning methods.

An Assessment of the Environmental-Economic Development of Urbanized Areas in Ukraine

The effective organic development of urbanized areas is a component determining high quality of the population’s life and related activities. The ongoing change in the environmental performance necessitates the making of a new roadmap for transformation and urbanization with consideration to tendencies and patterns of environmental phenomena.
 An overall assessment of the environmental-economic performance in urbanized areas is a rather complicated issue as it covers many dimensions that cannot be determined by one indicator. It is, therefore, obvious that it should be a set of indicators measuring the relationship between the phenomena occurring due to the environmental change.
 The main indicators of environmental friendliness of urbanized areas include ambient air quality, generation and treatment of waste of all the hazard categories, and wastewater treatment. Analysis of the structure and dynamics of these indicators as the main indicators of the environmental performance of urbanized areas is, therefore, critically important. 
 The article’s objective is to determine a set of indicators of the environmental friendliness of cities, explore their relationship, analyze and evaluate the mode of factor effects for the phenomena.
 The existing estimates of socio-economic development of cities do not provide the complete coverage of environmental indicators or do not include them at all. This, in particular, refers to the methods for assessment of territorial development in Ukraine (monitoring of regional development indicators).
 All the calculations required for the analysis were made using STATISTICA software, “Principal components” method, “Factor analysis” module. The graphic criterion “Screeplot” was used to visualize identification of the components.
 Using the Kaiser rule (eigenvalue (λj>1) allowed to identify three principal components attributable to 86.6% of the emissions of pollutants generated by stationary sources in urbanized areas of Ukraine.
 As shown by the research results, the heaviest polluters among the urbanized areas in 2020 were the cities of Dnipro with 31,100tons and Kyiv with 25,500tons. The analyzed tendencies give evidence of a positive move towards improvement in the air basin in the urbanized areas, although the problem of setting pollution margins required for environmental assessment for purposes of international comparisons is yet to be solved.
 As the environment preservation has become a challenge of today, issues of waste generation and treatment need to be assigned priority status in creating long-term, medium-term and short-term programs for development of urbanized areas.

The variance of causal effect estimators for binary v-structures

Abstract Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precisions. To investigate the extent of this variability, we consider the simplest non-trivial non-linear model of a v-structure on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading-order asymptotics, we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result that is not captured by the recent leading-order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size and cannot rely on purely graphical criteria.